Psychophysiology, 47 (2010), 797–798. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.00991.x
SPR AWARD, 2005
For distinguished contributions to psychophysiology: Cornelis H. M. Brunia
STEVEN A. HACKLEY Department of Psychology, University of Missouri, Columbia, Missouri, USA
lies on the mesial surface, deep within the interhemispheric fissure. This patch of cortex points towards the opposite side of the head, the side ipsilateral to the moving foot. Paradoxical lateralization had previously been reported for certain visual-evoked potentials. But I can tell you that in the field of visual electrophysiology this is viewed as little more than a laboratory curiosity. By contrast, paradoxical lateralization of movement-related potentials is important. It’s important because it allows M1 to be distinguished from other motor areas. M1 is the only motor area whose somatotopic mapping yields opposite lateralization for hand and foot movements. Kees’s third major contribution was isolating the Stimulus Preceding Negativity from other slow waves within the contingent negative variation (CNV) family. Here’s the historical context: John Rohrbaugh had presented evidence in 1976 that the late CNV, the part observed towards the end of the warning interval, could be entirely explained by the readiness potential. However, other psychophysiologists then presented evidence that the late CNVcan be observed even when no motor response is required at all. Hence, there should be no readiness potential. For example, Bob Simons, Arne O¨hman, and Peter Lang reported a late CNV when subjects were simply waiting to observe an emotion-inducing slide. But then critics responded that the subjects might have been preparing eye movements to scan the slide or, alternatively, the warning signal might have triggered a prolonged sensory evoked potential. Kees Brunia and his colleagues resolved this controversy by devising a clever paradigm that compared two serial time intervals. The first interval engaged motor preparation without stimulus anticipation. For the second interval, it was the reverse, stimulus anticipation without response preparation. In an extensive and systematic program of research, Kees Brunia and his colleagues used this paradigm to differentiate the Stimulus Preceding Negativity from the Readiness Potential. In summary, Kees’s three most important scientific contributions concerned reflex probes, paradoxical lateralization, and Stimulus Preceding Negativity. In terms of service to the field, the committee noted that Kees has served on the editorial boards of five relevant journals, that he helped organize several major conferences, and that he served both as president and as a member of the Board of Directors of SPR. SPR is proud to present its 2005 Distinguished Contributions to Psychophysiology Award to Cornelis H. M. Brunia.
The Distinguished Contributions to Psychophysiology Award is the highest recognition offered by our Society. Its importance is conveyed by the names of its previous recipients. These include John and Beatrice Lacey, Frances Graham, Yevgeny Sokolov, Steve Hillyard, Peter Lang, Manny Donchin, Risto Na¨a¨ta¨nen, and a small set of others in the Society’s history. To this eminent list we now add Cornelis H. M. Brunia. Criteria include scientific achievements, service to the Society, and the training of students and junior associates. The most important of these is scientific achievements, and Kees Brunia’s three most important accomplishments are clear: The integration of reflex probe methods into psychophysiology, the discovery of paradoxical lateralization of the readiness potential, and characterization of the Stimulus Preceding Negativity. As a neurologist, Kees had learned in medical school how to use reflexes to diagnose neuropathies and other disorders. It was during a visit to Paillard’s lab in Marseille that he saw how reflexes could be used to study psychologically relevant issues. He started with simple manipulations like task versus non-task conditions or drug versus placebo, and then progressed to cognitively more sophisticated designs. These studies helped disentangle the processes that can modulate reflexes during reaction time tasksFimmediate arousal, anticipatory attention, response preparation, response execution. They also introduced psychophysiology to the idea of using a reflex as a probe to measure something else. Kees Brunia’s reflex studies are still widely cited in action psychology and motor physiology. Kees’s second major contribution was the discovery of paradoxical lateralization of the readiness potential. Specifically, the negativity recorded prior to foot movements is bigger over the hemisphere on the same side as the foot rather than over the contralateral hemisphere. Of course, voluntary foot movements are controlled by contralateral motor cortex, just like movements of any other part of the body. The explanation for paradoxical lateralization, Kees realized, is that the foot representation in M1
At the forty-fifth annual meeting of the Society for Psychophysiological Research, the award for Distinguished Contributions to Psychophysiology was presented to Cornelis H. M. Brunia. This article comprises the citation given by Steven A. Hackley on behalf of the Society’s Awards Committee on October 9, 2005. Address reprint requests to: Steven A. Hackley, Department of Psychological Sciences, University of Missouri-Columbia, 210 McAlester Hall, Columbia, MO 65211, USA. E-mail:
[email protected] 797
798 SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article. APPENDIX S1. Author Bibliography
S. A. Hackley Please note: Wiley-Blackwell are not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.
Psychophysiology, 47 (2010), 799–808. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.00985.x
Non-linear EEG synchronization during observation: Effects of instructions and expertise
CLAIRE CALMELS,a MAGALY HARS,a,b GILBERT JARRY,c and CORNELIS J. STAMd Mission Recherche, Institut National du Sport et de l’E´ducation Physique, Paris, France Ecole doctorale ED 462, Universite´ de Reims Champagne-Ardenne, Reims, France c EA 4391, Universite´ Paris XII, Hoˆpital Henri Mondor, Cre´teil, France d Department of Clinical Neurophysiology, VU University Medical Centre, Amsterdam, The Netherlands a
b
Abstract The aim of this study was to examine the effects of instructions and expertise upon neuronal changes during observation of sequential finger movements. Professional pianists and musically naı¨ ve subjects observed these movements with the aim of either replicating or recognizing them at a later stage. A non-linear measure of functional coupling was used to investigate EEG activity. In the 10–13 Hz frequency band and in musically naı¨ ve subjects, functional coupling during observation for replica was greater within central and neighboring areas than during observation for recognition. An opposite pattern was found in the 4–8 Hz frequency band. In the 10–13 Hz band and in areas including the parietal cortex, functional coupling in musically naı¨ ve subjects was greater compared to professional pianists under observation for replica. Results are discussed in the light of recent findings from the cognitive and behavioral neuroscience literature. Descriptors: Observation, Synchronization, Instructions, Expertise, Motor skills, Musicians, Electroencephalography
ulated the semantic content of the action: it was either a meaningful action (i.e., familiar to the individual; e.g., pantomimes of transitive acts) or a meaningless action (i.e., unknown to the individual; e.g., actions emanating from the American Sign Language). They found that the instructional sets and the meaning of the actions led to changes in the observational neuronal profile. More specifically, Grezes et al. (1998) have shown that observing meaningful and meaningless actions with no specific instruction activated a common network of areas and specific areas. The left inferior frontal gyrus and the left fusiform gyrus were activated in meaningful actions and the dorsal pathway and the right cerebellum in meaningless actions. Zentgraf et al. (2005) used functional magnetic resonance imagery (fMRI) to observe movements involving the entire body under different instructions: observation for imitation and observation for evaluation. The results demonstrated distinctive activations within the supplementary motor area (SMA). Under an ‘‘imitative observation mode,’’ the SMA proper was highly activated, whereas under an ‘‘evaluative observation mode,’’ the pre-SMA was more strongly activated. Very recently, Suchan et al. (2008), with PET, have found activation differences whilst individuals observed hand movement for imitation or evaluation. The dorsal pathway was involved during observation for imitation whereas the ventral pathway was at play during observation for velocity judgment. A limited amount of research investigating the neural bases of the observation process has considered expertise as a variable (Babiloni, Del Percio, Rossini, Marzano, Iacoboni, et al., 2009; Calvo-Merino et al., 2005, 2006; Haslinger et al., 2005). Experts
Over the last decade, a large number of studies in the field of neuroscience have been conducted on observation and imitation (e.g., Calvo-Merino, Glaser, Grezes, Passingham, & Haggard, 2005; Calvo-Merino, Grezes, Glaser, Passingham, & Haggard, 2006; Decety, Grezes, Costes, Perani, Jeannerod, et al., 1997; Grezes, Costes, & Decety, 1998; Haslinger, Erhard, Altenmuller, Schroeder, Boecker, & Ceballos-Baumann, 2005; Iacoboni, Woods, Brass, Bekkering, Mazziotta, & Rizzolatti, 1999; Makuuchi, 2005; Muthukumaraswamy, Johnson, & McNair, 2004; Zentgraf, Stark, Reiser, Kunzell, Schienle, et al., 2005). However, with the exception of the studies undertaken by Decety et al. (1997), Grezes et al. (1998), Suchan, Melde, Herzog, Homberg, and Seitz (2008), and Zentgraf et al. (2005), the influence of the nature of instructions provided prior to observation of biological movements on brain activity has not been examined extensively. Decety et al. (1997) and Grezes et al. (1998) were the first researchers to take into account this factor. Using positron emission tomography (PET), they investigated the neural network involved when subjects were invited to observe an action with the purpose of: (i) later imitation; (ii) later recognition; or (iii) to observe the movement with no specific goal. They also manip-
This study was supported by a grant from the French Ministry of Health, Youth, and Sports. The authors are grateful to the participants who participated in the study. They would also like to thank M. Paisley for the video footage editing. Address reprint requests to: Claire Calmels, Mission Recherche, Institut National du Sport et de l’Education Physique, 11 Avenue du Tremblay, 75012 Paris, France. E-mail:
[email protected] 799
800 are defined as individuals who exhibit a high level of performance in a particular domain, which has been acquired through a long and high commitment (Ericsson, Krampe, & Tesch-Romer, 1993). Calvo-Merino et al. (2006) have shown that the activation of the premotor, parietal cortices, and cerebellum was a result of the motor competence of the observer and was closely associated with visual and motor familiarity of the action. More specifically, these authors have studied male and female expert classical dancers who possessed a similar visual familiarity of the ballet moves but not a motor familiarity since some of the ballet moves are specific to one sex and had, therefore, not been physically trained by the other gender group. Greater parietal, premotor, and cerebellar activations were found for observation of genderspecific ballet moves in contrast to visually familiar, but untrained, ballet moves. Haslinger et al. (2005) found similar results to those of Calvo-Merino et al. (2006) in the field of music and with the use of fMRI. They investigated cortical activations in expert pianists who had practiced the piano for 17 years and in neophyte subjects in music. They found greater activations within a fronto-parieto-temporal circuitry in professional pianists than in inexperienced pianists when passively viewing soundless piano playing movements using the right hand contrasted to the observation of a resting hand or sequential fingerto-thumb opposition movements. Very recently, Babiloni et al. (2009), with the use of electroencephalography (EEG), examined the cortical activation when elite rhythmic gymnasts and nongymnasts watched gymnastic sequences in order to subsequently judge the observed routines. They revealed a higher cortical activation in alpha frequency bands in non-gymnasts within the ventral and dorsal pathways. To our knowledge, no studies have manipulated simultaneously the two aforementioned variables (i.e., nature of instructions and level of expertise) when investigating neural processes of observation. Addressing the effects of these two variables in a single experiment would allow examining the influence of the observer intentions (instruction) and the influence of a long-term practice (expertise) upon the cortical observation network. More specifically, when experts and non-experts observe an action in order to replicate it or to recognize it at a later stage, examining the cerebral rhythmic activities may be worthwhile since it allows gaining knowledge about the mechanisms of action observation. Therefore, this was the aim of the present study. Experts were selected among a population of professional pianists. This specific population was chosen for two reasons. First, pianists are considered to be experts in manual dexterity, which has been acquired through their daily musical training. They could thus be easily compared with musically naı¨ ve subjects who did not have any experience in playing musical instruments. Second, movement of the fingers is a task that is perfectly appropriate to the constraints of EEG since it allows data collection during the execution stage of a movement. Since finger movements are performed at a distance from the scalp, no movement artifacts in the EEG are expected (Derambure, Defebvre, Bourriez, Cassim, & Guieu, 1999). The investigation of cerebral rhythmic activities has been completed with the synchronization likelihood (SL) measure, which is a marker of linear and non-linear changes in functional coupling between different brain areas (Montez, LinkenkaerHansen, van Dijk, & Stam, 2006; Stam & van Dijk, 2002). Central and neighboring cortical areas were considered since these are known to be involved during action observation (Pineda,
C. Calmels et al. 2005; Rizzolatti, Craighero, & Fadiga, 2002). First, the central zone, which comprises the sensorimotor cortex, is recognized as being activated during observation of actions performed by conspecifics (Lepage & The´oret, 2006; Muthukumaraswamy et al., 2004; Pineda, 2005). This activity is reflected by a suppression or a modulation of the mu rhythm, an 8–13 Hz rhythm generated in the sensorimotor cortex. Besides, the supplementary motor area, which is part of the mesial frontal cortex, is known to be the hand representation area (Chainay, Krainik, Tanguy, Gerardin, Le Bihan, & Lehe´ricy, 2004). Second, frontal and parietal areas are recognized as being interdependently connected to allow transformation of sensory information into actions (Matelli & Luppino, 2000; Rizzolatti, Luppino, & Matelli, 1998). These areas are also activated when humans observe actions performed by the hands or arms (Rizzolatti & Craighero, 2004; Rizzolatti et al., 2002). Finally, the parietal cortex seems to play a major role in action representation (Bonda, Petrides, Ostry, & Evans, 1996; Iacoboni et al., 1999; Sirigu, Duhamel, Cohen, Pillon, Dubois, & Agid, 1996). For example, Sirigu et al. (1996) suggested that this area was of prime importance to store or/and access motor representations. In the same vein, Iacoboni et al. (1999) suggested that a kinesthetic representation of an observed movement was generated in the right parietal lobe when viewing this movement. It was hypothesized that, in musically naı¨ ve subjects, interregional functional coupling during action observation with the purpose of replicating the action would be greater than during action observation with the aim of recognizing the action. Under the former observation condition, transformation of visual information into motor codes (Jeannerod, Arbib, Rizzolatti, & Sakata, 1995), argued to require additional population of neurons, would generate greater cognitive demands compared to those identified under observation with the aim of recognition, in which such transformation is unnecessary (Zentgraf et al., 2005). It was also expected that, in professional pianists, inter-regional functional coupling would not display significant differences between the two observation conditions, since musicians who are extremely well educated in processing visuomotor transformation (Palmer, 1997) do not need to recruit extra neuronal network under observation for replica. This last point has been supported by the idea that a great level of expertise is related to a reduced processing treatment (e.g., Haslinger, Erhard, Altenmuller, Hennenlotter, Schwaiger, et al., 2004; Jancke, Shah, & Peters, 2000; Koeneke, Lutz, Wustenberg, & Jancke, 2004; Krings, Topper, Foltys, Erberich, Sparing, et al., 2000, Meister, Krings, Foltys, Boroojerdi, Muller, et al., 2005). As a consequence of these two hypotheses, it was predicted that observation for replica among musically naı¨ ve subjects would elicit greater functional coupling than observation for replica among professional pianists. Modifications in functional coupling would be mainly conjectured to occur in theta band (4–8 Hz) and in alpha bands (8–13 Hz), since oscillations in these two bands play an important role during the encoding of information (Klimesch, 1999; Sauseng, Klimesch, Gruber, Doppelmayr, Stadler, & Schabus, 2002; Sauseng, Klimesch, Schabus, & Doppelmayr, 2005). Moreover, reverse patterns of coupling would be observed between theta and alpha bands. It is well recognized in the scientific literature that, during analyses of mental activity, EEG alpha activity desynchronizes in terms of power decrease, whereas EEG theta activity synchronizes in terms of power increase (Klimesch, 1996, 1999; Klimesch, Doppelmayr, Schimke, & Ripper, 1997a).
Observer’s intentions and expertise Experimental Methods Subjects Eleven right-handed professional pianists (eight men and three women; mean age 5 26.63, SD 5 4.99) and twelve right-handed musically naive subjects (eight males and four females; mean age 5 24.79, SD 5 3.61) were included in the study. Professional pianists played the piano for at least 10 years and had on average 25 h of piano practice per week. Musically naive subjects had no experiences in playing a musical instrument. All the subjects were assessed as right-handed by the Edinburgh Handedness Inventory (Oldfield, 1971) and did not report the presence of any neurological or psychiatric disorders. All subjects provided written informed consent and were kept blind to the goals of the study. The study was approved by the local institutional ethics committee. Task The task was a sequential finger movement, which consisted of touching the tip of the right thumb with the tip of the other right hand fingers with the hand in a supine position and resting on a pillow. The movement comprised four finger contacts performed at a rate of 2 Hz. There were 40 different movements with nonrepeated contact performed consecutively (e.g., 1321, 4312, 2423); 1 was the index finger, 2 the middle finger, 3 the ring finger, and 4 the little finger. Experimental Procedure The subjects were examined in three conditions: (i) a movement observation condition aimed at replicating the observed movement; (ii) a movement observation condition aimed at recognizing the observed movement; and (iii) a control condition. Multichannel EEG was recorded whilst participants sat in a darkened room with their forearms lying on armrests in a supine position. To reduce eye movement and other electromyographic artifacts throughout the EEG data collection, subjects were asked to refrain from blinking by maintaining their gaze on a fixation point placed in the middle of the screen situated 1.30 m in front of them and to keep their jaw relaxed. These instructions were provided before each condition. Movement Observation Condition for Replica Each subject observed 40 movements embedded in 40 trials. Each trial comprised five stages which were shown to the subject via a video display. During the first stage of each trial, lasting 4 s, the subject watched a blue monitor screen. Two seconds after the onset of this stage, the word ‘‘perform’’ appeared on the screen to invite the subject to observe the movement with the goal of replicating it at a later stage. During the second stage lasting 3.24 s, the subject observed a video of a human model performing a finger movement sequence at 2 Hz from an egocentric perspective. An egocentric perspective (i.e., a first-person perspective) has been chosen, since it is congruent to the visual perspective of the observer, whereas an allocentric perspective (i.e., a third-person perspective) is other centered. Moreover, an egocentric perspective yields more activity, during observation, in the contralateral sensorimotor cortex compared to an allocentric perspective (Jackson, Meltzoff, & Decety, 2006). This result is consistent with the findings of Maeda, Kleiner-Fisman, and Pascual-Leone (2002). Maeda et al. (2002) have shown a greater motor corticospinal excitability for observed hand actions under an egocentric perspective. In the third stage, lasting 3.76 s, an amber background was presented requiring the subject to stay focused. In the
801 fourth stage lasting 3.24 s, a black screen was displayed and invited the subject to perform the finger movement sequence viewed in the second stage. While the subjects performed the finger movements, the experimenter monitored the accuracy of the movements. Incorrect sequences of finger taps were discarded from subsequent analysis. In the fifth stage lasting 6 s, the screen changed to red and the subject was encouraged to relax, to think of nothing, and to blink her/his eyes if necessary (see Figure 1). The time interval between the beginning of viewing the movement and the onset of the next was 20.24 s (see Figure 1). Movement Observation Condition for Recognition The 40 trials under this condition were conducted in a similar way to those used in the movement observation condition for reproduction. However, in the first stage, the word ‘‘recognize’’ appeared on the blue screen in order to invite the subject to observe the movement with the goal of recognizing it at a later stage. The fourth stage was also different in that a video was presented to the subject. This video was similar or dissimilar than that viewed in the second stage. Fifty percent of the videos were similar. At the beginning of the fifth stage, the word ‘‘answer’’ appeared for 2 s on the red screen to prompt the subject to clench her/his fist whether the second video was different from the first video. When the second video was identical to the first one, the subject did nothing. Only correct answers were retained for analysis. Control Condition This condition followed a similar procedure to the observation conditions. However, instead of observing movements during the second and fourth stages, the subject observed a pillow during two periods each lasting 3.24 s (see Figure 1). All trials for each of the three conditions were triggered using a specifically designed interface based on a photoresistive diode, which responded to the screen color change. Four 10-min blocks of 30 trials were performed. Each block was separated by a 5-min rest period. The order of the 120 trials was randomized across the four blocks. During all the conditions apart from the fourth stage of the observation condition for replica, the experimenter verified that the subject did not move his/her fingers. Data Acquisition and Recording Electrical brain activity was recorded from 19 electrodes (Fp1, Fp2, Fz, F7, F8, F3, F4, Cz, C3, C4, PZ, P3, P4, T3, T4, T5, T6, O1, and O2) mounted in an elastic lycra cap (Electro-cap International, Eaton, OH) and placed in accordance with the international 10–20 system (Jasper, 1958). Mastoids were used for the reference electrodes, and the ground electrode was located on the forehead. Electro-oculograms (EOG) were also registered from the canthi of both eyes (horizontal EOG) and the supra and infra orbital of the right eye (vertical EOG). Electrode impedance was kept homogenously below 5 kO throughout the experimentation and was checked systematically between the four blocks of trials. Amplifier bandwidth was set between 0.15 and 114 Hz using a computer-based EEG recorder (Coherence, Deltamed, Paris, France). Baseline-corrected activity was sampled at 256 Hz. AD resolution was 16 bit. Synchronization Likelihood Synchronization Likelihood (SL) is a general measure of linear and non-linear correlations between EEG signals (Stam & van Dijk, 2002; Montez et al., 2006). This measure characterizes interchannel synchronization and is the likelihood that recurrence of a pattern in time series X at two times i and j will coincide with
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C. Calmels et al. Stage 4 (Execution or Observation)
Stage 5 (Rest)
3.76 s
3.24 s
6s
Amber
Black
Red
Stage 1
Stage 2 (Observation)
Stage 3
Stages duration
4s
3.24 s
Screen colour
Blue
Stages
Observation condition for replica
PERFORM
Observation condition for recognition
RECOGNIZE
RESPONSE
Control condition
Figure 1. Schema for one trial according to condition. The second stage lasted 3.24 s since two periods of 505 ms (i.e., 505 ms before the onset of the movement and 505 ms after its end) were added to the 2.23-s duration of the movement. During these time lapses, the subject could visualize the hand and fingers at rest. This was done to allow the subject to understand the context in which the movement was performed and to avoid abrupt video image changes.
recurrence of patterns in time series Y at the same times i and j. The patterns are defined in terms of state-space vectors obtained by time-delay embedding of the data. The SL takes on values between pref (no coupling) and 1 (complete coupling). Pref is a parameter of the computation of synchronization likelihood, and is usually chosen as Pref 5 0.01. Data Processing EEG data were analyzed in five frequency bands: 4–8 Hz, 8–10 Hz, 10–13 Hz, 13–20 Hz, and 20–30 Hz. The choice of these band widths was based on previous coherence, connectivity research (Andres, Mima, Schulman, Dichgans, Hallett, & Gerloff, 1999; Calmels, Hars, Holmes, Jarry, & Stam, 2008; Holz, Doppelmayr, Klimesch, & Sauseng, 2008; Stam, de Haan, Daffertshofer, Jones, Manshanden, et al., 2009). Data processing was composed of three steps: segmentation of EEG trials; removal of ocular artifacts; and SL computation. Segmentation of EEG trials. A total of 30 trials were completed during each 10-min block. Each trial was subdivided into five stages. Off-line analysis of the first stage revealed that muscular artifacts contaminated the data in the first second of some trials. Consequently, this stage was reduced to 3 s in length and the first second was discarded. The fifth stage, during which the subject was prompted to relax, was also removed, since this stage contained numerous ocular and muscular artifacts. Four stages were distinguished within a trial after the off-line analysis: the first stage (0–3 s), the second stage (3–6.24 s), the third stage (6.24–10 s), and the fourth stage (10–13.24 s) (see Figure 1). Only the second stage (3–6.24 s) was used for the EEG analysis. Removal of ocular artifacts. Ocular artifacts were corrected via Semlitsch, Anderer, Schuster, and Presslich’s (1986) method. This artifact removal was performed with Neuroscan 4.1 software (Revision A, 1999). The following options were selected: trigger: positive direction, threshold: 20%; blink values: 2 min-
imum sweeps, average artifact duration: 400 ms, and VEOG as the channel used in this computation; review: maxima and blinks enabled; and output: LDR1CNT. SL computation. SL was computed for all the 171 electrode pairs (stemming from 19 electrode sites) for the second stage for each of the trials of the three conditions, for each subject and frequency band. The 171 SL values were averaged across trials for each subject, condition, and frequency band. Parameters for the computation of the synchronization likelihood were: 1 sample for the lag; 8 for the embedding dimension; 8 for the Theiler correction (w1); and 0.010 for Pref. These parameter choices were necessary because of the very short duration of the stage to which the SL was implemented (see Appendix 1). To diminish the variability between subjects and electrode pairs, the SL value under the control condition was subtracted from the SL value under the observation condition as stated by the formula: SLfinal 5 SLobservation condition – SLcontrol condition (Manganotti, Gerloff, Toro, Katsuda, Sadato, et al., 1998; Gerloff, Richard, Hadley, Schulman, Honda, & Hallett, 1998). A positive SLfinal value indicated a SL increase, whereas a negative value represented a SL decrease. Statistical Analysis All statistical analyses were performed using Statistica software 7. From the electrode montage, F4, F3, C4, C3, P4, P3, FZ, CZ, and PZ were chosen for analysis because these electrode sites overlie the central and neighboring cortical areas that are known to be involved during action observation (Pineda, 2005; Rizzolatti et al., 2002). These electrode sites also displayed the highest EEG synchronization under the conditions of observation and execution of finger movements (Calmels, Holmes, Jarry, Hars, Lopez, et al., 2006) and revealed EEG patterns during hand/ finger action observation akin to those revealed during hand/ finger action execution (Babiloni, Babiloni, Carducci, Cincotti, Del Percio, et al., 2002; Calmels et al., 2008).
Observer’s intentions and expertise
803
In the present study, the number of links between the nine electrode sites belonging to the central and neighboring areas (i.e., the zone of interest) was 36 (C4-C3, C4-FZ, C4-CZ, C3-FZ, C3-CZ, FZ-CZ, F4-F3, F4-P4, F4-P3, F4-PZ, F3-P4, F3-P3, F3-PZ, P4-P3, P4-PZ, P3-PZ, F4-C4, F4-C3, F4-FZ, F4-CZ, F3-C4, F3-C3, F3-FZ, F3-CZ, C4-P4, C4-P3, C4-PZ, C3-P4, C3-P3, C3-PZ, P4-FZ, P4-CZ, P3-FZ, P3-CZ, FZ-PZ, CZ-PZ) (see Figure 2). For each of the five frequency bands and subjects, we averaged the 36 SL values within the central and neighboring areas to obtain one overall SL value. Two (conditions) ! 2 (groups) multivariate analysis of variance (MANOVAs) were completed for each of the frequency bands. There was one within-subject factor; condition (two levels: observation for replica, observation for recognition) and one between-subject factor; group (two levels: musically naı¨ ve subjects, professional pianists). Post hoc comparisons were calculating using Fisher’s LSD test where MANOVA results were significant. Before the MANOVA computations, the normality of the data was checked with the Kolmogorov-Smirnov test. Results Behavioral Results During observation for replica, the percentages of correct finger taps performed by the musically naı¨ ve subjects and the professional pianists were, respectively, 84% and 96.8%. This difference was statistically significant (Mann-Whitney, U 5 15.50, po.002). During observation for recognition, the percentages were 91% for the musically naı¨ ve subjects and 99.3% for the professional pianists. This difference was statistically significant (Mann-Whitney, U 5 15.50, po.002). Percentages of correct finger taps between the observation for replica and the observation for recognition were statistically different in musically naı¨ ve
F4
Fz
F3
C4
Cz
C3
P4
Pz
P3
Figure 2. Connections between electrode sites within the central and neighboring areas.
Table 1. Summary of the 2 (Conditions) ! 2 (Groups) MANOVAs for Each Frequency Band 4–8 Hz Interactions
F
Conditions ! Groups 7.06
p n
8–10 Hz F
P
10–13 Hz
13–20 Hz
20–30 Hz
F
F
F
0.67 .42 7.98
p n
p
p
1.54 .23 0.19 .67
Note: po.015. n
subjects (Wilcoxon, T 5 .00, po.004) and in professional pianists (Wilcoxon, T 5 3, po.04). Synchronization Likelihood The EEG data were normally distributed. Only results for conditions by groups are reported since they were directly linked to the goals of the present study (see Table 1). Five 2 (conditions) ! 2 (groups) MANOVAs were computed. Significant conditions ! groups interactions were found in the observation stage for: (1) the 4–8 Hz band, F(1,21) 5 7.06, po.015; (2) the 10–13 Hz band, F(1,21) 5 7.98, po.015 (see Table 1). Fisher’s LSD post hoc test analyses were computed and three results were found. First, Fisher’s LSD post hoc test analysis revealed significant differences among musically naı¨ ve subjects between the observation condition for replica and the observation condition for recognition in the 4–8 Hz frequency band. In this band, under the condition for replica, a weak SLfinal decrease was developed, whereas under the condition for recognition a SLfinal increase was produced (see Figure 3). Second, Fisher’s LSD post hoc test analysis also identified a significant difference among musically naı¨ ve subjects between the observation condition for replica and the observation condition for recognition in the 10–13 Hz frequency band. SLfinal increase was greater in the observation condition for replica compared to the observation condition for recognition. Finally, post hoc test analysis also yielded a significant difference, under the observation condition for replica, between musically naı¨ ve subjects and professional pianists for the 10–13 Hz frequency band (i.e., a SLfinal increase for the musically naı¨ ve subjects and a SLfinal decrease for the professional pianists) (see Figure 3). Discussion Observation for Replica and Recognition Among Musically Naı¨ve Subjects Consistent with our first prediction, significant differences in SLfinal across the observation conditions for replica and recognition were observed among musically naı¨ ve subjects in the zone of interest for the theta and upper alpha (10–13 Hz) frequency bands. The presence of these differences in theta and upper alpha bands is not a surprising result. It is recognized that theta and alpha rhythms are related to memory process. More specifically, theta oscillations facilitate long-term synaptic potentiation, which is a cellular mechanism involved in memory encoding process (Bliss & Collingridge, 1993). The present result is in line with the findings of Klimesch and co-workers, which showed that theta power is found to be closely associated to the encoding of new information in episodic short-term memory, whereas upper alpha rhythm is of great importance for long-term memory in retrieval processes. The authors also highlighted that a particular interaction between these two frequency bands is indicative of a transfer
804
C. Calmels et al. Electrode connections F4
Fz
F3
C4
Cz
C3
P4
Pz
P3
4-8 Hz
10-13 Hz
Observation condition for replica Observation condition for recognition
Figure 3. Significant results from the Fisher’s LSD post hoc test where conditions ! groups interactions were significant. Schema in the left indicates the electrode connections within the area of interest. Charts display the significant differences between musically naı¨ ve subjects and professional pianists under the two observation conditions and for the 4–8 Hz and 10–13 Hz frequency bands.
of information between the working and the long-term memories (Sauseng et al., 2002). In the present case, the results obtained in theta and alpha frequency bands are a consequence of the experimental paradigm that has been employed. This paradigm resembles traditional memory paradigms. Data employed for the EEG analysis stem from the observation stage, which is, in fact, an encoding stage. Individuals encode information to store it temporarily in the working memory before performing a recognition task or reproduction task after a delay of a few seconds. The process of encoding also implies that the individuals retrieve from long-term memory the information that will allow them to identify the perceived event before its storage in the working memory (Magill, 2004; Shiffrin & Geisler, 1973). The present differences detected between observation for replica and observation for recognition corroborated the findings of Decety et al. (1997) and Zentgraf et al. (2005), who found different neuronal profiles according to the instructions provided to the individuals. For instance, Decety et al. (1997) found activation of the right parahippocampal gyrus, while subjects observed an action in order to recognize it, and this irrespective of the meaning of the action. While subjects observed an action in order to repeat it at a later stage, the dorsolateral prefrontal cortex in both hemispheres, the left pre-supplementary motor area, the left middle occipital gyrus, precuneus, and the right cuneus were activated. Results from a more recent investigation by Zentgraf et al. (2005) showed different kinds of activation within the SMA during gymnastics movement observation tasks where instructions differed. However, comparison with Zentgraf et al.’s (2005) work should be made cautiously, since they invited the subjects, after the observation stage, to mentally simulate the movement from a first-person perspective under the ‘‘imitative observation mode.’’ Although mentally performing an action can be perceived as an extremely close motor-related task (Decety & Grezes, 1999; Grafton, Arbib, Fadiga, & Rizzolatti, 1996; Grezes & Decety, 2001; Jeannerod, 2001), nobody, at the present time, is in a position to confirm that observing an action with the aim of replicating it generates cortical phenomena similar to those elicited during the observation of an action with the aim of imagining it. A direct examination of this point would certainly warrant further investigation. The analysis of the 10–13 Hz frequency band showed that the SLfinal increase was greater under the observation condition for replica than under the observation condition for recognition in musically naı¨ ve subjects. This expected finding may be explained by two different mechanisms: a visuomotor transformation (Jeannerod et al., 1995; Zentgraf et al., 2005) and a spatial transformation (Anquetil & Jeannerod, 2007). First, the visuomotor transformation, similar to that requested for reaching and
grasping objects (Jeannerod et al., 1995; Stark & Zohary, 2008), was at play in the observation condition for replica. Neurophysiological mechanisms transform perceptual information stemming from the observation of an action performed by others into motor outputs used to guide the execution of the action. Zentgraf et al. (2005) suggested that these mechanisms are not necessary as an individual observes an action with the purpose of recognizing it at a later stage, since under this (observation) condition, no subsequent execution is required. The recognition of an action can be viewed as a distant facet of motor performance, observing an action for recognition is thus treated as a visual event (Jeannerod, 2006) and elicits activation in memory encoding structures such as the parahippocampal area (Decety et al., 1997; Grezes et al., 1998). The higher level of synchronization revealed under the condition for replica may be due to an additional recruitment of a pool of neurons, which would allow the visuomotor transformation to occur. Second, the spatial transformation (Anquetil & Jeannerod, 2007) reflects a mechanism in which the observer has to transpose the body of the person she/he observed from an egocentric perspective at her/his own body. This body transposition, which is probably not necessary under the observation condition for recognition, may have a cost that can be expressed by an increase in synchronization under the observation condition for replica. However, as pointed out by Anquetil and Jeannerod (2007), the neural basis related to the perspective change from the observed person to the observer is still unknown and needs to be investigated. The greater increase in synchronization revealed in upper alpha band under observation for replica compared to observation for recognition is also compatible with the behavioral results and the informal talks of the musically naı¨ ve subjects. Musically naı¨ ve subjects made more mistakes under the observation for replica than under the observation for recognition. These also reported having experienced more memorization effort, more energy, and more attention when they observed a finger movement in order to replicate it at a later stage. In the theta band, the relationship between synchronization values under observation for replica and observation for recognition is reversed compared to that obtained in the 10–13 Hz frequency band. In upper alpha band, a greater synchronization value under observation for replica was revealed, whereas, in theta band, the changes in synchronization across the two conditions were opposite in sign: the synchronization value was positive under observation for recognition; the synchronization value was negative and near zero under the observation for replica. These results are consistent with the findings of the ERD/ERS (Event-Related Desynchronization/Synchronization) literature related to memory tasks, which have shown that theta and al-
Observer’s intentions and expertise pha react in different and antagonistic manners during the presentation of visual stimuli (Doppelmayr, Klimesch, Pachinger, & Ripper, 1998; Klimesch, 1996, 1999; Klimesch, Schimke, & Schwaiger, 1994; Klimesch, Doppelmayr, Pachinger, & Ripper, 1997b; Klimesch et al., 1997a). These stimuli were, in fact, verbal materials such as letters, numbers, or words, and subjects were invited to provide a semantic judgment (Klimesch et al., 1997b) or/and to perform an episodic memory task (Klimesch, Doppelmayr, Pachinger, & Russegger, 1997c). In the present study, where movements were presented, covariations display quite different patterns compared to those observed by Klimesch et al. This may be due to the use of different kinds of stimuli; movement sequences vs verbal materials; and/or the use of different kinds of indicator; SL in the present study vs ERD/ERS in studies conducted by Klimesch’s research group. These different covariations also add weight to the idea that alpha and theta activities are produced by different mechanisms (Andersen & Anderson, 1968; Klimesch, 1996, 1999; Steriade, Gloor, Llinas, Lopes da Silva, & Mesulam, 1990). Cortical oscillations in alpha band translate activity stemming from the thalamus and induced to the cortex via thalamo-cortical pathways, whereas oscillations in theta band reflect activity emanating from the hippocampus and induced to the cortex via hippocampal cortical pathways (Andersen & Anderson, 1968; Klimesch, 1996, 1999; Steriade et al., 1990). Observation for Replica and Recognition Among Professional Pianists In accordance with our second hypothesis, data tend to provide evidence for an EEG equivalence between the two observation conditions among professional pianists. Interpretation of this result has been made with great care since a lack of significant difference between two experimental conditions does not necessarily imply equality. The lack of synchronization difference between the observation conditions for replica and recognition among the professional pianists may be explained by the reduced cortical network involved when these pianists watch an action which must be replicated in the near future. These individuals have acquired a high degree of musical expertise through an extensive and daily practice over, at least, a 10-year period. This musical training requires skills such as processing complex visuomotor transformations (Palmer, 1997), and learning by listening and observation in order to reproduce the teacher’s actions (Haslinger et al., 2005). Under the observation condition for replica, we can thus conjecture that the recruitment of additional neuronal resources is not necessary to allow the visuomotor transformations to take place, since the pianists of the present study are used to treating visuomotor transformations from their earliest years. In other words, it can be supposed that pianists treat these transformations quasi-automatically or even effortlessly, and that the cost for this processing is negligible compared to musically naı¨ ve subjects. In the first part of the discussion related to musically naı¨ ve subjects, we suggested that the synchronization difference between the two observation conditions was due to the cost generated by an extra recruitment of a pool of neurons needed for the visuomotor transformation. Since this cost is negligible among the pianist population, such a difference disappears. EEG equivalence detected between the two observation conditions in the professional pianists do not concur with their behavioral results and informal reports. The best scores in the task were obtained under observation for recognition, and professional pianists and these declared, as did the musically naı¨ ve subjects,
805 that they perceived the replica condition to be more difficult than the recognition condition. This mismatch is difficult to explain.
Observation for Replica Among Musically Naı¨ve Subjects and Professional Pianists The findings from this study also fit our third hypothesis. Functional coupling during observation for replica is greater in musically naı¨ ve subjects than in professional pianists. This result is consistent with the behavioral results. Musically naı¨ ve subjects committed more mistakes than professional pianists as they performed the sequential finger movements. This result is also deemed logical when examining the aforementioned findings of this study. As suggested by the authors, the greater coupling observed in musically naı¨ ve subjects is due to the mechanisms related to visuomotor/spatial transformation, which differs according to the musical expertise of the individuals. To perform visuomotor/spatial transformation, physically naı¨ ve subjects need to recruit sets of neurons more extensively than professional pianists. These points were reviewed thoroughly and earlier in the discussion. The next point that needs to be addressed is related to the nature of the task of the present study. The task was a sequential finger-to-thumb opposition movement with the right hand in a supine position. It was not a piano hand movement, which is characterized by up-and-down movements of the fingers striking keys on a piano’s keyboard and with the hands in a prone position. While the sequential finger-to-thumb opposition movements were unfamiliar for all subjects, the EEG results suggest that, contrary to musically naı¨ ve subjects, professional pianists were able to process the visuomotor transformation with no cost or, at least, with a reduced processing treatment. This difference can be explained by the fact that the sequential finger-to-thumb opposition movements may have been perceived by the professional pianists as a closed piano-related task and that the task may have appeared more meaningful to them compared to the musically naı¨ ve subjects. If the task had displayed more remote characteristics of musical hand movements, the results would have been probably different. This hypothesis has yet to be tested. Finally, the difference observed between the pianists and the musically naı¨ ve subjects corroborates recent studies that compared cortical activations during observation sessions in expert and inexpert individuals using the fMRI technique (Calvo-Merino et al., 2005, 2006; Haslinger et al., 2005). For example, Calvo-Merino et al. (2005) reported stronger bilateral activations in premotor cortex, intraparietal sulcus, right superior parietal lobe, and left posterior superior temporal sulcus in expert dancers when observing passively familiar dance movements that they were used to performing daily, in comparison to movements they had never performed. In the field of music, Haslinger et al. (2005) found a greater involvement of the fronto-parieto-temporal network in pianists compared to inexperienced pianists as they passively observed piano-playing movements. They also demonstrated that primary and second auditory areas were bilaterally activated only among pianists during the viewing of soundless piano-playing movements. While there is evidence for a difference between experts and novices in the literature, comparison with research that has not used the EEG technique should be made cautiously. Activation of a given area, expressed by blood oxygen level dependent (BOLD) signal, does not reflect similar aspects of cortical activity as does interregional functional coupling.
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Conclusion The use of the non-linear technique (Stam & van Dijk, 2002; Montez et al., 2006) provided an opportunity to analyze in detail
several mechanisms underlying observation. The results indicated that the nature of instructions provided prior observation and the level of expertise of the observer led to different cerebral rhythmic activities within central and neighboring areas.
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807 Sauseng, P., Klimesch, W., Gruber, W., Doppelmayr, M., Stadler, W., & Schabus, M. (2002). The interplay between theta and alpha oscillations in the human electroencephalogram reflects the transfer of information between memory systems. Neuroscience Letters, 324, 121– 124. Sauseng, P., Klimesch, W., Schabus, M., & Doppelmayr, M. (2005). Fronto-parietal EEG coherence in theta and upper alpha reflect central executive functions of working memory. International Journal of Psychophysiology, 57, 97–103. Semlitsch, H., Anderer, P., Schuster, P., & Presslich, O. (1986). A solution for reliable and valid reduction of ocular artifacts, applied to the P300 ERP. Psychophysiology, 23, 695–703. Shiffrin, R. M., & Geisler, W. (1973). Visual recognition in a theory of information processing. In R. L. Solso (Ed.), Contemporary Issues in Cognitive Psychology: The Loyola Symposium (pp. 53–101). Washington: Winston. Sirigu, A., Duhamel, J. R., Cohen, L., Pillon, B., Dubois, B., & Agid, Y. (1996). The mental representation of hand movements after parietal cortex damage. Science, 273, 1564–1568. Stam, C. J., de Haan, W., Daffertshofer, A., Jones, B. F., Manshanden, I., van Cappellen van Walsum, A. M., et al. (2009). Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer’s disease. Brain, 132, 213–224. Stam, C. J., Jelles, B., Achtereekte, H. A. M., Rombouts, S. A. R. B., Slaets, J. P. J., & Keunen, R. W. M. (1995). Investigation of EEG non linearity in dementia and Parkinson’s disease. Electroencephalography and Clinical Neurophysiology, 95, 309–317. Stam, C. J., & van Dijk, B. W. (2002). Synchronization likelihood: An unbiased measure of generalized synchronization in multivariate data sets. Physica D, 163, 236–251. Stark, A., & Zohary, E. (2008). Parietal mapping of visuomotor transformations during human tool grasping. Cerebral Cortex, 18, 2358– 2368. Steriade, M., Gloor, P., Llinas, R. R., Lopes da Silva, F. H., & Mesulam, M. M. (1990). Basic mechanisms of cerebral rhythmic activities. Electroencephalography and Clinical Neurophysiology, 76, 481–508. Suchan, B., Melde, C., Herzog, H., Homberg, V., & Seitz, R. J. (2008). Activation differences in observation of hand movements for imitation or velocity judgement. Behavioural Brain Research, 188, 78–83. Woyshville, M. J., & Calabrese, J. R. (1994). Quantification of occipital EEG changes in Alzheimer’s disease utilizing a new metric: The fractal dimension. Biological Psychiatry, 35, 381–387. Zentgraf, K., Stark, R., Reiser, M., Kunzell, S., Schienle, A., Kirsch, P., et al. (2005). Differential activation of pre-SMA and SMA proper during action observation: Effects of instructions. NeuroImage, 26, 662–672. (Received June 2, 2009; Accepted September 5, 2009)
APPENDIX 1 Epoch length is an important parameter to take into consideration in non linear analyses. Epochs should be long enough for reliable estimates of non linear measures but not too long, otherwise the requirement of stationarity is not fulfilled (Stam, Jelles, Achtereekte, Rombouts, Slaets, & Keunen, 1995). In the literature, employing short epoch length to compute non linear analyses is not frequent (Besthorn, Sattel, Geiger-Kabisch, Zerfass, & Forstl, 1995; Pijn, van Neerven, Noest, & Lopes da Silva, 1991; Stam et al., 1995; Woyshville & Calabrese, 1994). However, it has been proven that employing short EEG data segments allows reliable results. For example, Besthorn et al. (1995) investigated the influence of epoch length on the estimation of dimensional complexity in normal healthy subjects. Four epoch lengths of 512, 1,024, 2,048, and 4,096 points were considered (sampling frequency 5 200 Hz). Besthorn et al. (1995) found that, irrespective of the epoch length, values of dimensional complexity were robust from 512 to 1,046 points (i.e., 2.5 s to 20 s).
Similarly, Stam et al. (1995) examined the influence of epoch length on the estimations of correlation dimension (D2), Lyapunov exponent (L1), and Kolmogorov entropy (K2) based on spatial delay embedding in healthy subjects. Five epoch lengths of 100, 200, 300, 400, and 500 points were considered (sampling frequency 5 200 Hz). Stam et al. (1995) demonstrated a significant increase in all measures when epoch length was increased from 200 to 300 (i.e., 1 s to 1.5 s) and from 300 to 400 points (i.e., 1.5 s to 2 s). Further increase of the sample size to 512 displayed no significant increase in any of the measures. As far as SL is concerned, one of the issues in developing this measure was to have a general, unbiased measure of synchronization that could track rapid changes in levels of synchronization. The high temporal resolution of this measure has been proven for simulated data and real data in Stam and van Dijk’s paper (2002). In particular, it should be noted that SL displayed multiple rapid changes of gamma band synchronization in healthy subjects. The fact that most studies using SL measure
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employed longer epochs compared to that used in the present study should not be seen as an intrinsic limitation of this technique. What is required is an epoch length that is long enough to incorporate the lagn embedding dimension. Thus, if the values of the lag and the embedding dimension are small, short epoch can be analyzed. For instance, if the values were 1 sample for the lag, 10 for the embedding dimension, and 10 for the Theiler correction, epochs of 50–100 samples can be analyzed. To extend this work, a simulation has been undertaken to demonstrate that SL can be used on short epoch lengths. In the present case, the epoch length was 100 samples, and there were 21 channels. Parameters for the SL computation were: 1 sample for
the lag; 5 for the embedding dimension; 5 for the Theiler correction (w1); 0.05 for Pref; and 1 for the speed. The epoch, which has been examined, was filled with uncorrelated white noise. In the middle part, namely, from sample 30 to sample 80, correlation has been induced by copying channel 1 to all the other channels (see Figure A1). SL was then computed and the results were displayed in Figure A2. From sample 30 to sample 80, a great increase of synchronization was detected. This simulation shows that: (i) SL can be used with short epoch lengths, and (ii) even within a very short epoch length, SL can still pick up reliably very brief fluctuations of synchronization.
Figure A1. Experimental data used for the simulation.
Figure A2. The SL within a 100 sample-epoch length. The abscissa represents time (in seconds), the ordinate indicates the EEG channels. The value of the SL for each channel and each time point is indicated through a color scale; red shades correspond to higher levels of synchronization whereas blue shades represent lower levels of synchronization. The numbers on the right scale indicate the average synchronization values for each of the channels. The curve at the top of each diagram reflects the time course of the SL averaged over all channels.
Psychophysiology, 47 (2010), 809–813. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.00986.x
Evidence for a new late positive ERP component in an attended novelty oddball task
CRAIG G. McDONALDa, FRANCES H. GABBAYb, JEREMY C. RIETSCHELc, and CONNIE C. DUNCANb a
Department of Psychology, George Mason University, Fairfax, Virginia, USA Clinical Psychophysiology and Psychopharmacology Laboratory, Department of Psychiatry, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA c Neuroscience and Cognitive Science Program, Department of Kinesiology, University of Maryland, College Park, Maryland, USA b
Abstract In attended novelty oddball tasks, rare nontarget stimuli can elicit two late positive ERP components: P3a and P300. In passive oddball tasks, P300 is not elicited by these stimuli. In passive tasks, however, P3a is accompanied by another positive component, termed eP3a, which may have evaded detection in attended oddball tasks because of its spatiotemporal overlap with P300. To address this, temporal-spatial principal components analysis was used to quantify ERPs recorded in attended three-tone and novelty oddball tasks. As expected, novel stimuli elicited both P3a and P300. The analysis also identified a third component, evident in novelty ERPs as an inflection on the leading edge of P3a. This component has the same antecedent conditions as P3a, but is earlier and more centrally distributed. Its spatiotemporal characteristics suggest that it may be the eP3a component recently described in passive oddball tasks. Descriptors: Event-related potentials, eP3a, P3a, P300, PCA, Information processing
stimuli (Comerchero & Polich, 1999; Katayama & Polich, 1998). This observation is consistent with evidence that the novelty P3 is indistinguishable from the P3a (Simons, Graham, Miles, & Chen, 2001; Spencer, Dien, & Donchin, 2001), a component originally identified using a two-tone oddball sequence (Squires, Squires, & Hillyard, 1975). Squires et al. (1975) found that the P3a was elicited by rare tones, regardless of whether subjects attended to or ignored the stimulus train. Collectively, these findings suggest that a more appropriate label for this frontal component is P3a (Dien, Spencer, & Donchin, 2004). The P3a can be elicited by rare nontarget stimuli in both active and passive three-stimulus oddball tasks. In an active task, subjects are required to respond either overtly or covertly to rare target stimuli, whereas in a passive task, subjects are instructed not to respond. In passive oddball tasks, novel stimuli (complex environmental sounds) elicit a P3a that can be parsed into two separate components: early P3a (eP3a) and late P3a (lP3a) (Escera, Alho, Winkler, & Na¨a¨ta¨nen, 1998; Yago, Escera, Alho, Giard, & Serra-Grabulosa, 2003). The eP3a has a central topography, whereas lP3a has a fronto-central topography similar to the P3a elicited in active oddball tasks. Moreover, although both eP3a and lP3a were elicited only by novel stimuli, Escera et al. (1998) found that the auditory lP3a was larger when subjects were instructed to ignore auditory stimuli and respond to visual stimuli in a bimodal stimulus train than when they were instructed to ignore the auditory stimuli and read a book. In contrast, the amplitude of eP3a did not differ between these tasks. Escera et al. argued that the two tasks likely imposed different attentional demands and that, therefore, lP3a but not eP3a was modulated by attention. The
Unexpected complex auditory stimuli elicit a late positive component of the event-related brain potential (ERP) frequently referred to as the novelty P3 (Courchesne, Kilman, Galambos, & Lincoln, 1984). This component occurs in the latency range of the P300 (or P3b) but can be distinguished from it by its more frontal scalp distribution. Although the functional significance of the novelty P3 continues to be debated, a common interpretation is that it reflects a brain electrical correlate of the orienting response (Friedman, Cycowicz, & Gaeta, 2001; Knight, 1996). More recently, it has been shown that simple tones can elicit the novelty P3, provided that the standard-target discrimination is difficult and the rare nontarget stimulus is distinct from the other This research was conducted in the Clinical Psychophysiology and Psychopharmacology Laboratory in the Department of Psychiatry at the Uniformed Services University of the Health Sciences, and was supported by grants from NIDA (DA09475 and DA018674) and the Department of Veterans’ Affairs (DOD-83). We thank Joe Dien, PhD, for helpful advice on principal components analysis. We thank Christopher T. Lovelace, PhD, for technical assistance, and Jennifer D. Alder, Virginia L. Yoerg, Tracey Shissler, Erin K. Morris, and Mihyun Stacey Lee for their assistance with data collection. We also thank the anonymous reviewers and the editor for their insightful comments. The opinions and assertions expressed herein are those of the authors and are not to be construed as reflecting the views of the Uniformed Services University or the U.S. Department of Defense. Address reprint requests to: Frances H. Gabbay, PhD, Clinical Psychophysiology and Psychopharmacology Laboratory, Department of Psychiatry, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Rd., Bethesda, MD 20814-4799 USA. E-mail:
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810 precise nature of the cognitive process indexed by eP3a remains to be determined. At present, all that can be said is that eP3a is elicited when the expected regularity of the sensory environment is interrupted by stimuli of sufficient salience (Escera et al., 1998). Whether P3a in fact comprises two distinct components in an attended oddball task has not been established. We suggest that this may be due to the inherent difficulty in parsing components with extensive temporal and spatial overlap (Dien et al., 2004). Given that Escera and his colleagues employed a passive task in which responses to non-novel deviant stimuli were not required, a P300 was not elicited. It is possible that the absence of this component facilitated identification of the remaining late positive components. The recent development of temporal-spatial (or, alternatively, spatial-temporal) principal components analysis (PCA) has made this problem more accessible (Spencer, Dien, & Donchin, 1999, 2001; Goldstein, Spencer, & Donchin, 2002; Curran & Dien, 2003; Dien et al., 2004). In the current study, we used temporal-spatial PCA to parse the late positive components of the ERP elicited in two threestimulus oddball tasks. Our primary aim was to determine whether rare nontarget stimuli, presented in the context of an attended oddball task, elicit a late positive component comparable to eP3a. Both a three-tone task and a novelty oddball task were used to evaluate whether, as is the case with passive tasks, eP3a is selectively elicited by novel stimuli.
C. G. McDonald et al. Cycowicz, & Friedman, 1996). The mean duration of these 48 stimuli was 347 ms (SD 5 53, range 5 214–399 ms); all tones were 336 ms in duration. Stimuli were presented binaurally at 79 dB SPL over headphones, at the rate of one stimulus per second. Each task comprised a single block of 480 trials. Participants were instructed to respond by pressing a button with the thumb of their dominant hand as soon as they heard the target tone. Both speed and accuracy of performance were emphasized. Electrophysiological Recording Grass amplifiers were used with Polyview Pro/32 software (Grass Technologies, West Warwick, RI) to acquire data. Data collection and stimulus presentation were controlled by a desktop computer (Gateway 2000, Gateway, Inc., Irvine, CA) using InstEP software (InstEP Systems, Ottawa, Canada). The sampling frequency was 256 Hz. A commercially available electrode cap (Electro-Cap International, Eaton, OH), outfitted with Ag/ AgCl electrodes, was used to record the electroencephalogram (EEG). A customized version permitted EEG to be recorded from 30 scalp electrodes. This set included but was not limited to the 20 scalp electrodes specified by the International 10-20 System (Jasper, 1958). Data were recorded from ten midline sites:
Methods Participants Participants were healthy volunteers (n 5 32 men, n 5 30 women) who were paid for their participation (mean age 5 25.4, SD 5 4.6). The research protocol was approved by the Uniformed Services University Institutional Review Board, and written informed consent was obtained for all subjects. To minimize the effects of menstrual cycle variation on ERP components (O’Reilly, Cunningham, Lawlor, Walsh, & Rowan, 2004), an effort was made to schedule the sessions between Day 2 and Day 10 of the menstrual cycle, when estrogen and progesterone levels are minimal. Procedures Subjects were seated in a comfortable armchair in a sounddampened room. The experimenter observed the participant via a television monitor from an adjacent room in which the stimulus-generating and recording apparatus were located. An intercom permitted communication between the participant and the experimenter. During ERP recordings, participants were asked to keep their eyes open, to minimize eye and body movements, to keep their feet flat on the floor, and to perform the designated task. Cushioning was used as necessary to minimize movement of the neck and head. Oddball Tasks Subjects performed both a three-tone oddball task and a novelty oddball task; task order was counterbalanced across subjects. In each, standard stimuli (p 5 0.8) were 1000-Hz tones and targets (p 5 0.1) were 2000-Hz tones. Rare nontarget stimuli (p 5 0.1) were 500-Hz tones in the three-tone oddball and novel environmental sounds in the novelty oddball. Forty-eight unique novel sounds were taken from a larger set of 96 sounds obtained from the New York State Psychiatric Institute (Fabiani, Kazmerski,
Figure 1. Grand-average ERP waveforms at the midline electrode sites. The data from the three stimulus categories are superimposed for the three-tone and novelty oddball tasks. Rare targets in both tasks elicited a large positive component (labeled P300) that was maximal at Pz. In addition, rare nontargets in the novelty task elicited a fronto-central positivity, labeled P3a. Note the inflection on the leading edge of this positivity, labeled eP3a.
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Figure 2. The temporal loadings (left panel) display the time courses of the first four factors extracted by the PCA. T1 and T3 correspond to P3a/P300 and eP3a, respectively. T2 and T4 likely correspond to the reorienting negativity and N2b, respectively. The latter factors were not considered further. The spatial factor loadings (right panel) are presented in the form of topographic plots. The first three spatial factors derived from T1 and T3 are shown. Temporal-spatial factors T3S1, T1S2, and T1S1 correspond to eP3a, P3a, and P300, respectively. Factor loadings are voltage-scaled.
Fpz, AFz, Fz, FCz, Cz, CPz, Pz, POz, Oz, and Iz, as well as from Fp1, Fp2, F3, F4, F7, F8, FT7, FT8, FC3, FC4, C3, C4, T7, T8, P3, P4, P7, P8, O1, and O2 (Daly & Pedley, 1990). All scalp electrodes were referred to linked earlobes; the impedances of the two reference electrodes were kept comparable throughout testing (Miller, Lutzenberger, & Elbert, 1991). In addition, Ag/AgCl electrodes were placed at left supraorbital and suborbital sites to record vertical electro-oculographic (EOG) activity, at left and right outer canthal sites to monitor horizontal EOG, and at AF3 (ground). Electrode impedance was checked before and after each task, and was maintained below 5 kO. The EEG and EOG were recorded with Grass amplifiers set to a bandpass of 0.01 to 30 Hz. Data Quantification Single-trial EEG data were digitized online and stored for offline analysis. Trials with muscle and movement artifacts were discarded, and the remaining data were corrected for EOG artifact using the frequency-domain EOG-correction algorithm described by Woestenburg, Verbaten, and Slangen (1983). Separate artifact-free averages were computed for each category of trial. Trials with incorrect responses were discarded. Average ERPs from both oddball tasks were subjected to temporal-spatial PCA (Curran & Dien, 2003). Because our goal was to characterize the overlapping late positive components, the PCA was restricted to the epoch from 200 to 475 ms (see Simons et al., 2001). The temporal-spatial approach involved performing a temporal PCA, followed by spatial PCAs of temporal factors of interest. The variables for the temporal PCA were the timepoints of the average ERPs. For the spatial PCAs, the variables were the factor scores for the 30 scalp sites. A covariance matrix was used for all PCAs, and factor loadings were subjected to Promax rotation to obtain simple structure. The PCAs were conducted using the Matlab ERP PCA toolbox1.22 (Dien & Frishkoff, 2005; http://sourceforge.net/projects/erppcatoolkit/ files/). Factor scores, which reflect the contribution of each factor to each ERP, were subjected to repeated-measures analysis of variance (ANOVA). The Greenhouse-Geisser correction for violation of sphericity was applied where appropriate. Follow-up comparisons were made using paired t-tests corrected for multiple comparisons (Howell, 2002).
Results Accuracy in response to target stimuli was high for both the three-tone (mean accuracy 5 97.4%) and novelty oddball tasks (mean accuracy 5 98.0%). Average response time (RT) to targets was 362 ms for the three-tone task and 376 ms for the novelty oddball task. Accuracy did not differ significantly between the two tasks; there was, however, a significant difference between tasks with respect to RT (t(61) 5 2.76; po.01). Figure 1 shows the grand-average ERPs recorded at selected midline sites for the three-tone and novelty oddball tasks. In both tasks, targets elicited a large positive component with a parietal maximum. In the novelty task, rare nontarget stimuli elicited prominent parietal and fronto-central positivities of comparable latency. An inflection on the leading edge of the fronto-central positivity (labeled at Cz) can also be seen in the grand-average waveforms for the novelty task (Figure 1). The temporal PCA produced a factor (T1) with a time course (peak latency 5 370 ms) corresponding to the parietal positivity and its more anterior counterpart; this factor accounted for 49% of the data variance. A second factor (T3), which accounted for 14% of the data variance, exhibited a time course (peak latency 5 271 ms) corresponding to the inflection observed on the leading edge of the fronto-central positivity (Figure 2). Spatial PCA of T1 produced two factors comparable to those described in recent PCA investigations of the late positive components elicited in three-stimulus oddball tasks (Spencer et al., 2001; Goldstein et al., 2002; Dien et al., 2004). The first, T1S1, had a parietal distribution consistent with the topography of the classic P300. The second, T1S2, exhibited a fronto-central topography characteristic of P3a. The first spatial factor derived from T3 (T3S1) had a central scalp distribution comparable to that of the eP3a component identified by Escera et al. (1998) and Yago et al. (2003) (Figure 2).1 These components are identified in the grand-average ERPs shown in Figure 1. Statistical analysis of factor scores was carried out to evaluate the effects of stimulus category and task on these three factors.
1 Because this component has spatiotemporal characteristics that are remarkably similar to the eP3a described by Escera et al. (1998), we chose to use the label ‘eP3a’ throughout the paper.
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Figure 3. Factor scores for the temporal-spatial factors corresponding to eP3a, P3a, and P300. For each factor, mean scores for standard, rare nontarget, and target stimuli are plotted for the three-tone and novelty tasks.
For the sake of clarity, factors T1S1, T1S2, and T3S1 are hereafter referred to as P300, P3a, and eP3a, respectively. Significant main effects of task (F(1,60) 5 73.9, po.0001) and stimulus category (F(2,120) 5 112.8, po.0001, e 5 .65) were observed for the P300 factor. There was also a significant task by stimulus category interaction (F(2,120) 5 142.5, po.0001, e 5 .78). For the three-tone task, this factor was significantly larger for targets when compared to rare nontargets (po.0001) or standards (po.0001). Scores for standards and rare nontargets did not differ. In the novelty task, target (po.0001) and rare nontarget (po.0001) scores were significantly higher than those for standards. Rare nontarget and target scores did not differ significantly in the novelty task (Figure 3). For the P3a factor, there were significant effects of task (F(1,60) 5 48.3, po.0001) and stimulus category (F(2,120) 5 17.6, po.0001, e 5 .70), and a task by stimulus interaction (F(2,120) 5 103.7, po.0001, e 5 .84). For the threetone task, factor scores for all three stimulus categories were low and did not differ with respect to one another. In the novelty task, the P3a factor was significantly larger for rare nontargets as compared to standards (po.0001) and targets (po.0001). Factor scores for standards and targets did not differ significantly from each other in the novelty task (Figure 3). For the eP3a factor, significant main effects of task (F(1,60) 5 11.7, po.005) and stimulus category (F(2,120) 5 5.2, po.025, e 5 .72) and a task by stimulus category interaction (F(2,120) 5 12.6, po.001, e 5 .69) were observed. For the three-tone task, pairwise comparisons revealed no significant differences as a function of stimulus category. For the novelty task, factor scores for rare nontargets were significantly greater than those for targets (po.0001) and standards (po.01), which did not differ (Figure 3).
oddball task elicited the P300 component. In the three-tone task, rare nontarget stimuli failed to elicit eP3a, P3a, or P300 components. Consistent with previous investigations, P3a was elicited at the same latency as the parietal P300 but had a fronto-central scalp topography (Goldstein et al., 2002; Spencer et al., 1999, 2001). In contrast, both the temporal and spatial characteristics of P3a and eP3a differed, with the latter characterized by a shorter latency and a more central topography. In this study, eP3a was elicited by rare nontargets in the novelty task (i.e., by novel sounds) but not in the three-tone task (i.e., not by simple tones). This is consistent with the findings for eP3a elicited in a passive oddball taskFin which only novel sounds elicited eP3aFand is therefore consistent with the argument that the two components reflect the same aspect of processing. However, it has been reported that P3a can be elicited in a three-tone task in which the standard-target discrimination is difficult, a phenomenon that may reflect attentional modulation of adaptation to infrequent stimuli (Comerchero & Polich, 1999; Kimura, Katayama, & Murohashi, 2008).2 It remains to be seen if eP3a would also be elicited by rare nontargets in a more difficult three-tone task. The present study provides evidence of a component with properties similar to those of the eP3a described by Escera et al. (1998). Although eP3a and P3a shared the same antecedent conditions in our study, there is evidence that, in the passive oddball paradigm, the putative equivalents of these components can be functionally dissociated. Specifically, P3a, but not eP3a, can be modulated by stimulus valence (Holeckova, Fischer, Giard, Delpuech, & Morlet, 2006; Roye, Jacobsen, & Schro¨ger, 2007) and attention (Escera et al., 1998). The finding that eP3a appears to be unaffected by attentional manipulation suggests that this component is not a neural correlate of the orienting response (Escera et al., 1998; Friedman et al., 2001). In both passive and active oddball tasks, eP3a appears to be elicited only
Discussion In the attended novelty oddball task, rare nontarget stimuli elicited three late positive ERP components. The first may be the eP3a component, observed previously only in passive oddball tasks. The second component, P3a, has been observed consistently in passive as well as active oddball paradigms. Also in accord with previous work, rare nontarget stimuli in the novelty
2 The P300 elicited by rare nontargets has also been shown to be influenced by task difficulty (Comerchero & Polich, 1999). One might have expected a relatively small P300 to be elicited in the three-tone task used in the present study (i.e., a task with an easy standard-target discrimination). It is not clear why no P300 (vs. a smaller P300) was elicited by rare nontargets in this task. As expected, however, a large P300 was elicited by target stimuli in both tasks (Duncan-Johnson & Donchin, 1977; Sutton, Braren, Zubin, & John, 1965).
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by stimuli that deviate substantially from the expected uniformity of the sensory stream (Escera et al., 1998; Roye et al., 2007; Wetzel & Schro¨ger, 2007). Thus, the findings of the present study are consistent with the proposal, based on studies using passive oddball tasks, that eP3a instead reflects preattentive processing of particularly salient stimuli (Escera, Yago, & Alho, 2001; Roye et al., 2007). Additional studies will be required to further evaluate whether the component described here is comparable to the
eP3a reported previously for passive oddball tasks, and to ascertain the functional significance of eP3a. In conclusion, the current study provides evidence that three late positive componentsFP300, P3a, and eP3aFcan be elicited by unexpected novel nontarget stimuli in an attended oddball task. The spatiotemporal characteristics of the latter component suggest that it may be equivalent to the eP3a component described previously in passive oddball tasks.
REFERENCES Comerchero, M. D., & Polich, J. (1999). P3a and P3b from typical auditory and visual stimuli. Clinical Neurophysiology, 110, 24–30. Courchesne, E., Kilman, B. A., Galambos, R., & Lincoln, A. J. (1984). Event-related brain potentials (ERPs) of 13–21-year-old autistic subjects and age-matched controls were elicited by auditory stimuli in a variation of orienting. Electroencephalography and Clinical Neurophysiology, 59, 238–248. Curran, T., & Dien, J. (2003). Differentiating amodal familiarity from modality-specific memory processes: An ERP study. Psychophysiology, 40, 979–988. Daly, D. D., & Pedley, T. A. (1990). Current practice of clinical electroencephalography. New York: Raven Press. Dien, J., & Frishkoff, G. A. (2005). Introduction to principal components analysis of event-related potentials. In T. Handy (Ed.), Eventrelated potentials: A methods handbook (pp. 189–208). Cambridge, MA: MIT Press. Dien, J., Spencer, K. M., & Donchin, E. (2004). Parsing the late positive complex: Mental chronometry and the ERP components that inhabit the neighborhood of the P300. Psychophysiology, 41, 665–678. Duncan-Johnson, C. C., & Donchin, E. (1977). On quantifying surprise: The variation of event-related potentials with subjective probability. Psychophysiology, 14, 456–467. Escera, C., Alho, K., Winkler, I., & Na¨a¨ta¨nen, R. (1998). Neural mechanisms of involuntary attention to acoustic novelty and change. Journal of Cognitive Neuroscience, 10, 590–604. Escera, C., Yago, E., & Alho, K. (2001). Electrical responses reveal the temporal dynamics of brain events during involuntary attention switching. European Journal of Neuroscience, 14, 877–883. Fabiani, M., Kazmerski, V. A., Cycowicz, Y. M., & Friedman, D. (1996). Naming norms for brief environmental sounds: Effects of age and dementia. Psychophysiology, 33, 462–475. Friedman, D., Cycowicz, Y. M., & Gaeta, H. (2001). The novelty P3: An event-related brain potential (ERP) sign of the brain’s evaluation of novelty. Neuroscience and Biobehavioral Reviews, 25, 355–373. Goldstein, A., Spencer, K. M., & Donchin, E. (2002). The influence of stimulus deviance and novelty on the P300 and novelty P3. Psychophysiology, 39, 781–790. Holeckova, I., Fischer, C., Giard, M. H., Delpuech, C., & Morlet, D. (2006). Brain responses to a subject’s own name uttered by a familiar voice. Brain Research, 1082, 142–152. Howell, D. C. (2002). Statistical methods in psychology. Pacific Grove, CA: DuxburThomas Learning. Jasper, H. H. (1958). The ten-twenty electrode system of the International Federation. Electroencephalography and Clinical Neurophysiology, 10, 371–375.
Katayama, J., & Polich, J. (1998). Stimulus context determines P3a and P3b. Psychophysiology, 35, 23–33. Kimura, M., Katayama, J., & Murohashi, H. (2008). Underlying mechanisms of the P3a task-difficulty effect. Psychophysiology, 45, 721– 741. Knight, R. T. (1996). Contribution of human hippocampal region to novelty detection. Nature, 383, 256–259. Miller, G. A., Lutzenberger, W., & Elbert, T. (1991). The linked reference issue in EEG and ERP recording. Journal of Psychophysiology, 5, 273–276. O’Reilly, M. A., Cunningham, C. J., Lawlor, B. A., Walsh, C. D., & Rowan, M. J. (2004). The effect of the menstrual cycle on electrophysiological and behavioral measures of memory and mood. Psychophysiology, 41, 592–603. Roye, A., Jacobsen, T., & Schro¨ger, E. (2007). Personal significance is encoded automatically by the human brain: An event-related potential study with ringtones. European Journal of Neuroscience, 26, 784– 790. Simons, R. F., Graham, F. K., Miles, M. A., & Chen, X. (2001). On the relationship of P3a and the novelty-P3. Biological Psychology, 56, 207–218. Spencer, K. M., Dien, J., & Donchin, E. (1999). A componential analysis of the ERP elicited by novel events using a dense electrode array. Psychophysiology, 36, 409–414. Spencer, K. M., Dien, J., & Donchin, E. (2001). Spatiotemporal analysis of the late ERP responses to deviant stimuli. Psychophysiology, 38, 343–358. Squires, N. K., Squires, K. C., & Hillyard, S. A. (1975). Two varieties of long-latency positive waves evoked by unpredictable auditory stimuli in man. Electroencephalography and Clinical Neurophysiology, 38, 387–401. Sutton, S., Braren, M., Zubin, J., & John, E. R. (1965). Evoked-potential correlates of stimulus uncertainty. Science, 150, 1187–1188. Wetzel, N., & Schro¨ger, E. (2007). Modulation of involuntary attention by the duration of novel and pitch deviant sounds in children and adolescents. Biological Psychology, 75, 24–31. Woestenburg, J. C., Verbaten, M. N., & Slangen, J. L. (1983). The removal of the eye-movement artifact from the EEG by regression analysis in the frequency domain. Biological Psychology, 16, 127–147. Yago, E., Escera, C., Alho, K., Giard, M. H., & Serra-Grabulosa, J. M. (2003). Spatiotemporal dynamics of the auditory novelty-P3 eventrelated brain potential. Cognitive Brain Research, 16, 383–390. (Received January 21, 2009; Accepted September 12, 2009)
Psychophysiology, 47 (2010), 814–821. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.00992.x
The relation of aerobic fitness to neuroelectric indices of cognitive and motor task preparation
KEITA KAMIJO,a,b KEVIN C. O’LEARY,a MATTHEW B. PONTIFEX,a JASON R. THEMANSON,c and CHARLES H. HILLMANa a
Department of Kinesiology & Community Health, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA Japan Society for the Promotion of Science, Tokyo, Japan Department of Psychology, Illinois Wesleyan University, Bloomington, Illinois, USA
b c
Abstract The relation of aerobic fitness to task preparation was examined in a sample of young adults separated into higher- and lower-fit groups according to their maximal oxygen consumption. Participants performed a modified Sternberg working memory task under speed and accuracy instructions while measures of task performance and contingent negative variation (CNV) were collected. Analyses revealed no significant fitness differences between groups on task performance measures. However, frontal CNV amplitude was significantly larger for lower-fit participants compared to higher-fit participants during the speed instructions, an effect not found for the accuracy instructions. These results suggest that lower-fit individuals may rely to a greater extent on cognitive control processes to respond under speeded conditions, whereas higher-fit individuals may maintain a more constant level of control irrespective of the task instructions. Descriptors: Aerobic fitness, Cognitive control, Task preparation, Contingent negative variation
Gage, 1999). Further, recent functional magnetic resonance imaging research has demonstrated that aerobic fitness training increases brain volume, including both gray and white matter (Colcombe et al., 2006), and improves functional connectivity between brain regions during a task requiring variable amounts of cognitive control (Colcombe et al., 2004). Thus, of particular interest are recent studies that have indicated a disproportionally larger beneficial relation for tasks or task components requiring extensive cognitive control (Colcombe & Kramer, 2003). Cognitive control is used to describe the ability to filter and suppress irrelevant information, thoughts, and actions in favor of relevant ones (Casey, Galvan, & Hare, 2005), with working memory, inhibition, and mental flexibility thought to comprise core processes underlying such abilities (Diamond, 2006). Most studies investigating the relation of aerobic fitness to adult cognition have focused on inhibition, with considerably fewer studies examining other aspects of cognitive control (i.e., working memory). In addition, a relatively small literature has employed event-related brain potentials (ERPs) to understand this relationship (Hillman et al., 2006; Kamijo & Takeda, 2009; Pontifex, Hillman, & Polich, 2009; Themanson & Hillman, 2006; Themanson, Hillman, & Curtin, 2006; Themanson, Pontifex, & Hillman, 2008). These ERP studies have indicated benefits of aerobic fitness or physical activity on cognitive control in not only older adults but also in younger adults. The advantage of the ERP approach is that it provides information regarding discrete cognitive processes that occur between stimulus evaluation and response execution. Two ERP
Evidence for a positive relationship between aerobic fitness and cognitive function has been extensively provided over the last several decades (for review, see Hillman, Erikson, & Kramer, 2008). The beneficial effects of aerobic fitness have been found using a wide variety of tasks that tap a number of psychological processes from simple reaction time (RT; Dustman et al., 1984; Spirduso & Clifford, 1978) to more effortful cognitive control (Hillman, Kramer, Belopolsky, & Smith, 2006; Kramer et al., 1999; but, for review, see Colcombe & Kramer, 2003). Although the underlying mechanisms for the beneficial effects of aerobic fitness are not well understood, nonhuman animal models have suggested that aerobic exercise increases nerve growth factors such as brain-derived neurotrophin factor (Neeper, Gomez-Pinilla, Choi, & Cotman, 1995) and insulin-like growth factor I (Carro, Trejo, Busiguina, & Torres-Aleman, 2001), which may result in an increase in the number of synaptic connections and the development of new neurons that support learning and memory (Lu & Chow, 1999; van Praag, Christie, Sejnowski, & Support for our research and the preparation of this article were provided by the Japan Society for the Promotion of Science Postdoctoral Fellowships for Research Abroad to Keita Kamijo and grants from the National Institute of Mental Health (F31 MH076463) to Jason Themanson and the National Institute of Child Health and Human Development (RO1 HD060385) to Charles Hillman. Address reprint requests to: Keita Kamijo, Ph.D., Department of Kinesiology & Community Health, 313 Louise Freer Hall, 906 South Goodwin Avenue, University of Illinois, Urbana, IL 61801, USA. E-mail:
[email protected] 814
Aerobic fitness and task preparation components of interest relative to response processes are the lateralized readiness potential (LRP) and the contingent negative variation (CNV). A previous investigation assessing the relationship between physical activity and the LRP, which reflects preparation for a motor response (Coles, 1989; Gratton, Coles, Sirevaag, Eriksen, & Donchin, 1988), has indicated shorter LRP latency (closer to response onset) for active individuals compared to sedentary individuals, suggesting that active individuals prepare their response more rapidly compared to sedentary individuals (Kamijo & Takeda, 2009). Further, Hillman, Weiss, Hagberg, and Hatfield (2002) investigated aerobic fitness effects on response preparation processes using CNV, a negative slowwave cortical potential of an ERP elicited during the interval between warning (S1) and imperative (S2) stimuli. It is believed that terminal CNV reflects preparatory motor activity when a motor response is required to the S2 (Brunia & van Boxtel, 2001; van Boxtel & Brunia, 1994), which is thought to be superimposed on the readiness potential (Brunia, 1988). Hillman et al. (2002) observed increased CNV amplitude for lower-fit individuals relative to higher-fit individuals, suggesting that lower-fit individuals may allocate more neural resources for response preparation. In other words, response preparation processes of higher-fit individuals may be more efficient than lower-fit individuals. These previous ERP studies (Hillman et al., 2002; Kamijo & Takeda, 2009) imply that aerobic fitness influences not only cognitive processes but also response preparation processes. If this assumption is correct, it stands to reason that aerobic fitness may improve basic motor processes such as simple RT regardless of the cognitive requirements, the latter of which have been extensively studied. Hence, understanding possible differences in fitness effects on cognitive and motor processes may lead to a better understanding of the potential benefits of fitness on brain and cognition. The terminal CNV reflecting response preparation (Brunia & van Boxtel, 2001; van Boxtel & Brunia, 1994) has typically demonstrated a central topographic maximum (van Boxtel & Brunia, 1994). However, several studies have suggested that frontal CNV is associated with cognitive preparation rather than response preparation processes. That is, whereas predominately motoric tasks present with a central CNV, the frontal CNV is evidenced during the encoding of words (Leynes, Allen, & Marsh, 1998) as well as when tasks are more cognitively demanding (Wild-Wall, Hohnsbein, & Falkenstein, 2007) or require greater amounts of cognitive control (Lorist et al., 2000). Thus, it is plausible that central CNV is associated with response preparation processes and frontal CNV is associated with cognitive preparation processes, which under certain task constraints may reflect cognitive control requirements. In the present study, we investigated whether aerobic fitness influenced cognitive processes and/or response processes as reflected by CNV amplitude and topography. A modified Sternberg task (Sternberg, 1966) required participants to encode a memory set (S1) containing an array of three, five, or seven letters and decide whether a single probe (S2), presented at a variable delay, appeared in the encoded array. In this task, increased working memory capacity (i.e., necessitating greater amounts of cognitive control) was required to encode and maintain relevant information for larger set sizes, resulting in decreased response speed (Sternberg, 1966) and accuracy (Marshall, Molle, Siebner, & Born, 2005). To our knowledge, previous studies investigating the relation of aerobic fitness or physical activity on cognitive control have only focused on stimulus evaluation (Hillman et al.,
815 2006; Kamijo & Takeda, 2009) and action monitoring (Themanson & Hillman, 2006; Themanson et al., 2006, 2008) processes. Thus, the goal of this study was to better understand the contribution of aerobic fitness to individual aspects (i.e., cognitive, motor) of the stimulus–response relationship. Accordingly, CNV amplitude and topographic distribution were examined to determine whether aerobic fitness differentially influences cognitive and motor processes using the same paradigm. Lastly, task instructions were varied to emphasize either speed (speed instructions) or accuracy (accuracy instructions) to better examine the fitness–cognition relationship. Falkenstein, Hoormann, Hohnsbein, and Kleinsorge (2003) manipulated task instructions and indicated that fronto-central CNV was larger for the speed relative to the accuracy condition, suggesting that the increase in CNVamplitude may reflect increased effort allocation during speeded tasks. In addition, Wild-Wall et al. (2007) showed that increased frontal CNV amplitude was observed for middleaged individuals compared to young individuals only during a more effortful task condition (i.e., search task vs. nonsearch task), indicating that middle-aged individuals compensate for age-related decline in cognitive function through the use of a more effortful task preparation strategy. Taken together, more effortful task conditions (e.g., speed instructions) may be more sensitive for detecting subtle changes in cognitive function. Based on previous research, it was hypothesized that smaller CNV amplitude, reflecting increased efficiency, would be observed for the higher-fit group at both frontal and central regions compared to the lower-fit group, indicating more efficient cognitive and motor preparation. Further, for the frontal CNV, it was expected that aerobic fitness effects would be selectively greater during more cognitively demanding tasks (i.e., larger encoding requirements), and during task conditions requiring more effortful response preparation (i.e., speed instructions).
Methods Participants Seventy-two undergraduate students were recruited from undergraduate kinesiology courses at the University of Illinois at Urbana-Champaign. All participants reported being free of neurological disorders, cardiovascular disease, any medications that influenced central nervous system function, and had (corrected-to) normal vision. Data from 6 participants were discarded because of excessive noise in the electroencephalogram (EEG) signal. Further because body mass index (BMI) has been associated with cognitive function (Cournot et al., 2006), two participants with a BMI greater than 3 SD above the mean (M 5 23.1 kg/m2, SD 5 3.9) were excluded from analyses. Thus, analyses were conducted on 64 participants (39 women, 25 men), and a median split was performed on maximal oxygen consumption (VO2max) values within each sex to divide the participants into higher- and lower-fit groups. Two male participants who exhibited the same median value were classified into the same group (i.e., higher-fit group, VO2max ! 50 ml/kg/min), and a female participant who exhibited the median value was classified into the lower-fit group. The demographic and fitness information for both groups are summarized in Table 1. All participants provided informed consent to participate in the experiment, which was approved by the Institutional Review Board of the University of Illinois at Urbana-Champaign.
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Table 1. Group Means (" SD) for Demographic Information Categorized by Sex Higher-fit
n Age (years) BMI (kg/m2) VO2max (ml/kg/min)n HRmax (bpm)
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Women
Men
Women
Men
19 19.7 " 1.5 21.8 " 2.2 41.6 " 2.3
13 20.2 " 2.3 22.3 " 1.6 56.5 " 5.3
20 19.5 " 1.1 23.0 " 3.2 35.0 " 2.5
12 21.3 " 3.4 23.9 " 3.0 46.1 " 2.7
190.5 " 5.7
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192.1 " 7.6
193.8 " 7.6
Note: VO2max norms are available in the American College of Sports Medicine (2006) Guidelines for Exercise Testing and Prescription (7th Edition). n Significant difference, unpaired Student’s t test between groups, po.05.
Sternberg Working Memory Task A modified Sternberg task (Sternberg, 1966) asked participants to encode a memory set containing an array of three, five, or seven letters (S1) and decide whether a single probe letter was present in the encoded array (S2). The memory sets were comprised of all capitalized consonants (e.g., RKBXL) and contained no alphabetical consonant strings, whereas the probe letters were lowercase consonants, bilaterally flanked by one, two, or three ‘‘?’’ to match the memory set in physical size and visual content (e.g., ??k??). The participants were instructed to press one of two buttons with their thumbs corresponding to whether the probe was present (right) or absent (left) from the encoded letter array. Probe presence or absence and the three set sizes appeared with equal probability in a random order. The task was performed under the instruction to respond as quickly as possible (speed instructions) and under the instruction to respond as accurately as possible (accuracy instructions). After the 20 practice trials, participants completed 208 trials (52 trials ! 4 blocks) in each instruction condition (i.e., speed, accuracy). The order of instructions was counterbalanced among the participants to minimize potential practice effects. The viewing distance was approximately 1 m. All stimuli were 7-cm-tall white letters presented on a black background for 2000 ms (encoded array: S1) and 200 ms (probe letter: S2), respectively, with a 1500-ms response window (from S2 offset to S1 onset). An equiprobable but randomized interstimulus interval (ISI; from S1 onset to S2 onset) of 4000, 4500, or 5000 ms was used throughout the task block.
Cardiorespiratory Fitness Assessment VO2max was measured using a motor-driven treadmill and a modified Balke protocol (American College of Sports Medicine, 2006), which involved walking/running on a treadmill at a constant speed with increasing grade increments of 2% every 2 min until volitional exhaustion occurred. Oxygen consumption was measured using a computerized indirect calorimetry system (ParvoMedics True Max 2400) with averages for VO2 and respiratory exchange ratio (RER) assessed every 30 s. A polar heart rate (HR) monitor (Model A1, Polar Electro, Finland) was used to measure HR throughout the test. VO2max is determined by the highest oxygen consumption that corresponds to at least two of the following criteria: (1) a plateau in VO2 values despite an increase in exercise workload, (2) maximal heart rate (HR) within 10 beats per minute (bpm) of the age-predicted maximum (220 bpm minus age in years), or (3) a RER greater than 1.10.
ERP Recording Electroencephalographic activity was measured from 64 electrode sites according to the International 10–10 system (Chatrian, Lettich, & Nelson, 1985), referenced to averaged mastoids, with AFz serving as the ground electrode, and impedances kept below 10 kO. To monitor possible artifacts due to eye movements, vertical electrooculogram (EOG) was recorded using electrodes placed above and below the left orbit, and a horizontal EOG was recorded from the outer left and right canthi. Continuous data were digitized at a sampling rate of 500 Hz, amplified 500 times with a DC to 70-Hz filter, and a 60-Hz notch filter using a Neuroscan Synamps amplifier (Neuro, Inc., Charlotte, NC). Off-line EOG correction was applied to the individual trials prior to averaging using a spatial filter (Compumedics Neuroscan, 2003). Data were filtered using a zero phase shift 10-Hz (24 dB/octave) low-pass filter. Trials with a response error or artifact exceeding " 75 mV were rejected. Across groups, a mean of 19 trials (SE 5 0.5) were averaged in each task condition. S1-locked epochs were created from # 100 to 4000, 4500, or 5000 ms around the stimuli based on ISI duration. CNV amplitudes were measured relative to a 100-ms pre-S1 baseline, and the mean amplitude was calculated from the 200-ms period prior to S2 onset. Procedure For each participant, testing occurred on two separate days. On the first visit to the laboratory, participants completed an informed consent, health history, and demographics questionnaire, a handedness inventory (Chapman & Chapman, 1987), and had their height and weight measured to calculate their BMI. After completing all questionnaires, a cardiorespiratory fitness test was then conducted. On the second visit, the Sternberg task was conducted. Individual participants were prepared for neuroelectric measurement in accordance with the Society for Psychophysiological Research guidelines (Picton et al., 2000). The participant was given the task instructions and allowed practice trials before each experimental task. Upon completion of the last condition, all electrodes were removed and participants were briefed on the purpose of the experiment. Statistical Analysis Behavioral data (i.e., RT, response accuracy) were submitted to a 2 (Fitness: higher-fit, lower-fit) ! 2 (Instruction: speed, accuracy) ! 3 (Set Size: three letters, five letters, seven letters) ! 3 (ISI: short, medium, long) mixed-model ANOVA with repeated measures. CNVamplitude was analyzed using a 2 (Fitness) ! 2 (Instruction) ! 3 (Set Size) ! 3 (ISI) ! 5 (Region: Fz, FCz, Cz, CPz, Pz) mixed-model ANOVA with repeated measures. Analyses with three or more within-subject levels employed the Greenhouse–Geisser statistic if the assumption of sphericity was violated. Partial eta squared (Z2p ) values are reported to demonstrate the effect sizes, with .01–.059 representing a small effect, .06–.139 a medium effect, and 4.14 a large effect (Cohen, 1973). Post hoc comparisons were conducted using univariate ANOVAs and Bonferroni corrected t tests. The significance level was set at .05. Results Given the number of variables included in the study design, not all findings are reported in the Results section. With regard to the
Aerobic fitness and task preparation purpose of the study, only those findings that involve Fitness, Instruction, and Set Size factors are presented. Briefly though, the scalp topography of the CNV component indicated continuity of amplitude from frontal to parietal midline sites with the largest amplitude noted at the FCz site (Fz: M 5 ! 2.4 mV, SE 5 0.6; FCz: M 5 ! 3.0 mV, SE 5 0.5; Cz: M 5 ! 2.6 mV, SE 5 0.5; CPz: M 5 ! 2.7 mV, SE 5 0.5; Pz: M 5 ! 1.0 mV, SE 5 0.6). Further, CNV amplitude differed as a function of ISI duration with larger amplitude for longer ISIs (short: M 5 ! 1.2 mV, SE 5 0.4; medium: M 5 ! 2.4 mV, SE 5 0.4; long: M 5 ! 3.4 mV, SE 5 0.5), t(1,63) " 4.2, po.001. Finally, a Task Order # Instruction interaction, F(1,59) 5 6.5, p 5 .014, Z2p ¼ :09, indicated larger CNV amplitude for the speed instruction when that condition preceded accuracy (M 5 ! 4.5 mV, SE 5 0.7) compared to when accuracy preceded speed (M 5 ! 1.5 mV, SE 5 0.6). Task Performance RT analysis revealed main effects for Instruction, F(1,62) 5 204.1, po.001, Z2p ¼ :77, with shorter RT latency during the speed compared to the accuracy instruction, and Set Size, F(1.5,92.3) 5 217.7, po.001, Z2p ¼ :78, with follow up Bonferroni-corrected t tests indicating significantly longer RTs from three letters to seven letters, t(1,63) " 6.6, po.001. These main effects were superseded by an Instruction # Set Size interaction, F(2,124) 5 16.4, po.001, Z2p ¼ :21. However, post hoc analyses revealed Instruction differences for each Set Size, t(1,63) " 13.0, po.001. This interaction indicated that the Instruction differences were larger for larger set sizes (three letters: M 5 169.8 ms, SE 5 11.5; five letters: M 5 197.1 ms, SE 5 15.2; seven letters: M 5 220.0 ms, SE 5 16.4). No significant Fitness effect was observed on RT. Response accuracy analysis revealed main effects for Instruction, F(1,62) 5 28.2, po.001, Z2p ¼ :31, with higher response accuracy during the accuracy compared to the speed instruction, and Set Size, F(1.5,96.1) 5 200.7, po.001, Z2p ¼ :76, with follow up Bonferroni-corrected t tests indicating significant decreases in response accuracy from three letters to seven letters (three letters: M 5 89.2%, SE 5 0.8; five letters: M 5 85.3%, SE 5 1.0; seven letters: M 5 74.3%, SE 5 1.3), t(1,63) " 6.9, po.001. No significant Fitness effect was observed on response accuracy. CNV Figure 1 shows the grand averaged ERP waveforms for each fitness group, instruction as a function of ISI duration, and electrode site. CNV analyses revealed main effects for Instruction, F(1,62) 5 5.0, p 5 .03, Z2p ¼ :08, with larger CNV amplitude during the speed compared to the accuracy instruction, and Set Size, F(2,124) 5 9.6, po.001, Z2p ¼ :13, with follow up Bonferroni-corrected t tests indicating that CNVamplitude for the seven letter set size was larger than the other set sizes, t(1,63) " 2.7, p % .01. A Fitness # Instruction # Region interaction was also observed, F(3.2,200.6) 5 2.6, p 5 .046, Z2p ¼ :04. Breaking down the three-way interaction by examining Fitness # Instruction at each Region revealed a significant interaction only at Fz, F(1,62) 5 5.4, p 5 .02, Z2p ¼ :08. Post hoc analyses indicated that CNV amplitude was significantly larger for lower-fit participants compared to higher-fit participants during the speed instructions, t(1,62) 5 2.8, p 5 .007, with no such effect observed for the accuracy instructions, t(1,62) 5 0.6, p 5 .58 (Figure 2). No interaction was observed at other regions, F(1,62) % 0.8, p " .37, Z2p % :01. Further, the two-way ANOVAs (Fitness #
817 Instruction at each Region) revealed a main effect for Instruction at the Cz and CPz sites, F(1,62) " 8.9, p % .004, Z2p " :13, with larger CNV amplitude during the speed compared to the accuracy instruction (Figure 2). Lastly, the two-way ANOVA also indicated a marginal Fitness effect at Cz, F(1,62) 5 3.5, p 5 .066, Z2p ¼ :05, with larger CNV amplitude for the lower-fit compared to the higher-fit group. Discussion In this study, the relation of aerobic fitness to task preparation processes was investigated to understand whether a specific relation of fitness to either cognitive or motor processes existed or whether the relationship was generalized across both components of preparatory response processing. We used two task instructions that emphasized either speed or accuracy and manipulated encoding requirements (three, five, or seven letters) to vary cognitive control demands. Analyses revealed that participants responded more slowly and accurately during the accuracy instructions relative to the speed instructions and more slowly and less accurately from three letters to seven letters. Although a group difference in behavioral measures was not observed, selective effects of aerobic fitness based on task instructions were found for frontal CNV. Alternatively, encoding set size was not associated with aerobic fitness for either frontal or central CNV amplitude, suggesting that the processes reflected by the CNV are not sensitive to this cognitive manipulation. Frontal CNV The current findings indicated that frontal CNV amplitude was larger for the lower-fit relative to the higher-fit participants during the speed instruction condition. Recent studies have suggested that frontal CNV increases when greater amounts of cognitive control are required to act correctly within the stimulus environment (Lorist et al., 2000; Wild-Wall et al., 2007) or when an increase in resource allocation is required by effortful demands associated with task instruction (i.e., speed instructions; Falkenstein et al., 2003). Based on these findings, and the known beneficial relation of fitness to other cognitive processes (Hillman et al., 2006; Kamijo & Takeda, 2009; Pontifex et al., 2009; Themanson & Hillman, 2006; Themanson et al., 2006, 2008), it is plausible that the increased frontal CNVamplitude evidenced by the lower-fit participants was associated with the necessity to allocate greater amounts of cognitive control to respond within the more demanding task environment when instructions required speeded responding. Such a relationship is supported by the literature indicating that CNVamplitude is augmented under speeded task instruction resulting from increased task demands (Falkenstein et al., 2003). The current results suggest that such a task environment places disproportionately larger demands upon lower-fit individuals, necessitating larger CNV amplitude, which likely reflects greater preparation for action. Alternatively, higher-fit individuals appear to maintain constant control over the relevant cognitive processes irrespective of the task instructions, resulting in more efficient cognitive preparation during the speed condition. This efficiency is especially apparent given the lack of fitness-related differences in task performance. The current findings also support previous studies examining fitness effects on the error-related negativity potential (ERN; Themanson & Hillman, 2006; Themanson et al., 2006). The ERN is maximal over fronto-central electrode sites and is con-
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sidered a neuroelectric correlate of a subset of cognitive control processes involved in action monitoring (Holroyd & Coles, 2002). Previous ERN studies have indicated smaller amplitude for higher-fit relative to lower-fit individuals during a task with speeded response task instructions, suggesting that increased topdown control among higher-fit individuals decreased activation of action monitoring processes (Themanson & Hillman, 2006; Themanson et al., 2006) or, in other words, the cognitive control of action. In a similar manner, the present findings provide additional evidence that higher-fit individuals may increase topdown cognitive control to efficiently allocate task preparation processes. It is noteworthy that a previous ERN study manipulating task instructions indicated that aerobic fitness effects were only observed for the accuracy instructions (Themanson et al., 2008). Such a finding on the surface may appear to conflict with the present findings. However, ERN amplitude is increased when accuracy is emphasized over speed due to changes in the action monitoring system (Gehring, Goss, Coles, Meyer, & Donchin, 1993). That is, when accuracy is stressed, the monitoring system is more sensitive because of the increased salience of the error (Gehring et al., 1993) or increased attentional focus (Yeung, Botvinick, & Cohen, 2004). Thus, it appears that greater amounts of cognitive control are required for the accuracy condition relative to the speed condition during action monitoring processes, with aerobic fitness enhancing ERN amplitude during this condition. Alternatively, in the current study, frontal CNV amplitude was larger for the speed condition compared to the accuracy condition, which is consistent with the findings of Falkenstein and colleagues (2003). These conflicting findings may be the result of the differential processes reflected by the two components (i.e., ERN, CNV). In the S1-S2 task, participants may need to allocate more resources because of the effortful demands of the speed condition (Falkenstein et al., 2003) to prepare a response as quickly as possible. In contrast, less resource allocation may be needed during task preparation processes for the accuracy instructions, but more cognitive control would be required after S2 presentation (i.e., stimulus evaluation processes and action monitoring processes). Thus, changes in cognitive control requirements resulting from task instructions may differ depending on the aspect of cognition studied. Accordingly, the ERP components (i.e., ERN or CNV) would be expected to modulate differentially by fitness based on the cognitive processes reflected by each component. Taken together though, they provide convergent evidence for a beneficial relationship of aerobic fitness on the cognitive control of action. In the context of the relationship of fitness to cognitive control, we hypothesized that group differences in frontal CNV amplitude would increase with the set size required at encoding, because larger set sizes are thought to place greater demands upon working memory to encode and to maintain relevant information (Marshall et al., 2005; Sternberg, 1966). However, our data did not support this hypothesis. One possible explanation for the nonselective relationship based on encoding set size is that working memory demands were relatively high even in the smallest set size (i.e., three letters). That is, participants had to hold three letters in their memory store to response to S2. Indeed, no differences in CNVamplitudes were observed between the threeand five-letter set sizes. Thus, aerobic fitness effects on frontal CNVamplitude might not differ based on this variable because of relatively high working memory demands across set sizes. A second explanation may be that encoding processes were completed
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prior to response preparation processes. Ruchkin, Johnson, Grafman, Canoune, and Ritter (1992) observed differences in a parietal slow wave between 1000 and 3000 ms after S1 onset as a function of working memory load (three-, four-, and five-letter set sizes), and suggested that this activity reflected long duration encoding processes. The present ISIs were between 4000 and 5000 ms, and thus encoding processes may have been completed prior to the late CNV component. Further investigation into these possibilities is needed to clarify this issue. Central CNV At central and centro-parietal regions, CNV amplitude during speed instructions was larger than during the accuracy instructions. It is well established that central CNV is associated with response preparation processes (Leynes et al., 1998; van Boxtel & Brunia, 1994), with larger CNV amplitude observed with increases in time pressure (Falkenstein, Hohnsbein, & Hoormann, 1994; Wascher, Verleger, Jaskowski, & Wauschkuhn, 1996). The current data suggest that participants required more resources in preparation to act under conditions emphasizing speed relative to conditions emphasizing response accuracy. More importantly, a trend for larger central CNV amplitude was observed for lower-fit compared to higher-fit participants (although only marginally significant), indicating that lower-fit individuals might require more neural resources to prepare for action compared to higher-fit individuals. Similar to cognitive preparation (i.e., frontal CNV), these findings suggest that the response preparation processes of higher-fit individuals may be more efficient than lower-fit individuals, indicating that aerobic fitness influences both cognitive and motor preparation processes, albeit to a different extent. Interestingly, aerobic fitness trends observed for central CNV amplitude were found across task instructions, which contrasts with the frontal CNV findings. Colcombe and Kramer (2003) conducted a meta-analysis using only randomized clinical trials to clarify which aspects of cognition are most susceptible to the influence of aerobic fitness. Their findings indicated that aerobic fitness had the greatest effect on cognitive control processes relative to all other aspects of cognition. In the present study, the selective effects for frontal CNV based on the task instructions support this notion, as the largest fitness effects were observed for task conditions requiring the most extensive amount of cognitive control. Importantly, Colcombe and Kramer also indicated that fitness had a general effect across multiple aspects of cognition, with disproportionately smaller effects on other processes, including simple RT. The present data support this notion, as selectively larger effects were noted for cognitive relative to motor processes as a function of fitness. Additionally, the fitness trend observed for central CNV provides some convergence across these relatively diverse aspects of cognition. That is, fitness trends observed for simple RT might result from more efficient motor preparation in higher-fit compared to lower-fit individuals. The present central CNV findings are also consistent with those of
Hillman et al. (2002) and Kamijo and Takeda (2009), suggesting that the beneficial relation of aerobic fitness to response preparation may be independent of task conditions. Thus, the aerobic fitness effects on motor preparation processes appear to be independent of cognitive control requirements. Limitations In this study, a median split was performed on VO2max values to bifurcate the participants into higher- and lower-fitness groupings. According to the American College of Sports Medicine (2006) guidelines, the participants who comprised the higher-fit group are indeed considered high-fit, with mean VO2max values above the 70th percentile for women and above the 90th percentile for men. However, mean VO2max values of the lower-fit group corresponded to around the 40th percentile for women and the 50th percentile for men, indicating that this group may be considered moderately fit, rather than low-fit. Thus, the beneficial relation of aerobic fitness to task preparation processes may have been attenuated in the present study because of the relatively smaller fitness difference between groups. In this context, no group differences were observed for behavioral measures, and aerobic fitness effects were marginal for central CNV amplitude. However, a lack of fitness group differences for behavioral measures in the present study are consonant with a previous CNV study (Hillman et al., 2002) in which higher-fit older and younger adults demonstrated smaller CNV amplitude than their lower-fit peers with no differences in behavioral measures. Thus, one might speculate that when a warning stimulus (S1) precedes an imperative stimulus (S2), fitness effects on task performance may be attenuated relative to other experimental designs that do not employ a warning stimulus (Hillman et al., 2006; Kamijo & Takeda, 2009; Pontifex et al., 2009; Themanson & Hillman, 2006; Themanson et al., 2006, 2008). As such, future research should examine truly lower-fit participants and continue to manipulate experimental parameters to further investigate task performance relative to aerobic fitness. Summary In sum, aerobic fitness selectively influenced frontal CNV as a function on task demands, whereas more general fitness effects were observed for central CNV. These alterations in frontal CNV amplitudes support previous findings that aerobic fitness may exert its most beneficial effects on cognitive control operations (Colcombe & Kramer, 2003). The present study also provides new insight, suggesting that aerobic fitness may influence cognitive preparation processes reflected by CNV, extending the fitness and cognitive control database. Additional evidence is also provided suggesting that aerobic fitness influences motor preparation processes and that this relationship may be independent of cognitive control requirements. Accordingly, aerobic fitness appears to exert differential benefits to processes underlying cognitive and motor preparation.
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821 Kamijo, K., & Takeda, Y. (2009). General physical activity levels influence positive and negative priming effects in young adults. Clinical Neurophysiology, 120, 511–519. Kramer, A. F., Hahn, S., Cohen, N. J., Banich, M. T., McAuley, E., Harrison, C. R., et al. (1999). Ageing, fitness and neurocognitive function. Nature, 400, 418–419. Leynes, P. A., Allen, J. D., & Marsh, R. L. (1998). Topographic differences in CNV amplitude reflect different preparatory processes. International Journal of Psychophysiology, 31, 33–44. Lorist, M. M., Klein, M., Nieuwenhuis, S., De Jong, R., Mulder, G., & Meijman, T. F. (2000). Mental fatigue and task control: Planning and preparation. Psychophysiology, 37, 614–625. Lu, B., & Chow, A. (1999). Neurotrophins and hippocampal synaptic transmission and plasticity. Journal of Neuroscience Research, 58, 76– 87. Marshall, L., Molle, M., Siebner, H. R., & Born, J. (2005). Bifrontal transcranial direct current stimulation slows reaction time in a working memory task. BMC Neuroscience, 6, 23. Neeper, S. A., Gomez-Pinilla, F., Choi, J., & Cotman, C. (1995). Exercise and brain neurotrophins. Nature, 373, 109. Picton, T. W., Bentin, S., Berg, P., Donchin, E., Hillyard, S. A., Johnson, R. Jr., et al. (2000). Guidelines for using human event-related potentials to study cognition: Recording standards and publication criteria. Psychophysiology, 37, 127–152. Pontifex, M. B., Hillman, C. H., & Polich, J. (2009). Age, physical fitness, and attention: P3a and P3b. Psychophysiology, 46, 379–387. Ruchkin, D. S., Johnson, R. Jr., Grafman, J., Canoune, H., & Ritter, W. (1992). Distinctions and similarities among working memory processes: An event-related potential study. Cognitive Brain Research, 1, 53–66. Spirduso, W. W., & Clifford, P. (1978). Replication of age and physical activity effects on reaction and movement time. Journal of Gerontology, 33, 26–30. Sternberg, S. (1966). High-speed scanning in human memory. Science, 153, 652–654. Themanson, J. R., & Hillman, C. H. (2006). Cardiorespiratory fitness and acute aerobic exercise effects on neuroelectric and behavioral measures of action monitoring. Neuroscience, 141, 757–767. Themanson, J. R., Hillman, C. H., & Curtin, J. J. (2006). Age and physical activity influences on action monitoring during task switching. Neurobiology of Aging, 27, 1335–1345. Themanson, J. R., Pontifex, M. B., & Hillman, C. H. (2008). Fitness and action monitoring: Evidence for improved cognitive flexibility in young adults. Neuroscience, 157, 319–328. van Boxtel, G. J., & Brunia, C. H. (1994). Motor and non-motor aspects of slow brain potentials. Biological Psychology, 38, 37–51. van Praag, H., Christie, B. R., Sejnowski, T. J., & Gage, F. H. (1999). Running enhances neurogenesis, learning, and long-term potentiation in mice. Proceedings of the National Academy of Sciences, USA, 96, 13427–13431. Wascher, E., Verleger, R., Jaskowski, P., & Wauschkuhn, B. (1996). Preparation for action: An ERP study about two tasks provoking variability in response speed. Psychophysiology, 33, 262–272. Wild-Wall, N., Hohnsbein, J., & Falkenstein, M. (2007). Effects of ageing on cognitive task preparation as reflected by event-related potentials. Clinical Neurophysiology, 118, 558–569. Yeung, N., Botvinick, M. M., & Cohen, J. D. (2004). The neural basis of error detection: Conflict monitoring and the error-related negativity. Psychological Review, 111, 931–959. (Received July 10, 2009; Accepted October 2, 2009)
Psychophysiology, 47 (2010), 822–830. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.00996.x
Auditory sensory memory and language abilities in former late talkers: A mismatch negativity study
NICOLA GROSSHEINRICH, STEFANIE KADEMANN, JENNIFER BRUDER, JUERGEN BARTLING and WALDEMAR VON SUCHODOLETZ Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
Abstract The present study investigated whether (a) a reduced duration of auditory sensory memory is found in late talking children and (b) whether deficits of sensory memory are linked to persistent difficulties in language acquisition. Former late talkers and children without delayed language development were examined at the age of 4 years and 7 months using mismatch negativity (MMN) with interstimulus intervals (ISIs) of 500 ms and 2000 ms. Additionally, short-term memory, language skills, and nonverbal intelligence were assessed. MMN mean amplitude was reduced for the ISI of 2000 ms in former late talking children both with and without persistent language deficits. In summary, our findings suggest that late talkers are characterized by a reduced duration of auditory sensory memory. However, deficits in auditory sensory memory are not sufficient for persistent language difficulties and may be compensated for by some children. Descriptors: Auditory sensory memory, Late talker, Mismatch negativity, MMN, Specific language impairment, SLI
Deficiencies in auditory short-term memory are among the postulated causes of SLI. In a number of studies, auditory shortterm memory deficits were reported in children with SLI (Montgomery, 2003) and dyslexia (Jeffries & Everatt, 2004; SmithSpark & Fisk, 2007). These deficits are markers of SLI and are assumed to be predictive of language development in these children (Botting & Conti-Ramsden, 2001; Conti-Ramsden & Hesketh, 2003; Gathercole & Baddeley, 1990). In contrast, there is a lack of knowledge regarding the neurobiological basis of late talking. The relationship between SLI and auditory short-term memory has been interpreted using Baddeley and Hitch’s (1974) working memory model (e.g., Montgomery, 2003). This model proposes a multicomponent capacity-limited system that comprises a ‘‘phonological loop’’ for verbal information processing and a ‘‘visuospatial sketch pad’’ for processing visual information. The ‘‘central executive’’ coordinates and integrates both subsystems. Deficiencies in auditory short-term memory found in SLI are explained by reductions in both the storage capacity of the phonological loop and the encoding speed of language input. Such deficits are assumed to lead to difficulties establishing phonological representations, consequently impacting vocabulary acquisition and the establishment of grammatical rules (Baddeley, Gathercole, & Papagno, 1998). A limitation of Baddeley’s model however, is that the initial steps of information processing are not well described. In comparison, Cowan’s (1988, 1995) model specifies the reception and storage of sensory information in greater detail. According to this model, incoming sensory information is consecutively integrated within a sensory store for the purpose
Language delay in the absence of other medical conditions is found in approximately 10%–20% of 2-year-olds (Klee et al., 1998; Rescorla & Alley, 2001) who are referred to as ‘‘late talkers’’ (LTs; Horwitz et al., 2003; Rescorla, 1989). According to several studies (e.g., Miniscalco, Westerlund, & Lohmander, 2005; Rice, Taylor, & Zubrick, 2008), language delay is a risk factor for specific language impairment (SLI). For example, Dale, Price, Bishop, and Plomin (2003) examined 8,386 twins (LTs: n 5 802; non-LTs: n 5 7,584) and reported that 40.2% of LTs had language difficulties at the age of 4 years in contrast to 8.5% in normally developing children. Children with SLI have a higher risk of developing socioemotional problems. For example, they show lower achievements in school in a broad range of subjects including mathematics (Snowling, Adams, Bishop, & Stothard, 2001). Moreover, later in adulthood a twofold increase in the incidence of psychiatric disorders, such as dissocial behavior and anxiety disorders was found (Beitchman et al., 2001). Therefore, it seems important to investigate the underlying neurophysiological mechanisms contributing to the development of SLI in order to enable and enhance possibilities for early intervention.
We thank Monika Hage and Elisabeth Greiner for their assistance in collecting the EEG data. We are also particularly grateful to all the children and their parents for participating in this study. Address reprint requests to: Nicola Grossheinrich, Ph.D., Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilian University Munich, Waltherstrasse 23, D-80336 Munich, Germany. E-mail:
[email protected] 822
Auditory sensory memory and language abilities of resolving component features. This first part of the sensory store is designed to briefly (200–400 ms) hold large amounts of data. From this system the sensory information is transferred to a second, longer-lasting division of the sensory memory store, where it is kept available for further processing in working memory. Information is suggested to decay from this second part after a period of about 10–20 s. Auditory sensory memory is presumed to operate automatically and preattentively. It is hypothesized that a reduced duration of sensory memory could be the neurophysiological background of disturbed language acquisition in children with SLI (Barry et al., 2008). Auditory short-term memory is commonly assessed with behavioral tasks in which subjects are typically asked to verbally repeat sequences of tones, syllables, words, or numbers of increasing length. Difficulties successfully completing these tasks have been attributed not only to working memory deficits but also to language difficulties (Barry et al., 2008). Because repetition accuracy depends on lexical and sublexical properties, the repetition of nonwords is a powerful tool to identify children with language impairments (Coady & Evans, 2008), but less appropriate for the evaluation of short-term memory capacities in subjects with language deficits. Moreover, repetition tasks demand immediate responses from subjects, and thus results are affected by attention and motivation. For these reasons, behavioral tasks are not ideal for young children and subjects with language difficulties. An objective method for assessing auditory sensory memory is the event-related potential (ERP) known as mismatch negativity (MMN; Na¨a¨ta¨nen, 2003). MMN is generally obtained in an acoustic oddball paradigm, in which rare deviant sounds are presented within a stream of reoccurring standard sounds. The MMN operates at the sensory memory level and reflects an automatic preattentive process of comparisons between acoustic stimuli. Thus, the MMN is observed regardless of attention to the stimuli (e.g., Na¨a¨ta¨nen, Paavilainen, Tiitinen, Jiang, & Alho, 1993). It is assumed that regular aspects of consecutively presented standards form a memory trace in the sensory store and that violation of these regularities by deviants induces an MMN (Na¨a¨ta¨nen & Winkler, 1999). MMN is used in basic and clinical research to determine auditory discrimination accuracy and the duration of sensory memory. Discrimination accuracy is generally investigated in oddball paradigms with constant and relatively short stimulus offset-to-onset intervals (interstimulus interval, ISI). In children with SLI, diminished MMN amplitudes have been repeatedly reported for speech-sound stimuli, but less consequently for tone stimuli. These results suggest that children with SLI have discrimination deficiencies specific to speech sounds (e.g., Bishop, 2007; Shafer, Morr, Datta, Kurtzberg, & Schwartz, 2005; Uwer, Albrecht, & von Suchodoletz, 2002). To determine the duration of sensory memory, ISIs of different lengths are used. MMN is only found when the memory trace of the standard stimulus has not yet decayed from sensory memory. Therefore, sensory memory duration can be examined by varying the ISIs. It is thought that investigating the lifetime of the memory trace using MMN probes the second phase of sensory memory storage described by Cowan (Na¨a¨ta¨nen, Jacobsen, & Winkler, 2005). Several studies have used MMN experiments with variable ISIs to probe the duration of auditory sensory memory in healthy children and adults. In newborns, a prominent MMN was found after a stimulus delay of 0.7 s, but not after 1.4 s (Cheour et al.,
823 2002). Glass, Sachse, and von Suchodoletz (2008a, 2008b) found memory traces between 1 and 2 s in 2- and 3-year-olds, greater than 2 s in 4-year-olds, and between 3 and 5 s in 6-year-olds. Gomes et al. (1999) investigated the duration of auditory sensory memory in school-age children and adults (age groups: 6–7, 8– 10, 11–12, and 22–38 years) and obtained a robust MMN at an ISI of 1 s in all age groups. An MMN for the ISI of 8 s was found only in the groups with subjects older than 10 years. In healthy adults an MMN was detected up to an ISI of approximately 10 s (Bo¨ttcher-Gandor & Ullsperger, 1992; Sams, Hari, Rif, & Knuutila, 1993). In summary, the duration of the auditory sensory memory trace demonstrates a maturational development from approximately 0.7 s in newborns to at least 10 s in adults. Only a few studies have addressed the question of whether there is evidence for a diminished duration of auditory sensory memory in clinical samples. The lifetime of a memory trace in the sensory store has been reported to be reduced in patients with chronic alcoholism (Grau, Polo, Yago, Gual, & Escera, 2001; Zhang, Cohen, Porjesz, & Begleiter, 2001) and Alzheimer’s disease (Engeland, Mahoney, Mohr, Ilivitsky, & Knott, 2002; Pekkonen, Jousmaki, Kononen, Reinikainen, & Partanen, 1994). The findings suggest that MMN can objectively identify sensory memory deficits in patients with memory impairments. To our knowledge, in children, auditory sensory memory duration has only been investigated in CATCH syndrome (Cheour et al., 1997) and oral clefts (Ceponiene` et al., 1999). Both studies reported shorter auditory sensory memory duration in comparison to healthy peers and attributed this deficit causally to the children’s language impairments. Therefore, children with other language acquisition disturbances, such as SLI, might exhibit similar deficits. To our knowledge, only one MMN study has investigated auditory sensory memory duration in SLI (Barry et al., 2008). In this study, parents of children with SLI were compared to parents with typically developing children using ISIs of 800 ms and 3000 ms. Reduced MMN was found for the 3000-ms ISI in parents of language-impaired children. This result was independent of the parents’ language abilities. The authors therefore postulated a shortened lifetime for auditory sensory memory traces in parents of children with SLI, providing evidence for persistent and heritable auditory sensory memory deficits. Taken together, the results of previous MMN studies show that the duration of auditory traces in the sensory memory store is limited, that this limitation is age dependent, and that the duration is reduced in patients with memory or language impairments as well as in parents of children with SLI. Moreover, a deficient auditory sensory memory seems to be persistent because of its assumed heritability. To our knowledge, no study has examined the auditory sensory memory of children at risk for SLI and its meaning for the persistence of language disabilities. For this reason the present study addresses the question of whether former LTs show a sensory memory deficit in the auditory modality. If a deficient auditory sensory memory is linked to persistent difficulties in language acquisition, this deficit should be found in LTs with persisting language disabilities but not in LTs with resolved language problems, so-called late bloomers. Additionally, we analyzed neuropsychological memory scores and correlations between MMN and neuropsychological memory data in an exploratory manner between groups.
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A deviant
deviant
standard
standard
deviant
standard stimulus used for averaging deviant stimulus used for averaging
500 ms
500 ms
B standard
standard
2000 ms
deviant
2000 ms
Figure 1. Oddball paradigm for probing the duration of auditory sensory memory with 500-ms (A) and 2000-ms (B) interstimulus interval condition.
In detail we used MMN to examine auditory sensory memory with two ISI durations (500 ms and 2000 ms) inserted between trains of four tones (Figure 1). We hypothesized a reduced MMN in LTs in comparison to control children for the longer ISI condition only. Additionally, if intact sensory memory is essential for normal language development, we should find no difference in mean MMN amplitude between late bloomers and control children.
Methods Sample Seventy-one German-speaking children participated in the study at the age of 4 years and 7 months (M 5 55.04 ! 0.26 months). All children took part in a longitudinal study beginning at 2;1 years of age, with follow-ups at 3;1 and 4;7 years. To recruit children with and without language delay, we used birth announcements to contact parents of 2-year-old children (for details, see Sachse & von Suchodoletz, 2008). Children were classified as LTs at 2 years of age via a parent questionnaire (Elternfragebogen fuer die Frueherkennung von Risikokindern, ELFRA; Grimm & Doil, 2002), a German version of the McArthur Communicative Development Inventories (CDI, Toddler Form; Fenson, Dale, & Reznick, 1993), and a standardized language test (Sprachentwicklungstest fuer 2-jaehrige Kinder, SETK-2; Grimm, 2000) composed of two receptive and two productive language subtests. Children with poor results in ELFRA–2 (vocabularyo50 words or vocabulary between 50 and 79 words and deficient morpho-syntactic abilities) as well as in SETK-2 (z-score [M 5 0; SD 5 1] " # 1.5 at least in one subtest) were classified as LTs (n 5 60). Children with normal results in ELFRA-2 (vocabulary480 words and normal morpho-syntactic abilities) and SETK-2 (z-score4 # 1 in all subtests) were defined as control children (n 5 47). Children with results between these two classifications were not included in the analysis, with the aim to construct two clearly defined groups. Information about developmental milestones, medical history (complications during pregnancy or birth, prematurity, chronic
disorders, history of otitis media or other ear disorders), and socioeconomic characteristics were obtained by having the parents complete a questionnaire. There were no critical incidents reported for all of the participating children. Forty-six LTs (77%) and 40 (85%) control children were reassessed at the age of 4 years and 7 months. Children with abnormal otoacoustic emission results due to common colds or other unspecific reasons at the time of measurement were excluded from analysis (n 5 8). Other children were excluded because they refused to participate in the auditory screening (n 5 3) or the ERP recording (n 5 3) and because of emigration into a country with a foreign language (n 5 1). This resulted in a total inclusion of 37 LTs (62%) and 34 (72%) control children. All children had a normal nonverbal intelligence score (Snijders-Oomen nonverbal intelligence test: IQ $ 80), normal hearing abilities (measured by otoacoustic emission screening or audiometry), and normal results on otoacoustic emission screening at least for one ear at the time of electroencephalogram (EEG) recording. We additionally classified the LTs at the age of 4;7 years into late bloomers and non-late bloomers (z-score4 # 1 in all language tests vs. " # 1 in at least one language score including subtests sentence comprehension, sentence repetition, plural creation, and expressive vocabulary). Twenty-one of 37 (57%) LTs met the late bloomer criteria and 30 of 34 (8 %) control children had language abilities within or beyond the normal range (z-score4 # 1) in all language tests (‘‘language category’’; see Table 1). The characteristics of the sample are shown in Table 1. The groups (LTs vs. control children) differed in their frequencies for language category (normal vs. impaired: w2 5 8.68, po.01). Significant differences were also found for nonverbal intelligence (T 5 3.46, po.01) and language abilities (sentence comprehension: T 5 2.32, po.05; plural creation: T 5 3.83, po.01; sentence repetition: T 5 3.94, po.01; expressive vocabulary: T 5 3.93, po.01). No differences were observed for gender (w2 5 1.03, p4.1) and handedness (w2 5 5.69, p4.05) frequencies. All parents gave their written informed consent for their children to participate in the study. The study was approved by the
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Table 1. Characteristics of Late Talkers (LTs) Divided into Late Bloomers (LBs) versus Non-LBs and Control Children at the Age of 4;7 Years LTs (n 5 37)
General characteristics (n) Boys/girls Handedness (right/left/ambidextrous) Language category (SD4 ! 1/SD " ! 1)
IQ (M, SD) Non-verbal intelligencea Language (raw data: M, SD) Sentence comprehensionb Plural creationb Sentence repetitionb Expressive vocabularyc
25/12 27/2/8 21/16
102.32 # 11.16 10.05 21.68 76.22 16.03
LB (n 5 21)
# # # #
2.74 5.47 16.19 2.49
Non-LB (n 5 16)
15/6 16/2/3 21/0
104.76 # 10.91 11.1 23.71 86.48 16.81
# # # #
1.92 2.95 9.36 1.86
10/6 11/0/5 0/16
99.13 # 11.0 8.69 19.00 62.75 15.00
# # # #
3.1 6.84 13.08 2.88
LTs vs. Controls
Controls (n 5 34)
19/15 30/3/1 30/4
110.53 # 8.5 11.41 26.32 91.15 18.32
# # # #
2.12 4.70 15.73 2.43
w2
p
1.03 6.09 8.68
.31 .06 .00
T
p
3.46
.00
2.32 3.83 3.94 3.93
.02 .00 .00 .00
a
Four subtests of the Snijders-Oomen nonverbal intelligence test (Tellegen et al., 1998). SETK 3-5 (Grimm, 2001). c K-ABC (Melchers & Preuss, 1991). b
ethics commission of the medical faculty of the University of Munich (LMU Munich). Stimuli and Procedure Neuropsychological and EEG data were assessed on two consecutive days at the age of 4;7 years. Neuropsychological assessment. Language abilities were assessed via standardized language tests comprising expressive vocabulary (Kaufman Assessment Battery for Children, K-ABC; Melchers & Preuss, 1991), grammar production, and comprehension (Sprachentwicklungstest fuer 3- bis 5-jaehrige Kinder, SETK 3-5; Grimm, 2001). Grammar production was quantified by sentence repetition and plural creation. The latter ability is more complex in German than in English because there is a larger range of plural forms in German. Grammar comprehension was assessed by means of sentence comprehension. Here, the children were required to carry out verbal instructions. Short-term memory was measured using a nonword repetition task (NRT; subtest of SETK 3-5) and the subtest ‘‘word order’’ of the K-ABC. For the latter subtest, children listened to word sequences of increasing length; after each sequence children pointed to the corresponding pictures in the same order. Handedness was evaluated using a preference inventory based on the Edinburgh Handedness Inventory (Oldfield, 1971). The children were asked to demonstrate how they would carry out everyday activities: to bring someone a book, to comb one’s hair, to hammer, to switch on the light, and to throw a ball. Nonverbal intelligence scores were calculated by four subtests of the Snijders-Oomen nonverbal intelligence test (Tellegen, Winkel, Wijnberg-Williams, & Laros, 1998) at the age of 3 years. Neurophysiological assessment. Duration of auditory sensory memory was assessed using an oddball paradigm with varying ISI conditions. MMN was investigated using standard tones of 1000 Hz and deviant tones of 1200 Hz (duration 100 ms, rise and fall time 10 ms). The same frequencies had been used in previous studies exploring auditory sensory memory duration in children and adults (Barry et al., 2008; Glass et al., 2008a, 2008b; Gomes
et al., 1999). Because of difficulties associated with ERP recording in young children, we used a time-saving oddball paradigm described by Grau, Escera, Yago, and Polo (1998; Figure 1). Stimuli were grouped in trains of four tones with an interval of 500 ms between the tones within the trains. The trains began with either the standard or the deviant stimulus in a pseudorandomized order, and all nonleading stimuli were standards only, resulting in 1400 standards and 200 deviants (7:1) for each condition. The experiment was divided into four blocks of 400 stimuli each, with a 4 s break between blocks. We expected that LTs would exhibit a MMN comparable to those of controls for shorter ISI conditions, and therefore we chose a control condition with an ISI of 500 ms. This assumption was based on previous findings showing that even newborns generate an MMN with ISIs of 700 ms (Cheour et al., 2002). For the experimental condition, we employed an ISI of 2000 ms. For normally developing 4-year-old children, it was shown that auditory information remains in sensory memory for longer than 2 s (Glass et al., 2008b). Accordingly, we considered an ISI of 2000 ms as the critical duration of sensory memory. Stimuli from the control (ISI: 500 ms) and the experimental condition (ISI: 2000 ms) were presented in separate blocks in a balanced order. During the EEG recording, children were seated in an upright child’s seat and were shown a silent video. The child’s guardian remained in the testing room and silently completed questionnaires. Stimuli were presented through a loudspeaker placed in front of the child (distance: 2.3 m; sound pressure level: 74 dB). The total duration of the experiment was 42 min. EEG Recording The EEG was recorded using 20 Ag/AgCl sintered electrodes attached to an elastic electrode cap (Easy Cap, Herrsching, Germany). Electrodes were placed according to the International 10– 20 System (Jasper, 1958). The horizontal electrooculogram (HEOG) was recorded from electrodes placed at the outer canthus of each eye. For vertical electrooculogram (VEOG) Fp2 and one electrode placed under the eye were used. One child refused the electrode placement underneath the eye, and therefore only
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Fp2 was used for elimination of vertical eye artifacts for this child. The EEG electrodes were referenced to the right mastoid during the recording. Data acquisition was carried out using a BrainAmp system (Brain Products, Gilching, Germany). The online bandpass filter was set to 0.16 and 30 Hz (sampling rate: 250 Hz; impedances at the beginning of measurements:o5 kO). Data were analyzed off-line using Vision Analyzer. First, the scalp EEG was high-pass (0.8 Hz) and low-pass (20 Hz) filtered. Artifact correction was done in two steps. First, an independent component analyses (ICA) was conducted (Kalyakin, Gonzalez, Karkkainen, & Lyytinen, 2008) and eye movement and muscle artifacts were removed. Second, resting artifacts were rejected after re-referencing to linked mastoids by an amplitude criterion of ! 80 mV for all central and frontal electrodes. Finally, the data were segmented ( " 100 to 600 ms) and averaged. Segmentation resulted in a mean number of 192 ! 6 epochs (range: 177– 198) for the control children and a mean number of 194 ! 3 epochs (range 185–198) for the LTs. The mean number of epochs did not differ between groups (Mann-Whitney U test, Z 5 " 1.25, p4.2). Data Analysis ERP. Event-related responses were averaged using the first tone of the trains in order to ensure that the number (200) and relative position of standards and deviants were comparable. MMN was obtained by subtracting standard from deviantevoked responses for each ISI condition. The MMN was prevalent over frontal electrodes, and therefore F3, Fz, and F4 were used for further analyses. A frontal MMN maximum was also described in a previous study with 4–5.5-year-old children (Martin, Shafer, Morr, Kreuzer, & Kurtzberg 2003). Mean amplitudes of the MMN were calculated to quantify the MMN response. The time window for the mean amplitude was chosen based on running t tests (against zero) from the evoked responses of the combined group (LTand control group) for each ISI condition separately (po.05 at # 4 consecutive data points). The
Table 2. Time Window of Significant Differences between Standard and Deviant Responses in the Combined Group ISI (ms)
n
F3
Fz
F4
500 2000
71 71
124–272 84–148
120–260 92–148
128–260 92–156
Note: ISI: interstimulus interval. Running t test: po.05 at # 4 consecutive data points.
resulting time window covered all intervals of significant differences in any of the three frontal electrodes (Table 2). According to this procedure, the MMN time windows were 120 to 272 ms after stimulus onset for the 500-ms ISI control condition and 84 to 156 ms for the 2000-ms experimental condition (see gray areas of Figures 2 and 3). Statistical analysis. Statistical analysis of the ERP and neuropsychological data was performed using analysis of variance (ANOVA). Main effects and interactions were calculated for the between-subject factors group (LTs vs. control children) and language category (normal vs. impaired) to control for differences in language abilities between groups. For the ERPs, mean amplitudes of F3, Fz, and F4 were averaged. Additionally, the within-subject factor ISI (500 ms vs. 2000 ms) was part of the ERP analysis. In the case of significant interactions, follow-up analyses were conducted. Finally, because nonverbal intelligence differed between groups (see Table 1), this score was subsequently implemented as a covariate (analysis of covariance, ANCOVA). The NRT score was determined only for children without articulation difficulties (LTs 5 23; control children 5 28), because incorrect NRT responses may have arisen because of poor articulation rather than limited short-term memory capacity. Pearson correlations were calculated between MMN (2000 ms ISI) and the short-term memory measures word order and NRT to examine the relationship between neuropsychological
A
Grand Average
Grand Average
Grand Average
B
Grand Average
Grand Average
Grand Average
Figure 2. Mismatch negativity (MMN) as a function of interstimulus interval (ISI). MMN in the 500-ms control (A) and 2000-ms experimental (B) ISI condition for control children (solid lines) and late talkers (dashed lines). The gray area illustrates the interval of the mean amplitude.
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A
Grand Average
Grand Average
Grand Average
B
Grand Average
Grand Average
Grand Average
Figure 3. Mismatch negativity (MMN) as a function of interstimulus interval (ISI). MMN in the 500-ms control (A) and 2000-ms experimental (B) ISI condition for control children (solid lines) and late bloomers (dashed lines). The gray area illustrates the interval of the mean amplitude.
and neurophysiological memory parameters. Because neuropsychological scores depend on various cognitive abilities, the neuropsychological data were analyzed with principal component analysis (PCA; orthogonal transformation varimax solution) in order to distinguish memory abilities. Therefore, each test score and the mean amplitude of the MMN in the experimental condition (ISI: 2000 ms) was z-transformed. Missing data were substituted by means. Only factors with an eigenvalue greater than 1 were extracted. Significant effects are reported for po.05.
No main effects or interactions were found for word order. The NRT analysis revealed a main effect for language category, F(1,47) 5 20.41, po.01, because children with average language abilities achieved better NRT scores. In addition, the interaction between language category and group was significant, F(1,47) 5 6.88, p 5 .01. To explore this interaction further, t tests for independent samples were conducted separately for LTs and controls, resulting in a significant effect for the control group, T(26) 5 4.97, po.01, but not for the LTs, T(21) 5 1.44, p4.1. Including nonverbal intelligence as a covariate did not alter the significance of the results.
Results Descriptive data for MMN mean amplitudes and neuropsychological memory performance are listed in Table 3. Behavioral Results ANOVAs were performed for the neuropsychological memory scores word order and NRT with the between-subject factors group (LTs vs. control children) and language category (normal vs. impaired).
MMN Results An ANOVA for mean MMN amplitude was performed with the between-subject factors group (LTs vs. control children) and language category (normal vs. impaired) and the within-subject factor ISI (500 ms vs. 2000 ms). Main effects for the between-subject factors group and language category were not found, but the within-subject factor ISI was significant, F(1,67) 5 7.73, po.01, with higher amplitudes in
Table 3. Means and Standard Deviations for Mean Amplitude of Mismatch Negativity (MMN) and Neuropsychological Memory Tests for Late Talkers (LTs) Divided into Late Bloomers (LBs) versus Non-LBs and Control Children Group
MMN ISI: 500 ms ISI: 2000 ms Neuropsychological tests Word order NRTa
LTs (n 5 37)
LB (n 5 21)
Non-LB (n 5 16)
Controls (n 5 34)
! 2.73 " 3.25 ! .27 " 1.56
! 1.97 " 3.25 ! .09 " 1.6
! .3.73 " 3.05 ! .52 " 1.52
! 2.33 " 2.53 ! 1.49 " 2.48
6.27 " 1.97 9.09 " 3.01
6.71 " 1.87 9.73 " 2.28
5.69 " 1.99 7.88 " 3.94
7.41 " 2.34 12.25 " 3.16
Note: ISI: inter-stimulus-interval; NRT: nonword repetition task. a LTs: n 5 23 (LB: n 5 15, non-LB: n 5 8), control children: n 5 28.
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Table 4. Analysis of Mismatch Negativity (MMN) Mean Amplitudes for Interstimulus Interval (ISI, 500 ms, 2000 ms), Group (Late Talkers, Controls) and Language Category (Normal, Impaired), N 5 71, df 5 1
ISI Group Language category ISI ! Group ISI ! Language category Language category ! Group Language category ! Group ! ISI
0.5
Mean amplitude (mV) F
MSE
p
7.73 0.02 0.13 6.06 0.06 2.44 2.00
37.05 0.13 0.99 29.05 0.27 17.49 15.56
.01 .90 .72 .02 .82 .12 .16
factor 2
Source
1.0
0.0
−0.5
−1.0 -1.0
the 500-ms ISI condition. A significant interaction was detected for ISI ! Group, F(1,67) 5 6.06, po.05, in accordance with our hypothesis (Table 4). This interaction resulted from differences between control children and LTs in the 2000-ms ISI condition, F(1,68) 5 6.81, po.05, but not in the 500-ms ISI condition, F(1,68) 5 0.54, p4.4 (Figure 2). This discrepancy described above was also significant for late bloomers compared to control children in the 2000-ms ISI condition, F(1,52) 5 4.56, po.05, but not in the 500-ms ISI condition, F(1,52) 5 0.02, p40.8 (Figure 3). Entering nonverbal IQ as a covariate did not alter the significance of the interaction between ISI and group, F(1,66) 5 6.85, po.05. Correlations To examine the relationship between neuropsychological and neurophysiological memory parameters, Pearson correlations were obtained. A significant correlation was observed between word order and MMN (2000-ms ISI condition; r 5 " .24, po.05). Here, high test scores were associated with larger MMN amplitudes (signed negative). The correlation between MMN and NRT did not reach significance (r 5 " .09, p4.5). PCA performed on the neuropsychological and neurophysiological scores yielded two factors with an eigenvalue 41. Each test measure was sorted into a two-dimensional vector space (Figure 4). Both identified factors accounted for 59.4% of the variance. Late Bloomers versus Non-Late Bloomers LTs who performed well in four language tests at the age of 4;7 years were classified as late bloomers (n 5 21), whereas the remaining children showed persistent language deficits and were categorized as non-late bloomers (n 5 16). Both groups differ in all language scores (t test, po.05). No group differences were found for nonverbal intelligence and memory achievements (NRT and the K-ABC’s word order subtest; t test, p4.1). The two groups did not differ in terms of handedness and sex (chiquadrat test, p4.2). Additionally, MMN mean amplitude differences for both ISI conditions were not observed (t test, 500 ms: p4.1; 2000 ms: p4.4). Finally, a logistic binary regression analysis showed that none of the variables measured at the age of 2 years, including sex, handedness, intelligence, and receptive and productive language abilities, could predict the outcome of late bloomers or non-late bloomers at the age of 4;7 years.
-0.5
0.0 factor 1
0.5
1.0
Figure 4. Neuropsychological measurements and mean amplitude of mismatch negativity (MMN) of the experimental condition (interstimulus interval of 2000 ms) illustrated by two main factors of a principal component analysis.
Discussion The goals of this study were to investigate whether LTs show auditory short-term memory deficits, measured by MMN and neuropsychological tests, and to determine if such deficits are related to language impairment of LTs at preschool age. Neurophysiological Findings As hypothesized, MMN responses between LTs and control children did not differ in the 500-ms ISI control condition. These results imply that former LTs can normally discriminate preattentively between the tones of 1000 and 1200 Hz, and information in the auditory sensory memory store was still available after 500 ms. In contrast, prominent MMN was observed in the 2000-ms ISI condition for the control children only, suggesting that the auditory sensory memory trace had decayed before 2000 ms in LTs. These findings cannot be explained by differences in intelligence, handedness, or sex and suggest that LTs are characterized by a persistent shortened duration of auditory sensory memory. Unexpectedly, MMN (2000-ms ISI condition) was also reduced in late bloomers compared to the control group and not only in the children with persistent language deficits. No significant differences were observed between late bloomers and nonlate bloomers in both MMN conditions and in all reported neuropsychological tests with the exception of language achievements. Shortened sensory memory duration measured via MMN has been described for children with CATCH syndrome (Cheour et al., 1997) and oral clefts (Ceponiene` et al., 1999). The authors assumed that a deficient auditory sensory memory could be causally associated with disturbed language and cognitive development in these children. Our results do not support this assumption, as MMN was reduced in the 2000-ms ISI condition even in late bloomers who had normal language abilities (Figure 3). Moreover, mean MMN amplitudes did not distinguish late bloomers from non-late bloomers. Our outcome argues for a deficient auditory sensory memory in LTs that is not predictive for persistent language difficulties
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and is in accordance with recent findings (Barry et al., 2008). Barry et al. examined parents with typically developing children and parents with children affected by SLI. They observed MMN attenuation for ISIs of 3000 ms but not for 800 ms in parents of children with SLI. An analysis of actual language impairment in the parents (self-report or direct test) revealed no effect for SLI. On the one hand, our results suggest that the duration of auditory sensory memory is not causally associated with further language development in LTs. On the other hand, a higher frequency of children with SLI is consistently found in former LTs (Dale et al., 2003). Two explanatory issues for the relationship between LTs, auditory sensory memory, and SLI can be drawn. First, one could assume that a common factor exists behind memory achievements and SLI that is causally linked to both and that might also be moderated by other factors. For example, a genetic predisposition might be the common factor, which could lead to both reduced duration of auditory sensory memory and SLI. The second interpretation is that there is a causal relationship between auditory sensory memory and language acquisition. All LTs demonstrate deficient auditory sensory memory, but some children (late bloomers) can compensate for this deficit. However, the specific mechanisms that enable some LTs to compensate for sensory memory deficits are unclear. Neuropsychological Findings In contrast to sensory memory deficits found in the ERP data, no abnormalities were found in former LTs for neuropsychological short-term memory dimensions. The NRT yielded a significant group effect, which could be explained by different language abilities in both groups. All reported neuropsychological results were not affected by nonverbal intelligence scores. The observed relationship between NRT results and language abilities is in line with the observation that the NRT is sensitive for identifying children with language impairment (Coady & Evans, 2008). Moreover, it underlines the argument that the NRT is not suitable for measuring pure memory capacity because it depends heavily on language abilities (Barry et al., 2008). Neuropsychological data are generally confounded with other cognitive processes besides language. The PCA yielded two factors, which can be interpreted as a language factor and an auditory short-term memory factor. This interpretation is underlined by the distribution of the test scores (Figure 4). The MMN mean amplitude accounts for one factor, which can arguably be interpreted as a factor representing auditory shortterm memory. Language test scores form the language factor.
The word order test score lies between the two factors, whereas NRT clusters closer to the language factor. This is reflected by a moderate correlation between MMN mean amplitude (ISI: 2000 ms) and word order and no correlation between MMN mean amplitude and NRT. One possibility for the lack of correlation between NRT and MMN may be the diminished sample size. However, Barry and co-workers (2008) also found a lack of correlation between MMN and NRT. A further reason why the neuropsychological memory data failed to confirm the ERP data could be because of the different underlying memory processes. It is assumed that MMN measurements with different ISI durations reflect how long auditory information is passively held in the sensory memory store, whereas neuropsychological data provide an indication of the capacity of working memory. It is therefore reasonable that the MMN findings and the results of the neuropsychological tests word order and NRT do not remarkably correlate. Limitation In our study we chose an ISI of 2000 ms as the experimental condition because normal developing children at the age of 4;7 years have an auditory sensory memory duration of at least 2000 ms (Glass et al., 2008b). However, it is conceivable that the duration of auditory short-term memory increases in late bloomers more than in non-late bloomers. Should differences in the duration of auditory sensory memory within the time window from 0.5 s to 2 s between these groups exist, we would not be able to detect them with this paradigm. Conclusion In summary, a deficient auditory sensory memory is related to late talking but is not sufficient for the development of SLI at 4;7 years of age. This is shown primarily from the results of the late bloomers, who exhibited a reduced MMN much like the LTs for the longer ISI condition, but did not show the subsequent language impairments. The reduced duration of auditory short-term memory is most likely not reflected in the neuropsychological memory performance due to task-specific language requirements or due to the taxing of possibly different memory components. Therefore, a reduced duration of auditory sensory memory is, similar to late talking, a risk but not a predictive factor for SLI. To date, more exploratory neuroscientific work is needed to detect the probable moderator variables that could explain why some LTs become late bloomers whereas others continue to have persistent language difficulties.
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Psychophysiology, 47 (2010), 831–837. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.01000.x
Relationship between prepulse inhibition of acoustic startle response and schizotypy in healthy Japanese subjects
HIDETOSHI TAKAHASHI,a,b MASAO IWASE,a LEONIDES CANUET,a YUKA YASUDA,a,b,c KAZUTAKA OHI,a,b MOTOYUKI FUKUMOTO,a,b NAOMI IIKE,a,b TAKAYUKI NAKAHACHI,a KOJI IKEZAWA,a MICHIYO AZECHI,a RYU KURIMOTO,a RYOUHEI ISHII,a TETSUHIKO YOSHIDA,a,b HIROAKI KAZUI,a RYOTA HASHIMOTO,a,b,c and MASATOSHI TAKEDAa,c a
Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Osaka, Japan CREST (Core Research for Evolutionary Science and Technology), JST (Japan Science and Technology Agency), Kawaguchi, Saitama, Japan c The Osaka-Hamamatsu Joint Research Center for Child Mental Development, Osaka University Graduate School of Medicine, Suita, Osaka, Japan b
Abstract Prepulse inhibition (PPI) of the acoustic startle reflex (ASR) is the most common psychophysiological index of sensorimotor gating. Several studies have investigated the relationship of PPI of ASR to schizotypy in Caucasians. However, little has been reported on this relationship in Asians. We investigated a possible relationship between PPI of ASR and schizotypy in 79 healthy Japanese subjects. Schizotypy was assessed by the Schizotypal personality Questionnaire (SPQ). PPI was evaluated at signal-to-noise ratios (SnRs: difference between background noise intensity and prepulse intensity) of 112, 116, and 120 dB. The total SPQ score, cognitive/perceptual score, and interpersonal score correlated negatively with PPI at SnR of 116 and 120 dB. We conclude that PPI is associated with the trait of schizotypy in healthy Asian subjects. Descriptors: Prepulse inhibition, Sensorimotor gating, Schizotypy, Acoustic startle response, Asians
tients with schizophrenia (reviewed by Braff, Geyer, Light, Sprock, Perry, et al., 2001). Reports of PPI reductions not only in schizophrenia patients but also in unaffected relatives (Cadenhead, Swerdlow, Shafer, Diaz, & Braff, 2000; Kumari, Das, Zachariah, Ettinger, & Sharma, 2005) suggest a substantial heritability of PPI impairment (Anokhin, Heath, Myers, Ralano, & Wood, 2003). Deficient PPI is also seen in patients with schizotypal personality disorder (SPD) (Cadenhead, Geyer, & Braff, 1993; Cadenhead et al., 2000), and to a lesser extent in normal participants scoring high on psychometric measures of psychosisproneness (Kumari, Toone, & Gray, 1997; Simons & Giardina, 1992; Swerdlow, Filion, Geyer, & Braff, 1995). Since the profile of startle measures is thought to differ across race (Hasenkamp, Norrholm, Green, Lewison, Boshoven, et al., 2008; Swerdlow, Sprock, Light, Cadenhead, Calkins, et al., 2007; Swerdlow, Talledo, & Braff, 2005), PPI should be comprehensively explored in Asian subjects. Recent reports indicate that, as well as in Caucasians, PPI is impaired in Asian patients with schizophrenia (Kunugi, Tanaka, Hori, Hashimoto, Saitoh et al., 2007; Takahashi, Iwase, Ishii, Ohi, Fukumoto, et al., 2008). However, to our knowledge, the relationship between PPI and schizotypy has not yet been investigated in non-Caucasian subjects. In this study, we aimed at determining a possible association between PPI and schizotypy in a sample of 79 healthy Japanese subjects. We evaluated PPI at signal-to-noise ratios (SnRs:
Sensorimotor gating is thought to be a process which regulates sensory input by filtering out irrelevant or distracting stimuli, prevents sensory information overflow, and allows for selective and efficient processing of relevant information. Prepulse inhibition (PPI), which is usually defined as a reduction of the startle reflex due to weak sensory prestimulation (Braff, Stone, Callaway, Geyer, Glick, et al., 1978) of the acoustic startle reflex (ASR), is the most common psychophysiological index of sensorimotor gating. Recently, PPI has been considered a candidate intermediate phenotype (endophenotype) of schizophrenia (Braff & Light, 2005; Turetsky, Calkins, Light, Olincy, Radant, & Swerdlow, 2007) and schizotypy (Cadenhead & Braff, 2002). Previous studies have consistently demonstrated PPI reductions in paThe authors gratefully thank Harumi Koyama and Masako Kiribayashi for technical assistance. This work was supported in part by Grants-in-Aid from the Japanese Ministry of Health, Labor and Welfare (H18-kokoro-005, H19-kokoro-002), the Japanese Ministry of Education, Culture, Sports, Science and Technology (17591211, 18689030, 20591402, 21791130), CREST of JST, and Japan Foundation for Neuroscience and Mental Health. Address reprint request to: Hidetoshi Takahashi, Department of Psychiatry, Osaka University Graduate School of Medicine, D3, 2-2, Yamadaoka, Suita, Osaka, 5650871, Japan. E-mail address: hidetaka@ psy.med.osaka-u.ac.jp 831
832 difference between background noise intensity and prepulse intensity) of 112, 116, and 120 dB. To measure schizotypy, we used the three factor model (Raine, Reynolds, Lencz, Scerbo, Triphon, & Kim, 1994) of the Schizotypal Personality Questionnaire (SPQ; Raine, 1991).
Method Participants One hundred and nine Japanese volunteers were recruited by local advertisements in Osaka, as psychiatrically, medically, and neurologically healthy volunteers who were not receiving psychiatric medication, and had no first- or second-degree relatives with psychosis. Volunteers were screened for psychiatric disorder with the non-patient edition of the modified structured clinical interview for the Diagnostic and Statistical ManualF4th Edition, Axis I Disorders (SCID-I/NP) (First, Spitzer, Gibbon, & Williams, 1997), which was conducted by a well-trained psychologist. Two volunteers were excluded because they had psychiatric disorder (both anxiety disorder), and seven volunteers were excluded because they had first- or second-degree relatives with psychosis. As a result, 100 healthy Japanese subjects participated in this study. A portion of the subjects in the present study was from our previous sample (Takahashi et al., 2008). According to the screening interview, these subjects did not have clinically significant distress or impairment in social, occupational, or other important areas of functioning, which is necessary to be diagnosed as a personality disorder. None of the participants had any hearing impairments. Pregnant or lactating women were not included. The study procedure was conducted according to the Helsinki Declaration and approved by the Research Ethical Committee of Osaka University. All participants gave written informed consent after the study procedures were fully explained to them. Schizotypy Questionnaire Schizotypy was assessed using the SPQ. This is a 74-item questionnaire with a dichotomous response format (yes/no). The SPQ was developed to measure all of the nine diagnostic criteria stipulated by the Diagnostic and Statistical Manual of Mental Disorders (DSM-III-R) (American Psychiatric Association, 1987) for SPD. The factor analytical study of schizotypal personality by Raine et al. (1994) showed that the nine diagnostic subscales for SPD can be reduced to three latent factors: cognitive-perceptual, interpersonal, and disorganization. The cognitive-perceptual factor reflects the positive symptoms of schizotypy, characterized by ideas of reference, odd beliefs/magical thinking, unusual perceptual experiences, and suspiciousness/paranoid ideation. The interpersonal factor reflects the negative symptoms of schizotypy, characterized by suspiciousness/paranoid ideation, constricted affect, lack of close friends, and excessive social anxiety. The disorganization factor, which represents the disorganized schizotypy, consists of symptoms such as odd speech and odd or eccentric behavior. Scores for all 74 items are summed to produce the total SPQ score and the three SPQ latent factor scores. In this study, a Japanese version of the SPQ was used, and all participants filled out the questionnaire. The questionnaire had been administered to 258 Japanese college students in a validation study (Someya, Sasaki, & Takahashi, 1994), and the validity and reliability properties of this Japanese version of the SPQ were found to be similar to those of the original version of Raine (1991).
H. Takahashi et al. Startle Response Measurement The methods for the startle paradigm, eyeblink acquisition, scoring parameters, and the procedure are described in detail in one of our earlier publications (Takahashi et al., 2008). A commercial computerized human startle response monitoring system (Startle Eyeblink Reflex Analysis System Map1155SYS, Nihonsanteku Co., Osaka, Japan) was used to deliver acoustic startle stimuli, and record and score the corresponding electromyographic activity. Stimulus presentation and data acquisition were controlled through a laptop computer with Windows XP operating system installed on it. All the auditory stimuli and the background noise were produced by a custom-built tone and noise generator and delivered binaurally to the subjects through stereophonic headphones (type DR-531, Elega Acous. Co. Ltd., Tokyo, Japan) with hard plastic bells. Startle eyeblink electromyographic responses were recorded from the left orbicularis oculi muscle with a pair of Ag/AgCl disposable electrodes (sensor area 15 mm2) filled with wet gel. The first electrode (Blue Sensor N-00-S, Ambu, Ballerup, Denmark) was positioned approximately 1 cm directly below the pupil of the left eye and low enough to not touch the lower eyelid, while the second electrode (Blue Sensor M-00-S, Ambu) was placed laterally and slightly superior to the first one, with the centers of the electrodes separated by approximately 2 cm. The impedance between the two electrodes was measured and deemed acceptable if below 10 kO. The impedance was measured with an electrode impedance meter (MaP811, Nihonsanteku Co.) at a measurement frequency of 30 Hz. The ground electrode (Blue Sensor M-00-S) was placed on the left angle of the mandible. The skin area at the electrode site was cleaned with a cotton swab saturated with rubbing alcohol, then prepared by gently rubbing a small amount of Nuprep EEG & ECG Skin Prepping Gel (Bio-Medical Instruments Inc., Warren, MI), and cleaned with a cotton swab saturated with rubbing alcohol again. Electromyography (EMG) data were measured with an EMG Telemeter (PolyTele EMG, Nihonsanteku Co.). The measurement condition was adjusted as follows: the time constant was 0.03 s, which was equivalent to the low frequency filter of 5 Hz; the high frequency filter was 300 Hz. The sensitivity of the amplifier was 1000 times. The amplification gain control for the EMG signal was kept constant for all subjects. EMG data were digitized with a 12-bit A/D converter (MaP222, Nihonsanteku Co.) and collected on the PC. The sampling frequency was 1 kHz. Sampling on each trial began 1000 ms prior to the onset of the startle eliciting stimulus and continued for 1000 ms after the onset of the startle eliciting stimulus. The resulting data were baseline corrected with a moving average. The eyeblink magnitude of every startle response was defined as the voltage of the peak activity of the EMG within a latency window of 20–85 ms following startle eliciting stimulus onset. The data were stored and exported for analyses in microvolt values. Participants were tested in a startle paradigm, which consisted of 3 blocks with a continuously presented 70 dB sound pressure levels (SPL) background white noise. Pulse stimuli consisted of broadband white noises with an instantaneous rise/fall time lasting for 40 ms presented at 115 dB SPL. Prepulse stimuli were also broadband white noises with an instantaneous rise/fall time lasting for 20 ms presented at three different intensities (82, 86, and 90 dB SPL, equivalent to SnR of 112 dB, 116 dB, and 120 dB, respectively). The lead interval (from prepulse onset to pulse onset) was 120 ms. In block 1, the startle response for pulse alone trial (PA trial) was recorded 6 times. Block 2 consisted of PA
Prepulse inhibition and schizotypy in Japanese trials or trials of pulse with prepulse at the three different intensities (PP trials) performed eight times for each condition. Block 3 was the same as block 1 to explore the habituation phenomenon in one of our previous publications (Takahashi et al., 2008). However, because habituation was not assessed in this study, Block 3 was not used for analysis of these data. All trials were presented in a fixed pseudorandom order, separated by inter-trial intervals of 15–25 s (20 s on average). The startle paradigm consisted of a total of 44 trials. The session lasted approximately 20 min, including 5 min acclimation to the background noise. The following startle measures were examined: PPI82, PPI86, PPI90: prepulse inhibition at prepulse intensities of 82 dB, 86 dB, and 90 dB SPL, respectively. PPI for each prepulse intensity was computed as the percentage of magnitude reduction between PA and PP trials in block 2 by the formula: (1 – average eyeblink magnitude of startle response to PP trials in block 2/average eyeblink magnitude of startle response to PA trials in block 2) ! 100. Prior to data analyses, exclusion criteria were established for both trials and subject data. Trials were discarded if the voltage of their peak activity of the EMG within a latency window of 0–20 ms following startle eliciting stimulus onset was more than 30 microvolt. Subjects were excluded from further analyses as nonresponders if the voltages of their peak activity of the EMG within a latency window of 20–85 ms following startle eliciting stimulus onset were less than 30 microvolt in more than half of the trials in block 1. Analyses of PPI were not conducted if more than half of the PP trials at any prepulse intensity or PA trials in block 2 were discarded. Upon arriving at the laboratory, each subject read and signed an informed consent form and completed a brief medical history questionnaire including demographic data. The subjects were informed about the general purpose of the study, about the stimuli and procedure, and that they could withdraw from the study at any time. Subjects were told that the experiment aimed to measure their reactivity to a number of noise bursts. There was no restriction on smoking intake, but we took care to avoid testing smokers within 30 min of smoking a cigarette, as this could potentially increase PPI (Kumari, Soni, & Sharma, 2001). Subjects were then seated in the testing room. During the task, the subjects were instructed to keep their eyes open and to maintain their gaze on a fixed point 100 cm away. Thereafter, the skin area at the electrode site was cleaned and the electrodes were attached. The door to the experimental chamber was closed. Nineteen subjects were excluded from the analyses. One female subject could not stand the startle stimuli and did not complete the session. There were a total of 8 nonresponders. Ten subjects were excluded from analyses because their PPI were not evaluated according to the above exclusion criteria. There was one outlier, who was more than 3 standard deviations above or below the mean of all subjects, in cognitive/perceptual scores, and there was also another outlier in disorganization scores. We excluded these two outliers from further analyses. Thus, the final sample size was 79 (males N 5 33, females N 5 46; age [years]: M 5 38.5, SD 5 10.7, range 21–60). The percentage of smokers was higher in males than females (nonsmoker/smoker: males 22/ 11; females 39/7), although the difference did not reach statistical significance (w2(1) 5 3.58, p 5 .101, Fisher’s exact test). Age did not differ significantly across sex (males: M 5 36.5, SD 5 9.4; females: M 5 39.8, SD 5 11.4; t[77] 5 " 1.36, p 5 .179) and smoking status (nonsmokers: M 5 38.5, SD 5 11.4; smokers: M 5 38.3, SD 5 7.7; t[41.2] 5 " .10, p 5 .922). Those subjects excluded from the analyses did not differ significantly from the
833 included subjects in demographic characteristics, such as age, sex distribution, smoking status, and also in the SPQ scores. Statistical Analysis None of the SPQ scores and startle measures was normally distributed based on the Shapiro–Wilkes W statistic (po.001 for all SPQ scores; PPI82, W 5 .963, p 5 .021; PPI86, W 5 .957, p 5 .009; PPI90, W 5 .969, p 5 .049). Therefore, we performed nonparametric analyses. The Mann-Whitney U test was used for comparison of mean SPQ scores and startle measures. Within group differences in PPI across the three prepulse intensities were analyzed using the non-parametric Friedman w2 test. Spearman’s rank order correlations examined the relationship of PPI to psychiatric symptoms. All p-values reported here were two-tailed. Statistical significance was considered when p-value waso.05. Statistical analyses were performed using SPSS Ver. 12 (SPSS Japan, Tokyo, Japan).
Results Difference in Schizotypal Personality Questionnaire Scores and Startle Measures Across Sex and Smoking Status The SPQ scores and startle measures of the subjects in the present study are shown in Table 1. Since sex and smoking status may affect startle measures (Abel, Waikar, Pedro, Hemsley, & Geyer, 1998; George, Termine, Sacco, Allen, Reutenauer, et al., 2006; Kumari, Aasen, & Sharma, 2004; Kumari, Checkley, & Gray, 1996; Kumari et al., 2001; Rissling, Dawson, Shell & Nuechterlein, 2007; Swerdlow, Auerbach, Monroe, Hartston, Geyer, & Braff, 1993; Swerdlow, Hartman, & Auerbach, 1997), and are also related to schizotypy (Badcock & Dragovic, 2006; Esterberg, Jones, Compton, & Walker, 2007; Kremen, Faraone, Toomey, Seidman, & Tsuang, 1998; Wan, Crawford, & Boutros, 2007), we assessed the difference in SPQ scores and startle measures across sex groups and smoking status. PPI differed significantly across the three prepulse intensities (w2 (2) 5 36.9, po.01), with PPI82 showing the lowest PPI, and PPI90 showing the highest PPI. This difference was also observed after separate analyses for sex (male: w2 (2) 5 25.9, po.01, female: w2 (2) 5 14.39, po.01), and smoking status (smoker: w2 (2) 5 9.33, po.01, nonsmoker: w2 (2) 5 30.33, po.01). As shown in Table 1, females had significantly decreased PPI for all three prepulse intensities compared to males, and SPQ scores did not differ significantly across sex. We did not find significant difference in SPQ scores or startle measures between smokers and nonsmokers (PPI82, U 5 442, p 5 .211; PPI86, U 5 463, p 5 .315; PPI90, U 5 514, p 5 .682; the total SPQ score, U 5 469, p 5 .349; cognitive/perceptual score, U 5 525, p 5 .776; interpersonal score, U 5 463, p 5 .312; disorganization score, U 5 484.5, p 5 .445). Relationship of Startle Measures to Schizotypy1 Figure 1 shows scatterplots of prepulse inhibition by scores on SPQ. PPI86 correlated negatively with the total SPQ score, p 5 .002, as well as with cognitive/perceptual scores, p 5 .026, and with interpersonal scores, p 5 .003. PPI90 also correlated negatively with the total SPQ score, p 5 .020, as well as with 1 Since smoking status might have affected our results, we additionally investigated the relationship of startle measures and SPQ scores in nonsmokers. We found significant correlation between PPI86 and interpersonal scores in nonsmokers, rho 5 " .268, p 5 .037.
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H. Takahashi et al.
Table 1. Scores on Schizotypal Personality Questionnaire and Startle Measures All (N 5 79)
Scores on SPQ
Startle measures
Total SPQ score Cognitive/perceptual score Interpersonal score Disorganization score PPI82 (%) PPI86 (%) PPI90 (%)
Male (N 5 33)
Female (N 5 46)
M
SD
Skewness
Kurtosis
M
SD
M
SD
U
p
Effect size
9.4 2.7 4.6 2.6 27.2 31.9 40.7
6.5 2.6 3.8 2.3 27.2 27.5 26.0
0.76 1.04 1.00 0.60 ! 0.65 ! 0.38 ! 0.39
! 0.21 1.08 0.46 ! 0.61 0.28 ! 0.82 ! 0.36
9.2 2.7 4.5 2.6 37.6 41.4 50.7
7.1 3.0 3.7 2.5 27.0 25.4 23.6
9.5 2.8 4.7 2.6 19.7 25.2 33.5
6.1 2.2 3.9 2.1 25.2 27.1 25.5
703.5 682.5 725.0 732.0 448.0 506.5 462.0
0.580 0.440 0.734 0.786 0.002 0.012 0.003
0.057 0.053 0.075 0.018 0.656 0.590 0.662
Note: SPQ: Schizotypal personality questionnaire; PPI82, PPI86, PPI90: prepulse inhibition of acoustic startle reflex in prepulse of 82 dB, 86 dB, and 90 dB, respectively. U: Mann-Whitney U test.
cognitive/perceptual scores, p 5 .037, and with interpersonal scores, p 5 .048. There was no other significant correlation between SPQ scores and PPI. Since there was gender difference in PPI, we investigated the relationship of startle measures to SPQ scores separately for sex groups. In female subjects, PPI86 correlated negatively with the total SPQ score, rho 5 ! .41, p 5 .005, with cognitive-perceptual scores, rho 5 ! .30, p 5 .042, and with interpersonal scores, rho 5 ! .41, p 5 .005, whereas PPI90 correlated negatively with
a
Discussion In this study, we investigated a possible association between PPI and SPQ scores in a sample of 79 healthy Japanese subjects. We found that the total SPQ score, cognitive/perceptual score, and interpersonal score correlated negatively with PPI86 and PPI90.
b
(%) 100
PPI82
the total SPQ score, rho 5 ! .31, p 5 .037. However, we found no significant correlation between PPI and SPQ scores in male subjects.
rho=–.19
c rho=–.17
d rho=–.19
rho=–.05
50 0 –50
PPI86
e
f
100
rho=–.34**
g rho=–.25*
h rho=–.33**
rho=–.15
50 0 –50
PPI90
i
j
100
rho=–.26*
k rho=–.23*
l rho=–.22*
rho=–.05
50
–50
0
10 20 total SPQ score
30
0 10 20 0 10 20 cognitive / perceptual score interpersonal score
0
10 20 disorganization score
Figure 1. Scatterplot of prepulse inhibition by scores on schizotypal personality questionnaire (N 5 79) Scatterplots of (a) PPI82 for the total SPQ score, (b) PPI82 for cognitive/perceptual score, (c) PPI82 for SPQ interpersonal score, (d) PPI82 for SPQ disorganization score, (e) PPI86 for the total SPQ score, (f) PPI86 for cognitive/perceptual score, (g) PPI86 for SPQ interpersonal score, (h) PPI86 for SPQ disorganization score, (i) PPI90 for the total SPQ score, (j) PPI90 for cognitive/perceptual score, (k) PPI90 for SPQ interpersonal score, and (l) PPI90 for SPQ disorganization score. Variables are rho. SPQ: Schizotypal personality questionnaire; PPI82, PPI86, PPI90: prepulse inhibition of acoustic startle reflex in prepulse of 82 dB, 86 dB, and 90 dB, respectively. Spearman’s rank order correlations; npo.05; nnpo.01.
Prepulse inhibition and schizotypy in Japanese
835
To our knowledge, this is the first study to investigate the relationship between PPI and schizotypy in non-Caucasian subjects. Since the profile of startle measures, which includes PPI, appears to be different in Caucasians compared with non-Caucasian populations (Hasenkamp et al., 2008; Swerdlow et al., 2005, 2007), the relationship between PPI and schizotypy might be different across race. However, we found that PPI86 and PPI90 negatively correlated with the total SPQ scores in healthy Japanese subjects. Our results indicate that the association of PPI to schizotypy might be detected across race. Further replication studies in non-Caucasian participants will be necessary to confirm this argument. We used SPQ to assess schizotypy in relation to PPI. Although SPQ is a rather novel questionnaire to assess schizotypy, a recent study (Wuthrich & Bates, 2006) reported that SPQ scores showed good correlation with an established questionnaire of schizotypy, the Chapman schizotypy scales, which include the Chapman Magical Ideation (Eckblad & Chapman, 1983), Perceptual Aberration (Chapman, Chapman, & Raulin, 1978), and Revised Social Anhedonia (Eckblad, Chapman, Chapman, & Mishlove, 1982) scales. The SPQ assesses the nine diagnostic subscales of DSM-defined SPD, and the factor analytical study (Raine et al., 1994) showed that these nine diagnostic subscales for SPD can be reduced to three dimensions of schizotypy. Similarities between the three symptom factors of schizophrenia (Arndt, Alliger, & Andreasen, 1991; Bilder, Mukherjee, Rieder, & Pandurangi, 1985; Gruzelier, 1996; Liddle & Barnes, 1990) and the three SPQ dimensions of schizotypy suggested that analysis of the SPQ dimensions of schizotypy could be useful for evaluating the different components of schizotypy. In fact, the three-factor model (Raine et al., 1994) of SPQ has been suggested to underlie individual differences across samples of normal and schizophrenic patients (Rossi & Daneluzzo, 2002). Thus, the SPQ has been widely used to investigate the relationship of schizotypy to cognitive functions (Chen, Hsiao, Hsiao, & Hwu, 1998; Noguchi, Hori, & Kunugi, 2008) or to a psychophysiological index, such as P50 (Wan, Crawford, & Boutros, 2006, 2007; Wang, Miyazato, Hokama, Hiramatsu, & Kondo, 2004), P300 (Mannan, Hiramatsu, Hokama, & Ohta, 2001) or prefrontal activation patterns measured with near-infrared spectroscopy (Hori, Nagamine, Soshi, Okabe, Kim, & Kunugi, 2008; Hori, Ozeki, Terada, & Kunugi, 2008). By using SPQ, we could find a negative correlation between PPI and the trait of schizotypy. Our result that females exhibited smaller PPI than males is consistent with findings of most previous PPI studies (Aasen, Kolli, & Kumari, 2005; Abel et al., 1998; Della Casa, Ho¨fer, Weiner, & Feldon, 1998; Kumari et al., 2004; Swerdlow et al., 1993, 1995, 1997, 1999, 2006). In addition, the analysis of the data by sex difference indicated that association between SPQ scores and PPI remained significant among female subjects but not among male subjects. However, it is important to point out that, with the analytic approach used herein, we cannot ensure that reliable associations exist between sex and PPI in schizotypy in our data. Our results also showed a significant relationship
between PPI and schizotypy exclusively for PPI86 and PPI90. This supports recent reports of a significant impact of stimulus SnR on PPI of ASR (Blumenthal, Noto, Fox, & Franklin, 2006; Franklin, Bowker, & Blumenthal, 2009; Franklin, Moretti, & Blumenthal, 2007). Of note, the correlation of PPI with schizotypy for PPI82 nearly reached statistical significance (the total SPQ score, p 5 .103; cognitive/perceptual scores, p 5 .128; interpersonal scores, p 5 .097). Thus, it is conceivable that an increase in sample size could also result in significant difference for this PPI intensity. Overall, although effects of sex and SnR on PPI may be interesting, the present study was not specifically designed to examine this issue but to assess the cross-cultural variability of the PPI–schizotypy relationship. Further studies will be necessary to clarify the effects of these factors on the relationship between PPI and schizotypy. There are several limitations to the current study. First, we enrolled only healthy volunteers who have no family history of psychosis and were relatively mature-aged subjects. This might have restricted the range on the SPQ and influenced the relationship between PPI and SPQ scores. SPQ scores are reported high in relatives of patients with schizophrenia (Bora & Veznedaroglu, 2007) and are thought to become lower with increasing age (Badcock & Dragovic, 2006). Although SPQ scores in our sample were similar to those of mature, healthy populations (Chen et al., 1998), including those with participants without family history of psychiatric illness (Hori, Nagamine, et al., 2008; Hori, Ozeki, et al., 2008; Noguchi et al., 2008; Wang et al., 2004) (these studies have reported mean total SPQ scores ranging from 8.1 to 12.9), SPQ scores in our study were relatively smaller than those of previous studies on schizotypy, for instance, a study by Raine (1991) found a mean total SPQ score of 26.9. In addition, although symptom dimensions of schizotypy in relatives of patients with schizophrenia are reported in association with patient symptoms (Schu¨rhoff, Laguerre, Szo¨ke, Me´ary, & Leboyer, 2005), little is known about the relationships of symptom dimensions of schizotypy and schizophrenia. Further studies investigating the relationship between SPQ and PPI in relatives and non-relatives of patients with psychiatric disorders are needed. Second, some SPQ scores, such as the interpersonal, cognitive-perceptual, and total SPQ scores, are associated with traitanxiety (Braunstein-Bercovitz, 2000). Some previous studies (Duley, Hillman, Coombes, & Janelle, 2007; Franklin et al., 2009; Ludewig, Ludewig, Geyer, Hell, & Vollenweider, 2002) have reported a relationship between PPI and anxiety. Because we did not assess trait-anxiety of our subjects, the possibility that anxiety is more responsible for PPI than symptom dimension of schizotypy is not testable in this study. Future studies are needed to evaluate the association of PPI to psychiatric symptoms, including symptom dimension of schizotypy and anxiety.
Conclusion In the present study, PPI correlated negatively with the trait of schizotypy in healthy Asian subjects.
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(Received December 25, 2008; Accepted October 12, 2009)
Psychophysiology, 47 (2010), 838–845. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.01001.x
Methylphenidate enhances prepulse inhibition during processing of task-relevant stimuli in attention-deficit/ hyperactivity disorder
REBECCA L. ASHARE,a LARRY W. HAWK, Jr.,a KERI SHIELS,a JESSICA D. RHODES,a WILLIAM E. PELHAM, Jr.,b and JAMES G. WAXMONSKYc a
Department of Psychology, University at Buffalo, SUNY, Buffalo, New York Departments of Psychology and Pediatrics, Center for Children and Families, University at Buffalo, SUNY, Buffalo, New York c Department of Psychiatry, Center for Children and Families, University at Buffalo, SUNY, Buffalo, New York b
Abstract ADHD is characterized by inattention, hyperactivity, and disinhibition, including the inability to screen out distracting stimuli. Prepulse inhibition (PPI) of startle indexes a related gating process and is enhanced during attended compared to ignored stimuli. We predicted that PPI during attended stimuli would be enhanced by the stimulant methylphenidate (MPH) and that this effect would be moderated by baseline PPI. Children with ADHD (n 5 36) completed a baseline day and a randomized, double-blind medication trial (placebo vs. sustained release MPH). Bilateral startle eyeblink EMG was measured during a tone discrimination task. MPH enhanced PPI during attended, but not during ignored stimuli. Extending findings that pretreatment functioning moderates stimulant effects on PPI, this effect tended to be inversely related to baseline PPI. These data fit with the clinical literature on ADHD and the hypothesis that MPH enhances interference control for important environmental stimuli. Descriptors: Startle eyeblink, Cognition, Psychopharmacology, Psychopathology
measures of response inhibition (Lijffijt, Kenemans, Wal, Quik, Kemner et al., 2006; Oosterlaan, Logan, & Sergeant, 1998; Tannock, Schachar, & Logan, 1995), and sustained attention (Losier, McGrath, & Klein, 1996; Riccio, Waldrop, Reynolds, & Lowe, 2001). The smaller literature on interference control is mixed, with some studies demonstrating that MPH improves the screening out of distracting stimuli in ADHD (Scheres, Oosterlaan, Swanson, Morein-Zamir, Meiran, et al., 2003), whereas others do not (Langleben, Monterosso, Elman, Ash, Krikorian, & Austin, 2006). Differences in the measurement of interference control may partially explain these mixed findings (Lansbergen, Kenemans, & van Engeland, 2007). To elucidate the effect of MPH on the controlled ability to screen out distracting stimuli, the present work employs an attentional modification of prepulse inhibition of startle paradigm. Prepulse inhibition (PPI), a decrease in the magnitude of the startle response when a weak, nonstartling stimulus is presented 30–500 ms before startle probe onset, is widely used to investigate early cognitive processes (Filion, Dawson, & Schell, 1998) and their psychopharmacology (Braff, Geyer, & Swerdlow, 2001; Swerdlow, Weber, Qu, Light, & Braff, 2008). At these short-lead stimulus onset asynchronies (SOAs), PPI is thought to reflect partially automatic protection of processing of sensory stimuli (Graham, 1975). More generally, it has been conceptualized as a sensorimotor gating mechanism that serves a critical inhibitory function for sensory, cognitive, and motor output processing (Braff & Geyer, 1990).
Attention-Deficit/Hyperactivity Disorder (ADHD) is characterized by impulsivity (e.g, trouble waiting turns, interrupting others), hyperactivity, and inattention (American Psychiatric Association, 2000). According to a leading model, inhibitory control is theorized to be a core process in ADHD (Barkley, 1997). Nigg (2000) describes several facets of inhibitory control. These include behavioral (response) inhibition, or the inhibition of a prepotent response, and interference control, or the suppressed processing of a distracting stimulus to carry out a primary response. An important prediction for any putative core process in ADHD is that deficits in that process should be reduced by effective treatments. Stimulants continue to be a leading treatment for ADHD (Zuvekas, Vitiello, & Norquist, 2006), and the stimulant methylphenidate (MPH) improves performance on The authors thank Brian Gangloff, Sarah Spencer, Rebecca Mazzullo, Amanda Krol, Michael Strand, Jacob Via, and Joyce Mixson for assisting with data collection and providing comments on an earlier draft. The authors thank Mark Kutgowski for assisting with computer programming. We also express our gratitude to the children and families who participated in this study. This research was supported by grants R01MH069434 from the National Institute of Mental Health (LWH, PI) and by F31DA024532 (to RLA) from the National Institute on Drug Abuse. Address reprint requests to: Larry W. Hawk, Jr., Ph.D., 230 Park Hall, Box 604110, University at Buffalo, SUNY, Buffalo, NY 142604110. E-mail:
[email protected] 838
Methylphenidate, ADHD, and PPI Though basic PPI does not require conscious awareness, the effect is enhanced by directed attention (Filion, Dawson, & Schell, 1998). That is, PPI is enhanced during attended prepulses relative to ignored prepulses (e.g., Ashare, Hawk, & Mazzullo, 2007; Filion et al., 1993; Hawk, Yartz, Pelham, & Lock, 2003). Differences in these early inhibitory processes have been conceptualized as measures of controlled attention (Dawson, Schell, Swerdlow, & Filion, 1997) and selective inhibition (Hawk et al., 2003) that are central to the interference control construct. Although passive PPI is not reduced in children (Castellanos, Fine, Kaysen, Marsh, Rapoport, & Hallett, 1996; Ornitz, Hanna, & Traversay, 1992) or adults (Feifel, Minassian, & Perry, 2008; Hanlon, Karayanidis, & Schall, 2009) with ADHD compared to controls, preliminary evidence from a small sample of 9to 12-year-old boys suggests that PPI during attended prestimuli is selectively diminished in ADHD (Hawk et al., 2003). Most germane to the present work is the finding that among the boys with ADHD, a .3mg/kg dose of MPH enhanced PPI during attended prestimuli, eliminating group differences. This finding was consistent with the clinical effects of MPH, but was difficult to reconcile with extensive preclinical work on the psychopharmacology of PPI. Passive PPI is generally disrupted, or decreased, by dopamine (DA) agonists such as amphetamine and apomorphine (Geyer, Krebs-Thomson, Braff, & Swerdlow, 2001; Swerdlow, Geyer, & Braff, 2001). Therefore, the MPH findings in Hawk et al. (2003) contradict much of the animal literature since MPH, an indirect DA agonist, enhanced PPI during attended stimuli. However, there are at least two ways of incorporating the apparently incompatible data from ADHD. First, the effects of dopaminergic drugs on PPI may depend on baseline levels of PPI in humans (Bitsios, Giakoumaki, & Frangou, 2005; Swerdlow, Wasserman, Talledo, Casas, Bruins, & Stephany, 2003; Talledo, Sutherland Owens, Schortinghuis, & Swerdlow, 2009) and animals (e.g., Talledo et al., 2009). Specifically, the disruptive effects of DA agonists on passive PPI have been most robust among those with high levels of baseline PPI; among those with low baseline PPI, there are either no significant effects or trends toward PPI enhancement. Second, the effects observed by Hawk et al. (2003) may have been due to noradrenergic effects of MPH, a prediction consistent with recent evidence that noradrenergic drugs can increase PPI (Bakshi, Swerdlow, & Geyer, 1994; Gould, Rukstalis, & Lewis, 2005). Clearly, conclusions from Hawk et al. (2003) were tentative. In addition to the inconsistency with much of the literature regarding the effects of DA agonists on PPI in rats, the sample was small and limited to boys. Furthermore, the sample had been chosen for a positive clinical response to MPH, limiting the generalizability of the findings. The present study attempted to more definitively test the effects of MPH on PPI during attended and ignored prepulses among a larger sample of both boys and girls with ADHD who were not pre-selected for clinical response to MPH. Importantly, the present study is one of a relatively small number to examine the controlled effects of a therapeutic agent on PPI among persons with psychopathology (Wynn, Green, Sprock, Light, Widmark, et al., 2007)Fa strikingly sparse literature given the hundreds of preclinical and clinical studies linking PPI abnormalities to psychopathology (Braff et al., 2001). Based on our preliminary work, we predicted that MPH would selectively enhance PPI during attended stimuli but have no effect during ignored stimuli. We examined two therapeutic doses of MPH to provide initial data regarding the dose-response curve. Finally, based on evidence from non-clinical samples that
839 drug effects on PPI depend in part on baseline levels of processing (Baschnagel & Hawk, 2008; Bitsios et al., 2005; Swerdlow et al., 2003), and given the marked heterogeneity in ADHD (Nigg, Willcutt, Doyle, & Sonuga-Barke, 2005), we predicted that MPH enhancement of PPI during attended stimuli would be strongest among those with poor gating during attended tones at baseline.
Methods and Materials Participants Participants were 36 9- to 12-year-old children (8 females) diagnosed with ADHD. Sample characteristics are listed in Table 1. Participants were recruited from a university psychiatric clinic and pediatricians’ offices. Parents were remunerated with money; children were rewarded with toys and gift cards. Children were excluded based on the following criteria: Full Scale IQ below 80; history of seizures, neurological disorders, and other medical problems contraindicating psychostimulant treatment; current use of non-ADHD psychotropic medications; history or concurrent diagnosis of pervasive developmental disorder or psychosis; and sensory problems that would make it difficult to complete the task. Diagnostic Assessment All participants had a DSM-IV (American Psychiatric Association, 2000) diagnosis of ADHD based on a structured interview with the primary caregiver (Diagnostic Interview Schedule for Children Version IV (DISC-IV); Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000). In addition, parents and teachers completed the Disruptive Behavior Disorder (DBD) rating scale (Pelham, Fabiano, & Massetti, 2005; Pelham, Gnagy, Greenslade, & Milich, 1992) and the Impairment Rating Scale (Fabiano, Pelham, Waschbusch, Gnagy, Lahey, et al., 2006) to Table 1. Sample Characteristics, Mean (SD) Sample characteristics Age, mean (SD) Gender (male:female) Ethnicity Caucasian African-American Mixed race WISC Full Scale IQ, mean (SD) WJ Test of Achievement, mean (SD) Letter-Word Identification Calculation Spelling DBD rating scale Hyp/Imp, mean (SD) Parent report Teacher report Inattentive, mean (SD) Parent report Teacher report CBCL, mean T-score (SD) Attention Problems Externalizing Problems Internalizing Problems Comorbid diagnoses (percent subjects) ODD:CD
Mean (SD) 10.5 (1.1) 28:8 80% 17% 3% 102 (13) 103 (9) 104 (11) 104 (12) 13 (6) 10 (7) 16 (6) 13 (7) 68 (9) 61 (9) 57 (9) 47%:17%
Note: These values represent the total score of the items within each subtype domain on the DBD rating scale.
840 document the presence of functionally impairing ADHD symptoms in multiple realms. Regarding subtype, 61% were Combined, 31% were Predominantly Inattentive, and 8% were Predominantly Hyperactive/Impulsive. Additionally, 47% of the sample met criteria for Oppositional Defiant Disorder and 17% for Conduct Disorder. Standardized measures of intellectual ability and achievement included the Vocabulary and Block Design subtests from the Wechsler Intelligence Scale for ChildrenFFourth Edition (WISC-IV; Kaplan, Fein, Kramer, Delis, & Morris, 2004). Table 1 presents descriptive statistics for diagnostic symptom, intellectual, and achievement variables. Setting. The Summer Research Camp was held from 7:30 am to 5:00 pm Monday through Friday. On Monday through Thursday, cohorts of 5 children completed a variety of computerized tasks measuring neurocognitive processes implicated in ADHD, classroom periods, and recreational activities. Task order was randomized between participants but remained consistent for each child. The current report is limited to the tone discrimination task described below. Children earned points for task participation and appropriate behavior. These points were exchanged for toys and gift cards at the end of each day. Medication Assessment After an initial baseline day, each child participated in a 3-day, double-blind, placebo-controlled medication assessment. Two doses of long-acting osmotic release oral system MPH (OROSMPH; Concerta, ALZA Corporation, Titusville, NJ) were employed. One provided equivalent effects to TID IR (three times daily, immediate release) MPH at .3mg/kg dose, producing a total daily dose of .9mg/kg; the other dose was equivalent to TID IR MPH at .6mg/kg dose, producing a total daily dose of 1.8mg/ kg. As per current dosing guidelines by the American Academy of Child and Adolescent Psychiatry (Greenhill, Pliszka, Dulcan, Bernet, Arnold, et al., 2002; Pliszka, 2007), these represent medium to large doses, respectively (c.f., Pliszka, 2007). OROSMPH was selected to ensure consistent blood levels of MPH over the 8-hr testing period. Most (81%) participants were taking stimulant medication at the time of the study or had prior exposure to stimulants. Participants discontinued any stimulant medication at least 24 hr prior to participation; participants taking atomoxetine completed a 1-week washout period. To promote reasonable tolerability of the medication in subjects who were stimulant naı¨ ve or previously prescribed only very low doses (o.4 mg/kg/day), thirteen children were order restricted so that they received the .3 mg/kg TID-equivalent dose prior to receiving the .6 mg/kg TID-equivalent dose. Among those who were not order restricted, six possible drug orders were counterbalanced across participants. Medication was administered when the child arrived in the morning, 90 min prior to the initial cognitive task. Two prior studies have established that OROS-MPH significantly separates from placebo in this timeframe (Pelham et al., 2001; Swanson et al., 2003). Wilens et al. (2003) also established that a 20% higher OROS-MPH dose produces comparable effects to a TID IR MPH dose over a 12-hr period (i.e., 18 mg of OROSMPH 5 15mg of IR MPH dosed as 5 mg TID). We used similar procedures as this study to calculate matching doses of OROS-MPH. Doses ranged from 18 to 90 mg (dose was capped at 90 mg for safety reasons). By using combinations of commercially available doses (we used 18, 27, and 36 mg) and providing each child with either two or three capsules each morning
R. L. Ashare et al. (number of capsules remained constant for a given child), we were able to provide most within 4.5 mg OROS-MPH of the ideal weight-based dose up to the 90 mg maximum (no medium doses were affected by the maximum, but 9 children would have received high doses greater than 90 mg [median 5 99 mg] were it not for this cap). The mean of the medium dose was 40 mg OROS-MPH (SD 5 9.2) and the mean of the high dose was 76.5 mg OROS-MPH (SD 5 13.2). Adverse events were rated daily by camp counselors and parents using the Pittsburgh Side Effect Rating Scale, which inquires about common side effects seen with stimulants (rated none to severe) (Pelham, 1993). Blood pressure and pulse were also measured daily during times of peak medication effects; no child had marked elevation of either cardiovascular parameter. Any subject reporting significant distress or exhibiting marked side effects was evaluated by the study nurse or physician. Apparatus VPM 10.5 software (Cook, Atkinson, & Lang, 1987) running on a Pentium-class computer (Gateway; North Sioux City, SD) controlled the presentation of tone prestimuli and startle probes and sampled the eyeblink electromyogram (EMG). Stimulus parameters were based on earlier work (Filion et al., 1993; Hawk et al., 2003). Startle probes were 50-ms, 100-dB(A) bursts of white noise with near-instantaneous rise/fall times, and prestimuli were 5- and 8-s, 70-dB(A), 400- and 1200-Hz tones with 25ms rise/fall times. The prepulse tones and startle probes were presented via a Soundblaster 64 AWE Gold sound card, amplified with an Optimus SA-155 (Radio Shack; Fort Worth, TX) stereo receiver, and played through matched Telephonics TDH49-P headphones. Ambient background noise was approximately 55 dB(A). The eyeblink startle response was measured electromyographically from orbicularis oculi, using TDE-23 Ag/AgCl surface electrodes (Med Associates, East Fairfield, VT) placed about 1 cm below the pupil and outer canthus of each eye. EMG was amplified by Grass Instruments bioamplifiers (7P3/7DA; West Warwick, OH) with a bandpass of 10–500 Hz. Amplifier output was fed to the A/D converter of a Scientific Solutions (Solon, OH) Lab Master DMA interface, which sampled the amplified EMG at 1000 Hz from 50 ms before until 300 ms after startle probe onset. Procedure All procedures were approved by the University at Buffalo Children and Youth Institutional Review Board. Parents provided written consent for participation in the study and assent to participate was obtained from all children. Children were tested individually in a sound-attenuated booth. A cover story of going into space to intercept and decode messages from space was employed to engage children in the setting and the task. Children earned up to 100 points for following the rules, including: 1) follow directions, 2) stay in your assigned area, 3) use material and possessions appropriately, and 4) try your best. Children were also informed that, following a warning, they would lose 25 points per rule violation. Behavior was well maintained, with only 2 children losing any points. Sensors were attached for measurement of bilateral eyeblink EMG. Two tone series were presented over headphones to ensure consistent pitch and length discrimination. After training, 5 habituation probes were presented, and the following instruc-
Methylphenidate, ADHD, and PPI tions were provided based on whether the child was told to respond to high or low pitched tones: During the mission, you’ll begin to hear some tones like the ones you’ve already heard. Some will be high, and some will be low. Most will be of the regular length, but some will be longer-thanusual. Like I mentioned before, we think that we’ll be closer to understanding the message once we know more about the LOW tones that are longer than usual. So, pay attention to the LOW tones. You can ignore the HIGH tones. Every time there is a longer than usual LOW tone, I want you to click the LEFT mouse button. Remember to wait until right after the tone to click the mouse and to press it only if the tone was a LOW one and longer-thanusual. Now, there’s a bonus for astronauts who do this well. If you ‘‘mark’’ all of the LOW, longer-than-usual tones, then you’ll get a 500-point bonus! If you miss one or click the mouse when there wasn’t a longer-than-usual LOW tone, you’ll lose 50 points, so then you would have 450 points. If you did this twice, then you would only have 400 points . . . and so on.
Then, participants completed 60 trials (3 blocks of 20 trials, separated by brief breaks) of a tone discrimination paradigm modeled after that of Dawson and colleagues (e.g., Filion et al., 1993; see Hawk et al., 2003). Tones were of two pitches (400 and 1200 Hz); within each pitch one-third were longer-than-usual (8 s vs. 5 s). Bilateral startle eyeblink EMG responses to probes presented at 120-, 180-, and 4500-ms prepulse-probe stimulus onset asynchronies (SOAs) were assessed on 75% of trials. The two early SOAs assessed short-lead prepulse inhibition, whereas the 4500-ms SOA assessed long-lead prepulse facilitation. The remaining 25% of trials were equal numbers of no-startle trials and trials containing intertrial interval (ITI) startle probes. Following tone offset, participants had a 3-s window in which to respond to the tone via computer mouse. Performance measures, such as correct hits and false alarms as well as reaction time data, were recorded via E-Prime software (Psychology Software Tools, Pittsburgh, PA). Children were informed of their performance and given their points at the end of each session. Data Reduction and Analysis A total of 50 children were run in one or more sessions but were excluded due to an equipment problem resulting in EMG artifact (n 5 4), missing multiple testing sessions (n 5 4, namely due to reported nervousness regarding the testing), or outlying or missing data following data reduction, as outlined below (n 5 6), resulting in 36 participants with usable data. As in recent work (Ashare et al., 2007), startle responses were digitally integrated off-line (rectified, low-pass filtered with a 50ms time constant, and high-pass filtered with 30 Hz cutoff; van Boxtel, Boelhouwer, & Bos, 1998) and scored using the computer program of Balaban, Losito, Simions, & Graham (1986), and trials were also excluded on the basis of excessive baseline range if 1) baseline range exceeded 5 mV or 2) baseline range was greater than 3 mV and response magnitude was o5 times the baseline range. Using these criteria, approximately 4.0% of trials on both eyes were excluded. If baseline range was between 0 and 1 mVand the response magnitude was less than the baseline range, the magnitude was set to 0. All available trials were used to compute average eyeblink EMG magnitude for each SOA (120, 180 ms) ! Attend (at-
841 tended pitch vs. ignored pitch) ! Day ! Eye cell of the design. These eyeblink EMG magnitude subject averages were used to compute percent inhibition, relative to the magnitude on probealone (ITI startle) trials ([(Mprepulse_trials – MITI_trials)/(MITI_trials)] ! " 100). A parallel series of analyses were conducted on percent prepulse facilitation (PPF) at the long-lead 4500-ms SOA.1 For each condition, an average was considered an outlier when it was greater than three times the interquartile range above the 75th percentile (as in our previous work; Baschnagel & Hawk, 2008). For subjects with no outlying averages, percent modification scores were averaged across right and left eyes. If outliers were evident at only one eye, then data for the other eye were retained for analyses. If data for both eyes contained outlying values, then the participant’s data were excluded from analyses (n 5 6, as noted above). Data analyses were repeated measures ANOVAs with drug as a within-subjects factor, except where noted. For drug effects, orthogonal contrasts of medication, comparing placebo v. active drug (average of .3 mg/kg and .6 mg/kg), and dose, comparing the .3 mg/kg vs. .6 mg/kg, were employed. Sex and drug order were tested in preliminary models, but because neither significantly moderated the findings, both were removed from the final models. For percent PPI, SOA (120, 180), and attend (attended vs. ignored) were additional within-subjects factors. Exploratory analyses consider the possible roles of ADHD subtype, comorbid externalizing disorder (ODD/CD), and previous treatment with stimulant medication (stimulant naı¨ ve n 5 7). Based on recent work suggesting that baseline processing may be important in evaluating medication effects on PPI (Baschnagel & Hawk, 2008; Bitsios et al., 2005; Swerdlow et al., 2003; Talledo et al., 2009), we employed regression analyses to examine the role of baseline (Monday) startle modification in predicting the effect of placebo vs. active drug on PPI. Separate analyses were conducted for attended and ignored prestimuli such that in each model the drug effect on PPI was regressed on PPI during attended or ignored prestimuli during the baseline day, respectively. Because 6 children did not complete the entire baseline session, this analysis is based on n 5 30. The number of responses to longer-than-usual attended tones (i.e., ‘‘hits’’; Max 5 10) was the primary performance outcome. False alarms were examined in a parallel ANOVA. Results ITI startle magnitude. MPH did not affect ITI startle magnitude, ps4.2, Placebo vs. MPH mean difference 5 .68 mV (S.E. 5 1.2); Dose mean difference 5 1.4 mV (S.E. 5 1.1). Percent prepulse inhibition. Figure 1 depicts the mean percent prepulse modification for ignored and attended prestimuli (averaged across SOAs), for placebo and both active doses of MPH. Although there was a significant main effect of MPH on prepulse inhibition, Placebo vs. MPH F(1,35) 5 7.6, po.01, d 5 .46, this effect was influenced by the attentional nature of the prepulse, 1 Because analyses involving PPI were primary, PPF results are summarized here. As expected, long-lead PPF was significantly greater during attended compared to ignored stimuli, F(1,35) 5 9.0, po.01. Although this attentional modification of long-lead PPF was significant under active medication (means 5 104% and 43% for attended and ignored, respectively; po.01), but not under placebo (60% and 38%, p 5 .18), the interaction was marginal, F(1,35) 5 3.0, p 5 .09.
R. L. Ashare et al.
Change in % PPI (MPH - Placebo)
842 100 80 60 40 20 0 –20 –40 –60 –80 –80
–60
–40 –20 40 0 20 Baseline % PPI (Day 1) Attend
Figure 1. Mean (SE) percent prepulse inhibition for all Dose ! Attend conditions.
Placebo vs. MPH ! Attend interaction, F(1,35) 5 5.2, po.05. Follow-up tests revealed that MPH enhanced PPI compared to placebo during attended tones, po.02, d 5 .61, but not during ignored tones, p 5 .46, d 5 .17. There was little evidence of dose separation, .3 mg/kg vs. .6 mg/kg dose and Dose ! Attend Fs(1,35)o2.0, ps4.2. There were no significant differences between the 120- and 180-ms SOAs, Fso1. In exploratory analyses, neither ADHD subtype (combined vs. inattentive; hyperactive/impulsive was not included because n 5 3) nor the presence of comorbid externalizing disorders (ODD and CD combined) moderated the Dose ! Attend interactions, all Fso1. However, previous treatment tended to moderate the effect of MPH on PPI to attended and ignored tones, Treatment History ! Placebo vs. Active MPH ! Attend interaction, F(1,34) 5 3.0, p 5 .1 and Treatment History ! .3 mg/kg vs. .6 mg/kg Dose ! Attend interaction, F(1,34) 5 5.1, po.05. Among those with a history of stimulant treatment, the high dose (mean PPI 5 43%, S.E. 5 5.7%) appeared to be necessary to demonstrate a statistically significant increase in PPI during attended stimuli, .6 mg/kg vs. Placebo, po.01 and .6 mg/ kg vs. .3 mg/kg Dose, p 5 .06; .3 mg/kg vs. Placebo, p 5 .18; Placebo mean 5 19% (S.E. 5 7%), .3 mg/kg mean 5 34% (S.E. 5 8%). In contrast, among the small number of children who were stimulant naı¨ ve, both active doses (.3 mg/kg mean 5 25%, S.E. 5 16%; .6 mg/kg mean 5 29%, S.E. 5 11%) significantly enhanced PPI to attended tones compared to placebo (mean 5 11%, S.E. 5 13.4), both pso.05. However, there was no further enhancement from .3 mg/kg to .6 mg/kg, p 5 .8. There were no significant differences for ignored stimuli, all ps4.28, with PPI ranging from 21% to 37%. Consistent with our hypothesis regarding moderation of medication effects by baseline PPI, lower baseline PPI during attended tones tended to predict greater increases with MPH relative to placebo during attended tones, F(1,29) 5 3.4, p 5 .08, b 5 " .35 (see Figure 2). However, baseline PPI during ignored tones was not related to the effect of MPH on PPI during these stimuli, Fo1, b 5 " .03. Task performance. Hits, or correct responses to longer-thanusual attended tones, were greater during active drug compared to placebo (mean 5 7.7, S.E 5 .4), Placebo vs. Active drug F(1,35) 5 7.8, po.01; this seemed to be due in part to a marginally significant increase in hits with the .6 mg/kg dose
60
80
Ignore
Figure 2. The effect of MPH vs. Placebo on PPI of startle during attended and ignored prepulses as a function of baseline PPI.
(mean 5 9.1, S.E. 5 .25) compared to the .3 mg/kg dose (mean 5 8.4, S.E. 5 .4), .3 mg/kg vs. .6 mg/kg F(1,35) 5 3.7, p 5 .06. The number of false alarms to short attended tones was reduced during active drug compared to placebo (mean 5 2.4, S.E. 5 .6), Placebo vs Active drug F(1,35) 5 4.5, po.05 and tended to be reduced during the .6 mg/kg dose (mean 5 1.5, SE 5 .5) compared to the .3 mg/kg dose (mean 5 2.3, S.E. 5 .7), F(1,35) 5 3.1, p 5 .09. There were no discernable effects of MPH on false alarms to ignored tones, ps4.4.
Discussion Based on theories of ADHD (Nigg, 2000) and preliminary evidence regarding PPI during attended and ignored stimuli in ADHD (Hawk et al., 2003), we predicted that stimulant medication, a frontline treatment for ADHD, would selectively enhance PPI during attended stimuli. The ability to suppress processing of a distracting stimulus that may prevent one from carrying out a primary response, or interference control, is theorized to be part of a core inhibitory process in ADHD (Nigg, 2000). Although PPI is generally thought to reflect a partially automatic sensorimotor gating process (Braff et al., 2001; Graham, 1975), PPI is enhanced when attention is directed toward a relevant stimulus. This may reflect the ability to protect attention to task-relevant stimuli from interference caused by the taskirrelevant startle probe (Filion et al., 1993). Consequently, we tested school-aged children with ADHD in a paradigm in which some tone prepulses were to be attended whereas others could be ignored under conditions of acute administration of placebo and two different doses of the stimulant MPH in a double-blind, randomized study. Consistent with predictions, MPH enhanced PPI among children with ADHD, but only when they were engaged in effortful cognitive processing (i.e., directing their attention to relevant tones); PPI during ignored tones was not affected by medication. The effect size of MPH compared to placebo on PPI during attended stimuli was moderate, d 5 .61. Behaviorally, stimulant treatment also tended to reduce errors of omission and commission, though these effects were weaker, likely because of the relatively low task difficulty in the present paradigm. We have previously observed a beneficial effect of 0.3 mg/kg immediate-release MPH on PPI in a relatively homogeneous
Methylphenidate, ADHD, and PPI sample of boys with a documented positive response to ongoing stimulant treatment (Hawk et al., 2003). The present study replicates and extends our earlier findings. The present sample is more than twice as large as the original study, and it is more heterogeneous and generalizable with respect to important demographics and clinical characteristics, namely sex and stimulant treatment history, respectively. Regarding sex, because ADHD is diagnosed 3–5 times more frequently among boys than girls (see Arnold, 1996), we recruited a sample to match this distribution but was not powered to detect sex effects; hence the null results in supplementary analyses of sex effects are not taken as convincing evidence of the absence of such effects. Regarding stimulant treatment history, it was important to recruit a sample that was more varied in clinical response to stimulant treatment. In the current study, a small number of children (n 5 7) had never taken stimulant medication before completing this short-term study. Results of exploratory analyses of stimulant-naı¨ ve children compared to children with a history of stimulant treatment suggested that the findings of Hawk et al. (2003) were not driven by ‘‘responder’’ status. Although the higher dose of MPH (nearest OROS-MPH equivalent of .6 mg/kg MPH IR TID) enhanced PPI to during attended tones regardless of stimulant treatment history, the lower dose (nearest equivalent of .3 mg/kg TID) significantly enhanced PPI during attended tones only among children who were naı¨ ve to stimulant medication. Although the sample size of stimulant naı¨ ve children was quite small and the results should be interpreted cautiously, the findings are certainly not consistent with speculation that MPH effects on PPI would be present only among those with a positive clinical response and ongoing stimulant treatment. Overall, it appears that acute MPH improves PPI during attended prestimuli among a relatively broad range of children (and perhaps adults; see Hanlon et al., 2009) with ADHD. Together, these data suggest that MPH alleviates deficits in the ability to suppress distracting stimuli in ADHD. Although typically described as a measure of early selective attention (e.g., Dawson et al., 1997) or selective inhibition (e.g., Hawk et al., 2003), PPI during the tone discrimination paradigm may reflect interference control, which incorporates both attention and inhibition constructs (Barkley, Murphy, & Fischer, 2007). Evidence regarding the construct validity of PPI and its attentional modification is needed (see, e.g., Filion, Kelly, & Hazlett, 1999) in both adults and children, and we will directly address this issue in a larger sample of children with ADHD. At present, there are mixed results regarding the effects of MPH on interference control. For example, Scheres and colleagues (2003) found that MPH marginally enhanced performance on the Flanker task but had no effect on Stroop interference. However, Langleben et al. (2006) found that MPH reduced interference on a Stroop task in children with ADHD. One possibility for understanding these mixed findings is that there are key moderators at play. The present work focused on one such moderator that is also relevant to the broader PPI and psychopharmacology literature, baseline startle modulation. Work with nonclinical samples in passive PPI paradigms suggests that the degree, and even the direction, of medication effects on PPI may be a function of the amount of PPI observed at baseline (Baschnagel & Hawk, 2008; Bitsios et al., 2005; Swerdlow et al., 2003; Talledo et al., 2009). The present data extend this to those with psychopathology. Specifically, sup-
843 plemental analyses suggested that the MPH enhancement of PPI during attended tones was strongest for those who exhibited poorest PPI to attended tones during the baseline session. Importantly, as Swerdlow et al. (2008) noted, the presence of an actual baseline session is critical for demonstrating such effects while ruling out regression to the mean (c.f., Baschnagel & Hawk, 2008, for a more limited statistical approach). Given the heterogeneity of disorders such as ADHD and schizophrenia, not to mention healthy controls, true baselines seem well worth the trouble in studies of treatment effects on PPI. Of course, the finding observed here was only marginally statistically significant, and replication of the effect in ADHD will be important. The baseline effect also provides one perspective on how the present findings fit in with the broader literature on the psychopharmacology of PPI. That is, although DA agonists such as amphetamine and apomorphine typically disrupt passive PPI (Braff et al., 2001; Swerdlow et al., 2001), this may depend upon where participants fall along the PPI continuum. Specifically, several studies of adults now suggest that the disruptive effects of DA agonists on passive PPI are greater among those with high levels of baseline PPI. At the other end of the spectrum, persons with low baseline PPI exhibit trends toward improvement in PPI (e.g., Bitsios et al., 2005; Talledo et al., 2009). Children with ADHD show reduced PPI during attended tones, compared to typically developing children (Hawk et al., 2003), perhaps putting them in a range that is consistent with improved PPI with DA agonists. Even more generally consistent with the baseline perspective is the present finding that the enhancement of PPI with MPH during attended tones appears to be inversely related to the amount of PPI exhibited during attended tones in a true baseline session. Such individual differences in baseline PPI may, in turn, reflect variation in resting DA tone (Bitsios & Giakoumaki, 2005; Roussos, Giakoumaki, Rogdaki, Pavlakis, Frangou, & Bitsios, 2008; Talledo et al., 2009). Alternatively, the fact that MPH also prevents reuptake of norepinephrine may be important for interpreting the present findings. Animal studies suggest that PPI is also sensitive to noradrenergic regulation (Swerdlow, Bongiovanni, Tochen, & Shoemaker, 2006). Both clonidine, an alpha-2 agonist (Sallinen, Haapalinna, Viitamaa, Kobilka, & Scheinin, 1998; Swerdlow et al., 2006) and atomoxetine, a selective norepinephrine-reuptake inhibitor used to treat ADHD (Gould et al., 2005) enhance passive PPI in animals. Of course, it is difficult to generalize across paradigms and species. Future research should examine more selective therapeutic agents in order to better elucidate the relative contributions of dopamine and norepinephrine to PPI enhancement in ADHD. The differences between the passive (no task) paradigms used in most pharmacologic studies, particularly in non-humans, and the active (tone discrimination) PPI paradigm used here are important to consider in efforts to integrate the literature. Passive PPI is not reduced in ADHD (Castellanos et al., 1996; Ornitz et al., 1992), and a similar pattern has been observed for ignored stimuli in an active paradigm (Hawk et al., 2003). However, results from the two paradigms are not necessarily interchangeable. ‘Ignored’ stimuli must be processed to determine that they can be ignored, and even subsequent ‘ignoring’ may involve active processes that influence PPI (e.g., Filion & Poje, 2003; Hawk, Pelham, & Yartz, 2002; c.f., Jennings, Schell, Filion, & Dawson, 1996; Thorne, Dawson, & Schell, 2005). Analogues to active paradigms are just beginning to emerge (e.g., Baschnagel, Hawk, Colder, & Richards, 2007; see Li, Du, Li, Wu, & Wu,
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2009, for a review; Roskam & Koch, 2006). Ultimately, research using both active and passive paradigms in both species will be necessary to fully elucidate the psychological and neurobiological processes in PPI and its top-down regulation. Stepping back from task parameters, the present data add to a very small but important literature regarding controlled medication effects on PPI among persons with mental disorders. This literature is important because many of the hundreds of human and animal studies of PPI over the past few decades have been aimed at modeling and understanding psychological and neurobiological processes involved in psychopathology. The small but growing literature on baseline effects discussed above suggests that it will be quite important to actually include studies of those with the relevant psychopathology. Yet, the literature on controlled within-subjects medication studies of patient populations is remarkably small, including just one schizophrenia study (see review by Swerdlow et al., 2008; Wynn et al., 2007) and now two studies of ADHD (Hawk et al., 2003; present study). Though
such studies are challenging to complete, they are critical for realizing the full translational promise of PPI. At the clinical end of the spectrum, the present findings are consistent with, but insufficient for testing directly, the hypothesis that MPH exerts its therapeutic effects, at least in part, by enhancing the ability to screen out distracting stimuli and attend to relevant stimuli. We will soon test this mediational hypothesis in a larger sample. In sum, acute treatment with the stimulant methylphenidate enhanced PPI of startle, but only during attended/task-relevant stimuli, among children with ADHD. This effect tended to be greatest among those children with the weakest baseline PPI, extending preclinical dimensional work on the psychopharmacology of PPI. The present data fit nicely within the literature on the effects of stimulants on attention and inhibition. Further work is needed to elucidate the neurobiological basis, construct validation, and clinical implications of stimulant enhancement of PPI during attended prestimuli in ADHD.
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Psychophysiology, 47 (2010), 846–856. Wiley Periodicals, Inc. Printed in the USA. Journal compilation r 2010 Society for Psychophysiological Research No claim to original US government works DOI: 10.1111/j.1469-8986.2010.01004.x
Antisaccade performance in schizophrenia patients, their first-degree biological relatives, and community comparison subjects: Data from the COGS study
ALLEN D. RADANT,a DORCAS J. DOBIE,a MONICA E. CALKINS,b ANN OLINCY,c DAVID L. BRAFF,d KRISTIN S. CADENHEAD,d ROBERT FREEDMAN,c MICHAEL F. GREEN,e TIFFANY A. GREENWOOD,d RAQUEL E. GUR,b RUBEN C. GUR,b GREGORY A. LIGHT,d SEAN P. MEICHLE,a STEVE P. MILLARD,a JIM MINTZ,f KEITH H. NUECHTERLEIN,e NICHOLAS J. SCHORK,d LARRY J. SEIDMAN,g LARRY J. SIEVER,h,i JEREMY M. SILVERMAN,h WILLIAM S. STONE,g NEAL R. SWERDLOW,d MING T. TSUANG,d,g BRUCE I. TURETSKY,b and DEBBY W. TSUANGa a Department of Psychiatry and Behavioral Sciences, University of Washington and Department of Veteran Affairs, VISN-20, Mental Illness Research, Education, and Clinical Center, Seattle, Washington, USA b Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA c Department of Psychiatry, University of Colorado Health Sciences Center, Aurora, Colorado, USA d Department of Psychiatry, University of California, San Diego, San Diego, California, USA e Department of Psychiatry and Biobehavioral Sciences, Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California, USA f Department of Epidemiology and Biostatistics, University of Texas Health Science Center, San Antonio, Texas, USA g Massachusetts Mental Health Center Public Psychiatry Division of the Beth Israel Deaconess Medical Center, Harvard Medical School, Department of Psychiatry, and Harvard Institute of Psychiatric Epidemiology and Genetics, Boston, Massachusetts, USA h Department of Psychiatry, Mount Sinai School of Medicine, New York, New York, USA i Department of Veteran Affairs, VISN-3, Mental Illness Research, Education, and Clinical Center, James J. Peters VA Medical Center, Bronx, New York, USA
Abstract The antisaccade task is a widely used technique to measure failure of inhibition, an important cause of cognitive and clinical abnormalities found in schizophrenia. Although antisaccade performance, which reflects the ability to inhibit prepotent responses, is a putative schizophrenia endophenotype, researchers have not consistently reported the expected differences between first-degree relatives and comparison groups. Schizophrenia participants (n 5 219) from the large Consortium on the Genetics of Schizophrenia (COGS) sample (n 5 1078) demonstrated significant deficits on an overlap version of the antisaccade task compared to their first-degree relatives (n 5 443) and community comparison subjects (CCS; n 5 416). Although mean antisaccade performance of first-degree relatives was intermediate between schizophrenia participants and CCS, a linear mixed-effects model adjusting for group, site, age, and gender found no significant performance differences between the first-degree relatives and CCS. However, admixture analyses showed that two components best explained the distributions in all three groups, suggesting two distinct doses of an etiological factor. Given the significant heritability of antisaccade performance, the effects of a genetic polymorphism is one possible explanation of our results. Descriptors: Oculomotor, Endophenotype, Antisaccade, Schizophrenia, Family
Impaired inhibitory function of the prefrontal cortex is well documented in schizophrenia and appears to play a significant role in schizophrenia-related cognitive and functional impairment (Daskalakis, Fitzgerald, & Christensen, 2007; Liu, Fitzgerald, Daigle, Chen, & Daskalakis, 2009; Volk & Lewis, 2002). Description of inhibitory function is therefore important for pathology, treatment, and genetic studies of schizophrenia. One way of assessing inhibitory processes is through carefully designed cognitive tasks such as the antisaccade task, which was
This material is based upon work supported (or supported in part) by the Office of Research and Development Medical Research Service (or) Health Services R&D Service, Department of Veterans Affairs. This study was supported by NIMH grants R01 MH65571, R01 MH65588, R01 MH65562, R01 MH65707, R01 MH65554, R01 MH65578, and R01 MH65558. Address reprint requests to: Allen D. Radant, VAPSHCS S-116 MHC, 1660 S. Columbian Way, Seattle, WA 98108. E-mail: aradant @u.washington.edu 846
Antisaccade performance in schizophrenia first described by Hallett (1978). Although a number of cognitive processes may be important for the correct execution of an antisaccade (Hutton, 2008; McDowell, Dyckman, Austin, & Clementz, 2008), there is compelling evidence that inhibitory processes are active during the performance of antisaccade tasks (Nyffeler, Mu¨ri, Bucher-Ottiger, Pierrot-Deseilligny, Gaymard, & RivaudPechoux, 2007; Reuter, Kaufmann, Bender, Pinkpank, & Kathmann, 2010). Behaviorally, the antisaccade task relies on the fact that, when presented with a visual stimulus, higher primates will reflexively make a saccade to focus on the stimulus. Unlike some reflexes, this reflex can be inhibited with conscious effort. To assess antisaccades systematically, subjects sit in a dark room and focus on a stimulus located in the center of a video monitor. This stimulus is extinguished, and an antisaccade cue is presented to the left or right. The subject is then instructed to look in the opposite direction. To perform the task correctly, the subject must be able to understand and remember task instructions, inhibit the reflexive saccade toward the cue, translate cue information into an internal representation of the contralateral position, and execute the antisaccade. More than 40 studies have found that schizophrenia patients perform significantly worse on this task than controls, and no studies have failed to replicate this result. This fact, combined with evidence that antisaccade performance depends on prefrontal cortex function (Ettinger, Ffytche, Kumari, Kathmann, Reuter, et al., 2008; Nyffeler et al., 2007; PierrotDeseilligny, Mu¨ri, Ploner, Gaymard, Demeret, & RivaudPechoux, 2003), an area impacted by schizophrenia, makes the antisaccade task an ideal candidate schizophrenia endophenotype. One potentially productive use of tasks that reflect a basic illness-related neurophysiological deficit, such as the antisaccade task, is as intermediate phenotypes for genetic studies. First described by Gottesman and Shields (1972), these intermediate phenotypes, which are often referred to as endophenotypes, are defined as traits that are stable over time (i.e., not related to fluctuations in illness severity), are more common among affected persons, cosegregate with the illness in families, and are also more common among biological relatives of persons with the illness. Ideally, endophenotypes are phenomenologically much simpler than the illness itself and hence much easier to quantify. The physiologically elemental nature of endophenotypes raises the hope that they will have a simpler genetic architecture than that of the illness itself and thus that the responsible genes will be easier to identify (Braff, Freedman, Schork, & Gottesman, 2007; Gould & Gottesman, 2006). The antisaccade task has other characteristics of a high quality schizophrenia endophenotype (for review, see Hutton & Ettinger, 2006; Radant, Dobie, Calkins, Olincy, Braff, et al., 2007; Turetsky, Calkins, Light, Olincy, Radant, & Swerdlow, 2007), and a number of investigators have used it in this capacity (Greenwood, Braff, Light, Cadenhead, Calkins, et al., 2007; Kumari, Ettinger, Crawford, Zachariah, & Sharma, 2005; Myles-Worsley, Coon, McDowell, Brenner, Hoff, et al., 1999; Price, Michie, Johnston, Innes-Brown, Kent, et al., 2006). An important characteristic of an endophenotype is that the performance of unaffected first-degree relatives on pertinent tasks is distinct from the performance of the general population on those tasks (Berrettini, 2005; Braff et al., 2007; Gottesman & Gould, 2003). Meta-analytic results support a difference of moderate effect size in antisaccade performance between controls and first-degree relatives of schizophrenia patients, indicating poorer performance in relatives across studies (Calkins, Curtis, Iacono, & Grove, 2004; Calkins, Iacono, & Ones, 2008; Levy, Bowman,
847 Abel, Krastoshevsky, Krause, & Mendell, 2008; Levy, O’Driscoll, Matthysse, Cook, Holzman, & Mendell, 2004). However, the interpretation of this difference has been debated. Some investigators have argued that subtle differences in inclusion-exclusion criteria between relatives and controls spuriously cause the differences between controls and first-degree relatives (Levy et al., 2004). On the other hand, evidence has also been presented showing that comparably screened relatives and controls still exhibit significant differences in performance (Calkins et al., 2004; Ettinger, Kumari, Crawford, Corr, Das, et al., 2004; Karoumi, Saoud, d’Amato, Rosenfeld, Denise, et al., 2001). These research issues clearly illustrate the importance of attending to the influence of participant selection factors on study outcome. An effective method for assessing the nature of an endophenotype’s genetic architecture is through admixture analysis. Admixture analysis determines whether two distinct doses of an important etiological factor are present as opposed to a continuous range of doses or a mixture of a large number of factors. Some traits, such as height, have a very high heritability yet nevertheless form a Gaussian distribution because many different genes have a significant impact (Lettre, 2009). On the other hand, traits with simpler genetic architecture are more likely to result in a population distribution that is clearly a mixture of subcomponent distributions (Friedlander, Kark, Sinnreich, Edwards, & Austin, 1999). If the admixture analysis suggests the presence of a two-dose etiological factor, that factor is not necessarily genetic; however, absent some obvious environmental factor, genetic polymorphisms are the most common dichotomous traits for explaining such a distribution (van Koolwijk, Healey, Hitchings, Mitchell, Sham, et al., 2009). Admixture analysis has been used successfully in the field of psychiatry to analyze age of onset of illness (Delorme, Golmard, Chabane, Millet, Krebs, et al., 2005; Schu¨rhoff, Golmard, Szo¨ke, Bellivier, Berthier, et al., 2004; Slama, Courtet, Golmard, Mathieu, Guillaume, et al., 2009) and smooth pursuit eye movements (Ross, Olincy, Mikulich, Radant, Harris, et al., 2002). Admixture analysis of putative endophenotypes, such as antisaccade performance, may provide clues as to the genetic architecture underlying these traits and hence be helpful with the overall analysis of schizophrenia genetics. This study used data obtained from the Consortium on the Genetics of Schizophrenia (COGS) sample to examine antisaccade error rate, latency, and gain in a large group of schizophrenia participants, their first-degree relatives, and community comparison subjects (CCS). The COGS study is a seven-site, National Institute of Mental Health–funded project (Calkins, Dobie, Cadenhead, Olincy, Freedman, et al., 2007) that was designed to analyze the genetic architecture of multiple schizophrenia-related endophenotypes using a family-based linkage design. The large sample size, detailed characterization of COGS participants, and information about reliability and between-site effects (Radant et al., 2007) make the COGS study an ideal resource for teasing out the complex ascertainment and demographic issues that might influence between-group differences in antisaccade performance. These factors, especially the large sample size, also allowed us to perform a reliable admixture analysis.
Methods Previous reports have described in detail both the general study design (Calkins et al., 2007) and the specific oculomotor methods
848 (Radant et al., 2007) that were used in the COGS study. Radant et al. (2007) reported on 338 schizophrenia participants and CCS, which are a subset of the 1078 total subjects who participated in this study. All participants underwent a standardized diagnostic and clinical assessment protocol and a medical record review. The COGS enrolled families with at least one person diagnosed with schizophrenia and age-matched CCS. Families consisted of at least one schizophrenia proband, an unaffected sibling, and the parents of the proband. Schizophrenia participants, their relatives, and CCS were excluded for a history of electroconvulsive therapy (ECT) in the past 6 months, a positive drug or alcohol screen, a diagnosis of substance abuse disorder in the past 30 days or substance dependence in the past 6 months, or an estimated premorbid IQ of less than 70 as determined by the Wide Range Achievement Test-Third Edition (WRAT-3). We excluded participants with a history of ocular, neurological, or major systemic medical problems that could influence antisaccade performance. Additionally, we excluded CCS if they had a personal history of Cluster A Personality Disorder, a personal history of psychosis, or a family history of psychosis in first- or second-degree relatives. Schizophrenia participants all met Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition diagnostic criteria for schizophrenia based on a bestestimate consensus diagnostic procedure, which included the Diagnostic Interview for Genetic studies (DIGS; Nurnberger, Blehar, Kaufmann, York-Cooler, Simpson, et al., 1994). Siblings (n 5 7) and parents (n 5 2) who were diagnosed with schizophrenia were excluded from this analysis. In a large study such as the COGS, very strict exclusion criteria are impractical. To determine whether inclusion/exclusion criteria might have affected the primary results, a subset of participants who met very strict exclusion criteria were designated as ‘‘narrow’’ (see also Turetsky, Greenwood, Olincy, Radant, Braff, et al., 2008). By this definition, all narrow participants had no history of minor head injury, significant medical or neurological conditions, or substance abuse. Additionally, schizophrenia participants who were designated as narrow had no history of other Axis I disorders (except adjustment disorder) or ECT. Relatives who were designated as narrow had an IQ greater than 70 and no history of ECT, Axis I or cluster A personality disorder diagnoses, or treatment with psychotropic medications, and CCS who were designated as narrow had no history of nonpsychotic Axis I disorders or treatment with psychotropic medications. For each subject group, we analyzed antisaccade performance of the narrowly defined subjects and compared this with the group as whole. Participation required approximately 10 h, spread over two days, and consisted of an interview, blood draw, and the measurement of neurophysiological and cognitive endophenotypes, including antisaccade performance. To permit participant acclimation to oculomotor testing, a prosaccade task was administered prior to the antisaccade tasks. The local institutional review board of each site approved the study, and all participants provided signed informed consent before commencing the study procedures. The COGS investigators constructed an antisaccade task using a 200-ms overlap between central fixation and cue; antisaccade tasks constructed with these task parameters were previously reported to be sensitive to differences between firstdegree relatives and CCS (McDowell, Myles-Worsley, Coon, Byerley, Clementz, et al., 1999; Myles-Worsley et al., 1999). Participants viewed antisaccade stimuli on a video monitor in a dark room with their heads stabilized. The square stimuli sub-
A. D. Radant et al. tended a visual angle of 0.351 and differed only by color (the cue stimulus was yellow; all other stimuli were blue). Each antisaccade trial consisted of 2400 to 3600 ms of the central fixation stimulus, 200 ms of overlap between the central fixation and cue stimulus, 800 ms of cue presentation alone, a 500-ms duration stimulus to indicate the location of a correct antisaccade (i.e., contralateral to the cue), and finally, a return to central fixation and the beginning of the next trial. Cue stimuli were presented pseudorandomly at four locations: 101 to the left, 101 to the right, 151 to the left, and 151 to the right. Participants performed three blocks of 20 antisaccade trials each, for a total of 60 antisaccade trials. Prior to commencing the antisaccade task, all participants established that they understood the task by pointing to the correct location during each phase of a quarter-speed practice version. Eye position was acquired at 500 Hz using infrared oculography (ASL 310 infrared system, Applied Science Laboratories, Bedford, MA). All oculomotor technicians were trained in person and met competency standards (for more details, see Calkins et al., 2007; Radant et al., 2007). Oculomotor technicians performed a standardized calibration procedure by having participants focus at 01, !7.51, and !151 prior to each block of 20 antisaccade trials. Occasionally, calibration was so poor that subject data could not be reliably analyzed. To acclimate participants to the oculomotor laboratory, technicians had participants perform one block of a prosaccade task before the antisaccade task. For each trial, custom software (Radant & Hommer, 1992; Ross, Harris, Olincy, Radant, Adler, & Freedman, 1998) identified and characterized the primary response saccade (correct versus error) or flagged trials that could not be analyzed due to prominent artifacts. This software has been used successfully for more than 15 years. Saccades have a characteristic waveform and are reliably characterized and distinguished from artifacts by the software. We required an initial acceleration of at least 20001 s " 2, a minimum velocity of 4301/s, and epochs of near zero-velocity pre- and post-saccade to designate an eye movement as a true saccade. The first saccade of at least 31 amplitude beginning 80 ms after cue presentation was considered the ‘‘response’’ saccade. Saccades beginning after the onset of the stimulus indicating correct target position were not analyzed. Direction of the response saccade determined whether the participant made a correct or error saccade. Some trials were so contaminated by artifacts that a response saccade could not be identified. These trials were designated uninterpretable and were not included in determination of the error rate. Occasionally, entire 20-trial blocks were so contaminated by artifacts or poor calibration that they were discarded. Data from participants with less than 20 interpretable response saccades were discarded. After computerized analysis, at least one oculomotor specialist (ADR or SPM, each with a minimum of 5 years’ experience), who was blinded to participant group, reviewed all of the tracings; tracings with unacceptable calibration or that were so contaminated by artifacts that the software could not reliably analyze them were discarded. Oculomotor specialists never changed the decision of the software but did identify blocks of data of such poor quality that they needed to be discarded. This decision was always made by the senior author (ADR). A preliminary variable, which was designated ‘‘proportion of interpretable trials,’’ was defined as the total number of antisaccade trials minus the tracings that could not be analyzed due to artifacts, divided by the total number of antisaccade trials. This variable reflected data quality rather than antisaccade performance. The
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primary outcome measure, ‘‘proportion correct,’’ was defined as the number of correct antisaccade responses divided by the number of interpretable saccades. Thus, a participant who had 40 interpretable trials and made 10 correct responses would have a proportion correct of 0.25. Saccadic latency was defined as duration from the onset of the cue to the onset of the response and was calculated separately for correct and incorrect responses. Gain of correct antisaccades was defined as the saccadic amplitude divided by the distance between the central fixation point and the cue stimulus. Additionally, proportion correct was also calculated separately for 101 and 151 cue locations. Between-group differences in demographic variables were assessed with either one-way ANOVA (for continuous variables) or chi-square analyses (for dichotomous variables). Post hoc comparisons were based on Fisher’s least significant difference test. The proportion of interpretable saccades, proportion of correct saccades, saccade gain, saccade latencies, and effects of cue distance from the center were analyzed separately with linear mixed-effects models (Pinheiro & Bates, 2000) in which family membership served as a random effect to account for the relatedness of observations among family members. The initial model for proportion of interpretable response saccades included the potential effects of group, site, and group ! site interaction. The initial model for proportion of correct antisaccades, saccade gain, and saccade latencies included the potential effects of group, site, age, gender, smoking status, and parental education (maximum grade level of mother and father), as well as group ! site, group ! age, and group ! smoking interactions. The final model for each variable contained only those covariates with a statistically significant (po.05) contribution to the model. Parental education and smoking status contained missing values that were imputed based on regressing parental education on age, site, and cohort (parent, schizophrenia participant/siblings, CCS) and regressing smoking status on age, site, and group (Little & Rubin, 2002). Effect size was computed as the difference
in group means adjusted for other variables in the model and then divided by the estimated population standard deviation. Because the distribution of proportion correct antisaccades is skewed by nature, analyses of this measure were repeated after arcsine transformation. The admixture analysis was performed with the publicly available NOCOM program (see linkage.rockefeller.edu; Ott, 1979). This program uses an iterative, least likelihood algorithm to determine how many theoretical Gaussian components are present in a distribution. The program requires seeding with initial starting values; for each analysis, we checked a large range of initial values, almost all of which yielded the same solutions. A common standard deviation was used for all components, as recommended on the NOCOM web site. Because arcsine-transformed data were used, no skew parameter was included in the model. To avoid the spurious influence of significant covariates, the residuals of the arcsine-transformed data were used for admixture analyses. Maximum likelihood estimates were determined for one to four distributions. Note that the G2 statistic (2[ln(maximum likelihood 1) " ln (maximum likelihood 2)]) used to determine significance is only approximately chi-squared (linkage.rockefeller.edu); therefore, our confidence in significant but high p values is limited. All other analyses were performed using S-PLUS version 8 (Insightful Corporation, Palo Alto, CA). Because the distribution of proportion correct antisaccades is skewed by nature, analyses of these measures were repeated after arcsine transformation.
Results Valid oculomotor data were obtained on 219 schizophrenia participants, 293 siblings, 150 parents, and 416 CCS (Table 1). Data from 16 subjects were invalid due to poor technical quality (i.e., 1094 total subjects completed the antisaccade task). Schizophre-
Table 1. Demographic Data for Schizophrenia Participants, Their First-Degree Relatives, and Community Comparison Subjects
Number of participants Age (years)a % Male Parent education (years)a,b,c % Right-handed % Smokersb % on Antipsychoticsb GAFd scorea % Narrow a
Schizophrenia participants (S)
Parents (P)
Siblings (SB)
Community comparison subjects (C)
219 34.4 (11.1)
150 55.6 (4.9)
293 36.3 (11.7)
416 36.2 (12.6)
73
39
42
42
15.7 (3.3) 2 NAs 87
12.9 (3.6) 5 NAs 93
89
15.1 (3.1) 24 NAs 89
45
9 1 NA 3 3 NAs 78.8 (13.6) 9 NAs 59
15
13
0 4 NAs 80.4 (11.3) 10 NAs 52
0 8 NAs 83.7 (9.4) 21 NAs 73
94 1 NA 45.8 (12.6) 12 NAs 55
Test for group differencese
Post hoc comparisonsf
F(3,1074) 5 134.5 po.0001 chi-squared (3) 5 69.5 po.0001 F(2,751) 5 36.1 po.0001 chi-squared (3) 5 4.1 p 5 .25 chi-squared (3) 5 114.2 po.0001 chi-squared (3) 5 960.0 po.0001 F(3,1022) 5 574.0 po.0001 chi-squared (3) 5 38.0 po.0001
P4 S, SB, C S4 P, SB, C S4 C 4 P
S4 P, SB, C S4 P, SB, C C4 P, SB 4 S C4 S, P, SB
Mean (# SD). NA indicates number of missing values. Maximum value of parents’ education level when both available; otherwise based on the parent for whom information is available. NA refers to the case when education is missing for both parents. d Global Assessment of Functioning. e One-way ANOVA for continuous variables; chi-squared test for categorical variables. f Age post hoc comparisons based on Tamhane’s method to account for unequal variances; all other post hoc comparisons based on Fisher’s LSD. b c
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nia participants were significantly more likely to smoke and to be male than siblings, parents, and CCS. The parental education of the schizophrenia participants was slightly, but significantly, higher than that of the CCS. Handedness did not vary among groups. Most of the schizophrenia participants and a small proportion of the relatives were on antipsychotic medications. The Global Assessment of Functioning (GAF) score of CCS was significantly higher than that of the first-degree relatives and schizophrenia participants, and the GAF score of first-degree relatives was significantly higher than that of the schizophrenia participants. The Scale for the Assessment of Negative Symptoms (Andreasen, 1983) scores of the schizophrenia participants were 9.7 ! 5.8, and the Scale for the Assessment of Positive Symptoms (Andreasen, 1984) scores of the schizophrenia participants were 6.2 ! 4.2, which together indicated a moderate degree of current illness. All participants tolerated testing well. Occasionally an artifact contaminated the epoch of the saccade response, precluding the identification of an interpretable response saccade. Schizophrenia participants had a significantly lower proportion of interpretable saccades than the first-degree relatives or CCS (Table 2). Although there was a main effect of site on interpretable saccades, the range in the proportion of interpretable saccades was small across sites (from 0.93 to 0.97), reflecting excellent data quality at all sites; also, the group " site interaction was not significant (see also Radant et al., 2007). For all of the linear mixed-effects models, the results that were based on omitting participants with missing covariate values were similar to the results that were based on imputing missing covariate values; we used imputation for all analyses. Also, the analysis of arcsine-transformed data (for proportion correct) and raw data yielded nearly identical results; only analyses of raw proportion-correct data are reported.
The linear mixed-effects model indicated significant differences between the three groups in the main outcome variable, the proportion of correctly performed antisaccades (Table 2). Schizophrenia participants performed significantly worse than the other two groups. Although the mean of first-degree relatives fell between that of the schizophrenia participants and CCS, post hoc analyses indicated that the CCS and first-degree relatives did not differ significantly. The effect sizes (difference in the means of the residuals) were 1.18 for the schizophrenia-CCS difference, 1.10 for the schizophrenia-relative difference, and 0.09 for the CCS-relative difference. The linear mixed-effects model also indicated that males performed slightly but significantly better than females in the main outcome variable. Performance of all three groups declined significantly with age. Some authors have argued that analysis of covariance can spuriously obscure real findings (Miller & Chapman, 2001). To insure this did not occur with our results with respect to age, we compared the antisaccade performance of younger CCS (o50 years old) to that of siblings and the antisaccade performance of older CCS (449 years old) to that of the parents. In both age cohorts, there was no significant difference in antisaccade performance (younger CCS versus siblings: proportion correct 0.83 ! 0.14, 0.81 ! 0.20, respectively, p 5 .085; older CCS versus parents: proportion correct 0.76 ! 0.19, 0.74 ! 0.20, respectively, p 5 .52). There was a main effect of site but no significant site " group interaction. All participants performed significantly better when presented with a 151 cue rather than a 101 cue; the magnitude of this effect was similar in all groups (i.e., there was no significant group " degree interaction effect). Admixture analysis was performed separately for each group and for the entire participant pool. Because antisaccade performance is a proportion, it has a naturally skewed distribution, a
Table 2. Antisaccade Performance for Schizophrenia Participants, Their First-Degree Relatives, and Community Comparison Subjects
Schizophrenia participants (S)
First-degree relatives (R)
Community comparison subjects (C)
Number of participants Proportion interpretablea
219 0.92 (0.12)
443 0.96 (0.09)
416 0.96 (0.09)
Proportion correcta
0.60 (0.26)
0.79 (0.20)
0.82 (0.16)
Proportion correct 101a Proportion correct 151a Latency to correct (ms)a,b,f
0.57 (0.27) 0.64 (0.27) 425 (99) 2 NAs
0.75 (0.22) 0.82 (0.20) 401 (72) 1 NA
0.79 (0.17) 0.84 (0.16) 392 (70)
Latency to incorrect (ms)a,c,f
243 (70)
255 (74)
256 (72)
Gain of correcta
0.88 (0.20)
0.96 (0.15)
0.95 (0.16)
a
d
Test for differences Group Site Group Site Age Sex Degree
F(2,404) 5 13.6, po.0001 F(6,665) 5 4.0, p 5 .0007 F(2,401) 5 116.0, po.0001 F(6,665) 5 5.1, po.0001 F(1,401) 5 30.0, po.0001 F(1,401) 5 4.8, p 5 .03 F(1,1077) 5 352.3, po.0001
Group " Age
F(2,394) 5 5.1, p 5 .007
Group " Smoker Site Sex Parent Ed Group " Age Site Sex Group Site
F(2,394) 5 3.4, p 5 .03 F(6,664) 5 3.1, p 5 .005 F(1,394) 5 19.3, po.0001 F(1,394) 5 14.4, p 5 .0002 F(2,393) 5 4.4, p 5 .01 F(6,660) 5 1.9, p 5 .08 F(1,393) 5 4.7, p 5 .03 F(2,402) 5 21.2, po.0001 F(6,664) 5 2.5, p 5 .02
Mean (! SD). Final model includes group, site, age, sex, smoker, parent education, group " age, and group " smoker. c Final model includes group, site, age, sex, and group " age. d Marginal conditional F-test based on linear mixed-effect model. e Based on Fisher’s LSD from linear mixed-effect model. f Test for main effect of group is not applicable due to significant interaction terms that include group. b
Post hoc comparisonse So R, C So R, C
101o151
So R, C
Antisaccade performance in schizophrenia
851
Figure 1: Admixture analyses of antisaccade data for the CCS (top), schizophrenia participants (middle), and the first-degree relatives of schizophrenia participants (bottom). To account for the effects of significant main factors and the naturally skewed distribution of antisaccade performance measures, residuals of arcsine-transformed data were used for these analyses. A minimum likelihood algorithm (Ott, 1979) was used to identify the underlying components. Two-component solutions were significantly better than one-component solutions for all three groups.
852 circumstance that is problematic for admixture analysis; our data were therefore arcsine-transformed prior to analysis. After accounting for group, site, gender, and smoking status, we used the residuals of the arcsine-transformed data for admixture analyses (Figure 1). Two components accounted for the distributions significantly better than one component for all three groups (po10 ! 6, po10 ! 6, and po.005 for the CCS, relatives, and schizophrenia participants, respectively). Although three components were significantly better than two components for the CCS and schizophrenia participants but not the relatives (po.01, p 5 .48, and p 5 .054 for the CCS, schizophrenia participants, and relatives, respectively), the significance of three-component solutions as compared to two-component solutions was much less than the difference between the one- and two-component solutions. No four-component solution was superior to any three-component solution. The intersection between the Gaussian curves was used to define the point of rarity between the two components. This point was ! .026 for the CCS, ! 0.29 for the relatives, ! 0.18 for the schizophrenia participants, and ! 0.28 for the participant pool as a whole. Therefore, a threshold of ! 0.28 was used to define participants as having good versus bad antisaccade performance. This corresponds to approximately 0.53 proportion correct in raw antisaccade performance. Twenty-six of 219 schizophrenia participants (11.9%), 33 of 443 relatives (7.9%), and 10 of 416 CCS (3.8%) had poor antisaccade performance, a statistically significant difference (chi square 5 22.8, df 5 2, po.0001). The difference between the relatives and CCS was significant (chi square 5 11.5, df 5 1, po.001), whereas the difference between schizophrenia participants and relatives was marginally significant (chi square 5 3.53, df 5 1, p 5 .06). There was no significant main effect of group on latency to correct antisaccades (Table 2). Male sex and higher parental education were both significantly associated with shorter latencies to correct antisaccades. There was a significant interaction between group and smoking status: being a smoker negatively impacted the correct antisaccade latency of schizophrenia participants more than the other two groups. Similarly, there was an interaction between group and age such that the correct antisaccade latency of schizophrenia participants increased more significantly with age than that of the other two groups. Finally, antisaccade gain was significantly lower in the schizophrenia participants than the other two groups. Some experts (Levy et al., 2004) have argued that asymmetric inclusion/exclusion criteria have caused spurious differences between controls and first-degree relatives. To address this important issue, we reanalyzed the data, using only subjects from all three groups that met narrow criteria for study enrollment. This distinction was made in the exact same way as reported in Turetsky et al. (2007). The proportion of correct antisaccades for all narrow subjects was almost identical to that from the participant pool as a whole (0.753 " 0.215 versus 0.761 " 0.220). Unexpectedly, schizophrenia participants meeting narrow criteria performed worse than the average schizophrenia participant (0.604 " 0.259 and 0.570 " 0.260 proportion correct, respectively, for all schizophrenia participants versus narrow schizophrenia participants). Antisaccade performance of CCS and relatives who met narrow criteria was nearly identical to the performance of these groups as a whole (0.817 " 0.157 and 0.820 " 0.160 proportion correct, respectively, for all CCS versus narrow CCS; 0.780 " .202 and 0.797 " 0.200 proportion correct, respectively, for all relatives versus narrow relatives).
A. D. Radant et al. Discussion In this large sample from the COGS, the antisaccade performance of schizophrenia participants was impaired compared to both CCS and unaffected first-degree relatives. The main findings of this study are similar to previous findings that the antisaccade performance of schizophrenia participants is inferior to that of controls and that the antisaccade performance of firstdegree relatives of schizophrenia participants is intermediate between schizophrenia participants and controls (Clementz, McDowell, & Zisook, 1994; Curtis, Calkins, Grove, Feil, & Iacono, 2001). The control-relative difference reached statistical significance in some, but not all, of these studies (for review of this issue, see Calkins et al., 2004; Levy et al., 2004). Our study was adequately powered to detect small but meaningful betweengroup differences and to detect the effects of parental education, GAF scores, smoking, site, and sex. Furthermore, admixture analyses indicated that two Gaussian components fit the data much better than one in all three groups. Post hoc analysis showed that the sample sizes accrued in this study yielded 90% power to detect a difference of less than 0.05 in the proportion of correct antisaccades between the relatives and CCS. Within groups, the performance of medically and psychiatrically healthy non-schizophrenia participants who met our narrow, strict exclusion criteria was nearly identical to the performance of all subjects. Thus, factors such as substance abuse and comorbid nonpsychotic psychiatric illness had no effect on antisaccade performance. This suggests that antisaccade deficits are relatively specific for schizophrenia pathology. Thus, the differences between first-degree relatives and controls that have been reported in previous studies are likely due to factors related to schizophrenia rather than subtle between-group differences in inclusion/exclusion criteria (Calkins et al., 2004). Admixture analyses of the antisaccade data of CCS, relatives, and schizophrenia participants in the current study resulted in two components for each group. The proportion of subjects in the poorer performing component (i.e., below the point of rarity) was greatest among the schizophrenia participants and least among the CCS. Three-component solutions were slightly more significant than two-component solutions, but given the uncertainty about determining the significance of the test statistic at marginal p values and the fact that inspection of the distributions did not lend face validity to three-component solutions, we had little confidence in the three-component solutions. The highly significant advantage in the explanatory power of two-component versus one-component solutions implies that two distinct doses of some important etiological factor influence the antisaccade performance of our participants. Furthermore, the proportion of participants below the threshold identified by admixture analysis increased significantly from the CCS to the relatives and from the relatives to the schizophrenia participants. Although many factors might explain this pattern of results, one possibility is a single major gene effect with a frequency of the deleterious allele that is highest among schizophrenia participants, intermediate among relatives, and lowest among CCS. This finding, combined with the significant heritability of antisaccade performance (h2 5 .42) that has been previously reported in the COGS sample (Greenwood et al., 2007) and in healthy twins (h2 5 .57; Malone & Iacono, 2002), supports a genetic influence on antisaccade performance. Other putative schizophrenia endophenotypes evidence a similar pattern: for example, a polymorphism in the alpha-7 subunit of the nicotinic receptor is
Antisaccade performance in schizophrenia associated with magnitude of suppression of the P50 waveform of the auditory evoked potential (Freedman, Olincy, Ross, Waldo, Stevens, et al., 2003) in the presence of a priming stimulus. In addition to our findings concerning the overall differences in antisaccade performance between schizophrenia participants, their first-degree relatives, and CCS, our study also provides insight into secondary attributes of oculomotor performance in these groups. For example, unlike some studies (Boudet, Bocca, Chabot, Delamillieure, Brazo, et al., 2005; Calkins et al., 2004; Crawford, Sharma, Puri, Murray, Berridge, & Lewis, 1998; Curtis, Calkins, Grove, et al., 2001), but not all studies (Olincy, Ross, Young, & Freedman, 1997), we found a significant correlation between antisaccade performance and age (despite our exclusion of participants over the age of 65). Therefore, age was included in the mixed-effects model that was used in this study. Notably, if age was not included as a covariate in the model, proportion correct was significantly lower in the first-degree relatives (which included both siblings and parents) than in the CCS. Due to methodological differences between studies, the impact of target excursion size on antisaccade performance is not well understood. In our study, all participants, regardless of group, performed better with the 151 excursion than the 101 excursion, but McDowell et al. (1999) used the same two excursions and found that between-group differences were accentuated using the 151 excursion as compared to the 101 excursion. Other studies have used different target excursions (Curtis, Calkins, Grove, et al., 2001) and as many as five different excursion distances. A mix of target excursions may be important in preventing the use of predictive strategies by participants, whereas excursion size may be less important. Studies designed to focus specifically on this issueFfor example, studies using a large range of target excursionsFwould be required to illuminate the reasons for this. Saccadic gain was lower among schizophrenia participants in our study. Normal saccadic gain, with respect to an antisaccade, requires accurate perception of a cue stimulus, generation of a sensorimotor representation of that location in the opposite hemifield, and generation of an accurate saccade based on the representation (Barash & Zhang, 2006; Everling & Fischer, 1998). Correctly matching cue amplitude with antisaccade amplitude (saccadic gain) likely reflects the quality of the sensorimotor transformation. Although antisaccade performance itself most likely depends on dorsolateral prefrontal cortex areas (Hutton & Ettinger, 2006; Nyffeler et al., 2007), abnormalities of antisaccade amplitude in schizophrenia may reflect function of the parietal and supplementary oculomotor cortex (McDowell et al., 2008; Moon, Barton, Mikulski, Polli, Cain, et al., 2007). The significantly lower saccadic gain among schizophrenia participants is consistent with the results of most other studies that have examined this issue; however, the relatives in this study performed no differently than the CCS. Relatively few studies have examined antisaccade gain in relatives, and the results thus far have been conflicting (Ettinger et al., 2004; Levy et al., 2008), rendering its status as a candidate endophenotype unclear. Although antisaccade performance itself probably depends on dorsolateral prefrontal cortex areas (Hutton & Ettinger, 2006; Nyffeler et al., 2007), saccadic gain may reflect function of the parietal and supplementary oculomotor cortex. In addition to deficits in inhibitory processing, there is significant evidence for impairment in other aspects of attention in schizophrenia, such as reaction time (Luck & Gold, 2008). Ha-
853 raldsson, Ettinger, Magnusdottir, Sigmundsson, Sigurdsson, et al. (2008) have identified possible anatomic and neurochemical substrates of reaction time that are relevant to schizophrenia. With respect to the antisaccade task, the latency (i.e., reaction time) of correctly performed antisaccades reflects both basic reaction time and the extra processing time required to inhibit a prepotent response (Hutton & Ettinger, 2006). Therefore, we anticipated longer latencies in our schizophrenia participants than our relatives and CCS. However, we found that, although the latency to correct antisaccades of schizophrenia participants was longer than that of the other two groups, the difference fell short of significance. This finding differs from the results of a number of previous studies (e.g., Curtis, Calkins, Grove, et al., 2001; Myles-Worsley et al., 1999) that have identified significantly increased latency to correct responses in schizophrenia patients. Moreover, recent meta-analyses of a small number of family studies indicate longer latencies to correct responses in schizophrenia relatives, with small mean effect sizes ranging from .33 (Levy et al., 2008) to .39 (Calkins et al., 2008). A possible explanation for the unexpected finding in our study may be that our latency data had very high variances, making proof of statistical significance difficult. Also, many of these other studies used the no-overlap version of the task, and perhaps the overlap version is less sensitive to between-group differences in latency. We also noted a more rapid decay in saccadic latency with age in schizophrenia participants than in relatives and CCS. This finding has not been reported before, perhaps because a large sample is required to detect this effect. Our study included 1078 participants, which is, to our knowledge, much larger than any other study of antisaccade performance in these specific groups. Previous studies suggest that the effects of age on cognition differ for schizophrenia patients and healthy study participants or patients with other neuropsychiatric illnesses (Brodaty, Sachdev, Koschera, Monk, & Cullen, 2003; Friedman, Harvey, Coleman, Moriarty, Bowie, et al., 2001). Kirkpatrick, Messias, Harvey, Fernandez-Egea, and Bowie (2007) have argued that many manifestations of schizophrenia are attributable to an abnormal aging process. However, little research has been done specifically investigating whether aging affects basic neurocognitive abilities such as reaction time differently in schizophrenia participants than controls. Given the putative neurodevelopmental antecedents of schizophrenia (Rapoport, Addington, Frangou, & Psych, 2005; Weinberger, 1996) and the unique forms of dementia that are sometimes associated with schizophrenia (de Vries, Honer, Kemp, & McKenna, 2001; Friedman et al., 2001), this interesting observation is worthy of further exploration. The major limitation of this study relates to our ascertainment strategy, which may have biased families and relatives in the direction of less psychopathology. The overall COGS research design necessitated enrollment of families who were functional enough to participate jointly in a comprehensive research protocol, and this requirement may have unintentionally led to the exclusion of families with estranged or more severely affected probands; likewise, these excluded families may also have included more relatives with overt schizophrenia-spectrum disorders. Consistent with this, the higher level of parental education in the schizophrenia participants than in the CCS suggests that schizophrenia families in our study were less ill than the average schizophrenia family. Moreover, even in COGS families, the healthiest relatives may have been more likely to participate in endophenotype testing than siblings with a diathesis toward schizophrenia. Indeed, 38% of siblings refused to participate in
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the COGS study, and their unwillingness to participate might be partially explained by an increased frequency of subclinical schizotypal traits in these siblings. Also, because the COGS research design required that at least one sibling was unaffected (Braff, Greenwood, Swerdlow, Light, Schork, et al., 2008; Calkins et al., 2007), families in which all siblings were affected, a condition that is likely related to a higher genetic diathesis for schizophrenia, were by definition excluded from the COGS study. In our study, we enrolled only 13 families with two affected persons and just 1 family with three affected persons. Another related limitation is that there was a significant main effect of site, suggesting across-site differences in recruitment or data collection. Despite the significant main effect, the absolute differences between sites were quite small. We have discussed this issue in detail in previous reports (Radant et al., 2007; Calkins et al., 2007). No other study of antisaccade performance in relatives of schizophrenia patients has used such exacting requirements for family structure. Two recent meta-analyses (Calkins et al., 2004; Levy et al., 2004) found that the average antisaccade performance of first-degree relatives was approximately 67% (range 55% to 83%) correct. In contrast, relatives in the COGS study performed 78.5% of antisaccades correctly, suggesting that they were not as impaired in antisaccade performance as ‘‘average’’ first-degree relatives of schizophrenia patients. Thus, ascertainment biases secondary to our recruitment strategy may have biased both schizophrenia participants and their relatives to be higher functioning, less neurophysiologically impacted by genetic diatheses toward schizophrenia and, hence, less prone to poor antisaccade performance. If it were possible to test relatives from a truly random sample of families with at least one member diagnosed with schizophrenia, a significant difference might emerge between relatives and CCS. However, the obstacles encountered in the COGS recruitment would be unavoidable in any similarly designed, family-based genetic study of schizophrenia. Another potential limitation of our study is our decision to use a 200-ms overlap between fixation and cue rather than the more widely used no-overlap version. We selected the overlap paradigm because it has yielded highly significant relative-control differences (McDowell et al., 1999) with effect sizes between relatives and controls that were considerably larger than in studies using the no-overlap version (Clementz et al., 1994; Curtis,
Calkins, & Iacono, 2001; Karoumi et al., 2001). The choice of the overlap version is supported by the fact that we found a highly significant heritability using the overlap task (Greenwood et al., 2007). However, the no-overlap version of the task may have some advantages: in contrast to the overlap version of the task, with the no-overlap when the antisaccade cue is presented, no stimulus is present and visual attention is not actively engaged. Suppression of a prepotent response, which is required for correct performance on the antisaccade task, may be more difficult during a task situation where attention is not engaged (Klein, Bru¨gner, Foerster, Mu¨ller, & Schweickhardt, 2000) than in a task situation where attention is engaged (i.e., during the overlap version). In support of this suggestion, Curtis, Calkins, and Iacono (2001) administered two versions of the antisaccade task (an overlap and a no-overlap version) to schizophrenia patients, their biological relatives, and controls, and they found that the no-overlap task produced greater differences between relatives and controls. More reflexive errors were committed by all groups during the no-overlap task than during the overlap task, and the schizophrenia and relative groups had disproportionately more errors. Thus, it is unclear which type of antisaccade task is most sensitive to schizophrenia-related psychopathology. Unfortunately, it was not possible for us to systematically compare the overlap antisaccade task version to the no-overlap antisaccade task version in the large and time-consuming COGS study. Our study confirms the impairment of schizophrenia participants in performing the antisaccade task. Although schizophrenia relatives and CCS did not differ significantly in antisaccade performance, admixture analysis showed that antisaccade distributions were best explained by two components. Given the significant heritability of antisaccade performance in the COGS sample, one explanation of this is a major gene effect on antisaccade performance. We also identify the importance of age, target excursion, and demographics as well as task parameters such as the presence of overlap in analyzing antisaccade performance. Future studies should focus on further genetic analysis and further exploration of the genetic architecture of antisaccade performance with careful attention to task- and participant-related variables that might influence the ability to detect between-group differences in antisaccade performance.
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(Received December 23, 2008; Accepted October 23, 2009)
Psychophysiology, 47 (2010), 857–862. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.01005.x
BRIEF REPORT
MAOA T941G polymorphism and the time course of emotional recovery following unpleasant pictures
CHRISTINE L. LARSON, LAUREN E. TAUBITZ, and JORDAN S. ROBINSON Department of Psychology, University of Wisconsin–Milwaukee, Milwaukee, Wisconsin, USA
Abstract Difficulty down-regulating negative affect has been linked with anxiety and depression. In addition, recent studies have identified specific polymorphisms of the MAOA gene related to affective psychopathology. Here we examined whether genetic variation in MAOA was associated with the time course of responses to affective stimuli. Emotion-modulation of the startle blink response was measured during and after affective pictures. Women with the G/G genotype of the MAOA T941G single nucleotide polymorphism showed sustained reactivity to unpleasant stimuli, as evidenced by continued blink potentiation during the picture-offset period. These data suggest that the MAOA T941G polymorphism, which has been previously linked with mood disorders, is associated with a maladaptive pattern of affective responding in women. Descriptors: Startle response, Individual differences, Genetics
during a sadness induction (Haas, Constable, & Canli, 2008). Furthermore, potentiation of startle blink responses following the offset of unpleasant pictures is present among unselected subjects exhibiting a pattern of hemispheric asymmetry associated with distress disorders (Jackson, Mueller, Dolski, Dalton, Nitschke, et al., 2003; Larson & Davidson, 2001). As potentiation of startle blink has been shown to be mediated by amygdala influences on brainstem startle circuitry (Antoniadis, Winslow, Davis, & Amaral, 2007; Campeau & Davis, 1995), together these neuroimaging and psychophysiological studies implicate both the amygdala and medial prefrontal cortex in sustained negative affect. Thus, this brief snapshot of affective recovery, continuing to respond to unpleasant stimuli in the first few seconds following offset of unpleasant stimuli, may serve as a useful index of emotion dysregulation in distress disorders and negative affective style. A separate line of research has begun to identify candidate genes associated with neural and behavioral emotion regulation mechanisms that confer risk for affective psychopathology (Canli & Lesch, 2007; Ebstein, 2006). One candidate gene implicated in risk for affective disorders is the monoamine oxidase A (MAOA) gene. Variation in this gene has been linked with depression (Du, Bakish, Ravindran, & Hrdina, 2004; Schulze, Muler, Krauss, Scherk, Ohlraun, et al., 2000) and anxiety (Samochowiec, Hajduk, Samochowiec, Horodnicki, Stepien, et al., 2004). The MAOA gene encodes the enzyme MAOA, which catalyzes the degradation of a number of neurotransmitters, including serotonin, norepinephrine, and dopamine. Inhibition of MAO activity is associated with improvement of symptoms of depression (Quitkin, Rifkin, & Klein, 1979) and anxiety (van Vliet, Westenberg, & Den Boer, 1993), possibly through promoting lasting increases in firing of midbrain dopamine neurons (Mercuri, Scarponi, Bonci, Siniscalchi, & Bernardi, 1997).
Emotion dysregulation is a core feature of numerous forms of affective pathology. Difficulty down-regulating negative affect is particularly evident in depression, anxiety and a number of other Axis I and II disorders (American Psychiatric Association, 2000; Taylor & Liberzon, 2007). A substantial body of research has demonstrated that individuals with these disorders exhibit greater intensity of negative affect (Clark & Watson, 1991). In addition, recent literature indicates that individuals with depression, dysphoria, and anxiety also show longer lasting responses to negatively valenced stimuli (Deldin, Deveney, Kim, Casa, & Best, 2001; Larson, Nitschke, & Davidson, 2007; Siegle, Steinhauer, Thase, Stenger, & Carter, 2002). Sustained processing in the period following offset of an unpleasant stimulus has been observed in depressed individuals as indexed by event-related potentials, pupil dilation, and respiratory sinus arrhythmia (Deldin et al., 2001; Deveney & Deldin, 2004; Rottenberg, Kasch, Gross, & Gotlib, 2002; Siegle, Granholm, Ingram, & Matt, 2001; Siegle, Steinhauer, Carter, et al., 2003). Prolonged responses to negative stimuli in depression have also been associated with sustained activation of the amygdala (Siegle, Steinhauer, Thase, et al., 2002). Even at sub-clinical levels, anxious and dysphoric subjects show slower decay of negative affect (Larson et al., 2007). Neuroticism, a robust risk factor for internalizing disorders (Trull & Sher, 1994), is linked with sustained subjective experience of negative affect (Shuls, Green, & Hillis, 1998) and sustained medial prefrontal activation We wish to thank the numerous students who assisted with data collection and data processing for this study. Address reprint requests to: Christine L. Larson, Department of Psychology, University of Wisconsin–Milwaukee, 2441 E. Hartford Avenue, Milwaukee, WI 53211. E-mail:
[email protected] 857
858 One specific polymorphism of the MAOA gene, the T941G SNP, has been linked with depression and anxiety (Tadic, Muller, Rujescu, Kohne, Stassen, et al., 2007; Tadic, Rujescu, Szegedi, Gigling, Singer, et al., 2003). The T-allele of this polymorphism is associated with lower MAOA enzyme activity (Hotamisligil & Breakefield, 1991), which results in decreased amine degradation and increased availability of neurotransmitters implicated in depression and anxiety. This idea is supported by findings that T-homozygotes show improved long-term affective recovery, exhibiting faster and more significant response to the antidepressant mirtazapine (Tadic et al., 2007). The G-allele of the T941G SNP, which is associated with higher MAOA enzyme activity, has the opposite effect, increasing amine degradation and decreasing the availability of neurotransmitters such as serotonin, dopamine, and norepinephrine. In the current study, we examined whether the MAOA T941G polymorphism1 is associated with more short-term emotion regulation processes that have been linked with trait negative affectivity and affective disorders. We used an emotionmodulated startle paradigm to assess whether variation in this polymorphism was associated with individual differences in affective recovery during the first several seconds following offset of emotionally laden stimuli. Given the advantage in recovery from depression conferred by the T/T genotype, we predicted that individuals with this variant would show faster recovery following unpleasant images. The sample was limited to women for both conceptual and practical reasons. First, internalizing disorders are more prevalent in women (Kessler, McGonagle, Swartz, Blazer, & Nelson, 1993), and second, the T941G SNP is located on the X-chromosome; therefore men have only one copy of either the Tor G allele and the effects of heterozygosity cannot be assessed.
Method Participants Ninety-seven undergraduate women participated (mean age 5 18.92, SD 5 0.85). All were free of psychotropic medication and were native English speakers. These data were collected as part of a larger project in which electroencephalographic data were collected, so participants were free of head injury or neurological disorder and were right-handed. The sample was predominantly white (N 5 91), and also included one African American, one Hispanic, and four Asian participants.2 Median family annual income was rated as between $60,001 and $80,000. Materials Forty-two pictures of each of three valences (unpleasant, pleasant, and neutral) were selected from the International
C. L. Larson et al. Affective Picture System3 based on published norms for valence and arousal (Lang, Bradley, & Cuthbert, 2008). Unpleasant and pleasant pictures were selected to be toward the extreme ends of the bipolar valence rating scale and high on arousal. Neutral pictures were selected to be in the middle of the valence scale and low on arousal. Unpleasant pictures included mutilated bodies, physical assault scenes, guns aimed at the viewer, and accident scenes. Pleasant pictures depicted sports and adventure, nature scenes, money, and erotica (four pictures of romantic couples). The neutral pictures primarily consisted of household objects, but also included plants and neutral faces. Procedure Informed consent was obtained from participants upon arrival at the laboratory. Prior to picture presentation, electrodes for recording blink responses were applied. In order to familiarize the participants with the procedure and habituate them to the acoustic startle probe, they first viewed an introductory set of 9 pictures, during 8 of which startle probes were presented. Pictures were presented on a 21-in LCD monitor in a quasi-random order (not more than two of each valence presented consecutively) for 6 s with a 12–18 s blank screen intertrial interval. The startle probe was a 50 ms 100 dB burst of white noise presented binaurally via pneumatic intra-ear headphones. Probes were presented during 36 pictures of each condition at one of four probe times: 1.5, 4.5, 7.5, or 9 s post-stimulus onset. Nine trials of each valence and probe time combination were presented. Stimuli were presented in six blocks with 21 trials per block (7 of each valence). Startle Recording and Quantification Electromyography (EMG) was recorded using two SensorMedics mini-electrodes (Conshohocken, PA) placed approximately 36 mm apart on the inferior left orbicularis oculi muscle (van Boxtel, Boelhouwer, & Bos, 1998) (impedance o20 Kohms). EMG signals were amplified 10,000 times and filtered with a bandpass of 1–200 Hz using bioelectric amplifiers (SAI Instrumentation, Caroga Lake, NY). A 30 Hz high pass filter was applied prior to integration and rectification of the raw EMG signals (time constant 5 20 ms). A PC running SnapMaster software (HEM Data Corporation, Springfield, MI) and a 12-bit analog-to-digital board (Analogic Corporation, Wakefield, MA) was used to digitize signals at 500 Hz. Blink magnitude, computed as the difference between magnitude at peak and at blink onset, was calculated for a window between 20 and 120 ms following probe onset. Trials with no perceptible eye-blink reflex were assigned a magnitude of zero and included in analysis. The mean number of no-response trials was 7.54 (SD 5 4.22), representing 6.9% of the total number of trials. To correct for individual variance in blink magnitude, 3
1
Based on mounting evidence (e.g., Hariri, Mattay, Tessitore, Kolachana, Fera, et al., 2002; Heinz, Braus, Smolka, Wrase, Puls, et al., 2005), we also hypothesized that individuals with the S-allele of the serotonin transporter promoter (5-HTTLPR) gene would show sustained reactivity to unpleasant affective stimuli. However, we did not find any significant effects for affect modulation of startle at any probe time as a function of this gene. Thus, for brevity’s sake the full results for this gene are not reported here. 2 Due to potential problems associated with population stratification, analyses were also run without the six non-Caucasian participants, but the findings were unchanged. Thus, participants from all ethnic groups were included in the final analyses.
IAPS pictures used in this study were: Unpleasant: 3000, 3010, 3015, 3030, 3051, 3053, 3060, 3071, 3080, 3100, 3102, 3120, 3130, 3140, 3150, 3168, 3170, 3266, 3350, 3400, 3500, 3530, 6212, 6230, 6260, 6312, 6313, 6350, 6360, 6510, 6560, 6570, 9040, 9252, 9410, 9500, 9560, 9570, 9800, 9810, 9910, 9921; Neutral: 1670, 2620, 5510, 5520, 5531, 5532, 5533, 5534, 5731, 6150, 7000, 7002, 7006, 7009, 7010, 7025, 7030, 7034, 7035, 7040, 7050, 7060, 7080, 7090, 7100, 7130, 7140, 7150, 7170, 7190, 7207, 7217, 7224, 7233, 7234, 7235, 7490, 7500, 7700, 7710, 7920, 9210; Pleasant: 1710, 2216, 2391, 4599, 4660, 4670, 4680, 5260, 5270, 5450, 5460, 5470, 5480, 5621, 5623, 5629, 5700, 5910, 7230, 7270, 7502, 8030, 8034, 8080, 8170, 8180, 8185, 8190, 8200, 8210, 8300, 8340, 8370, 8380, 8400, 8420, 8470, 8500, 8501, 8502, 8510, 8531.
MAOA and startle time course
Results Allele Frequencies Genotypic frequencies were 6.5% G/G (N 5 6), 39.8% G/T (N 5 37), and 53.8% T/T (N 5 50), which is in keeping with Hardy-Weinberg equilibrium, w2 5 0.06, df 5 1, p 5 0.81. Emotion-modulated Startle Blink A Genotype (G/G, G/T, T/T) ! Picture Condition (pleasant, unpleasant, neutral) ! Probe Time (1.5, 4.5, 7.5, 9 s) mixed model ANOVA was computed (Huyhn-Feldt correction applied to all ANOVA statistics). Most importantly for the hypotheses in the current study, the Genotype ! Picture Condition ! Probe Time interaction was significant, F(12,540) 5 2.074, po.03, Z2 5 .102. To isolate the differences driving this interaction, Genotype ! Picture Condition ANOVAs were conducted separately for each probe time. While the 1.5, 4.5, and 7.5 s probe times all yielded the expected main effect for Picture Condition (pso.002), no interactions with Genotype emerged at any of these points in time (ps4.69). However, at the 9 s probe time, the Picture Condition ! Genotype interaction was significant, F(4,180) 5 4.40, po.005, Z2 5 .089. Post-hoc comparisons revealed significantly greater blink potentiation for unpleasant compared to neutral pictures for G/G compared to T/T, t(54) 5 3.40, po.002, and G/T participants, t(41) 5 2.74, po.01 (all post-hoc comparisons Bonferroni corrected). There was no difference between T/Tand G/T individuals (p 5 .70). At 9 s, G/G women also showed potentiation of blinks for pleasant versus neutral pictures compared to G/T, t(41) 5 2.52, p 5 .01, and T/T women, t(54) 5 3.68, po.002. For ease of interpretation, emotion modulation difference scores (unpleasant-neutral, pleasant-neutral) are presented in Figure 1. Table 1 presents the mean blink magnitudes for each cell in the design. In addition to the findings for genotype and time course, the expected main effect for Picture Condition was significant, F(2, 180) 5 29.98, po.001, Z2 5 .364, indicating that across probe time and genotype blinks were largest for unpleasant compared
z-transformed Blink Magnitude
Genotyping DNA was sampled using Oragene saliva collection kits (DNA Genotek, Ottawa, ON, Canada). The MAOA T941G SNP (position 106, exon 8, chromosome Xp11) was amplified using the following primers: forward (5 0 GCT TCC AGC AGA GAG AAA CCA 3 0 ) and reverse (5 0 GGC CTC CTT GTA ATA CAT CAT GCA 3 0 ). PCR was performed in a 5 ml volume (3 ml of master mix plus 2 ml of DNA) in a 384-well plate using the ABI 7900HT Fast Real-Time PCR System (Applied Biosystems, Foster City, CA). An initial denaturation step was run at 951C for 10 min followed by 50 cycles at 921C for 15 s and 601C for 1 min. Finally, samples were held at 41C. ABI SNP analysis software (Foster City, CA) was used to determine allele status. Four participants were dropped because the DNA could not be amplified, yielding a final sample of 93 participants.
0.6
Unpleasant - Neutral
0.5 0.4
T/T G/T G/G
0.3 0.2 0.1 0 −0.1 −0.2 −0.3
z-transformed Blink Magnitude
reflex magnitudes were z-transformed within subject. Blinks greater than 3 standard deviations from each participant’s mean were excluded. Approximately 12.9% of eyeblink reflexes were excluded (treated as missing values) due to an unstable baseline (50 ms preceding probe onset), or because reflex onset occurred prior to 20 ms following probe onset. All participants had at least three good startle responses of the nine possible for each cell (Picture Condition ! Probe Time).
859
1.5 s
0.6
4.5 s
7.5 s
9s
Pleasant - Neutral
0.5 0.4 0.3 0.2 0.1 0 −0.1 −0.2 −0.3
1.5 s
4.5 s
7.5 s
9s
Probe Time Figure 1. Mean z-transformed emotion-modulation blink difference scores at each probe time for the three MAOA T941G genotypes. Pictures were presented for 6 s, thus the last two probe times followed picture offset. The top panel depicts unpleasant minus neutral blink magnitudes. Positive numbers reflect greater potentiation to unpleasant pictures. The bottom panel depicts pleasant minus neutral blink magnitudes. Negative numbers reflect greater attenuation of the blink response to pleasant compared to neutral stimuli. Error bars represent standard error.
to pleasant, po.001, and neutral, po.002, pictures. Blink magnitudes for pleasant and neutral pictures did not differ (p4.90). In addition, there was a main effect for Probe Time, F(3,270) 5 5.425, po.002, Z2 5 .171. Post-hoc comparisons indicated that blinks were larger at 9 s than at both 1.5, p 5 .024, and 7.5 s, po.001. A significant Picture Condition ! Probe Time interaction was also present, F(6,540) 5 4.059, po.002, Z2 5 .230, and indicated that blink magnitudes were greater for unpleasant pictures compared to both neutral and pleasant at the 1.5, 4.5, and 7.5 s probe times, but not at the 9 s probe time. Following up on the significant main effect for Condition, at 1.5, 4.5, and 7.5 s for each probe time, blink reflexes were significantly larger for unpleasant compared to neutral pictures (all pso.001). Furthermore, this held true for each of the three genotype groups (all pso.04). However, significant modulation of blink responses to pleasant pictures, defined as significant attenuation of blink magnitudes for pleasant compared to neutral pictures, was not evident for any of the three probe times (ps4.33). Examining each of the three genotype groups separately also revealed no significant differences between pleasant and neutral pictures (ps4.27). Thus, for the first three probe
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Table 1. Mean z-Transformed Blink Magnitudes for Each Picture Condition, Probe Time, and MAOA T941G Genotype Probe time
T/T Unpleasant Neutral Pleasant G/T Unpleasant Neutral Pleasant G/G Unpleasant Neutral Pleasant
1.5 s
4.5 s
7.5 s
9s
.12 (.32) ! .19 (.22) ! .12 (.27)
.29 (.34) ! .08 (.22) ! .06 (.21)
.12 (.27) ! .14 (.24) ! .10 (.27)
.01 (.28) .19 (.29) ! .03 (.29)
.20 (.39) ! .16 (.31) ! .13 (.28)
.22 (.24) ! .04 (.27) ! .04 (.19)
.05 (.26) ! .11 (.22) ! .06 (.28)
! .02 (.28) .10 (.33) .05 (.28)
.12 (.24) ! .10 (.24) ! .21 (.37)
.27 (.18) ! .14 (.28) ! .15 (.34)
.09 (.26) ! .10 (.32) ! .16 (.22)
.28 (.19) ! .14 (.23) .29 (.23)
times, the typically observed potentiation of blink responses to unpleasant pictures was observed; however, blink attenuation to pleasant pictures was not evident (see Figure 1). Therefore, while a significant difference was present for modulation following offset of pleasant pictures at the 9 s probe time, the lack of modulation at earlier probe times makes this finding difficult to interpret. As such, strong conclusions based on the pleasant picture data seem unwarranted and are reported primarily for completeness. Out of concern that the unequal sample sizes of the genotype groups may have affected the results, the emotion-modulation difference scores for the 9 s time point were further examined via Kruskal-Wallis test (case-level data for these difference scores are also presented in Figure 2). Results of this test supported the ANOVA findings. Genotype had a significant effect on blink magnitude for both unpleasant and pleasant compared to neutral pictures (H(2) 5 10.885, po0.01; H(2) 5 12.69, po0.01, respectively) at the 9 s time point. Post hoc comparisons of genotype groups were done using Mann-Whitney tests, Bonferroni corrected so that all significant effects are po.0167. G/G women had significantly larger blinks for unpleasant compared to neutral pictures than did the G/T (U 5 31, po0.01, r 5 0.43) and T/T women (U 5 24, po0.01, r 5 0.45). Similarly, G/G women had significantly larger responses to pleasant compared to unpleasant pictures than did women with the G/T (U 5 29, po0.01, r 5 0.44) or T/T genotypes (U 5 30, po0.01, r 5 0.42). Finally, G/T and T/T women did not differ for either unpleasant compared to neutral (U 5 844, p 5 0.49, r 5 0.07) or pleasant compared to neutral responses (U 5 724, p 5 0.08, r 5 0.19).
Discussion MAOA T941G was associated with recovery following affective stimuli in women. Specifically, women homozygous for the high-activity G-allele showed continued potentiation 3 s after the offset of unpleasant pictures, while women either homozygous or heterozygous for the low-activity T allele did not. The pattern of sustained potentiation following the offset of unpleasant pictures in women homozygous for the G-allele is consistent with that observed in anxious and dysphoric individuals (Larson et al., 2007).
Figure 2. Mean z-transformed emotion-modulation blink difference scores for each participant at the 9 s probe time (3 s post-picture offset). The top panel represents unpleasant minus neutral blink magnitudes, and the bottom panel shows pleasant minus unpleasant magnitudes. The horizontal lines indicate the mean difference score for each genotype groups. Note that the y-axis scale is different for the top and bottom graphs.
There is also evidence from the pharmacogenetic literature supporting the idea that MAOA genotype may influence affective recovery. In comparison with G-carriers, homozygosity for the T allele of the T941G SNP has been previously linked with faster and more robust response to mirtazapine, an antidepressant acting on both serotonin and norepinephrine transmission (Tadic et al., 2007). Thus, women T-carriers appear to show faster affective recovery than women with the G/G genotype, both in terms of brief natural recovery following unpleasant stimuli, and in more long-term recovery from clinical depression. Similarly, Domschke and colleagues (2005) reported that women with high-activity genotypes of a 30 bp variable number tandem repeat (VNTR) in the promoter region of the MAOA gene exhibited a slower and less efficient response to antidepressant treatment. This provides additional evidence that individuals with low-activity MAOA genotypes may show enhanced recovery from depression in comparison to individuals with high-activity MAOA genotypes. These findings are in concordance with what would be expected from the literature regarding the function of MAOA. MAOA degrades amine neurotransmitters, and medications that inhibit MAOA activity have been found to be effective in the treatment of internalizing disorders (Fitton, Faulds, & Goa, 1992; Papakostas & Fava, 2006). In addition, Meyer, Ginovart, Boovariwala, Sagrati, Hussey, et al. (2006) found that MAOA density was significantly elevated in individuals with depression, suggesting a possible cause of the significant loss of monoamines evident in depression. Studies examining other polymorphisms of the MAOA gene have found associations between high-activity
MAOA and startle time course
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MAOA genotypes and depression (Schulze et al., 2000) and panic disorder (Deckert, Catalano, Syagailo, Bosi, Okladnova, et al., 1999) in women. Along similar lines, Hotamisligil and Breakefield (1991) found that the occurrence of a G at position 941 of the MAOA gene results in heightened levels of MAO activity, while a T results in lower levels of MAO activity. Because sustained negative affect has been observed in individuals with anxiety and depression and high MAOA activity has been associated with these disorders, the observation in the present study that women with the high-activity genotype of the T941G SNP exhibit sustained potentiation to unpleasant stimuli is consistent with previous literature. While a growing body of research suggests that recovery from unpleasant stimuli in the seconds following stimulus-offset is linked with depression and likely anxiety (e.g., Larson et al., 2007; Siegle et al., 2001, 2003), the link between these brief snapshots of sustained negative affect and long-term mood pathology is less clear. One likely mechanism linking these phenomena is rumination, defined as perseverating on one’s own sad or depressed mood (Nolen-Hoeksema, 1991). Rumination has been repeatedly shown to increase risk for depression, and prolong and intensify episodes of depression once they begin (Nolen-Hoeksema, 1991; Nolen-Hoeksema, Morrow, & Fredrickson, 1993). The risk for depression conferred by trait negative affectivity appears to be mediated in part by rumination (Roberts, Gilboa, & Gotlib, 1998). Of more direct relevance, sustained amygdala response to negative words in depressed individuals is correlated with increased rumination (Siegle et al., 2003). More broadly, these data show initial support for combining molecular genetic and psychophysiological techniques for understanding affective recovery and emotion regulation. While the combination of genetic and psychophysiological or neuroimaging techniques to understand emotion regulation and psychopathology is becoming more common, we are aware of only one other candidate gene study of emotion-modulation of startle blink. In this study, blink potentiation to unpleasant stimuli was observed in Met homozygotes of the catechol-O-methyltransferase Val158Met polymorphism (Montag, Buckholtz, Hartmann, Merz, Burk, et al., 2008); however, this was examined only during, not following, picture presentation. Together, the results of the present study and the results of Montag and colleagues’ (2008) study provide an impetus for future examination of the relationship between genotype and performance on psychophysiological measures. As noted in Footnote 1, we also examined the time course of affective responding as a function of the serotonin transporter
promoter gene (5-HTTLPR), but found no significant effects. The lack of findings for this SNP may be in part due to one of the limitations of this or any other candidate gene association study. While candidate gene studies have become common and have made important contributions to understanding individual differences in complex behaviors and traits, recent work has also made clear that such studies are haunted by failures to replicate and Type I error concerns (Sullivan, 2007; van den Oord, 2008). Recently, authors have suggested selecting a two-tail alpha value of about 0.0005 rather than the traditional value of 0.05 in order to minimize the rate of false positive findings (Sullivan, 2007; van den Oord, 2008). While the p-values obtained in this study fall beneath the traditional 0.05 threshold, they are greater than the suggested value of 0.0005. Thus, while the results of the present study are promising, the potential for Type I error cannot be ignored, and replication of the findings will be important for determining the robustness of the observed effect. A number of other limitations should also be taken into consideration. First, our sample was limited to women. While others who have examined the T941G SNP and other MAOA polymorphisms have found that MAOA genotype only has a significant effect in women, assessment of a sample of men is needed to rule out the possibility that T941G genotype affects startle potentiation in men as well as in women. Within our sample of women, the number of women homozygous for the G allele was also quite small. While the group differences in sustained blink potentiation are large and all of the G/G women showed clear potentiation at the 9 s probe time, the small sample size for this group also indicates a need for further replication. Finally, although our findings are consistent with much of the literature on MAOA functioning, the extant literature on the T941G SNP is small, and at least one study that we are aware of yielded seemingly contradictory results (Tadic et al., 2003). Thus, future work is needed to more precisely specify the role of the MAOA T941G SNP in relation to mechanisms conferring risk for psychopathology. Overall, however, using the T941G SNP, the present study provides support for the idea that females with low-activity MAOA genotypes may show enhanced recovery from depression in comparison to females with high-activity MAOA genotypes. Specifically, it provides evidence that female T-carriers of the MAOA T941G SNP show faster recovery following negative affective stimuli compared to women with the G/G genotype. More broadly, it suggests that it may be useful to combine molecular genetic and psychophysiological measures in the study of affective recovery and emotion regulation.
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(Received April 1, 2009; Accepted October 25, 2009)
Psychophysiology, 47 (2010), 863–873. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.01006.x
Beta-adrenergic receptor gene polymorphisms and cardiovascular reactivity to stress in Black adolescents and young adults
ROBERT M. KELSEY,a BRUCE S. ALPERT,a MARY K. DAHMER,b JULIA KRUSHKAL,c and MICHAEL W. QUASNEYb a
Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, USA Department of Pediatrics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA c Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA b
Abstract Cardiovascular reactivity to stress and b-adrenergic receptor (b-AR) function may contribute to the development of hypertension. As Black Americans have an increased risk of hypertension, we evaluated associations between b1-AR (Arg389Gly) and b2-AR (Arg16Gly, Gln27Glu) gene variants and cardiovascular reactivity in 500 Black youth. Heart rate, preejection period, total peripheral resistance, and blood pressure reactivity were measured during cold and psychological stress. The Arg389Gly polymorphism in the b1-AR was associated with preejection period reactivity in males but not in females. The Arg16Gly polymorphism in the b2-AR was associated with diastolic blood pressure reactivity only during video game stress. An association between the Gln27Glu polymorphism in the b2-AR and vascular reactivity depended on sex. Thus, specific patterns of associations emerged between genetic variations in b-ARs and cardiovascular reactivity in young Blacks. Descriptors: Beta-adrenergic receptor, Genetics, Hemodynamics, Impedance cardiography, Stress, African Americans
intyre, 2003; Matthews, Katholi, McCreath, Whooley, Williams, et al., 2004; Matthews, Salomon, Brady, & Allen, 2003; Moseley & Linden, 2006; Treiber, Kamarck, Schneiderman, Sheffield, Kapuku, et al., 2003). The heritability of cardiovascular responses to stress and their association with the development of hypertension indicate that these responses can be used as intermediate phenotypes for investigating possible genetic contributions to cardiovascular risk. Moreover, given that cardiovascular reactivity to stress involves a change in cardiovascular function evoked by environmental stress, a genetic study of cardiovascular reactivity to stress is inherently a study of gene-environment interaction. Different types of stressors tend to elicit different patterns of cardiovascular reactivity (Kamarck & Lovallo, 2003; Kasprowicz, Manuck, Malkoff, & Krantz, 1990; Obrist, 1981; Saab et al., 1992; Sherwood, Dolan, & Light, 1990). Stressors that involve effortful active coping (e.g., mental arithmetic, challenging video games) elicit primarily cardiac and vasodilatory responses, whereas stressors that involve passive coping (e.g., cold exposure) elicit primarily vasoconstrictive responses. Nevertheless, there are substantial individual differences in the magnitude and pattern of cardiovascular responses that cut across different types of stressors, so that some individuals are characteristically hyperreactive whereas others are characteristically hypo-reactive; moreover, some are primarily cardiac reactors whereas others are primarily vascular reactors (Kamarck & Lovallo, 2003; Kasprowicz et al., 1990; Obrist, 1981; Saab et al., 1992; Sher-
The prevalence and severity of hypertension and related cardiovascular diseases are greater among Black/African Americans than among other ethnic groups in the United States (Ong, Cheung, Man, Lau, & Lam, 2007). Although the precise reasons for this increased risk among Blacks are unknown, cardiovascular reactivity to stress has been identified as a likely contributory mechanism (Anderson, 1989). Previous research has found that cardiovascular responses to cold and psychological stressors are greater in Blacks than in Whites (Anderson, 1989; Kelsey, Alpert, Patterson, & Barnard, 2000; Murphy, Alpert, & Walker, 1992; Saab, Llabre, Hurwitz, Frame, Reineke, et al., 1992; Sherwood, May, Siegel, & Blumenthal, 1995; Treiber, Musante, Braden, Arensman, Strong, et al., 1990). Moreover, these responses are reliable (Kamarck, 1992; Kelsey, Ornduff, & Alpert, 2007; McGrath & O’Brien, 2001; Swain & Suls, 1996), heritable (De Geus, Kupper, Boomsma, & Snieder, 2007; Snieder, Harshfield, Barbeau, Pollock, Pollack, et al., 2002), and predictive of future elevations in resting blood pressure (BP) and the development of hypertension (Carroll, Ring, Hunt, Ford, & MacThis work was supported by Grant Numbers HL-72375 and RR00211 from the National Institutes of Health. The contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health. Address reprint requests to: Robert M. Kelsey, Ph.D., Department of Pediatrics, Division of Cardiology, The University of Tennessee Health Science Center, 777 Washington Avenue, Suite 215, Memphis, TN 38105, USA. E-mail:
[email protected] 863
864 wood, Dolan, & Light, 1990). These individual differences are an important prerequisite for using cardiovascular reactivity as a marker for hypertensive risk because only a subset of the population develops hypertension. Adrenergic systems play key roles in regulating BP and cardiovascular responses to stress. The sympathetic nervous system (SNS) responds to stress by releasing norepinephrine (NE) and epinephrine (EPI), which stimulate b- and a-adrenergic receptors (ARs) in the heart and vasculature to alter cardiovascular function and BP. In addition to their direct effects, these catecholamines exert indirect effects by stimulating or enhancing the activity of other vasoactive substances, including angiotensin-II and endothelin-1 (Damon, 1998; Rodriguez-Garcia, Paule, Dominguez, Garcia-Escribano, & Vazquez, 2000). Several lines of evidence implicate excessive adrenergic activation in the pathophysiology of hypertension, including altered b-AR and a-AR function in borderline and established hypertension (Michel, Brodde, & Insel, 1990; Sherwood & Hinderliter, 1993), the effectiveness of b-AR and a-AR antagonists as antihypertensive medications (Flamenbaum, Weber, McMahon, Materson, Carr, & Poland, 1985; Julius & Nesbitt, 1996), and exaggerated adrenergic reactivity to stress in hypertensive or high-risk normotensive subjects (Julius & Nesbitt, 1996; Noll, Wenzel, Schneider, Oesch, Binggeli, et al., 1996; Sherwood, Hinderliter, & Light, 1995). In addition, pharmacological studies indicate that b-adrenergically mediated vasodilatory reactivity is diminished in Blacks (Cardillo, Kilcoyne, Cannon, & Panza, 1999; Light & Sherwood, 1989; Stein, Lang, Singh, He, & Wood, 2000). Other studies suggest that b-adrenergically mediated cardiac reactivity is exaggerated in Blacks (Kelsey, Alpert, et al., 2000; Light & Sherwood, 1989). Three distinct b-AR subtypes have been identified (b1, b2, and b3; Brodde, 2008; Kirstein & Insel, 2004). The b1-AR is located primarily in the heart and mediates increases in cardiac rate (chronotropy) and contractility (inotropy) when stimulated by NE and EPI. The b2-AR is located primarily at postsynaptic vascular sites and mediates increases in vasodilation in response to EPI. In contrast, the b3-AR is located primarily in adipose tissue and plays only a minor role in cardiovascular function. Common, functional, nonsynonymous single nucleotide polymorphisms (SNPs) have been identified in the b1-AR and b2-AR genes (ADRB1 and ADRB2; Brodde, 2008; Kirstein & Insel, 2004). These polymorphisms include three functional, nonsynonymous SNPs with minor allele frequencies of 10% or greater in individuals of African descent, as reported in both the International HapMap Project (HAPMAP) and SeattleSNPs Programs for Genomic Applications (PGA) databases (available at http:// gvs.gs.washington.edu/GVS/): (a) a SNP in ADRB1 that results in either arginine (Arg) or glycine (Gly) at codon 389 (Arg389Gly, rs1801253); (b) a SNP in ADRB2 that results in either Arg or Gly at codon 16 (Arg16Gly, rs1042713); (c) a SNP in ADRB2 that results in either glutamine (Gln) or glutamic acid (Glu) at codon 27 (Gln27Glu, rs1042714). These SNPs have functional molecular effects, changing the amino acid sequence of the corresponding receptor protein and altering the response to catecholamines (Brodde, 2008; Kirstein & Insel, 2004). The genes associated with established hypertension and its resultant target organ damage may be different from those associated with the initiation and evolution of the disease. Accordingly, we have focused on cardiovascular reactivity in normotensive adolescents and young adults at increased risk for developing hypertension with the aim of identifying genes associated with the evolution, rather than the sequelae, of the disease. The present
R. M. Kelsey et al. study evaluated associations between the above-described common, functional, nonsynonymous SNPs in the b1-AR and b2-AR genes and cardiovascular reactivity to cold and psychological stress in healthy, normotensive Black adolescents and young adults. Previous studies have found some evidence for associations of these b-AR SNPs with cardiovascular function and hypertension (Brodde, 2008; Kirstein & Insel, 2004), but the present study is the first to focus on associations between these SNPs and cardiovascular reactivity to cold and psychological stress in this vulnerable population. Based on the principal cardiovascular effects of the b-ARs, we hypothesized that the b1-AR polymorphism would be associated primarily with cardiac reactivity to stress, whereas the b2-AR polymorphisms would be associated primarily with vascular reactivity to stress.
Methods Participants Unrelated, normotensive Black adolescents and young adults (N 5 535; 267 females, 268 males; age 15 to 21 years, mean ! SD 5 17.8 ! 1.9 years) were recruited from the Memphis, Tennessee area between February 2004 and January 2008. All participants identified themselves as ‘‘Black or African-American’’ in response to a census-style ethnic self-identification question from the Multigroup Ethnic Identity Measure (Phinney, 1992). Potential participants who reported a significant medical condition or the use of any medication that would affect BP or cardiovascular responses were excluded from the study. All participants were asked to refrain from eating for at least 2 h prior to testing, and from smoking, drinking beverages containing caffeine or alcohol, or taking any medication or drugs for at least 8 h prior to testing. Informed consent was obtained from each young adult, and assent and informed consent were obtained from each adolescent and a parent or legally authorized representative. The Institutional Review Board of the University of Tennessee Health Science Center approved the protocol, and all procedures conformed to institutional guidelines. Each participant received $100 for participating in the study. Data from 35 participants were excluded. Three participants were excluded for health reasons (one with significant cardiac arrhythmia, one with hypertensive resting BP, and one with acute gastric distress); eleven were excluded because of equipment failure; fifteen were excluded because of poor signal quality of cardiovascular recordings; six were excluded because of outlying values on multiple cardiovascular measures. Thus, the final sample included 500 participants (254 females, 246 males). Apparatus and Measures The study was conducted in the cardiovascular laboratory at the University of Tennessee General Clinical Research Center. The laboratory has a central equipment/control room, an adjoining participant testing room (approximately 3 " 2 " 2.5 m, maintained at a temperature of 221C), and an adjoining cold chamber (approximately 2 " 1.5 " 2.5 m, maintained at a temperature of 91C to 121C). Body mass index (BMI, kg/m2) was calculated from measurements of weight (kg) and height (cm) taken with a standard hospital balance beam scale and a stadiometer. Cardiovascular measures. Systolic and diastolic blood pressure (SBP and DBP, mmHg) were measured once per minute
Genetics of cardiovascular reactivity from the brachial artery region of the non-dominant arm using a cuff of appropriate size and an automated oscillometric BP monitor (model 9300 OscilloMate, CAS Medical Systems, Inc., Brantford, CT). Heart rate (HR, bpm), preejection period (PEP, ms), and cardiac output (CO, L/min) were measured continuously with a standard tetrapolar band-electrode system and an HIC-2000 Impedance Cardiograph (model 2000, Bio-Impedance Technology, Inc., Chapel Hill, NC) according to established guidelines (Sherwood, Allen, Fahrenberg, Kelsey, Lovallo, & van Doornen, 1990). Impedance cardiographic data were acquired and scored with commercial software (COP-Win 6.2, BioImpedance Technology, Inc.). As in previous studies (Kelsey, Reiff, Wiens, Schneider, Mezzacappa, & Guethlein, 1998; Kelsey et al., 2007; Kelsey, Soderlund, & Arthur, 2004; Mezzacappa, Kelsey, & Katkin, 1999), HR was measured using the internal electrocardiographic signal from the impedance cardiograph, and PEP was measured as the interval between the peak of the electrocardiographic R-wave and the B-point of the impedance cardiographic dZ/dt waveform. Total peripheral resistance (TPR, dyne-s/cm5) was derived from concurrent measures of CO and MAP, using the formula TPR 5 (MAP/CO) ! 80 (Sherwood, Allen, et al., 1990). Prior research has established the reliability and validity of these cardiovascular measures (Alpert, 1996; Berntson, Cacioppo, Binkley, Uchino, Quigley, & Fieldstone, 1994; Kelsey et al., 1998; Kelsey et al., 2007; Mezzacappa et al., 1999; Sherwood, Allen, et al., 1990; also see Results below for reliability data). Stress appraisals. Participants rated their experience of stress immediately after each task by answering five stress appraisal questions (Kelsey, Blascovich, Leitten, Schneider, Tomaka, & Wiens, 2000): (a) ‘‘How stressful was the task you just completed?’’; (b) ‘‘How threatening was the task you just completed?’’; (c) ‘‘How demanding was the task you just completed?’’; (d) ‘‘How well were you able to cope with the task?’’; (e) ‘‘How well do you think you performed the task?’’ Each item included an appropriately anchored 7-point Likerttype scale, ranging from ‘‘not at all’’ to ‘‘extremely.’’ The fourth and fifth items were reverse scored, and the five items were summed to create a stress appraisal scale for each task (Kelsey, Blascovich, et al., 2000), with scores ranging from 5 (not at all stressful) to 35 (extremely stressful). Our previous research has demonstrated that these stress appraisals are reliable and are sensitive to the effects of task repetition and evaluative threat (Kelsey, Blascovich, et al., 2000; Kelsey et al., 2004). In the present study, the 5-item appraisal scales were reliable, with internal consistency coefficients ranging from a 5 0.65 to 0.80. Psychological stressors. Participants completed two 5-min mental arithmetic (MA) tasks and a 3-min video game (VG) task. Numerous studies have established that MA elicits increases in HR and BP and decreases in PEP (Al’Absi, Bongard, Buchanan, Pincomb, Licinio, & Lovallo, 1997; Kasprowicz et al., 1990; Kelsey, Blascovich, Tomaka, Leitten, Schneider, & Wiens, 1999; Kelsey, Blascovich, et al., 2000; Kelsey et al., 2004; Kelsey et al., 2007; McGrath & O’Brien, 2001), along with increases in cortisol (Al’Absi et al., 1997) and both NE and EPI (Rostrup, Westheim, Kjeldsen, & Eide, 1993; Schoder, Silverman, Campisi, Sayre, Phelps, et al., 2000). Likewise, research has shown that VG elicits increases in HR, BP, and TPR (Kelsey et al., 2007; Murphy et al., 1992; Treiber, Jackson, Davis, Pollock, Kapuku, et al., 2000), decreases in PEP (Kelsey et al., 2007), and increases in NE
865 and EPI (Eisenhofer, Lambie, & Johnson, 1985; Goldstein, Eisenhofer, Sax, Keiser, & Kopin, 1987; Skosnik, Chatterton, Swisher, & Park, 2000). For the MA tasks, participants counted backward aloud by 7s from a 4-digit number for 5 min. A 5-min rest period separated the two tasks, and performance during the second task was overtly videotaped to increase evaluative threat. The pre-recorded instructions, procedures, and equipment have been described in detail elsewhere (Kelsey, Blascovich, et al., 2000; Kelsey et al., 2004, 2007). For the VG task, participants played three 1-min games of ‘‘Centipede’’ (Atari) on a handheld device (Game Boy, Nintendo; Kelsey et al., 2007). As in previous studies (Kelsey et al., 2007; Murphy et al., 1992), the games progressed through three levels of challenge: for the first game, participants were instructed to see how well they could do; for the second game, they were instructed to try harder and improve their scores; for the third game, they were instructed to try harder and exceed their previous scores to win a monetary reward. Cold stressors. Participants were exposed to a 3-min forehead cold pressor (CP) test and 10 min of whole-body cold exposure (CE). Prior research has documented that CP elicits increases in BP and TPR (Allen & Matthews, 1997; Jacob, Costa, Shannon, Robertson, & Biaggioni, 2000; Kelsey et al., 2007; Saab et al., 1992; Stein et al., 2000; Treiber et al., 1990, 2000), as well as increases in NE and EPI (Jacob et al., 2000; Rostrup et al., 1993; Stein et al., 2000). We have shown that whole-body CE elicits increases in BP and TPR, decreases in PEP (Kelsey, Alpert, et al., 2000; Kelsey, Patterson, Barnard, & Alpert, 2000), and increases in NE (Alpert, Kelsey, Cook, & Jennings, 2001). For CP, participants sat upright in a comfortable chair with their heads tilted back while the experimenter applied a plastic bag containing crushed ice and water (approximately 31C to 41C) to their foreheads for 3 min (Kelsey, Patterson, et al., 2000; Kelsey et al., 2007; Treiber et al., 1990). For CE, participants sat upright in a comfortable chair in a cold chamber for 10 min (Kelsey, Alpert, et al., 2000; Kelsey, Patterson, et al., 2000). A refrigerated ventilation system maintained the cold chamber at a constant temperature of 91C to 121C (50% to 60% humidity). Participants were instructed to sit quietly but remain awake, and were observed continuously through an observation window. Experimental Procedure After informed consent and assent were obtained, the participant provided a buccal swab sample for genetic analysis and then removed all outer garments from the upper body and put on a hospital gown. The experimenter measured each participant’s height and weight, administered a battery of psychosocial (data not reported) and demographic questionnaires, and then connected the participant to the cardiovascular recording devices. Participants sat quietly in a comfortable chair for a 10-min baseline rest period before each stress task, and cardiovascular data were recorded during the last 3 min of each baseline period. Previous research has demonstrated that a 10-min rest period is sufficient to establish a stable cardiovascular baseline (Jennings, Kamarck, Stewart, Eddy, & Johnson, 1992; Kelsey et al., 1999; Murphy, Alpert, & Walker, 1994). The MA, VG, and CP tasks were presented in counterbalanced order, and cardiovascular data were recorded during each minute of each task. After completing these tasks, participants rested for an additional 10-min baseline period and then were seated in the cold chamber. Cardiovascular measures were recorded during the last 3 min of the baseline period and each minute of CE. Following CE, the
866 cardiovascular recording devices were removed and any remaining questions by the participant and/or parent were answered. Genetic Methods Buccal swabs were stored at ! 201C until extraction. DNA was extracted from the buccal swabs using the Epicentre MasterAmp Buccal Swab DNA Extraction kit (MB79015, Epicentre Biotechnologies, Madison, WI) and stored at ! 801C. Genotyping focused on functional, nonsynonymous SNPs in ADRB1 and ADRB2 that have a minor allele frequency "10% in individuals of African descent, as reported in both the HAPMAP and PGA databases (available at http://gvs.gs.washington. edu/GVS/). All genotypic analyses were performed blind with respect to phenotypic data. All assays included DNA samples with known genotypes as controls. At least 10% of the samples were analyzed a second time for each polymorphism and in each instance gave the same result, thus confirming reproducibility. Initially, samples were genotyped using PCR amplification and restriction digestion. However, as new technology became available during the course of the study, samples collected later were genotyped using Taqman SNP Genotyping assays (Applied Biosystems, Foster City, CA), which discriminate between alleles (genotypes at a specific SNP) using a 5’nuclease assay and allelespecific fluorogenic probes. For assays using PCR amplification and restriction digestion, two researchers independently assessed the results from the analyses and assigned genotypes. A subset of samples was assayed using both of these methods, and the results were identical for each SNP, thus verifying the equivalence of the methods. Assay of the b1-AR polymorphism (ADRB1). The b1-AR SNP C/G (rs1801253) located in chromosomal region 10q25.3, resulting in either Arg or Gly at amino acid position 389 (Arg389Gly), was assayed using either the Taqman Assay (N 5 294) or a slight modification of the PCR amplification and restriction digestion method (N 5 206) of Mason, Moore, Green, and Liggett (1999). There were two genotyping failures, leaving N 5 498 for this SNP. Assay of the b2-AR polymorphisms (ADRB2). The b2-AR SNPs located in chromosomal region 5q33.1, A/G resulting in Arg or Gly (rs1042713) at amino acid position 16 (Arg16Gly) and C/G resulting in Gln or Glu (rs1042714) at amino acid position 27 (Gln27Glu), were assayed using either the Taqman Assay (N 5 212) or the PCR amplification and restriction digestion method (N 5 288) described previously (Aynacioglu, Cascorbi, Gungor, Ozkur, Bekir, et al., 1999). Genetic Data Processing The genetic data were analyzed using both SAS/GENETICS v.9.2 (SAS Inc., Cary, NC) and the genhwi routine of StataSE 9 (StataCorp, College Station, TX; Cleves, 1999). The three b-AR SNPs were tested for deviation from Hardy-Weinberg equilibrium using both w2 and permutation exact tests. All three SNPs were in Hardy-Weinberg equilibrium (p 5 0.60–1.00) for the total sample and for each sex. The genotype counts and minor allele frequencies for the three SNPs are shown in Table 1. Linkage disequilibrium measures between the two SNPs in ADRB2 were computed using SAS/Genetics and the pwld routine of the genassoc software package (by Dr. David Clayton, available at http://www-gene.cimr.cam.ac.uk/clayton/) for StataSE 9. These two SNPs (rs1042713 and rs1042714) were in linkage disequilibrium (D’ 5 1.0, although r2 5 0.2 because of differences
R. M. Kelsey et al. in the minor allele frequencies for these SNPs, as shown in Table 1). Haplotypes were inferred for these SNPs using SAS/Genetics and PHASE software v. 2.1.1 (Stephens & Donnelly, 2003; Stephens & Scheet, 2005). Only three haplotypes (Arg16-Gln27, Gly16-Gln27, and Gly16-Glu27 with frequencies of 0.50, 0.33, and 0.17, respectively) were predicted for individuals with no missing genetic data for both SNPs (N 5 499). We evaluated associations between these haplotypes and cardiovascular baseline and reactivity measures; however, because the haplotype results were in full agreement with the individual SNP results, only the latter are reported. Cardiovascular Data Processing and Analysis Cardiovascular data were averaged over the last 3 min of each 10min baseline rest period to compute baseline means, and over all minutes of each task period to compute stress means. Reactivity was evaluated for HR, PEP, TPR, SBP, and DBP by subtracting the means for each pre-stress baseline period from the means for each corresponding stress period. Focusing on these five measures served to minimize the number of statistical tests while still providing broad coverage of adrenergically mediated cardiovascular reactivity. Measures of cardiovascular reactivity were analyzed using repeated measures multivariate analysis of covariance (MANCOVA) in a mixed factorial design. Sex (2 levels) and genotype (3 levels) were the between-subjects factors, and stressor (4 levels) was the within-subjects factor. Note that this design is multivariate only for tests involving the within-subjects factor (i.e., the stressor main effect and interactions involving type of stressor); tests of the sex and genotype main effects and the sex # genotype interaction effect are actually univariate, as they involve reactivity averaged over all stressors (i.e., the grand mean). The covariates included age, BMI, and two dummy-coded variables for stress task order (MA first and VG first). A family-wise Type I error rate of 5% was preserved for these analyses by using a modified hierarchical Bonferroni adjustment procedure (Simes, 1986). Adjusting for significance tests over five cardiovascular measures and three SNPs resulted in an initial adjusted a 5 0.00333, with subsequent a-levels increasing by integer multiples of two (i.e., a 5 0.00667) through fifteen (i.e., a 5 0.05). Significant sex # genotype interaction effects for overall reactivity were evaluated further using multiple regression techniques for simple effects analysis (Jaccard & Turrisi, 2003), whereas significant interaction effects involving stress period were evaluated further using MANCOVA techniques for simple effects analysis. Effect sizes were estimated using partial eta squared (Z2) for MANCOVA and the squared semipartial correlation (sri2) for multiple regression analysis. Similar analyses were conducted for pre-stress baseline levels (sex # genotype # baseline period MANCOVAs). Results Table 2 presents means and standard errors (SEM) for age, BMI, overall cardiovascular baseline levels (mean resting levels over all baseline periods), overall cardiovascular reactivity scores (mean reactivity scores over all stress tasks), and overall post-task stress appraisals (mean appraisal scores over all stress tasks) for males, females, and the combined sample. The cardiovascular baseline and reactivity measures were highly stable, as indicated by intraclass correlation coefficients based on an absolute agreement definition (random effects coefficients, Model 2, Shrout & Fleiss,
Genetics of cardiovascular reactivity
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Table 1. Genotype Counts and Minor Allele Frequencies for b-adrenergic Receptor (b-AR) Gene Polymorphisms in Black Youth Gene SNP b1-AR Arg389Gly (rs1801253) N 5 498 Male (n 5 246) Female (n 5 252) b2-AR Arg16Gly (rs1042713) N 5 500 Male (n 5 246) Female (n 5 254) b2-AR Gln27Glu (rs1042714) N 5 500 Male (n 5 246) Female (n 5 254)
Genotype count Arg389Arg 164 77 87 Arg16Arg 125 66 59 Gln27Gln 345 176 169
Minor allele frequency (%)
Arg389Gly 240 121 119 Arg16Gly 247 121 126 Gln27Glu 140 65 75
1979), which ranged from 0.98 to 0.99 for resting levels over all baseline minutes and from 0.84 to 0.89 for reactivity scores over all task minutes. All five measures of overall cardiovascular reactivity differed significantly from baseline for both sexes, all po0.0005. Likewise, overall post-task stress appraisals exceeded minimal levels for both sexes, all po0.0005. There were no significant sex differences in age or BMI. Preliminary tests for sex differences in overall cardiovascular baseline levels and overall cardiovascular reactivity were conducted using analysis of covariance with sex as the between-subjects factor, and age, BMI, and stress task order as covariates. Males had lower resting HR, longer resting PEP, and higher resting SBP as compared to females, all po0.0005. Significant covariate effects on baseline levels occurred for age (higher TPR, SBP, and DBP levels with increasing age, all po0.03), BMI (longer PEP and higher SBP and DBP levels with increasing BMI, all po0.003), and task order (higher SBP levels with MA first, po0.01). During stress, males showed larger overall increases in TPR, SBP, and DBP, whereas females showed larger overall increases in HR, all po0.008. Significant covariate effects on reactivity occurred for age (higher HR and SBP reactivity and lower TPR reactivity with increasing age, all po0.05) and task order (higher HR and SBP reactivity with VG first, both po0.03). Similar analyses of post-task stress appraisals showed
Gly389Gly 94 48 46 Gly16Gly 128 59 69 Glu27Glu 15 5 10
Gly 43 Arg 50 Glu 17
higher overall ratings by females than males, po0.02. The only significant covariate effects for stress appraisals involved task order (higher stress appraisals with either MA or VG first, both po0.008). Genetic Association Analyses There were no significant associations between any of the b-AR SNPs and cardiovascular baseline levels. However, there were significant associations between the three b-AR SNPs and specific measures of cardiovascular reactivity to stress (see Table 3). The Arg389Gly SNP in the b1-AR was associated with overall PEP reactivity, although this association was observed only in males, as indicated by a significant sex ! genotype interaction effect, F(2,488) 5 6.32, MSE 5 38.20, po0.002, Z2 5 0.025. As shown in Figure 1, this effect was primarily due to diminished PEP reactivity in males who were homozygous for the Gly389 allele as compared to males carrying the Arg389 allele (po0.0005, sri2 5 0.025); no such effect occurred in females (p40.20). For the Arg16Gly SNP in the b2-AR, there was a significant genotype ! stressor effect on DBP reactivity, Wilks’ L 5 0.966, multivariate F(6,976) 5 2.82, po0.01, Z2 5 0.017. As indicated in Figure 2, this effect was largely due to diminished DBP reactivity during VG in Arg16/Arg16 homozygotes as compared to
Table 2. Sample Characteristics
Age (yr) BMI (kg/m2) Overall baseline levels HR (bpm)n PEP (ms)n TPR (dyne-s/cm5) SBP (mmHg)n DBP (mmHg) Overall reactivity (D) HR D (bpm)w PEP D (ms) TPR D (dyne-s/cm5)w SBP D (mmHg)w DBP D (mmHg)w Stress appraisals#
Male (n 5 246)
Female (n 5 254)
Combined (N 5 500)
17.7 " 0.1 25.4 " 0.4
17.9 " 0.1 26.4 " 0.4
17.8 " 0.1 25.9 " 0.3
62.6 73.2 1023.7 116.0 68.5
" " " " "
0.6 0.6 15.8 0.5 0.4
70.8 69.3 1025.4 109.0 68.4
" " " " "
0.5 0.6 15.5 0.5 0.4
66.7 71.2 1024.6 112.5 68.5
" " " " "
0.4 0.4 11.1 0.4 0.3
3.2 # 3.0 118.6 7.0 6.5 15.4
" " " " " "
0.2 0.2 6.0 0.2 0.2 0.3
4.5 # 2.6 87.7 6.2 5.2 16.2
" " " " " "
0.2 0.2 5.9 0.2 0.2 0.3
3.8 # 2.8 103.2 6.6 5.8 15.8
" " " " " "
0.2 0.1 4.2 0.2 0.2 0.2
Note: Data are shown as mean " SEM. BMI, body mass index; HR, heart rate; PEP, preejection period; TPR, total peripheral resistance; SBP, systolic blood pressure; DBP, diastolic blood pressure. n Sex difference, po0.0005. w Sex difference, po0.008. # Sex difference, po0.02.
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R. M. Kelsey et al.
Table 3. Summary of F-tests and Effect Sizes (Z2) for Genetic Associations with Cardiovascular Reactivity ADRB1 Arg389Gly Measure Heart rate
Preejection period
Total peripheral resistance
Systolic blood pressure
Diastolic blood pressure
ADRB2 Arg16Gly 2
Source
F
df
p
Z
Genotype Sex ! Genotype Genotype ! Stressor Sex ! Genotype ! Stressor Genotype Sex ! Genotype Genotype ! Stressor Sex ! Genotype ! Stressor Genotype Sex ! Genotype Genotype ! Stressor Sex ! Genotype ! Stressor Genotype Sex ! Genotype Genotype ! Stressor Sex ! Genotype ! Stressor Genotype Sex ! Genotype Genotype ! Stressor Sex ! Genotype ! Stressor
0.35 1.02 0.37 0.96 1.47 6.32 1.26 0.61 1.52 0.48 0.56 0.97 0.78 0.08 1.21 1.41 1.59 0.00 0.79 1.82
2/488 2/488 6/972 6/972 2/488 2/488 6/972 6/972 2/488 2/488 6/972 6/972 2/488 2/488 6/972 6/972 2/488 2/488 6/972 6/972
0.703 0.360 0.900 0.451 0.230 0.002 0.276 0.726 0.220 0.617 0.764 0.443 0.457 0.927 0.297 0.209 0.206 1.000 0.579 0.092
0.001 0.004 0.002 0.006 0.006 0.025 0.008 0.004 0.006 0.002 0.003 0.006 0.003 0.000 0.007 0.009 0.006 0.000 0.005 0.011
ADRB2 Gln27Glu 2
F
df
p
Z
F
df
p
Z2
1.67 0.75 1.09 0.79 2.21 0.54 1.59 1.53 0.06 1.16 0.91 1.54 0.89 0.12 1.17 0.49 1.28 0.48 2.82 0.59
2/490 2/490 6/976 6/976 2/490 2/490 6/976 6/976 2/490 2/490 6/976 6/976 2/490 2/490 6/976 6/976 2/490 2/490 6/976 6/976
0.190 0.474 0.367 0.575 0.111 0.586 0.147 0.165 0.941 0.315 0.487 0.163 0.410 0.886 0.320 0.815 0.279 0.618 0.010 0.741
0.007 0.003 0.007 0.005 0.009 0.002 0.010 0.009 0.000 0.005 0.006 0.009 0.004 0.000 0.007 0.003 0.005 0.002 0.017 0.004
0.33 0.91 0.67 0.85 0.01 0.20 0.31 2.43 0.10 5.13 1.34 0.30 0.04 0.30 0.28 1.55 0.44 2.87 2.41 0.85
2/490 2/490 6/976 6/976 2/490 2/490 6/976 6/976 2/490 2/490 6/976 6/976 2/490 2/490 6/976 6/976 2/490 2/490 6/976 6/976
0.717 0.404 0.676 0.534 0.989 0.823 0.934 0.025 0.908 0.006 0.237 0.935 0.965 0.738 0.948 0.159 0.646 0.058 0.026 0.532
0.001 0.004 0.004 0.005 0.000 0.001 0.002 0.015 0.000 0.020 0.008 0.002 0.000 0.001 0.002 0.009 0.002 0.012 0.015 0.005
Note: Bold typeface indicates a significant effect.
allele (p40.60). A parallel but weaker trend toward a sex ! genotype interaction effect also occurred for overall DBP reactivity, F(2,490) 5 2.87, MSE 5 11.46, po0.058, Z2 5 0.012. There was a marginal sex difference in the linear association between the number of copies of the Gln27 allele and DBP reactivity, F(1, 490) 5 4.74, po0.03, but the interaction was again primarily attributable to differences in reactivity between males and females who were homozygous for the Gln27 allele (po0.0005, sri2 5 0.043). DBP reactivity was relatively enhanced in Gln27/Gln27 males (Mean " SEM 5 6.7 " 0.3 mmHg) and diminished in Gln27/Gln27 females (Mean " SEM 5 5.0 " 0.3 mmHg), as compared to male or female carriers of the Glu27 allele (Mean " SEM 5 6.0 " 0.4 and 5.5 " 0.4 mmHg, respectively). Finally, there was a marginal genotype ! stressor interaction effect for the Gln27Glu SNP and DBP reactivity, Wilks’ L 5 0.971, multivariate F(6,976) 5 2.41, po0.026, Z2 5 0.015. This marginal effect was mainly attributable to greater DBP
0
Arg /Arg
–0.5
Arg /G ly G ly /G ly
–1
12
–1.5
10
DBP ∆ (mm Hg)
PEP ∆ (ms)
carriers of the Gly16 allele, F(1,492) 5 11.34, MSE 5 43.95, po0.001, Z2 5 0.023. The Gln27Glu SNP in the b2-AR was associated with overall TPR reactivity, but this association also depended on sex, as indicated by a significant sex ! genotype interaction effect, F(2,490) 5 5.13, MSE 5 34673.9, po0.006, Z2 5 0.020. As illustrated in Figure 3, males and females showed opposite linear associations between the number of copies of the Gln27 allele and TPR reactivity, F(1,490) 5 10.21, po0.001, Z2 5 0.020; however, the linear associations were not significant for either sex (both p4.055). Rather, as Figure 3 indicates, the interaction effect was primarily due to differences in TPR reactivity between males and females who were homozygous for the Gln27 allele (po0.0005, sri2 5 0.040), with Gln27/Gln27 males showing enhanced vascular reactivity and Gln27/Gln27 females showing diminished vascular reactivity; there was no difference in vascular reactivity between males and females who carried the Glu27
–2 –2.5 –3 –3.5 –4
8 6 4 2
Fe m al es
p < 0.0005 M al es
p < 0.001
Gly/Gly Arg/Gly Arg/Arg
Figure 1. Arg389Gly polymorphism in the b1-adrenergic receptor and cardiac preejection period reactivity to stress (PEPD, mean " SEM) in young Black males and females (N 5 498).
0 MA
CP
VG
CE
Stressor Figure 2. Arg16Gly polymorphism in the b2-adrenergic receptor and diastolic blood pressure reactivity (DBPD, mean " SEM) during mental arithmetic (MA), video game (VG), forehead cold pressor (CP), and wholebody cold exposure (CE) in Black adolescents and young adults (N 5 500).
Genetics of cardiovascular reactivity
869 Gln/Gln Gln/Glu
p < 0.0005
Glu/Glu
TPR∆ (dyne-s/cm^5)
160 140 120 100 80 60 40 20 es al m Fe
M al es
0
Figure 3. Gln27Glu polymorphism in the b2-adrenergic receptor and total peripheral resistance reactivity to stress (TPRD, mean ! SEM) in young Black males and females (N 5 500).
reactivity during VG in Glu27/Glu27 homozygotes (Mean SEM 5 7.7 ! 1.8 mmHg) than in carriers of the Gln27 allele (Mean ! SEM 5 4.0 ! 0.3 mmHg), F(1,492) 5 7.22, MSE 5 44.08, po0.007, Z2 5 0.014.
Compliance with pre-testing instructions. Most of the participants complied with the pre-testing instructions to refrain from eating (88.0%), drinking beverages containing caffeine (84.3%) or alcohol (99.6%), smoking (96.4%), or taking medications or drugs (95.4%) within the specified time before the testing session. Nevertheless, we evaluated whether noncompliance with pretesting instructions influenced the observed genetic associations with cardiovascular reactivity. First, we assessed the associations between the b-AR SNPs and compliance with the pre-testing instructions. Two significant associations emerged for the Gln27Glu SNP in the b2-AR: the use of caffeine within 8 h of the study was positively associated with the number of copies of the Gln27 allele, w2(2) 5 8.58, po0.014, whereas the use of prescription or nonprescription medications within 8 h of the study was positively associated with the number of copies of the Glu27 allele, w2(2) 5 14.63, po0.001. There were no other significant genetic associations with pre-testing compliance. Second, we evaluated the associations between the b-AR SNPs and cardiovascular reactivity after controlling for compliance with each pretesting instruction. These analyses yielded results that were virtually the same as those in our principal association analyses, implying that noncompliance with pre-testing instructions was not responsible for the observed genetic associations with cardiovascular reactivity.
Discussion Secondary Analyses We conducted secondary analyses of potential confounding factors that might have contributed to the observed genetic associations with cardiovascular reactivity to stress, including sex differences in genetic associations. Hormonal status. We collected information from our female participants about date of last menses and oral contraceptive use (Slauterbeck, Fuzie, Smith, Clark, Xu, et al., 2002; Wegienka & Baird, 2005), so we were able to evaluate whether menstrual cycle phase or birth control medication contributed to the sex differences in genetic associations with cardiovascular reactivity. Analyses comparing males to females in the follicular vs. luteal phase, or comparing males to females with vs. without birth control medication, yielded results that were substantially the same as those in our principal genetic association analyses. Thus, it is unlikely that hormonal status contributed appreciably to the observed sex differences in genetic associations with cardiovascular reactivity. Stress appraisals. There were significant sex differences in post-task appraisals of stress, so we evaluated whether these differences contributed to the sex differences in genetic associations with cardiovascular reactivity. A series of 2 (sex) " 3 (genotype) " 4 (stressor) mixed factorial MANCOVAs paralleling the principal association analyses showed no significant associations between any of the b-AR SNPs and stress appraisals. Furthermore, analyses of the associations between the b-AR SNPs and cardiovascular reactivity with post-task stress appraisals as covariates yielded results that were virtually identical to those of our principal association analyses. Thus, sex differences in appraisals of stress were unrelated to the associations between the b-AR SNPs and cardiovascular reactivity to stress.
We evaluated the effects of common, functional, nonsynonymous SNPs in the b1-AR and b2-AR genes on cardiovascular reactivity to cold and psychological stress in Black adolescents and young adults, a population at high risk for essential hypertension. Significant associations emerged between the Arg389Gly SNP in the b1-AR and inotropic cardiac reactivity, the Arg16Gly SNP in the b2-AR and DBP reactivity, and the Gln27Glu SNP in the b2-AR and vascular reactivity. However, these genetic associations with cardiovascular reactivity were moderated by sex in the first and third cases and by type of stressor in the second. As predicted, cardiac reactivity to stress was associated with the Arg389Gly SNP in the b1-AR, but this association depended on sex. Males showed the expected association between PEP reactivity and b1-AR function; specifically, PEP reactivity was diminished in males who were homozygous for the Gly389 allele as compared to males who carried the Arg389 allele. Females showed no such association. The effect for PEP reactivity in males is consistent with prior research showing that the Gly389 allele is associated with diminished b1-AR function (Brodde, 2008; Kirstein & Insel, 2004; Mason et al., 1999), that the Arg389 allele is associated with enhanced cardiovascular responses to b-blockers (Johnson, Zineh, Puckett, McGorray, Yarandi, & Pauly, 2003; Liu, Liu, Tan, Chen, Wang, et al., 2003), and that Arg389 homozygotes show enhanced inotropic myocardial responses to dobutamine and isoproterenol (Bruck, Leineweber, Temme, Weber, Heusch, et al., 2005; La Rosee, Huntgeburth, Resenkranz, Bohm, & Schnabel, 2004; Liggett, Mialet-Perez, Thaneemit-Chen, Weber, Greene, et al., 2006). Interestingly, much of the evidence associating the Arg389Gly SNP with cardiovascular responses to b-AR agonists and antagonists comes from studies that included only males (Bruck et al., 2005; Liu et al., 2003; La Rosee et al., 2004). Our findings add to this evidence by showing that endogenous sympathetic cardiac
870 responses to environmental stress in young Black males are associated with the expected functional effects of the Arg389Gly SNP. This novel finding indicates that a functional b1-AR polymorphism is associated with variability in inotropic cardiac reactivity to stress in a manner consistent with the role of the b1-AR in cardiac control (Brodde, 2008; Kirstein & Insel, 2004), and provides further evidence relating PEP reactivity to b-adrenergic influences on the heart (Berntson et al., 1994; Mezzacappa et al., 1999; Sherwood, Allen, et al., 1990). The potential impact of these findings for the prevention and treatment of hypertension is underscored by recent research showing that the Arg389 allele is associated with elevated mortality risk (Pacanowski, Gong, Cooper-Dehoff, Schork, Shriver, et al., 2008) and with therapeutic responses to b-blocker treatment in hypertensive patients (Johnson et al., 2003; Pacanowski et al., 2008). Thus, the association between the Arg389Gly SNP and PEP reactivity may provide an early marker for cardiovascular risk in Black males. Our findings are generally consistent with the reported literature, but we are the first to identify an association between the Arg389Gly SNP and inotropic cardiac reactivity in Blacks and the first to report a sex difference in this association. The Arg16Gly SNP in the b2-AR was associated with DBP reactivity to stress, but the association was limited to a particular stressor. Specifically, DBP reactivity during VG was diminished in participants who were homozygous for the Arg16 allele as compared to those who carried one or two copies of the Gly16 allele. Prior research has found that homozygous carriers of the Gly16 allele show enhanced regional (i.e., brachial artery or hand vein) vasodilation to local infusions of b-AR agonists (Brodde, 2008; Cockcroft, Gazis, Cross, Wheatley, Dewar, et al., 2000; Garovic, Joyner, Dietz, Boerwinkle, & Turner, 2003), but diminished total peripheral vasodilation and enhanced BP reactivity to systemic infusions of b-AR agonists (Brodde, 2008; Gratze, Fortin, Labugger, Binder, Kotanko, et al., 1999; Hoit, Suresh, Craft, Walsh, & Liggett, 2000; Snapir, Koskenvuo, Toikka, Orho-Melander, Hinkkas, et al., 2003). Our finding of diminished DBP reactivity during VG in Arg16/Arg16 homozygotes is consistent with the latter research on systemic infusions. However, this finding is contrary to a report of enhanced DBP reactivity to MA and CP in White adults with the Arg16Arg genotype (Li, Faulhaber, Rosenthal, Schuster, Jordan, et al., 2001). This discrepancy may be due to differences in participant characteristics (age, race/ethnicity) or methods of cardiovascular phenotyping. Mirroring the stressor-specific association between the Arg16Gly SNP and DBP reactivity, the Gln27Glu SNP was marginally associated with DBP reactivity during VG, with Glu27/Glu27 homozygotes showing relatively enhanced reactivity. Together, these findings agree with those of recent haplotype studies in suggesting that the Gly16 and Glu27 alleles are associated with potentially adverse BP and vascular responses to stress (Brodde, 2008; Poole, Snieder, Davis, & Treiber, 2006; Trombetta, Batalha, Rondon, Laterza, Frazzatto, et al., 2005). Moreover, it is worth noting that BP reactivity to VG is heightened in Black adolescents and predicts future elevations in resting BP (Murphy et al., 1992; Treiber et al., 2003). As expected, the Gln27Glu SNP in the b2-AR was associated with vascular reactivity to stress; this effect also was moderated by sex. The overall increase in TPR during stress was largest for males who were homozygous for the Gln27 allele and smallest for females who were homozygous for the Gln27 allele. Carriers of the variant Glu27 allele showed intermediate increases in TPR that were equivalent for males and females. A similar but weaker
R. M. Kelsey et al. pattern of associations appeared for DBP reactivity. The variant Glu27 allele in the b2-AR is associated with resistance to receptor down-regulation and enhanced receptor function (Brodde, 2008; Kirstein & Insel, 2004; Xie, Stein, Kim, Xiao, He, et al., 1999), as well as increased forearm blood flow during exercise and psychological stress (Trombetta et al., 2005), whereas the common Gln27 allele is associated with attenuated vasodilatory responses to b-adrenergic agonists (Brodde, 2008; Cockcroft et al., 2000). The enhanced vasoconstrictive reactivity that we observed in homozygous Gln27/Gln27 males is consistent with these observations, and may reflect diminished b2-AR vasodilatory function during stress. As in our study (see Table 2), previous studies have found significant sex differences in vascular reactivity to stress, with males showing greater reactivity than females (Allen & Matthews, 1997; Allen, Stoney, Owens, & Matthews, 1993; Girdler, Turner, Sherwood, & Light, 1990). Our findings suggest that such sex differences in vascular reactivity may depend, at least in part, on variation at the Gln27Glu SNP in the b2-AR, and thus are more likely to emerge in studies that include sufficient numbers of males and females who are homozygous for the common Gln27 variant. Nevertheless, our findings for the Gln27Glu SNP are complex and should be viewed with caution, especially since there were few Glu27/Glu27 homozygotes in our sample and unexpected associations between this SNP and pretesting use of caffeine and medications. The low minor allele frequency and the small number of Glu27/Glu27 homozygotes in our study limited the power to detect significant linear associations between the number of copies of the minor allele and cardiovascular reactivity or significant sex differences in cardiovascular reactivity for Glu27/Glu27 homozygotes. We did not find any significant associations for the b-AR SNPs and cardiovascular baseline levels. Although other studies have reported associations between resting cardiovascular measures and genetic variations in b-ARs (Brodde, 2008; Kirstein & Insel, 2004), findings have often been contradictory, analyses have often failed to control adequately for Type I error, and participants have typically been older and from different racial/ ethnic groups than those in our study. One association study of the b2-AR SNPs (Arg16Gly and Gln27Glu) and resting hemodynamics in young European American and African American twins found several significant associations for Whites, but few associations for Blacks (Snieder, Dong, et al., 2002). It is possible that genetic associations with resting cardiovascular function change over the lifespan or depend on environmental factors that vary among different racial/ethnic groups. The fact that we found significant genetic associations for cardiovascular reactivity to stress, but not for resting cardiovascular baseline levels, underscores the importance of gene-environment interactions in cardiovascular regulation. There is growing recognition of the importance of considering sex differences in genetic associations with disease-related traits, especially for diseases such as hypertension that have different prevalence rates in men and women (Rana, Insel, Payne, Abel, Beutler, et al., 2007; Seda, Tremblay, Gaudet, Brunelle, Gurau, et al., 2008; Weiss, Pan, Abney, & Ober, 2006). Sex differences in b-AR function and cardiovascular reactivity have been observed (Bowyer, Brown, & Jones, 2001; Freedman, Sabharwal, & Desai, 1987; Kneale, Chowienczyk, Brett, Coltart, & Ritter, 2003; Rana et al., 2007), as have sex differences in genetic associations with BP levels and related cardiovascular phenotypes (Rana et al., 2007; Seda et al., 2008; Weiss et al., 2006), including associations involving genetic variations in the b1-AR and the
Genetics of cardiovascular reactivity
871
b2-AR (Rana et al., 2007). Our study is the first to find significant sex differences in associations between b-AR gene variants and cardiovascular reactivity to stress. Such sex differences have important implications for genetic association analyses and may explain some of the past failures to replicate genetic associations with cardiovascular phenotypes, especially as prior studies have generally focused on a single sex or evaluated sex as a covariate rather than as a moderator of genetic associations. Nevertheless, the biological origins of these sex differences are complex (Mendelsohn & Karas, 2005). Previous studies have found significant sex differences in adrenergic function and cardiovascular reactivity regardless of female menstrual cycle phase or oral contraceptive use (Bowyer et al., 2001; Freedman et al., 1987; Kneale et al., 2003). Consistent with those studies, our secondary analyses suggest that the observed sex differences in the associations between the b-AR SNPs and cardiovascular reactivity were not attributable to variations in female menstrual cycle phase or use of birth control medication. Moreover, our secondary analyses suggest that the sexual dimorphisms in the genetic associations with cardiovascular reactivity were not attributable to observed sex differences in appraisals of stress. Given these results and evidence that sex differences in cardiovascular reactivity tend to emerge primarily after puberty (Allen & Matthews, 1997), it seems likely that sexual dimorphisms in genetic associations with cardiovascular reactivity result from post-pubertal transformations in adrenergic function. The genetic associations with cardiovascular reactivity that we observed were relatively small, but such modest associations are typical for complex, heterogeneous, polygenic traits and disorders (Shih & O’Connor, 2008). It is generally assumed that such traits and disorders involve additive and interactive effects of genetic contributions from multiple biological pathways. Although our findings should be viewed with caution pending replication in an independent sample, the NCI-NHGRI Working Group on Replication in Association Studies has stated that such
initial findings still provide valuable information when a study has been carefully designed and described (Chanock, Manolio, Boehnke, Boerwinkle, Hunter, et al., 2007). Future studies will be required to determine whether similar effects are observable in other populations. However, studies of these genetic associations in other racial/ethnic groups may require different sample sizes to ensure adequate statistical power (Pacanowski et al., 2008), as there are substantial racial/ethnic differences in the published allele frequencies for b-AR SNPs (Brodde, 2008; Kirstein & Insel, 2004). Although the present study did not examine SNPs in noncoding regions, we are planning future studies to evaluate genetic associations with cardiovascular reactivity involving additional SNPs in or near these genes that do not necessarily lead to changes in amino acid sequence.
Conclusions Our findings indicate that b-AR polymorphisms are associated with cardiovascular reactivity to stress in Black adolescents and young adults, a population at increased risk of developing hypertension and related cardiovascular diseases. The genetic associations with cardiovascular reactivity in Black males may be particularly relevant for cardiovascular risk, as males who carried a combination of the Arg389 allele(s) in ADRB1 and the Gln27Gln genotype in ADRB2 showed a potentially adverse pattern of elevated cardiac and vascular reactivity to stress. Given that measures of cardiovascular reactivity qualify as intermediate phenotypes for hypertensive risk, the identification of genetic variations associated with increased cardiovascular reactivity to stress in young Blacks may provide valuable insights into the pathophysiology of the evolution of hypertension, and may lead to the development of new methods for early detection, prevention, and treatment in this population.
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873 Skosnik, P. D., Chatterton, R. T., Swisher, T., & Park, S. (2000). Modulation of attentional inhibition by norepinephrine and cortisol after psychological stress. International Journal of Psychophysiology, 36, 59–68. Slauterbeck, J. R., Fuzie, S. F., Smith, M. P., Clark, R. J., Xu, K. T., Starch, D. W., et al. (2002). The menstrual cycle, sex hormones, and anterior cruciate ligament injury. Journal of Athletic Training, 37, 275–280. Snapir, A., Koskenvuo, J., Toikka, J., Orho-Melander, M., Hinkkas, S., Saraste, M., et al. (2003). Effects of common polymorphisms in the a1a-, a2b-, b1-, and b2-adrenoreceptors on haemodynamic responses to adrenaline. Clinical Science, 104, 509–520. Snieder, H., Dong, Y., Barbeau, P., Harshfield, G. A., Dalageogou, C., Zhu, H., et al. (2002). Beta2-adrenergic receptor gene and resting hemodynamics in European and African American youth. American Journal of Hypertension, 15, 973–979. Snieder, H., Harshfield, G. A., Barbeau, P., Pollock, D. M., Pollock, J. S., & Treiber, F. A. (2002). Dissecting the genetic architecture of the cardiovascular and renal stress response. Biological Psychology, 61, 73–95. Stein, C. M., Lang, C. C., Singh, I., He, H. B., & Wood, A. J. J. (2000). Increased vascular adrenergic vasoconstriction and decreased vasodilation in blacks: Additive mechanisms leading to enhanced vascular reactivity. Hypertension, 36, 945–951. Stephens, M., & Donnelly, P. (2003). A comparison of Bayesian methods for haplotype reconstruction from population genotype data. American Journal of Human Genetics, 73, 1162–1169. Stephens, M., & Scheet, P. (2005). Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation. American Journal of Human Genetics, 76, 449–462. Swain, A., & Suls, J. (1996). Reproducibility of blood pressure and heart rate reactivity: A meta-analysis. Psychophysiology, 33, 162–174. Treiber, F. A., Jackson, R. W., Davis, H., Pollock, J. S., Kapuku, G., Mensah, G. A., & Pollock, D. M. (2000). Racial differences in endothelin-1 at rest and in response to acute stress in adolescent males. Hypertension, 35, 722–725. Treiber, F. A., Kamarck, T., Schneiderman, N., Sheffield, D., Kapuku, G., & Taylor, T. (2003). Cardiovascular reactivity and development of preclinical and clinical disease states. Psychosomatic Medicine, 65, 46– 62. Treiber, F. A., Musante, L., Braden, D., Arensman, F., Strong, W. B., Levy, M., et al. (1990). Racial differences in hemodynamic responses to the cold face stimulus in children and adults. Psychosomatic Medicine, 52, 286–296. Trombetta, I. C., Batalha, L. T., Rondon, M. U. P. B., Laterza, M. C., Frazzatto, E., Alves, M. J. N. N., et al. (2005). Gly161Glu27 b2adrenoceptor polymorphisms cause increased forearm blood flow responses to mental stress and handgrip in humans. Journal of Applied Physiology, 98, 787–794. Wegienka, G., & Baird, D. D. (2005). A comparison of recalled date of last menstrual period with prospectively recorded dates. Journal of Women’s Health, 14, 248–252. Weiss, L. A., Pan, L., Abney, M., & Ober, C. (2006). The sex-specific genetic architecture of quantitative traits in humans. Nature Genetics, 38, 218–222. Xie, H-G., Stein, C. M., Kim, R. B., Xiao, Z-S., He, N., Zhou, H-H., et al. (1999). Frequency of functionally important beta-2 adrenoceptor polymorphisms varies markedly among African-American, Caucasian and Chinese individuals. Pharmacogenetics, 9, 511–516. (Received June 11, 2009; Accepted October 26, 2009)
Psychophysiology, 47 (2010), 874–878. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.01007.x
In the face of anger: Startle modulation to graded facial expressions
JONATHAN P. DUNNING, ANTHONY AURIEMMO, CLAUDE CASTILLE, and GREG HAJCAK Department of Psychology, Stony Brook University, Stony Brook, New York, USA
Abstract In the present study, the startle reflex was examined with respect to the degree of anger displayed in facial expressions. To this end, 52 participants viewed faces that were morphed to display 0, 20, 40, 60, 80, or 100% anger. As the percentage of anger in faces increased from 0 to 100%, faces were perceived as increasingly angry; however, relative to neutral facial expressions, startle amplitude was only potentiated to maximally angry faces. These data imply a nonlinear relationship between the intensity of angry faces and defensive physiological activity. This pattern of startle modulation suggests a categorical distinction between threatening (100% anger) and other facial expressions presented. These results are further discussed in terms of existing data, and how this paradigm might be utilized in psychopathology research. Descriptors: Emotion, Startle, Faces, Anger
ipants view threatening emotional faces. This is particularly surprising in light of the rather large functional neuroimaging literature on the amgydala that has utilized threatening faces as emotional stimuli (Blair, Morris, Frith, Perrett, & Dolan, 1999; Breiter, Etcoff, Whalen, Kennedy, Rauch, et al., 1996; Vuilleumier, Armony, Driver, & Dolan, 2001; Whalen, Rauch, Etcoff, McInerney, Lee, & Jenike, 1998; Whalen, Shin, McInerney, Fischer, Wright, & Rauch, 2001). In one developmental study, 5-month-old children had larger startle amplitudes when viewing angry compared to happy or neutral faces (Balaban, 1995; but see Spangler, Emlinger, Meinhardt, & Hamm, 2001). In adult samples, potentiated startle has also been found in the context of angry compared to happy and neutral faces (Hess, Sabourin, & Kleck, 2007; Springer, Rosas, McGetrick, & Bowers, 2007). Thus, angry expressions reflect viewer-directed threat and effectively engage withdrawal or escape motivation (Springer et al., 2007). However, one study only found this effect when expressers were male (Hess et al., 2007); and Hess and colleagues suggest that male compared to female faces may indicate more direct threat since men are more associated with social dominance and aggressive acts than women. Activation of the amygdala depends on the affective strength of the eliciting stimuli, such that amygdala activation increases as ratings of emotional intensity/arousal also increase (Canli, Zhao, Brewer, Gabrieli, & Cahill, 2000; Phan, Taylor, Welsh, Decker, Noll, et al., 2003; Phan, Taylor, Welsh, Ho, Britton, et al., 2004). Data suggests that this type of dose-response relationship may also exist between stimulus aversiveness and potentiation of the startle reflex. For example, Bradley, Codispoti, Cuthbert, and Lang (2001) found potentiated startle responses in the presence of a wide variety of aversive picture content, including contamination, mutilation, and scenes depicting human and animal attack.
The startle response is a primitive defensive reflex that is observed across species in response to abrupt and intense sensory stimuli (Davis, 1984; Grillon & Baas, 2003). In humans, the eyeblink reflex is one of the most reliable components of the startle response (Grillon & Baas, 2003; Landis & Hunt, 1939). According to the motivational priming hypothesis, a defensive reflex should be potentiated when the aversive motivational system is active, but should be attenuated when the appetitive motivational system is active (Lang, Bradley, & Cuthbert, 1997). Indeed, research has supported this notion by demonstrating that eyeblink responses to startle probes are enhanced while participants view unpleasant scenes and attenuated when they view pleasant scenes (Lang, 1995); this pattern holds across various probe (e.g., acoustic, visual, tactile; Bradley, Cuthbert, & Lang, 1990) and affective stimulus modalities (e.g., sounds, movies, odors; Bradley, Cuthbert, & Lang, 1999). Extensive research in non-human animals suggests that potentiation of the startle reflex by fear-eliciting stimuli depends critically on the amygdala (Davis, Falls, Campeau, & Kim, 1993; Davis, Walker, & Lee, 1999; Lang, Davis, & Ohman, 2000). Studies that employ functional neuroimaging confirm that startle modulation in humans is likewise dependent upon the amygdala (Pissiota, Frans, Michelgard, Appel, Langstrom, et al., 2003). Despite ample evidence that the human startle reflex is potentiated by complex aversive pictures, fewer studies have assessed whether the startle response is similarly increased while particThe authors would like to thank Paul Ekman for providing permission to reproduce Figure 1. Address reprint requests to: Greg Hajcak, Ph.D., Department of Psychology, Stony Brook University, Stony Brook, NY 11794-2500. E-mail:
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Interestingly, when the unpleasant content was ordered according to participant ratings of arousal, a linear relationship between startle amplitude and arousal was evident such that startle responses were larger with higher arousal ratings (Bradley et al., 2001). Although pictures of both complex scenes and emotional faces activate the amygdala and potentiate the magnitude of the defensive startle reflex, it is unclear whether the degree of unpleasant emotion in facial displays would be related to increases in startle response, as was shown with pictures from the International Affective Picture System. Facial stimuli are ideal for investigating dose-response relationships because the same face can be easily manipulated to display gradations of emotional intensity. No studies to date have examined whether ‘more anger’ in faces relates to larger startle responses. Therefore, the present study sought to determine whether the startle reflex would linearly track increasing levels of anger in faces, or whether startle potentiation would be evident at some threshold in the anger continuum. To this end, participants viewed faces that displayed 0, 20, 40, 60, 80, or 100% anger while acoustic startle probes were presented and eyeblink electromyographic (EMG) activity was measured. Participants also completed self-report ratings of perceived anger.
The acoustic startle probe was a 50-ms burst of white noise that was set to a volume of 105 dB and was delivered through headphones using a tone generator (Contact Precision Instruments, Cambridge, MA). All stimuli and psychophysiological responses were presented and recorded using PSYLAB hardware and PSYLAB 8 software (Contact Precision Instruments).
Methods
Data Recording, Reduction, and Analysis Startle responses were recorded from EMG activity using a PSYLAB Stand Alone Monitor Unit (SAM) and BioAmplifier (Contact Precision Instruments). Two 4 mm Ag-AgCl electrodes were positioned approximately 25 mm apart over the orbicularis oculi muscle beneath the left eye, and an isolated ground was positioned on the forehead. EMG activity was sampled at 500 Hz, and band-pass filtered between 30 and 500 Hz. Startle EMG was rectified in a 200-ms window beginning 50 ms before the startle probe and smoothed using a 6-point running average. Startle amplitude was quantified as the maximum response in a 100-ms post-probe window relative to the average activity in the 50-ms pre-probe baseline period. No trials were excluded due to artifact or movement, and all trials (including non-response trials) were included in the present analyses. Startle amplitude for each subject was converted to t scores to reduce between-subject variability unrelated to variables of interest. Comparable results, however, were obtained when raw scores were analyzed. All measures were statistically analyzed using SPSS 15.0 through repeated-measures analysis of variance (ANOVA) with Greenhouse-Geisser correction applied. Startle response and self-reported ratings of perceived anger were each examined by conducting separate one-way (degree of anger: 0, 20, 40, 60, 80, & 100%) repeated-measures ANOVAs. To further examine significant omnibus effects for startle responses, post hoc pairedsamples t-tests were performed between neutral (0%) and all other angry faces (20, 40, 60, 80, 100%). For self-reported anger, post hoc paired-samples t-tests were performed between each level of anger and the one before it in the continuum. Bonferroni’s correction was used for multiple comparisons (0.05/ 5 5 0.01).
Participants Fifty-two undergraduate students (29 female) participated in the present study. All participants gave written informed consent and received course credit for their participation. This research was approved by the Stony Brook University Institutional Review Board. Stimuli Twelve photographs were selected from a standardized (Ekman & Friesen, 1976) picture set. For each of six male actors (male expressors were selected based on the aforementioned findings of Hess et al. (2007)1), we chose their neutral and angry facial expression; within each actor, neutral and angry faces were morphed with digital morphing software (MorphMan 3.0, STOIK Imaging, Moscow, Russia) to create six different facial expressions that ranged from neutral to angry (0%–100%) in increments of 20% (see Figure 1). Each picture was 732 ! 452 pixels and presented in black and white on a 19-inch monitor set with a resolution of 1024 ! 768 pixels. At a viewing distance of 25 inches, each picture occupied approximately 331 of visual angle horizontally and 271 vertically. 1 Given the findings of Hess and colleagues (2007), we explored whether the gender of our participants may have played a role in the effects of startle potentiation or ratings of perceived anger in faces by conducting separate 6 (degree of anger: 0, 20, 40, 60, 80, & 100%) ! 2 (gender) mixed-model ANOVAs. Results of startle amplitude revealed no main effect of gender (F(1,50)o1) and no interaction between gender and degree of anger (F(5,250)o1). Results of perceived anger ratings in facial expressions revealed no main effect of gender (F(1,50) 5 1.72, p4.15) but a significant interaction between gender and degree of anger (F(5,250) 5 4.10, po.01). Post-hoc comparisons revealed that females compared to males rated 40% (t(50) 5 2.23, po.05) and 60% (t(50) 5 2.22, po.05) angry faces as slightly more angry. In addition, within males, 20% and 40% angry faces were not rated differently (t(22) 5 " 2.06, p4.05), but all other within-gender effects matched the present study’s overall findings.
Procedure After obtaining informed consent, participants were given detailed task instructions. The experiment began with a four-trial startle habituation phase used to elicit initial extreme startle responses. For the remainder of the experiment, participants were presented with three blocks of 12 trials; each block contained two faces displaying each level of anger (0, 20, 40, 60, 80, and 100%) distributed randomly. On each trial, faces were presented for 10 s; startle probes occurred randomly between 3 to 5 s following picture onset. In addition, each block contained one or two intertrial interval (ITI) startle probes that occurred randomly between 3 to 7 s following stimulus offset in order to reduce probe predictability. Finally, all participants completed a self-report rating of perceived anger for each of the 36 faces. Every picture was rated using an 11-point Likert-type scale that ranged from ‘‘neutral’’ (0) on one end to ‘‘angry’’ (100) on the other end in increments of 10 units; the midpoint was unlabeled.
Results As evident in Figure 2, self-reported ratings of perceived anger in facial expressions significantly differed across stimuli (F(5,
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Figure 1. An example of one actor’s morphed faces used in the present study. Starting from the left, faces demonstrate 0, 20, 40, 60, 80, and 100% anger.
Discussion
Figure 2. Average self-reported perceived anger present in each facial expression (0, 20, 40, 60, 80, and 100% anger). The scale ranged from 0 (neutral) to 100 (angry). Bars represent standard errors of the mean.
255) 5 620.19, po.001). Post-hoc comparisons revealed that each set of increasingly angry faces were rated as significantly more angry than the one before (2040%, (t(51) 5 ! 5.20, po.001); 40420%, (t(51) 5 ! 6.22, po.001); 60440%, (t(51) 5 ! 18.24, po.001); 80460%, (t(51) 5 ! 15.26, po.001); and 100 480%, (t(51) 5 ! 7.34, po.001). Consistent with the impression from Figure 3, startle amplitude differed as a function of the degree of anger in the facial stimuli (F(5,255) 5 2.70, po.03). Unlike the self-report findings, startle amplitude was significantly potentiated relative to neutral expressions (0%) only when faces were maximally angry (100%; t(51) 5 ! 3.05, po.005). Relative to neutral expressions, there were no differences in startle amplitude at 20% (t(51) 5 ! .71, p4.45), 40% (t(51) 5 ! .91, p4.35), 60% (t(51) 5 ! .89, p4.35), or 80% (t(51) 5 .19, p4.80) anger. Overall then, perceived anger increased linearly as a function of the percent of anger displayed in each facial expression. However, relative to neutral facial expressions, startle amplitude was significantly potentiated only by maximally angry faces.2
2 To explore possible habituation effects over the course of the study, we compared startle amplitudes in the first and second halves of the task. In order to maintain an adequate number of trials per average in each half of the task, we collapsed levels of facial anger into the following 3 groups: 0–20%, 40–60%, and 80–100%. A 3 (degree of anger) " 2 (task half) repeated measures ANOVA revealed a main effect of task half (F(1,51) 5 114.82, po.001), such that larger startles were present in the first half of the task, and an interaction between task half and degree of facial anger (F(2,102) 5 9.26, po.001). Post-hoc comparisons revealed no differences in startle between levels of facial anger in the first half of the task (all tso2.20, ps4.03; did not meet family-wise alpha corrections). However, in the second half of the task, only 80–100% angry faces elicited larger startles than 0–20% angry faces (t(51) 5 ! 3.42, po.001). Hence, the second half of the task exhibited results most similar to the original analyses in which only maximally angry faces elicited potentiated startle amplitudes.
The present study sought to determine whether the defensive startle reflex would be sensitive to the intensity of anger in facial expressions. Self-report ratings of perceived anger linearly tracked increasing levels of anger in faces, confirming that participants were aware of the relatively subtle differences of displayed anger. However, measures of defensive psychophysiology exhibited a different pattern of results: startle amplitude was potentiated only by maximally angry relative to neutral faces; startle amplitude did not differ between any of the less angry (20, 40, 60, 80%) compared to neutral facial expressions. In regard to the maximally angry faces, the present findings are in line with recent studies demonstrating startle modulation to angry compared to neutral facial expressions (Hess et al., 2007; Springer et al., 2007). However, the threshold evident in our startle findings initially appear in contrast to that of Bradley et al. (2001), in which startle potentiation linearly increased with arousal ratings of unpleasant pictures. Such a comparison, although, may be between non-equivalent stimuli; Bradley et al. (2001) used unpleasant, complex pictorial scenes while our study focused on finer gradations of emotion in facial stimuli. A recent study found that, although viewing IAPS and facial expressions activates similar brain regions (e.g., amygdala, ventromedial prefrontal cortex, and visual cortex), participants rated expressive faces lower on dimensions of arousal and valence compared to IAPS (Britton, Taylor, Sudheimer, & Liberzon, 2006). Hence, one possibility is that there may exist more of a dose-response relationship between aversiveness and startle potentiation at the upper end of the arousal spectrum, whereas this relationship is binary/categorical for stimuli that are relatively low in arousal. Consistent with this possibility, Hess et al. (2007) only found startle potentiation to angry male, but not female, faces; these data are consistent with the possibility that only the most threat-
Figure 3. Standardized EMG activity elicited by startle probes as a function of degree of anger present in the facial expressions (0, 20, 40, 60, 80, and 100% anger). Bars represent standard errors of the mean.
Startle and anger
877
ening faces in a given context are sufficient to activate the defensive motivational system. When examining startle amplitudes between each half of the task, we found evidence of overall habituation, such that larger startles were present in the first compared to second half of the task. Interestingly, startle potentiation to angry faces was not present in the first half of the task; however, in the second half results were similar to the overall findings such that startle was potentiated only by the most angry faces. These data imply that modulation of startle by maximally angry faces may depend on experience with the full-range of stimuli, such that the categorization of anger with respect to other stimuli develops over time. Our results do imply a disconnect between the perceived intensity of an aversive facial expression and modulation of the startle reflex. In this study, individuals were able to accurately perceive increasing increments of anger in faces via self-report ratings, but the startle response was potentiated in a binary manner, and only in response to maximally angry faces. In this way, the startle reflex was relatively insensitive to small gradations of anger in the present study. It would be interesting to evaluate whether physiological or self-report measures better predict behavioral responses and individual differences in future studies. Along the same lines, the present paradigm may also have utility for investigating the role that individual differences play in patterns of startle modulation. By examining certain individual differences that might be linked to susceptibility to psychopathology (e.g., personality traits such as neuroticism, and individual differences in anxiety and depression), it is possible that group differences may emerge in patterns of startle modulation. For instance, social phobia and high levels of social anxiety have been associated with interpretation biasesFthe tendency to interpret ambiguous social information as
negative (Franklin, Huppert, Langner, Leiberg, & Foa, 2005; Hirsch & Clark, 2004; Stopa & Clark, 2000). It is possible that social anxiety may be associated with a lower threshold for startle potentiation when viewing morphed faces. If this were the case, patterns of startle reflex attenuation/potentiation in paradigms that use graded facial expressions of emotion might be useful in psychopathology studies. Startle potentiation to maximally angry faces in the present study would also suggest modulation of the amygdala (Blair et al., 1999; Breiter et al., 1996; Pissiota et al., 2003; Vuilleumier et al., 2001; Whalen et al., 1998, 2001). Given this notion, it would be interesting for future studies to determine if the amygdala similarly demonstrates a binary response to faces that vary in expressed anger. A final possible future direction would be to determine if the present findings generalize to different emotions such as fear or even happiness. Given that startle inhibition typically occurs in the presence of pleasant scenes (Bradley et al., 2001; Lang et al., 1997), future studies could determine if startle attenuation, as opposed to potentiation, similarly requires intense emotional stimuli or whether a dose-response relationship between startle inhibition and emotional intensity would be evident when viewing graded facial expressions of happiness. Overall, our study demonstrated that, in the context of angry facial expressions, the defensive startle reflex was only potentiated by maximally angry expressions. Interestingly, self-reported perceived anger did not follow this pattern, but instead linearly tracked increasing levels of anger. Results indicate that aversive facial expressions differentially engage the startle reflex and conscious emotional perception. Future studies should determine if this effect generalizes to different emotions, and determine whether or not different patterns of startle modulation would be evident among those more sensitive to social signals of rejection and threat.
REFERENCES Balaban, M. T. (1995). Affective influences on startle in five-month-old infants: Reactions to facial expressions of emotion. Child Development, 66, 28–36. Blair, R. J., Morris, J. S., Frith, C. D., Perrett, D. I., & Dolan, R. J. (1999). Dissociable neural responses to facial expressions of sadness and anger. Brain, 122, 883–893. Bradley, M. M., Codispoti, M., Cuthbert, B. N., & Lang, P. J. (2001). Emotion and motivation I: Defensive and appetitive reactions in picture processing. Emotion, 1, 276–298. Bradley, M. M., Cuthbert, B. N., & Lang, P. J. (1990). Startle reflex modification: Emotion or attention? Psychophysiology, 27, 513–522. Bradley, M. M., Cuthbert, B. N., & Lang, P. J. (1999). Affect and the startle reflex. In M. E. Dawson, A. M. Schell, & A. H. Bohmelt (Eds.), Startle modification: Implications for neuroscience, cognitive science, and clinical science (pp. 157–183). New York: Cambridge University Press. Breiter, H. C., Etcoff, N. L., Whalen, P. J., Kennedy, W. A., Rauch, S. L., Buckner, R. L., et al. (1996). Response and habituation of the human amygdala during visual processing of facial expression. Neuron, 17, 875–887. Britton, J. C., Taylor, S. F., Sudheimer, K. D., & Liberzon, I. (2006). Facial expressions and complex IAPS pictures: Common and differential networks. NeuroImage, 31, 906–919. Canli, T., Zhao, Z., Brewer, J., Gabrieli, J. D., & Cahill, L. (2000). Event-related activation in the human amygdala associates with later memory for individual emotional experience. The Journal of Neuroscience, 20, RC99. Davis, M. (1984). The mammalian startle response. In R. C. Eaton (Ed.), Neural mechanisms of startle behavior (pp. 287–351). New York, NY: Plenum Press.
Davis, M., Falls, W. A., Campeau, S., & Kim, M. (1993). Fear-potentiated startle: A neural and pharmacological analysis. Behavioural Brain Research, 58, 175–198. Davis, M., Walker, D. L., & Lee, Y. (1999). Neurophysiology and neuropharmacology of startle and its affective modification. In M. E. Dawson, A. M. Schell, & A. H. Bohmelt (Eds.), Startle modification: Implications for neuroscience, cognitive science, and clinical science (pp. 95–113). New York: Cambridge University Press. Ekman, P. E., & Friesen, W. V. (1976). Pictures of facial affect. PaloAlto: Consulting Psychologists Press. Franklin, M. E., Huppert, J. D., Langner, R., Leiberg, S., & Foa, E. B. (2005). Interpretation bias: A comparison of treated social phobics, untreated social phobics, and controls. Cognitive Therapy and Research, 29, 289–300. Grillon, C., & Baas, J. (2003). A review of the modulation of the startle reflex by affective states and its application in psychiatry. Clinical Neurophysiology, 114, 1557–1579. Hess, U., Sabourin, G., & Kleck, R. E. (2007). Postauricular and eyeblink startle responses to facial expressions. Psychophysiology, 44, 431–435. Hirsch, C. R., & Clark, D. M. (2004). Information-processing bias in social phobia. Clinical Psychology Review, 24, 799–825. Landis, C., & Hunt, W. A. (1939). The startle pattern. New York: Farrar and Rinehart Inc. Lang, P. J. (1995). The emotion probe: Studies of motivation and attention. American Psychologist, 50, 372–385. Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1997). Motivated attention: Affect, activation and action. In P. J. Lang, R. F. Simons, & M. T. Balaban (Eds.), Attention and orienting: Sensory and motivational processes (pp. 97–135). Hillsdale, NJ: Erlbaum.
878 Lang, P. J., Davis, M., & Ohman, A. (2000). Fear and anxiety: Animal models and human cognitive psychophysiology. Journal of Affective Disorders, 61, 137–159. Phan, K. L., Taylor, S. F., Welsh, R. C., Decker, L. R., Noll, D. C., Nichols, T. E., et al. (2003). Activation of the medial prefrontal cortex and extended amygdala by individual ratings of emotional arousal: A fMRI study. Biological Psychiatry, 53, 211–215. Phan, K. L., Taylor, S. F., Welsh, R. C., Ho, S., Britton, J. C., & Liberzon, I. (2004). Neural correlates of individual ratings of emotional salience: A trial-related fMRI study. NeuroImage, 21, 768–780. Pissiota, A., Frans, O., Michelgard, A., Appel, L., Langstrom, B., Flaten, M. A., et al. (2003). Amygdala and anterior cingulate cortex activation during affective startle modulation: A PET study of fear. European Journal of Neuroscience, 18, 1325–1331. Spangler, G., Emlinger, S., Meinhardt, J., & Hamm, A. (2001). The specificity of infant emotional expression for emotion perception. International Journal of Psychophysiology, 41, 155–168.
J. P. Dunning et al. Springer, U. S., Rosas, A., McGetrick, J., & Bowers, D. (2007). Differences in startle reactivity during the perception of angry and fearful faces. Emotion, 7, 516–525. Stopa, L., & Clark, D. M. (2000). Social phobia and interpretation of social events. Behaviour Research and Therapy, 38, 273–283. Vuilleumier, P., Armony, J. L., Driver, J., & Dolan, R. J. (2001). Effects of attention and emotion on face processing in the human brain: An event-related fMRI study. Neuron, 30, 829–841. Whalen, P. J., Rauch, S. L., Etcoff, N. L., McInerney, S. C., Lee, M. B., & Jenike, M. A. (1998). Masked presentations of emotional facial expressions modulate amygdala activity without explicit knowledge. The Journal of Neuroscience, 18, 411–418. Whalen, P. J., Shin, L. M., McInerney, S. C., Fischer, H., Wright, C. I., & Rauch, S. L. (2001). A functional MRI study of human amygdala responses to facial expressions of fear versus anger. Emotion, 1, 70–83. (Received September 18, 2009; Accepted November 2, 2009)
Psychophysiology, 47 (2010), 879–887. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.01008.x
The dynamics of cardiac defense: From attention to action
ISABEL RAMI´REZ,a PEDRO GUERRA,b MIGUEL A´NGEL MUN˜OZ,c PANDELIS PERAKAKIS,b LOURDES ANLLO-VENTO,b,d and JAIME VILAb a
Department of Personality, Spanish National Open University, Madrid, Spain Department of Clinical Psychology, University of Granada, Granada, Spain Department of Psychology, University of the Balearic Islands, Palma de Mallorca, Spain d F. Olo´riz Institute of Neurosciences, University of Granada, Granada, Spain b c
Abstract The attentional and motivational significance of cardiac defense is examined in two studies. In Study 1, cardiac defense was evoked by an intense acoustic stimulus in the context of either a visual search or a memory search task using letters as stimuli. Results showed a potentiation of the long latency acceleration of cardiac defense in the visual search task. In Study 2, participants performed the same visual search task using pleasant, neutral, and unpleasant pictures as stimuli. Results showed a further potentiation of the long latency acceleration of cardiac defense when the visual search task was performed with unpleasant, compared to pleasant or neutral pictures. These results indicate that cardiac defense has both attentional and motivational contributions, where the attentional significance is related to increased sensory processing, whereas the motivational significance is associated with preparation for active defense. Descriptors: Cardiac defense, Visual attention, Aversive motivation, Habituation
rived largely from animal research (Blanchard & Blanchard, 1988; Bracha, 2004; Facchinetti, Imbiriba, Azevedo, Vargas, & Volchan, 2006; Fanselow, 1994; Lang, Bradley, & Cuthbert, 1997; Marx, Forsyth, Gallup, Fuse´, & Lexington, 2008). The new approach views defense as a dynamic sequence or cascade of reactions, from attentive freezing to active defense (flight and fight), which takes place depending primarily on proximity of the source of danger and availability of an escape route. Lang and colleagues (see Lang et al., 1997; Lang, Davis, & O¨hman, 2000) were the first to propose an adaptation of the animal cascade model of defense to explain human psychophysiological reactions to threatening stimuli. They suggested that the increase in arousal induced in the laboratory as stimuli become more aversive may be analogous to the increase in arousal induced in natural settings by increased proximity of a predator. Thus, the initial stages would be characterized by a progressive increase of physiological indices of attention, including increased heart-rate deceleration, but, as the arousal intensifies and the threat becomes more imminent, the heart rate reverses the direction of change ‘‘from a vigilance related fear bradycardia to action mobilization and cardiac acceleration’’ (Lang et al., 2000, p. 150). A similar approach to cardiac defense was proposed by Vila and colleagues after a series of studies on attentional modulation of the heart rate response to intense auditory stimulation (Ferna´ndez & Vila, 1989; Pe´rez, Ferna´ndez, Vila, & Turpin 2000; Vila, Pe´rez, Ferna´ndez, Pegalajar, & Sa´nchez, 1997). Previous research (Eves & Gruzelier, 1984; Turpin & Siddle, 1978; Vila, Ferna´ndez, & Godoy, 1992) had repeatedly demonstrated a
Two major psychophysiological approaches to cardiac defense can be identified: the cognitive and the motivational. The cognitive approach, built on Pavlov’s (1927) and Sokolov’s (1963) distinction between orienting and defense reflexes, assumes that cardiac responses to environmental stimuli reflect cognitive mechanisms aimed at facilitating or inhibiting stimulus processing (Graham, 1992; Graham & Clifton, 1966). The motivational approach, built on Cannon’s (1929) and Selye’s (1956) concepts of the fight–flight and the stress response, assumes that cardiac changes reflect metabolic mechanisms aimed at providing the body with the necessary energy for adaptive behaviors (Obrist, 1981). These two approaches have been difficult to reconcile in the past. From the cognitive perspective, the functional significance of cardiac defense was understood as an attentional mechanism contrary to cardiac orienting. From the motivational perspective, the functional significance of cardiac defense was understood as a response mobilization mechanism contrary to cardiac relaxation. A different approach to cardiac defense has been proposed in recent years, based on a naturalistic perspective of defense de-
This research was supported by a joint grant from the Spanish Ministry of Science and Education and the European Union (Project: SEJ2004-07956). L. Anllo-Vento’s participation was financed by a ‘‘Ramo´n y Cajal’’ contract from the Spanish Ministry of Science and Education. Address reprint requests to: Isabel Ramı´ rez, Department of Personality, Faculty of Psychology, National Open University, Calle Juan del Rosal, 10 - 28040 Madrid, Spain. E-mail:
[email protected] 879
880 complex pattern of heart rate response to an unexpected intense noise that comprises two accelerative/decelerative components in alternating order. The attentional modulation of this response pattern was investigated by Vila and colleagues by manipulating the direction of attention (external versus internal) in line with the intake-rejection hypothesis (Lacey & Lacey, 1974). Results revealed a positive relationship between cardiac defense and attentional processes of sensory intake. No relation with attentional processes of sensory rejection was found. Based on these findings, it was proposed (Vila et al., 1997; Vila, Guerra, Mun˜oz, Vico, Viedma-del Jesu´s, et al., 2007) that the pattern of heart rate changes in response to unexpected intense aversive stimuli reflects the succession of two defensive phases: an attentional protective phase linked to the short latency acceleration/deceleration and a motivational protective phase linked to the long latency acceleration/deceleration. This interpretation of cardiac defense is contrary to the classic cognitive model of defense (‘shut down sensory processing,’ ‘rejection of the stimulus’) but is consistent with recent models of attention developed within cognitive neuroscience (Posner, 1994). Posner’s attentional model assumes the existence of three anatomical and functional attentional networks: the alertness network (involved in maintaining an appropriate vigilance state), the anterior attentional network (involved in executive control), and the posterior attentional network (involved in selection of information from sensory input). In his model, Posner (1994) proposed an excitatory relationship between the alerting and posterior attentional networks and an inhibitory relationship between the alerting and anterior attentional networks (see Callejas, Lupia´n˜ez, & Tudela, 2004; Cohen, Semple, Gross, Holcomb, Dowling, & Nordahl, 1998; Fan, McCandliss, Sommer, Raz, & Posner, 2002). Accordingly, if cardiac defense elicited by an intense noise represents activation of the alerting system, the response would tend to be potentiated by the simultaneous performance of a posterior attentional task (e.g., visual search) and inhibited by the performance of an anterior attentional task (e.g., memory search). The objective of the two studies reported here was to test the attentional and motivational significance of cardiac defense. Study 1 was designed to replicate the potentiation of the second accelerative component of cardiac defense by external attention. We selected two similar but opposite attentional tasks with respect to the direction of attention: Sternberg’s visual search task (external attention) and Sternberg’s memory search task (internal attention). The present study was also intended to confirm whether the attentional modulation depends on the direction of attention and not on task demands, as partially demonstrated by Pe´rez et al. (2000). Study 2 was designed to test the motivational significance of cardiac defense by adding emotional content to the visual search task in Study 1. Instead of searching for letters, participants had to search for unpleasant, neutral, or pleasant pictures while the cardiac defense response was being evoked. Previous studies (Ruiz-Padial, Mata, Rodrı´ guez, Ferna´ndez, & Vila 2005; Sa´nchez, Guerra, Mun˜oz, Mata, Bradley, Lang, & Vila, 2009; Sa´nchez, Ruiz-Padial, Pe´rez, Ferna´ndez, Cobos, & Vila, 2002) using the startle probe paradigm (Lang, 1995) demonstrated the potentiation of cardiac defense by the visualization of unpleasant and fearful pictures presented a few seconds before the defense stimulus. Based on these data, it was hypothesized that adding an aversive content to the visual search task would further potentiate cardiac defense.
I. Ramı´rez et al. STUDY 1 Method Participants Participants were 80 student volunteers (40 women) with ages ranging between 17 and 39 years. None of the participants had auditory or visual deficits or cardiovascular problems and none were under pharmacological or psychological treatment. They received course credit for their participation, after signing an informed consent form approved by the local ethics review board. Design Participants of each gender were randomly assigned to one of four experimental groups derived from the 2 ! 2 factorial design used in the study, with 2 attentional tasks (visual search and memory search) each one with 2 levels of task difficulty (easy and difficult). Physiological Test The physiological test consisted of three presentations of an intense white noise of 105 dB, 500-ms duration, and instantaneous rise time, capable of eliciting the cardiac defense response (Ramı´ rez, Sa´nchez, Ferna´ndez, Lipp, & Vila, 2005), in the following sequence: (a) 10 min of rest period, (b) three presentations of the white noise with an inter-stimulus interval of 100 s, and (c) a final period of 60 s with no stimulation. Each presentation of the white noise was followed by the initiation of the corresponding attentional task performed during 80 s. The noise was generated by a Coulbourn audio system (Coulbourn Instruments, L.L.C., Allentown, PA) (modules S81-02, S84-04, S82-24, and S22-18) and presented binaurally through earphones (Telephonic TDH Model- 49, Telephonic Corporation, Farmingdale, NY), calibrating the intensity of the sound by using a sound pressure meter (Bruel & Kjaer, model 2235, Naerum, Denmark) and artificial ear (Bruel & Kjaer, model 4153). Behavioral Tasks The tasks were programmed using MEL software (Schneider, 1988) and presented on a Pentium computer using a 35-cm (14inch) CRT monitor located approximately 50 cm from the participant’s eyes. Memory search. We used a variant of Sternberg’s Memory Search Task (Sternberg, 1966), identical to that used by Pe´rez et al. (2000). Participants had to memorize a set of letters and then indicate whether a target letter belonged to the memorized set. Each trial consisted of the following sequence: (1) A fixation point (n) was presented in the center of the screen for 500 ms, and was followed by (2) an array of horizontally displayed capital letters (2 or 7, depending on the level of difficulty) with a duration of 2500 ms; after which (3) an array of masks (#) was presented in the positions previously occupied by the letters for 500 ms. Finally, (4) a target letter was shown in the center of the screen until the participant emitted the response. Participants were instructed to respond, as fast and accurately as possible, by pressing with the index finger of the right hand a key with a green circle on it (key ‘B’) if the target letter belonged to the array, and another key with a yellow circle on it (key ‘N’) if the target letter did not belong to the array. A new trial started 5 sec after the onset of the previous trial. A total of 16 trials (lasting 80 sec) were performed following the defense noise. Letters and trials were randomly
Dynamics of cardiac defense selected so that half of the time (50%) the target belonged to the array. Visual search. We used a variant of Sternberg’s Visual Search Task (Sternberg, 1969). Participants had to search for a target letter (always the letter ‘A’) and detect whether it was present in an array of letters randomly distributed over the computer screen. In each trial: (1) A fixation point (n) in the center of the screen was presented for 500 ms, and was followed by (2) the target letter (‘A’) also in the center of the monitor with a duration of 2500 ms; (3) a mask (#), occupying the same position of the target letter, was presented for 500 ms, and was followed by (4) the onset of an array of 4 or 12 capital letters (depending on the level of difficulty), which were scattered around the monitor and presented until the participant emitted the response. As in the Memory Search condition above, participants were instructed to respond, as fast and accurately as possible, by pressing with the index finger of the right hand a key with a green circle on it (key ‘B’) if the target letter belonged to the array, and another key with a yellow circle on it (key ‘N’) if the target letter did not belong to the array. In all other respects, both tasks were identical. Dependent Variables Cardiac defense. Electrocardiogram was recorded using a Grass polygraph (model Rps 7c), with a 7P4 preamplifier, and standard Beckman electrodes at lead III (left arm and left foot with ground electrode in right foot). A band-pass filter of 10–35 Hz and a sampling rate of 1000 Hz were used. Weighted averaged sec-by-sec heart rate was obtained from the R-R intervals analyzed using the VPM program (Cook, 1999). The 80 heart rate values after onset of each auditory stimulus were then expressed as difference scores with respect to baseline level (15 s before each trial). To facilitate the statistical analysis, we followed the same procedure used in previous studies: for each participant, the 80 sec-by-sec heart rate values in each trial were reduced to 10 heart rate values corresponding to the medians of 10 progressively longer intervals: two of 3 s, two of 5 s, three of 7 s, and three of 13 s (Vila et al., 1992). This procedure allows reduction of the cardiac response without altering the response form. Behavioral measures. Reaction time (in milliseconds) and number of correct, incorrect, and missed responses were recorded for each behavioral task. Subjective measures. Participants completed a post-experimental questionnaire with three rating scales to assess the intensity and unpleasantness of the first noise and the difficulty of the task on a scale from 0 (not at all) to 100 (extremely). Procedure Each participant attended a single laboratory session that lasted approximately 60 min. Upon arrival, the participant was invited to sit in an armchair and then received information about the experimental session and signed the informed consent form. A brief interview followed to establish that selection criteria were met. The electrodes, filled with electrolyte paste, were attached after cleaning the skin at attachment sites. The computer monitor and keyboard were placed in front of the participant, who then read the instructions for performing the attentional task. A practice session with the specific task corresponding to the participant’s group was carried out to confirm that the task was
881 correctly understood. The physiological recording was then checked, the earphones were placed, and the participant was left alone in the room. After the test, the experimenter removed the earphones and electrodes, and the participant completed the post-experiment questionnaire. Finally, the participant was debriefed and given the credit for his/her participation. Statistical Analysis Cardiac defense was analyzed by means of a 2 ! 2 (! 3 ! 10) analysis of variance (ANOVA) with two between-group factors, Task (memory search vs. visual search) and Difficulty (easy vs. difficult) and two repeated measures factors, Trials (the three noise presentations) and Time (the 10 heart rate medians). The Greenhouse-Geisser epsilon correction was applied to the repeated measures factors. The behavioral and subjective data were analyzed by means of 2 ! 2 ANOVAS, with Task and Difficulty as the two between-group factors. Results Cardiac Defense The 2 ! 2 (! 3 ! 10) ANOVA yielded significant main effects of Task, F(1,76) 5 6.15, po.02, Trials, F(2,152) 5 27.37, po.0001, and Time, F(9,684) 5 12.81, po.0001, and two significant interaction effects: Trials ! Time, F(18,1368) 5 22.15, po.0001, and Task ! Trials ! Time, F(18,1368) 5 2.93, po.002. No significant main or interaction effect was found for Difficulty. Figure 1 plots the triple Task ! Trials ! Time interaction. Both tasks showed the expected cardiac defense response pattern in the first trial and the rapid habituation of the long-latency acceleration/deceleration in the second and third trials. In addition, larger accelerative responses were observed with the visual search task than with the memory search task, evident in the long-latency acceleration of trial 1. Follow-up analyses of the three-way interaction revealed significant differences between the two tasks in trials 1 and 3. The significant differences in trial 1 appeared in medians 4 (po.02), 5 (po.005), 6 (po.001), and 7 (po.003), corresponding to the long-latency acceleration. The significant differences in trial 3 appeared in median 1 (po.005), corresponding to the shortlatency acceleration. In both trial 1 and trial 3, a larger accelerative response was observed with the visual search task than with the memory search task. Behavioral Data Table 1 presents the means and standard deviations for the behavioral data as a function of Task and Difficulty. The 2 ! 2 ANOVAs for reaction time and number of correct and missed responses only revealed a significant effect of Difficulty (reaction time: F(1,76) 5 53.33, po.0001; correct responses: F(1, 76) 5 6.06, po.02; missed responses: F(1,76) 5 4.77, po.04). Participants in the easy condition had a shorter reaction time, higher number of correct responses, and lower number of missed responses in comparison to participants in the difficult condition. The 2 ! 2 ANOVA for incorrect responses yielded significant effects of Task, F(1,76) 5 42.71, po.0001, Difficulty, F(1, 76) 5 24.77, po.0001, and Task ! Difficulty, F(1,76) 5 19.16, po.0001. Follow-up analysis of the Task ! Difficulty interaction revealed significant differences between the easy and difficult conditions only in the memory search task (po.0001) and between both tasks in the difficult condition (po.0001). No differences were found between the easy and difficult condition in the
882
I. Ramı´rez et al. TRIAL 1
Heart Rate Change (bpm)
25
Table 1. Mean and (Standard Deviation) of the Behavioral and Subjective Data as a Function of Task (Visual Search vs. Memory Search) and Difficulty (Easy vs. Difficult)
Visual Memory
20 15
Visual search Easy (N 5 20)
10 5 0 –5
0
10
20
30 40 50 Time (seconds)
60
70
80
TRIAL 2 Heart Rate Change (bpm)
25
Visual Memory
20
Memory search Easy (N 5 20)
Difficult (N 5 20)
679 (118) 916 (137) 708 (102) 881 (140) 45.25 (7.5) 42.85 (4.8) 43.65 (8.7) 38.85 (4.1) 1.25 (1.8) 1.5 (1.9) 2.15 (1.7) 6.05 (2.1) 1.60 (5.8) 3.65 (4.9) 0.70 (1.3) 2.80 (3.6) 22.5 (18.9) 26.5 (20.3) 21.5 (16.6) 59.0 (19.2) 86.7 (8.1) 81.2 (13.2) 79.3 (11.3) 81.5 (16.7) 93.4 (7.1) 84.0 (17.3) 90.5 (8.8) 87.9 (7.5)
15 10 5 0 –5
0
10
20
30 40 50 Time (seconds)
60
70
80
Visual
20
Memory
15 10 5 0 –5
0
10
20
30 40 50 Time (seconds)
Finally, the 2 ! 2 ANOVAs for the rating of the intensity and unpleasantness of the noise revealed only a significant main effect of Difficulty in the rating of unpleasantness, F(1,76) 5 5.97, po.02. Participants in the easy condition of both tasks rated the noise as more unpleasant than participants in the difficult condition. No other significant effects were found.
Discussion
TRIAL 3
25 Heart Rate Change (bpm)
Behavioral data Reaction time Correct responses Incorrect responses Missed responses Subjective data Task difficulty Noise intensity Noise unpleasantness
Difficult (N 5 20)
60
70
80
Figure 1. Cardiac Defense elicited when participants were performing the Memory Search Task versus the Visual Search Task in trials 1 (top), 2 (middle), and 3 (bottom).
visual search task (p4.65) or between the two tasks in the easy condition (p4.11).
Subjective Data Table 1 also shows the means and standard deviations for the subjective data as a function of Task and Difficulty. The 2 ! 2 ANOVA for the rating of the difficulty of the task yielded significant effects of Task, F(1,76) 5 14.04, po.0001, Difficulty, F(1,76) 5 24.36, po.0001, and Task ! Difficulty interaction, F(1,76) 5 15.87, po.0001. Follow-up analysis of the Task ! Difficulty interaction revealed significant differences between the easy and difficult conditions only in the memory search task (po.0001) and between the two tasks in the difficult condition (po.0001). No differences were found between the easy and difficult condition in the visual search task (p4.5) or between the two tasks in the easy condition (p4.8).
As predicted, the amplitude of the second accelerative component of cardiac defense, elicited in the first defense trial, was larger when participants were simultaneously performing the visual search task than when they were performing the memory search task. Although not predicted, due to the expected rapid habituation of the response, a larger amplitude of the first accelerative component was also observed when participants were performing the visual versus memory search task in the third defense trial. These results are consistent with previous findings supporting the association of cardiac defense with attentional factors related to external rather than internal attention (Ferna´ndez & Vila, 1989; Pe´rez et al., 2000; Vila et al., 1997). Unlike previous studies, however, the present study used a visual search task for external attention that was matched, in terms of perceptual stimuli and motor responses, with the internal attention task under comparison, thereby providing stronger support for the attentional interpretation of results. The difficulty of the task did not have any effect on cardiac defense, also confirming previous findings (Pe´rez et al., 2000). The behavioral data (reaction time and number of correct and missed responses) confirmed the effectiveness of the manipulation of the task demand. In general, participants seemed to have emphasized accuracy (few incorrect and missed responses) over speed, yielding the clearest statistical results for reaction time. However, the number of incorrect responses and the retrospective ratings did not confirm the effectiveness of the difficulty manipulation in the visual search task. Hence, conclusions concerning the effect of difficulty on the visual search task should be drawn with caution. Finally, an unexpected finding was the effect of task difficulty on the subjective rating of the noise. The rating of unpleasantness was lower for difficult versus easy tasks, which may be due to reduced attentional resources to process the quality of the noise under higher task demands.
Dynamics of cardiac defense STUDY 2 Method
883 Dependent Variables Cardiac defense. Cardiac defense was defined as in Study 1.
Participants Participants were 60 student volunteers (30 women) between 18 and 30 years of age. None of the participants had auditory or visual deficits or cardiovascular problems, and none were under pharmacological or psychological treatment. They received course credit for their participation.
Behavioral and subjective measures. The same measures as in Study 1 were used, except that the difficulty of the task was not rated. In addition, nine pictures (the three targets plus two randomly selected pictures in each emotional category) were rated using the Self-Assessment Manikin valence and arousal scales (Lang et al., 2005). These final measures were included to confirm the a priori selection of the pictures.
Design All participants performed a visual search task that was identical to the easy task in Study 1 except that the letters were replaced with emotional pictures. The task was superimposed on the evocation of cardiac defense in three successive trials. Each defense noise presentation was followed by performance of the visual search task with one of three different picture valences: pleasant, neutral, or unpleasant. The order of picture valence along the three defense trials was counterbalanced according to a Latin square design. Participants were randomly distributed among the different orders, balancing the gender of participants (10 men and 10 women per order of picture valence).
Procedure We used the same procedure as Study 1 except that, after the physiological test and the subjective ratings of the noise, the participants assessed the emotional pictures. The assessment procedure was as follows: (a) the participant was given instructions on using the Self-Assessment Manikin scales; (b) each picture was projected on the wall in front of the participant for 15 s using a Kodak 9000 Ektapro slide projector; and (c) the participant rated the valence and arousal of the picture on the two SelfAssessment Manikin scales printed on a sheet of paper (one for each picture). Four random orders of picture presentation were used.
Physiological Test and Material The physiological test was identical to that used in Study 1. A set of 17 pleasant pictures (erotic couples), 17 neutral pictures (household objects), and 17 unpleasant pictures (mutilated bodies) were selected from the International Affective Picture System (IAPS; Lang, Bradley, & Cuthbert, 2005) using the Spanish norms (Molto´, Montan˜e´s, Poy, Segarra, Pastor, et al., 1999). The three sets significantly differed in valence (pleasant: M 5 7.39, SD 5 .39; neutral: M 5 5.11, SD 5 .28; unpleasant: M 5 1.82, SD 5 .36). Pleasant and unpleasant pictures did not significantly differ in arousal (pleasant: M 5 6.37, SD 5 .67; unpleasant: M 5 6.87, SD 5 1.0), whereas neutral pictures were significantly lower in arousal (M 5 3.05, SD 5 .57). IAPS codes for each picture are given in Footnote 1.1 The emotional pictures were presented on a 48-cm (19-in) CRT monitor using Visual Basic software. The presentation sequence in each visual search trial was the same as in Study 1: (a) fixation square of 15 ! 15 cm in the center of the monitor, presented for 500 ms; (b) the target picture, with same size of fixation square in the center of the monitor, for 2500 ms; (c) symbol # in the center of the monitor for 500 ms; and (d) an array of 4 pictures of identical target size scattered around the monitor and presented until the participant emitted the response. Participants had to respond whether the target picture was in the array. A total of 16 trials with the same target picture and the same affective valence (pleasant, neutral, or unpleasant) of the pictures presented in the arrays were performed after each defense noise. The 17 pictures belonging to the same valence category were randomly distributed along the 16 trials, with the restriction that (a) none of the 4 pictures in the same array was repeated, and (b) the target picture appeared in the array with a 50% probability.
Statistical Analysis Cardiac defense was analyzed by means of a 3 (! 3 ! 10) ANOVA with one between-group factor, Order of trials (the three groups with different order of picture valence), and two repeated-measures factors, Picture Valence (pleasant, neutral, and unpleasant) and Time (the 10 heart rate medians). Behavioral data were analyzed by means of 3 (! 3) ANOVAs with one between-group factor (Order of trials) and one repeated-measures factor (Picture Valence). The Greenhouse-Geisser epsilon correction was applied to the repeated-measures factors. Subjective assessment of the intensity and unpleasantness of the first noise was analyzed by means of ANOVAs with a single betweengroup factor (e.g., Picture Valence in trial 1). Analysis of the valence and arousal ratings of the nine pictures were analyzed by means of ANOVAs with a single repeated measures factor (e.g., Picture Valence).
1 Codes of IAPS pictures used in study 2. Pleasant pictures: 4651 (target), 4800, 4599, 4611, 4653, 4810, 4608, 4609, 4650, 4660, 4607, 4659, 4652, 4606, 4680, 4690, 4664. Neutral pictures: 7010 (target), 7000, 7009, 7025, 7030, 7034, 7035, 7040, 7050, 7060, 7080, 7090, 7150, 7190, 7235, 7233, 7002. Unpleasant pictures: 3010 (target), 3010, 3120, 3130, 3100, 3140, 3060, 3053, 3160, 3170, 3071, 3102, 3051, 3400, 3220, 3230, 3180.
Results Cardiac Defense The 3 (! 3 ! 10) ANOVA yielded significant main effects of Picture Valence, F(2,114) 5 4.94, po.009, and Time, F(9, 513) 5 18.68, po.0001, and three significant interaction effects: Picture Valence ! Time, F(18,1026) 5 2.44, po.01, Picture Valence ! Order, F(4,114) 5 16.83, po.0001, and Picture Valence ! Time ! Order, F(36, 1026) 5 13.13, po.0001. Figure 2 plots the triple interaction. As can be seen, a potentiated cardiac defense response is observed in the first trial, when participants were performing the visual search task with unpleasant pictures. In the second and third trial, the response pattern is markedly reduced, reflecting the rapid habituation of the longlatency acceleration/deceleration. Follow-up analysis of the three-way interaction revealed significant differences between the three picture valences in trials 1 and 2. The significant differences in trial 1 appeared in medians 4 (po.006), 5 (po.03), 6 (po.04), and 7 (po.05), corresponding to the long-latency acceleration. In all cases, pair-wise comparisons indicated that the cardiac acceleration was significantly
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higher for the unpleasant picture content than for the neutral (medians 4, 5, 6, and 7) and pleasant (medians 4, 5, and 7) picture contents. In trial 2, pair-wise comparisons also indicated a higher average heart rate response for the unpleasant versus neutral picture content. No other comparisons were significant. Behavioral Data Table 2 presents the mean and standard deviation of the behavioral data as a function of Picture Valence. The 3 (! 3) Order of trials ! Picture Valence ANOVAs yielded significant main effects of Picture Valence for reaction time, F(2,114) 5 19.33, po.0001, correct responses, F(2,114) 5 3.32, po.05, and missed responses, F(2,114) 5 5.87, po.008, and significant Order ! Picture Valence interactions for reaction time, F(4,114) 5 11.39, po.0001, correct responses, F(4,114) 5 5.35, po.002, incorrect responses, F(4,114) 5 3.91, po.005, and missed responses, TRIAL 1 Heart Rate Change (bpm)
25 Unpleasant Neutral Pleasant
20 15 10 5 0 –5
0
10
20
30 40 50 Time (seconds)
60
70
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TRIAL 2 Heart Rate Change (bpm)
25
Unpleasant Neutral Pleasant
20 15 10 5
0
10
20
30 40 50 Time (seconds)
60
70
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TRIAL 3 Heart Rate Change (bpm)
25
Unpleasant Neutral Pleasant
20 15 10 5 0 –5
Subjective Data Table 2 also presents the mean and standard deviation of the subjective data as a function of Picture Valence. The ANOVAs for the subjective ratings of the intensity and unpleasantness of the first noise revealed no significant effects of Picture Valence (intensity: F(2,57) 5 .97, p4.38; unpleasantness: F(2,57) 5 1.59, p4.2). Ratings of valence and arousal of the nine selected pictures confirmed the selection criteria for valence. As expected, pleasant, neutral, and unpleasant pictures differed significantly in valence, F(2,118) 5 217.58, po.0001), with significant differences among the three picture contents (all pso.0001). However, contrary to expectations, the three categories of pictures also differed in arousal, F(2,118) 5 108.33, po.0001, with significant differences among the three picture contents (all pso.0001). Pleasant and unpleasant pictures were not expected to differ in arousal, according to the IAPS selection criteria. However, whereas the ratings of unpleasant and neutral pictures were consistent with IAPS norms, the pleasant pictures were rated by our participants as somewhat less pleasant and arousing than in the original reference study. Discussion
0 –5
F(4,114) 5 4.48, po.005. Pair-wise comparisons for the main effect of Picture Valence revealed: (a) significant slower reaction time for pleasant and unpleasant pictures than for neutral pictures (both pso.0001), (b) significant lower number of correct responses for pleasant and unpleasant pictures than for neutral ones (both pso.05), and (c) significant higher number of missed responses for pleasant and unpleasant pictures than for neutral ones (both pso.02). No significant difference was found between pleasant and unpleasant picture valence in any variable. On the other hand, pair-wise comparisons between picture valences in the first trial revealed significant differences in reaction time only between pleasant and neutral and between unpleasant and neutral content (both pso.0001), with no significant difference between pleasant and unpleasant content (p4.75). Differences did not reach statistical significance in the remaining trials or in the trials for correct, incorrect, and missed responses.
0
10
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30 40 50 Time (seconds)
60
70
80
Figure 2. Cardiac Defense elicited when participants were performing the Visual Search Task in trials 1 (top), 2 (middle), and 3 (bottom) as a function of Picture Valence.
As predicted, the addition of an aversive emotional content to the visual search task resulted in larger amplitudes of the second accelerative component of cardiac defense in the first defense trial. No significant differences in cardiac defense were observed in this trial when pictures were pleasant or neutral. Although not predicted, due to the expected rapid habituation of the response, a larger accelerative pattern was also observed in the second trial when the visual search task was performed with unpleasant versus neutral pictures. The observed potentiation of cardiac defense when participants were performing the task with unpleasant pictures, and the lack of differences when participants were performing the task with pleasant and neutral pictures, is consistent with previous findings by our group (Ruiz-Padial et al., 2005; Sa´nchez et al., 2002, 2009). Sa´nchez et al. (2002) tested the emotional modulation of cardiac defense by using the startle probe paradigm. They reported that visualization of unpleasant pictures presented a few seconds before the defense noise resulted in a cardiac defense response characterized by a single large and prolonged acceleration. Visualization of pleasant and neutral pictures attenuated the typical response pattern, with no differences be-
Dynamics of cardiac defense
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Table 2. Mean and (Standard Deviation) of the Behavioral and Subjective Data as a function of Picture Valence (Pleasant, Neutral, and Unpleasant) Picture valence
Behavioral data Reaction time (N 5 60) Correct responses (N 5 60) Incorrect responses (N 5 60) Missed responses (N 5 60) Subjective data Noise intensity (N 5 20) Noise unpleasantness (N 5 20) Rating of Picture Valence (N 5 60) Rating of Picture Arousal (N 5 60)
Pleasant
Neutral
Unpleasant
890 (176) 14.3 (2.4) 0.4 (0.7) 1.3 (1.9)
747 (181) 15.0 (1.6) 0.4 (0.7) 0.6 (1.4)
863 (196) 14.3 (2.1) 0.4 (0.8) 1.5 (1.9)
87.1 (10.8) 88.2 (16.1)
91.5 (11.2) 87.3 (11.9) 93.8 (7.9) 86.0 (16.8)
6.6 (1.5)
5.2 (1.1)
1.9 (1.1)
5.7 (1.9)
2.6 (1.6)
6.8 (2.1)
tween these categories. A similar aversive potentiation of cardiac defense was reported, using the startle probe paradigm, when fearful participants were viewing fearful versus fear-irrelevant pictures (Sa´nchez et al., 2009), as also observed using masked and unmasked picture presentation (Ruiz-Padial et al., 2005). Potentiation of cardiac defense by viewing of unpleasant pictures parallels the eye-blink startle reflex potentiation consistently demonstrated in the context of the startle probe paradigm (see Bradley & Lang, 2007). Lang and colleagues (1997, 2000) consider this phenomenon as due to the congruence between the motivational system engaged by the perceptual stimuli (aversive) and the type of reflex being elicited (defensive). This explanation, known as the motivational priming hypothesis, also predicts inhibition of defensive reflexes by viewing pleasant pictures, due to the incongruence between the motivational system engaged by the pictures (appetitive) and the type of reflex being elicited (defensive). Both predictions have consistently been confirmed for the eye-blink startle reflex when pleasant and unpleasant pictures are compared to neutral ones. The present data and those published by Sa´nchez et al. (2002) support the predicted potentiation of cardiac defense by unpleasant pictures, in line with the motivational priming hypothesis, but they do not support its inhibition by pleasant versus neutral pictures. This finding may reflect a fundamental difference between cardiac and motor reflexes, or it may result from an ineffective manipulation of the appetitive motivational system by our pleasant pictures. Participants rated pleasant pictures as less pleasant and arousing than the IAPS reference population, supporting the second alternative. However, the first alternative is also supported by the behavioral data showing similar reaction times, correct responses, and missed responses for pleasant and unpleasant pictures compared to neutral pictures, indicating that the appetitive system was effectively manipulated. Implications for the Attention-Motivation Model of Cardiac Defense The results of our two studies provide new evidence to support an interpretation of cardiac defense in terms of both attentional and motivational significance. The attentional significance would be related to directing attention towards the processing of external cues, and the motivational significance towards activation of the
aversive motivational system. However, the attention-motivation model of cardiac defense proposed by Vila and colleagues (2007) suggests that the complex pattern of heart rate changes in response to unexpected intense acoustic or electrocutaneous stimuli represents the succession of two protective phases: an attentional protective phase linked to the short latency acceleration/decelerationFaimed at interrupting ongoing activity and heightening attention to the potential external dangerFand a motivational protective phase linked to the long latency acceleration/decelerationFaimed at motor preparation for active defense and recovery if no real danger occurs. Several questions arise concerning the present data. External attention and the short latency acceleration/deceleration. A first question concerns the effect of the visual search task on the long latency component, rather than the short latency one, the cardiac component presumably linked to the attentional phase. It should be noted that the visual search task used in our study did not only affect the long latency acceleration/deceleration. In both studies, significant differences in response pattern started in median 4, which, in the memory search task of Study 1 and in the pleasant and neutral picture conditions of Study 2, corresponded to the end of the short latency deceleration. Hence, the external attention task had a relatively generalized effect on cardiac defense, reducing the short latency deceleration and bringing forward the long latency acceleration. Vila and colleagues (1997) attributed this effect to the congruence between the type of attentional demands required by the defense stimulus (external) and the type of attentional demands required by the visual search task (also external). The shared attentional demands between stimulus and response would facilitate earlier completion of the attentional phase and a higher allocation of resources to the subsequent motivational phase. Aversive motivation and the long latency acceleration/deceleration. A second question concerns the effect of viewing unpleasant picturesFin the context of the startle probe paradigmFon the short latency component of cardiac defense, rather than the long latency one (Ruiz-Padial et al., 2005; Sa´nchez et al., 2002, 2009), the component presumably linked to the motivational phase. A methodological difference between the startle probe paradigm and the visual search task may explain this effect. Visualization of unpleasant pictures starts before presentation of the defense stimulus in the startle probe paradigm, whereas the unpleasant pictures are presented after the defense stimulus in the visual search task. When unpleasant pictures are presented before the defense stimulus, the typical cardiac defense response pattern changes from a complex pattern with two accelerative/ decelerative components to a single large and prolonged acceleration. One interpretation is that the attentional protective phase (linked to short latency acceleration/deceleration) has been pre-activated by the preceding threatening signals, temporally anticipating the motivational protective phase (long latency acceleration/deceleration) in preparation for active defense (Vila et al., 2007). In the visual search task, the threatening signals do not precede the defense stimulus and, therefore, no pre-activation of the attentional phase is expected. The space-time dimension in the cascade model and cardiac defense. A final theoretical question concerns the translation of the cascade model of defense, based primarily on increased spatial proximity of a distant visual cue (the approaching predator), to cardiac defense, based on a time sequence of cardiac responses
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to a fixed and close acoustic cue (the sudden unexpected intense noise). It can be argued that such intense noise represents the imminent presence of the danger (the predator) and that the timing of the short and long latency components of cardiac defense do not fit well with the necessity for fast action in self defense. Indeed, the long latency componentFwhich starts around 15 s after stimulus onset and reaches its maximum amplitude around 30 sFmay be considered too slow for an effective response mobilization (flight or fight). However, if the short latency component is considered analogous to the freezing response (attentive immobility), the absence of response mobilization during several seconds would play the adaptive function of passive defense against an unknown danger: reduced
detectability accompanied by increased attention towards the source of the potential danger (Marx et al., 2008). The physiological mechanism underlying the short latency component of cardiac defense, mediated exclusively by vagal influences (Reyes del Paso, Godoy, & Vila, 1993), would support this interpretation. In summary, the data presented here confirm the description and interpretation of cardiac defense as a dynamic sequence of accelerative and decelerative components with both attentional and motivational significance: the attentional significance would be related to directing attention to external cues and the motivational significance to a metabolic preparation of the organism for active defense.
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Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (2005). The International Affective Picture System. Gainesville, FL: Center for Research in Psychophysiology, University of Florida. Lang, P. J., Davis, M., & O¨hman, A. (2000). Fear and anxiety: Animal models and human cognitive psychophysiology. Journal of Affective Disorders, 61, 137–159. Marx, B. P., Forsyth, J. P., Gallup, G. G., Fuse´, T., & Lexington, J. M. (2008). Tonic immobility as an evolved predator defense: Implications for sexual assault survivors. Clinical Psychology Science and Practice, 15, 74–90. Molto´, J., Montan˜e´s, S., Poy, R., Segarra, P., Pastor, M. C., Tormo, M. P., et al. (1999). Un nuevo me´todo para el estudio experimental de las emociones: El International Affective Picture System (IAPS). Revista de Psicologı´a General y Aplicada, 52, 55–87. Obrist, P. A. (1981). Cardiovascular psychophysiology: A perspective. New York: Plenum Press. Pavlov, I. (1927). Conditioned reflexes. Oxford: Oxford University Press. Pe´rez, N., Ferna´ndez, M. C., Vila, J., & Turpin, G. (2000). Attentional and emotional modulation of cardiac defense. Psychophysiology, 37, 275–282. Posner, M. I. (1994). Attention: The mechanisms of consciousness. Proceedings of the National Academy of Sciences USA, 91, 7398– 7403. Ramı´ rez, I., Sa´nchez, M. B., Ferna´ndez, M. C., Lipp, O. V., & Vila, J. (2005). Differentiation between protective reflexes: Cardiac defense and startle. Pychophysiology, 42, 732–739. Reyes del Paso, G. A., Godoy, J., & Vila, J. (1993). Respiratory sinus arrhythmia as an index of parasympathetic cardiac control during the cardiac defense response. Biological Psychology, 35, 17–35. Ruiz-Padial, E., Mata, J. L., Rodrı´ guez, S., Ferna´ndez, M. C., & Vila, J. (2005). Non-conscious modulation of cardiac defense by masked phobic pictures. International Journal of Psychophysiology, 56, 271– 281. Sa´nchez, M. B., Ruiz-Padial, E., Pe´rez, N., Ferna´ndez, M. C., Cobos, P., & Vila, J. (2002). Modulacio´n emocional de los reflejos defensivos mediante visualizacio´n de ima´genes afectivas. Psicothema, 14, 702– 707. Sa´nchez, M. B., Guerra, P., Mun˜oz, M. A., Mata, J. L., Bradley, M. M., Lang, P. J., & Vila, J. (2009). Communalities and differences in fear potentiation between cardiac defense and eye-blink startle. Psychophysiology, 46, 1–4. Schneider, W. (1988). Micro experimental laboratory: Integrated system for IBM PC compatibles. Behaviour Research Methods, Instruments and Computers, 20, 206–271. Selye, H. (1956). The stress of life. New York: McGraw-Hill. Sokolov, F. N. (1963). Perception and the conditioned reflex. New York: Pergamon. Sternberg, S. (1966). High speed scanning in human memory. Science, 153, 652–654. Sternberg, S. (1969). The discovery of processing stages: Extension of Donders’ method. Acta Psychologica, 30, 276–315. Turpin, G., & Siddle, D. A. (1978). Cardiac and forearm plethysmographic responses to high intensity auditory stimulation. Biological Psychology, 6, 267–281.
Dynamics of cardiac defense Vila, J., Fernandez, M. C., & Godoy, J. (1992). The cardiac defense response in humans: Effects of stimulus modality and gender differences. Journal of Psychophysiology, 6, 140–154. Vila, J., Guerra, P., Mun˜oz, M. A., Vico, C., Viedma-del Jesu´s, M. I., Delgado, L. C., et al. (2007). Cardiac defense: From attention to action. International Journal of Psychophysiology, 66, 169–182.
887 Vila, J., Pe´rez, M. N., Ferna´ndez, M. C., Pegalajar, J., & Sa´nchez, M. (1997). Attentional modulation of the cardiac defense response in humans. Psychophysiology, 34, 482–487. (Received June 7, 2009; Accepted November 11, 2009)
Psychophysiology, 47 (2010), 888–904. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.01009.x
The effects of electrode impedance on data quality and statistical significance in ERP recordings
EMILY S. KAPPENMAN and STEVEN J. LUCK Center for Mind & Brain and Department of Psychology, University of California, Davis, Davis, California, USA
Abstract To determine whether data quality is meaningfully reduced by high electrode impedance, EEG was recorded simultaneously from low- and high-impedance electrode sites during an oddball task. Low-frequency noise was found to be increased at high-impedance sites relative to low-impedance sites, especially when the recording environment was warm and humid. The increased noise at the high-impedance sites caused an increase in the number of trials needed to obtain statistical significance in analyses of P3 amplitude, but this could be partially mitigated by high-pass filtering and artifact rejection. High electrode impedance did not reduce statistical power for the N1 wave unless the recording environment was warm and humid. Thus, high electrode impedance may increase noise and decrease statistical power under some conditions, but these effects can be reduced by using a cool and dry recording environment and appropriate signal processing methods. Descriptors: Electrode impedance, Signal-to-noise ratio, Skin potentials
subject or an increase in the number of subjects tested in an experiment to achieve a given S/N ratio in the averaged ERPs. To put this in terms that reflect the ‘‘bottom line’’ for most ERP researchers, recording the EEG under conditions that yield a lower S/N ratio will either decrease the probability of obtaining a statistically significant experimental effect (if the number of trials is held constant) or increase the amount of recording time necessary to obtain a significant effect (if the statistical power is held constant). In most cases, researchers would like to maintain the probability of obtaining a significant effect. However, the increased number of trials required for this is often higher than one might expect, because the S/N ratio of an average does not increase linearly with the number of trials in the average but instead increases with the square root of the number of trials (see Luck, 2005). If, for example, the S/N ratio of the raw EEG recording is half as great with high electrode impedances as with low electrode impedances, then it would be necessary to record four times as many trials with high electrode impedances as with low electrode impedances to achieve the same S/N ratio and hence the same statistical power in the averaged ERPs (all else being equal). However, it is not clear whether the S/N ratio will actually be meaningfully reduced when high electrode impedances are used in conjunction with an EEG recording system that was designed to tolerate these high impedances (which we will call high-impedance systems). There are several published papers describing the properties and performance of these systems (Ferree, Luu, Russell, & Tucker, 2001; Metting van Rijn, Peper, & Grimbergen, 1990; Tucker, 1993), but these papers do not provide direct, empirical, and quantitative comparisons of low and high electrode impedances under the conditions of a typical ERP experiment. For example, the study of Ferree et al. (2001) simply
In event-related potential (ERP) studies, researchers have traditionally minimized noise in the recordings by reducing the impedance between the recording electrodes and the living skin tissue (Luck, 2005; Picton, Bentin, Berg, Donchin, Hillyard, et al., 2000). When large numbers of electrodes are used, however, the process of reducing the impedances becomes very time consuming, and this has led manufacturers of electroencephalogram (EEG) recording systems to develop systems that can tolerate high electrode impedances. These systems have become quite popular, but many researchers are concerned that the quality of the EEG will be poorer with high electrode impedances than with low electrode impedances. High electrode impedances do not meaningfully reduce the size of the EEG signal (Johnson, de Haan, Oliver, Smith, Hatzakis, et al., 2001), but they might increase the noise level, resulting in a lower signal-to-noise (S/N) ratio. The goal of the present study was to determine whether the S/N ratio is meaningfully reduced when the EEG is recorded with high compared to low electrode impedances. If the S/N ratio of the EEG is lower, more trials will need to be averaged together to obtain a given S/N ratio in the averaged ERPs. That is, a decline in the S/N ratio of the EEG recordings will necessitate an increase in the number of trials tested in each This study was made possible by a grant to S.J.L. from the National Institute of Mental Health (R01 MH076226) and by a graduate research fellowship to E.S.K. from the National Science Foundation. We would like to thank Coen Metting van Rijn, Lloyd Smith, Ingmar Gutberlet, Phil Holcomb, and an anonymous reviewer for comments on this manuscript. Address reprint requests to: Emily S. Kappenman, UC-Davis Center for Mind & Brain, 267 Cousteau Place, Davis, CA 95618. E-mail:
[email protected] 888
Electrode impedance in ERP recordings recorded the resting EEG and assessed the power in different frequency bands as a function of impedance. The goal of the present study was to provide a quantitative evaluation of the effects of electrode impedance on the S/N ratio of ERP recordings by an independent laboratory with no significant ties to manufacturers of EEG recording equipment.1 In particular, the study was designed to determine whether more trials must be averaged together to obtain a significant experimental effect with high electrode impedances than with low electrode impedances. To provide a direct test of the effects of electrode impedance, unconfounded by other factors, we compared high and low electrode impedances within a single recording system rather than comparing a high-impedance system with a low-impedance system (as in the study of Johnson et al., 2001). A comparison across different systems would confound factors other than electrode impedance that vary across systems (e.g., amplifier noise levels, electrode composition, shielding effectiveness, analog-to-digital converter precision, etc.). Thus, we addressed the general issue of the effects of electrode impedance on S/N ratio rather than comparing the performance of specific commercial EEG recording systems. This is an important distinction, because the finding of a decreased S/N ratio when the electrode impedance is higher does not mean that the S/N ratio of a given high-impedance system will be lower than the S/N ratio of a completely different low-impedance system. There are simply too many other factors that differ across systems to draw such a conclusion. However, the finding of a large impact of electrode impedance on data quality would have important implications for the procedures used to collect data with a given EEG recording system. Before describing the study, we will define the term impedance and consider the reasons why high electrode impedance might or might not be expected to yield a lower S/N ratio. What is Impedance? Impedance is opposition to alternating current (AC) flow, and it has two components, resistance and reactance. Resistance by itself is opposition to direct current (DC) flow, and in the context of impedance is a frequency-independent opposition to AC current flow. A volume control on a radio, for example, is typically a device that creates a variable resistance. Reactance is a combination of capacitance and inductance, which oppose AC current
1
When evaluating studies like this, it is important to consider whether the researchers have any potential conflicts of interest that might lead to intentional or unintentional bias in the design, analysis, or presentation of the study. We would therefore like to make the following disclosures. Our laboratory uses high-impedance EEG recording systems manufactured by BioSemi but has not received any free or discounted equipment or any other financial considerations from BioSemi or from any other manufacturers. We lead a yearly summer workshop on ERP methods (the ERP Boot Camp), which has received a donation of a few electrode caps and a small amount of money from Cortech Solutions, the U.S. distributor for Biosemi. The ERP Boot Camp has also received modest financial support from several other vendors of ERP recording and analysis systems, including Brain Products GmbH, EasyCap GmbH, and Advanced Neuro Technologies, and software has been provided for the ERP Boot Camp by Compumedics Neuroscan, Megis GmbH, and Brain Products GmbH. These donations have been modest, have been used to support the ERP Boot Camp rather than our laboratory, and have come from vendors of both low- and high-impedance recording systems. Consequently, we believe that the research reported here was not significantly biased by financial, scientific, or personal ties to manufacturers or distributors of EEG systems.
889 flow in a manner that depends on the frequency content of the AC current. Because the EEG contains a strong AC signal, ERP researchers measure impedance rather than merely resistance. In the context of EEG recordings, impedance is typically measured by passing a small 10-Hz current between two or more electrodes and measuring the opposition to the flow of this current (for more details, see Chapter 3 in Luck, 2005). The goal is to measure the impedance between the electrode and the highly conductive living skin tissue that immediately overlies the skull (i.e., the electrode impedance). The living skin tissue is covered by a layer of dead skin cells, and these dead skin cells provide a relatively high-impedance interface between the electrode2 and the living skin tissue. When impedance is measured between two electrodes, the measured value reflects the impedance of everything between the two electrodes, which includes the impedance between each electrode and the living skin tissue lying right beneath it (with a small contribution from the impedance of the living skin between the electrodes). However, it is possible to estimate the impedance between each individual electrode and the underlying living skin (see Method section below and Chapter 3 in Luck, 2005). High Impedance and Common Mode Rejection We will now consider why the electrode impedance might be expected to impact the S/N ratio of EEG recordings. Two key issues are commonly raised in this context, namely, common mode rejection and cephalic skin potentials. Common mode rejection refers to the ability of a recording system to reject noise that is in common to the active and reference electrodes. That is, any noise sources that are identical in the active and reference recording electrodes are attenuated in a differential amplifier, because the output of the amplifier subtracts the voltage measured at the reference electrode from the voltage measured at the active electrode.3 This primarily eliminates noise induced by electrical devices in the recording environment (e.g., lights, video displays, wiring, etc.) rather than biological noise generated by the subject (e.g., muscle activity, eye blinks, etc.). As the common mode rejection of an amplifier increases, the contribution of noise signals decreases and the S/N ratio increases. To eliminate noise that is in common to the active and reference sites, the signals arising from these sites must be treated equivalently. The ability of an EEG amplifier to accomplish this depends, in part, on the ratio of the electrode impedance to the amplifier’s input impedance. The amplifier’s input impedance is its tendency to oppose the flow of current from the electrodes through the amplifier, and it is determined by the electronics used in the amplifier. The amplifier’s input impedance is a fixed value, typically in the Megohm range or higher. The electrode impedance, in contrast, is determined by the properties of the skin, which can vary considerably across individuals, across electrode sites, and across time. As the electrode impedance increases, the common mode rejection of the system decreases, and the S/N ratio of the recording decreases. This is primarily due to the fact that random differences between 2 The electrode does not typically directly contact the skin. Instead, a conductive gel or liquid typically provides the electrical connection between the electrode and the skin. We will use the term electrode to refer to the combination of the electrode and the conductive gel or liquid. 3 Some EEG recording systems now use single-ended amplifiers rather than differential amplifiers, meaning that they digitize the active and reference signals separately. The difference between active and reference can then be computed offline. However, the same issues apply to these systems as to differential amplification systems.
890 the impedances at the different electrode sites are typically magnified when the impedances are increased (Ferree et al., 2001). Thus, all else being equal, more trials will be needed to achieve a given level of statistical significance with high electrode impedances than with low electrode impedances. With traditional low-impedance EEG recording systems, this problem is typically solved by cleansing and abrading the skin. Abrasion of the skin reduces impedance by disrupting the external layer of dead skin cells, providing a more direct contact with the underlying living skin tissue. Oils on the surface of the skin may also play a role in impedance, and cleansing the skin may reduce the contribution of these oils. When large numbers of electrodes are used, a significant amount of time is required to reduce the impedance at each electrode site. In addition, abrasion of the skin makes it possible for blood-borne pathogens to be transferred from the subject to the electrodes and vice versa, and this could potentially lead to the transfer of illnesses such as hepatitis and AIDS from one subject to the next. Disinfection of the electrodes between subjects can reduce the possibility of disease transmission, but it is not practical to completely sterilize the electrodes and thereby completely eliminate the possibility of disease transmission. In addition, abrasion of the skin can leave a red mark or even a scab, which is particularly problematic for studies of infants and young children. For these reasons, many investigators would like an alternative to low-impedance recordings. To deal with the problem of decreased common mode rejection with high electrode impedances, it is possible to use amplifiers with a higher input impedance, thus yielding the same ratio of electrode impedance to input impedance that would be obtained in a traditional low-impedance system. In addition, highimpedance recording systems often include features that reduce sensitivity to induced noise from electrical devices near the subject, such as preamplifiers built into the electrodes (active electrodes) and shielding of the electrode cables (see review by Metting van Rijn et al., 1990). It should be noted that most of the induced electrical noise in most ERP experiments arises from AC devices near the subject or electrode cables (called line noise). This noise has a frequency of 50 or 60 Hz, depending on the nature of a country’s electrical system (e.g., 50 Hz in Europe, 60 Hz in North America). Consequently, when high electrode impedances lead to reduced common mode rejection, any increase in the noise level is typically largest at the 50- or 60-Hz line frequency. High Impedance and Cephalic Skin Potentials High electrode impedances may lead to a second problem that cannot be solved by means of changes to the amplifier’s input impedance, namely, an increase in skin potential artifacts. Skin potentials arise because of the standing electrical potential that is normally present between the inside and the outside of the skin (Edelberg, 1972). The magnitude of this potential depends on the conductance of the skin, which in turn depends on factors such as the thickness of the skin, the number of sweat glands and hair follicles, and the degree of skin hydration (Fowles, 1971; Tregear, 1966). When the voltage is recorded between two electrodes on the surface of the skin, any differences in the conductance of the skin under these two electrodes will lead to a different voltage offset for each electrode, which creates an electrical potential between the two electrodes. This potential will vary over time if the conductance of the skin under one electrode varies over time in a different manner than the conductance of the skin under the other electrode.
E. S. Kappenman & S. J. Luck Sweat glands form a variable-resistance bridge between the inside and the outside of the skin and play an important role in these changes in skin potential over time (see review by Fowles, 1986). When the air temperature is in the low-to-normal range and stress levels are low, the individual’s sweat glands will contain relatively little sweat, and they will not serve as good conductors. Under these conditions, the impedance between the outside and the inside of the skin will ordinarily be high (because conductance is inversely related to impedance). If the individual’s body temperature or stress level increases, sweat will begin to fill the sweat glands, and this will increase the conductance and thereby decrease the impedance, even if no sweat is actually excreted from the sweat gland onto the surface of the skin. As the impedance between the inside and the outside of the skin changes, the electrical potential also changes, creating a very large artifact (often several hundred microvolts). Thus, changes in temperature or psychological state may cause changes in the potential recorded between the scalp electrodes. Skin potentials usually consist of slow shifts in voltage over a period of many seconds. They are especially pronounced in certain body parts, such as the palms of the hands, and they also occur across the surface of the head (where they are termed cephalic skin potentials). Skin potentials become much more prominent under warm and humid recording conditions, where they provide a significant source of noise, distorting the amplitudes of relatively slow ERP components such as the P3 wave and the contingent negative variation (Corby, Roth, & Kopell, 1974; Picton & Hillyard, 1972). They also distort the baseline and therefore add noise to the measurement of faster ERP components as well. There are two main ways to decrease skin potential artifacts in EEG recordings. First, one can reduce the occurrence of changes in the sweat level within the sweat glands. For example, keeping the recording environment consistently cool and dry will reduce the occurrence of skin potentials. Second, one can reduce the size of the voltage change measured at the recording electrodes when the sweat level changes. Abrasion of the skin under the electrode will have this effect, because it creates a lowimpedance voltage shunt between the living skin tissue and the electrode. That is, because electricity tends to follow the path of least resistance, a low-impedance connection between the outside and the inside of the skin in one location will minimize the voltage change produced by a change in the impedance at a nearby location. Picton and Hillyard (1972) note that puncturing the skin with a needle at the recording site is the most effective means of eliminating skin potentials; gentle abrasion of the skin also appears to be quite effective at minimizing skin potentials and can be accomplished with little or no discomfort (see Chapter 3 in Luck, 2005).
The Present Study Unlike the problem of common mode rejection, the problem of increases in low-frequency noise due to skin potentials is not as easy to solve by means of changes to the circuitry of an EEG recording system.4 Thus, it is possible that the use of high 4 Some EEG recording systems have a very large input range, which indirectly helps to solve one consequence of skin potentials, namely saturation of the amplifier that can occur when a skin potential brings the voltage out of the amplifier’s operating range. However, the use of a large input range does not address the primary consequence of skin potentials, namely large voltage shifts.
Electrode impedance in ERP recordings electrode impedances will result in an increase in low-frequency noise even in recording systems that are designed to tolerate these high impedances, and that this will decrease the S/N ratio and increase the number of trials needed to obtain a statistically significant experimental effect. This problem would be expected to be more severe under warm recording conditions, especially when the humidity is high, because these conditions increase the incidence and magnitude of skin potentials. Thus, the present study was designed to quantify the S/N ratio and the number of trials required to obtain a statistically significant experimental effect in low- versus high-impedance recordings, using both cool and dry recording conditions and warm and humid recording conditions. It should be noted that the comparison of low and high electrode impedances by Ferree et al. (2001) did not examine frequencies below 1 Hz and did not report the temperature and humidity of the recording environment, so it does not provide any information about the potential problem of skin potential artifacts. High electrode impedances may also lead to an increase in 50or 60-Hz line noise. Our laboratory is well shielded to reduce sources of line noise, and so we were unable to accurately assess the effects of electrode impedance on line noise. This limitation will be addressed further in the Discussion. To make the present experiment relevant for a large number of ERP researchers, we used the most common ERP paradigm, the oddball task. The stimulus sequences consisted of 20% target stimuli and 80% standard stimuli. Our main analyses were focused on the P3 wave, which was expected to be larger for the targets than for the standards. We used a high-impedance recording system, but we abraded the skin under the electrodes in one hemisphere so that we could obtain low-impedance recordings from that hemisphere simultaneously with high-impedance recordings from the other hemisphere. Thus, one hemisphere served as the control for the other hemisphere, minimizing the contribution of global state factors and individual differences to the results. In addition, we kept the recording environment cool and dry for half of each recording session and warm and humid for the other half,5 allowing us to assess interactions between impedance and recording environment. Our main question was whether more trials would be necessary to obtain a statistically significant difference in P3 amplitude between targets and standards in the high-impedance hemisphere than in the low-impedance hemisphere. We predicted that the S/N ratio would be somewhat worse for the high-impedance recordings than for the low-impedance recordings under cool and dry conditions, with a further decline under warm and humid conditions. In addition, we predicted that these changes in S/N ratio would lead to increases in the number of trials required to obtain a statistically significant effect of probability on P3 amplitude. Because the P3 probability effect is much larger than most ERP effects, we also examined the effects of impedance on statistical significance for the somewhat subtler interaction between probability and electrode site. In addition, we also examined the N1 component to determine whether changes in impedance would also influence a smaller, earlier, shorterduration ERP component.
5 It did not seem worthwhile at this time to separately assess the effects of temperature and humidity. Cooling systems typically reduce humidity levels, so humidity and temperature will tend to covary under real-world conditions.
891 Method Participants Seventeen subjects between the ages of 18 and 30 were tested. Our laboratory always excludes any subject who has artifacts on greater than 25% of trials under our typical cool and dry testing conditions, and five subjects were eliminated for this reason in the present study. All reported analyses are from the remaining 12 subjects. Stimuli and Task The stimuli were black letters and digits, each measuring 2.5 ! 2.51 of visual angle, presented at the center of a cathode ray tube video monitor. The monitor was viewed at a distance of 70 cm and had a light gray background and a continuously visible fixation point. Each stimulus was presented for 200 ms, followed by a blank intertrial interval of 1100–1500 ms (rectangular distribution). Subjects alternated between blocks in which letters were 80% probable and digits were 20% probable, and blocks in which this was reversed. The starting point was randomized across subjects. Subjects pressed a button with the index finger of the dominant hand for one stimulus category and with the middle finger of the dominant hand for the other stimulus category; the assignment of stimuli to buttons was counterbalanced. Each block contained 160 trials, and a rest break was provided every 40 trials. For both the cool and dry condition and the warm and humid condition, each subject received 4 blocks, yielding a total of 128 target stimuli and 512 standard stimuli in each recording environment. Recording and Analysis The EEG was recorded inside an Eckel C-15A sound attenuating chamber with a radio frequency shielding package and no windows. The video monitor was enclosed within a Faraday cage (see pp. 114–115 in Luck, 2005) and powered via a shielded AC cable. Lighting was provided by strips of DC-powered light emitting diodes (LEDs; Nemalux LED Lighting, Model GSLED24-12-W, Calgary, AB, Canada). The temperature and humidity inside the recording chamber were either decreased (using fans and the building ventilation system) or increased (using a space heater and humidifiers) to achieve a cool and dry environment or a warm and humid environment. The fans, space heater, and humidifiers were turned off during the actual recordings. We measured the temperature and humidity at the beginning and end of each temperature/ humidity condition. At the start of the cool and dry testing condition, the temperature in the recording chamber was lowered to between 19.5 and 231C (67–731F), with a mean of 21.31C (70.31F) and a standard error of 0.291C. At the end of the cool and dry testing condition, the temperature in the recording chamber was between 19.8 and 22.21C (67.6–71.91F) with a mean of 21.21C (70.11F) and a standard error of 0.201C. At the start of the warm and humid testing condition, the temperature in the recording chamber was between 26.0 and 29.51C (79–851F), with a mean of 27.71C (81.81F) and a standard error of 0.291C. At the end of the warm and humid testing condition, the temperature in the recording chamber was between 25.0 and 27.61C (77–81.71F) with a mean of 26.11C (79.01F) and a standard error of 0.211C. The absolute humidity in the cool and dry testing session was a mean of 7.67 g/m3 and a standard error of 0.376 at the beginning of the session and a mean of 7.83 g/m3 and a standard error of 0.373 at the end of the session. For the warm
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Table 1. Mean Electrode Impedance (Z) as a Function of Recording Environment and Electrode Site (SEM in Parentheses) Electrode site F3/4 Session Before cool/dry block After cool/dry block Before warm/humid block After warm/humid block
C3/4
P3/4
P9/10 (reference)
Low Z
High Z
Low Z
High Z
Low Z
High Z
Low Z
High Z
CMS Low Z
DRL Low Z
3.11 (0.52) 2.51 (0.40) 2.52 (0.38) 2.63 (0.39)
56.44 (13.25) 29.43 (6.89) 32.95 (8.27) 42.68 (12.82)
2.82 (0.55) 2.43 (0.38) 2.38 (0.46) 2.41 (0.44)
68.48 (13.41) 34.2 (8.43) 33.27 (9.50) 34.5 (10.35)
3.85 (0.45) 3.03 (0.38) 2.83 (0.36) 2.90 (0.42)
88.84 (18.86) 43.83 (7.96) 43.70 (11.60) 38.12 (7.56)
2.99 (0.47) 2.65 (0.44) 4.20 (1.61) 2.78 (0.45)
81.02 (17.58) 24.72 (6.35) 34.26 (5.84) 33.53 (6.82)
3.45 (0.65) 2.90 (0.40) 2.97 (0.33) 5.76 (1.80)
2.49 (0.29) 2.03 (0.21) 2.44 (0.36) 4.25 (1.16)
and humid testing session, the absolute humidity was an average of 11.5 g/m3 with a standard error of 0.564 at the beginning of the session and an average of 9.11 g/m3 with a standard error of 0.339 at the end of the session. Given that 25.51C (781F) is a typical recommended summertime room temperature in the U.S., the average warm temperature of 27.71C (81.81F) in the warm and humid condition is well within the range of temperatures one might expect inside a small EEG recording chamber, especially in a poorly ventilated building. Moreover, the humidity level in this condition (which corresponds to 44% relative humidity) is also within a normal range for much of the world. It should be noted that warmer temperatures may make subjects drowsier and less attentive. However, because we were recording from low and high impedance electrode sites simultaneously, any differences in subject state between the cool and warm conditions would have equivalent effects on the low and high impedance sites. The order of the testing conditions was counterbalanced across subjects. Subjects completed both conditions in the same testing session, with a 1-h break between conditions to allow for the temperature and humidity of the recording chamber to be adjusted. To determine the effects of electrode impedance on data quality, we lowered electrode impedances in one hemisphere of each subject to less than 5 KO, and electrode impedances in the other hemisphere were allowed to remain at their naturally high levels (ranging from 10–190 KO). The impedances were lowered in the left hemisphere for half of the subjects and in the right hemisphere for the other half. Impedances were lowered using traditional scalp abrasion techniques and were measured using a Grass F-EZM4A impedance meter. Electrode impedances were measured before and after each half of the session, for a total of four times per subject. Table 1 provides the means and standard errors for the low- and high-impedance measurements across time points. The EEG was recorded using a Biosemi ActiveTwo EEG recording system (Biosemi B.V., Amsterdam, The Netherlands). This system has a number of features that are designed to optimize data quality, including a preamplifier within each electrode, a driven right leg circuit, very high electrical isolation, and a low bias current. In contrast to most EEG amplifiers, which amplify the difference between the active-ground voltage and the reference-ground voltage, the Biosemi ActiveTwo system amplifies and measures the single-ended voltage between each electrode site and a common mode sense (CMS) electrode. All referencing is accomplished offline. The electrodes were mounted in an elastic cap using a subset of the International 10/20 System sites (F3, C3, P3, P9, F4, C4, P4, P10), as depicted in Figure 1. Signa Gel (Parker Labs, Fair-
field, NJ) was used to create a stable electrical connection between each electrode and the scalp. The electrode offset was kept below 40 mV. The CMS electrode was located at site FC1, with a driven right leg (DRL) electrode located at site FC2. The impedance of the CMS and DRL electrodes were both lowered to less than 5 KO; any effect of the impedance at the CMS and DRL sites will equivalently affect both the low- and high-impedance sites. The single-ended signals were converted to differential signals offline, with right hemisphere electrodes referenced to electrode P10 (near the right mastoid) and left hemisphere electrodes referenced to electrode P9 (near the left mastoid). The impedance of the reference electrode for a given hemisphere was lowered if the impedances of the other electrodes in that hemisphere were lowered. The horizontal electrooculogram (EOG) was recorded from electrodes placed lateral to the external canthi and was used to measure horizontal eye movements. The vertical EOG was recorded from electrodes above and below the right eye to detect blinks. The monopolar EOG signals were converted to bipolar signals offline (left minus right for horizontal EOG and lower minus upper for vertical EOG). The EEG and EOG were lowpass filtered using a fifth order sinc filter with a half-power cutoff at 204.8 Hz and then digitized at 1024 Hz with 24 bits of resolution. Because the Biosemi ActiveTwo system uses active electrodes, in which each electrode contains a preamplifier, electrode impedances cannot be measured with the standard electrodes. Consequently, we purchased a set of passive electrodes from Biosemi and used them when we were measuring the impedances.6 Impedance is typically measured by passing a small alternating current between two or more electrodes connected to the skin. Thus, the measured impedance reflects contributions from more than a single electrode. To determine the impedance at one specific electrode, we tested impedances in sets of two electrodes and measured each electrode separately against two additional electrodes. We then computed the impedance of each individual electrode using a simple algebraic expression. Specifically, we defined A1B as the impedance measured between electrodes A and B, A1C as the impedance measured between electrodes A and C, and B1C as the impedance measured between electrodes B and C. With these three measurements, the impedance at electrode A can be computed as [(A1B)1(A1C) ! (B1C)] " 2. Monte Carlo analyses. One of our primary goals was to assess the number of trials necessary to achieve statistical significance 6 Additional testing demonstrated that the removal and reinsertion of electrodes has a negligible effect (o1% change) on impedances measured for both low- and high-impedance sites.
Electrode impedance in ERP recordings
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F3
F4
C3
P3
C4
P4 P10
P9
Figure 1. Electrode recording montage.
with low and high electrode impedances under cool and dry conditions and under warm and humid conditions. To achieve this goal, we performed Monte Carlo analyses, in which we simulated experiments with varying numbers of trials by sampling random subsets of the large number of trials that were recorded from each subject. We then determined whether a given simulated experiment yielded a significant difference in P3 amplitude between the target and standard stimulus categories using a conventional paired t-test. To obtain a robust estimate of the likelihood of achieving statistical significance with a given number of trials, we simulated 1000 experiments for a given number of trials by sampling different random subsets of trials for each simulated experiment. This allowed us to estimate the statistical power (e.g., the probability of attaining po.05) for a given number of trials. This was done separately for the four combinations of electrode impedance (low versus high) and recording condition (cool and dry versus warm and humid). In our P3 analyses, we simulated experiments with 10, 20, 30, 40, 50, 60, and 70 artifact-free trials (target:standard ratios of 2:8, 4:16, 6:24, 8:32, 10:40, 12:48, and 14:56 trials, preserving the 20% target and 80% standard stimulus probabilities in the experimental design). Because the data were recorded in a highly shielded environment with an EEG recording system that is designed to minimize induced electrical noise, we expected that electrode impedance would primarily impact low-frequency noise. That is, although increased electrode impedance would be expected to yield increased 60-Hz line noise, other aspects of the recording system and environment were expected to yield such a low level of line noise that any increase would be difficult to assess. To determine
whether filtering would mitigate the expected low-frequency noise and to assess the optimal filter cutoff value, we repeated the simulation after applying a high-pass filter with a half-amplitude cutoff value of 0.01, 0.05, 0.10, 0.50, or 1.0 Hz (noncausal Butterworth impulse response function, ! 24 dB/octave). The DC offset was removed before filtering by subtracting the mean voltage across the entire trial block. No low-pass filtering was applied. In total, we simulated 140,000 experiments, 1000 for each combination of electrode impedance, recording temperature, number of trials, and filter setting. The EEG data from each trial were sorted according to whether the stimulus was in the target category or in the standard category, and epochs were extracted from ! 1000 to 2000 ms. Each epoch was then baseline-corrected by subtracting the mean voltage from the 400 ms prior to stimulus onset. This is necessary because the EEG voltage fluctuations ride on top of a relatively large, slowly changing voltage offset, which would add enormous variance to the ERP measurements if not removed. Trials containing artifacts, defined as voltages exceeding " 100 mV, were marked for rejection and were excluded from all analyses described below.7 Trials with incorrect behavioral responses were also excluded. After these trials were excluded, we randomly selected the appropriate number of target and standard trials from the set of trials obtained from a given subject (e.g., 10 target trials 7 The same trials were rejected for both the low- and high-impedance recording sites and across the different filter settings. This makes it simpler to compare across sites and across filter settings, because the comparisons were based on identical trials. The raw unfiltered EEG was used to determine which trials contained artifacts.
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Figure 2. Grand average ERP waveforms for low- and high-impedance parietal electrode sites for the standard and target stimuli in the cool and warm recording environments. The waveforms were low-pass filtered with a half-amplitude cutoff of 30 Hz (noncausal Butterworth impulse response function, ! 24 dB/octave) in this and all subsequent average ERP figures.
and 40 standard trials when simulating an experiment with 50 trials). We then computed the averaged ERP waveforms from these trials. From these averaged waveforms, we measured P3 amplitude as the mean voltage between 350 and 650 ms at the P3 or P4 electrode site (depending on which hemisphere had the impedance that was currently being tested). This set of procedures was repeated for each subject, and we then performed a paired-samples t-test to compare the amplitudes of the target and standard trials. This same procedure was repeated 1000 times with different random selections of trials for each combination of electrode impedance, recording temperature, number of trials, and filter setting. All simulations were conducted in Matlab (The Mathworks, Inc., Natick, MA) using the EEGLAB Toolbox (Delorme & Makeig, 2004) and custom routines. To determine whether our results would generalize to other ERP components and other types of experimental effects, we conducted a similar set of analyses examining the scalp distribution of the N1 wave. Rather than assessing the differences between target and standard trials at a single electrode site, we assessed the differences in voltage between two electrode sites (P3 versus C3 or P4 versus C4, depending on which hemisphere had the appropriate impedance level) using only the data from the standard trials. N1 amplitude was measured as the mean voltage between 80 and 100 ms. We simulated experiments with 16, 32, 48, 64, 80, 96, and 112 artifact-free trials from each electrode site. The simulations were otherwise identical to those described for the P3 wave. Although we conducted simulations of 140 combinations of conditions for each of two ERP components, there are many other useful ways in which the data could be analyzed (e.g., different filter settings, measurement approaches, statistical analyses, etc.). We have therefore made the full data set available at http://erpinfo.org/impedance so that interested
researchers can test other signal processing and data analysis approaches.
Results Basic ERP Results Figure 2 shows the grand average ERP waveforms for the target and standard stimuli, based on all of the artifact-free trials for each subject. As would be expected on the basis of thousands of prior studies, the target stimuli elicited a greater positivity than the standard stimuli in the P3 latency range (see below for statistical analyses). The same basic pattern was observed for all four combinations of impedance and recording environment. Due to the large number of trials per condition, the noise level evident in the prestimulus baseline period was quite low, although it was clearly higher for the target stimuli than for the standard stimuli (reflecting the smaller number of target trials). As has been described previously (Johnson et al., 2001), higher impedance did not produce a substantially smaller overall signal size. However, the P2 wave (ca. 150 ms) was somewhat smaller for the high-impedance electrode sites than for the lowimpedance electrode sites for both the target and standard stimuli in both the cool and warm recording environments. This effect was primarily caused by a single subject who exhibited an extremely large hemispheric asymmetry in the P2 wave, and this effect was not statistically significant. Specifically, an analysis of variance (ANOVA) on the mean amplitude from 125–175 ms with factors of probability, impedance level, and recording environment yielded a significant main effect of probability (F(1,11) 5 7.09, p 5 .022), with no significant main effects of impedance (p 5 .650) or recording environment (p 5 .254) and no significant interactions between probability and impedance
Electrode impedance in ERP recordings (p 5 .203), probability and recording environment (p 5 .155), impedance and recording environment (p 5 .946), or the threeway interaction between probability, recording environment, and impedance (p 5 .495). Similarly, an ANOVA on the mean amplitude from 350–650 ms yielded a significant main effect of probability (F(1,11) 5 50.69, po.001). The main effects of impedance (p 5 .627) and recording environment (p 5 .385), and the interactions between probability and impedance (p 5 .189), probability and recording environment (p 5 .979), impedance and recording environment (p 5 .759), and the three-way interaction between probability, recording environment, and impedance (p 5 .920) all failed to reach significance. An analysis of the mean amplitude from 650–1000 also yielded a significant main effect of probability (F(1,11) 5 15.75, p 5 .002), with no significant main effects of impedance (p 5 .161) or recording environment (p 5 .639) and no significant interactions between probability and impedance (p 5 .788), probability and recording environment (p 5 .944), impedance and recording environment (p 5 .720), and no significant three-way interaction between probability, impedance, and recording environment (p 5 .688). These results indicate that the use of high electrode impedances does not produce a global attenuation of the ERP signal. EEG Quality To determine whether there was an increase in low-frequency voltage fluctuations in the high-impedance conditions, as would be expected from an increase in skin potentials, we visually inspected the data on a time scale that displayed the EEG over an entire trial block (approximately 5 min). Figure 3 shows the EEG from the low- and high-impedance electrode sites in a representative subject, recorded during a warm temperature, high humidity block and a cool temperature, low humidity block. Very slow voltage drifts can be seen across the 5-min recording period for all four combinations of impedance and recording environment. These very slow drifts did not appear to vary systematically across conditions when the data from all subjects were inspected, and they were much slower and larger than the skin potentials typically caused by changes in the level of sweat in the sweat glands. Instead, they likely reflect slower processes, such as changes in skin hydration (Fowles & Venables, 1970). Faster changes in voltage can be seen in the EEG from the high-impedance electrode in the warm and humid condition. These voltage fluctuations occurred over periods of tens of seconds, which is typical for skin potentials (Edelberg, 1972). They often consisted of quite large and relatively sudden changes in voltage (see arrows in Figure 3). As in the individual subject shown in Figure 3, almost all of the subjects exhibited more pronounced voltage fluctuations of this nature in the high-impedance recordings than in the low-impedance recordings, especially in the warm and humid recording condition. Quantitative support for these observations will be provided in the following sections. The waveforms in Figure 3 also show a number of small, sharp, spike-like potentials. Closer inspection of the data revealed that these were eyeblink artifacts (as indicated by the scalp distribution and opposite polarity above versus below the eye). Frequency Analysis To quantify these increases in low-frequency voltage fluctuations in the high-impedance conditions, we performed a Fast Fourier Transform (FFT) on the data from the standard stimuli to assess
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Figure 3. Raw EEG data from a representative subject for the first block of testing for both low- and high-impedance parietal electrode sites in each of the recording environments. Data were downsampled to 10 Hz to illustrate the low-frequency voltage fluctuations over time. The sharp downward spikes are eyeblink artifacts. Note that the data from the highand low-impedance sites were collected simultaneously, whereas the data from the warm and cool recording environments were collected in separate trial blocks.
the amplitude density in each frequency band. The raw EEG was epoched with a window of ! 500 ms to 1500 ms relative to stimulus onset and baseline corrected by subtracting the mean prestimulus voltage from the entire epoch to remove the DC offset. Epochs containing eyeblink artifacts were excluded. FFTs were computed on the remaining epochs, averaged across trials for each subject, and then averaged across subjects. The resulting grand average FFTs for the standards8 are shown in Figure 4A, using a log scale for the x-axis to enhance the visibility of the low frequencies. The amplitude density was similar across conditions for the higher frequencies (10–100 Hz) but differed in the lower frequencies. For the low frequencies, the amplitude density was lowest in the low-impedance conditions (with no effect of temperature), intermediate in the high impedance, cool temperature condition, and largest in the high impedance, warm temperature condition. A 3-way ANOVA with factors of impedance, recording environment, and frequency range (binned into fifty 2-Hz wide bins from 0–100 Hz) yielded significant main effects of impedance
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RMS Noise Level in the EEG and ERP Data To provide an additional measure of the overall noise level in the EEG recordings, we measured the root mean square (RMS) voltage on the same baseline-corrected, artifact-free epochs used for the FFT analyses. RMS voltage provides an overall measure of the magnitude of the signal being recorded, irrespective of frequency. Because the ERP signal was approximately the same across impedance levels and recording environments (see Figure 2), any differences in the single-trial EEG magnitude must reflect differences in the noise level. Thus, a larger RMS voltage is an indication of larger noise. Baseline correction was necessary prior to computation of RMS voltage to eliminate the contribution of the DC offset, which does not influence the S/N ratio of the averaged ERPs (because the averaged ERPs are computed from baseline-corrected EEG epochs). Figure 5A shows that the RMS amplitude was higher for the high-impedance recordings than for the low-impedance recordings, and this was confirmed by a 2-way ANOVA with factors of impedance level and recording environment, which yielded a significant main effect of impedance level (F(1,11) 5 19.55, p 5 .001). Although the RMS voltage at the high-impedance electrode was slightly larger for the warm and humid condition
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(F(1,11) 5 9.02, p 5 .012) and frequency (F(49,539) 5 34.21, po.001). In addition, there was a significant two-way interaction between impedance and frequency (F(49,539) 5 6.70, p 5 .025). It should be noted that the amplitude density in the 60-Hz line frequency band was quite low in all conditions (between 0.96 and 1.10 mV, which was only slightly higher than the amplitude density in surrounding frequency bands). This suggests that high electrode impedances did not lead to a meaningful increase in induced activity from environmental electrical sources, such as lights and video monitors (i.e., there was no clear reduction in common mode rejection). However, the low level of 60-Hz noise is probably also the result of the specific EEG recording system and the use of extensive shielding. High electrode impedances may yield meaningful increases in line noise in other recording systems and in poorly shielded recording environments. ERP researchers often reject epochs with large voltage excursions, such as those marked by the arrows in Figure 3. We therefore asked whether the differences in low-frequency noise would remain if we excluded epochs with voltages that exceeded ! 100 mV in the EEG from the P3 and P4 sites (after referencing and baseline correction). The resulting FFTs are shown in Figure 4B. Although rejecting epochs with large voltage excursions did indeed reduce the low-frequency activity in the warm temperature, high impedance condition, the low-frequency activity remained substantially higher in the high-impedance electrode sites compared to the low-impedance sites. A 3-way ANOVA with factors of impedance, recording environment, and frequency range (binned into fifty 2-Hz wide bins from 0–100 Hz) yielded significant main effects of impedance (F(1,11) 5 19.95, p 5 .001) and frequency (F(49,539) 5 104.37, po.001). In addition, there was a significant two-way interaction between impedance and frequency (F(49,539) 5 37.10, po.001). Thus, removal of epochs with large voltage excursions can greatly reduce low-frequency noise in high-impedance recordings in a warm environment, but it does not reduce this noise to the level obtained in low-impedance recordings. Because this type of artifact rejection is common practice in ERP research, all subsequent analyses were performed only on the artifact-free epochs (i.e., the epochs that survived artifact rejection).
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Figure 4. Amplitude density as a function of frequency, derived from Fast Fourier Transforms (FFTs) from the parietal electrode sites for the standard trials as a function of recording environment and impedance. The X-axis plots frequency on a log scale. Note that the low-impedance cool and warm recording environment values overlap almost completely. Panel A shows the FFT based on all epochs except those with eyeblink artifacts. Panel B shows the FFT after also excluding epochs with large voltage excursions in the parietal channels.
than for the cool and dry condition, the main effect of recording environment and the interaction between impedance level and recording environment did not reach significance (p 5 .832 and .552, respectively). These results indicate that the voltage had greater variability over time at the high-impedance sites than at the low-impedance sites, which presumably reflects a higher noise level. To assess whether these differences in noise could be detected after averaging, we also measured the RMS voltage during the 400-ms prestimulus period in the averaged ERP waveforms from each subject (all artifact-free trials were included in these averages). This analysis was conducted because the S/N ratio of the averaged ERP waveform is more relevant than the S/N ratio of the EEG for most ERP studies. This analysis was limited to the prestimulus interval, in which any deviation from zero should entirely reflect noise, so that the RMS amplitude values would not be influenced by the actual ERP signals. In general, the noise
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Monte Carlo Simulations of Statistical Significance for the Effect of Probability on P3 Amplitude To ascertain whether the increased low-frequency noise and greater voltage variability observed at the high-impedance electrode sites affects the ability to detect a significant experimental effect, we conducted Monte Carlo simulations to determine how many trials were necessary to find a significant target-versusstandard mean amplitude difference, as described in the Method section. Figure 6A shows the probability of obtaining a significant effect as a function of the number of trials with the unfiltered EEG data. The low-impedance conditions required the smallest number of trials to detect a significant experimental effect and exhibited no effect of recording environment; the high impedance conditions required substantially more trials to achieve a given likelihood of statistical significance, with somewhat more trials required under warm and humid conditions than under cool and dry conditions.
Figure 6A also indicates the number of trials required to obtain an 80% likelihood of having a significant effect (i.e., a statistical power of .80). The 80% point was reached for the two low-impedance conditions in approximately 12 trials; however, almost three times as many trials (approximately 35) were
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level of the prestimulus baseline period provides a useful metric of the overall noise level of the data. Figure 5B shows that the RMS voltage was larger for the targets than for the standards, presumably reflecting the smaller number of trials in the averages for the target stimuli. This effect was confirmed in a 3-way ANOVA with factors of probability, impedance, and recording environment, which yielded a significant main effect of probability (F(1,11) 5 31.36, po.001). The RMS voltage was also larger for the high-impedance recordings than for the low-impedance recordings, leading to a significant main effect of impedance (F(1,11) 5 5.75, p 5 .035). However, none of the main effects or interactions involving recording environment reached significance. This demonstrates that the higher RMS noise level observed in the EEG for the high-impedance recordings has a significant effect on the averaged ERP waveforms. Both the RMS and FFTanalyses indicated that the noise level was higher at the high-impedance electrode sites than at the lowimpedance electrode sites, but this effect was more pronounced in the warm and humid recording environment in the FFTanalyses than in the RMS analyses. This probably reflects the fact that RMS amplitude collapses activity across all frequency bands, and the interaction with recording environment was present only in the lower frequencies. This may have diluted the effects of recording environment on the RMS amplitude measures.
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Figure 6. (A) Probability of obtaining a significant P3 amplitude difference between the standards and targets in the Monte Carlo simulations as a function of the number of trials in the simulated experiment. (B) Probability of obtaining a significant P3 probability (target versus standard) " electrode site (F3/4, C3/4, P3/4) interaction effect in the Monte Carlo simulations as a function of the number of trials in the simulated experiment.
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9 We could not objectively quantify the number of trials that contained these large voltage excursions because eyeblinks also produce large voltage excursions.
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required for the high impedance conditions. Thus, all else being equal, substantially more trials may be necessary to achieve statistical significance with high electrode impedances than with low electrode impedances, at least for some kinds of experiments. Note that these Monte Carlo simulations were performed after epochs with ocular artifacts or large voltage excursions were excluded. We also conducted this set of simulations without excluding trials with large voltage excursions in the P3 and P4 channels. The probability of obtaining a significant p-value was much lower in these simulations for the high impedance, warm temperature condition (but not for the other conditions). A close examination of the averaged ERP waveforms from these simulations indicated that, although these large voltage excursions were relatively rare, they occurred often enough to produce a very large distortion in the waveforms from 1–2 of the subjects in many of the simulated experiments. These distortions led to outlier values that prevented the target-versus-standard difference from reaching significance in a large proportion of the simulated experiments. Fortunately, it is trivial to exclude epochs containing these artifacts, and they are rare enough that this does not produce a substantial decline in the number of trials available for averaging.9 Thus, these artifacts are unlikely to be a serious problem in most ERP experiments. The FFTs shown in Figure 4 demonstrated that the main difference between the low- and high-impedance conditions lies in the low frequencies. Therefore, we examined whether highpass filters that remove the low frequency noise could improve the quality of the high-impedance data. It is important to note, however, that high-pass filters with cutoffs above 0.1 Hz will tend to attenuate the P3 wave (as described many years ago by Duncan-Johnson & Donchin, 1979) and can produce substantial distortions of the entire ERP waveform (see Chapter 5 in Luck, 2005). To illustrate this, Figure 7 shows how the averaged ERPs for the target trials varied as a function of the filter cutoff in the high impedance, warm temperature condition. There was very little effect as the filter cutoff increased from DC to 0.1 Hz. When the filter cutoff was increased beyond 0.1 Hz, however, the amplitude of the P3 wave became attenuated, ranging from approximately 9 mV for the unfiltered waveforms to approximately 4 mV with a cutoff of 1.0 Hz. In addition, the target-minusstandard amplitude difference also decreased as the filter cutoff was increased. Therefore, although high-pass filtering attenuated the low-frequency noise in the high-impedance conditions, it also attenuated the signal being measured, limiting the ability to detect a significant target-versus-standard P3 amplitude effect. Figure 8 shows how the different high-pass filter settings impacted the Monte Carlo simulation results. In the two low impedance conditions, filtering had a negligible effect on the ability to detect a significant effect. This is exactly what would be expected given the low levels of low-frequency noise in these conditions. In the high impedance, cool temperature condition, the probability of obtaining a significant effect increased as the filter cutoff was increased from 0.01 Hz through 0.5 Hz. This is as would be expected given the high level of low-frequency noise in this condition. It is important to note that, as illustrated in Figure 7, the reduction in noise achieved by high-pass filtering is also accompanied by an attenuation of P3 amplitude. As a result, the probability of obtaining a significant effect in the cool temperature,
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high impedance condition decreased at the 1.0-Hz filter setting relative to the 0.5-Hz filter setting. In the high impedance, warm temperature condition, the probability of obtaining a statistically significant effect was again highest for the 0.5-Hz filter setting and slightly lower for the 1.0-Hz setting, but the effects of the lower filter settings were less systematic (perhaps reflecting the longer time constants of these filters, which could cause the occasional large voltage excursions to spread to adjacent epochs). It is important to note that the tradeoff between reduction in noise and attenuation of signal depends on many factors that will vary across experiments. Although the 0.5-Hz filter cutoff yielded the best tradeoff for the high-impedance conditions in the present experiment, this cutoff will not necessarily be optimal in other experiments. Furthermore, as shown in Figure 7, a cutoff greater than 0.1 Hz can severely distort the waveform (note, e.g., the artifactual negative potential produced from ! 50 to 1100 ms by the 0.5-Hz and 1.0-Hz filters). Thus, although a high-pass filter with a cutoff of 0.5 Hz or higher may maximize statistical significance, researchers must be very careful when using such filters. Monte Carlo Simulations of Statistical Significance for P3 Scalp Distribution Given the large nature of the P3 probability main effect, the lowimpedance conditions reached a high likelihood of obtaining significance even with the smallest number of trials tested. To assess the effect of electrode impedance under more typical conditions, we conducted an additional set of simulations examining the more modest electrode site " probability interaction effect. Specifically, we conducted Monte Carlo simulations for electrode sites F3/4, C3/4, and P3/4 to determine how many trials were necessary to obtain a significant electrode site " probability interaction effect in a repeated measures ANOVA. These simulations were conducted with the unfiltered data, excluding epochs with artifacts. Figure 6B shows the probability of obtaining a significant interaction effect as a function of the number of trials. In general, statistical power was highest for the low-impedance recordings, independent of the recording environment, was substantially reduced for high-impedance recordings in the cool and dry recording environment, and was further reduced for
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high-impedance recordings in the warm and humid recording environment. For the two low-impedance conditions, the probability of an interaction effect increased steadily as the number of trials increased, with an 80% likelihood of a significant interaction effect achieved with between 60 and 70 trials. For the highimpedance, cool temperature condition, the probability of an interaction effect reached 50% likelihood only with the largest number of trials tested. Furthermore, for the high-impedance, warm temperature condition, only a 27% likelihood of a significant interaction effect was reached with the largest number of trials tested. Thus, high electrode impedances can have a substantial effect on statistical power for relatively subtle effects, especially when the recording environment is warm and humid. Additional analyses indicated that this general pattern of results was not influenced by high-pass filtering. The effect of high electrode impedance was magnified in the warm and humid recording environment in this analysis and in the FFTs, but not in the analyses of the main effect of probability on P3 amplitude. The reason for this is not clear. One possibility is that the main effect of probability is so large that it is less sensitive to some types of noise. It is also possible that skin potentials are more problematic at the frontal and central electrode sites that were included in the electrode site ! probability interaction analyses (perhaps because the P3 is smaller at those sites). Further research is needed to identify the specific conditions under which high electrode impedances will be particularly problematic.
Monte Carlo Simulations of Statistical Significance for the N1 Wave Because the FFT analyses indicated that the noise in the highimpedance recordings was primarily present in relatively low frequencies (o5 Hz), one might suppose that relatively shortduration components would be unaffected by the noise or that high-pass filtering might do a better job of improving the S/N ratio. However, transient ERP responses contain a broad range of frequencies; indeed, an infinitesimally brief response contains equal amounts of all frequencies. Moreover, low-frequency noise may have a large impact on the baseline voltage used in measuring a given component, and noise in the baseline will be propagated to the measurement of the component. Moreover, filtering out the lower frequency components of an ERP waveform may create artifactual voltage deflections that distort the timing and amplitude of the higher frequency components (see Chapter 5 in Luck, 2005). Thus, it is not obvious whether relatively short-duration components would be significantly degraded by low-frequency noise in the data and, if so, whether they would be significantly aided by high-pass filtering. We therefore repeated the Monte Carlo simulations on the N1 wave, determining the probability of obtaining a significant difference in amplitude between the central and parietal electrode sites for the standard stimuli as a function of the number of trials (16–112 per electrode site). Figure 9 overlays grand average waveforms from the central (C3/4) and parietal (P3/4) electrode sites, based on averages that included all of the artifact-free trials
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for each subject. The mean amplitude of the N1 was larger at the C3/4 electrode sites compared with the P3/4 sites. A 3-way ANOVA with factors of electrode site, impedance, and recording environment yielded a significant main effect of electrode site (F(1,11) 5 34.79, po.001). The main effects of impedance (p 5 .489) and recording environment (p 5 .560), and the interactions between electrode site and impedance (p 5 .182), electrode site and recording environment (p 5 .170), impedance and recording environment (p 5 .570), and the three-way interaction between electrode site, impedance, and recording environment (p 5 .409) all failed to reach significance. Figure 10 shows the simulation results for the different highpass filter settings in the four conditions. The probability of obtaining a significant difference in amplitude between the central and parietal electrode sites for a given number of trials was approximately the same in all conditions, except that more trials were needed to obtain a given probability of significance in the high impedance, warm temperature condition. The broken line in Figure 10 shows the point at which a 50% probability of a significant effect was reached (50% was used rather than 80% because the N1 effect was much smaller than the P3 effect). Approximately 50% more trials were necessary to reach this probability in the high impedance, warm temperature condition than in the other three conditions. The cutoff of the high-pass filter had little influence on the probability of obtaining a significant effect, except that the probability was reduced when the filter cutoff was increased to 1.0 Hz.10 These results demonstrate 10 The reduction in the probability of obtaining statistical significance produced by the 1.0-Hz high-pass cutoff occurred because this filter took the low-frequency energy of the P3 wave, inverted it, and spread it both forward and backward in time, so that it added variance in the N1 latency range.
that short-duration components like the N1 may be relatively unaffected by low-frequency noise in high impedance recordings, especially when the recording environment is cool and dry. Note, however, that a much larger reduction in the probability of statistical significance was obtained when we did not first exclude epochs containing large voltage deflections in the C3/4 and P3/4 sites. Thus, it is essential that these artifacts are removed in highimpedance recordings.
Discussion By recording ERPs with low- and high-impedance electrodes simultaneously in each subject, this study was able to quantify the effects of impedance on data quality while holding all other factors constant. The main finding was that high electrode impedances led to a poorer S/N ratio and reduced statistical power when P3 amplitude was measured, especially under warm and humid recording conditions and especially when the electrode site ! probability interaction was examined. When N1 amplitude was measured, however, high electrode impedances produced no substantial decline in statistical power under cool and dry recording conditions and only a modest decline under warm and humid conditions. In addition, high electrode impedances produced an increase in the noise level of the EEG, which was confined primarily to relatively low frequencies (o5 Hz). Given that skin potentials consist primarily of low-frequency voltage changes and are known to be influenced by electrode impedance (Picton & Hillyard, 1972), this pattern of results suggests that the reduced data quality observed in the high-impedance recordings was caused in large part by an increased size or incidence of skin potentials. However, the effects of electrode impedance on low-
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Figure 10. Effect of high-pass filter cutoff on the probability of obtaining a significant N1 amplitude difference between the central and parietal electrode sites in the Monte Carlo simulations.
frequency noise observed here are equally important whether or not they are a result of skin potentials. The present study found no detectable impact of electrode impedance on 60-Hz line noise, but the recording environment was so well shielded that the line noise was too small to be accurately assessed. Thus, the present results do not provide conclusive evidence about the effects of electrode impedance on line noise. However, a previous study similarly found that increased electrode impedance yielded only a small and statistically insignificant increase in 60-Hz noise (Ferree et al., 2001). Moreover, moderate amounts of line noise are only a minor problem for most ERP experiments, because frequencies above 30 Hz can usually be filtered with little impact on the ERP waveform (see Chapter 5 in Luck, 2005). However, very high levels of line noise would be problematic in many ERP experiments, and even small amounts of line noise can be problematic for analyses of shortlatency components such as the auditory brainstem responses and for frequency-domain analyses that focus on the gamma band. Thus, investigators who wish to record with high electrode impedances should consider whether shielding and other noisereduction measures will be necessary to ensure that line noise does not degrade the data quality. It is important to note that the design of the present experiment rules out many alternative explanations of the results. That is, because we recorded simultaneously from low- and high-impedance electrode sites, differences in subject state or related factors cannot explain the results. For example, any additional
sweat secreted in the warm and humid condition would have impacted the electrolyte equally at the low- and high-impedance sites. Similarly, any increase in drowsiness in the warm and humid condition would have had equivalent effects on the recordings from the low- and high-impedance electrodes. The two most important metrics of data quality for most ERP researchers are the probability of reaching statistical significance for a given number of trials and the number of trials required to achieve a particular probability of achieving significance. The influence of impedance on these metrics will depend on the nature of the effect being assessed in a given experiment, and it will also depend on the nature of the signal processing operations that are applied to the data (e.g., the filter settings). For example, increases in low-frequency noise would be expected to have a larger impact on components that are dominated by relatively low frequencies; consistent with this, we found much larger effects of electrode impedance on a large, late, long-duration effect (the P3 probability effect and the P3 electrode site ! probability interaction effect) than on a small, early, short-duration effect (the N1 scalp distribution effect). In addition, we found that attenuating the low-frequency noise by increasing the half-amplitude cutoff frequency of the high-pass filter to 0.1 or 0.5 Hz greatly improved the statistical power in the high-impedance recordings when P3 mean amplitude was measured. However, further increases in the filter cutoff (up to 1.0 Hz) caused a decrease rather than an increase in statistical power. Moreover, substantial distortion of the ERP waveforms was observed with cutoffs of 0.5 Hz and
902 above, impacting both the N1 and P3 waves, and a 0.1-Hz filter may therefore be optimal in most cases. If very large amounts of low-frequency noise are presentFas may be the case for highimpedance recordings obtained in a warm recording environmentFthe probability of obtaining a statistically significant experimental effect may be increased by using a cutoff as high as 0.5 Hz. However, such severe filters distort the time course of the ERP waveforms and must be used with caution, even when relatively short-latency components such as the N1 wave are the focus of the analyses. It would be tempting to conclude from the present results that low-impedance recording systems are superior to high-impedance recording systems (when used with low and high electrode impedances, respectively). Such a conclusion would be unwarranted. The present study shows that, all else being equal, the lowfrequency noise level is lower for low electrode impedances than for high electrode impedances. However, all else is not usually equal when comparing a given low-impedance system with a given high-impedance system (or when comparing two high-impedance systems). Factors such as the use of preamplifiers in the electrodes, a stable conductive medium between the electrode and the skin, and a driven right leg circuit may have a substantial effect on data quality. Thus, recordings obtained with high electrode impedances from an optimized system may be as good as, or even better than, recordings obtained with low electrode impedances from an inferior system. Impedance is only one of several factors that influence data quality, and the impact of impedance will depend on the nature of the recording environment and the ERP measures being analyzed. However, the present data clearly indicate that researchers who are considering recording with high electrode impedances should think carefully about the impact this might have on the number of trials needed to reach statistical significance. This brings up an important issue, namely the degree to which the present results can be generalized. For example, would the same pattern of results be obtained using a different EEG recording system, a different experimental paradigm, a different set of signal processing procedures, etc.? There is no obvious reason to believe that the decline in S/N ratio observed here for high electrode impedancesFand the interaction between impedance and the temperature of the recording environmentFwould be more severe than in other recording systems, paradigms, etc. Indeed, by using an optimized recording system and a highly shielded recording environment, the present results may represent a best-case scenario for high-impedance recordings. It would be worthwhile, however, for other researchers to replicate this study under different conditions to establish the generality of the findings. In addition, it would be worthwhile for other investigators to apply different signal processing techniques to the data from the present experiment (available at http://erpinfo.org/ impedance) to see if the reduction in statistical power resulting from high electrode impedances can be mitigated by means of offline data processing. It is important to note that the difference in statistical power for low- versus high-impedance recordings was relatively modest under the cool and dry recording conditions for the N1 wave. For researchers who can maintain a cool and dry recording environment and who focus on relatively fast components, the benefits of high electrode impedances (reduced electrode application time, reduced subject discomfort, and reduced probability of disease transmission) may outweigh this reduction in statistical power. However, the decrease in statistical power was quite substantial
E. S. Kappenman & S. J. Luck for the P3 wave, even under cool and dry recording conditions. For the main effect of probability, achieving 80% power required 2–3 times as many trials for high-impedance recordings compared to low-impedance recordings (see Figure 6A). Similarly, the probability of obtaining a statistically significant probability ! electrode site interaction for a given number of trials was approximately half as great for high-impedance recordings as for low-impedance recordings under cool and dry recording conditions (see Figure 6B). Thus, the advantages of high electrode impedances may be more than offset by the reduction in statistical power for researchers who are focusing on relatively slow components such as P3 and N400. Moreover, the reduction in statistical power with high electrode impedances is even larger in a warm and humid recording environment. Therefore, researchers who focus on relatively slow components and who wish to use high electrode impedances would be well advised to spend the time and effort needed to ensure that the recording environment is cool and dry. This could halve the number of trials needed to obtain statistical significance in some experiments. Unfortunately, ensuring a cool and dry recording environment is often quite difficult, because most laboratories are located in large buildings with a single, centralized air cooling system. These systems are not designed to provide substantially different levels of cooling to different rooms. When a subject and electronic devices such as a video display are placed inside a small, closed recording chamber, substantial inflow of cool air from the ventilation system may be necessary to maintain a cool chamber temperature, and it may not be possible for a given air cooling system to provide this inflow without making other parts of the building uncomfortably cool. Indeed, many ERP recording chambers are not connected to the central cooling system and rely on a small fan to bring cool air into the chamber from the surrounding room. Thus, even in an air-conditioned building that is generally kept at a comfortable temperature (e.g., 25.51C or 781F), it may be difficult to maintain a cool temperature (e.g., 211C or 701F) in the recording chamber. Although we have not systematically investigated the effects of intermediate temperatures between the 211C and 281C values tested in the present study, our informal observations suggest that data quality is highest toward the low end of this range. Substantial effort may be necessary to ensure temperatures in this range, especially in locations with a hot and humid climate during significant portions of the year. At a minimum, researchers who record with high electrode impedances should carefully track the temperature within the recording environment. Researchers who are unable to ensure consistently cool and dry recording conditions will need to choose between (a) high electrode impedances and a concomitant increase in the number of trials needed to achieve statistical significance, and (b) low electrode impedances and a concomitant increase in electrode application time and in the risk of disease transmission. For some researchers, it may simply be impractical to obtain low electrode impedances because of the need to record from many channels or because of concerns about disease transmission or subject discomfort. For other researchers, however, large numbers of electrodes may be unnecessary, which minimizes the problem of increased electrode application time when the skin must be abraded. Indeed, the use of large numbers of electrodes may make it more difficult to ensure that high-quality data are being recorded at each site, because it may be difficult to carefully monitor the incoming EEG signals from many dozens of sites. For many research questions, it may be more important to have
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high-quality data from a small number of electrodes rather than low-quality data from a large number of electrodes. In addition, the risk of disease transmission is quite low in most research settings, and it can be further reduced by adequate disinfection procedures. Thus, the choice of high versus low electrode impedances will depend on the specific needs of a researcher. Researchers who are concerned about the temperature in the recording environment may consider whether they actually need to use an electrically shielded recording chamber.11 It may be much easier to provide adequate ventilation in a moderately large room than in a much smaller recording chamber, and the present findings indicate that lowering the temperature can dramatically reduce the difference in data quality between low- and highimpedance recordings. If a chamber is not used, induced electrical noise from the environment will almost certainly increase. However, this noise can be substantially reduced by means of active electrodes, shielding of cables and video monitors, DCpowered lighting systems, etc. Indeed, many researchers record inside shielded chambers but bring sources of line noise (e.g., video monitors) inside the chamber without shielding them. Induced electrical noise may be eliminated much less expensively by shielding the AC devices than by purchasing a shielded chamber, especially if AC devices are present in the chamber (see Chapter 3 in Luck, 2005). Moreover, in most ERP studies, a modest amount of 50- or 60-Hz noise arising from the ambient electrical environment may be much easier to filter than a large amount of low-frequency noise arising from skin potentials. Exceptions to this would include studies of gamma band activity and short- and mid-latency auditory evoked responses, in which line-frequency noise would overlap extensively with the frequency content of the signal being recorded. It is also possible that the increase in low-frequency noise we obtained with high-impedance recordings under warm and humid conditions could be reduced, at least to some degree, by the use of an electrode gel with a higher salt concentration. We have not tested this possibility, and we will probably not have the opportunity to provide a systematic test. However, we would encourage researchers who are using high-impedance recording
systems in warm recording environments to provide a systematic test of different electrolytes and to publish the results so that the entire ERP community can benefit from the knowledge gained. It is also worth asking whether psychological factors (e.g., emotional responses) could trigger skin potentials following specific types of stimuli in an experiment, leading to slow voltage changes that are interpreted as being ERPs even though they arise from the skin instead of the brain. This is unlikely in most cases, because electrodermal responses typically begin more than 1000 ms after the onset of the eliciting stimulus (see, e.g., Lim, Rennie, Barry, Bahramali, Lazzaro, et al., 1997). Thus, stimuluselicited skin potentials will not have an impact on most ERP components. However, they may be problematic in studies examining very slow ERPs (e.g., the contingent negative variation). Skin potentials triggered by one stimulus may also overlap with the ERPs elicited by subsequent stimuli, influencing the prestimulus baseline period and even the post-stimulus waveform for the subsequent stimuli. These overlapping skin potentials will distort the data in the same general manner as overlapping ERP components (see Woldorff, 1993, for an extensive discussion), except that the skin potentials will be slower and last longer. Consequently, overlapping skin potentials will typically be a problem only if they differ systematically across conditions, and the overlap can be minimized by the use of an appropriate highpass filter. We would like to stress that it should be possible for manufacturers of EEG recording equipment to produce systems that are designed for use with high electrode impedances but that also make it convenient to abrade the skin and obtain low electrode impedances. This would involve providing a reasonably large access hole for each electrode so that an implement for abrading the skin could be inserted. It would also involve providing a means of conveniently measuring the impedance at each electrode. Such a system would allow researchers to use high electrode impedances for experiments in which statistical power is high or in which large numbers of electrodes are necessary, but to switch to low electrode impedances for experiments in which statistical power would otherwise be too low.
REFERENCES Corby, J. C., Roth, W. T., & Kopell, B. S. (1974). Prevalence and methods of control of the cephalic skin potential EEG artifact. Psychophysiology, 11, 350–360. Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134, 9–21. Duncan-Johnson, C., & Donchin, E. (1979). The time constant in P300 recording. Psychophysiology, 16, 53–55. Edelberg, R. (1972). Electrical activity of the skin: Its measurement and uses in psychophysiology. In N. S. Greenfield & R. A. Sternbach (Eds.), Handbook of psychophysiology (pp. 367–418). New York: Rinehart and Winston. Ferree, T. C., Luu, P., Russell, G. S., & Tucker, D. M. (2001). Scalp electrode impedance, infection risk, and EEG data quality. Clinical Neurophysiology, 112, 536–544. Fowles, D. C. (1971). The reduction of palmar skin potential by epidermal hydration. Psychophysiology, 7, 254–261.
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We thank Lloyd Smith for this suggestion.
Fowles, D. C. (1986). The eccrine system and electrodermal activity. In M. G. H. Coles, E. Donchin, & S. W. Porges (Eds.), Psychophysiology (pp. 51–96). New York: Guilford Press. Fowles, D. C., & Venables, P. H. (1970). The reduction of palmar skin potential by epidermal hydration. Psychophysiology, 7, 254–261. Johnson, M. H., de Haan, M., Oliver, A., Smith, W., Hatzakis, H., Tucker, L. A., & Csibra, G. (2001). Recording and analyzing highdensity event-related potentials with infants using the Geodesic Sensor Net. Developmental Neuropsychology, 19, 295–323. Lim, C. L., Rennie, C., Barry, R. J., Bahramali, H., Lazzaro, I., Manor, B., & Gordon, E. (1997). Decomposing skin conductance into tonic and phasic components. International Journal of Psychophysiology, 25, 97–109. Luck, S. J. (2005). An introduction to the event-related potential technique. Cambridge, MA: MIT Press. Metting van Rijn, A. C., Peper, A., & Grimbergen, C. A. (1990). High quality recording of bioelectric events. I: Interference reduction, theory and practice. Medical & Biological Engineering & Computing, 28, 389–397. Picton, T. W., Bentin, S., Berg, P., Donchin, E., Hillyard, S. A., Johnson, R. Jr., et al. (2000). Guidelines for using human event-related potentials to study cognition: Recording standards and publication criteria. Psychophysiology, 37, 127–152.
904 Picton, T. W., & Hillyard, S. A. (1972). Cephalic skin potentials in electroencephalography. Electroencephalogray and Clinical Neurophysiology, 33, 419–424. Tregear, R. T. (1966). Physical properties of the skin. London: Academic Press. Tucker, D. M. (1993). Spatial sampling of head electrical fields: The geodesic sensor net. Electroencephalography & Clinical Neurophysiology, 87, 154–163.
E. S. Kappenman & S. J. Luck Woldorff, M. (1993). Distortion of ERP averages due to overlap from temporally adjacent ERPs: Analysis and correction. Psychophysiology, 30, 98–119.
(Received July 13, 2009; Accepted November 10, 2009)
Psychophysiology, 47 (2010), 905–912. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.00987.x
Cortical dynamics of the visual change detection process
TOMOKAZU URAKAWA, KOJI INUI, KOYA YAMASHIRO, and RYUSUKE KAKIGI Department of Integrative Physiology, National Institute for Physiological Sciences, Okazaki, Japan; and Department of Physiological Sciences, The School of Life Science, The Graduate University for Advanced Studies (SOKENDAI), Hayama, Japan
Abstract In this study, the cortical dynamics of the visual change detection process were investigated using an oddball paradigm similar to that used in auditory mismatch negativity studies. When subjects watched a silent movie, color stimuli were presented using 280 dual color LEDs arranged along the frame of the video screen. Task-irrelevant red and blue color stimuli were presented randomly at a probability of 10% and 90%, respectively, in one session and vice versa for the other one, and we traced brain responses using magnetoencephalography. Results show that activation in the middle occipital gyrus (MOG) was significantly enhanced for the infrequent stimulus, while early activities in Brodmann’s area 17/18 were comparable for the frequent and infrequent stimuli. These results suggest that automatic visual change detection is associated with the MOG activity. Descriptors: Magnetoencephalography, Visual change detection
Winkler, 2002; Czigler, Balazs, & Pato, 2004; Czigler, Weisz, & Winkler, 2006; Kenemans, Johg, & Verbaten, 2003; Kimura et al., 2009; Maekawa, Goto, Kinukawa, Taniwaki, Hanbu, & Tobimatsu, 2005; Pazo-Alvarez, Amenedo, & Cadaveira, 2004a, 2004b; Tales, Newton, Troscianko, & Butler, 1999; Stagg, Hindley, Tales, & Butler, 2004; for review, see Pazo-Alvarez, Cadaveira, & Amened, 2003). However, another further significant issue, that is, what cortical network is recruited in detecting an infrequent visual change and what the response dynamics of the network are, remains to be elucidated. In previous studies using EEG (e.g., Czigler et al., 2004; Amenedo et al., 2007) and functional magnetic resonance imaging (fMRI) (Yucel, McCarthy, & Belger, 2007), it has been shown that automatic visual change detection is associated with neural activities in visual areas. However, dynamics of cortical processing relevant to visual change detection remains to be further explored regarding the timing, origin, and nature of the change-related cortical activity. In the present study, we used magnetoencephalography (MEG) that has a higher spatial resolution compared to EEG and a higher temporal resolution compared to fMRI. Although some previous MEG studies reported activation of visual areas in response to visual changes (Tanaka, Inui, Kida, & Kakigi, 2009a, 2009b), the stimulation paradigm was not an oddball paradigm and, therefore, the visual change detection process needs to be further investigated under a specific stimulation paradigm. In the present study, we employed a visual oddball paradigm similar to that used in conventional auditory studies, where subjects watched a silent movie while the infrequent and frequent stimuli were delivered. Under this paradigm, we traced neural responses to the infrequent and frequent stimuli by MEG and compared them using a multi-dipole method to obtain an insight into the source of the cortical activity as well as its temporal profile. With this approach, the present study aimed to elucidate
Automatic detection of a change in the dynamic sensory environment is a cardinal task of the brain, and understanding its neural mechanism is one of the primary goals of neuroscience. For decades, electroencephalographic (EEG) studies using an auditory stimulus have revealed human brain activities that reflect the automatic detection of changes in the regularity of sensory events (for a recent review, see Na¨a¨ta¨nen, Paavilainen, Rinne, & Alho, 2007). In these studies, brain potentials in response to an infrequent stimulus embedded in a train of frequent stimuli shifted negatively in comparison with that in response to a frequent stimulus over the fronto-temporal sites with a latency of 100 to 200 ms (Na¨a¨ta¨nen, Gaillard, & Mantysalo, 1978; Na¨a¨ta¨nen and Winkler, 1999). This negative shift is called mismatch negativity (MMN), which is quantified by subtracting the response to the frequent stimulus from that to the infrequent stimulus. The neural process underlying the generation of MMN is regarded as the automatic detection of sensory changes based on a sensory memory of the preceding stimulus: a memorycomparison-based process (Schro¨ger, 1997; Na¨a¨ta¨nen & Winkler, 1999; Jacobsen & Schro¨ger, 2001; Na¨a¨ta¨nen, Jacobsen, & Winkler, 2005). For the visual modality, the change detection process has also been investigated using various oddball paradigms and a stronger posterior negativity has been reported for infrequent stimuli (e.g., Alho, Woods, Algazi, & Na¨a¨ta¨nen, 1992; Amenedo, Pazo-Alvarez, & Cadaveira, 2007; Astikainen, Lillstrang, & Ruusuvirta, 2008; Astikainen, Ruusuvirta, Wikgren, & Korhonen, 2004; Berti & Schro¨ger, 2001; Czigler, Balazs, & We thank Mr. Takeshima for technical assistance. Address reprint requests to: T. Urakawa, Department of Integrative Physiology, National Institute for Physiological Sciences, Myodaiji, Okazaki 444-8585, Japan. E-mail:
[email protected] 905
906 the nature of spatio-temporal dynamics of cortical processing for automatic visual change detection.
Methods Subjects Eight healthy right-handed volunteers (7 males and 1 female) participated in this study. They had normal or corrected-tonormal visual acuity. Informed consent was obtained from all subjects. This experiment was approved by the Ethics Committee of the National Institute for Physiological Sciences, Okazaki, Japan. Stimulus and Task We used dual color light emitting diodes (LEDs) 3.5 mm in diameter as the stimulus. The subjects were seated in front of a screen at a viewing distance of 2 m. The LEDs were arrayed along the frame of the screen extending 8.61 (vertical) ! 11.41 (horizontal). The number of LEDs was 280. The screen as a stimulus device is illustrated in Figure 1. The LEDs were arranged along the periphery of the screen, and a movie was presented at the center of the screen. The LEDs emitted blue or red light alternatively at 2 Hz for 100 ms. The locations of the LEDs were spatially segregated from the periphery of the area on which the movie was projected. During the experiment, the subjects watched a silent video projected at the center of screen. All electric devices except for the screen were outside of the magnetically shielded room. The colors of LEDs, blue and red, were switched
Figure 1. The method of stimulus is illustrated. Some of the diodes, arranged along the screen frame are enlarged. The color of the LEDs alternates between blue and red at 2 Hz. The subjects were required to watch the movie projected at the center of the screen. The locations of the LEDs are spatially separated from the area in which the movie was projected.
T. Urakawa et al. trial by trial. The luminance was 5.25 (cd/m2) for red and 0.99 (cd/m2) for blue. In a pilot study, the two colors were felt to be equally bright by the subjects. An oddball paradigm was used as in previous studies (Czigler et al., 2002). The probability of the infrequent and the frequent stimulus was 10% and 90%, respectively. There were two consecutive experimental sessions. For every subject, blue or red was randomly selected for the infrequent stimulus and the other color was used for the frequent stimulus in the first session. In the following session, the colors were reversed. Data Acquisition and Analysis The magnetic responses were recorded with a 306-channel whole head MEG system (Vectorview; ELEKTA Neuromag, Helsinki, Finland), which had 102 identical sensor points above the head. At each sensor element, there were 3 detectors coupled to a multiSQUID (super-conducting quantum interference device), one magnetometer, and two planner-typed gradiometers oriented orthogonally to each other. We analyzed the responses detected by the 204 planner-type gradiometers as in previous studies (Tanaka, Noguchi, Kakigi, & Kaneoke, 2007; Altmann, Nakata, Noguchi, Innui, Hoshiyama et al., 2008). The signals were digitized at 997 Hz and recorded with a 0.1–200 Hz band-pass filter on line. Before the recording, four head position indicator (HPI) coils were attached to specific sites on the scalp, and the locations of these coils and three anatomical landmarks, the nasion and bilateral preauriculars, were positioned. Current was fed into the HPI coils, and the resultant magnetic fields were measured by the magnetometers to relate the head locations positioned with HPI to the MEG sensor arrays. Then, using the locations positioned by the three anatomical landmarks, marked in advance for the individual magnetic resonance (MR) images obtained with a 3-T magnetic resonance imaging system (Allegra; Siemens, Erlangen, Germany), the individual MR images were integrated into the coordinate system of the sensor array. MEG signals over 3000 fT/cm were rejected. Throughout the recording, blinking and eye movements were monitored using an infrared eye tracker (ISCAN, Inc., Cambridge, MA) at a sampling rate of 60 Hz. Epochs in which subjects blinked or the eye deflection was over 3.81 from the approximate center of the screen were automatically excluded. The epochs in both conditions were averaged from 100 ms before to 400 ms after the onset of the stimulus for at least 100 infrequent trials. The baseline was corrected using the mean of MEG signals for the prestimulus period of 100 ms. Off line, the signals were low-pass filtered at 30 Hz. The response to the infrequent stimulus was compared with that to the frequent stimulus of the same color (blue and red, respectively). Thus, characteristics such as color and luminance were identical, but the frequency differed (infrequent or frequent). First, we performed a sensor-based analysis to examine the difference in the response between the infrequent and the frequent condition. In previous studies (Tarkiainen, Helenius, & Salmelin, 2003; Bonte, Parviainen, Hytonen, & Salmelin, 2006; Tanaka et al., 2007; Altmann et al., 2008; Ayabe, Ishizu, Kojima, Urakawa, Nishitani et al., 2008), the strength of the magnetic field was obtained by summing the squared MEG signals of each gradiometer pair and then calculating the square root of the sum. To detect the spatial difference of the magnetic field between the infrequent and frequent conditions, we subtracted the magnetic response to the frequent stimulus from that to the infrequent
Cortical dynamics of visual change detection process
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stimulus at the vector level, and obtained the strength. In calculating this differential response, we used the following formula: 102 !n o0:5" 1 X ðP1ðnÞinfreq $P1ðnÞfreq Þ2 þ ðP2ðnÞinfreq $P2ðnÞfreq Þ2 102 n¼1 where n is the sensor number. P1 and P2 correspond to the recorded magnetic fields detected by the 2 planner-type gradiometers oriented orthogonally, respectively. Based on this procedure, we obtained the difference in the response between the infrequent and the frequent condition at all 102 sensors. Like in previous studies (Tarkiainen et al., 2003; Altmann et al., 2008), we averaged across locations for an initial overview of the difference in response. After the sensor-based analysis, we performed a multi-dipole brain electric source analysis (BESA; NeuroScan, McLean, VA). As in previous studies (Altmann et al., 2008; Inui & Kakigi, 2006; Inui, Wang, Tamura, Kaneoke, & Kakigi, 2004; Tanaka et al., 2008), the model adequacy was assessed based on 1) percent variance (Hari, Joutsiniemi, & Sarvas, 1988), 2) the F-ratio (ratio of reduced chi-square values before and after adding a new source) (Supek and Aine, 1993), and 3) residual waveforms, the difference between the recorded data and the model. The integral probability of the F-ratio was calculated before and after a new dipole was added to evaluate whether the fit was significantly improved. If the P value was smaller than 0.05, the new dipole was considered significant. We continued to add a source to the model until the addition of a dipole did not significantly improve the fit. The locations of the dipoles were transformed to Talairach coordinates by the BESA and Brain Voyager (QX 1.4, Maastricht, The Netherlands). To evaluate whether the differential responses obtained in the sensor-based analysis differed between colors (blue and red), a paired-t test was used as in a previous study (Nakata, Inui, Wasaka, Akatsuka, & Kakigi, 2005). To assess the dipoles locations, we used a discriminant analysis with regard to color (blue and red) and frequency (infrequent and frequent). For evaluation of the peak latency and peak amplitude of the activities, we used 3-way ANOVAs with color, frequency, and hemisphere as factors. In all statistical analyses, the significance level was set at po.05. Data were expressed as the mean & SD. Results Sensor-Based Analysis Figure 2A shows the magnetic responses to the infrequent and frequent blue stimuli in a representative subject. The first prominent response appeared at a latency of 100 to 150 ms in both conditions but was larger in amplitude for the infrequent stimulus. At a latency ranging from 200 to 300 ms, evoked responses were also stronger for the infrequent condition. In Figure 2B, the difference in activity between the infrequent and frequent responses is depicted. The differential activity showed a prominent peak at around 150 ms, and several later components, indicating that the neural activity in response to the infrequent and frequent stimuli, diverged at around 150 ms, consistent with EEG studies (Czigler et al., 2002; Czigler et al., 2004; Pazo-Alvarez et al., 2004a). Figure 2C shows the spatial distribution of the magnetic fields shown in Figure 2A at two latency points. As in our previous study (Inui and Kakigi, 2006), a characteristic quadrupole pattern was observed at around 100 ms, suggesting a pair of dipoles near the occipital pole pointing laterally. The spatial distribution
Figure 2. Magnetic responses to the blue stimulus in a representative subject. (A) Responses to the infrequent and frequent blue stimuli recorded from all 204 planner-type gradiometers are superimposed. (B) The difference between the infrequent and frequent conditions illustrated in (A). (C) Iso-contour maps of the magnetic response at 110 and 150 ms. Note that the field distribution patterns differ between the two latency points. (D) Grand averages of the difference waveforms across subjects in the blue and red conditions.
908 of the response at around 100 ms shifted antero-laterally with an increase in latency for the infrequent and the frequent condition in a similar manner. At around 150 ms, the spatial pattern of the magnetic field was consistent with a pair of dipoles in a more lateral and anterior region orienting antero-superiorly. The field strength at this latency was apparently stronger for the infrequent stimulus, which was in line with the prominent peak in the difference waveform in Figure 2B. Similar results were obtained in the remaining subjects for both color conditions. The differential response to the infrequent and frequent condition for both colors is shown in Figure 2D. Results of a paired t-test across subjects for the differential waveform showed no significant difference between the blue and the red conditions at any latency point (p4.05). Multi-Dipole Analysis After the sensor-based analysis, we performed a multi-dipole analysis using BESA to determine when and where the neural process for the infrequent and frequent stimuli started to diverge. Results for a representative subject shown in Figure 3 clearly suggested that the activity in the cuneus in both hemispheres was responsible for the quadrupole-type field distribution at 100–130 ms and that in the middle occipital gyrus (MOG) was responsible for the later two-dipolar pattern at around 150 ms. As shown in Figure 3, the source of the MOG activity was clearly stronger for the infrequent condition while that of the cuneus activity was similar between the two conditions. The mean peak latency, peak amplitude, and location of each cortical activity are listed in Table 1. The source of both dipoles did not differ significantly between the two color conditions and between the two frequency conditions for either hemisphere (discriminant analysis, Figure 4). Figure 5 shows the time course of each cortical activity of all the subjects (thin lines) and the grand-averaged waveform (thick lines). As for the peak latency of each cortical activity, results of ANOVAs showed that none of the factors, frequency, color, or hemisphere, had a significant effect. As for peak amplitude, neither color nor hemisphere was a significant factor for the activity in the cuneus or MOG. However, the peak amplitude of the MOG activity differed significantly between the infrequent and frequent conditions (F(1,30) 5 20.7, po.001, 5 0.35). By contrast, frequency was not a significant factor determining the
T. Urakawa et al. peak amplitude of the cuneus activity (F(1,30) 5 0.5, p 5 .48, 5 0.014). In some subjects, sources were estimated to be located in cortical areas other than the cuneus or MOG, including the inferior frontal gyrus (IFG) (N 5 3) and precuneus (N 5 4). A source in the right IFG was active after the cuneus and MOG activities in the infrequent condition exclusively. The latency of the precuneus response was comparable to that of the MOG response, and the response’s amplitude was enhanced in the infrequent condition compared to the frequent condition. The IFG activity was located in the right hemisphere only in the infrequent condition. Among the subjects showing the precuneus activity, the source was estimated only in the infrequent condition in two subjects. For the subject with the precuneus response to both the infrequent and frequent conditions, the source was located in the right hemisphere and stronger for the infrequent condition (1.5 vs 8.9 nAm). Discussion In the present study, we tried to trace the neural response to the infrequent and frequent stimulus using MEG. The dipole analysis showed that the activity in the cuneus appeared at around 100 ms and was followed by activity in the MOG, peaking at around 150 ms. The MOG activity was stronger in the infrequent condition than frequent condition, while this was not the case for the activity in the cuneus, implying that the MOG plays an important role in automatic detection of a visual change. Activation at the Cuneus and MOG The pattern of activation in the MOG conforms to the topography of the EEG response. Since the MOG dipoles of both hemispheres were orientated anteriorly (direction of the intracellular current) at around 150 ms, bilateral enhancement of the MOG activity in the infrequent condition would give rise to a negative shift of potential over the posterior electrodes compared with that in the frequent condition, which is consistent with previous studies, showing that the difference in response was due to posterior negativity. As to the cuneus, its activity overlapped with the MOG activity. Thus, there is reason to believe that it also contributes to the posterior negativity. However, the amplitude of the cuneus activity peaking at 100–130 ms was comparable between the infrequent and frequent conditions,
Figure 3. Results of the multi-dipole analysis in a representative subject. Locations of dipoles overlaid on the subject’s own MR images and isocontour maps of the recorded data and the model at two latency points are shown for the infrequent condition (left side) and the frequent condition (right side). In the middle panel, the time course of the activity of each dipole is presented. MOG, middle occipital gyrus.
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Table 1. The Peak Latency, Peak Amplitude, and Talairach Coordinates of Estimated Sources Infrequent
Cuneus MOG Precuneus IFG
Hemi. (L/R)
N
L R L R L R R
6 6 8 6 1 3 3
Talairach coordinate
Latency (ms)
Amplitude (nAm)
x
y
z
115.1 ! 10.4 111.3 ! 11.1 144.0 ! 18.6 142.0 ! 10.9 159 177.0 ! 55.7 319.7 ! 11.0
4.3 ! 2.6 4.7 ! 3.0 10.7 ! 4.4 10.8 ! 7.4 15.4 7.5 ! 3.8 5.1 ! 9.9
" 25.4 ! 7.1 22.8 ! 5.3 " 37.3 ! 7.3 31.1 ! 7.5 " 26.2 20.4 ! 13.0 44.6 ! 8.4
" 93.3 ! 6.0 " 90.6 ! 10.0 " 73.4 ! 14.0 72.8 ! 12.4 " 57 " 67.6 ! 7.1 5.3 ! 31.1
" 2.2 ! 14.9 2.2 ! 11.0 11.8 ! 17.4 9.3 ! 10.4 36.7 41.8 ! 14.7 12.3 ! 19.6
Frequent
Cuneus MOG Precuneus
Hemi. (L/R)
N
Latency (ms)
L R L R L R
6 6 6 5 2 1
118.9 ! 12.7 114.9 ! 13.2 135.3 ! 21.8 139.7 ! 16.2 141.5 ! 21.9 144.0
Amplitude (nAm) 4.1 4.1 4.0 3.7 3.8
! 2.4 ! 2.2 ! 1.6 ! 1.5 ! 0.4 1.5
Talairach coordinate x " 25.9 ! 19.7 ! " 36.2 ! 37.2 ! " 27.5 ! 31.8
3.6 7.8 8.3 7.3 3.0
y
z
" 95.3 ! 9.4 " 90.5 ! 8.9 " 79.8 ! 6.5 " 74.8 ! 8.8 " 72.0 ! 5.2 " 74.9
" 0.3 ! 11.6 2.5 ! 10.5 11.5 ! 13.4 8.8 ! 12.0 42.2 ! 5.8 49.6
Note: N indicates the number of subjects.
indicating the main generator responsible for the posterior negativity (a differential response between the infrequent and frequent stimulus) to be located in the MOG. These results suggested that automatic visual change detection is associated
Figure 4. The mean location of the dipoles across all the subjects. The location for each condition is plotted on coronal slices of a standard brain.
with the MOG. In a previous fMRI study using a pattern of stimulation similar to an oddball paradigm, the MOG was activated when the stimulus was changed from one to another regardless of change directions (from A to B and B to A) (Downar, Crawley, Mikulis, & Davis, 2000). In addition, recent EEG and MEG studies (Tanaka et al., 2008, 2009a, 2009b) also showed the enhancement of the MOG response to changes of a visual stimulus, which seems to be, in addition, somewhat vulnerable to changes in luminance (Tanaka et al., 2009a). From these results, the MOG could be a cardinal area in the detection of a visual change in general. For the interpretation of the visual change detection process underlying the differential response between the infrequent and frequent conditions, two mechanisms, the neural refractoriness and memory-comparison processes, have been considered so far. With respect to refractoriness, activity in the cuneus was comparable between frequency conditions; thus, no change detection process seems to appear at this processing level, suggesting that the differential response is not due to a simple effect of refractoriness and/or habituation in earlier and lower areas than the cuneus. Nonetheless, at the MOG, the refractoriness effect cannot be excluded. According to the psychophysiological models of deviant information processing (Na¨a¨ta¨nen, 1990; Schro¨ger, 1997), the mismatch signals should appear only when the frequent stimulus exists, and not when the frequent stimulus is absent. On the basis of this notion, one might regard the differential MOG response as a result of refractoriness because the MOG response was elicited not only by the infrequent but also frequent stimulus. To support this, in a recent EEG (electroencephalogram) study, it was reported that the differential response between frequency conditions at the latency range which overlapped that for the MOG activity of the present study was due to refractoriness (Kimura et al., 2009). On the other hand, in other studies supporting the memory-comparison viewpoint, a differential response at the latency range similar to that for the MOG response could be based on the memory-comparison pro-
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Figure 5. Comparison of the source of activity in the cuneus and middle occipital gyrus between the infrequent and frequent stimuli. Waveforms for all the subjects (thin lines) and averages (thick lines) are shown.
cess (e.g., Czigler et al., 2002). The memory-comparison process was further confirmed by a study using a backward masking paradigm (Czigler, Weisz, & Winkler, 2007), in which the latency range of the differential response was comparable to that of the MOG response. Based on these facts, it is possible that the enhanced MOG response was related to the memory-comparison process, at least in part. Taking these previous and the present results together, the differential MOG response appears to be mainly due to refractoriness, but also possibly due to the memory-comparison process to some extent. From the current results, we cannot decisively deduce the neural mechanism underlying the differential MOG response. Future investigation is needed to clarify this issue. Activation in Other Areas In addition to the cuneus and MOG, the precuneus was found to be active in this study. The latency of the response in the precuneus was comparable to that in the MOG, and the response’s amplitude was enhanced in the infrequent condition compared to the frequent condition, implying that the precuneus is relevant to the change detection process. The recruitment of the parietal area
for detecting changes has been less well reported. In previous visual (Yucel et al., 2007) and auditory (Molholm, Martinez, Ritter, Javitt, & Foxe, 2005) studies using fMRI, a response in the parietal area was found. Although its functional role is not clear, the precuneus might be involved in the change detection process, for example, the spatial aspect, for both auditory and visual information. However, again, whether the neural processing in the precuneus in response to the infrequent stimulus is ascribable partly to a memory-comparison system or simply to neural refractoriness remains to be elucidated. In three subjects, the right IFG was active after the activation in the cuneus and MOG exclusively in the infrequent condition. This sequential activation through an infrequent stimulus-sensitive sensory-specific area and the IFG is consistent with findings of auditory studies, in which a MMN response in the temporal area was followed by a response in the frontal cortical area (e.g., Rinne, Alho, Ilmoniemi, Virtanen, & Na¨a¨ta¨nen, 2000). Regarding the frontal activity, two mechanisms have been proposed: the attention switching mechanism (e.g., Na¨a¨ta¨nen, 1990), and the contrast enhancement mechanism (e.g., Doeller, Opitz, Mecklinger, Krick, Reith, & Schro¨ger, 2003; Opitz, Rinne, Mecklin-
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ger, von Cramon, & Schro¨ger, 2002). In the attention switching mechanism, the frontal activity reflects the involuntary orienting of attention to an infrequent stimulus change, which is detected by mismatch processing. As for the contrast enhancement mechanism, the frontal activity is regarded as the process of sharpening the incoming deviant sensory signals. In the present visual study, the latency of the IFG response was later than that of the MOG response, which is in line with previous auditory studies (Rinne et al., 2000; but see also Yago, Escera, Alho, & Giard, 2001), and is consistent with the attention switching mechanism. Nonetheless, it is still unclear whether the IFG response can be interpreted based on the contrast enhancement mechanism because the magnitude of deviation of the infrequent stimulus was not systematically manipulated, as in previous auditory studies (e.g., Opitz et al., 2002; Doeller et al., 2003). Although we cannot clarify the mechanism underlying the visual IFG activity in detail, the overall nature of the detection process through modalityspecific and multi-modal cortical areas is similar for the visual and auditory systems. In fact, the IFG is one of the multi-modal cortical areas sensitive to sensory changes in fMRI (Downar et al., 2000) and MEG (Tanaka et al., 2009b) studies.
ulus. Second, the task was not to mark and/or to identify the characteristics of the infrequent events, which contributed to the generation of SN. Third, the selection negativity had a latency of around 200 ms, appearing later than the MOG response. However, effects of the attentional leak for the infrequent stimulus cannot be excluded. In the present study, subjects watched a silent movie displayed at the center of the screen while the stimuli were presented outside of the movie image. This stimulus paradigm could induce continuous eye movement throughout the experiment, which could contribute to the difference in response between the infrequent and frequent conditions. However, we employed an oddball paradigm with a low probability (p 5 .1) for the infrequent stimulus. Thus, the subjects could not anticipate the timing of the infrequent stimulus. Under this condition, it is not likely that subjects moved their eyes more frequently during the analysis period for the infrequent stimulus. Further, we rejected trials with eye movement larger than 3.81. Therefore, it is unlikely that the eye movement was critical to the differential response at the MOG.
Effect of Subject Attentional State and Eye Movement on the Response Modulation In the present study, the subjects passively watched the video, and the stimulus was presented at the periphery of the screen. With such a task, control of the attentional state of the subject is quite weak. One possible criticism is that the modulation of the MOG response was due to an attentional-related response such as selection negativity (SN) (Harter & Previc, 1978; Eimer, 1997). Nevertheless, the magnetic response reported here was less contaminated by SN for several reasons. First, the present study used the oddball paradigm in which the timing of the infrequent stimulus could not be anticipated. Therefore, it is not as easy for a subject to concentrate on the infrequent stimulus as in attention studies, where subjects selectively attend to a cued target stim-
Conclusion In the present study, we tried to uncover the cortical dynamics relevant to the automatic detection of visual changes. Results showed that an enhanced response to the infrequent stimulus in an oddball paradigm appeared in the MOG with a latency of around 150 ms, but not for earlier activity at around 100 ms in the cuneus. For some subjects, activity in the precuneus was also enhanced in the infrequent condition, and the IFG was exclusively active in the infrequent condition. The present results suggest the significant role of these cortical areas in the automatic detection of visual changes. The issue to what extent the enhanced activity was related to a memory-comparison process needs to be examined further.
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Psychophysiology, 47 (2010), 913–920. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.00988.x
Chronic medication does not affect hyperactive error responses in obsessive-compulsive disorder
EMILY R. STERN,a YANNI LIU,b WILLIAM J. GEHRING,b JAMES J. LISTER,a GANG YIN,b,c JUN ZHANG,b KATE D. FITZGERALD,a JOSEPH A. HIMLE,a JAMES L. ABELSON,a and STEPHAN F. TAYLORa a
Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA Department of Psychology, University of Michigan, Ann Arbor, Michigan, USA c Center for Neuroinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China b
Abstract Patients with obsessive-compulsive disorder (OCD) show an increased error-related negativity (ERN), yet previous studies have not controlled for medication use, which may be important given evidence linking performance monitoring to neurotransmitter systems targeted by treatment, such as serotonin. In an examination of 19 unmedicated OCD patients, 19 medicated OCD patients, 19 medicated patient controls without OCD, and 21 unmedicated healthy controls, we found greater ERNs in OCD patients than in controls, irrespective of medication use. Severity of generalized anxiety and depression was associated with ERN amplitude in controls but not patients. These data confirm previous findings of an exaggerated error response in OCD, further showing that it cannot be attributed to medication. The absence in patients of a relationship between ERN amplitude and anxiety/depression, as was found in controls, suggests that elevated error signals in OCD may be disorder-specific. Descriptors: Error-related negativity (ERN), Event-related potentials (ERPs), Anxiety, Anterior cingulate cortex (ACC)
et al., 2001; but see Nieuwenhuis, Nielen, Mol, Hajcak, & Veltman, 2005 for alternate finding) and undiagnosed subjects with high ratings of OC symptoms (Hajcak & Simons, 2002). While the error signal appears to be increased in OCD, previous studies have not always controlled for other factors that may influence the ERN. In particular, medications such as serotonin reuptake inhibitors (SRIs) are commonly used to treat patients with OCD, and many studies of error processing in OCD have tested groups where some or all patients were taking medication (Endrass et al., 2008; Gehring et al., 2000; Nieuwenhuis et al., 2005; Ursu, Stenger, Shear, Jones, & Carter, 2003). While the influence of dopamine in generating and/or modulating the ERN has perhaps received the most attention (Holroyd & Coles, 2002), evidence now also links serotonergic (Fallgatter et al., 2004) and noradrenergic systems to performance monitoring (for a review, see Jocham & Ullsperger, 2009), pointing to the need to consider medication status when investigating the ERN in psychiatric populations. A very few studies have examined unmedicated patients, but in small samples. Johannes and colleagues (2001) found increased ERNs in 10 unmedicated OCD patients compared to healthy controls, suggesting that a hyperactive ERN is not related to medication. In a direct comparison of medicated and unmedicated OCD subjects (Endrass et al., 2008), the ERN was significantly lower in patients taking medication, but it is possible that this effect was due to differences in illness severity between the groups. Given that severely ill patients are more likely to be on medication, disentangling the potential
Obsessive-compulsive disorder (OCD) is characterized by intrusive thoughts (obsessions) and/or repetitive behaviors (compulsions) that are often associated with intense doubt regarding the correctness of an act or excessive fear about the likelihood of a bad outcome. Proposals that symptoms are related to an overactive error detection mechanism that continually signals that ‘‘something is wrong’’ (Pitman, 1987; Schwartz, 1997) have been supported by research identifying alterations in the neural substrate for error processing in OCD. Errors elicit robust activation in a network of brain regions including medial frontal cortex (MFC) (Taylor, Stern, & Gehring, 2007). In particular, the errorrelated negativity (ERN, or Ne) component of the event-related brain potential (ERP), which onsets at the time of error commission (Falkenstein, Hohnsbein, Hoormann, & Blanke, 1990; Gehring, Goss, Coles, Meyer, & Donchin, 1993) and localizes to MFC (Dehaene, Posner, & Tucker, 1994; van Veen & Carter, 2002), has emerged as an index of dysfunctional error processing in OCD. Several groups have found an increased ERN in patients with clinical OCD (Endrass, Klawohn, Schuster, & Kathmann, 2008; Gehring, Himle, & Nisenson, 2000; Johannes Support for this research has been provided by the National Institute of Mental Health (NIMH) R01 MH071821 awarded to SFT and F32 MH082573 awarded to ERS. Address reprint requests to: Emily R. Stern, Ph.D., University of Michigan, 4250 Plymouth Road, 2506 Rachel Upjohn Building, Ann Arbor, MI 48106. E-mail:
[email protected] 913
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confound between medication use and illness severity remains a methodological challenge. In order to examine the effects of chronic medication on error responses in OCD, we examined ERNs in OCD patients free of psychotropic medication, OCD patients on SRIs, patient controls on SRIs but without OCD, and healthy control subjects free of psychotropic medication and psychiatric illness. Any effect of medication on the ERN that is unrelated to OCD would be expected to be found in both medicated OCD patients and patient controls, but absent in unmedicated OCD patients and healthy controls.
Methods Subjects Eighty subjects performed the task while electroencephalogram (EEG) data were acquired. However, two OCD patients were excluded due to performance on the task; the amount of errors exhibited by one patient was over 3 standard deviations from the entire group mean (4180 errors) and another made fewer than 10 errors. Due to a recent study reporting high internal reliability of the ERN with 10 errors and moderate reliability with 6 errors (Olvet & Hajcak, 2009), we also performed all analyses reported below with the inclusion of the subject who made under 10 errors, and found results to be unchanged. The final group of 78 subjects were distributed as follows: 1) 19 OCD patients who were unmedicated (uOCD), 2) 19 OCD patients taking medication (mOCD), all of whom were on at least one serotonin reuptake inhibitor (with the exception of one patient who was taking buproprion), 3) 19 medicated patient controls (mPC), all of whom were taking at least one serotonin reuptake inhibitor due to prior history of major depression (in full or partial remission, based on DSM-IV criteria), and 4) 21 healthy controls (uHC) free of psy-
chotropic medication and without current or past psychiatric diagnoses. Demographic and clinical variables of interest were examined for each group, as shown in Table 1. Unmedicated OCD and uHC groups were age- and education-matched, as were mOCD and mPC groups. Group differences in age, years of education, and scores from the Hamilton Anxiety Rating Scale (HARS) and Hamilton Depression Rating Scale (HDRS) were evaluated with separate 2 ! 2 ANOVAs using diagnosis (OCD vs. control) and medication (unmedicated vs. medicated) as between-subjects factors. Chi-square tests compared gender (all groups), the proportion of subjects with a history of depression (uOCD, mOCD, and mPC groups), the proportion in current treatment for OCD (uOCD and mOCD groups), and the proportion with a history of hospitalizations for OCD (uOCD and mOCD groups). Independent samples t-tests were used to compare uOCD and mOCD groups on Yale-Brown Obsessive-Compulsive Scale (YBOCS) scores (Goodman et al., 1989), age of onset of OCD, and illness duration. Subjects were recruited through paper advertisements posted around the local community, online advertisements through the University of Michigan Health System, and, for OCD patients, from referrals from the Anxiety Disorders Unit of the University of Michigan Depression Center/Ambulatory Psychiatry clinics. Patients in both OCD groups met DSM-IV criteria for primary diagnoses of OCD and were free of comorbid psychiatric disorders with the exception of tic disorder and/or specific phobia. Patients with hoarding as a primary symptom were excluded. OCD patients were accepted if they had current depressive disorder not otherwise specified (NOS) or had histories of major depressive disorder or dysthymia but were in partial or full remission from their depression (79% of mOCD patients and 53% of uOCD patients). While all 19 medicated OCD patients were in treatment for OCD, with 15 out of the 19 being seen in our Anxiety clinic, many unmedicated OCD patients (13/19) were recruited through advertisements and were not in any treatment
Table 1. Demographic Information uOCD (n 5 19)
mOCD (n 5 19)
mPC (n 5 19)
uHC (n 5 21)
Group differences
Post-hoc comparisons
Age
25.0 (8.0)
30.8 (9.5)
31.7 (10.6)
25.3 (7.5)
M: F(1,77) 5 9.2, p 5 .003
mOCD4uHC, uOCD mPC4uHC, uOCD all po.05
Education (years) Gender HARS
14.9 (1.8) 10 F, 9 M 8.9 (3.8)
16.1 (2.7) 10 F, 9 M 9.4 (4.8)
16.8 (2.1) 11 F, 8 M 4.4 (2.7)
16 (2.4) 10 F, 11 M 1.3 (1.6)
8.8 (2.8)
8.3 (3.6)
4.1 (2.8)
1.2 (1.4)
10/19
15/19
19/19
23.4 (3.5) 12.5 (4.6) 12.5 (8.7) 6/19
21.6 (4.2) 11.7 (5.9) 19.1 (12.3) 19/19
3/19
6/19
ns ns D: F(1,77) 5 67.1, po.001 M: F(1,77) 5 5.0, p 5 .028 D: F(1,77) 5 92.7, po.001 D ! M: F(1, 77) 5 7.9, p 5 .006 w2 (2) 5 12.2, p 5 .002 ns ns ns w2 (1) 5 19.8, po.001 ns
HDRS
History of depression YBOCS Age of onset Illness duration (years) In current treatment History of hospitalizations
mOCD4uHC, mPC uOCD4uHC, mPC mPC4uHC all po.001 mOCD4uHC, mPC uOCD4uHC, mPC mPC4uHC all po.001 mPC4uOCD p 5 .002
Note: uOCD 5 unmedicated OCD; mOCD 5 medicated OCD; mPC 5 medicated patient controls; uHC 5 unmedicated healthy controls. Values in parentheses represent standard deviations. HARS 5 Hamilton Anxiety Rating Scale; HDRS 5 Hamilton Depression Rating Scale; YBOCS 5 YaleBrown Obsessive Compulsive Scale. D 5 main effect of diagnosis factor; M 5 main effect of medication factor; D ! M 5 interaction between diagnosis and medication factors. Only those effects significant at po.05 are shown, and followed up with post-hoc comparisons using independentsamples t-tests, except history of depression, which used chi-square tests.
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Table 2. List of Medications in Medicated OCD (mOCD) and Medicated Patient Control (mPC) Groups
Medications SSRIs/SNRIs Citalopram Escitalopram Fluoxetine Fluvoxamine Paroxetine Sertraline Venlafaxine Benzodiazepines Alprazolam Clonazepam Lorazepam TCAs Clomipromine Other Buproprion Buspirone Gabapentin Methylphenidate
N in mOCD Average N in mPC Average group dosage (mg) group dosage (mg) 3 6 4 1 1 2 1
30 25 47.5 250 60 162.5 300
1 9 2
40 15 35
1 6
100 262.5
3 4 1
0.75 0.7 0.5
2 3
0.7 0.7
1
100
2
300
2 1
300 20
1
600 1
54
Note: SSRIs 5 selective-serotonin reuptake inhibitors; SNRIs 5 serotonin-norepinephrine reuptake inhibitors; TCAs 5 tricycle antidepressants. All subjects with the exception of one mOCD patient were taking a serotonin reuptake inhibitor (SSRI or SNRI). SSRIs were taken by 17/19 mOCDs and 13/19 mPCs, and SNRIs were taken by 1 mOCD and 6 mPC subjects. Ten mOCD and 8 mPC subjects were taking more than one medication. Benzodiazepines were taken as needed.
(the 6 patients who were in treatment were being seen in our clinic but were not on medication). Despite these differences, the two OCD groups were similar on many of the demographic and clinical measures we examined (see Table 1), although medicated OCD patients were older and had a trend toward longer illness duration (t(32.4) 5 1.9, p 5 .067). Subjects in the mPC group were taking very comparable medications to mOCD (see Table 2) due to a primary diagnosis of major depressive disorder that was in full remission (2 subjects with a history of a single episode and 8 with recurrent episodes) or partial remission (9 subjects with recurrent episodes). Importantly, these subjects had no history of OCD and exhibited minimal anxiety comorbidity (5 patients total: 1 with panic disorder, 1 with panic disorder and specific phobia, 1 with specific phobia, and 2 with anxiety disorder NOS), and thus provided partial control for effects related to histories of depressive episodes among OCD patients. All subjects were evaluated by a trained clinician using the Structured Clinical Interview for DSM-IV (First, Spitzer, Gibbon, & Williams, 1996). Depression and anxiety symptoms were evaluated using Hamilton Ratings Scales for Depression and Anxiety, respectively, and severity of OC symptoms in OCD patients was measured using the YBOCS. Written informed consent as approved by the institutional review board of the University of Michigan was obtained from all subjects following a complete description of the study. Task We used a modified version of a flanker task in which subjects pressed one of two buttons based on the identity of a target letter that was placed in the second, third, or fourth position in a string of 5 letters. ‘‘Low’’ interference trials were those where both
target and flankers signaled the same button press (‘‘S’’ and ‘‘K’’ lettersFleft button, ‘‘H’’ and ‘‘C’’ lettersFright button), while ‘‘high’’ interference trials elicited errors because the target and flankers designated opposing responses. Individually tailored response deadlines, set at 0.8–1.5 times the mean reaction time (RT) determined from a practice session, were used to generate commission error rates between 10–20%. Letter stimuli were presented on-screen for 300 ms, followed by a blank screen until the response deadline was achieved. Feedback was presented immediately following response in order to increase motivation to perform well and to ensure that subjects maintained their knowledge of the correct stimulus-response mappings throughout the experiment. A row of white asterisks were presented for correct responses, a row of red asterisks were presented for errors of commission, and a messageF‘‘Too Slow’’Fwas shown if responses were not within the deadline (i.e., omission error). Duration of feedback was 700–1300 ms depending on the individual subject’s RT on each trial, so that total time between stimulus presentation and end of feedback presentation was 1800 ms. Following feedback, a blank inter-trial interval (ITI) was shown for 2000 ms. The task also varied the incentive value of each trial so that an error (or correct response) could result in a loss of money (or a failure to lose), a failure to gain money (or a gain), or no change in money. Cues showing the amount of money at stake (0, 10, or 50 cents) preceded letter stimuli with durations of 1500–4500 ms and an average length of 2125 ms (in order to match a companion fMRI study). As the incentive was not found to have significant effects on the ERN, we focus the current report on group effects on the overall ERN. Money won or lost on each trial was real, and tallied to provide a bonus at the end of the experiment. A total of 480 trials (240 low and 240 high interference) were run over 10 blocks. In order to determine how subjects evaluated the task and their performance after completing the experiment, ratings to three debriefing questions were obtained on a five-point Likert scale ranging from ‘‘none/not at all’’ (1) to ‘‘always/very’’ (5): 1) ‘‘Did you make any mistakes?,’’ 2) ‘‘Were you ever frustrated with your performance?,’’ and 3) ‘‘When you made a mistake, were you flustered? Did you find it hard to get back on track?’’ Data Acquisition and Analysis Behavioral analyses examined RT on correct trials, commission error rates, and responses to debriefing questions as dependent measures in separate 2 ! 2 ANOVAs using diagnosis (OCD vs. control) and medication status (unmedicated vs. medicated) as between-subjects factors. Omission errors were excluded from all analyses. The EEG was recorded from 26 Ag/AgCl scalp electrodes (FP1, FP2, AFZ, F7, F3, FZ, F4, F8, FC3, FCZ, FC4, T7, C3, CZ, C4, T8, CP3, CPZ, CP4, P7, P3, PZ, P4, P8, O1, O2) embedded in a nylon mesh cap (Easy-Cap, Falk Minow Systems, Inc., http://www.easycap.de) using a left mastoid reference and forehead ground. Average mastoid reference was derived off-line using right mastoid data. The electro-oculogram (EOG) was recorded from Ag/AgCl electrodes above and below the left eye and external to the outer canthus of each eye. Impedances were kept below 10 KO. EEG and EOG were amplified by SYNAMPS DC amplifiers (Neuroscan Labs, Sterling, VA) and filtered on-line from .01 to 100 Hz (half-amplitude cutoffs). Data were digitized at 500 Hz and filtered with a nine-point Chebyshev II low-pass, zero-phase-shift digital filter (Matlab 7.04;
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Mathworks, Natick, MA), half-amplitude cutoff at 12 Hz. Ocular movement artifacts were corrected using the algorithm described by Gratton, Coles, and Donchin (1983). In order to correct for the fact that the response-locked ERN will actually reflect some combination of response and stimulusevoked components, we applied a method (Zhang, 1998) that allows for the unique recovery of the response-locked component based on the stimulus-locked ERP, response-locked ERP, and the time between stimulus and response on each trial (reaction time distribution). The method works as follows. We have experimentally obtained (a) stimulus-aligned ERP average waveform, denoted Fs(t); (b) response-aligned ERP average waveform, denoted Fr(t); and (c) distribution of reaction times g(t) across the trials. Suppose the stimulus-aligned and responsealigned ERP waveforms were generated by two underlying component waveforms, a stimulus-locked component (‘‘S-component’’), denoted fs(t), and a response-locked component (‘‘Rcomponent’’), denoted fr(t). By reflecting on how Fs(t) and Fr(t) were constructed and how fs(t) and fr(t) were defined, the following two mathematical equations were derived: Fs ðtÞ ¼ fs ðtÞ þ
Z
fr ðt % tÞgðtÞdt
ð1Þ
Fr ðtÞ ¼ fr ðtÞ þ
Z
fs ðt þ tÞgðtÞdt
ð2Þ
In convolution notation, they are Fs ðtÞ ¼ fs ðtÞ þ fr ðtÞ & gðtÞ
ð3Þ
Fr ðtÞ ¼ fr ðtÞ þ fs ðtÞ & gð%tÞ
ð4Þ
We can then solve these equations (3) and (4) either by Fourier transformation (in frequency domain) or by an iterative procedure (in time domain). In practice, due to discrete sampling in time domain, we can transform equation (3) and (4) into matrix notation (Yin, Zhang, Tian, & Yao, 2009), and apply singular value decomposition (SVD) method to the reaction-time distribution to deal with noise introduced by insufficient number of trials. In this paper, the time domain SVD method (same as Fourier transform method in nature) was used, by removing/ truncating small eigenvalues (of the reaction time distribution matrix) to reduce the influence of noise and improve stability. After recovery of the response-locked waveform, mean amplitude was calculated over a window of 20–120 ms post-response (identified from grand-averaged waveforms) with a baseline of 200 to 100 ms prior to response. Three-way ANOVAs with diagnosis (OCD vs. control) and medication status (unmedicated vs. medicated) as between-subjects factors and trial type (error vs. correct) as a within-subjects factor were performed separately at electrodes FZ and FCZ. Analyses were performed separately at these two electrodes because inspection of the topographical distribution of scalp activity indicated that the peak of the ERN was located at FCZ for control subjects but anterior to FCZ for OCD patients (see Figure 1). Pearson’s correlations (two-tailed) of error and correct trial amplitudes with anxiety (HARS) and depression (HDRS), and for OCD patients, YBOCS scores, were used to further interrogate effects identified
by the ANOVAs. The mean number of errors per subject contributing to the analysis was 63.94 (SD: 32.77, range: 15–150). Results Behavioral There were no significant differences between the groups for mean RT on correct trials, although there was a trend for an interaction between diagnosis and medication (F(1,74) 5 3.3, p 5 .072), with unmedicated healthy controls exhibiting the fastest RTs and medicated patient controls exhibiting the slowest RTs (uOCD: 569.2, mOCD: 566.7, mPC: 597.8, uHC: 532.7 ms). Similarly, there were no differences in the mean percentage of commission errors between groups (uOCD: 12.5%, mOCD: 14%, mPC: 12.9%, uHC: 13.5%). Analyses of responses to debriefing questions indicated no main effects or interactions of group factors on subjects’ evaluation of the amount of mistakes that were made (p4.25 for all). Interestingly, despite similar performance accuracy, OCD patients reported being significantly more frustrated with their performance (F(1,74) 5 22.1, po.001) and more flustered when making a mistake (F(1,74) 5 21.9, po.001) than control subjects. No other effects were found, with the exception of a trend toward unmedicated subjects being slightly more flustered when making a mistake than medicated subjects (F(1,74) 5 3.3, p 5 .073).
Electrophysiological As expected, there was a highly significant main effect of trial type (error vs. correct) at both electrodes (FZ: F(1,74) 5 175, po.001; FCZ: F(1,74) 5 199.9, po.001), such that amplitudes were more negative for errors as compared to correct trials. There were no main effects of group factors (diagnosis or medication status); however, there was a significant interaction between trial type and diagnosis (OCD vs. control) at electrode FZ (F(1, 74) 5 4.1, p 5 .047), indicating that OCD patients exhibited a greater ERN than control subjects ( % 4.0 vs. % 2.5 mV, t(76) 5 % 1.98, p 5 .05), with no difference in amplitude on correct trials (2.7 vs. 2.4 m V, t(76) 5 0.38, p 5 .71)1 (Figures 1 and 2). Importantly, there were no 2-way interactions between trial type and medication status or 3-way interactions involving trial type, medication status, and diagnosis (all ps40.6), indicating that the presence of chronic medication did not significantly influence the ERN. No effects of group factors were found at electrode FCZ. Among OCD patients, ERN amplitudes were not significantly correlated with YBOCS scores (r 5 .17, p 5 .32) or with generalized anxiety or depressive symptoms (HARS: r 5 .13, p 5 .43, HDRS: r 5 .10, p 5 .57) (Figure 3). By contrast, among control subjects, there was a significant correlation between ERN amplitude and HARS (r 5 % .35, p 5 .027) and HDRS (r 5 % .35, p 5 .026), such that greater symptom severity was associated with an increased ERN. As can be seen from Figure 3, these relationships were found within both uHC and mPC groups for HARS (r 5 % .49, p 5 .023 and r 5 % .41, p 5 .082, respectively) and HDRS (r 5 % .39, p 5 .081, and r 5 % .46, p 5 .050, respectively) scores. Correct trial amplitudes were not 1 When removing the one mOCD patient not taking a serotonin reuptake inhibitor (i.e., buproprion), the interaction remained significant, F(1,73) 5 7, p 5 .034.
Error response in obsessive-compulsive disorder
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Figure 1. Topographic maps of ERN amplitude. Error activity shows a medial frontal focus, with the peak of activity in OCD patients located slightly anterior to that of control subjects. Scale represents mean activity in mV between 20 and 120 ms post error response.
correlated with symptom severity measures for either OCD patients or controls. Discussion Previous studies have identified an increased ERN in patients with OCD (Endrass et al., 2008; Gehring et al., 2000; Johannes et al., 2001), yet it has been unclear whether the use of serotonin reuptake inhibitors among OCD patients may be influencing this finding given evidence linking serotonergic functioning to the ERN (Fallgatter et al., 2004; Jocham & Ullsperger, 2009). Methodologically, it is difficult to separate primary markers of a disorder from secondary phenomena related to treatment effects and comorbidity. As such, it may be particularly advantageous to compare treated and untreated patient groups matched on symptom severity, along with a treated ‘‘control’’ group with a diagnosis that is highly comorbid with the primary disorder, as was done in the current investigation. Our results indicated that OCD patients exhibited an increased ERN irrespective of medication use, and that medication use in patient controls was not associated with any elevation in the ERN compared to healthy
controls. This extends previous findings of no effect of acute administration of selective-serotonin reuptake inhibitor (SSRI) on the ERN in healthy controls (de Bruijn, Sabbe, Hulstijn, Ruigt, & Verkes, 2006) to include chronic administration of SRIs in a patient population. The current findings suggest that ERN hyperactivity in OCD is not an epiphenomenon of SSRI administration, and that if serotonergic activity does impact the ERN (Fallgatter et al., 2004; Jocham & Ullsperger 2009), it may not be a simple effect of reuptake inhibition. Does an increased ERN simply reflect greater anxiety and/or depression in OCD patients rather than being specific to OCD? Previous studies have indeed found greater ERNs associated with generalized anxiety (Hajcak, McDonald, & Simons, 2003) and major depressive disorder (Chiu & Deldin, 2007; Holmes & Pizzagalli, 2008). Our results indicated that, while OCD patients exhibited more anxiety and depressive symptoms (in addition to OC symptoms) than either control group, their level of these symptoms did not predict ERN amplitude. Intriguingly, within both control groups, greater anxiety and depression were associated with increased ERN amplitude. It is possible that the ERN is normally influenced by levels of negative affect, but that
Figure 2. Error and correct trial waveforms for OCD patients and control subjects. OCD patients (blue lines) exhibited greater amplitude for errors (solid) but not correct trials (dashed) as compared to control subjects (black lines). Bar graph shows the amplitude of the difference wave (errorFcorrect) in each group. uHC 5 unmedicated healthy controls; mPC 5 medicated patient controls; uOCD 5 unmedicated OCD patients; mOCD 5 medicated OCD patients.
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Figure 3. Correlations between ERN amplitude and symptoms of generalized anxiety and depression. Significant relationships were found in the control group but not in the OCD group. Trend lines are shown for correlations in all control subjects (solid black) and all OCD patients (solid blue), and for each group separately (unmedicated healthy controls: short dashed light gray; medicated patient controls: long dashed dark gray; unmedicated OCD patients: short dashed light blue; medicated OCD patients: long dashed dark blue).
in OCD this mechanism is overshadowed by a disorder-specific abnormality that is not related to the severity of generalized anxiety or depressive symptoms. Although symptoms of depression were related to an increased ERN in unmedicated healthy and medicated patient controls, a diagnosis of major depressive disorder in remission was not associated with an overall enhancement of the ERN in the mPC group. Such a distinction between effects of current versus prior depressive symptomatology on the ERN may help explain inconsistencies among previous studies examining the ERN in depression, which have identified no differences between remitted depressed patients and controls (Ruchsow et al., 2004, 2006), but an increased ERN in moderately depressed patients in a current depressive episode (Chiu & Deldin, 2007; Holmes & Pizzagalli, 2008). Our data indicated that ERNs were similarly increased for medicated and unmedicated OCD groups, which was unexpected given previous findings of a reduction in cingulate and orbitofrontal hyperactivity after a course of medication in OCD patients (Perani et al., 1995; Saxena et al., 1999). One possibility is that the medicated group was actually more severely ill prior to their initiation of treatment, and would have shown greater YBOCS scores and ERNs than unmedicated patients had they been tested in an untreated state. However, as ERN amplitudes were not correlated with YBOCS scores in the OCD group, our data do not
support the hypothesis that the increased ERN found in OCD is a function of OC symptom severity. Rather, this result provides support for the notion that ERN hyperactivity may be a trait marker of OCD that is not sensitive to fluctuations in symptom severity and does not decrease in response to treatment, consistent with a recent study reporting no change in ERN hyperactivity among children with OCD after treatment with cognitive behavioral therapy (Hajcak, Franklin, Foa, & Simons, 2008). While the ERN was robust for all groups at electrodes FZ and FCZ, the difference between OCD patients and controls was greatest at the more anterior electrode FZ. This is likely due to the slightly anterior topography of the ERN in OCD patients as compared to control subjects, which may have psychological significance. Although the ERN is typically thought to have a source in posterior/dorsal regions of medial frontal cortex/anterior cingulate gyrus (Dehaene et al., 1994; van Veen & Carter, 2002), it has also been shown to correlate with activity in anterior/rostral regions of medial frontal cortex (Mathalon, Whitfield, & Ford, 2003). Anterior MFC is involved in emotion and social processing (Amodio & Frith, 2006; Bush, Luu, & Posner, 2000; Steele & Lawrie, 2004), and it stands to reason that the relative contribution of posterior versus anterior regions of MFC to the ERN may, in fact, vary based upon the psychological reaction to making an error. In our study, OCD patients were
Error response in obsessive-compulsive disorder
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more frustrated with their performance and more flustered when making an error than control subjects. It is possible that the emergence of the group difference at the more anterior site is due to a greater contribution of anterior regions of MFC to the ERN in OCD patients, in relation to their heightened emotional reaction. Although speculative, such a hypothesis is consistent with previous fMRI data identifying error-related hyperactivation of anterior MFC in OCD patients (Fitzgerald et al., 2005). There are several limitations to the current study that suggest avenues for future research. First, performance feedback provided on a trial-by-trial basis was used to increase motivation, but may have reduced the response-locked signal by shifting attention away from the response toward the feedback. As it is conceivable that error feedback would be more salient, and hence divert more attention, than correct feedback, this feature of the study may have lead to a disproportionate reduction in the ERN compared to correct trial ERPs. However, given that there were no differences in accuracy between groups, this is unlikely to account for the greater ERN in OCD patients. Second, although mPCs and mOCDs were nearly all taking some form of SRI medication, we did not control for the concomitant use of benzodiazepines or non-SRI antidepressant medication. The medicated groups had similar proportions of subjects taking these additional medications, however, and greater use of benzodiazepines among OCD patients cannot explain their exaggerated ERN relative to controls, since benzodiazepines have been associated with a reduced ERN (de Bruijn, Hulstijn, Verkes, Ruigt,
& Sabbe, 2004; Riba, Rodrı´ guez-Fornells, Mu¨nte, & Barbanoj, 2005). Third, among SRI types, there was a greater proportion of SNRIs being taken by mPCs than mOCD patients. Yet, as norepinephrine release has been shown to increase the ERN (Riba, Rodrı´ guez-Fornells, Morte, Mu¨nte, & Barbanoj, 2005), this, too, would work against the finding of exaggerated the ERN in the OCD patients. Sample sizes were too small to segregate medicated subjects based on SSRI, SNRI, and additional medication use, so more detailed analysis of medication effects on the ERN will require replication with a larger sample. Subjects included here were not taking antipsychotic medications, but considering that these are sometimes used as adjunctive therapy in OCD and can reduce the ERN (de Bruijn et al., 2006), future studies should seek to track or exclude their use as well. In sum, our results indicate that OCD patients have exaggerated ERNs that are not due to SRI medication use. The data also show that greater severity of generalized anxiety and depressive symptoms is associated with increased ERNs among control subjects, but that these symptoms do not account for ERN hyperactivity in OCD patients. Instead, our results suggest that an increased ERN may be a neural endophenotype of OCD that does not fluctuate with symptom severity or decrease with pharmacological treatment. Future studies would benefit from comparing trait and state influences on ERN hyperactivity across different psychiatric disorders, and by examining genetic influences on the ERN in OCD, in order to further explore these effects.
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(Received September 22, 2009; Accepted September 23, 2009)
Psychophysiology, 47 (2010), 921–930. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.00989.x
Event-related brain potential correlates of identity negative priming from overlapping pictures
JO¨RG BEHRENDT,a,b HENNING GIBBONS,a,c HECKE SCHROBSDORFF,a,d MATTHIAS IHRKE,a,b J. MICHAEL HERRMANN,a,e and MARCUS HASSELHORNa,f,g a
Bernstein Center for Computational Neuroscience Go¨ttingen, Go¨ttingen, Germany University of Go¨ttingen, Georg-Elias-Mu¨ller-Institute for Psychology, Go¨ttingen, Germany Department of Medical Psychology and Medical Sociology, University Medical Center Go¨ttingen, Go¨ttingen, Germany d Institute for Nonlinear Dynamics, Go¨ttingen, Germany e University of Edinburgh, Edinburgh, UK f German Institute for International Educational Research (DIPF), Frankfurt, Germany g Center for Research on Individual Development and Adaptive Education of Children at Risk (IDeA), Frankfurt, Germany b c
Abstract Event-related potentials (ERPs) were obtained from an identity priming task, where a green target had to be selected against a superimposed red distractor. Several priming conditions were realized in a mix of control (CO), negative priming (NP), and positive priming (PP) trials. PP and NP effects in reaction times (RTs) were significant. ERP results conceptually replicate earlier findings of left-posterior P300 reduction in PP and NP trials compared to CO. This ERP effect may reflect the detection of prime-probe similarity corresponding to the concept of a retrieval cue. A novel finding concerned amplitude increase of the frontal late positive complex (LPC) in the order NP, CO, and PP. NP therefore seemed to induce brain activity related to cognitive control and/or memory processes, with reduced LPC amplitude indicating effortful processing. Overall, retrieval-based explanations of identity NP are supported. Descriptors: Negative priming, Identification, EEG, Event-related potentials, P300, Late positive complex, Episodic retrieval
ing tasks where the target is selected by means of a physical feature (e.g., color, location) and then responded to according to its identity. Over the years, various theories have been developed to explain NP, but in spite of a lively theoretical discussion, a consistent explanatory account of all NP phenomena is still missing. Two major theoretical approaches can be distinguished: persisting-inhibition (Houghton & Tipper, 1998; Neill, 1977; Tipper, 1985) and episodic-retrieval (Milliken, Joordens, Merikle, & Seiffert, 1998; Neill, 1997) theories. Initially, inhibition-based theories (Tipper, 1985) postulated that NP is a behavioral index of an inhibitory component of selection. For instance, it has been suggested that cognitive representations of irrelevant stimuli are ‘‘deactivated’’ to support selection of the relevant target stimuli (Neill, 1977), or that the link between stimulus representations and the response mechanism is blocked (Tipper & Cranston, 1985). Inhibition is assumed to persist for some time, and when a former distractor becomes the relevant target in the probe display, responding is impaired. However, in the last few years a majority of researchers interpreted NP according to the episodicretrieval view, that is, as a result of conflicting information caused by the automatic retrieval of prime information during probe processing (Neill, Valdes, Terry, & Gorfein, 1992). If the object information about the prime distractor from the retrieved episode is inconsistent with its current role as target object, a
Because of the limited capacity of the cognitive system, selective attention to the goal-relevant information is crucial for coherent behavior of individuals. Investigations applying the so-called negative-priming (NP) paradigm (Dalrymple-Alford & Budayr, 1966) showed that not only the process of extracting relevant information from the environment is important, but also the understanding of what happens to the ignored part of perceptual input. NP describes the phenomenon of prolonged reaction time (RT) and/or more errors when in a probe display subjects have to respond to a target that was the ignored distractor stimulus on a preceding prime display (i.e., the NP condition). The NP effect has been found in a wide variety of experimental tasks and can therefore be called quite a robust phenomenon (for reviews, see Fox, 1995; May, Kane, & Hasher, 1995; Tipper, 2001). In the present paper, we will particularly focus on visual identity primThis work was funded by the BMBF in the framework of the Bernstein Center for Computational Neuroscience Go¨ttingen, project C4, grant number 01GQ0432. The authors wish to thank Theo Geisel (BCCN Go¨ttingen) for continuous support of this project and Thorsten Wu¨stenberg (Charite´ Berlin), Ralph Meier (BCCN Freiburg), Jutta Stahl (University of Ko¨ln), and Cora Titz (DIPF Frankfurt) for fruitful discussions. Address reprint requests to Jo¨rg Behrendt, Georg-August-University of Go¨ttingen, Georg-Elias-Mu¨ller Institute for Psychology, Waldweg 26, D-37073 Go¨ttingen, Germany. E-mail:
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922 conflict is elicited whose resolution is time-consuming. Rothermund, Wentura, and De Houwer (2005) proposed an alternative response-retrieval account, assuming that the NP effect results from conflict between the required probe response and the retrieved (incorrect) prime response. There are findings in support of both inhibition and episodicretrieval views, but the body of empirical evidence does not clearly favor one approach over the other. According to Tipper (2001), distractor-inhibition and episodic-retrieval theories are not mutually exclusive. Supposedly, in most tasks NP will be caused by a mixture of persisting inhibition and retrieval-interference, depending on context and other experimental factors (Kane, May, Hasher, Rahhal, & Stolzfus, 1997). Because these processes may sometimes oppose each other, it is difficult to distinguish them by means of behavioral measures like RTs and error rates (cf., Gibbons, 2006). In view of the complexity of the effect, it appears desirable to include other sources of information to elucidate the mechanisms of NP. Therefore, several researchers used event-related potentials (ERPs) to further investigate the processes underlying the NP effect, because of the excellent temporal resolution of ERPs. Mayr, Niedeggen, Buchner, and Pietrowsky (2003) investigated RTand ERP effects of NP in the auditory domain. In this study, ERP analysis revealed an attenuation of the parietal late positive complex (LPC) for NP compared to control (CO). This LPC correlate of auditory NP was successfully replicated by Mayr, Niedeggen, Buchner, and Orgs (2006). The authors interpreted their results as support for episodic-retrieval accounts of NP, because the LPC is known to be sensitive to stimulus recognition and familiarity. In the following years, several ERP studies of identity NP in the visual domain have been published. A number of ERP components were discussed as candidates for correlates of behavioral NP, particularly the N200 and P300 potentials. Some results were interpreted as evidence for persisting inhibition; others seemed to support the episodic-retrieval view (for review, see Mayr & Buchner, 2007). N200. In general, the N200 potential of the ERP has been interpreted to reflect early stimulus evaluation and especially active or passive discrimination processes subsequent to the perceptual identification of the stimulus features (cf., Ritter, Ford, Gaillard, Harter, Kutas, et al., 1984; Sams, Paavilainen, Alho, & Na¨a¨ta¨nen, 1985). Using a classical flanker paradigm, Frings and Groh-Bordin (2007) found larger frontal N200 for NP trials relative to CO; this N200 effect was more pronounced in participants with above-average behavioral NP. The authors concluded that ERPs in the N200 time range represent cognitive processes that caused their NP effect, and interpreted them as additional effort required in NP probes to select a previously inhibited stimulus against (non-inhibited) incompatible distractors. Also Daurignac, Houde`, and Jouvent (2006) observed enhanced N200 in NP trials and interpreted this ERP correlate of NP as evidence for effective inhibition mechanisms. Note, however, that their task was not typical for identity NP, because strategies rather than stimulus features were repeated from prime to probe. P300. The most frequently observed ERP correlate of visual identity NP has been modulation of the P300 potential. The P300 is often interpreted as reflecting the updating of resources needed for stimulus evaluation within the context of a model of the environment (Donchin & Coles, 1988). Kathmann, Bogdahn, and
J. Behrendt et al. Endrass (2006) found increased P300 amplitude in NP relative to control trials in a visual identification task with two overlapping black and grey digits. Kathmann et al. (2006) suggested that their P300 correlate of NP supports a response-retrieval account of NP. In contrast, Gibbons (2006) argued that P300 enhancement seems to be well in line with the inhibition view of NP. Inhibition theory assumes that the activation level of the internal representation of an NP target is below baseline at probe display onset (cf., Houghton & Tipper, 1994). Therefore, more activation is needed to reach a criterion threshold required for stimulus identification, which could well be reflected in increased P300 amplitude and/or latency. Based on similar considerations, one can expect reduced P300 amplitude and/or latency in a priming condition involving prime-probe target repetitions, due to abovebaseline activation of the representation of the target at probe display onset (Stahl & Gibbons, 2007). In contrast to Kathmann et al.’s (2006) findings, Stahl and Gibbons (2007) found NP-related reduction of left-posterior P300 amplitude in a study of identity NP in the Eriksen flanker task (Eriksen & Eriksen, 1974). Stahl and Gibbons (2007) suggested that their P300 effect is better in line with episodic-retrieval than with inhibition view. For this conclusion, it was important that left-posterior P300 reduction was also observed in yet another priming condition, involving prime-probe target repetitions. Typical for this condition is the strong facilitation of responding known as positive priming (PP). If anything, there should be persisting activation for PP targets; consequently, reduced P300 amplitude in both PP and NP trials suggests persisting activation rather than persisting inhibition also in NP trials. Therefore, to explain the emergence of behavioral NP, a process operating at a later stage, which counteracts the presumed facilitatory NP effect during stimulus evaluation, has to be assumed. In a recent study, Gibbons (2009) provided a successful replication of NP-related left-posterior P300 reduction in the flanker task. In addition, a subsequent left-posterior N400 potential was specific for NP and was interpreted as a correlate of the effortful processing of the retrieved taskinappropriate information. An additional analysis in the study by Gibbons (2009) revealed that the N400 effect was restricted to the group of participants showing above-median behavioral NP, thus confirming the significance of this ERP effect for NP. The divergent empirical findings of Kathmann et al. (2006) and Stahl and Gibbons (2007; see also Gibbons, 2009) concerning P300 amplitude may be accounted for by several methodological differences, which cannot be discussed here in detail (see Stahl & Gibbons, 2007). Briefly, it seems that Stahl and Gibbons (2007) provided conditions that encouraged episodic retrieval to become effective, while Kathmann et al.’s (2006) experimental setup favored the emergence of persisting inhibition. Further ERP studies on visual identity NP have been published, but either there were no ERP effects of NP (Gibbons, 2006; Hinojosa, Villarino, Pozo, Elosua, Merino, et al., 2007), or the interpretation of the ERP correlates of NP was difficult because no NP effects were found at the behavioral level (Gibbons, Rammsayer, & Stahl, 2006). Two other studies using lexical decision tasks found small N400 amplitude reductions in the NP condition relative to control (Heil & Rolke, 2004; Wagner, Baving, Berg, Cohen, & Rockstroh, 2006). It has, however, been argued that this effect was a correlate of a detected prime-probe repetition, rather than a specific correlate of NP (cf., Heil & Rolke, 2004).
ERP correlates of identity negative priming To summarize, on the basis of the relatively small number of existing studies and the heterogeneous results, it has not been possible to get a clear picture of the ERP correlates of visual identity NP up to now. Overall, the majority of studies seem to support an episodic-retrieval explanation of NP. Yet, the inconsistency of previous results and especially the lack of replication of the ERP correlates of NP have hitherto prevented significant contributions of ERP research to the theoretical debate on NP. Only the finding of left-posterior P300 reduction for both NP and PP conditions compared to control (Stahl & Gibbons, 2007) was replicated by Gibbons (2009) using the same flanker task. In a next step, showing that the P300 effect generalizes over different identification tasks would substantially add to the literature. However, it should also be emphasized that previous findings only indirectly support retrieval explanations; at least, a genuine functional late-range ERP correlate of NP which could reflect the conflict between retrieved prime information and information extracted from the probe is still missing. The aim of the present study of identity-based NP was to test two predictions that can be derived from episodic-retrieval view. First, retrieval of prime information should only occur if the probe display in NP trials is perceived as somehow similar to the prime display. Second, because the retrieved prime information is inappropriate for the required probe response in NP trials, late processing conflict should emerge. For both processes ERP correlates should be found. The ERP reflection of perceived primeprobe similarity and/or the retrieval process itself should be largely the same for NP and PP trials (for both conditions, the probe target is a repeated prime stimulus). We therefore expect a conceptual replication of a repeatedly observed ERP correlate of visual NP, that is, P300 amplitude reduction in both PP and NP trials relative to CO (Gibbons, 2009; Stahl & Gibbons, 2007). By contrast, the second, late-range ERP correlate should distinguish NP from both PP and CO conditions, because only in NP trials should conflict emerge. Anterior predominance can be expected for this late-range ERP correlate of NP, given the crucial role of the frontal cortex in the processing of various types of conflict (see Ridderinkhof, van den Wildenberg, Segalowitz, & Carter, 2004, for review). The relatively small behavioral NP effects (10–15 ms) found in previous ERP studies of visual NP (e.g., Frings & Groh-Bordin, 2007; Gibbons, 2009; Kathmann et al., 2006; Stahl & Gibbons, 2007) may be one reason why no late-range, conflictrelated ERP effect of NP has been established until now. Therefore, in the present study a classical picture-naming task (cf., Tipper, 1985) is employed, which has not been used in ERP studies of NP so far but is known to produce relatively strong NP effects (30–50 ms; cf., Titz, Behrendt, Menge, & Hasselhorn, 2008). Obviously, this increases the chance to find a functional late-range ERP correlate of NP, if indeed a late conflict-related process is responsible for NP. Also, the facts that more complex stimuli are used and RT is generally larger than in the simple flanker task may increase the chance to find a late conflict-related ERP correlate of NP. Method Participants Sixteen right-handed young adults (undergraduate students from the University of Go¨ttingen, Germany), five males and eleven females with mean age of 23.3 years, SD 5 5.5, took part in the study and received course credit or were paid 8h (! $10). All
923 participants had normal or corrected-to-normal vision and were naive about the aims of the experiment. Materials and Apparatus Seven line drawings of familiar objects were prepared in green and red color (RGB coordinates were 0, 255, 0 for green pictures; 255, 0, 0 for red pictures). To minimize the potential systematic error in naming latencies between stimuli originating from differential triggering of the microphone, all objects began with the same initial plosive letter. The objects were: ball (German: Ball), tree (Baum), bench (Bank), book (Buch), boat (Boot), bus (Bus), and box (Box). The experiment was run on an IBM-compatible computer using Presentation software (Version 9.20, www.neurobs.com). A microphone was used to record naming latencies. In each trial, two superimposed objects were presented in the middle of a 19’’ computer monitor. The participants sat in front of the monitor at a distance of approximately 80 cm. Each stimulus subtended a horizontal visual angle of 5.01 and a vertical visual angle of 4.31. On the whole, every participant processed 840 successively presented displays (trials). The display sequences were portioned in 20 blocks containing 42 trials each. The first two trials in each block were not analyzed to exclude adaptation effects. The probability of presentation for the seven objects was balanced within each block. Each display contained a green target stimulus and a red distractor stimulus, or only one green target stimulus. Target and distractor were positioned in the center of the display. All line drawings appeared equally often as target and as distractor per experimental condition. By means of two successively presented displays, several priming conditions were realized (see Figure 1). In 16% of the prime-probe pairs, none of the prime stimuli were repeated in the probe display (control trials, CO). In 13% of the prime-probe pairs, the prime distractor reappeared as target in the probe display (NP). Thirteen percent of the trials realized the PP condition where the target from the prime repeated as the probe target. In addition to the above classical conditions, five single-target conditions were also realized, to investigate a research question not addressed by this paper. Three of these conditions showed only a target and no distractor in the probe trial (30% of all trials). The remaining trials were distractor-plustarget filler trials following single-target presentations (32% of all trials). These trials were excluded from analysis. All priming conditions were presented pseudo-randomized and in unpredictable order. Procedure Participants were tested individually in sessions that lasted no longer than 70 min. Prior to the main experiment, subjects were tested for their color discrimination abilities, by asking them to name different color patches printed on a card. The line drawings of the experimental stimuli along with their names printed in black were then shown to the subjects. They were told that they would see these objects overlapping one another, one drawn in green and the other in red. Participants were also instructed to name the green target object as quickly and correctly as possible while ignoring the superimposed red distractor object. To familiarize participants with the experimental procedure, a 30-trials practice session preceded the main session. Participants paced the succession of the instructions as well as the experimental blocks themselves by button presses. In a single trial, subjects saw the following series of events: (a) a fixation cross, centered on the screen for 500 ms; (b) a display
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J. Behrendt et al. Statistical Analysis of Behavioral Data An analysis of variance (ANOVA) with a repeated-measures factor Priming Condition (CO, NP, and PP) was computed for RTs, employing correction of p values according to Geisser and Greenhouse (1958). One-tailed Bonferroni-corrected t-tests were applied to test differences to control for conditions NP and PP (expecting NP and PP effects, respectively).
Figure 1. Example of a sequence of stimuli used in the identity priming task. In the actual experiment, the target was printed in green and the distractor was presented in red. Participants had to respond to a continuous stream of trials, where each display served as prime for the next one. A fixation cross was displayed for 500 ms before the presentation of every stimulus. The recurrence of prime objects in the probe defined the priming condition (meanings of the acronyms are introduced in Materials and Apparatus).
containing two superimposed (or a single) object(s) (until the subject responded, but no longer than 2 s); (c) a blank screen for a randomized duration between 0 and 1000 ms. The length of the response-to-stimulus interval (RSI) was thus randomized between 500 and 1500 ms (blank screen plus fixation cross), because this approach yielded good results in terms of strong NP effects in previous experiments. After each experimental block, subjects were allowed to take a short break. Behavioral errors were noted when subjects accidentally named the distractor, used a wrong name identifier, stuttered, or failed to answer. Once the participants had completed all trials, they were asked to comment on the experimental procedure (e.g., difficulties in identifying the presented pictures) to exclude strong outliers. Electrophysiological Recordings EEG (electroencephalogram) was continuously recorded from 63 head electrodes (see Figure 2) arranged in an extended 10–20 system, using a 64-channel BrainAmp MR amplifier and an electrode cap (Brain Products Inc., Munich, Germany) with sintered Ag/AgCl electrodes. The sampling rate was 1,000 Hz and band-pass was set to 0.1–70 Hz. Vertical electro-oculogram (EOG) was monitored from an electrode positioned 1 cm below the right eye. FCz served as active reference electrode. All impedances were kept below 5 kO. From continuous electroencephalogram, ERPs were derived separately for priming conditions (CO, NP, and PP) and participants, according to the following steps. First, EEG segmentation was performed, resulting in (! 100, 1500 ms) epochs, with zero indicating probe display onset. Epochs were then baselinecorrected with respect to the (! 100, 0 ms) interval, and an initial artifact rejection was performed to identify epochs with technical artifacts (i.e., amplitudes exceeding " 1 mV). Afterwards, data were subjected to EOG correction according to Gratton, Coles, and Donchin (1983). A final, more sensitive artifact rejection eliminated all epochs containing amplitude values exceeding " 100 mV. This resulted in a reduction of trials of less than 10% for each participant and priming condition. Epochs were then averaged separately for priming conditions and participants. In a final step, these individual averaged waveforms were re-referenced against algebraically linked mastoids (LM, RM).
ERP Analysis First, major ERP potentials were determined from grand-averaged ERP waveforms computed across the three priming conditions (see Figure 2). Identified potentials included posterior P1 (mean peak latency 102 ms) and N1 (mean peak latency 174 ms). A subsequent P2 potential peaked at around 230 ms, followed by N2 (270 ms). At posterior electrodes, P300 was observed at around 360 ms. In addition, over frontal areas a LPC occurred at around 700 ms after probe display onset. Second, in an attempt to systematically investigate ERP priming effects, amplitudes and latencies of these ERP peaks were determined separately for priming conditions and individuals. Separately for electrodes, the P1 peak was found as the most positive amplitude value between 80 and 120 ms. The N1 peak was found as the most negative amplitude value between 140 and 200 ms, and the P2 peak as the most positive amplitude value between 220 and 250 ms. N2 was found as the most negative amplitude value between 250 and 300 ms. The P300 peak was determined as the most positive amplitude value between 300 and 500 ms, and the frontal LPC was found as the most positive peak between 400 and 900 ms. Amplitudes and latencies of P1, N1, P2, N2, P300, and LPC potentials were subjected to separate ANOVAs, treating Priming Condition (CO, NP, and PP) and Electrode as repeated-measures factors, with levels of the latter factor depending on the potential of interest: For P1, N1, and P300, electrodes from the most posterior three rows (see Figure 2) were included in the analysis, whereas for P2 and N2, only electrodes from the most posterior two rows were considered. In the analysis of LPC, the most anterior four rows were included. Note that P300 amplitude was measured as mean voltage between 300 and 500 ms, to account for the broader temporal distribution of P300. Since analysis revealed a strong priming effect on LPC peak latency, LPC amplitude was not determined from a fixed time window. Rather, to assess priming effects on LPC amplitude and latency independently of each other, LPC amplitude was computed separately for participants, priming conditions and electrodes as mean voltage in 200-ms time windows adjusted for LPC peak latency. Intervals ranged from 500 to 700 ms in the PP condition, and from 550 to 750 ms in the NP and control conditions. Greenhouse-Geisser correction was applied when necessary (Geisser & Greenhouse, 1958) and significant effects were further explored using post-hoc Scheffe’s test. Results Behavioral Data Mean RTs in each priming condition, standard deviations, and difference effects for NP and PP trials compared with CO trials are shown in Table 1. Trials in which an error was committed (1.2 %) and subsequent trials were excluded from further RTanalysis. Trials with response latencies below 250 ms or more than two standard deviations above the individual mean for each participant and priming condition were excluded as outliers (4.7%).
ERP correlates of identity negative priming
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Figure 2. Grand-averaged ERP waveforms for each priming condition (CO 5 thick line, NP 5 dashed line, PP 5 dotted line). Negativity is plotted upwards. Note the reduced left-posterior P300 amplitude for the NP and the PP conditions as compared to CO trials (e.g., P7). Moreover, amplitude and latency of a frontal late positive complex (LPC) between 500 and 700 ms post-stimulus were found to be sensitive to the priming manipulation. NP trials showed significantly reduced LPC amplitude compared to CO trials, while PP trials produced significantly increased amplitude (see Discussion). In addition, the LPC peak occurred earlier for PP trials (e.g., FPz).
One-way ANOVA was used to analyze priming effects. The effect of Priming Condition (CO, NP, and PP) was significant F(2,30) 5 85.68, po.001; e 5 .85. Planned comparisons showed that mean RT for NP trials was significantly increased compared to CO trials [CO vs. NP: t(15) 5 ! 5.62, po.001]. As anticipated, RT for trials in the PP condition was significantly decreased [CO vs. PP: t(15) 5 8.57, po.001].
ERP Data Figure 2 displays the grand-grand average ERP waveforms computed across priming conditions. Components P1 (mean peak latency 102 ms), N1 (174 ms), P2 (230 ms), N2 (270 ms), and P300 (360 ms) can be identified. In addition, a frontal/fronto-central LPC component can be seen at around 700 ms poststimulus. Figure 2 suggests ERP priming effects on N2, P300,
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Table 1. Reaction Times (RTs) as a Function of Priming Condition (CO, NP, PP); RT Differences (Priming Effects) Mean Response Latencies in ms CO NP PP
765.8 (198.8) 793.3 (214.2) 632.9 (139.0) Priming Effects in ms a
CO-NP CO-PPa
" 27.5 132.9
Note: Standard deviations in brackets. CO 5 control trials, NP 5 negative priming trials, PP 5 positive priming trials, CO-NP 5 negative priming effect, CO-PP 5 positive priming effect. a RT differences between control condition and the respective prime condition.
and LPC. Posterior N2 was increased specifically in the NP condition. While P300 amplitude was reduced for both conditions PP and NP relative to CO, particularly over left posterior areas, amplitude of the anterior LPC increased in the order NP, CO, and PP. Moreover, LPC peaked earlier for PP compared to both NP and CO. By contrast, at first glance there seemed to be no priming effects on earlier ERP components P1, N1, and P2. To further investigate these findings, repeated-measures ANOVAs were carried out for peak amplitude and latency of components P1, N1, P2, N2, P300, and LPC, employing factors Priming Condition (CO, NP, and PP) and Electrode, with levels of the latter factor depending on analysis (see Method). P1. In the analysis of P1 latency, only the main effect of Electrode was significant, F(16,240) 5 5.0, po.01; e 5 .25. This effect was not further explored because it was irrelevant to priming. Neither the main effect of Priming Condition, F(2,30) 5 0.7, p 5 .47; e 5 .80, nor the interaction by Electrode, F(32, 480) 5 0.8, p 5 .51; e 5 .13, proved to be reliable. Also, the analysis of P1 amplitude did not yield any significant priming effects; main effect of Priming Condition, F(2,30) 5 1.7, p 5 .20; e 5 .88; interaction Priming Condition ! Electrode, F(32,480) 5 0.4, p 5 .84; e 5 .14. N1. There was a significant priming effect on N1 peak latency, F(2,30) 5 3.7, po.05; e 5 .97. According to Scheffe’s test, in the NP condition the N1 peak was reliably delayed compared to PP (176 and 172 ms, po.05). N1 latency in the CO condition (174 ms) was not significantly different from NP and PP conditions (p4.50). Neither the main effect of Priming Condition on N1 amplitude, F(2,30) 5 1.5, p 5 .23; e 5 .93, nor the interaction by Electrode, F(32,480) 5 0.8, p 5 .56; e 5 .19, were significant. P2. Also for P2 latency, a significant effect of Priming Condition could be established, F(2,30) 5 3.8, po.05; e 5 .79, which was due to P2 peaking slightly earlier for NP than CO (237 and 241 ms, po.05 according to Scheffe’s test). P2 latency in the PP condition (239 ms) did not differ significantly from the other two conditions (p4.50). The interaction Priming Condition ! Electrode was not significant, F(14,210) 5 1.1, p 5 .36; e 5 .31. Neither the main effect of Priming Condition on P2 amplitude, F(2,30) 5 0.4, p 5 .61; e 5 .75, nor the interaction by Electrode, F(14,210) 5 1.4, p 5 .24; e 5 .36, were significant.
N2. No reliable priming effects were observed for N2 latency; main effect of Priming Condition, F(2,30) 5 0.7, p 5 .50; e 5 .85; interaction by Electrode, F(14,210) 5 0.5, p 5 .67; e 5 .33. By contrast, a significant main effect of Priming Condition was established for N2 amplitude, F(2,30) 5 3.7, po.05; e 5 .91. N2 was significantly larger in the NP condition than in the CO condition ( " 0.8 mV and 0.0 mV; po.05 according to Scheffe’s test). Both the differences between NP and PP and between PP and CO were not significant (p4.30). The interaction by Electrode was not significant, F(14,210) 5 1.8, p 5 .14; e 5 .31. P300. The analysis of P300 latency yielded a significant main effect of Priming Condition, F(2,30) 5 4.3, po.05; e 5 .74. Scheffe’s test revealed that the P300 peak occurred in the PP condition reliably earlier than in the NP condition (351 and 361 ms, po.05). The difference between NP and CO was not significant (p 5 .83), nor was the difference between PP and CO (p 5 .10). The interaction by Electrode was not significant, F(32,480) 5 1.6, p 5 .16; e 5 .20. Also for P300 amplitude, the main effect of Priming Condition was significant, F(2,30) 5 4.3, po.05; e 5 .78. As indicated by Scheffe’s test, P300 amplitude was reliably larger in the CO condition (3.6 mV) than in both the PP condition (2.9 mV; po.05) and the NP condition (3.0 mV; po.05). PP and NP condition did not differ from each other (p 5 .95). The main effect was further qualified by a significant interaction by Electrode, F(32,480) 5 4.5, po.001; e 5 .19. Since an a priori hypothesis had been formulated based on the results by Stahl and Gibbons (2007) and Gibbons (2009), regarding leftparietal predominance of priming effects on P300 amplitude (see introduction), planned comparisons between priming conditions were performed for clusters of left-parietal (P1, P3, P5, P7) and homologous right-parietal electrodes (P2, P4, P6, P8). Over leftside parietal areas, P300 amplitude in the CO condition (4.3 mV) was significantly larger than in both the NP condition (3.6 mV, t[15] 5 4.1, p 5 .001), and the PP condition (3.5 mV, t[15] 5 2.7, po.05). By contrast, right-parietal P300 amplitude did not differ significantly for NP and CO (4.6 and 4.8 mV, t[15] 5 1.3, p 5 .22), and PP and CO (4.4 and 4.8 mV, t[15] 5 1.7, p 5 .11). LPC. For analysis of the frontal LPC at around 700 ms poststimulus, the four most anterior rows of electrodes were considered (see Figure 2). The main effect of Priming Condition on LPC peak latency was significant, F(2,30) 5 10.1, po.001; e 5 .99. According to Scheffe’s test, LPC peaked significantly earlier in the PP condition (711 ms) than in both the CO condition (789 ms; po.01) and the NP condition (769 ms; po.05). The difference between NP and CO was not significant (p4.50). There was no reliable interaction between Priming Condition and Electrode, F(50,750) 5 1.3, p 5 .20; e 5 .17. Analysis of LPC amplitude determined as mean voltage in a 200-ms window centered around the condition-specific LPC peak (see Method) yielded a significant main effect of Priming Condition, F(2,30) 5 13.7, po.001; e 5 .71. Scheffe’s test revealed significantly larger LPC amplitude in the PP condition (3.6 mV) compared to both NP (2.1 mV; po.001) and CO conditions (2.8 mV; po.05). Also the difference between NP and CO was significant at p 5 .05. The interaction by Electrode was not significant, F(50,750) 5 0.6, p 5 .66; e 5 .09. Given that there was substantial blink activity at around (or, immediately after) the overt response (see Figure 2, vEOG), it was necessary to ensure that the frontal LPC effects were not
ERP correlates of identity negative priming merely due to differences in vertical EOG activity between priming conditions. Mean EOG activity in the 550–750 ms interval (which is exactly the time window used for LPC amplitude analysis) was compared for CO and NP conditions. No significant difference was found, t(15) 5 1.5, p 5 .16. To compare CO and PP conditions, the time window had to be adjusted for the PP condition (500–700 ms) to compensate for LPC latency differences, thus exactly matching the time window chosen for the PP condition in the analysis of LPC amplitude. The t-test was also not significant, t(15) 5 0.4, p 5 .68. Discussion The present experiment was designed to investigate electrophysiological correlates of NP in a picture-naming task, striving for the main goal of finding evidence for two ERP correlates predicted from the episodic-retrieval view of NP. First, the finding of left-posterior P300 amplitude reduction for NP and PP conditions compared to control (Gibbons, 2009; Stahl & Gibbons, 2007), which may reflect the processing of prime-probe similarity, should be replicated in a completely different task. A second late conflict-related correlate of NP should be found that distinguishes the NP condition from both PP and CO conditions, and may reflect the NP-specific processing of conflict between retrieved prime information and information extracted from the probe. Only one such direct ERP correlate of visual identitybased NP has been reported before, in terms of NP-specific amplitude enhancement of a left-posterior, N400-like component (Gibbons, 2009). To find further evidence for a late-range ERP correlate of visual NP, we used a classical priming task with overlapping pictures (Tipper, 1985) that has not been used in ERP studies of NP so far, but is known to produce strong behavioral NP effects. Thereby, we aimed at broadening the empirical basis for the discussion of the mental processes and neuronal structures involved in NP effects. Behavioral Effects Relative to other ERP studies using digit-identification (Kathmann et al., 2006) or flanker tasks (Gibbons, 2009; Stahl & Gibbons, 2007), a strong advantage of the present picture-naming task is that it produces strong behavioral priming effects (cf., Titz et al., 2008). In the current study, mean RT was delayed by 28 ms in NP trials and accelerated by 133 ms in PP trials, compared to control. Because both effects, PP and NP, were sufficiently large, substantial differences in the corresponding ERPs can be expected, which should provide a good basis for valid interpretations of the obtained ERP correlates. ERP Effects The observed ERP correlates of NP mainly concerned two components, P300 and frontal LPC. Two other findings were not followed up further. The first was a small NP-related increase in N1 latency by 4 ms compared to the PP condition, which neither can account for the 28-ms behavioral NP effect nor for the 161ms RT difference between NP and PP conditions. The second was a small N2 amplitude increase for the NP condition relative to control, which, however, did not distinguish the NP condition from the PP condition. Thus, in contrast to the studies by Daurignac et al. (2006) and Frings and Groh-Bordin (2007), no significant NP-specific effects on early ERP components were observed, and there seems to be little evidence in the present data that would support the inhibition view of NP. In the following, possible explanations for the present priming effects on P300 and
927 LPC amplitudes and their consequences for theories of NP are discussed in more detail. Replication of the P300 ERP correlate of perceived primeprobe similarity. The present study provides a successful conceptual replication of earlier reports of priming effects on P300 amplitude. The finding of reduced left-posterior P300 in both NP and PP trials is well in line with the results obtained by Stahl and Gibbons (2007) and Gibbons (2009) in their Eriksen flanker tasks. Since these tasks are rather different from the present task employing overlapping objects, the convergent results regarding priming effects on P300, even with respect to the left-posterior scalp topography, are all the more remarkable. Note that the present left-posterior P300 effect was observed in a (300–500 ms) time window, whereas Gibbons (2009) and Stahl and Gibbons (2007) analyzed a somewhat earlier time window (300–400 ms). However, given the difference in mean RT between the present task using overlapping pictures (650–800 ms) and the two earlier studies using the flanker task (500–650 ms), it seems likely that functionally the same processes were involved in P300 reduction in both tasks. Therefore, in line with Gibbons (2009) we interpret PP- and NP-related reduction in P300 amplitude as reflecting processes related to perceived prime-probe similarity. Within the framework of episodic-retrieval view (Neill et al., 1992; Rothermund et al., 2005), this would correspond to the concept of a ‘‘retrieval cue.’’ One might argue that reduced P300 amplitude is at variance with the literature on (word) repetition effects on the ERP, usually reporting larger ERP positivity for repeated stimuli (see Rugg, 1995, for an early review). However, the ERP repetition effect typically has its maximum at around 600 ms, as opposed to the present joint PP/NP effect on P300 at around 400 ms. There are at least two other striking differences between the present priming task and the ERP repetition paradigm: First, in the present experiment all stimuli are repeated over and over, whereas in ERP repetitions studies first and second presentations of a stimulus are compared, with lags often ranging from many seconds to several minutes. Second, unlike the present task ERP repetition experiments do not involve selection; typically, on each trial one single stimulus is presented. The present P300 component, therefore, seems to be rather different from the late-range positive ERP components observed in ERP repetition studies. It should be mentioned that we did not find differences in P300 peak latency between NP and CO conditions, suggesting similar speed of stimulus evaluation processes in these two priming conditions. When the relatively strong behavioral NP effect is considered, in line with Stahl and Gibbons (2007) it can be argued that at least one additional process in the post-P300 time range has to be assumed to explain behavioral NP. A novel late conflict-related ERP correlate of visual NP. Two late-range ERP findings of the present study may provide a basis for a consistent explanation of the RT differences between priming conditions. These findings concern reduced amplitude and (qualitatively) delayed peak latency of the frontal LPC for the NP condition, as well as increased LPC amplitude and reduced LPC latency for the PP condition, both compared to control. The effects were widely distributed over the anterior scalp, including the first four rows of electrodes (fronto-polar to fronto-central). Note that LPC priming effects were analyzed between 500 and 700 ms, but frontal ERP differences-to-control already started at around 380 ms for PP and 520 ms for NP (see Figure 1). Given
928 that RTs ranged from 632 ms (PP condition) to 793 ms (NP condition), these LPC effects may well reflect processes that were responsible for priming effects on RT, and not merely consequences of the mean RT differences between conditions. Frontal brain activity is known to be particularly necessary in situations where an individual’s behavior is not simply controlled by stimulus-response relationships, but requires careful selection from different response options (see Ridderinkhof et al., 2004, for review). One possibility is to explain the present LPC differences in terms of the amount of cognitive control required. Processes related to cognitive control may become evident in laterange frontal ERP negativity, as it is often observed in situations of conflict processing (e.g., Krigolson & Holroyd, 2007; Lorist, Klein, Nieuwenhuis, De Jong, Mulder, & Meijman, 2000; West, Bowry, & McConville, 2004; Yeung, Botvinick, & Cohen, 2004). These processes should be strongest in the high-conflict NP condition, thereby reducing and/or delaying the ERP positivity that normally indicates the completion of trial processing. By contrast, in the low-conflict PP condition no strong cognitive control is required, causing earlier and stronger ERP positivity. The response-retrieval theory introduced by Rothermund et al. (2005) may provide an especially good means of explaining NP in terms of late processing conflicts. This approach suggests that the prime response is automatically reactivated when prime stimuli are repeated in the probe. In case of PP trials, both prime response retrieval and probe processing converge at the same response, which causes behavioral PP and, moreover, should be accompanied by particularly low conflict. No (frontal) brain activity related to cognitive control is then required. This can explain why in the PP condition particularly strong and early frontal ERP positivity was observed, given that positive ERP deflections typically occur when mental resources can be updated because they are no longer needed for ongoing information processing. In contrast, in the NP condition retrieval of the prime response and algorithmic probe processing activate different responses, which may necessitate cognitive control processes reflected in late-range frontal brain activation. This type of brain activity typically manifests in ERP negativity. It may then be superimposed on positive components indicating the completion of stimulus analysis, thereby causing amplitude reduction and/or delayed latency of these positive components (see Kok, 2001, for the general argumentation). In sum, the observed differences in frontal LPC amplitude between NP, CO, and PP conditions are well in line with the prime-response retrieval account of NP (Rothermund et al., 2005). A second explanation for the present LPC priming effects may directly refer to memory processes. Mayr et al. (2003, 2006) observed NP-related reduction of (posterior) LPC amplitude in the auditory domain. The authors interpreted their finding as support for episodic-retrieval mechanisms and argued as follows: In ERP repetition studies, familiarity of a stimulus is reflected in larger posterior LPC, compared to novel stimuli. If, however, NP is accompanied by reduced LPC amplitude, this may indicate that former distractor stimuli, when repeating as targets in the NP condition, are in some sense ‘‘less familiar’’ than the novel targets in the CO condition. This below-baseline reduced familiarity of NP targets may then result in less efficient processing of these stimuli, causing the NP effect. In principle, a similar mechanism related to memory processes may also be responsible for the present NP-related frontal LPC reduction. The different topographies of the present LPC effect and the LPC effect by Mayr
J. Behrendt et al. et al. (2003, 2006) might be explained in terms of modality differences (visual vs. auditory) and/or differences in task demands (naming a visual object vs. classifying a sound by means of a button press). Note that there is evidence in the literature that visual memory tasks indeed may involve frontal LPC effects (e.g., Hayama, Johnson, & Rugg, 2008; Wilding & Rugg, 1996). Ullsperger, Mecklinger, and Mu¨ller (2000) distinguished between a frontal LPC effect reflecting stimulus familiarity and a posterior LPC effect related to conscious recollection. The present priming effects on frontal LPC are, therefore, well in line with the assumption that familiarity of the probe target in the PP condition is particularly high (here, the largest LPC amplitude was observed). By contrast, in the NP condition showing smaller frontal LPC than control, familiarity of a recently ignored target seems to be below baseline. Theoretical Implications In sum, there seems to be little evidence in the present data for an interpretation in favor of the inhibition view of NP. In this respect, besides the fact that there were no significant NP correlates in the N200 time range, where according to the literature persisting inhibition can be expected to operate (Daurignac et al., 2006; Frings & Groh-Bordin, 2007), the finding of reduced leftposterior P300 amplitude for both PP and NP conditions relative to control has to be emphasized. P300 amplitude is often understood as an index of mental effort (e.g., Ullsperger, Metz, & Gille, 1988; see also Kok, 2001). Thus, a persisting-activation/ inhibition view of PP/NP would predict larger P300 amplitude for NP compared to PP trials. Reduced P300 amplitude in PP trials would reflect facilitation of processing due to persisting activation of internal representations of the (repeated) target stimulus. Increased P300 amplitude in NP trials would reflect greater mental effort, as the activation of NP targets is still below baseline, due to persisting inhibition. As noted earlier, however, this pattern of P300 amplitudes was not found in the present study. Episodic-retrieval view, therefore, seems most appropriate to interpret our ERP correlates of NP in a picture-naming task, because it can explain both reduced P300 amplitudes in both conditions NP and PP and the ‘‘parametric’’ modulation of LPC amplitude by priming. Regarding P300, recognized prime-probe similarity (cf., Fox & de Fockert, 1998), which is reflected in smaller P300 amplitude, may serve as a retrieval cue that triggers the retrieval of prime information (Neill et al., 1992). As explained above, the retrieved prime information then has opponent effects on further processing in PP and NP trials (beneficial vs. conflicting, respectively). This is consistent with our observation that up to the P300 time range ERPs did not reveal any major differences in the processing of NP and PP trials. Hence, the present NP effect seems to originate at a later, post-P300 stage of processing. Conclusion One of the two major results of our study, that is, smaller leftposterior P300 amplitude for NP and PP trials compared to the CO condition, represents a conceptual replication of the results by Stahl and Gibbons (2007) and Gibbons (2009) in a classical picture-naming priming task. Thus, the processing of primeprobe similarity seems to be important for visual NP in general and is not restricted to a certain task. In addition, the reduced LPC amplitude in NP trials suggests more effortful processes
ERP correlates of identity negative priming
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related to cognitive control and/or memory retrieval. The present NP effect on frontal LPC amplitude has been observed for the first time, which may point to a specific advantage of the picture-naming task. This task is more difficult than typical visual NP tasks (e.g., the flanker) and produces long RTs and strong behavioral NP effects. Both these aspects may provide good conditions to identify late-range ERP correlates of NP. Overall, our results seem to favor a retrieval-based explanation of the present NP effect. A cautionary note concerns the fact that the three priming conditions relevant to the present study (PP, NP, and CO) were randomly presented among other trial types not containing distractor objects. These trials were included to answer a separate research question not relevant to the present study. Their presence, however, may have affected possible strategies used by the participants and, thereby, processes related to NP. For example, the frequent inclusion of single-target trials may have increased the difficulty participants had when, on another trial, a distractor actually appeared. Insofar as this may have strengthened the NP
effect, it is consistent with the aim of the present study, i.e., to increase the chance of finding ERP correlates of NP by employing a task known to produce strong NP. However, the present results may encourage further ERP studies with the overlappingpictures task, which then should be composed only of the most relevant conditions PP, NP, and CO. Furthermore, future studies should go beyond mere attempts to find ERP correlates of NP. Rather, the size of the behavioral NP effect should be experimentally manipulated, and corresponding changes in the ERP should be determined. For example, by using a larger temporal interval between the probe and the next prime than between the prime and the probe, conditions favoring episodic retrieval processes can be realized (cf. Neill et al., 1992; Allport, Tipper, & Chmiel, 1985). If differences in behavioral NP are found and, moreover, certain ERP correlates of NP are sensitive to this manipulation, this would strongly increase our understanding of the exact mental processes that are tapped by these ERP correlates.
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(Received October 26, 2008; Accepted September 4, 2009)
Psychophysiology, 47 (2010), 921–930. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.00989.x
Event-related brain potential correlates of identity negative priming from overlapping pictures
JO¨RG BEHRENDT,a,b HENNING GIBBONS,a,c HECKE SCHROBSDORFF,a,d MATTHIAS IHRKE,a,b J. MICHAEL HERRMANN,a,e and MARCUS HASSELHORNa,f,g a
Bernstein Center for Computational Neuroscience Go¨ttingen, Go¨ttingen, Germany University of Go¨ttingen, Georg-Elias-Mu¨ller-Institute for Psychology, Go¨ttingen, Germany Department of Medical Psychology and Medical Sociology, University Medical Center Go¨ttingen, Go¨ttingen, Germany d Institute for Nonlinear Dynamics, Go¨ttingen, Germany e University of Edinburgh, Edinburgh, UK f German Institute for International Educational Research (DIPF), Frankfurt, Germany g Center for Research on Individual Development and Adaptive Education of Children at Risk (IDeA), Frankfurt, Germany b c
Abstract Event-related potentials (ERPs) were obtained from an identity priming task, where a green target had to be selected against a superimposed red distractor. Several priming conditions were realized in a mix of control (CO), negative priming (NP), and positive priming (PP) trials. PP and NP effects in reaction times (RTs) were significant. ERP results conceptually replicate earlier findings of left-posterior P300 reduction in PP and NP trials compared to CO. This ERP effect may reflect the detection of prime-probe similarity corresponding to the concept of a retrieval cue. A novel finding concerned amplitude increase of the frontal late positive complex (LPC) in the order NP, CO, and PP. NP therefore seemed to induce brain activity related to cognitive control and/or memory processes, with reduced LPC amplitude indicating effortful processing. Overall, retrieval-based explanations of identity NP are supported. Descriptors: Negative priming, Identification, EEG, Event-related potentials, P300, Late positive complex, Episodic retrieval
ing tasks where the target is selected by means of a physical feature (e.g., color, location) and then responded to according to its identity. Over the years, various theories have been developed to explain NP, but in spite of a lively theoretical discussion, a consistent explanatory account of all NP phenomena is still missing. Two major theoretical approaches can be distinguished: persisting-inhibition (Houghton & Tipper, 1998; Neill, 1977; Tipper, 1985) and episodic-retrieval (Milliken, Joordens, Merikle, & Seiffert, 1998; Neill, 1997) theories. Initially, inhibition-based theories (Tipper, 1985) postulated that NP is a behavioral index of an inhibitory component of selection. For instance, it has been suggested that cognitive representations of irrelevant stimuli are ‘‘deactivated’’ to support selection of the relevant target stimuli (Neill, 1977), or that the link between stimulus representations and the response mechanism is blocked (Tipper & Cranston, 1985). Inhibition is assumed to persist for some time, and when a former distractor becomes the relevant target in the probe display, responding is impaired. However, in the last few years a majority of researchers interpreted NP according to the episodicretrieval view, that is, as a result of conflicting information caused by the automatic retrieval of prime information during probe processing (Neill, Valdes, Terry, & Gorfein, 1992). If the object information about the prime distractor from the retrieved episode is inconsistent with its current role as target object, a
Because of the limited capacity of the cognitive system, selective attention to the goal-relevant information is crucial for coherent behavior of individuals. Investigations applying the so-called negative-priming (NP) paradigm (Dalrymple-Alford & Budayr, 1966) showed that not only the process of extracting relevant information from the environment is important, but also the understanding of what happens to the ignored part of perceptual input. NP describes the phenomenon of prolonged reaction time (RT) and/or more errors when in a probe display subjects have to respond to a target that was the ignored distractor stimulus on a preceding prime display (i.e., the NP condition). The NP effect has been found in a wide variety of experimental tasks and can therefore be called quite a robust phenomenon (for reviews, see Fox, 1995; May, Kane, & Hasher, 1995; Tipper, 2001). In the present paper, we will particularly focus on visual identity primThis work was funded by the BMBF in the framework of the Bernstein Center for Computational Neuroscience Go¨ttingen, project C4, grant number 01GQ0432. The authors wish to thank Theo Geisel (BCCN Go¨ttingen) for continuous support of this project and Thorsten Wu¨stenberg (Charite´ Berlin), Ralph Meier (BCCN Freiburg), Jutta Stahl (University of Ko¨ln), and Cora Titz (DIPF Frankfurt) for fruitful discussions. Address reprint requests to Jo¨rg Behrendt, Georg-August-University of Go¨ttingen, Georg-Elias-Mu¨ller Institute for Psychology, Waldweg 26, D-37073 Go¨ttingen, Germany. E-mail:
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922 conflict is elicited whose resolution is time-consuming. Rothermund, Wentura, and De Houwer (2005) proposed an alternative response-retrieval account, assuming that the NP effect results from conflict between the required probe response and the retrieved (incorrect) prime response. There are findings in support of both inhibition and episodicretrieval views, but the body of empirical evidence does not clearly favor one approach over the other. According to Tipper (2001), distractor-inhibition and episodic-retrieval theories are not mutually exclusive. Supposedly, in most tasks NP will be caused by a mixture of persisting inhibition and retrieval-interference, depending on context and other experimental factors (Kane, May, Hasher, Rahhal, & Stolzfus, 1997). Because these processes may sometimes oppose each other, it is difficult to distinguish them by means of behavioral measures like RTs and error rates (cf., Gibbons, 2006). In view of the complexity of the effect, it appears desirable to include other sources of information to elucidate the mechanisms of NP. Therefore, several researchers used event-related potentials (ERPs) to further investigate the processes underlying the NP effect, because of the excellent temporal resolution of ERPs. Mayr, Niedeggen, Buchner, and Pietrowsky (2003) investigated RTand ERP effects of NP in the auditory domain. In this study, ERP analysis revealed an attenuation of the parietal late positive complex (LPC) for NP compared to control (CO). This LPC correlate of auditory NP was successfully replicated by Mayr, Niedeggen, Buchner, and Orgs (2006). The authors interpreted their results as support for episodic-retrieval accounts of NP, because the LPC is known to be sensitive to stimulus recognition and familiarity. In the following years, several ERP studies of identity NP in the visual domain have been published. A number of ERP components were discussed as candidates for correlates of behavioral NP, particularly the N200 and P300 potentials. Some results were interpreted as evidence for persisting inhibition; others seemed to support the episodic-retrieval view (for review, see Mayr & Buchner, 2007). N200. In general, the N200 potential of the ERP has been interpreted to reflect early stimulus evaluation and especially active or passive discrimination processes subsequent to the perceptual identification of the stimulus features (cf., Ritter, Ford, Gaillard, Harter, Kutas, et al., 1984; Sams, Paavilainen, Alho, & Na¨a¨ta¨nen, 1985). Using a classical flanker paradigm, Frings and Groh-Bordin (2007) found larger frontal N200 for NP trials relative to CO; this N200 effect was more pronounced in participants with above-average behavioral NP. The authors concluded that ERPs in the N200 time range represent cognitive processes that caused their NP effect, and interpreted them as additional effort required in NP probes to select a previously inhibited stimulus against (non-inhibited) incompatible distractors. Also Daurignac, Houde`, and Jouvent (2006) observed enhanced N200 in NP trials and interpreted this ERP correlate of NP as evidence for effective inhibition mechanisms. Note, however, that their task was not typical for identity NP, because strategies rather than stimulus features were repeated from prime to probe. P300. The most frequently observed ERP correlate of visual identity NP has been modulation of the P300 potential. The P300 is often interpreted as reflecting the updating of resources needed for stimulus evaluation within the context of a model of the environment (Donchin & Coles, 1988). Kathmann, Bogdahn, and
J. Behrendt et al. Endrass (2006) found increased P300 amplitude in NP relative to control trials in a visual identification task with two overlapping black and grey digits. Kathmann et al. (2006) suggested that their P300 correlate of NP supports a response-retrieval account of NP. In contrast, Gibbons (2006) argued that P300 enhancement seems to be well in line with the inhibition view of NP. Inhibition theory assumes that the activation level of the internal representation of an NP target is below baseline at probe display onset (cf., Houghton & Tipper, 1994). Therefore, more activation is needed to reach a criterion threshold required for stimulus identification, which could well be reflected in increased P300 amplitude and/or latency. Based on similar considerations, one can expect reduced P300 amplitude and/or latency in a priming condition involving prime-probe target repetitions, due to abovebaseline activation of the representation of the target at probe display onset (Stahl & Gibbons, 2007). In contrast to Kathmann et al.’s (2006) findings, Stahl and Gibbons (2007) found NP-related reduction of left-posterior P300 amplitude in a study of identity NP in the Eriksen flanker task (Eriksen & Eriksen, 1974). Stahl and Gibbons (2007) suggested that their P300 effect is better in line with episodic-retrieval than with inhibition view. For this conclusion, it was important that left-posterior P300 reduction was also observed in yet another priming condition, involving prime-probe target repetitions. Typical for this condition is the strong facilitation of responding known as positive priming (PP). If anything, there should be persisting activation for PP targets; consequently, reduced P300 amplitude in both PP and NP trials suggests persisting activation rather than persisting inhibition also in NP trials. Therefore, to explain the emergence of behavioral NP, a process operating at a later stage, which counteracts the presumed facilitatory NP effect during stimulus evaluation, has to be assumed. In a recent study, Gibbons (2009) provided a successful replication of NP-related left-posterior P300 reduction in the flanker task. In addition, a subsequent left-posterior N400 potential was specific for NP and was interpreted as a correlate of the effortful processing of the retrieved taskinappropriate information. An additional analysis in the study by Gibbons (2009) revealed that the N400 effect was restricted to the group of participants showing above-median behavioral NP, thus confirming the significance of this ERP effect for NP. The divergent empirical findings of Kathmann et al. (2006) and Stahl and Gibbons (2007; see also Gibbons, 2009) concerning P300 amplitude may be accounted for by several methodological differences, which cannot be discussed here in detail (see Stahl & Gibbons, 2007). Briefly, it seems that Stahl and Gibbons (2007) provided conditions that encouraged episodic retrieval to become effective, while Kathmann et al.’s (2006) experimental setup favored the emergence of persisting inhibition. Further ERP studies on visual identity NP have been published, but either there were no ERP effects of NP (Gibbons, 2006; Hinojosa, Villarino, Pozo, Elosua, Merino, et al., 2007), or the interpretation of the ERP correlates of NP was difficult because no NP effects were found at the behavioral level (Gibbons, Rammsayer, & Stahl, 2006). Two other studies using lexical decision tasks found small N400 amplitude reductions in the NP condition relative to control (Heil & Rolke, 2004; Wagner, Baving, Berg, Cohen, & Rockstroh, 2006). It has, however, been argued that this effect was a correlate of a detected prime-probe repetition, rather than a specific correlate of NP (cf., Heil & Rolke, 2004).
ERP correlates of identity negative priming To summarize, on the basis of the relatively small number of existing studies and the heterogeneous results, it has not been possible to get a clear picture of the ERP correlates of visual identity NP up to now. Overall, the majority of studies seem to support an episodic-retrieval explanation of NP. Yet, the inconsistency of previous results and especially the lack of replication of the ERP correlates of NP have hitherto prevented significant contributions of ERP research to the theoretical debate on NP. Only the finding of left-posterior P300 reduction for both NP and PP conditions compared to control (Stahl & Gibbons, 2007) was replicated by Gibbons (2009) using the same flanker task. In a next step, showing that the P300 effect generalizes over different identification tasks would substantially add to the literature. However, it should also be emphasized that previous findings only indirectly support retrieval explanations; at least, a genuine functional late-range ERP correlate of NP which could reflect the conflict between retrieved prime information and information extracted from the probe is still missing. The aim of the present study of identity-based NP was to test two predictions that can be derived from episodic-retrieval view. First, retrieval of prime information should only occur if the probe display in NP trials is perceived as somehow similar to the prime display. Second, because the retrieved prime information is inappropriate for the required probe response in NP trials, late processing conflict should emerge. For both processes ERP correlates should be found. The ERP reflection of perceived primeprobe similarity and/or the retrieval process itself should be largely the same for NP and PP trials (for both conditions, the probe target is a repeated prime stimulus). We therefore expect a conceptual replication of a repeatedly observed ERP correlate of visual NP, that is, P300 amplitude reduction in both PP and NP trials relative to CO (Gibbons, 2009; Stahl & Gibbons, 2007). By contrast, the second, late-range ERP correlate should distinguish NP from both PP and CO conditions, because only in NP trials should conflict emerge. Anterior predominance can be expected for this late-range ERP correlate of NP, given the crucial role of the frontal cortex in the processing of various types of conflict (see Ridderinkhof, van den Wildenberg, Segalowitz, & Carter, 2004, for review). The relatively small behavioral NP effects (10–15 ms) found in previous ERP studies of visual NP (e.g., Frings & Groh-Bordin, 2007; Gibbons, 2009; Kathmann et al., 2006; Stahl & Gibbons, 2007) may be one reason why no late-range, conflictrelated ERP effect of NP has been established until now. Therefore, in the present study a classical picture-naming task (cf., Tipper, 1985) is employed, which has not been used in ERP studies of NP so far but is known to produce relatively strong NP effects (30–50 ms; cf., Titz, Behrendt, Menge, & Hasselhorn, 2008). Obviously, this increases the chance to find a functional late-range ERP correlate of NP, if indeed a late conflict-related process is responsible for NP. Also, the facts that more complex stimuli are used and RT is generally larger than in the simple flanker task may increase the chance to find a late conflict-related ERP correlate of NP. Method Participants Sixteen right-handed young adults (undergraduate students from the University of Go¨ttingen, Germany), five males and eleven females with mean age of 23.3 years, SD 5 5.5, took part in the study and received course credit or were paid 8h (! $10). All
923 participants had normal or corrected-to-normal vision and were naive about the aims of the experiment. Materials and Apparatus Seven line drawings of familiar objects were prepared in green and red color (RGB coordinates were 0, 255, 0 for green pictures; 255, 0, 0 for red pictures). To minimize the potential systematic error in naming latencies between stimuli originating from differential triggering of the microphone, all objects began with the same initial plosive letter. The objects were: ball (German: Ball), tree (Baum), bench (Bank), book (Buch), boat (Boot), bus (Bus), and box (Box). The experiment was run on an IBM-compatible computer using Presentation software (Version 9.20, www.neurobs.com). A microphone was used to record naming latencies. In each trial, two superimposed objects were presented in the middle of a 19’’ computer monitor. The participants sat in front of the monitor at a distance of approximately 80 cm. Each stimulus subtended a horizontal visual angle of 5.01 and a vertical visual angle of 4.31. On the whole, every participant processed 840 successively presented displays (trials). The display sequences were portioned in 20 blocks containing 42 trials each. The first two trials in each block were not analyzed to exclude adaptation effects. The probability of presentation for the seven objects was balanced within each block. Each display contained a green target stimulus and a red distractor stimulus, or only one green target stimulus. Target and distractor were positioned in the center of the display. All line drawings appeared equally often as target and as distractor per experimental condition. By means of two successively presented displays, several priming conditions were realized (see Figure 1). In 16% of the prime-probe pairs, none of the prime stimuli were repeated in the probe display (control trials, CO). In 13% of the prime-probe pairs, the prime distractor reappeared as target in the probe display (NP). Thirteen percent of the trials realized the PP condition where the target from the prime repeated as the probe target. In addition to the above classical conditions, five single-target conditions were also realized, to investigate a research question not addressed by this paper. Three of these conditions showed only a target and no distractor in the probe trial (30% of all trials). The remaining trials were distractor-plustarget filler trials following single-target presentations (32% of all trials). These trials were excluded from analysis. All priming conditions were presented pseudo-randomized and in unpredictable order. Procedure Participants were tested individually in sessions that lasted no longer than 70 min. Prior to the main experiment, subjects were tested for their color discrimination abilities, by asking them to name different color patches printed on a card. The line drawings of the experimental stimuli along with their names printed in black were then shown to the subjects. They were told that they would see these objects overlapping one another, one drawn in green and the other in red. Participants were also instructed to name the green target object as quickly and correctly as possible while ignoring the superimposed red distractor object. To familiarize participants with the experimental procedure, a 30-trials practice session preceded the main session. Participants paced the succession of the instructions as well as the experimental blocks themselves by button presses. In a single trial, subjects saw the following series of events: (a) a fixation cross, centered on the screen for 500 ms; (b) a display
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J. Behrendt et al. Statistical Analysis of Behavioral Data An analysis of variance (ANOVA) with a repeated-measures factor Priming Condition (CO, NP, and PP) was computed for RTs, employing correction of p values according to Geisser and Greenhouse (1958). One-tailed Bonferroni-corrected t-tests were applied to test differences to control for conditions NP and PP (expecting NP and PP effects, respectively).
Figure 1. Example of a sequence of stimuli used in the identity priming task. In the actual experiment, the target was printed in green and the distractor was presented in red. Participants had to respond to a continuous stream of trials, where each display served as prime for the next one. A fixation cross was displayed for 500 ms before the presentation of every stimulus. The recurrence of prime objects in the probe defined the priming condition (meanings of the acronyms are introduced in Materials and Apparatus).
containing two superimposed (or a single) object(s) (until the subject responded, but no longer than 2 s); (c) a blank screen for a randomized duration between 0 and 1000 ms. The length of the response-to-stimulus interval (RSI) was thus randomized between 500 and 1500 ms (blank screen plus fixation cross), because this approach yielded good results in terms of strong NP effects in previous experiments. After each experimental block, subjects were allowed to take a short break. Behavioral errors were noted when subjects accidentally named the distractor, used a wrong name identifier, stuttered, or failed to answer. Once the participants had completed all trials, they were asked to comment on the experimental procedure (e.g., difficulties in identifying the presented pictures) to exclude strong outliers. Electrophysiological Recordings EEG (electroencephalogram) was continuously recorded from 63 head electrodes (see Figure 2) arranged in an extended 10–20 system, using a 64-channel BrainAmp MR amplifier and an electrode cap (Brain Products Inc., Munich, Germany) with sintered Ag/AgCl electrodes. The sampling rate was 1,000 Hz and band-pass was set to 0.1–70 Hz. Vertical electro-oculogram (EOG) was monitored from an electrode positioned 1 cm below the right eye. FCz served as active reference electrode. All impedances were kept below 5 kO. From continuous electroencephalogram, ERPs were derived separately for priming conditions (CO, NP, and PP) and participants, according to the following steps. First, EEG segmentation was performed, resulting in (! 100, 1500 ms) epochs, with zero indicating probe display onset. Epochs were then baselinecorrected with respect to the (! 100, 0 ms) interval, and an initial artifact rejection was performed to identify epochs with technical artifacts (i.e., amplitudes exceeding " 1 mV). Afterwards, data were subjected to EOG correction according to Gratton, Coles, and Donchin (1983). A final, more sensitive artifact rejection eliminated all epochs containing amplitude values exceeding " 100 mV. This resulted in a reduction of trials of less than 10% for each participant and priming condition. Epochs were then averaged separately for priming conditions and participants. In a final step, these individual averaged waveforms were re-referenced against algebraically linked mastoids (LM, RM).
ERP Analysis First, major ERP potentials were determined from grand-averaged ERP waveforms computed across the three priming conditions (see Figure 2). Identified potentials included posterior P1 (mean peak latency 102 ms) and N1 (mean peak latency 174 ms). A subsequent P2 potential peaked at around 230 ms, followed by N2 (270 ms). At posterior electrodes, P300 was observed at around 360 ms. In addition, over frontal areas a LPC occurred at around 700 ms after probe display onset. Second, in an attempt to systematically investigate ERP priming effects, amplitudes and latencies of these ERP peaks were determined separately for priming conditions and individuals. Separately for electrodes, the P1 peak was found as the most positive amplitude value between 80 and 120 ms. The N1 peak was found as the most negative amplitude value between 140 and 200 ms, and the P2 peak as the most positive amplitude value between 220 and 250 ms. N2 was found as the most negative amplitude value between 250 and 300 ms. The P300 peak was determined as the most positive amplitude value between 300 and 500 ms, and the frontal LPC was found as the most positive peak between 400 and 900 ms. Amplitudes and latencies of P1, N1, P2, N2, P300, and LPC potentials were subjected to separate ANOVAs, treating Priming Condition (CO, NP, and PP) and Electrode as repeated-measures factors, with levels of the latter factor depending on the potential of interest: For P1, N1, and P300, electrodes from the most posterior three rows (see Figure 2) were included in the analysis, whereas for P2 and N2, only electrodes from the most posterior two rows were considered. In the analysis of LPC, the most anterior four rows were included. Note that P300 amplitude was measured as mean voltage between 300 and 500 ms, to account for the broader temporal distribution of P300. Since analysis revealed a strong priming effect on LPC peak latency, LPC amplitude was not determined from a fixed time window. Rather, to assess priming effects on LPC amplitude and latency independently of each other, LPC amplitude was computed separately for participants, priming conditions and electrodes as mean voltage in 200-ms time windows adjusted for LPC peak latency. Intervals ranged from 500 to 700 ms in the PP condition, and from 550 to 750 ms in the NP and control conditions. Greenhouse-Geisser correction was applied when necessary (Geisser & Greenhouse, 1958) and significant effects were further explored using post-hoc Scheffe’s test. Results Behavioral Data Mean RTs in each priming condition, standard deviations, and difference effects for NP and PP trials compared with CO trials are shown in Table 1. Trials in which an error was committed (1.2 %) and subsequent trials were excluded from further RTanalysis. Trials with response latencies below 250 ms or more than two standard deviations above the individual mean for each participant and priming condition were excluded as outliers (4.7%).
ERP correlates of identity negative priming
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Figure 2. Grand-averaged ERP waveforms for each priming condition (CO 5 thick line, NP 5 dashed line, PP 5 dotted line). Negativity is plotted upwards. Note the reduced left-posterior P300 amplitude for the NP and the PP conditions as compared to CO trials (e.g., P7). Moreover, amplitude and latency of a frontal late positive complex (LPC) between 500 and 700 ms post-stimulus were found to be sensitive to the priming manipulation. NP trials showed significantly reduced LPC amplitude compared to CO trials, while PP trials produced significantly increased amplitude (see Discussion). In addition, the LPC peak occurred earlier for PP trials (e.g., FPz).
One-way ANOVA was used to analyze priming effects. The effect of Priming Condition (CO, NP, and PP) was significant F(2,30) 5 85.68, po.001; e 5 .85. Planned comparisons showed that mean RT for NP trials was significantly increased compared to CO trials [CO vs. NP: t(15) 5 ! 5.62, po.001]. As anticipated, RT for trials in the PP condition was significantly decreased [CO vs. PP: t(15) 5 8.57, po.001].
ERP Data Figure 2 displays the grand-grand average ERP waveforms computed across priming conditions. Components P1 (mean peak latency 102 ms), N1 (174 ms), P2 (230 ms), N2 (270 ms), and P300 (360 ms) can be identified. In addition, a frontal/fronto-central LPC component can be seen at around 700 ms poststimulus. Figure 2 suggests ERP priming effects on N2, P300,
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Table 1. Reaction Times (RTs) as a Function of Priming Condition (CO, NP, PP); RT Differences (Priming Effects) Mean Response Latencies in ms CO NP PP
765.8 (198.8) 793.3 (214.2) 632.9 (139.0) Priming Effects in ms a
CO-NP CO-PPa
" 27.5 132.9
Note: Standard deviations in brackets. CO 5 control trials, NP 5 negative priming trials, PP 5 positive priming trials, CO-NP 5 negative priming effect, CO-PP 5 positive priming effect. a RT differences between control condition and the respective prime condition.
and LPC. Posterior N2 was increased specifically in the NP condition. While P300 amplitude was reduced for both conditions PP and NP relative to CO, particularly over left posterior areas, amplitude of the anterior LPC increased in the order NP, CO, and PP. Moreover, LPC peaked earlier for PP compared to both NP and CO. By contrast, at first glance there seemed to be no priming effects on earlier ERP components P1, N1, and P2. To further investigate these findings, repeated-measures ANOVAs were carried out for peak amplitude and latency of components P1, N1, P2, N2, P300, and LPC, employing factors Priming Condition (CO, NP, and PP) and Electrode, with levels of the latter factor depending on analysis (see Method). P1. In the analysis of P1 latency, only the main effect of Electrode was significant, F(16,240) 5 5.0, po.01; e 5 .25. This effect was not further explored because it was irrelevant to priming. Neither the main effect of Priming Condition, F(2,30) 5 0.7, p 5 .47; e 5 .80, nor the interaction by Electrode, F(32, 480) 5 0.8, p 5 .51; e 5 .13, proved to be reliable. Also, the analysis of P1 amplitude did not yield any significant priming effects; main effect of Priming Condition, F(2,30) 5 1.7, p 5 .20; e 5 .88; interaction Priming Condition ! Electrode, F(32,480) 5 0.4, p 5 .84; e 5 .14. N1. There was a significant priming effect on N1 peak latency, F(2,30) 5 3.7, po.05; e 5 .97. According to Scheffe’s test, in the NP condition the N1 peak was reliably delayed compared to PP (176 and 172 ms, po.05). N1 latency in the CO condition (174 ms) was not significantly different from NP and PP conditions (p4.50). Neither the main effect of Priming Condition on N1 amplitude, F(2,30) 5 1.5, p 5 .23; e 5 .93, nor the interaction by Electrode, F(32,480) 5 0.8, p 5 .56; e 5 .19, were significant. P2. Also for P2 latency, a significant effect of Priming Condition could be established, F(2,30) 5 3.8, po.05; e 5 .79, which was due to P2 peaking slightly earlier for NP than CO (237 and 241 ms, po.05 according to Scheffe’s test). P2 latency in the PP condition (239 ms) did not differ significantly from the other two conditions (p4.50). The interaction Priming Condition ! Electrode was not significant, F(14,210) 5 1.1, p 5 .36; e 5 .31. Neither the main effect of Priming Condition on P2 amplitude, F(2,30) 5 0.4, p 5 .61; e 5 .75, nor the interaction by Electrode, F(14,210) 5 1.4, p 5 .24; e 5 .36, were significant.
N2. No reliable priming effects were observed for N2 latency; main effect of Priming Condition, F(2,30) 5 0.7, p 5 .50; e 5 .85; interaction by Electrode, F(14,210) 5 0.5, p 5 .67; e 5 .33. By contrast, a significant main effect of Priming Condition was established for N2 amplitude, F(2,30) 5 3.7, po.05; e 5 .91. N2 was significantly larger in the NP condition than in the CO condition ( " 0.8 mV and 0.0 mV; po.05 according to Scheffe’s test). Both the differences between NP and PP and between PP and CO were not significant (p4.30). The interaction by Electrode was not significant, F(14,210) 5 1.8, p 5 .14; e 5 .31. P300. The analysis of P300 latency yielded a significant main effect of Priming Condition, F(2,30) 5 4.3, po.05; e 5 .74. Scheffe’s test revealed that the P300 peak occurred in the PP condition reliably earlier than in the NP condition (351 and 361 ms, po.05). The difference between NP and CO was not significant (p 5 .83), nor was the difference between PP and CO (p 5 .10). The interaction by Electrode was not significant, F(32,480) 5 1.6, p 5 .16; e 5 .20. Also for P300 amplitude, the main effect of Priming Condition was significant, F(2,30) 5 4.3, po.05; e 5 .78. As indicated by Scheffe’s test, P300 amplitude was reliably larger in the CO condition (3.6 mV) than in both the PP condition (2.9 mV; po.05) and the NP condition (3.0 mV; po.05). PP and NP condition did not differ from each other (p 5 .95). The main effect was further qualified by a significant interaction by Electrode, F(32,480) 5 4.5, po.001; e 5 .19. Since an a priori hypothesis had been formulated based on the results by Stahl and Gibbons (2007) and Gibbons (2009), regarding leftparietal predominance of priming effects on P300 amplitude (see introduction), planned comparisons between priming conditions were performed for clusters of left-parietal (P1, P3, P5, P7) and homologous right-parietal electrodes (P2, P4, P6, P8). Over leftside parietal areas, P300 amplitude in the CO condition (4.3 mV) was significantly larger than in both the NP condition (3.6 mV, t[15] 5 4.1, p 5 .001), and the PP condition (3.5 mV, t[15] 5 2.7, po.05). By contrast, right-parietal P300 amplitude did not differ significantly for NP and CO (4.6 and 4.8 mV, t[15] 5 1.3, p 5 .22), and PP and CO (4.4 and 4.8 mV, t[15] 5 1.7, p 5 .11). LPC. For analysis of the frontal LPC at around 700 ms poststimulus, the four most anterior rows of electrodes were considered (see Figure 2). The main effect of Priming Condition on LPC peak latency was significant, F(2,30) 5 10.1, po.001; e 5 .99. According to Scheffe’s test, LPC peaked significantly earlier in the PP condition (711 ms) than in both the CO condition (789 ms; po.01) and the NP condition (769 ms; po.05). The difference between NP and CO was not significant (p4.50). There was no reliable interaction between Priming Condition and Electrode, F(50,750) 5 1.3, p 5 .20; e 5 .17. Analysis of LPC amplitude determined as mean voltage in a 200-ms window centered around the condition-specific LPC peak (see Method) yielded a significant main effect of Priming Condition, F(2,30) 5 13.7, po.001; e 5 .71. Scheffe’s test revealed significantly larger LPC amplitude in the PP condition (3.6 mV) compared to both NP (2.1 mV; po.001) and CO conditions (2.8 mV; po.05). Also the difference between NP and CO was significant at p 5 .05. The interaction by Electrode was not significant, F(50,750) 5 0.6, p 5 .66; e 5 .09. Given that there was substantial blink activity at around (or, immediately after) the overt response (see Figure 2, vEOG), it was necessary to ensure that the frontal LPC effects were not
ERP correlates of identity negative priming merely due to differences in vertical EOG activity between priming conditions. Mean EOG activity in the 550–750 ms interval (which is exactly the time window used for LPC amplitude analysis) was compared for CO and NP conditions. No significant difference was found, t(15) 5 1.5, p 5 .16. To compare CO and PP conditions, the time window had to be adjusted for the PP condition (500–700 ms) to compensate for LPC latency differences, thus exactly matching the time window chosen for the PP condition in the analysis of LPC amplitude. The t-test was also not significant, t(15) 5 0.4, p 5 .68. Discussion The present experiment was designed to investigate electrophysiological correlates of NP in a picture-naming task, striving for the main goal of finding evidence for two ERP correlates predicted from the episodic-retrieval view of NP. First, the finding of left-posterior P300 amplitude reduction for NP and PP conditions compared to control (Gibbons, 2009; Stahl & Gibbons, 2007), which may reflect the processing of prime-probe similarity, should be replicated in a completely different task. A second late conflict-related correlate of NP should be found that distinguishes the NP condition from both PP and CO conditions, and may reflect the NP-specific processing of conflict between retrieved prime information and information extracted from the probe. Only one such direct ERP correlate of visual identitybased NP has been reported before, in terms of NP-specific amplitude enhancement of a left-posterior, N400-like component (Gibbons, 2009). To find further evidence for a late-range ERP correlate of visual NP, we used a classical priming task with overlapping pictures (Tipper, 1985) that has not been used in ERP studies of NP so far, but is known to produce strong behavioral NP effects. Thereby, we aimed at broadening the empirical basis for the discussion of the mental processes and neuronal structures involved in NP effects. Behavioral Effects Relative to other ERP studies using digit-identification (Kathmann et al., 2006) or flanker tasks (Gibbons, 2009; Stahl & Gibbons, 2007), a strong advantage of the present picture-naming task is that it produces strong behavioral priming effects (cf., Titz et al., 2008). In the current study, mean RT was delayed by 28 ms in NP trials and accelerated by 133 ms in PP trials, compared to control. Because both effects, PP and NP, were sufficiently large, substantial differences in the corresponding ERPs can be expected, which should provide a good basis for valid interpretations of the obtained ERP correlates. ERP Effects The observed ERP correlates of NP mainly concerned two components, P300 and frontal LPC. Two other findings were not followed up further. The first was a small NP-related increase in N1 latency by 4 ms compared to the PP condition, which neither can account for the 28-ms behavioral NP effect nor for the 161ms RT difference between NP and PP conditions. The second was a small N2 amplitude increase for the NP condition relative to control, which, however, did not distinguish the NP condition from the PP condition. Thus, in contrast to the studies by Daurignac et al. (2006) and Frings and Groh-Bordin (2007), no significant NP-specific effects on early ERP components were observed, and there seems to be little evidence in the present data that would support the inhibition view of NP. In the following, possible explanations for the present priming effects on P300 and
927 LPC amplitudes and their consequences for theories of NP are discussed in more detail. Replication of the P300 ERP correlate of perceived primeprobe similarity. The present study provides a successful conceptual replication of earlier reports of priming effects on P300 amplitude. The finding of reduced left-posterior P300 in both NP and PP trials is well in line with the results obtained by Stahl and Gibbons (2007) and Gibbons (2009) in their Eriksen flanker tasks. Since these tasks are rather different from the present task employing overlapping objects, the convergent results regarding priming effects on P300, even with respect to the left-posterior scalp topography, are all the more remarkable. Note that the present left-posterior P300 effect was observed in a (300–500 ms) time window, whereas Gibbons (2009) and Stahl and Gibbons (2007) analyzed a somewhat earlier time window (300–400 ms). However, given the difference in mean RT between the present task using overlapping pictures (650–800 ms) and the two earlier studies using the flanker task (500–650 ms), it seems likely that functionally the same processes were involved in P300 reduction in both tasks. Therefore, in line with Gibbons (2009) we interpret PP- and NP-related reduction in P300 amplitude as reflecting processes related to perceived prime-probe similarity. Within the framework of episodic-retrieval view (Neill et al., 1992; Rothermund et al., 2005), this would correspond to the concept of a ‘‘retrieval cue.’’ One might argue that reduced P300 amplitude is at variance with the literature on (word) repetition effects on the ERP, usually reporting larger ERP positivity for repeated stimuli (see Rugg, 1995, for an early review). However, the ERP repetition effect typically has its maximum at around 600 ms, as opposed to the present joint PP/NP effect on P300 at around 400 ms. There are at least two other striking differences between the present priming task and the ERP repetition paradigm: First, in the present experiment all stimuli are repeated over and over, whereas in ERP repetitions studies first and second presentations of a stimulus are compared, with lags often ranging from many seconds to several minutes. Second, unlike the present task ERP repetition experiments do not involve selection; typically, on each trial one single stimulus is presented. The present P300 component, therefore, seems to be rather different from the late-range positive ERP components observed in ERP repetition studies. It should be mentioned that we did not find differences in P300 peak latency between NP and CO conditions, suggesting similar speed of stimulus evaluation processes in these two priming conditions. When the relatively strong behavioral NP effect is considered, in line with Stahl and Gibbons (2007) it can be argued that at least one additional process in the post-P300 time range has to be assumed to explain behavioral NP. A novel late conflict-related ERP correlate of visual NP. Two late-range ERP findings of the present study may provide a basis for a consistent explanation of the RT differences between priming conditions. These findings concern reduced amplitude and (qualitatively) delayed peak latency of the frontal LPC for the NP condition, as well as increased LPC amplitude and reduced LPC latency for the PP condition, both compared to control. The effects were widely distributed over the anterior scalp, including the first four rows of electrodes (fronto-polar to fronto-central). Note that LPC priming effects were analyzed between 500 and 700 ms, but frontal ERP differences-to-control already started at around 380 ms for PP and 520 ms for NP (see Figure 1). Given
928 that RTs ranged from 632 ms (PP condition) to 793 ms (NP condition), these LPC effects may well reflect processes that were responsible for priming effects on RT, and not merely consequences of the mean RT differences between conditions. Frontal brain activity is known to be particularly necessary in situations where an individual’s behavior is not simply controlled by stimulus-response relationships, but requires careful selection from different response options (see Ridderinkhof et al., 2004, for review). One possibility is to explain the present LPC differences in terms of the amount of cognitive control required. Processes related to cognitive control may become evident in laterange frontal ERP negativity, as it is often observed in situations of conflict processing (e.g., Krigolson & Holroyd, 2007; Lorist, Klein, Nieuwenhuis, De Jong, Mulder, & Meijman, 2000; West, Bowry, & McConville, 2004; Yeung, Botvinick, & Cohen, 2004). These processes should be strongest in the high-conflict NP condition, thereby reducing and/or delaying the ERP positivity that normally indicates the completion of trial processing. By contrast, in the low-conflict PP condition no strong cognitive control is required, causing earlier and stronger ERP positivity. The response-retrieval theory introduced by Rothermund et al. (2005) may provide an especially good means of explaining NP in terms of late processing conflicts. This approach suggests that the prime response is automatically reactivated when prime stimuli are repeated in the probe. In case of PP trials, both prime response retrieval and probe processing converge at the same response, which causes behavioral PP and, moreover, should be accompanied by particularly low conflict. No (frontal) brain activity related to cognitive control is then required. This can explain why in the PP condition particularly strong and early frontal ERP positivity was observed, given that positive ERP deflections typically occur when mental resources can be updated because they are no longer needed for ongoing information processing. In contrast, in the NP condition retrieval of the prime response and algorithmic probe processing activate different responses, which may necessitate cognitive control processes reflected in late-range frontal brain activation. This type of brain activity typically manifests in ERP negativity. It may then be superimposed on positive components indicating the completion of stimulus analysis, thereby causing amplitude reduction and/or delayed latency of these positive components (see Kok, 2001, for the general argumentation). In sum, the observed differences in frontal LPC amplitude between NP, CO, and PP conditions are well in line with the prime-response retrieval account of NP (Rothermund et al., 2005). A second explanation for the present LPC priming effects may directly refer to memory processes. Mayr et al. (2003, 2006) observed NP-related reduction of (posterior) LPC amplitude in the auditory domain. The authors interpreted their finding as support for episodic-retrieval mechanisms and argued as follows: In ERP repetition studies, familiarity of a stimulus is reflected in larger posterior LPC, compared to novel stimuli. If, however, NP is accompanied by reduced LPC amplitude, this may indicate that former distractor stimuli, when repeating as targets in the NP condition, are in some sense ‘‘less familiar’’ than the novel targets in the CO condition. This below-baseline reduced familiarity of NP targets may then result in less efficient processing of these stimuli, causing the NP effect. In principle, a similar mechanism related to memory processes may also be responsible for the present NP-related frontal LPC reduction. The different topographies of the present LPC effect and the LPC effect by Mayr
J. Behrendt et al. et al. (2003, 2006) might be explained in terms of modality differences (visual vs. auditory) and/or differences in task demands (naming a visual object vs. classifying a sound by means of a button press). Note that there is evidence in the literature that visual memory tasks indeed may involve frontal LPC effects (e.g., Hayama, Johnson, & Rugg, 2008; Wilding & Rugg, 1996). Ullsperger, Mecklinger, and Mu¨ller (2000) distinguished between a frontal LPC effect reflecting stimulus familiarity and a posterior LPC effect related to conscious recollection. The present priming effects on frontal LPC are, therefore, well in line with the assumption that familiarity of the probe target in the PP condition is particularly high (here, the largest LPC amplitude was observed). By contrast, in the NP condition showing smaller frontal LPC than control, familiarity of a recently ignored target seems to be below baseline. Theoretical Implications In sum, there seems to be little evidence in the present data for an interpretation in favor of the inhibition view of NP. In this respect, besides the fact that there were no significant NP correlates in the N200 time range, where according to the literature persisting inhibition can be expected to operate (Daurignac et al., 2006; Frings & Groh-Bordin, 2007), the finding of reduced leftposterior P300 amplitude for both PP and NP conditions relative to control has to be emphasized. P300 amplitude is often understood as an index of mental effort (e.g., Ullsperger, Metz, & Gille, 1988; see also Kok, 2001). Thus, a persisting-activation/ inhibition view of PP/NP would predict larger P300 amplitude for NP compared to PP trials. Reduced P300 amplitude in PP trials would reflect facilitation of processing due to persisting activation of internal representations of the (repeated) target stimulus. Increased P300 amplitude in NP trials would reflect greater mental effort, as the activation of NP targets is still below baseline, due to persisting inhibition. As noted earlier, however, this pattern of P300 amplitudes was not found in the present study. Episodic-retrieval view, therefore, seems most appropriate to interpret our ERP correlates of NP in a picture-naming task, because it can explain both reduced P300 amplitudes in both conditions NP and PP and the ‘‘parametric’’ modulation of LPC amplitude by priming. Regarding P300, recognized prime-probe similarity (cf., Fox & de Fockert, 1998), which is reflected in smaller P300 amplitude, may serve as a retrieval cue that triggers the retrieval of prime information (Neill et al., 1992). As explained above, the retrieved prime information then has opponent effects on further processing in PP and NP trials (beneficial vs. conflicting, respectively). This is consistent with our observation that up to the P300 time range ERPs did not reveal any major differences in the processing of NP and PP trials. Hence, the present NP effect seems to originate at a later, post-P300 stage of processing. Conclusion One of the two major results of our study, that is, smaller leftposterior P300 amplitude for NP and PP trials compared to the CO condition, represents a conceptual replication of the results by Stahl and Gibbons (2007) and Gibbons (2009) in a classical picture-naming priming task. Thus, the processing of primeprobe similarity seems to be important for visual NP in general and is not restricted to a certain task. In addition, the reduced LPC amplitude in NP trials suggests more effortful processes
ERP correlates of identity negative priming
929
related to cognitive control and/or memory retrieval. The present NP effect on frontal LPC amplitude has been observed for the first time, which may point to a specific advantage of the picture-naming task. This task is more difficult than typical visual NP tasks (e.g., the flanker) and produces long RTs and strong behavioral NP effects. Both these aspects may provide good conditions to identify late-range ERP correlates of NP. Overall, our results seem to favor a retrieval-based explanation of the present NP effect. A cautionary note concerns the fact that the three priming conditions relevant to the present study (PP, NP, and CO) were randomly presented among other trial types not containing distractor objects. These trials were included to answer a separate research question not relevant to the present study. Their presence, however, may have affected possible strategies used by the participants and, thereby, processes related to NP. For example, the frequent inclusion of single-target trials may have increased the difficulty participants had when, on another trial, a distractor actually appeared. Insofar as this may have strengthened the NP
effect, it is consistent with the aim of the present study, i.e., to increase the chance of finding ERP correlates of NP by employing a task known to produce strong NP. However, the present results may encourage further ERP studies with the overlappingpictures task, which then should be composed only of the most relevant conditions PP, NP, and CO. Furthermore, future studies should go beyond mere attempts to find ERP correlates of NP. Rather, the size of the behavioral NP effect should be experimentally manipulated, and corresponding changes in the ERP should be determined. For example, by using a larger temporal interval between the probe and the next prime than between the prime and the probe, conditions favoring episodic retrieval processes can be realized (cf. Neill et al., 1992; Allport, Tipper, & Chmiel, 1985). If differences in behavioral NP are found and, moreover, certain ERP correlates of NP are sensitive to this manipulation, this would strongly increase our understanding of the exact mental processes that are tapped by these ERP correlates.
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(Received October 26, 2008; Accepted September 4, 2009)
Psychophysiology, 47 (2010), 931–941. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.00990.x
The effects of response sharing and stimulus presentation frequency on event-related potentials in an auditory oddball paradigm
JA´NOS HORVA´TH,a URTE ROEBER,b and ERICH SCHRO¨GERb a
Institute for Psychology, Hungarian Academy of Sciences, Budapest, Hungary Institute of Psychology I, University of Leipzig, Leipzig, Germany
b
Abstract An experimental model for investigating the processes involved in reacting to unpredictable events is the oddball paradigm. We investigated how the commonality or independence of response options (i.e., many-to-one vs. one-toone stimulus-response mappings) influences processing in an auditory oddball paradigm. Participants performed a discrimination task with two one-to-one and one two-to-one mappings. The pattern of conflict- and oddball-related N2 event-related potentials suggest that information that would allow correct responding is represented at the latency of the N2. Integration of this information takes place only by the latency of P3b, and longer reaction times to rare stimuli are probably due to processes preventing the utilization of this information. We also suggest that, in the given task context, conflict-related N2 may reflect the number of alternative stimuli leading to alternative response options. Descriptors: Attention, Conflict, Oddball paradigm, Categorization, Event related potentials (ERP), N2, P3a, P3b
approach to separate the behavioral effects of stimulus-related and response-related processing is to utilize tasks with many-toone instead of one-to-one stimulus-response (S-R) mappings, which makes it possible to separate stimulus- and response-frequency effects (see, e.g., Bertelson & Tisseyre, 1966). In the present study, we investigated how the commonality or independence of response options (i.e., many-to-one vs. one-to-one S-R mappings) influences processing in an auditory oddball paradigm. In the following, we briefly describe the ERPs typically elicited in active oddball paradigms, then we introduce two hypotheses regarding how response independence and commonality may influence these ERPs: 1) The first is based on the notion that stimulus-response events are represented in a combined network of stimulus and response features (event-files). Features shared between events result in processing interference, which suggests higher interference for stimuli mapped to the same response. 2) The second hypothesis is based on the idea that S-R mappings prescribed by the given task influence the categorization of stimuli, and this imposed categorization influences stimulus processing.
Producing adequate reactions to unpredictable events occurring in the environment is crucial for the survival of the organism. An experimental model for investigating processes involved in such reactions is the oddball paradigm. In the oddball paradigm, regular stimuli presented in a sequence are, occasionally, unpredictably replaced by irregular stimuli. Whereas most analyses of behavioral and event-related potential (ERP) responses in an oddball paradigm are concerned with the differential processing of frequent and rare stimuli, it is obvious that in many variations of the paradigm a comparison of behavioral and ERP responses to stimuli involves not only stimulus, but response differences as well. For example, rare stimuli may require a left-hand response, whereas frequent stimuli may require a right-hand response. Thus, right-hand responses are more frequent than left-hand responses, and the stimulus frequency factor is perfectly confounded with response frequency. Since stimulus-response associations are often one-to-one mappings, the separation of stimulus- and response-related effects is often problematic. One
This study was supported by the European Commission under the Marie Curie Intra-European Fellowship Project MEIF-CT-2006023924. The experiment was realized using Cogent 2000 developed by the Cogent 2000 team at the FIL and the ICN and Cogent Graphics developed by John Romaya at the LON at the Wellcome Department of Imaging Neuroscience. We thank two anonymous reviewers for constructive comments on an earlier version of the manuscript. Address reprint requests to: Ja´nos Horva´th, Institute for Psychology, Hungarian Academy of Sciences, P.O.B. 398, Szondi u 83/85, H-1394 Budapest, Hungary. E-mail:
[email protected] ERPs Elicited in Oddball Paradigms The sequence of processes triggered by infrequent stimuli in oddball paradigms has been widely studied using ERPs. In the present context, two components, the conflict-related N2 and the P3b, are the most important ones. Both components are elicited in active paradigms (i.e., in which participants perform a task on the stimulus sequence). 931
932 There are many negative components emerging around 200– 300 ms in different paradigms (for a review, see Folstein & Van Petten, 2008). Relevant to the present study is the fronto-central N2 component, which reflects a cognitive control process playing a role in the inhibition of prepotent response options or conflict monitoring (see, e.g., Nieuwenhuis, Yeung, van den Wildenberg, & Ridderinkhof, 2003; Donkers & van Boxtel, 2004). This component is described for many visual arrangements and is generally interpreted in terms of a response conflict. Response conflict is usually induced by two kinds of experimental manipulations (often applied in combination): 1) Response conflict may result from the high similarity of stimuli associated with different responses, which leads to the simultaneous activation of different response options when any of them is presented (e.g., Azizian, Freitas, Parvaz, & Squires, 2006). In a certain sense, this manipulation could be interpreted as a departure from a strictly oneto-one S-R mapping: due to the high similarity of the stimuli, essentially, a one-to-many S-R mapping is realized (which still makes it possible to assure that participants perform the assigned task). 2) Response conflict may also be present in paradigms in which response frequencies are different, that is, in oddball paradigms (see, e.g., Braver, Barch, Gray, Molfese, & Snyder, 2001). It seems plausible that a frequent response (or withholding/inhibiting a response) becomes a prepotent reaction to stimuli presented in the experimental situation, and the prepotent reaction to the presentation of a stimulus and the (infrequent) response based on the identification of the stimulus are both activated, causing a response conflict. Moreover, it seems plausible that the magnitude of conflict increases with the asymmetry of the stimulus- and therefore response-ratio. Note that the conflict-related N2 has been hardly investigated in the auditory domain. Here, its presence seems to be contingent upon the careful control of similarity of the frequent and infrequent sounds corresponding to the different response options (Nieuwenhuis, Yeung, & Cohen, 2004). Szmalec and colleagues (2008) found that a frontal N2 was elicited in a pitch-discrimination choice-reaction time task, whereas no N2 was elicited when the same stimuli were presented in the context of a simple reaction time task. Moreover, the N2 amplitude was higher when the discrimination was difficult. It is important to note that, when participants are required to make a discriminative response to infrequent sounds, a central negative component at around 200– 250 ms, termed N2b, is elicited (Na¨a¨ta¨nen & Gaillard, 1983). It was suggested that the N2b reflects a modality-aspecific process: the controlled registration of the occurrence of an infrequent task-relevant event (as opposed to its automatic detection reflected by the mismatch negativity, MMN, see Ritter, Paavilainen, Lavikainen, Reinikainen, Alho et al., 1992). Based on its similarity to N2s obtained in visual studies, it is also possible that the N2b reflects ‘‘template mismatch’’ (Folstein & Van Petten, 2008). Although the N2b is obtained in situations including conflicts, its specific relation to the conflict-related N2 described above has not yet been investigated. Infrequent, unpredictable stimuli also elicit ERPs from the family of P3 components. If the oddball stimulus is infrequent with respect to a task-irrelevant stimulus feature, it usually elicits the P3a, a fronto-central positivity peaking at about 300 ms from the onset of the irrelevant feature. P3a is assumed to reflect a process related to the involuntary orienting of attention (Escera, Alho, Schro¨ger, & Winkler, 2000; Friedman, Cycowicz, & Gaeta, 2001; Knight & Scabini, 1998; Schro¨ger, 1996); however, the precise role of P3a is still debated (see Dien, Spencer, & Donchin,
J. Horva´th et al. 2004). If the infrequent stimulus-feature is task-relevant, the stimulus elicits a slow parietal positivity starting around 350–400 ms, termed P3b. It has been proposed that P3b reflects a memory function, which maintains and updates the working-memory representation of the stimulus context describing the subjective probabilities of the stimuli in task categories (Donchin & Coles, 1988; Polich, 2007; but see Verleger, 1988) or it might be related to task-related monitoring or decision processes (Verleger, 2008). Whether P3b reflects purely stimulus-related processing or whether it reflects response-related processing as well is a debated topic (for summaries, see, e.g., Dien et al, 2004; Verleger, 1997; Verleger, Jaskowski, & Wascher, 2005). Goal of the Present Study The series of ERPs elicited in an active oddball paradigm are often interpreted as correlates of distinct processing stages bridging the gap between ‘‘early’’ sensory processing and ‘‘late’’ motor responses, starting with stimulus registration, stimulus selection, stimulus identification, and, finally, its categorization in taskrelated terms. It is implied that, whereas these stages may be temporally more or less separable, they are not necessarily serial; that is, processing at a later stage might not depend upon the completion of earlier stages, and it would be deceptive to describe these as consecutive stages in the chain of a stimulus-response translation process (Dien et al., 2004). Indeed, many studies suggest that, apart from the processing taking place ‘‘early’’ in the perceptual system and ‘‘late’’ in the motor-system, stimulusand response-related (or action- and sensory consequence-related) processing cannot be separated (see, e.g., Magen & Cohen, 2007). The Theory of Event Coding (TEC, for a summary, see Hommel, Mu¨sseler, Aschersleben, & Prinz, 2001) suggests that any given stimulus and the response associated with it or an action and its sensory consequences are represented in a combined network of stimulus and response features, the so-called event file (Hommel, 1998). Note that, whereas TEC is mainly based on experiments conducted with visual stimulation, there is evidence supporting the existence of event files in the auditory domain as well (Mondor & Leboe, 2008; Zmigrod & Hommel, 2009). The goal of the present study was to investigate whether the formation of such combined stimulus-response representations influences certain ERP components with relatively wellestablished functional interpretations. According to TEC, if two events share certain (stimulus or response) features, the occurrence of one of these events may lead to the activation of the other event-representation as well, through the activation of the common feature-representations. This way, a shared (stimulus or response) feature between events of a given context may lead to the simultaneous activation of incompatible sets of feature-representations, which cause processing interference. In these terms, response-conflict (i.e., the simultaneous activation of more response options) can be described as interference between event-representations activated by shared stimulus-features. Similarly, if stimulus-response events are represented in the form of event files, then interference might also emerge during the processing of a stimulus associated with the same response as another potential stimulus in the given context. The goal of the present study was to investigate whether response-sharing between stimulus-response events influences cognitive processing as reflected by ERP and behavioral responses in the context of an oddball paradigm. To investigate the two sources of processing interference, response-sharing and stimulus infrequency, we used a set of S-R
Response sharing in an oddball paradigm mappings (see Figure 1), in which two stimuli were associated with two independent response options, and two other stimuli with a common, third response option. One stimulus associated with an independent response and one stimulus associated with the shared response were presented frequently, whereas the other two were presented infrequently. By analyzing response times to and ERPs elicited by the four types of stimuli, it can be determined whether the two sources of interference influence processing; if any of them do, the temporal locus of these effects may be specified. In accord with the studies described above, we hypothesized that processing interference will be higher for 1) stimuli with shared response options compared to those associated with independent responses, and 2) rare stimuli compared to frequent ones, because frequent events may have more robust event-representations on average than rare ones. Specifically, on the basis of this event-file-interference hypothesis, we expected longer response times for trials with higher interference; moreover, it seems reasonable to assume that such effects mainly influence the conflict-related N2 and P3b, which should both be elicited with higher amplitudes. However, other components may be affected as well. For example, the P3a was shown to be affected by learned action-effect associations (Waszak & Herwig, 2007). It should be noted that the event file approach was actually developed to account for trial-to-trial effects and that we extend this framework by test for effects between rare vs. frequent stimuli and between stimuli with shared vs. independent responses. An alternative hypothesis can be based on the notion that S-R mappings impose a categorization on the stimuli (S-R mappingimposed stimulus categorization hypothesis). According to this hypothesis, the four stimuli are treated as instances of three categories formed on the basis of stimulus-response mappings (i.e., the two stimuli with the shared response are grouped together). This hypothesis is mainly based on the task-related stimuluscategorization effects on P3b. For example, in the study of Johnson and Donchin (1980), where the task was to count 1000 Hz tones and disregard tones with higher pitch (1400 Hz and 1800 Hz), P3b amplitude for a 33%-probable target was the same, regardless of whether the nontargets were a single stimulus with 67% probability, or two 33%-probable stimuli. The categorization, of course, did not overwrite perception: participants could clearly distinguish that there were two higher tones, as indicated by sequential probability effects on P3b amplitude (e.g., nontarget-A elicited a larger P3b if preceded by nontargetB than if preceded by another nontarget-A). In a mirror image of the Johnson and Donchin study, Breton, Ritter, Simson, & Vaughan (1988) showed that, in a visual oddball letter-discrimination task, the 80%-probable nontarget stimuli elicited similar waveforms regardless of the number of stimuli (25 vs. 1) in the target category. It seems evident that a given task-imposed stimulus categorization may or may not fit well the perceptual organization of
Figure 1. Stimulus-response mappings and stimulus probabilities used in the experiment.
933 the stimulus material. In the Johnson and Donchin study, it would probably have been more difficult to form two categories corresponding to the response options if the middle-frequency tones would have to be counted, because a single category boundary along the pitch dimension could not be set (see Mecklinger & Ullsperger, 1993). It seems plausible that, if perceptual processing extracts information that helps the grouping or separation of the relevant stimuli into the categories imposed by the S-R mapping, then it will be relied upon, whereas ‘‘unhelpful’’ parts of the perceptual evaluation will be suppressed. Mecklinger and Ullsperger (1993) found that P3b amplitude elicited by a stimulus depended not simply on the probability of the given stimulus, but it was also influenced by the category-membership of the stimulus within the given context. In a task associating responses to tones with different frequencies, they found that easily separable tones (i.e., the lowest and highest ones) elicited larger P3bs than those which were difficult to separate from the others (i.e., those in the middle), and resulted in faster behavioral responses. On the basis of these results, in the present study, it can be hypothesized that stimuli with independent responses will elicit a larger P3b and trigger faster responses than those with a shared one. Indeed, if the imposed categorization would be perfectly supported by the stimulus set, this hypothesis would suggest that P3b amplitudes would reflect the probabilities of the associated responses (see remark on the possible influence of response-frequency below). Stimulus categorization may also affect ERP components emerging in the N2 latency range; however, evidence on this matter is rather ambiguous (see Folstein & Van Petten, 2008; Pritchard, Shappell, & Brandt, 1991). If S-R mappings result in a stimulus categorization reflecting response-probabilities, stimuli in infrequent categories may elicit an N2b in comparison to those in frequent ones. Moreover, rarity of stimulus features may also influence the N2 even within the same category: Folstein, Van Petten and Rose (2008) found that stimuli with rare features elicited larger N2s than those with more frequent features, even though the two sorts of stimuli shared a 33%-probable response. The number of stimuli within a category might also influence the N2 amplitude: in the oddball visual letter-discrimination experiment mentioned above, Breton et al. (1988) found that the anterior N2 elicited by targets was larger and reactions were slower in the 25- than in the 1-target condition. However, this effect might have been caused by differences in the average discriminability of targets and nontargets as well. The suggested S-R mapping-imposed stimulus categorization might be achieved by ‘‘tuning’’ selective attention set in the given stimulus context to optimize the processing of task-relevant information. This could be seen as a weighing of information provided by early perceptual processing that is achieved by 1) focusing on those perceptually extracted pieces of information that make it possible to better distinguish stimuli corresponding to separate responses, and 2) drawing attention away from pieces of information (or suppressing the processing of information) that may distinguish stimuli mapped to the same response. Thus, both event-file-interference and S-R mapping-imposed categorization hypotheses predict reaction time prolongation and N2-P3 amplification in trials with rare vs. frequent stimuli. With respect to the processing of stimuli with shared relative to independent responses, the event-file-interference hypothesis predicts response delay and N2-P3 amplification for stimuli with shared responses compared to those with independent responses; the S-R mapping-imposed categorization hypothesis
934 predicts a P3 amplification is in the opposite direction, that is, enlarged P3 with independent responses. With respect to behavioral responses and N2, it is not that obvious what to predict; however, if the number of stimuli within a category affects N2 (see Breton et al., 1988), then stimuli with a shared response should elicit a larger N2 and reaction times to these should be longer. On the other hand, if the results solely depend on the postulated three categories formed on the basis of stimulus-response mappings, no response and N2 differences between stimuli for shared and independent responses are to be expected. It is apparent that both hypotheses described above depend on the nature of stimulus material. Interference between a set of stimulus-response event representations may arise not only from sharing response but also stimulus features, and categorization probably takes place only if there is some perceptually extractable information that allows the simplification of the stimulus context. In the present study, we presented feature-rich, dynamic stimuli mapped arbitrarily (randomly) to response options. By randomizing the stimulus roles, the number of shared features can be, on average, balanced out, while feature-rich, dynamic stimuli most likely provide features that help distinguish one but not other pairs of stimuli. Task-relevant sensory information may be delivered with high temporal dispersion within feature-rich, dynamic sounds, which could lead to stimulus-specific delays in behavioral and ERP responses. In order to temporally constrain the delivery of stimulus information, we used sounds of very brief duration (20 ms). To eliminate ERP effects stemming from physical stimulus differences, the stimulus-response type associations were counterbalanced between, whereas stimulus probabilities were counterbalanced within participants. Note that the response-sharing factor is confounded by response frequency in the present design: the three response options are used with different frequencies (50%, 45%, and 5%). Indeed, it seems possible that the processes reflected by the ERPs react first and foremost to stimulus or response infrequency. This has to be considered when interpreting the results. Whereas the present design does not aim for a complete dissociation of stimulus and response frequencies, it still eliminates the one-to-one correspondence between stimuli and responses, thereby, it may provide insight on stimulus- and response-related processing. Methods Participants Twelve healthy volunteers (7 women, aged 24–34 years; mean 24 years; all right-handed) participated in the experiment for course credit or monetary compensation. All participants reported normal hearing status and gave written informed consent after the experimental procedures were explained to them. Stimuli and Procedure The participants were seated in a comfortable chair in a soundattenuated and electrically shielded room during the experiments. Sequences comprising four spectrally rich, dynamic sounds were presented through headphones (Sennheiser HD-25, Wedemark, Germany). We used edited and modified versions of sounds from the Freesound Project (http://freesound.iua.upf.edu; see Appendix). The duration of the sounds was 20 ms. Peak amplitudes corresponded to an approximate intensity level of 82 dB (SPL). A detailed description of the sounds can be found in Horva´th, Roeber, and Schro¨ger (2009).
J. Horva´th et al. Participants were instructed to respond to each sound according to a four-to-three S-R mapping (i.e., two sounds were associated with the same response, see Figure 1) that was constant throughout the experimental session. The three response options were one button under each of the index, middle, and ring finger of the right hand. The S-R mappings were generated randomly for the twelve participants with the following constraints: (1) the shared response was mapped to the index, middle, and ring finger for four participants each; (2) each sound was presented to six participants as a stimulus corresponding to a shared response, and to six participants as a stimulus corresponding to an independent response; and (3) no participants had the same S-R mapping. The experiment started with the learning of the S-R mapping and practicing the task. In the learning phase, participants listened to random sequences of the four sounds presented with a stimulus onset asynchrony (SOA, onset-to-onset inter-stimulus interval) of 3000 ms. In the learning and practice phase, the sequences consisted of random 4-element permutations of the four sounds. 1000 ms before each sound, one of three horizontally presented squares were highlighted on a display in front of the participants. Participants were instructed to press the button corresponding to the position of the highlighted square (leftFindex finger, middleFmiddle finger, rightFring finger) as soon as they heard the sound. We drew the participants’ attention to the fact that the goal of this phase was to learn the sound-response mapping, and that one of the buttons had to be pressed in response to two sounds. In the learning phase, three 40-sound–long learning sequences were presented. In the practice phase, six sequences of 100 sounds were presented without the visual aid, with an SOA of 1500 ms. The experimental part consisted of twelve experimental blocks, presented in random order. In each block, 204 sounds were presented with an SOA of 1500 ms. The first four stimuli of each block were a random permutation of the four sounds. In the remaining 200 trials, one of the stimuli corresponding to a shared response was presented 90 times, the other stimulus associated with the same response 10 times; similarly, one of the stimuli associated to an independent response was presented 90 times, the other stimulus associated to another independent response 10 times. The presentation frequencies of the stimuli corresponding to the same type of response were exchanged in half of the blocks to avoid effects that can be explained by the physical characteristics of the stimuli. The orthogonal variation of the stimulus frequencies corresponding to independent and shared responses resulted in four configurations (e.g., if S1 and S2 were associated with independent responses, then S1 was frequent and S2 was infrequent in two configurations, and vice versa; combined with the orthogonal variation of the S3 and S4 frequencies, this results in four stimulus configurations). Each configuration was presented in three blocks. To avoid excessive eye movements, participants were asked to keep looking at a puppet placed in front of them at a comfortable (2 m) distance during the experimental blocks. The stimulus blocks were separated by short breaks. During the breaks, a feedback about the correct response rate in the last block was given. Only correct responses given in the [100, 1400] ms interval following the onset of the stimuli were included in the reaction times analysis. Reaction times and error rates were analyzed in two-way repeated measures analyses of variance (ANOVA) using Stimulus (frequent vs. rare) and Response (shared vs. independent) as factors. Partial eta-squares are reported (Zp).
Response sharing in an oddball paradigm EEG Recording and Analysis Electroencephalogram (EEG) was recorded using Ag/AgCl electrodes with an Active-Two system (BioSemi, Amsterdam, The Netherlands), with 512 Hz sampling rate (DC-coupled; on-line low-pass, 5th order sinc filtering with -3 dB point at 102.4 Hz) from the scalp-locations Fp1, Fp2, AF3, AF4, F7, F3, Fz, F4, F8, FC5, FC1, FC2, FC6, T7, C3, Cz, C4, T8, CP5, CP1, CP2, CP6, P7, P3, Pz, P4, P8, PO3, PO4, O1, Oz, O2 (according to the 10% system, Nuwer, Comi, Emerson, Fuglsang-Frederiksen, Gue´rit et al., 1998). Electrodes were also placed on the mastoids (left: Lm and right: Rm). The reference electrode (for off-line rereferencing) was placed on the tip of the nose. Horizontal eye movements were monitored with electrodes placed lateral to the outer canthi of the two eyes; vertical eye movements were monitored with electrodes placed above and below the right eye. The EEG was band-pass filtered offline (Kaiser-windowed sinc filter with 0.5 and 15 Hz cutoff frequencies, 0.4 Hz transition width, maximum passband attenuation of 0.003 dB, and at least 70 dB stopband attenuation). 1500 ms long epochs were extracted including a 200 ms pre-stimulus interval. Amplitude calculations were referred to the average signal measured in the prestimulus interval. Epochs corresponding to the first six trials of each block and epochs with a signal range exceeding 150 mV on any channel were discarded from the analyses. Epochs corresponding to frequent and infrequent shared-response and independent-response stimuli were averaged separately (but collapsed across the different stimuli). ERP components were visually identified in the group-average ERPs, and infrequentminus-frequent or independent-minus-shared response difference waveforms. The amplitudes of components peaking with similar latency in each condition were submitted to repeated measures ANOVAs using Anterior/Posterior (A/P in the following) and Laterality factors over the AF3, AF4, FC1, FC2, CP1, CP2, PO3, and PO4 electrode sites in combination with Stimulus (frequent vs. infrequent) and Response (shared vs. independent) factors. In the following, we will use the AF, FC, CP, and PO abbreviations to denote the electrode lines over the A/P direction. The individual average amplitudes were calculated in a 40 ms long window centered at the group-average amplitude peaks. Because some later ERP waveforms could not be described by a single peak-amplitude difference, and the durations of these ERP effects were longer, individual average amplitudes were calculated in two 130 ms long epochs and entered to ANOVA with the same factor structure as described above. Greenhouse-Geisser correction was applied where applicable. Only significant effects involving the Stimulus or Response factors are reported. Interactions involving the A/P factor were also investigated by normalizing the amplitude measurements separately for the levels of the interacting non-electrode factors (or their permutations, if both Stimulus and Response factors were involved) and conducting an ANOVA of the normalized amplitudes using the factors in question. In this type of analysis, interactions between the electrode factors and the Stimulus or Response factors indicate a topographical shape difference, which may suggest that amplitude differences are attributable to a difference in the generator structure of the components and not a simple modulation of the amplitude of the given component (McCarthy & Wood, 1985). For these types of analyses, only the presence or absence of interaction is reported. Topographical distributions were further specified by pair-wise t-tests between electrode sites.
935 Results Behavioral Data Group average reaction times and error rates are presented in Table 1. The ANOVA of the reaction times showed a main effect of Stimulus: F(1,11) 5 103.79, Zp 5 .90, po.001; and Stimulus ! Response interaction: F(1,11) 5 9.62, Zp 5 .47, po.01. To resolve the interaction, t-tests were calculated between independent and shared-response reaction times at the two Stimulus levels. A significant difference was only found for frequent stimuli: shared responses were significantly faster than independent responses: t(11) 5 3.43, po.01. The rare-minus-frequent reaction time difference was also larger for stimuli with shared responses than those with independent responses (196 vs. 144 ms): t(11) 5 3.10, po.05. The ANOVA of the error rates showed a main effect of Stimulus: F(1,11) 5 22.01, Zp 5 .67, po.001. In summary, responses were generally slower to rare than to frequent stimuli, and participants made more mistakes when responding to rare stimuli. Also, shared responses were faster to frequent stimuli than independent ones. Event-related Potentials Figure 2 shows the ERPs (left column) and difference waveforms (right column) elicited in the experiment. The different stimuli elicited the N1, N2, and early P3 with different amplitudes at very similar latencies, similar to ERP effects emerging in this latency range. These component amplitudes were measured in 40 ms long windows centered at the group average peak latency. For the mean amplitudes standard errors are reported. N1-effect Rare stimuli elicited an increased negativity compared to the frequent ones peaking at 95 ms. The ANOVA of the amplitudes showed a Stimulus main effect: F(1,11) 5 7.83, Zp 5 .42, po.05; and a Stimulus ! A/P interaction: F(3,33) 5 19.59, e 5 .71, Zp 5 .64, po.001. The Stimulus ! A/P ANOVA of the normalized amplitudes (averaged over Response and Laterality levels) showed an interaction: F(3,33) 5 11.22, e 5 .73, Zp 5 .50, po.001, indicating that the infrequency related N1-increment is (at least in part) attributable to an additional, negative posterior component. Whereas frequent stimuli elicited an N1 that was significantly larger at FC ( " 3.79 # 0.85 mV) and CP ( " 3.61 # 0.70 mV) than at AF ( " 2.41 # 0.52 mV, t[11] 5 3.44; po.01, and t[11] 5 2.58; po.05, respectively), and larger at CP than at PO ( " 2.58 # 0.53 mV, t[11] 5 3.33; po.01; see Figure 2, left column), the rare-minus-frequent difference waveform was larger at the CP and PO sites (t-values 42.32 and p-values o.05 for all comparisons with the AF and FC sites, see Figure 2, right column; the difference amplitudes were " 0.28 # 0.26 mV, " 0.65 # 0.36 mV, " 1.21 # 0.35 mV, " 1.34 # 0.31 mV, respectively, for AF, FC, CP, and PO). Table 1. Group-average Reaction Times and Error Rates (with Standard Errors of Means) Reaction time (ms)
Independent response Shared response
Error rate (%)
Frequent stimulus
Rare stimulus
Frequent stimulus
Rare stimulus
424 (16) 453 (20)
620 (29) 597 (29)
5 (1) 5 (1)
8 (1) 9 (2)
936
J. Horva´th et al.
Figure 2. Group-average ERPs (left column) and difference waveforms (right column) over anterio-frontal (AFFaverage of AF3 and AF4), frontocentral (FCFaverage of FC1 and FC2), centro-parietal (CPFaverage of CP1 and CP2), parieto-occipital (POFaverage of PO3 and PO4) sites. Response differences are independent-minus-shared differences; frequency differences are rare-minus-frequent differences. Stimulus onset is at 0 ms. Note that the calibration of the difference waveforms (right) differs from that of the ERPs (left).
N2-effects There was an N2-increment observable both in the rare-minus-frequent and the independent-minus-shared response difference waveforms, peaking at 201 ms (see Figure 3 for the potential maps of the differences). Whereas the Response effect was a modulation of the N2 amplitude with central maximum, stimulus infrequency led to a topographical shape difference observable as a high-amplitude, wide-spread, right-lateralized distribution having a parietal focus (as supported by the analyses below). The ANOVA of the amplitudes showed a Stimulus main effect: F(1,11) 5 54.21, Zp 5 .83, po.001; a Response main
effect: F(1,11) 5 7.64, Zp 5 .41, po.05; a Stimulus ! Laterality interaction: F(1,11) 5 13.71, Zp 5 .55, po0.01; a Stimulus ! A/P interaction: F(3,33) 5 23.41, e 5 .47, Zp 5 .68, po.001; and a Response ! A/P ! Laterality interaction: F(3,33) 5 4.11, e 5 .65, Zp 5 .27, po.05. The three-way interaction was further investigated by an ANOVA of the normalized amplitudes (averaged across Stimulus levels), which showed no significant interaction. The independent-minus-shared response difference amplitude was larger at FC1 ( " 1.61 # 0.52 mV) and FC2 ( " 1.42 # 0.53 mV) than at AF1 ( " 1.18 # 0.37 mV) and AF2 ( " 0.94 # 0.42 mV), respec-
Response sharing in an oddball paradigm
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Figure 3. Potential maps of the N2 difference waveforms at 201 ms following stimulus onset.
tively; it was larger at CP2 (! 1.57 " 0.50 mV) than at AF2, and it was larger at CP1 (! 1.41 " 0.47 mV) and CP2 than at PO1 (! 0.98 " 0.49 mV) and PO2 (! 0.94 " 0.54 mV), respectively (t-values 42.40 and p-values o.05; see Figure 2, right column and Figure 3, right column). The Stimulus # A/P interaction was resolved by a Stimulus # A/P ANOVA over the normalized amplitudes (averaged across Response and Laterality levels), which showed an interaction: F(3,33) 5 8.57, e 5 .45, Zp 5 .44, po.01, indicating that the infrequency related N2-increment is (at least in part) attributable to an additional, negative posterior component. The rare-minusfrequent difference amplitude was larger (more negative) at the CP (! 4.32 " 0.59 mV) and PO (! 4.07 " 0.63 mV) sites than at AF (! 1.8 " 0.22 mV) or FC (! 3.29 " 0.46 mV; t-values 42.49 and p-values o.05); it was larger at FC than at AF [t(11)45.06; po.01; see Figure 2, right column and Figure 3, left column]. The Stimulus # Laterality interaction was resolved by t-tests between frequent and rare stimuli on the left and right side (averaged across A/P and Response levels), which showed significant differences (t-values 46.93, p-values o.001). A t-test between the rare-minus-frequent differences at the left and right sides (averaged across A/P and Response levels; ! 3.21 " 0.46 mV and ! 3.53 " 0.46 mV, respectively) showed that the Stimulus-related negative effect was right-lateralized [t(11) 5 3.70, po.01]. Because of its theoretical relevance (see Discussion), we explicitly tested whether the response-related N2-effect was significant for frequent stimuli: the Response # A/P # Laterality ANOVA over the amplitudes elicited by frequent stimuli showed a Response main effect: F(1,11) 5 5.73, Zp 5 .34, po.05. Early P3 interval A sharp positivity peaking at 320 ms was present for all ERPs, identified as an early P3. The ANOVA of the amplitudes showed a Stimulus # A/P interaction: F(3,33) 5 6.21, e 5 .50, Zp 5 .36,
po.01. The interaction was followed up by t-tests between the frequent and rare stimuli at the different A/P electrode sites (averaged over sides and response types), which showed no significant differences. Pair-wise comparisons of the rare-minusfrequent difference amplitudes between A/P electrode sites showed that the differences at AF (1.05 " 0.62 mV) and FC (0.89 " 0.95 mV) were significantly more positive than those at CP (! 0.39 " 0.90 mV) and PO (! 0.64 " 0.70 mV; t-values 42.36, p-values o.05). This may indicate the presence of a P3a. The ANOVA of the normalized amplitudes (averaged across Response and Laterality levels) showed no significant interaction. Late P3 interval As shown in Figure 2 (left column), after the early P3 peak, the ERP waveforms cannot be uniformly described by single peak amplitude effects: for the frequent stimuli, positive signals slowly drift towards the baseline, whereas for rare stimuli, high amplitude positive peaks can be observed. Comparisons of the Stimulus and Response effects (Figure 2, right column), show that Stimulus effects (i.e., the rare-minus-frequent waveforms, represented with blue and red lines) and Response effects (i.e., independent-minus-shared response waveforms, represented with solid and dotted black lines) show a diverging pattern. There are, however, two common regions where the effects are maximal: all four difference waveforms exhibit posterior maxima between 484 and 565 ms; and peaking at 662 and 699 ms there are later positive maxima for both rare-minus-frequent difference waveforms. In accord with these observations, mean amplitudes were calculated in the [460, 590] ms and the [615, 745] ms intervals and submitted to Stimulus # Response # A/P # Laterality ANOVAs. As the analyses below confirm, stimulus infrequency and response independency resulted in higher amplitudes in both cases.
938 In the first interval, however, the two experimental factors produced an interaction: the effect of infrequency was larger for stimuli with independent responses than for those with sharedresponses, whereas in the second interval no interaction was found. For the second interval, the infrequency related P3beffect was slightly left lateralized. 460–590 ms interval. The ANOVA of the amplitudes in the first interval showed a Stimulus main effect: F(1,11) 5 8.52, Zp 5 .44; po.05; a Response main effect: F(1,11) 5 16.94, Zp 5 .61, po.001; a Stimulus ! Response interaction: F(1,11) 5 7.67, Zp 5 0.41, po.05; a Stimulus ! A/P interaction: F(3,33) 5 13.94, e 5 .46, Zp 5 0.56, po.001; and a Response ! A/P interaction: F(3,33) 5 16.75, e 5 .55, Zp 5 0.60, po.001. The Stimulus ! Response interaction was resolved by t-tests between frequent and rare stimuli corresponding to independent and shared responses and also between independent and shared responses for frequent and rare stimuli (averaged across A/P and Laterality), which showed that infrequent stimuli or those associated with an independent response elicited significantly larger ERPs (t-values 42.27, p-values o.05). A t-test between the independent and shared-response rare-minus-frequent differences showed that the difference was larger for stimuli with independent responses [t(11) 5 2.76, po.05]. (The mean amplitudes and standard errors: frequent stimulus with shared response: 5.21 " 0.97 mV; frequent stimulus with independent response: 6.31 " 1.07 mV; rare stimulus with shared response: 7.56 " 1.48 mV; rare stimulus with independent response: 10.45 " 1.94 mV.) Both interactions with the A/P factors were followed-up by an ANOVA of the normalized amplitudes (averaged across Laterality and Stimulus, or Laterality and Response levels), neither of which showed a significant interaction. To characterize the topographies, pair-wise t-tests between independent-minus-shared difference amplitudes at the A/P sites showed that the amplitude was significantly larger for the posterior site within each comparison, except for CP and PO (t-values 43.10 and p-values o.01, see Figure 2, right column; the difference amplitudes were 1.01 " 0.48 mV, 1.57 " 0.60 mV, 2.56 " 0.55 mV, 2.84 " 0.43 mV, respectively, for AF, FC, CP, and PO). 615–745 ms interval. The ANOVA of the amplitudes in the second interval showed a Stimulus main effect: F(1,11) 5 25.74, Zp 5 .70, po.001; a Stimulus ! Laterality interaction: (1,11) 5 10.98, Zp 5 .50, po0.01; a Stimulus ! A/P interaction: F(3,33) 5 11.43, e 5 .44, Zp 5 0.51, po.01; a Response ! A/P interaction: F(3,33) 5 25.46, e 5 .62, Zp 5 0.70, po.001; and a Stimulus ! Response ! A/P interaction: F(3,33) 5 9.20, e 5 .51, Zp 5 0.46, po.01. To resolve the three-way interaction, separate Stimulus ! Response ANOVAs were calculated at all four Anterior/Posterior levels (averaged across Laterality levels), which showed a Stimulus main effect at FC [F(1,11) 5 8.14, Zp 5 .42, po.05], and both Stimulus and Response main effects at CP [F(1,11) 5 34.52, Zp 5 .76, po.001; and F(1,11) 5 7.72, Zp 5 .41, po.05 respectively], and PO sites [F(1,11) 5 55.77, Zp 5 .83, po.001; and F(1,11) 5 10.93, Zp 5 .50, po.01, respectively]. The ANOVA of the normalized amplitudes (averaged across Laterality levels) showed no significant interactions. To characterize the topographies, pair-wise t-tests between the rare-minus-frequent difference amplitudes were calculated for the stimuli with independent responses, which showed that the
J. Horva´th et al. amplitude was significantly larger for the posterior site within each comparison, except for CP and PO (t-values 43.35 and pvalues o.01, see Figure 2, right column; the difference amplitudes were 0.23 " 0.76 mV, 2.10 " 0.74 mV, 5.17 " 0.71 mV, 5.37 " 0.62 mV, respectively, for AF, FC, CP, and PO). The Stimulus ! Laterality interaction was resolved by comparing the rare-minus-frequent amplitude differences on the two sides, which showed that the differences were larger on the left (3.43 " 0.64 mV) than on the right (3.12 " 0.66 mV) side: t(11) 5 3.31, po.01. Discussion The pattern of behavioral and ERP responses for the stimulus frequency factor are compatible with both the event-file-interference and the S-R mapping-imposed categorization hypotheses: behavioral responses were prolonged and N1-N2-P3 enlarged for rare as compared with that for frequent stimuli. Regarding the response-sharing/independence factor, however, the results clearly contradict the event-file-interference hypothesis: the hypothesized high-conflict trials resulted in faster responses (when compared between frequent stimuli), and lower conflict-related ERP amplitudes. Of course, this relates to our extension of the TEC to the present setting, not to the TEC per se. The finding that the P3b amplitudes were higher for stimuli with independent responses is compatible with the alternative SR mapping-imposed categorization hypothesis. However, the finding of enhanced N2 amplitudes for stimuli with independent responses requires a reformulation of the S-R mapping-imposed categorization hypothesis. Behavioral Responses Responses were strikingly delayed for rare stimuli, and they were significantly slower for frequent stimuli associated with an independent response than for those associated with a shared response. Participants made more erroneous responses for infrequent than for frequent sounds. At a first glance, the effect of response-sharing for the frequent tones should be interpreted with care, because it might as well show a response-frequency effect: the response to the frequent stimulus associated with a shared-response may be faster than the one associated with the independent response because it was used more frequently (50% vs. 45%). There are two arguments that can be put forward against this explanation: 1) Other studies with larger response frequency differences did not find such a reaction time difference (e.g., 70% vs. 30%, Bertelson & Tisseyre, 1966), and 2) If response frequency played a role in this effect, then it should have been manifested also for rare stimuli, because the response frequency difference was even more substantial in this case (45% vs. 5%). Thus, the behavioral results do not support the event-fileinterference hypothesis, which would predict prolonged reaction times (due to larger conflict) for stimuli sharing the response. ERP Effects At electrophysiological level, the processing of stimulus infrequency and response-sharing information seem to follow separate, but temporally overlapping processing routes that converge only at the latency of the P3b. The processing of stimulus infrequency information was indexed by an N1-effect, an infrequency-related (oddball) N2-effect, a P3a, and the early and late P3b components. On the other route, response-sharing resulted in a topographically distinct N2- and an (early) P3b-effect. In the
Response sharing in an oddball paradigm following, we first discuss those ERP components which were selectively affected by the two factors, then commence with a discussion of P3b-effects. Stimulus-infrequence related ERP effects Infrequent stimuli elicited an unusual posterior N1-increment which was probably brought about by the unique stimulus characteristics of the present study (very short, feature-rich, dynamic sounds). The same stimulation did not result in N1-differences between rare and frequent stimuli in a passive situation (i.e., in which participants had no task regarding the stimuli and watched a silent movie during stimulation, Horva´th, Roeber, & Schro¨ger, 2009). From numerous selective attention studies and a longlasting debate on that issue, it is widely accepted that attention does increase the N1-amplitude (Hillyard, Hink, Schwent, & Picton, 1973; Na¨a¨ta¨nen, Alho, & Schro¨ger, 2002). To the best of our knowledge, it is not settled whether this attentional N1-enhancement is especially pronounced in rare relative to frequent stimuli. However, based on the assumption that the N1 reflects stimulus encoding (Na¨a¨ta¨nen & Winkler, 1999) it seems likely that the encoding of the difference between frequent and rare stimuli may benefit from attentional enhancement as well, which, in turn, can pay out in an especially enlarged N1 for oddball stimuli. This is a common explanation for attentional effects on the mismatch negativity (Sussman, 2007). Although the cause of the distribution-effect is unclear, it should be noted that it somewhat resembles the posterior distribution sometimes reported for N2-effects (see next paragraph). Stimulus infrequency was manifested in a wide, right-lateralized N2-effect with a parietal focus. Due to its topography, this effect is unlikely to be an MMN (Na¨a¨ta¨nen, 1992), an auditory ERP also elicited by infrequent, irregular stimuli presented in a regular sound sequence, because MMN exhibits a fronto-centrally negative peak for these stimuli (Horva´th, Roeber, & Schro¨ger, 2009). The posterior N2 topography is rather unusual in an auditory oddball paradigm, however, similar negativities have been reported before in auditory distraction paradigms (Schro¨ger & Wolff, 1998) in which the P3a did not overlap in the same latency interval (Horva´th, Czigler, Birka´s, Winkler, & Gervai, 2009; Horva´th, Roeber, Bendixen, & Schro¨ger, 2008), or in oddball paradigms using meaningful, emotionally, or personally significant sounds as distractors (Celsis, Doyon, Boulanouar, Pastor, Demonet, & Nespoulous, 1999; Czigler, Cox, Gyimesi, & Horva´th, 2007; Roye, Jacobsen, & Schro¨ger, 2007). Posterior N2s are often elicited in visual oddball paradigms, which are generally interpreted as direct correlates of the attentional processing demands of the given stimulus (see Folstein & Van Petten, 2008). The present study and the auditory studies mentioned above, however, suggest that a similar N2-effect may be present in auditory experiments as well, therefore, it may reflect a modality-aspecific process, probably related to stimulus categorychange, infrequency, or ‘‘oddness’’ of the stimuli. A small P3a was also elicited by infrequent stimuli. Since P3a is usually elicited if the oddball stimulus comprises a task-irrelevant change, this may indicate that participants used a (probably implicit) perceptual strategy to reach the highest response speed possible for frequent stimuli; that is, they might have tuned their attention set to the feature that made the fastest discrimination of the two frequent sounds possible. After presenting a number of frequent sounds, features that did not contribute strongly to the discrimination of these might have become temporarily less relevant, thereby resulting in a P3a elicitation when a
939 rare sound was presented. Note that this explanation does not contradict the notion that the four stimuli cannot be readily grouped into fewer categories, because for these sounds, the best feature-dimension for distinguishing two of them when presented frequently is most likely to differ between each pair of sounds. That is, while it may be possible to optimize performance for the frequent set of stimuli by focusing on the best feature distinguishing them, this strategy does not group any of the stimuli leading to different responses together, thereby does not simplify the network of response decisions: it just speeds up decisions for the frequent stimuli. Response-sharing related ERP effects Stimuli associated with independent responses (compared to those associated with shared responses) elicited larger N2 components, which were topographically distinct from the oddballrelated N2-effects (the effect was present in the comparison at the frequent stimulus-level as well). This result contradicts the eventfile-interference hypothesis, which suggests that stimuli associated with shared responses would cause more processing interference, therefore eliciting a larger conflict-related N2. It also contradicts the assumption that a higher number of stimuli within a single category would lead to higher N2 amplitude and slower reaction times (see Breton et al., 1988). Similar to the reaction time effect, this N2-effect might be explained as a response-frequency effect as well, but because the highly different response frequency differences (50% vs. 45% and 45% vs. 5%) did not bring effects of different magnitudes about, this effect is probably related to response independence. An interpretation of this effect has to accommodate two findings: the N2 increment in trials with independent responses and the associated reaction time delay that was observable in frequent stimulus trials. One possibility is that the N2-effect reflects S-R mappingimposed categorization of stimuli. Indeed, it has been suggested that one variant of the N2 reflects categorization; however, evidence regarding this component is highly inconclusive (N2c, see Pritchard et al., 1991). The N2-effects are similar to the P3beffects observed in the present study (see below), therefore the N2 increment may result from the more unambiguous categorization of stimuli corresponding to a one-to-one than to a two-to-one SR mapping (see Mecklinger & Ullsperger, 1993). On the other hand, if stimuli with independent responses were easier to categorize, then responses to them should be faster, which is not the case in the present study (at least for frequent stimuli). Alternatively, it might be possible that the N2-increment is an N2b reflecting the infrequency of the given stimulus category (Ritter et al., 1992), but this interpretation is problematic because of the highly different stimulus ratios described above. The second possibility is that the response independence-related N2-effect is a conflict-related N2-effect. It seems generally accepted that conflict-related N2 amplitude reflects the effort needed to activate a response in the face of other potential response options. In the present paradigm, this would imply that processing a stimulus from a one-to-one S-R mapping poses a larger conflict than the processing of the corresponding stimulus from the two-to-one S-R mapping. Since response frequency differences do not seem to influence the conflict-related N2 amplitude in the present paradigm, the larger amplitude, and therefore larger conflict, may be explained by assuming that the processes reflected by the N2 are influenced by the number of other possible stimuli which would converge onto different response options. That is, in the present paradigm, if a stimulus
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with a one-to-one S-R mapping is presented, then there are three other stimuli which are associated with a different response; however, when a stimulus from a two-to-one mapping is presented, there are only two stimuli associated with alternate responses. In other words, what matters is the number of alternative stimuli for which a different response has to be given, rather than the number or frequency of the competing response options. In this sense, processing interference does come into play, but its role differs from that predicted by the event-file-interference hypothesis. P3b-effects Both stimulus infrequency and response independence resulted in higher P3b amplitudes. The P3b-effects exhibited two distinct peaks at around 525 and 680 ms, the later of which only present in the rare vs. frequent comparisons (with a responseeffect and interaction nonetheless present from the ‘‘tail’’ of the early P3b-effect). For the early P3b, the two factors interacted: the combination of stimulus infrequency and response independency resulted in larger P3b than the combination of the two effects alone. This suggests that the early P3b reflects a process which operates on an integrated representation of stimulus and response features. Both peak-regions followed mean reaction times approximately by 30–50 ms. The left dominance of the later peak (i.e., contralateral to the responding hand) may indicate the involvement of response-related processes in the generation of this component. These results are compatible with the S-R mapping-imposed categorization hypothesis; however, they also suggest that the stimulus material could not be perfectly ‘‘cast into’’ the categories determined by the S-R mappings. Convergence of the Two Routes of Processing The present results show a somewhat perplexing phenomenon: on the basis of the ERP amplitudes in the N2-interval, the cognitive system apparently already held information on which response was appropriate in the given trial at around 200 ms (i.e., in
theory, it could be unequivocally determined which response should be given simply by measuring the peak amplitudes in the N2-interval). Behavioral responses to infrequent sounds were, however, delayed by about 150 ms compared to frequent ones. The only component affected by both factors is the early P3b at approximately the same latency range (around 525 ms). It seems plausible that stimulus-infrequency and response-independence (or -infrequency) information was already integrated at this latency. Because mean reaction times to rare stimuli followed 80– 100 ms after the first P3b peak latency, it seems reasonable to assume that, though an integrated representation of stimulusinfrequency and response-independence information is present, infrequent stimuli trigger processes that prevent the utilization of this information. The existence of such processes have been suggested before in other contexts (cf. ‘‘red flag,’’ a process initiating active response inhibition, Kok, 1986). In summary, we found that the event-file interference did not influence the ERPs elicited in the present experiment as expected. The results are compatible with the S-R mapping-imposed categorization hypothesis, but they also suggest that the stimulus material could not be perfectly ‘‘cast into’’ the categories determined by the S-R mappings. Infrequent stimuli elicited ERP components in the N1 and N2 latency range in addition to the component modulated by response-independence (the conflictrelated N2). These results suggest that the processing of stimulus infrequency follows a different (though temporally overlapping) route from that of response independency as reflected by the ERPs, with the results of the two evaluations integrated only after the N2, but not later than the early P3b latency. The striking response-delay for infrequent stimuli may stem from processes preventing the application of this integrated information. Furthermore, we suggest that conflict-related N2 may reflect the number of expectable stimuli that would lead to alternative responses, and not response-conflict per se.
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(Received March 19, 2009; Accepted October 1, 2009)
APPENDIX The list of sound files from the Freesound Project used in the experiment: ‘‘cracklebox- pweowp.wav’’ by tombola, (http:// freesound.iua.upf.edu/samplesViewSingle.php?id=1122), ‘‘cork.aif’’ by Traveler (http://freesound.iua.upf.edu/samplesViewSingle.php?id=13635), ‘‘Tick 01.wav’’ by A43 (http:// freesound.iua.upf.edu/samplesViewSingle.php?id=9444), and ‘‘clickity 21.glass_bowl.wav’’ by vitriolix (http://freesound. iua.upf.edu/samplesViewSingle.php?id=781).
Psychophysiology, 47 (2010), 942–948. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.00993.x
Backward masking during rapid serial visual presentation affects the amplitude but not the latency of the P3 event-related potential
BENOIT BRISSON,a,b NICOLAS ROBITAILLE,a ALEXANDRINE DELAND-BE´LANGER,a THOMAS M. SPALEK,b VINCENT DI LOLLO,b and PIERRE JOLICŒURa a
Research Center in Neuropsychology and Cognition, University of Montreal, Montreal, Quebec, Canada Department of Psychology, Simon Fraser University, Burnaby, British Columbia, Canada
b
Abstract Masking of the first target in the attentional blink (AB) paradigm increases the magnitude of the AB relative to when the first target is not masked. We examined the underlying causes of this effect in an experiment in which a single target was presented in a rapid visual serial presentation stream. The P3 to the target was isolated by subtracting infrequent target category trials from frequent target category trials. The item immediately trailing the target (i.e., the mask) was present in the masked condition and replaced by a blank screen in the not-masked condition, reproducing conditions known to modulate the AB. Masking the target significantly reduced the amplitude of the target-locked P3 but had no effect on P3 latency. Results are discussed in relation to previous findings in the AB literature. Descriptors: Event-related potentials, Frequency-related P3, Masking, Rapid serial visual presentation, Attentional blink
T1. In light of these and other observations, it has been suggested that when attentional mechanisms are engaged in processing T1, they are not available to process a subsequent target (T2) effectively, which leads to a transient functional blindness observed as the AB. Specifically, it has been proposed that consolidation into working memory, which is thought to be a necessary step for a perceptual representation to reach consciousness, is time-consuming and capacity limited and thus acts as a processing bottleneck (Chun & Potter, 1995; Jolicœur, 1998). According to bottleneck models of the AB, T1 and T2 perceptual representations cannot be consolidated at the same time as efficiently as when T1 and T2 can be processed independently. While a representation of T1 is being consolidated into working memory for later report, consolidation of T2 has to wait or undergoes slower processing (Jolicœur, 1998; Tombu & Jolicœur, 2003). During the period of waiting (or slower consolidation) the representation of T2 decays and is more susceptible to overwriting by a subsequent item in the RSVP stream before being recoded in a more stable format that supports conscious report. One important finding in the AB literature is that when the item immediately trailing T1 (i.e., the T111 item) is removed, the AB is significantly reduced (Chun & Potter, 1995; Raymond et al., 1992; Seiffert & Di Lollo, 1997; Visser, 2007). This manipulation is often characterized as an effect of masking of T1 by the T111 item (often referred to as the T1 mask). It has been shown that more effective T1 masks (i.e., T1 masks that have a greater effect on T1 accuracy) tend to increase the AB (Seiffert & Di Lollo, 1997). Given the relationship between the effect of the T1 mask on T1 accuracy and the amplitude of the AB, some have
In typical attentional blink (AB) paradigms, two targets (T1 and T2) are embedded in a rapid serial visual presentation (RSVP) stream of distractors presented at fixation. Responses are made off-line, at the end of each trial, without speed pressure. If participants are instructed to report the identity of both T1 and T2, the accuracy of responses to T2 is often impaired when it is presented at short intertarget lags, within 500 ms of T1 in a typical version of the paradigm, with maximum deficit usually between 200 and 300 ms when there is no task switch across T1 and T2 (Chun & Potter, 1995; Jolicœur, 1998). It is this decrease in T2 performance at short lags that is known as the AB. When T1 can be ignored, no AB is observed, meaning that participants often do not have difficulty in reporting T2 (e.g., Raymond, Shapiro, & Arnell, 1992), even though it is presented shortly after This research was supported by grants from the Natural Sciences and Engineering Research Council of Canada (NSERC) awarded to P.J., grants from the Canada Research Chairs Program, and the Universite´ de Montre´al awarded to P.J., by NSERC postgraduate scholarship awarded to B.B. and N.R., and by a Fonds Que´be´cois de la Recherche sur la Nature et les Technologies (FQRNT) postdoctoral fellowship awarded to B.B., as well as by an infrastructure grant from the Fonds de Recherche en Sante´ du Quebec (FRSQ). We thank Nathalie Bouloute, Manon Robert, Hugo Chenier, Kevin Murphy, Kristelle Alunni-Menichini, and Rosalie Perron for technical assistance. Address reprint requests to: Benoit Brisson, De´partement de Psychologie, Universite´ de Montre´al, C.P. 6128, succursale Centre-ville, Montre´al, Que´bec, H3C 3J7, Canada. E-mail:
[email protected] 942
Masking attenuates but does not delay the P3 proposed that the T1 mask increases the AB indirectly by degrading perception of T1, thereby increasing T1 processing demands (e.g., Seiffert & Di Lollo, 1997), which in turn creates a longer waiting period in which T2 has to wait before being consolidated. These results are consistent with the relationship observed by Jolicœur and colleagues in which a longer processing time for T1 is associated with a larger AB for T2 (Jolicœur, 1998, 1999a, 1999c). Nonetheless, the exact locus of the purported longer period of processing of T1 caused by visual masking is not known. According to the locus-of-slack logic (see Pashler, 1994; Schweickert, 1980), carry-forward effects of T1 difficulty on T2 performance can occur when difficulty of T1 affects processing before or at the bottleneck. This means that masking of T1 could lengthen processing at a perceptual level or at a later, central stage. Ouimet and Jolicœur (2007) noted that T1 accuracy is usually very similar and very high (near ceiling) between T1 masking conditions. They argued that the near-ceiling performance could be interpreted as an indication that, although the T1 mask can affect early perceptual processing (resulting in poorer performance when the T1 mask is present), it probably has an effect primarily in making the processing of T1 lengthier at later stages of processing. In other words, masking would degrade the perceptual representation of T1, in turn affecting how efficiently this information could be used by later, central stages, such as response selection and decision making. Recent distractor-based theories of the AB have even postulated that the T111 item does not affect T1 processing at all, especially in T1 correct trials, which are usually those included in the analyses of T2 performance in AB studies. According to these models, the items that follow T1, and especially the T111 item, influence the AB directly, without influencing T1 processing, either by inducing an inhibition response (boost and bounce theory; Olivers & Meeter, 2008) or by disrupting an input filter established to accept targets and discard distractors optimally (temporary loss of control; Di Lollo, Kawahara, Ghorashi, & Enns, 2005). To examine the effects of masking on processing of T1, we used the electroencephalogram and the event-related potential (ERP) method, focusing on the P3 ERP component (hereafter used to mean the P3b), an electrophysiological index of ‘‘context updating’’ or encoding into working memory (e.g., Donchin, 1981; Luck, 1998; but see Verleger, 1988). We measured the amplitude and the latency of the P3 elicited by T1 in correctly reported trials, when the T111 item was present in the RSVP stream and when the T111 item was removed from the stream. According to most models of dual-task interference, notwithstanding preparation differences, T1-only trials (i.e., single target trials) engage similar processing mechanisms associated with T1 as those engaged under dual-task conditions. Given that we were specifically interested in effects of masking on T1 processing and that we wanted to avoid all possible contamination of T2 processing on T1-related activity, we chose to use single-target RSVP streams (see Marois, Chun, & Gore, 2000, for a similar approach). Several properties of the P3 make it an ideal component for the question at hand. First, the latency of the P3 has often been used as a measure of the duration of perceptual processes prior to encoding in working memory (i.e., stimulus-evaluation time: see Coles, Smid, Scheffers, & Otten, 1995; Duncan-Johnson, 1981; Kutas, McCarthy, & Donchin, 1977; see Verleger, 1997), and its amplitude can inform us of the strength of the perceptual rep-
943 resentation that is encoded in working memory. Therefore, straightforward interpretations of the P3 results can be formulated. A delay in the onset latency of the T1-locked P3 in a condition where the T1 mask is presented, compared to a condition where the T1 mask is removed, would indicate that the mask affects the length of processing at or before encoding into working memory (i.e., perceptual processing), and a reduced amplitude would indicate that the mask degraded the perceptual representation of T1. On the other hand, if more resources must be allocated to a masked target in order to report it correctly, it is also possible that the P3 amplitude will be larger in masked trials than not-masked trials, because P3 amplitude is also thought to be sensitive to attentional demand (see Johnson, 1986). Such a result would corroborate resource-sharing accounts of the AB (e.g., Shapiro, Schmitz, Martens, Hommel, & Schnitzler, 2006), which postulate that allocating more resources to T1 leaves less resources available to T2, hence producing a greater blink in T1 masked trials. Finally, the absence of an effect of the mask on both P3 amplitude and latency would favor distractor-based accounts of the AB. Another advantage of the P3 is that difference waves can be easily computed in order to isolate the P3 from overlapping activity. Indeed, when multiple stimuli are presented in close temporal succession, as in RSVP streams, the ERPs associated to the processing of one stimulus (e.g., T1) will overlap with the ERPs associated to the onset and/or processing of the other stimuli (e.g., distractors). Disentangling the ERP waveform elicited by a specific stimulus (e.g., T1) can be particularly challenging. Fortunately, under most conditions, voltage fields summate linearly (Luck, 2005), enabling the use of subtraction methods to compute difference waves that reflect specific processes uncontaminated by overlapping activity. It is well known that the P3 component is larger for infrequent task-defined target categories than for frequent task-defined target categories (Donchin & Coles, 1988; Kutas et al., 1977; Luck, 1998; Vogel, Luck, & Shapiro, 1998). For example, if a target is randomly chosen equally often from a set of letters (for example {A, B, C, D}) and the participant has to respond whether the target was an A or not an A, the P3 will be larger in A trials (infrequent target category; 25% of all trials) than in not-A trials (frequent target category; 75% of all trials). It is therefore possible to manipulate target frequency and isolate the frequency-related P3 component from other frequency-insensitive activity by computing difference waves in which the ERP for frequent-target category trials are subtracted from the ERP for infrequent-target category trials. As mentioned above, this subtraction technique is particularly valuable when multiple stimuli are presented in rapid succession, as in RSVP streams, and when multiple tasks overlap, as in dual-task situations. This technique, employed on the second of two targets, has provided valuable information on the locus of dual-task interference in the AB (e.g., Arnell, 2006; Vogel & Luck, 2002; Vogel et al., 1998). Vogel et al. (1998) were the first to manipulate the relative frequency of T2 in an AB experiment in order to isolate the T2-locked frequency-related P3 from overlapping ERP activity from other stimuli such as T1 and distractor items in RSVP streams. They found that the P3 was strongly attenuated (abolished even) when T2 was presented during the AB period. They also provided evidence suggesting that that earlier, sensory components (the occipital visual P1 and N1) were not affected by the AB. Perhaps most surprising was that the N400, which is sensitive to semantic mismatch (see Luck, Vogel, & Shapiro, 1996;
944 Rolke, Heil, Streb, & Hennighausen, 2001; but see Giesbrecht, Sy, & Elliott, 2007), was not affected by lag during the blink (although the N400 amplitude was reduced, at all lags, under dual-task conditions relative to single-task conditions). The results of this study were consistent with the consolidation bottleneck models of the AB, such as the two-stage model (Chun & Potter, 1995) and the central interference theory (Jolicœur & Dell’Acqua, 1998; Jolicœur, 1998, 1999a, 1999b). Vogel and Luck (2002) extended their earlier P3 findings by demonstrating that the frequency-related P3 is abolished in the blink period only when T2 is overwritten (i.e., masked) by a subsequent item in the RSVP stream. If T2 is the last item of the RSVP stream (and therefore is not masked by a subsequent item), the P3 component is not reduced in amplitude: Instead, the P3 is delayed (Vogel & Luck, 2002; see also Ptito, Arnell, Jolicœur, & MacLeod, 2008). These electrophysiological results are consistent with behavioral studies that found no loss of accuracy when T2 was presented at the end of the RSVP stream (Giesbrecht & Di Lollo, 1998; Jolicœur, 1999b).1 The delay of the P3 component observed by Vogel and Luck was also observed independently of whether T1 and T2 were presented in the same sensory modality (T1 visual/T2 visual or T1 auditory/T1 auditory) or in different modalities (T1 auditory/T2 visual or T1 visual/T2 auditory; Arnell, 2006; Ptito et al., 2008), which is consisted with an amodal bottleneck, and it was observed in the absence of a task switch across T1 and T2 (Ptito et al., 2008). In the present study, we employed the same technique to isolate the frequency-related P3 response to a visual target in an RSVP stream and measured its onset latency and amplitude under conditions where the distractor immediately trailing the target was present (masked condition) or replaced by a blank (not-masked condition). Employing the same technique as previously used to study masking of T2 in the AB (e.g., Vogel & Luck, 2002) will enable direct comparison between this study and previous ones. Moreover, computing such a difference wave was also particularly important in the present context in order to eliminate differential overlapping activity that could have resulted from the difference in stimulation in the T111 position across masking conditions. Given that masked and not-masked conditions were presented equally often in both frequency conditions, we can safely assume that the difference in stimulation between masking conditions is equivalent in frequent and infrequent trials and therefore is eliminated in the frequency-related difference wave (for similar assumptions, see Vogel & Luck, 2002).
Method Participants Nine paid volunteers participated in this experiment. Data from 1 participant were excluded because of excessive alpha activity. Therefore, 8 participants (7 women), aged 19–25 years (mean age: 21.4 years) remained in the final sample. All participants declared to be neurologically intact and reported having normal or corrected-to-normal visual acuity and color vision. Written 1 Note here that because the first-stage representation is said to be short lasting and decays with time, bottleneck models would predict that, under appropriate conditions (e.g., if T2 intensity is low and if the waiting period is long enough), a blink in accuracy could occur without T2 being masked, as has been found for processing delays due to task switching (e.g., Kawahara, Zuvic, Enns, & Di Lollo, 2003).
B. Brisson et al. consent was obtained from each participant at the beginning of the experiment. The procedure was vetted by the appropriate ethics committee at the Universite´ de Montre´al. Stimuli and Procedure Stimuli were presented on a 17-in. computer screen (cathode-ray tube) at a viewing distance of 57 cm, in a dimly lit, electrically shielded room. Each participant performed one practice block of 32 trials followed by 12 experimental blocks of 64 trials. Each trial began with a central feedback symbol indicating if the response of the preceding trial was correct [1] or incorrect [ ! ]. Participants initiated each trial by simultaneously pressing the response keys assigned to the left and right index fingers (see below). The feedback symbol disappeared and was immediately replaced by the fixation point. After a random interval selected between 300 and 700 ms, an RSVP stream was presented. Intermixed, from trial to trial, masked and not-masked conditions were randomly presented equally often in each block. In the masked condition, three to eight distractors preceded the target and three to five distractors trailed it. The total RSVP stream therefore contained between 7 and 14 items (each subtending a visual angle of 0.981), presented at the center of the computer screen at a rate of 100 ms per item, without any interstimulus interval. The target was randomly chosen among the uppercase letters A, B, C, or D, and distractors were randomly chosen among the other 22 uppercase letters of the alphabet, with the constraint that the same letter could not be presented in two temporally adjacent frames. The distractor immediately trailing the target (i.e., the mask) was replaced by a 100-ms blank in the not-masked condition. For each subject, one of the target letters was singled out. This character became the infrequent response category target for this subject. This choice was counterbalanced between participants. Two participants had to respond whether the target was A (infrequent response category) or not A (frequent response category), two had to respond whether it was B or not B, two whether it was C or not C, and two whether it was D or not D. Thus, even though we presented each character equally often as the target character, three of them were associated with a frequent response category (e.g., not A, required in 75% of the trials), whereas the remaining character was associated with the infrequent response category (e.g., A, required in 25% of the trials). Six hundred to 1000 ms after the end of the stream, a response display was presented. On one side of the screen, the infrequent response category target was presented three times (for example AAA for participants assigned to the A vs. not-A counterbalancing group), and on the other side of the screen the letters comprised in the frequent response category were presented to represent the other three possible characters (i.e., BCD; see Figure 1). Infrequent and frequent response category targets were randomly presented to the left and right hemifields. Participants responded, without pressure about speed, by pressing the response key ipsilateral to the correct response. For example, if a frequent response category target was presented in the RSVP stream and the three frequent response category letters were presented in the left visual hemifield of the response display, the correct response would be the left key (i.e., [Z]), whereas if the three frequent response category letters were presented in the right hemifield, the correct response would be the right key (i.e., [,]). This type of response was used to ensure that response preparation activity did not overlap with the P3. Given the random side of presentation of the response alternatives, which varied
Masking attenuates but does not delay the P3
945 Response display AAA ·BCD
ipants, none had fewer than 50% of trials remaining after trial rejection (with an average of more than 86%) in all Masking ! Frequency conditions. None of the participants had residual eye movements that deviated more than 3.3 mV (i.e., corresponding to about 0.21 of visual angle; Lins, Picton, Berg, & Scherg; 1993) after rejection criteria were applied (see Luck, 2005).
Response display
Results
P L X Target
AAA ·BCD
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Behavioral Results Mean accuracy was high in both masking conditions, but it was significantly higher in the not-masked (98%) than in the masked condition (94%; F(1,7) 5 10.48, MSE 5 0.0014, po.015). The tendency toward a higher accuracy in the frequent (98%) relative to the infrequent (96%) condition did not reach significance (Fo1). A marginal interaction was observed between these two variables, F(1,7) 5 4.53, MSE 5 0.001, p4.07, indicating that the masking effect tended to be greater in the infrequent (notmasked 5 99%, masked 5 92%) than in the frequent condition (not-masked 5 97%, masked 5 95%). This interaction likely reflects the tendency to guess the more frequent response in the absence of information (i.e., under masking of infrequent-response stimuli; see Robitaille, Jolicœur, Dell’Acqua, & Sessa, 2007). The masking effects observed here are in the range of magnitude known to modulate the AB (e.g., Brisson, Spalek, & Di Lollo, 2009).
Figure 1. Example of stimulus sequence.
from trial to trial, response preparation could only begin at the onset of the response display. After the response, the feedback symbol appeared. Participants were instructed to maintain central eye fixation throughout the trial and to blink only when the feedback was on the screen. Electrophysiological Recording and Analysis A BioSemi ActiveTwo system was used to record the electroencephalogram (EEG) from 64 active Ag/AgCl electrodes mounted on an elastic cap and referenced to the average of the left and right mastoids after the recording. Electrodes were placed according to the International 10–10 system at Fp1, Fpz, Fp2, AF7, AF3, AFz, AF4, AF8, F7, F5, F3, F1, Fz, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCz, FC2, FC4, FC6, FT8, T7, C5, C3, C1, Cz, C2, C4, C6, T8, TP7, CP5, CP3, CP1, CPz, CP2, CP4, CP6, TP8, P9, P7, P5, P3, P1, Pz, P2, P4, P6, P8, P10, PO7, PO3, POz, PO4, PO8, O1, Oz, O2, and Iz sites. The horizontal electrooculogram (HEOG), recorded as the voltage difference between electrodes placed lateral to the external canthi, was used to measure horizontal eye movements. The vertical electrooculogram (VEOG), recorded as the voltage difference between two electrodes placed above and below the left eye, was used to detect eyeblinks. A low-pass filter of 30 Hz was applied, and the EEG and EOG signals, digitized at 256 Hz, were averaged off-line. Trials with eyeblinks (VEOG4100 mV), large horizontal eye movements (HEOG435 mV), and artifacts at POz (4100 mV) were rejected, as well as trials in which the target was not correctly reported. The EEG was averaged starting 200 ms prior to the target onset and ending 800 ms after target onset and baseline corrected based on the 200-ms pretarget period. ERPs were computed at the POz electrode site, where the frequency-related P3 is often maximal. One participant was rejected for excessive alpha activity in the EEG signals. Of the remaining 8 partic-
Electrophysiology The grand-average ERP waves for frequent and infrequent trials, in the masked and not-masked conditions, are shown in Figure 2 at the POz electrode site, where the P3 amplitude was maximal. The corresponding frequency-related P3 difference waves (infrequent minus frequent waves) are shown in Figure 3 (left panel), and the scalp distributions of the difference waves are shown in Figure 3 (right panel). P3 measurements (mean amplitude of the difference wave at POz during the 400–800-ms posttarget time window) was submitted to an analysis of variance (ANOVA) in which masking condition (mask or not-masked) was included as a within-subject factor. As suggested in Figure 3, the P3 was significantly attenuated in the masked condition (mean amplitude 5 4.74 mV) compared to the not-masked condition (mean amplitude 5 7.72 mV), F(1,7) 5 8.31, MSE 5 4.29, po.024. µV 12
Not-masked – Infrequent
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Figure 2. Grand average waveforms time-locked to the target in all four Masking ! Frequency conditions at POz. A 10-Hz low-pass filter was applied to these waveforms for display purposes.
946
B. Brisson et al. Not-masked
µV 12 10
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Figure 3. Left panel: Grand average infrequent minus frequent difference waves time-locked to the target for masked and not-masked conditions at POz. A 10-Hz low-pass filter was applied to these waveforms for display purposes. Right panel: Scalp distribution of the electrical potentials measured from the difference waves during the P3 (400–800 ms) posttarget onset time window for both conditions.
This effect was present in both the 400–600-ms time window, F(1,7) 5 5.85, MSE 5 5.50, po.05, and in the 600–800-ms time window, F(1,7) 5 10.64, MSE 5 3.67, po.015. Including the participant with excessive alpha did not change the overall patterns of results. To evaluate whether masking had an effect on the P3 onset latency, an additional 10-Hz low-pass filter was applied, and the time at which the waveforms reached 2 mV was measured and analyzed using the jackknife method, which has been shown to provide greater statistical power than traditional single-participant methods, with no inflation of Type 1 errors (Kiesel, Miller, Jolicœur, & Brisson, 2008; Miller, Patterson, & Ulrich, 1998; Ulrich & Miller, 2001). With the jackknife method, N grand average waveforms are computed with N–1 participants (a different participant is removed for each waveform). Latency measures are obtained for each of these n grand average waveforms, and the values are submitted to a conventional ANOVA, but for which the F values must be adjusted according to
tency analysis on the 5- Hz low-pass filtered waveforms also failed to reveal a significant effect (Fo1). As is often the case in frequency-related difference waves, the P3 is preceded by a positive deflection around 200 ms, referred to as the frequency-related P2. Although the P2 was not the main focus of this paper, differences in this time range could inform us as to the efficiency to which the computation of the difference wave eliminated sensory differences between masking conditions. P2 measurements (mean amplitude during the 170–270-ms posttarget time window) was submitted to an ANOVA in which masking condition (mask or not-masked) was included as a within-subject factor. This analysis revealed no significant difference between the masked condition (mean amplitude 5 2.39 mV) and the not-masked condition (mean amplitude 5 0.99 mV), F(1,7) 5 3.37, MSE 5 2.33, p4.10.
Fcorrected ¼ F=ðN # 1Þ2
Removing the T1 mask in the AB paradigm typically improves performance not only to T1 but also to T2, so much so that the AB effect is significantly reduced when T1 is not masked (Chun & Potter, 1995; Raymond et al., 1992; Seiffert & Di Lollo, 1997; see also Jolicœur, 1999b). It has been proposed that the T1 mask increases the AB indirectly by degrading perception of T1, thereby increasing T1 processing demands (e.g., Seiffert & Di Lollo, 1997). However, because it is usual to remove T1 incorrect trials in the calculation of T2 accuracy, it is unclear to what extent this also reduces T1 processing differences related to masking. To investigate whether masking effects, usually observed in terms of
(see Ulrich & Miller, 2001, for a general proof of this adjustment). This analysis reveal no effect of masking on P3 onset latency when a fixed criterion of 2 mV was used (masked condition 5 339 ms, not-masked condition 5 344 ms), Fcorrected (1,7) o1. The same pattern of results was observed when the 50% maximal amplitude criterion was employed to measure P3 latency (masked condition 5 355 ms, not-masked condition 5 384 ms), Fcorrected (1,7) 5 1.07, p4.33. Conventional individual peak la-
Discussion
Masking attenuates but does not delay the P3
947
reduced accuracy, can also be reflected in correctly reported trials and, if present, whether masking effects occur at or before encoding in working memory, a single-target RSVP experiment was conducted in which correct report of the target and the targetlocked P3 were measured in a masked condition, where the item immediately trailing the target (i.e., the mask) was present, and in a not-masked condition, where the mask was replaced by a blank. As mentioned in the introduction, a second target was not included because we were specifically interested in effects of masking on processing of T1, and we wanted to avoid all possible contamination of T2 processing on T1-related activity. According to most models of dual-task interference, notwithstanding preparation differences, T1-only trials (i.e., single target trials) engage processing mechanisms associated with T1 similar to those engaged under dual-task conditions. Although our single-target paradigm did not allow us to directly measure effects of T1 masking on the AB (i.e., T2 accuracy), the masking effect observed on correct report of the single target (i.e., T1 accuracy) is in the range known to affect the AB (e.g., Brisson et al., 2009), demonstrating that the P3 results observed in the present study can be linked to the AB literature. To isolate the target-locked P3 from other overlapping activity, the frequency of the target category was manipulated and the frequent-trial waveforms were subtracted from infrequent-target waveforms. This type of subtraction method has been used extensively to isolate the T2-locked P3 in dual-task paradigms such as the psychological refractory period (PRP: Dell’Acqua, Jolicœur, Vespignani, & Toffanin, 2005; Luck, 1998) and the AB (e.g., Arnell, 2006; Ptito et al., 2008; Robitaille et al., 2007; Vogel & Luck, 2002; Vogel et al., 1998). A clear attenuation of the amplitude of the P3 was observed in the masked condition compared to the not-masked condition, demonstrating that masking effects can be observed in trials with a correct response. This result goes against distractor-based models that would assume that the T111 item does not affect T1 processing in correctly reported trials. Furthermore, if we assume that the amplitude of the P3 is modulated by the amount of attentional resources dedicated to a target, the observed amplitude effects are opposite to what would be expected by resourcesharing models, which would predict that more resources should be allocated to the target in the masked condition, leading to a larger P3. In regard to T1 masking effects in the AB literature, this would leave fewer resources for T2, which would be reflected by a larger blink (e.g., Shapiro et al., 2006). Contrary to what was observed for the P3 amplitude, effects of masking on P3 onset latency were clearly absent. Based on the assumption that the mask degrades perception (Seiffert & Di Lollo, 1997), that perceptual degradation leads to a prolongation of perceptual processing, and that the P3 is often taken as a good marker of the duration of perceptual processing (Coles et al., 1995; Duncan-Johnson, 1981; Kutas et al., 1977), this result is somewhat surprising. In fact, a lengthening of the P3 onset latency in masked conditions would have been the most direct prediction of bottleneck models, which assume that delaying processes before or at the bottleneck stage(s) will equally lengthen the period T2 has to wait at short lags (also known as the slack period; see Pashler, 1994) and hence increase the AB.
From the present study, it remains unclear how an attenuation of the P3 to a first target can influence processing of a second target. One possible explanation is that the attenuation of the P3 indicates that the working memory representation of the target is weaker when T1 is masked, and, as a consequence, the difficulty of subsequent processing is increased. Ouimet and Jolicœur (2007) proposed a similar hypothesis, and, in line with this suggestion, Hsieh and Yu (2003) have shown that smaller P3 amplitudes were followed by later onsets of the stimulus-locked lateralized readiness potentials (LRP, an index of response-selection processes) in a task-switching paradigm that required a speeded response. Such an explanation would be in line with the central interference theory (Jolicœur, 1998, 1999a, 1999b), which postulates that several central processes can interfere with consolidation into working memory. Although further work will be necessary to clarify how the increase in processing demands reflected by the attenuation of the P3 is related to the finding that the AB is larger when T1 is masked, the present study unambiguously demonstrates that differences in processing of masked versus not-masked targets are observable in trials with a correct response. Furthermore, the main mechanism of action by which masking of T1 increases the AB appears not to be by a lengthening of the processing duration of stages leading up to the generation of the P3. If we take the onset and peak latency of the P3 as general indicators of when information about the target is consolidated in working memory, the present results suggest that consolidation time is not strongly affected by masking, at least not under the present conditions (and those typically used in AB experiments). Note that the term masking here is used loosely, because the masking effect was actually very small in terms of the loss of accuracy of response for T1 (only 4%). It might be more appropriate to refer to the presence of the T111 item as light masking. Interestingly, however, in contrast to the small difference in behavioral manifestation of the presence or absence of the T111 item, we observed a very robust decrease in the amplitude of the P3 response to correctly identified T1 stimuli (Figure 3). There also appears to be a multiplier effect of the light masking of T1 on accuracy of report of T2 (when T2 is also lightly masked). If we interpret the lower amplitude of the P3 as a measure of the strength of the encoded representation in working memory, then light masking results in a weaker memory trace. The results suggest strongly that light masking of T1 by the presence of a T111 item does not have consequences only for the processing of items that follow in the RSVP stream. Rather, masking of T1 has a clear effect on the electrophysiological response to T1 in a way that suggests a weaker memory trace. Perhaps surprisingly, the creation of this memory trace does not appear to be delayed by masking. If the carry-forward effect of masking T1 on T2 is mediated by an increase in the duration of some aspect of T1 processing, then this effect would appear to involve a stage that occurs after the processes that led to the P3 and that is presumably slower when the P3 is smaller (given clear latency effects on the P3 to T2; Ptito et al., 2008; Vogel & Luck, 2002). Isolating this process remains an important challenge for the field.
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Psychophysiology, 47 (2010), 949–954. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.00994.x
BRIEF REPORT
What and where in mirror reading
RU¨DIGER ILG,a RUTH DAUNER,b AFRA MARIA WOHLSCHLA¨GER,c YASMIN LIEBAU,a JOSEF ZIHL,b,d and MARK MU¨HLAUa a
Department of Neurology, Technische Universita¨t Mu¨nchen, Munich, Germany Department of Psychology, Neuropsychology, Ludwig-Maximilians-Universita¨t, Munich, Germany Department of Neuroradiology, Technische Universita¨t Mu¨nchen, Munich, Germany d Max Planck Institute of Psychiatry, Neuropsychology Research Group, Munich, Germany b c
Abstract In a combined voxel-based morphometry and functional magnetic resonance imaging study on the practice of mirror reading, we recently found a shift of activation from right superior parietal to right dorsal occipital cortex and a corresponding increase of gray matter. We interpreted this shift of activation and the corresponding structural changes as a shift from effortful visuospatial transformation to a more direct processing of mirrored words (Ilg et al., 2008). To test this hypothesis, we now analyzed brain activation patterns associated with different aspects of mirror reading. Activation at the dorsal occipital cortex and bilateral parietal cortex (dorsal visual stream) was related to inverse text processing, whereas activation of areas at the inferior and ventral occipitotemporal cortex (ventral visual stream) was associated with decoding of mirrored words. This indicates that the dichotomy of content-related (‘‘what’’) and process-related (‘‘where’’) higher visual functions also applies to mirror reading. Descriptors: Functional plasticity, Practice, Mirror reading, fMRI, Ventral/dorsal visual stream
aspects of mirror reading; for this purpose, we pooled the data before and after practice and compared the activation by different mirror reading conditions, which had higher or lower demands on procedural and content-related aspects of mirror reading. In detail, we analyzed the following contrasts of activation: (a) mirror reading versus conventional reading (i.e., mirror reading specific activation), (b) mirror reading of long words versus mirror reading of short words (i.e., high vs. low demands on inverse visuospatial scanning and reading), and (c) mirror reading of words versus ‘‘mirror reading’’ of meaningless dot patterns (i.e., decoding of mirrored words without effects by visuospatial exploration).
As already suggested by Tinker and Goodenough (1931), mirror reading is an interesting paradigm with which to study text processing. Functional imaging studies have demonstrated that reading activates a widely distributed set of areas in occipital, occipito-temporal, posterior temporal, parietal, and precentral regions as well as in inferior frontal and dorsolateral prefrontal regions (Price & Mechelli, 2005; Price et al., 2003). Beyond striate cortex (V1), visual text processing involves areas in the ventral visual stream (Brewer, Liu, Wade, & Wandell, 2005), including parts of the posterior fusiform gyrus (Nobre, Allison, & McCarthy, 1994), the posterior occipito-temporal junction (Leff et al., 2001), and parietal areas (Meyler et al., 2007). In a recent study on the acquisition of mirror reading skills using longitudinal voxel-based morphometry (VBM) and functional magnetic resonance imaging (fMRI), we found a shift from right superior parietal to right dorsal occipital activation and a corresponding increase of gray matter at the right dorsal occipital cortex (Ilg et al., 2008). We interpreted this shift of activation and the corresponding structural changes as a shift from effortful visuospatial transformation processes (i.e., procedural aspects of mirror reading) to a more direct recognition of mirrored words (i.e., content-related aspects of mirror reading). In the present study, we further examined the role of engaged cortical areas in different
Method Participants Twenty male university students volunteered to participate in the study and were paid for their participation. Mean age was 23 years (range: 20–27). All participants had normal or correctedto-normal vision (contact lenses; minimal acuity for near and far vision: 1.00); all were right-handed (Edinburgh handedness inventory: mean, 88; SD, 11.55) and native German speakers. Prior to their participation in the study, all subjects gave their written informed consent in accordance with the Declaration of Helsinki. Two participants did not finish the fMRI study and were therefore not included in the analysis of the fMRI data (n 5 18). Participants were the same as in our previous study (Ilg et al., 2008).
R.I. and R.D. contributed equally to the study. Address reprint requests to: Ru¨diger Ilg, Department of Neurology, Klinikum rechts der Isar, Technische Universita¨t Mu¨nchen, 81675 Munich, Germany. E-mail:
[email protected] 949
950 Functional Magnetic Resonance Imaging fMRI data were acquired with a 1.5 T Siemens Magnetom Symphony whole-body scanner using an eight-channel head coil. Multislice T2n-weighted echoplanar images (EPI) were obtained from a gradient-echo sequence (GRE: plane, oblique axial; number of slices, 33; slice thickness, 4 mm; inter-slice gap, 0.4 mm; flip angle, 901; in-plane resolution, 3.125 ! 3.125 mm2; field of view, 200 mm; echo time, 50 ms; repetition time, 3000 ms). MRI scanning was performed in two identical sessions before and after 2 weeks of practice (Ilg et al., 2008). The following analysis refers to the data of both scans. Participants were instructed to silently read conventional and mirrored short and long words that were presented on a projection screen in front of the scanner. Participants were instructed to read as fluently and accurately as possible. Reading performance was defined as the number of words read per trial. The words were presented on slides of three words (a so-called triad, black letters on green background) in a blocked, self-paced design. To control for effects of eye movements, we implemented a baseline condition consisting of horizontal dot patterns to be ‘‘read’’ from left to right (conventional condition) or from right to left (mirrored condition). The number of dots was matched with the number of fixations that were measured by eye movement recordings. An arrow indicated the direction of fixation shifts (reading direction). Mirrored and conventional words were matched with respect to length (long words, 8.5 letters; short words, 3.7 letters). Each fMRI session consisted of two functional runs and different sets of words. One run consisted of 12 blocks including two trials of each condition (conventional and mirrored short words; conventional and mirrored long words; conventional and mirrored dot patterns). The two trials of each condition were randomly assigned to the first or second half of the run. The sequence of runs, trials, triads within trials, and assignment of words to the mirrored or conventional condition was randomized. Participants were instructed to silently read the presented words and press a button with their right index finger to trigger the presentation of the next triad as soon as they had deciphered all three words. On average, participants read 177 mirror word triads and 551 conventional word triads in the first session and 281 mirror word triads and 615 conventional word triads in the second session. Statistical Analysis of the fMRI Data The fMRI analysis was performed with Statistical Parametric Mapping software, version 5 (SPM5; Wellcome Department of Imaging Neuroscience, London, UK). The first three images of each run were discarded to allow equilibration of longitudinal magnetization. The preprocessing steps were (a) realigning and unwarping the images to correct for movement artifacts and related susceptibility artifacts, (b) co-registration of anatomical and functional images, (c) segmentation and normalizing of the anatomical image to standard stereotactic space (Montreal Neurological Institute, MNI), (d) application of normalization transformation to the functional images, and (e) smoothing with a Gaussian kernel of 8 mm for group analysis. Low-frequency components of data were removed by high-pass filtering. For each participant, images were subjected to a fixed effects analysis. At the group level, a random effects analysis was performed. The number of responses (i.e., the number of triads deciphered per block) was modeled as a covariate to control for the number of attended word triads and performance-related task effects. We regarded this as a reasonable approximation of mirror-reading performance because the number of silently read mirrored words in the scanner correlated significantly with the number of correctly deciphered mirrored words
R. Ilg et al. outside the scanner (r 5 .86, po.001). We computed three differential contrasts of activation (all vice versa): (a) conventional reading versus mirror reading, (b) mirror reading of long words versus mirror reading of short words, (c) mirror reading of words versus mirror reading of dot patterns. As implemented in SPM, all corrections for multiple statistical tests are predicated on the theory of Gaussian random fields (Friston, Holmes, Poline, Price, & Frith, 1996; Nichols & Hayasaka, 2003). We will refer to thresholds of voxel-level inference as height thresholds and to thresholds of cluster-level inference as extent thresholds. For all analyses, we applied a height threshold of 0.001 uncorrected and an extent threshold of 0.05 corrected (note that most peak voxel also survived a family-wise error correction of po.05).
Results Mirror-Reading-Specific Activation Compared to conventional reading, mirror reading resulted in higher activation of the dorsal occipital lobe bilaterally (inferior, middle, and superior occipital gyrus), occipito-temporal cortex (bilateral fusiform gyrus), parietal cortex (bilateral superior parietal lobule, left intraparietal sulcus, bilateral posterior precuneus, left somatosensory cortex), and medial and dorsolateral prefrontal cortex (left pre-SMA, right middle cingulate cortex, left frontal eye field, bilateral precentral gyrus) as well as activation of the cerebellum and right anterior insula (see Figure 1A in red and Table 1, panel A). In contrast, conventional reading resulted in higher activation of the bilateral medial and dorsolateral prefrontal cortex (left anterior cingulate cortex, right superior medial frontal gyrus, left middle and superior frontal gyrus), motor cortex (right precentral gyrus), and bilateral inferior and medial parietal cortex (angular gyrus and anterior precuneus) as well as of the right temporal cortex (superior temporal gyrus; see Figure 1A in green and Table 1, panel B). Activation Associated with Inverse Text Processing and Reading Pattern To focus on activation related to the inverse text processing and reading, we compared reading of long mirrored words (which required high demands of inverse visuospatial scanning) and short mirrored words (which required low demands of inverse visuospatial scanning). The reading of long mirrored words was associated with higher activation of the medial and dorsolateral occipital cortex (cuneus and middle occipital gyrus), parietal cortex (superior parietal lobule), left motor cortex, right dorsolateral prefrontal (middle frontal gyrus) and medial prefrontal cortex (bilateral SMA), right anterior insula, and cerebellum (see Figure 1B in purple and Table 1, panel C). In contrast, reading of short mirrored words resulted in higher activation of the left inferior parietal cortex (angular gyrus), bilateral medial and dorsolateral prefrontal areas (left superior medial frontal gyrus, left superior and middle frontal gyrus, right operculum), and the right motor cortex (precentral gyrus; see Table 1, panel D). Activation Associated with Decoding of Mirrored Words To investigate activation associated with the decoding of mirrored words (i.e., content-related aspects of mirror reading) apart from inverse text processing and inverse eye movements, we compared the reading of mirrored words (short words and long words) with the ‘‘reading’’ of dot patterns (i.e., leftward visuospatial scanning and eye movements). This analysis
What and where in mirror reading
951
A
Mirror reading vs. conventional reading
B
Inverse visuospatial scanning and reading
C
Decoding of mirror words
Figure 1. Neural correlates of different aspects of mirror reading. A: Activation specifically associated with conventional reading (green) and mirror reading (red). Compared with mirror reading, conventional reading resulted in a higher activation at the bilateral medial and dorsolateral prefrontal cortex, motor cortex, bilateral inferior (angular gyrus) and medial parietal cortex (precuneus, not shown), and right temporal cortex. In contrast, mirror reading resulted in a higher bilateral activation of the dorsal occipital lobe, occipito-temporal cortex, parietal cortex, and medial and dorsolateral prefrontal cortex as well as right anterior insula and cerebellum. B: High demands on inverse visuospatial scanning and reading (in purple: reading of long mirrored words vs. reading of short mirrored words) resulted in higher activation of the medial and dorsolateral occipital cortex, superior parietal cortex, right dorsolateral prefrontal cortex, primary and supplementary motor cortex, and right anterior insula. C: In contrast, the decoding of mirrored words (in pink: mirror reading of words vs. mirror reading of meaningless dot patterns) was associated with a specific activation of the bilateral dorsal and ventral occipito-temporal cortex (inferior occipital gyrus and fusiform gyrus) and left inferior frontal gyrus. Activation is superimposed on a surfacerendered MNI template (voxel-threshold, po.001 uncorrected; height threshold, po.05 corrected).
revealed that activation of the bilateral dorsal and ventral occipito-temporal cortex (inferior occipital gyrus and fusiform gyrus), left inferior frontal gyrus, and right cerebellum was specifically associated with the decoding of mirrored words (see Figure 1C in pink and Table 1, panel E).
Discussion In a recent study we found that the practice of mirror reading resulted in a shift of activation from the right superior parietal cortex to the right dorsolateral occipital cortex, which we interpreted as a shift from effortful visuospatial scanning to a more direct recognition of mirrored words. We now performed an ad-
ditional analysis of different conditions of mirror reading that were associated with high and low demands of visuospatial scanning and the mere decoding of mirror words. Areas Engaged in Mirror Reading In contrast to conventional reading, mirror reading involved a higher activation of the dorsal occipital lobe, ventral occipitotemporal cortex, bilateral superior parietal cortex, and posterior precuneus as well as of the dorsolateral and medial prefrontal cortex. This pattern corresponds to several imaging studies that reported activation of both the dorsal and, to a lesser extent, the ventral visual stream in spatial transformations of written language (Dong et al., 2000; Goebel, Linden, Lanfermann, Zanella, & Singer, 1998; Mochizuki-Kawai, Tsukiura, Mochizuki, &
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R. Ilg et al.
Table 1. Neural Correlates of Different Aspects of Mirror Reading MNI coordinates (mm) Region
Localization of peak voxels
x
y
z
R
36 42 ! 42 ! 36 36 ! 30 ! 30 27 ! 24 ! 36 ! 36 39 33 ! 18 ! 24 27 24 ! 45 ! 15 12 ! 42 !6 !3 9 ! 30 ! 51 51 36 36 15 9
! 84 ! 75 ! 60 ! 87 ! 84 ! 84 ! 93 ! 69 ! 69 ! 48 ! 69 ! 69 ! 48 ! 63 ! 66 ! 57 ! 66 ! 39 ! 75 ! 69 ! 42 12 3 18 !6 !3 9 24 21 ! 72 ! 72
!3 ! 12 ! 12 ! 12 9 12 18 27 33 ! 21 ! 12 ! 18 ! 18 39 54 45 51 42 45 45 54 48 54 39 51 48 36 9 0 ! 21 ! 21
5.97a 5.52a 5.86a 5.46a 5.69a 5.76a 5.23a 5.60a 5.86a 5.75a 5.56a 5.71a 5.68a 5.66a 5.27a 5.45a 5.28a 5.47a 5.18a 5.24a 5.05a 5.55a 5.45a 5.16a 5.45a 5.29a 5.12a 5.21a 5.12a 5.11a 5.33a
21 9 !6 ! 27 !9 ! 48 54 !3 60
! 30 54 33 27 60 ! 72 ! 69 51 ! 24
66 18 !3 42 27 36 30 30 12
4.86a 4.50 4.87a 4.47 4.47 4.77 4.23 4.56 4.01
! 48 33 33 !3 ! 24 24 12 ! 15 33 ! 24 18
!9 36 18 3 ! 66 ! 57 ! 90 ! 90 ! 84 ! 90 ! 57
57 24 12 57 54 57 6 !6 15 12 ! 45
4.83a 4.69 4.48 5.16a 5.27a 5.40a 6.39a 6.34a 5.45a 5.33a 4.14
36 15 !9 !6 ! 39 ! 48
! 18 ! 30 51 42 15 ! 72
18 63 30 54 51 36
4.43 4.23 4.21 3.80 3.93 4.46
! 36 30 ! 48 ! 24
! 87 ! 87 12 27
!9 !3 27 !3
5.56a 5.40a 4.67 4.63
A. Mirror reading4conventional reading Dorsal occipital Inferior occipital gyrus (BA 18/19/37)
L
Occipito-temporal
Middle occipital gyrus (BA 18/19)
R L
Superior occipital gyrus (BA 19/7)
R L L
Fusiform gyrus (BA 19/37)
R Parietal
Frontal
Superior/inferior parietal lobule
L
(BA 7/40)
R
Intraparietal sulcus (BA 40) Precuneus (BA7)
L L R L L
Somatosensory cortex (BA 2) Pre-SMA (BA 6) Middle cingulate cortex (BA 24/32) Frontal eye field (BA 6) Precentral gyrus (BA 6/9)
Limbic
Insula lobe
Cerebellum
Posterior lobe Vermis B. Conventional reading4mirror reading Frontal Precentral gyrus (motor cortex) Superior medial frontal gyrus Anterior cingulate cortex Middle frontal gyrus Superior frontal gyrus Parietal Angular gyrus Precuneus Temporal Superior temporal gyrus C. Long mirrored words4short mirrored words Frontal Precentral gyrus (motor cortex) Middle frontal gyrus Anterior insular lobe Supplementary motor area (SMA) Parietal Superior parietal lobule Occipital
Z values (peak voxel)
Hemisphere
Medial occipital cortex (Cuneus, BA 17/18) Middle occipital gyrus (BA 18/19)
Cerebellum Posterior lobe D. Short mirrored words4long mirrored words Frontal Operculum Precentral gyrus (motor cortex) Superior medial frontal gyrus Superior frontal gyrus Middle frontal gyrus Parietal Angular gyrus E. Mirrored words4mirrored dot words Occipito-temporal Inferior occipital gyrus/ Fusiform gyrus Frontal Inferior frontal gyrus Note: Voxel-threshold po.001 uncorrected; height threshold po.05 corrected. a Marked voxels also survived a FWE correction of po.05 at the voxel level.
R L L4R R R4L R R L L4R L4R R L R R/L L R R L R L R R L L L4 R L
What and where in mirror reading
953
Kawamura, 2006; Poldrack, Desmond, Glover, & Gabrieli, 1998; Poldrack & Gabrieli, 2001). Poldrack et al. were the first to examine practice effects in mirror reading with fMRI. Comparable to our results, they found widespread bilateral activation of occipital, parietal, and inferior temporal regions. To control for oculomotor effects and focus on the visuospatial transformation and cognitive aspects of mirror reading, Dong et al. used Japanese kana words, which are read from the top down. As in our study, they found widespread activation of predominantly occipital and parietal areas. The role of the parietal cortex in the visuospatial processing of letter-strings is further supported by results of a remediation study on poor readers (Meyler et al., 2007). Prior to instruction, poor readers had significantly less bilateral activation in the parietal cortex than good readers. Immediately after instruction, poor readers showed a significant increase in activation at the left angular gyrus and the left superior parietal lobule, which reached normal levels after 1 year of systematic practice. These changes of activation have been interpreted as changes in reading-associated word processing, which fits the observed activation in our experiment. Finally, the observed activation of medial and dorsolateral prefrontal areas is in line with studies on mental rotation of objects (M. S. Cohen et al., 1996; Hanakawa et al., 2002) and supports the role of spatial mental rotation in mirror reading. Activation Related to Inverse Reading and a Visuospatial Scanning Pattern during Mirror Reading The ability to read mirrored words (i.e., letter strings) requires visuospatial scanning from right to left and the translation of mirrored graphemes (i.e., letters) into meaningful semantic information (i.e., phonemes). Several studies indicated that the visuospatial transformation of mirrored words is primarily accomplished by specialized areas in the dorsal visual stream (Dong et al., 2000; Poldrack & Gabrieli, 2001; Poldrack et al., 1998). To focus on procedural aspects of mirror reading (i.e., the inverse visuospatial scanning and reading pattern), we compared activation by long and short mirrored words. This analysis revealed that the activation by the reading of long mirrored words largely matched the activation associated with the mirror reading condition per se, indicating that the strong mirror-reading-related activation at the (superior and inferior) dorsal occipital lobe, superior parietal, and dorsolateral prefrontal cortex was predominantly related to procedural aspects of mirror reading. In contrast, reading short mirrored words resulted in activation of a set of areas that largely resembled the conventional reading pattern, indicating that reading short mirrored words was less effortful and more similar to conventional reading. Furthermore, it is worth mentioning that the activation pattern also comprised the inferior occipital areas that were primarily associated with the decoding of mirror words. This indicates that mirror reading of long words as compared to mirror reading of short words not only implied higher demands on the
visuospatial scanning but also that longer words were likewise more difficult to decode and to reassemble. Activation Related to Decoding of Mirrored Words To identify the activation that was specifically associated with the decoding of mirrored words and to avoid a confoundation of the analysis by effects of the inverse reading and eye movement pattern, we compared reading of mirrored words with ‘‘mirror reading’’ of dot patterns. This comparison showed that mirror reading of meaningful letter strings as opposed to meaningless dot patterns was associated with increased activation at the dorsal occipital cortex and ventral visual stream (i.e., inferior occipital gyrus and fusiform gyrus) and the inferior frontal cortex of the dominant hemisphere (i.e., the left inferior frontal gyrus). With respect to our previous study, it is noteworthy that the dorsal occipital activation corresponds to the mirror-reading-specific activation and the site of an observed gray matter increase (Ilg et al., 2008). It has been indicated that parts of the inferior occipito-temporal cortex (including the fusiform gyrus) might be specifically involved in the processing of written words (Devlin, Jamison, Gonnerman, & Matthews, 2006). Parametric fMRI and EEG source analyses showed that activation and event-related potentials at the left fusiform gyrus were influenced by lexical word frequency (Hauk, Davis, & Pulvermuller, 2008; Proverbio, Zani, & Adorni, 2008) and led to the description of a ‘‘visual word form area’’ in the posterior fusiform gyrus. Yet, it remains unclear whether the shown activation in the ventral occipito-temporal cortex is specific to word processing or whether it is associated with a more general processing of complex symbol strings (L. Cohen et al., 2000; Dehaene, Le Clec, Poline, Le Bihan, & Cohen, 2002; Devlin et al., 2006; McCandliss, Cohen, & Dehaene, 2003; Starrfelt & Gerlach, 2007). Aside from that, the role of the inferior frontal cortex in word processing is well established (Bookheimer, 2002; Price & Mechelli, 2005; Price et al., 2006). Interpretation of the Practice-Related Parieto-Occipital Shift of Activation With respect to the reported practice-related shift of activation from superior parietal to dorsal occipital areas (Ilg et al., 2008), the observed associations of high demands on visuospatial scanning with dorsal occipital and superior parietal activation and of decoding of mirror words with inferior and ventral occipital activation supports our interpretation of a shift from effortful visuospatial transformation to a more direct processing of mirrored words. Furthermore, our results support the idea that the established dichotomy of content-related (ventral visual stream: ‘‘what’’) and process-related (dorsal visual stream: ‘‘where’’) visual processes (Ungerleider & Haxby, 1994) also applies to mirror reading with functions related to the inverse visuospatial scanning pattern being located in the dorsal visual stream and functions associated with decoding of the mirror words being located in the ventral visual stream.
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954 activity during mental rotation. A mapping study using functional MRI. Brain, 119(Pt 1), 89–100. Dehaene, S., Le Clec, H. G., Poline, J. B., Le Bihan, D., & Cohen, L. (2002). The visual word form area: A prelexical representation of visual words in the fusiform gyrus. NeuroReport, 13, 321–325. Devlin, J. T., Jamison, H. L., Gonnerman, L. M., & Matthews, P. M. (2006). The role of the posterior fusiform gyrus in reading. Journal of Cognitive Neuroscience, 18, 911–922. Dong, Y., Fukuyama, H., Honda, M., Okada, T., Hanakawa, T., Nakamura, K., et al. (2000). Essential role of the right superior parietal cortex in Japanese kana mirror reading: An fMRI study. Brain, 123(Pt 4), 790–799. Friston, K. J., Holmes, A., Poline, J. B., Price, C. J., & Frith, C. D. (1996). Detecting activations in PET and fMRI: Levels of inference and power. NeuroImage, 4, 223–235. Goebel, R., Linden, D. E., Lanfermann, H., Zanella, F. E., & Singer, W. (1998). Functional imaging of mirror and inverse reading reveals separate coactivated networks for oculomotion and spatial transformations. NeuroReport, 9, 713–719. Hanakawa, T., Honda, M., Sawamoto, N., Okada, T., Yonekura, Y., Fukuyama, H., et al. (2002). The role of rostral Brodmann area 6 in mental-operation tasks: An integrative neuroimaging approach. Cerebral Cortex, 12, 1157–1170. Hauk, O., Davis, M. H., & Pulvermuller, F. (2008). Modulation of brain activity by multiple lexical and word form variables in visual word recognition: A parametric fMRI study. NeuroImage, 42, 1185–1195. Ilg, R., Wohlschlager, A. M., Gaser, C., Liebau, Y., Dauner, R., Woller, A., et al. (2008). Gray matter increase induced by practice correlates with task-specific activation: A combined functional and morphometric magnetic resonance imaging study. Journal of Neuroscience, 28, 4210–4215. Leff, A. P., Crewes, H., Plant, G. T., Scott, S. K., Kennard, C., & Wise, R. J. (2001). The functional anatomy of single-word reading in patients with hemianopic and pure alexia. Brain, 124, 510–521. McCandliss, B. D., Cohen, L., & Dehaene, S. (2003). The visual word form area: Expertise for reading in the fusiform gyrus. Trends in Cognitive Science, 7, 293–299. Meyler, A., Keller, T. A., Cherkassky, V. L., Lee, D., Hoeft, F., Whitfield-Gabrieli, S., et al. (2007). Brain activation during sentence
R. Ilg et al. comprehension among good and poor readers. Cerebral Cortex, 17, 2780–2787. Mochizuki-Kawai, H., Tsukiura, T., Mochizuki, S., & Kawamura, M. (2006). Learning-related changes of brain activation in the visual ventral stream: An fMRI study of mirror reading skill. Brain Research, 1122, 154–160. Nichols, T., & Hayasaka, S. (2003). Controlling the familywise error rate in functional neuroimaging: A comparative review. Statistical Methods in Medical Research, 12, 419–446. Nobre, A. C., Allison, T., & McCarthy, G. (1994). Word recognition in the human inferior temporal lobe. Nature, 372, 260–263. Poldrack, R. A., Desmond, J. E., Glover, G. H., & Gabrieli, J. D. (1998). The neural basis of visual skill learning: An fMRI study of mirror reading. Cerebral Cortex, 8, 1–10. Poldrack, R. A., & Gabrieli, J. D. (2001). Characterizing the neural mechanisms of skill learning and repetition priming: Evidence from mirror reading. Brain, 124, 67–82. Price, C. J., Gorno-Tempini, M. L., Graham, K. S., Biggio, N., Mechelli, A., Patterson, K., et al. (2003). Normal and pathological reading: Converging data from lesion and imaging studies. NeuroImage, 20(Suppl 1), S30–S41. Price, C. J., McCrory, E., Noppeney, U., Mechelli, A., Moore, C. J., Biggio, N., et al. (2006). How reading differs from object naming at the neuronal level. NeuroImage, 29, 643–648. Price, C. J., & Mechelli, A. (2005). Reading and reading disturbance. Current Opinion in Neurobiology, 15, 231–238. Proverbio, A. M., Zani, A., & Adorni, R. (2008). The left fusiform area is affected by written frequency of words. Neuropsychologia, 46, 2292–2299. Starrfelt, R., & Gerlach, C. (2007). The visual what for area: Words and pictures in the left fusiform gyrus. NeuroImage, 35, 334–342. Tinker, M. A., & Goodenough, F. L. (1931). Mirror reading as a method of analyzing factors involved in word perception. Journal of Educational Psychology, 22, 493–502. Ungerleider, L. G., & Haxby, J. V. (1994). ‘What’ and ‘where’ in the human brain. Current Opinion in Neurobiology, 4, 157–165. (Received September 30, 2009; Accepted October 5, 2009)
Psychophysiology, 47 (2010), 955–960. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.00995.x
Correction of blink artifacts using independent component analysis and empirical mode decomposition
JOB P. LINDSENa and JOYDEEP BHATTACHARYAa,b a
Department of Psychology, Goldsmiths, University of London, New Cross, London, UK Commission for Scientific Visualization, Austrian Academy of Sciences, Vienna, Austria
b
Abstract Blink-related ocular activity is a major source of artifacts in electroencephalogram (EEG) data. Independent component analysis (ICA) is a well-known technique for the correction of such ocular artifacts, but one of the limitations of ICA is that the ICs selected for removal contain not only ocular activity but also some EEG activity. Straightforward removal of these ICs might, therefore, lead to a loss of EEG data. In this article a method is proposed to separate blinkrelated ocular activity from actual EEG by combining ICA with a novel technique, empirical mode decomposition. This combination of two techniques allows for maximizing the retention of EEG data and the selective removal of the eyeblink artifact. The performance of the proposed method is demonstrated with simulated and real data.
Eyeblinks constitute a major source of artifacts in electroencephalographic (EEG) recordings. This is primarily caused by the movement of the eyelids over the eyeball despite the lack of any concomitant movement of the eyeball (Matsuo, Peters, & Reilly, 1975). It is suggested that the eyeball forms an electric dipole and the eyelid acts as a sliding potential source; therefore the movement of eyelids over eyeballs causes potential shifts and contaminates EEG signals (Berg & Scherg, 1991). In general, the eyeblink artifacts are characterized by a larger amplitude and a lower frequency than the true EEG, with a fronto-polar distribution of activity that gradually declines toward posterior electrode positions; indeed, blink-related artifact amplitude is inversely proportional to the square of the distance (Croft & Barry, 2000). A common way to remove these artifacts is to reject blink-contaminated epochs. However, a disproportionate number of trials might be discarded this way; not only are task-relevant neural responses thrown away but the power of statistical tests might also be severely reduced. A practical alternative to the rejection of artifact-laden epochs is the procedure of artifact correction, whereby the epochs are ‘‘cleaned’’ by eliminating the blink artifacts; various methods have been designed and compared to this end (see, for details, Croft & Barry, 2000; Croft, Chandler, Barry, Cooper, & Clarke, 2005; Hoffmann & Falkenstein, 2008; Ille, Berg, & Scherg, 2002; Joyce, Gorodnitsky, & Kutas, 2004; Picton et al., 2000). One popular class of ocular artifact correction methods is based on regression in the time domain, that is, a proportion of the electro-oculographic (EOG) recording is ‘‘subtracted’’ from each scalp electrode (Gratton, Coles, & Donchin, 1983; Quilter, MacGillivray, & Wadbrook,
1977; Schlogl et al., 2007). Another popular method for ocular artifact correction is based on blind source separation, usually by means of independent component analysis (ICA), which is based on the assumption that the blink artifact is represented by a limited number of independent components (ICs). Once identified, these ICs can be selectively removed during the inverse ICA transformation to yield the clean data (Jung et al., 2000; Vigario, 1997). A potential problem using these methods, albeit one often overlooked, is that both the recorded EOG signals and the ICs selected for removal will contain some EEG data in addition to the blink artifacts (Castellanos & Makarov, 2006; Frank & Frishkoff, 2007). So, using this method to correct blink artifacts is likely to result in the loss of some true EEG data as well (Barbati, Porcaro, Zappasodi, Rossini, & Tecchio, 2004; Ille et al., 2002; Joyce et al., 2004). In this article, a novel method is proposed for the correction of blink artifacts that aims to separate these artifacts from the EEG data, resulting in a more exclusive removal of the artifact and therefore ensuring a better reconstruction of EEG data from the artifact-contaminated epochs. Essentially, this method consists of three steps: (1) transforming the original data into ICs, (2) performing an empirical mode decomposition (EMD; Huang et al., 1998) of the ICs containing blink artifacts, and (3) classifying the intrinsic mode functions (IMFs) obtained by means of EMD as either blink or EEG and selectively removing the blink-related intrinsic modes. These three steps can be performed automatically. In the following sections the proposed method is presented and its performance in removing blink artifacts is demonstrated using simulated and real data.
The research is supported by JST.ERATO project (J.B.). Address reprint requests to: Joydeep Bhattacharya, Department of Psychology, Goldsmiths, University of London, New Cross, London, SE14 6NW, UK. E-mail:
[email protected] Selectively Removing Blink Artifacts Using ICA and EMD The first step in the proposed method is to transform the data by means of ICA. The underlying assumption of the ICA is that the 955
956 signals recorded at each electrode position are linear mixtures of a number of sources with an independent time course. The aim of ICA is to find a set of spatial filters that inverts this mixture and recover the original sources, called independent components (ICs). So, if the multichannel EEG recording is considered as a mixture of brain and artifactual blink signals, ICA provides a spatial filter that captures these blink artifacts in a limited number of ICs (Jung et al., 2000). Once these ICs are identified, they can be excluded from the inverse transformation back to the signal space. The use of ICA to capture eyeblink activity is a well-established method, and discussions of its strengths and limitations can be found elsewhere (Frank & Frishkoff, 2007; Hoffmann & Falkenstein, 2008). One limitation of using ICA for ocular artifact correction is that a straightforward removal of the ICs containing artifacts most likely results in some loss of EEG data, because those ICs will rarely consist of only blink-related EOG activity. Furthermore, when eyeblink activity is not captured by a single IC but spreads out over multiple components, that is, when the ICA suffers from a split variance, the amount of lost EEG data could be considerable when these multiple ICs are removed (Frank & Frishkoff, 2007). To retain the true EEG activity in those selected ICs containing blinks, a recently introduced technique, empirical mode decomposition, for an adaptive representation of nonstationary signals is applied (Huang et al., 1998). Unlike other signal decomposition methods, such as the Fourier or the wavelet transforms, EMD is purely data driven, that is, the basis of the decomposition is adaptively derived from the data. Furthermore, because the EMD is based on the local characteristic time scale of the data, it is applicable to nonlinear and nonstationary signals, such as large-scale brain responses (Liang, Bressler, Buffalo, Desimone, & Fries, 2005). The EMD procedure considers a signal or time series to be composed of one oscillation riding on top of another, slower oscillation that is, in turn, riding on still slower oscillations. Therefore, application of the EMD to a signal amounts to an iterative procedure that identifies each of these intrinsic oscillatory modes by their characteristic time scales. Here, the effective steps of EMD are briefly summarized: 1. Given a signal x(t), identify all local extremes (maxima and minima). 2. Interpolate between maxima to estimate the upper envelope xup(t) and between minima to estimate the lower envelope xlow(t). 3. Compute the mean of the two envelopes, m(t) 5 (xup(t)1 xlow(t))/2, and subtract it from the data: d(t) 5 x(t) ! m(t). Steps 1–3 are repeated on d(t) until the resulting signal, c1(t), satisfies the three following criteria: (1) the number of extremes and the number of zero crossings must either be equal or differ at most by one, (2) the mean value of the envelope defined by the local maxima and the envelope defined by the local minima must be zero at any time point, and (3) the difference between two consecutive repetitions, in terms of standard deviation, must not be smaller than some predetermined criterion. Once this is achieved, the resulting signal is considered to be the first IMF. After an IMF is identified it is subtracted from the original data, and the residue is taken as the new data from which the next IMF can be identified by repeating the steps that yielded the first IMF. No more IMFs are extracted from the data when the residue
J.P. Lindsen & J. Bhattacharya becomes a monotonic function or when it becomes negligibly small. Therefore, the end result of the EMD method is a set of IMFs ordered according to their characteristic time scales plus a residual that, when linearly summed together, constitute the original signal. Although EMD ensures a complete reconstruction of the data by summing all IMFs, removing the artifactual IMFs selectively from the summation could effectively filter out the artifact from the original data. So, instead of removing the entire IC from the inverse ICA transformation, the artifact-free part of the IC can be incorporated together with other ICs to reconstruct the data. Consequently, the proposed method of combining ICA and EMD allows for the selective removal of only those modes of the IC predominantly associated with blink artifacts and minimizes the loss of true EEG data. A crucial step in the proposed EMD-based method for the successful correction of the blink artifacts is the classification of the IMFs as either a blink- or an EEG- related mode. To fully automate the whole correction procedure, a simple criterion, albeit ad hoc, is applied for the classification of the IMFs based on their standard deviations (SDs). Though the SDs of the IMFs representing the EEG-related components will differ to a certain extent depending on the strengths of constituent frequency bands (i.e., alpha, beta, gamma), they will still be considerably lower than the SDs of the IMFs representing the blink artifacts, because of the characteristic differences in amplitudes of EEG and blink signals. Based on this property, the following classification rule is suggested: If the SD of an IMF is larger than p times the SD of the first IMF, then that IMF and all other lower order IMFs will be classified as blink-related modes. Simulation results that are described later in this article suggest that this classification procedure produces optimal results with p between 1.5 and 2.5.
Illustration of the EMD Method An illustrative example of how the EMD of an IC results in multiple IMFs, which subsequently can be used to separate a superimposed blink and EEG signal, is shown in Figure 1. The top panel shows a signal (in black) that is composed of a simulated eyeblink superimposed onto an IC obtained from an ICA decomposition of 64-channel EEG data. Application of the EMD method to this signal produces seven IMFs (bottom panel). Using a p ( 5 the SD of second or higher order IMFs/the SD of the first IMF) of 2, the first four IMFs (in blue) are classified as true EEG activity, that is, the SDs of the second, third, and fourth IMF are smaller than 2 times the SD of the first IMF. Visual inspection of these four modes confirms that they are a reasonable represention of the relatively high frequency oscillatory components in the original data and do not evidently show any characteristics of artifactual blink-related activity. The fifth IMF (in red) has a SD larger than 2 times the SD of the first IMF, and hence this IMF and the two remaining lower-order IMFs (in red) are classified as blink-related activity. Here, visual inspection of the IMFs confirms that these last three modes (in red) are characterized by relatively large amplitudes and low frequencies that are also observed in the blink activity. As can be seen in the top panel of Figure 1, the sum of the first four IMFs (in blue) provides a good approximation of the original IC activity (in black), whereas the sum of the last three IMFs (in red) provides a good approximation of the eyeblink activity. Therefore, this ex-
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Figure 1. Empirical mode decomposition (EMD) of an EEG signal with an eyeblink artifact. The top panel shows the signal plus artifact (black line), the cleaned signal (blue line), and the artifact removed by EMD (red line). The bottom panel shows the seven independent mode functions (IMFs) generated by an EMD of the signal plus artifact. The first four IMFs (blue lines) are classified as containing EEG, and their sum is taken as the cleaned signal. The last three IMFs (red lines) are classified as containing the blink artifact, and these are removed from the signal. The inset shows in detail the differences between the original and cleaned signals.
ample shows how EMD could separate blink-related activity from neural activity based on the differences in both time scale and amplitude of intrinsic oscillatory modes.
Validation of the EMD Based Method A major problem in EOG correction, including the eyeblink removal research, is the lack of an obvious quantitative method of validation. This is partly because, in an experimental situation, there is no access to the uncontaminated EEG activity, and therefore there is no quantitative criterion against which the performance of a correction method could be rigorously measured (Croft & Barry, 2000). Often the success of a correction method is measured qualitatively, whereby the corrected waveforms are supposed to have face validity based on visual inspection, therefore promoting subjectivity (Verleger, Gasser, & Mocks, 1982). In the earlier example (Figure 1), visual inspection of the IMFs that are retained in the data set do not show any blink activity, yet the IMFs that are selected for removal contains some low frequency EEG components as well. Therefore, it is important to use some additional quantitative measures in addition by which the success and the error of the correction method can be quantified, and this is only possible through simulation studies. Here a simulation was performed in which a blink template was superimposed onto artifact-free IC data and
subsequently removed using EMD, allowing for a direct comparison between the original and the corrected signal. This simulation also allowed for an evaluation of the performance of the classification method based on the relative SDs of the IMFs by systematically varying the values of p and the size of the artifacts relative to the variation in the data. In the simulation, 100 segments of 4 s (2,048 sample points) of artifact-free IC data were selected from a 64-channel EEG study conducted in our laboratory (see, for details, Lindsen, Jones, Shimojo, & Bhattacharya, 2010). An eyeblink template of 2 s duration was obtained by averaging 140 peak-aligned blink intervals from 2 randomly chosen participants in this study. This eyeblink template was superimposed onto the IC data with a random offset varying between 0 and 2 s, creating waveforms resembling those data segments containing real blink artifacts. The relative size of this artifact, defined as the peak-to-peak amplitude of the blink template, was taken as s times the SD of the data segment on which it was superimposed, with s varying between 20 and 40 in steps of 2. The value of p (the multiplication factor that determines the upper limit of acceptable SDs in the classification of IMFs) varied between 1 and 7 in steps of 0.25. At every pair {p, s}, EMDs were obtained for the 100 segments with superimposed blink artifacts, and for each of these EMDs, the IMFs that were classified by our proposed classification method as containing blink-related components were discarded. Then, for each segment, the correlation coeffi-
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cient between the cleaned data, that is, the sum of the IMFs classified as containing EEG and the original data without the artifact, was computed as a measure of the goodness of fit. As a result, 100 values were obtained for every {p, s} pair, and the average was taken as an indication of how much of the original signal was maintained after artifact correction. Figure 2 shows the results of this simulation. In addition to the correlation coefficient, the mutual information between the original and the corrected signal was also calculated. The mutual information is sensitive to both linear and higher order correlations between two signals whereas correlation coefficient is sensitive to only linear correlation (Li, 1990). Over the intervals of varying artifact sizes tested in this simulation, the optimal performance of the EMD1ICA method is fairly constant between correlation values of .55 and .60. The mutual information estimates showed a similar pattern as the correlation coefficients but were, in general, slightly lower with maximum values ranging from .35 to .4 nats. The simulation also shows that classification performance deteriorates for both too high and too low values of p due to the exclusion of IMFs containing EEG data and to the inclusion of IMFs containing artifacts, respectively. The classification is optimal when p is between 1.5 and 2.5. Next, the improvement of the combined ICA and EMD method over the traditional ICA method was assessed by directly comparing the outcomes of the two methods for the data of one randomly chosen participant from the same data set used in the simulation study. An ICA decomposition of the data (64 EEG, two VEOG, and two HEOG channels) was obtained with the infomax algorithm implemented in the EEGLAB toolbox (Delorme & Makeig, 2004). One single IC was identified by visual inspection as containing blink artifacts. First, the IC containing blink artifacts was removed before the inverse transformation recalculated the data. Next, the IMFs of the IC containing blinks were obtained with an EMD, and the IMFs representing the artifact were excluded before the inverse ICA transformation. The difference in the amount of data lost between removing entire ICs and removing only the blink components of the ICs by means of EMD during inverse ICA transformation is illustrated qualitatively in Figure 3. This figure shows the data from five
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midline electrodes, Fpz, Fz, Cz, Pz, and Oz. In the top panel, the original data show an eyeblink artifact occurring between approximately 2 and 4 s with a maximal impact on the frontopolar electrode. The reconstructed data are shown in the bottom panel of Figure 3. Although traditional ICA-cleaned (red lines) and the proposed ICA1EMD-cleaned data (blue lines) are largely similar, differences do appear on close inspection (see figure insets), and these differences are more pronounced on anterior than on posterior electrodes. When the cleaned frontopolar signals are overlapped with the original data (inset A), it is evident that the combined ICA1EMD method retains more information in the data, especially on shorter time scales. The superiority of the ICA1EMD method over traditional ICA gets more apparent during the time period containing the blink: The ICA-cleaned data show spiky behavior locally that does not seem to be present in the original data, and such spurious peaks are not present in the ICA1EMD-cleaned data (inset B). To further quantify the improvements of the ICA1EMD method over the traditional ICA method, the correlation coefficient (and the mutual information) between the ICA1EMD-corrected data and the original data was compared with the correlation coefficient between the ICA-only corrected data and the original data. These comparisons focused on the data segments outside the blink intervals, because these segments allow for an assessment of the amount of EEG signal lost by correcting for blinks with either the traditional ICA or the combined ICA1EMD method. For electrode Fpz, where most of the data loss is to be expected, the correlation between the corrected and original data was .81 for the traditional ICA method and .86 for the proposed EMD1ICA method. So, although the amount of data lost with traditional ICA does not seem to be very large, the ICA1EMD method manages to reduce this loss considerably. The mutual information values were .32 and .40 nats between the traditional ICA method and the original data, and between the ICA1EMD method and the original data, respectively. Altogether, these values indicate a considerable increase in the information retained by using the combined ICA1EMD method as compared to the traditional ICA method.
Discussion and Conclusion In this article, a novel way to correct for blink-related ocular artifacts has been proposed, based on the combination of EMD and ICA decompositions of multichannel EEG recordings. Whereas ICA provides a spatial filter that conveniently captures widespread blink activity in a small set of ICs, the EMD method can be used as a frequency filter (Flandrin, Rilling, & Goncalves, 2004) to selectively exclude the artifacts from these ICs and retain useful EEG data that might be present in these ICs as well. The approach described here is conceptually quite similar to that recently proposed by Castellanos and Makarov (2006), who applied a digital wavelet transform (DWT) to the ICs to separate the blink artifacts from the EEG data. However, on purely theoretical grounds, it might be argued that the EMD is a more suitable decomposition method for EEG/EOG data than the DWT. In essence, EMD is an adaptive method, that is, it does not assume that the same basis for decomposition is suitable for an entire data set, but takes its basis from local dynamics in the data. This property makes it highly suitable for the analysis of nonstationary and nonlinear signals such as EEGs and transient eyeblink artifacts. In contrast, the DWT assumes that the same
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Figure 3. Comparison of ICA and ICA1EMD method for correction of blink artifacts. The top panel shows the midline electrodes from a 64-channel EEG recording containing a blink artifact. The bottom panel shows the same electrodes after removing the independent component (IC) that contained this blink artifact (red line) and after the cleaning this IC using EMD (blue line). Inset A shows in detail the difference between the original signal and the two methods of correcting the blink artifact outside the blink interval. Inset B shows in detail the difference between the two methods of correcting during the blink interval; note the spurious peaks in the ICA cleaned data that do not seem to be present in the original data.
basis is suitable for an entire data set and might yield spurious harmonics because of the nonstationary and nonlinear nature of the signals. Furthermore, performing EMD is computationally straightforward, and the only assumption on the input is that it has at least one minimum and one maximum. Surprisingly, despite the appropriateness of EMD as a suitable technique for EEG analysis, only a few applications have been reported so far (Liang et al., 2005; Sweeney-Reed & Nasuto, 2007). Here, it is shown how the EMD method, in combination with ICA, could be applied to correct blink artifacts in EEG data by excluding selective modes in data that contain relatively pure blink activity. Interestingly, the reverse process of excluding the modes that contain EEG data might be of potential interest for another popular method of blink artifact correction based on regression in the time domain (e.g., Gratton et al., 1983). The regression method uses EOG recordings to subtract from each scalp electrode a portion of the EOG signal, but is also likely to subtract some EEG signal, as the EOG recording is most likely not a ‘‘pure’’ recording of blink artifacts. So, EMD has the potential to be applied to EOG signals for selectively eliminating those IMFs associated with EEG, therefore ‘‘cleaning’’ the EOG recordings before the subtraction process and improving the performance of the regression method. The main limitation of the combined ICA1EMD method is that in addition to eliminating blink-related artifacts in the EEG, it will also eliminate very slow time scale EEG activity, comparable to or slower than the time scale of blink artifacts.1 For example, the inset of Figure 1 shows that although the profile of
the original signal on a smaller time scale is maintained, the largescale low-frequency trend in the original signal is removed together with the blink-related activity. Potentially, this might greatly reduce EEG activity in the delta (up to 4 Hz) range as well as ERP components with a time course of several hundreds of milliseconds. But it must be noted that the loss of slow drift due to the removal of lower order IMFs will occur only if there is a significant overlap between both the frequency characteristics and the spatial distribution of the eyeblink activity and the original EEG as captured by the selected ICs. Importantly, this data loss is relative to the original, uncorrected data; when only ICA is used to correct for blink artifacts, the entire ICs containing blinks would be removed regardless of their frequency content. Eyeblink artifacts are typically asymmetric with respect to the horizontal axis, that is, the positive peak amplitude is higher than the negative peak amplitude. In contrast, the IMFs are, by definition, symmetrical, as the mean of the upper and lower envelopes is required to be zero at any time point. Therefore, the blink artifact cannot be completely captured by one single IMF but will be distributed over multiple lower order IMFs; see, for example, the bottom panel of Figure 1. For this reason, in the proposed classification criterion, the first IMF that has a SD larger than p times the SD of the first IMF is excluded together with all lower order IMFs, regardless of the SD of these IMFs. 1 It is shown that for a structured broadband Gaussian process, EMD works as a dyadic filter bank; the mean oscillation of a given IMF is roughly twice that of the previous one (Flandrin et al., 2004).
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In addition to eyeblinks, saccades are another prominent source of artifacts in the EEG. Whereas eyeblinks have a characteristic frequency profile that falls outside the frequency range of interest in the study of many EEG components, the frequencies involved in saccade artifacts typically overlap with this range. For this reason, a frequency-based method for artifact correction is less suitable for the correction of artifacts caused by saccades. Furthermore, the amplitude of saccade artifacts falls generally in the same range as other fluctuations in the raw EEG, rendering a classification method based on the relative size of the SDs of the IMFs problematic. Finally, eyeblink artifacts are not only a problem in the recording and analysis of EEG data but provide similar problems for the analysis of magnetoencephalogram (MEG) data. Despite some general differences between EEG and MEG signals, the characteristic properties of blink artifacts that the proposed EMD-assisted ICA procedure exploits, that is, its relatively slow time scale and relatively large amplitude com-
pared to neurally generated signals, are present in both methods of recording. So, the proposed method for blink artifact correction has potential merit for use in both EEG and MEG data. In conclusion, combining ICA with EMD provides a useful method to retain EEG activity in ICs representing eyeblink activity. The EMD procedure is an intuitive and computationally straightforward method that separates a signal in modes with different characteristic time scales, allowing for the selective removal of the modes with a slow time scale and large amplitude activity typical of eyeblink artifacts. The instantaneous and adaptive nature of the EMD makes it especially suitable for the analysis of nonstationary and nonlinear processes like EEGs and transient eyeblink artifacts. In this article the effectiveness of the ICA1EMD method was illustrated with both real and simulated data, and a criterion was provided for the classification of IMFs as either containing EEG or blink activity that allows for a fully automated correction of blink artifacts.
REFERENCES Barbati, G., Porcaro, C., Zappasodi, F., Rossini, P. M., & Tecchio, F. (2004). Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals. Clinical Neurophysiology, 115, 1220–1232. Berg, P., & Scherg, M. (1991). Dipole models of eye movements and blinks. Electroencephalography and Clinical Neurophysiology, 79, 36– 44. Castellanos, N. P., & Makarov, V. A. (2006). Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis. Journal of Neuroscience Methods, 158, 300–312. Croft, R. J., & Barry, R. J. (2000). Removal of ocular artifact from the EEG: A review. Neurophysiologie Clinique, 30, 5–19. Croft, R. J., Chandler, J. S., Barry, R. J., Cooper, N. R., & Clarke, A. R. (2005). EOG correction: A comparison of four methods. Psychophysiology, 42, 16–24. Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134, 9–21. Flandrin, P., Rilling, G., & Goncalves, P. (2004). Empirical mode decomposition as a filter bank. Signal Processing Letters, IEEE, 11, 112–114. Frank, R. M., & Frishkoff, G. A. (2007). Automated protocol for evaluation of electromagnetic component separation (APECS): Application of a framework for evaluating statistical methods of blink extraction from multichannel EEG. Neurophysiologie Clinique, 118, 80–97. Gratton, G., Coles, M. G., & Donchin, E. (1983). A new method for offline removal of ocular artifact. Electroencephalography and Clinical Neurophysiology, 55, 468–484. Hoffmann, S., & Falkenstein, M. (2008). The correction of eye blink artefacts in the EEG: A comparison of two prominent methods. PLoS ONE, 3, e3004. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., et al. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 454, 903–995. Ille, N., Berg, P., & Scherg, M. (2002). Artifact correction of the ongoing EEG using spatial filters based on artifact and brain signal topographies. Journal of Clinical Neurophysiology, 19, 113–24.
Joyce, C. A., Gorodnitsky, I. F., & Kutas, M. (2004). Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. Psychophysiology, 41, 313–325. Jung, T. P., Makeig, S., Humphries, C., Lee, T. W., McKeown, M. J., Iragui, V., et al. (2000). Removing electroencephalographic artifacts by blind source separation. Psychophysiology, 37, 163–178. Li, W. (1990). Mutual information functions versus correlation functions. Journal of Statistical Physics, 60, 823–837. Liang, H., Bressler, S. L., Buffalo, E. A., Desimone, R., & Fries, P. (2005). Empirical mode decomposition of field potentials from macaque V4 in visual spatial attention. Biological Cybernetics, 92, 380– 392. Lindsen, J. P., Jones, R., Shimojo, S., & Bhattacharya, J. (2010). Neural components underlying subjective preferential decision making. NeuroImage, 50, 1626–1632. Matsuo, F., Peters, J. F., & Reilly, E. L. (1975). Electrical phenomena associated with movements of the eyelid. Electroencephalography and Clinical Neurophysiology, 38, 507–511. Picton, T. W., van Roon, P., Armilio, M. L., Berg, P., Ille, N., & Scherg, M. (2000). The correction of ocular artifacts: A topographic perspective. Clinical Neurophysiology, 111, 53–65. Quilter, P. M., MacGillivray, B. B., & Wadbrook, D. G. (1977). The removal of eye movement artifact from the EEG signals using correlation techniques. Random Signal Analysis, IEEE Conference Publication, 159, 93–100. Schlogl, A., Keinrath, C., Zimmermann, D., Scherer, R., Leeb, R., & Pfurtscheller, G. (2007). A fully automated correction method of EOG artifacts in EEG recordings. Clinical Neurophysiology, 118, 98– 104. Sweeney-Reed, C. M., & Nasuto, S. J. (2007). A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition. Journal of Computational Neuroscience, 23, 79–111. Verleger, R., Gasser, T., & Mocks, J. (1982). Correction of EOG artifacts in event-related potentials of the EEG: Aspects of reliability and validity. Psychophysiology, 19, 472–480. Vigario, R. N. (1997). Extraction of ocular artifacts from EEG using independent component analysis. Electroencephalography and Clinical Neurophysiology, 103, 395–404. (Received February 26, 2009; Accepted September 30, 2009)
Psychophysiology, 47 (2010), 961–967. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.00997.x
Anxiety and feedback negativity
RUOLEI GU, YU-XIA HUANG, and YUE-JIA LUO State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
Abstract It has been suggested that anxious individuals are more prone to feel that negative outcomes are particularly extreme and to interpret ambiguous outcomes as negative compared to nonanxious individuals. Previous studies have demonstrated that the feedback negativity (FN) component of event-related brain potential (ERP) is sensitive to outcome evaluation and outcome expectancy. Hence, we predicted that the FN should be different between high trait-anxiety (HTA) and low trait-anxiety (LTA) individuals. To test our hypothesis, the ERPs were recorded during a simple monetary gambling task. The FN was measured as a difference wave created across conditions. We found that the amplitude of the FN indicating negative versus positive outcomes was significantly larger for LTA individuals compared to HTA individuals. However, there was no significant difference in the FN between groups in response to ambiguous versus positive outcomes. The results indicate that there is a relationship between the FN and individual differences in anxiety. We suggest that these results reflect the impact of anxiety on outcome expectation. Our results challenge the reinforcement learning theory of error-related negativity, which proposes that ERN and FN reflect the same cognitive process. Descriptors: Feedback negativity (FN), Trait anxiety, Decision making, Outcome expectancy, Outcome evaluation, Reinforcement learning theory of the error-related negativity, RL-ERN theory
Besides, according to previous studies, a high level of anxiety is associated with an increased likelihood of holding negative interpretations of ambiguous stimuli (cf. Blanchette & Richards, 2003; see also Bensi & Giusberti, 2007; Kverno, 2000; Richards et al., 2002). Although it is still unclear whether this phenomenon plays a role in high anxiety decision making, it has been reported that anxious adolescents are more intolerant to uncertainty in a decision-making task (Krain et al., 2008). It would be worthwhile to determine whether high anxiety produces a more negative evaluation of ambiguous outcomes than low anxiety. Feedback negativity (FN; Yeung & Sanfey, 2004), also known as feedback error-related negativity (fERN; Holroyd & Coles, 2002), is a negative-going component of the event-related potential (ERP) spanning the interval between 200 and 300 ms after the onset of feedback stimuli. Gehring and Willoughby (2002) reported that the FN became larger after losses than after gains in simple monetary gambling tasks. These data indicate that the amplitude of the FN is sensitive to the valence of the experienced outcome. The characteristics of the FN have subsequently become a focus of research on decision making. The reinforcement learning theory of the ERN (RL-ERN theory) is currently the only theory that attempts to explain the functional significance of the FN (Nieuwenhuis, Holroyd, Mol, & Coles, 2004). The RL-ERN theory suggests that the FN, as well as ERN (Gehring, Coles, Meyer, & Donchin, 1990), reflects the evaluative information conveyed via the midbrain dopamine system (Holroyd, Hajcak, & Larsen, 2006). According to RL-ERN theory, the FN amplitude depends on the relationship between the actual versus the expected outcome (Nieuwenhuis et al., 2004). Therefore, the FN is a suitable
Anxiety is an emotional state accompanied by expectations of negative outcomes and concerns about the severity of those outcomes (cf. Eisenberg, Baron, & Seligman, 1998). The perception of threat and danger are central for evoking anxiety (Mitte, 2007). Anxiety can exert a profound influence on cognition, potentially heightening the perception of threat (Maner & Schmidt, 2006). A number of different experimental approaches have been used to examine the impact of anxiety on cognitive processes such as attention, working memory, behavioral control, and decision making (see Paulus, Feinstein, Simmons, & Stein, 2004). Decision making can be divided functionally into distinct processes, one of which is outcome evaluation or experience (Paulus, 2005). It has been suggested that outcome evaluation can be influenced by anxiety level. First, anxious individuals are more prone to feel that negative outcomes are particularly extreme (Eisenberg et al., 1998; Maner & Schmidt, 2006; Wray & Stone, 2005). For instance, Mitte (2007) asked participants to choose between different scenarios and found that there was a significant correlation between trait anxiety and the subjective cost of negative events. However, most data in this field come from subjective self-reports. This research was supported by the NSFC (30930031, 30800298), National Key Technologies R&D Program (2009BAI77B01), Ministry of Education (PCSIRT, IRT0710), and Global Research Initiative Program, NIH, USA (1R01TW007897). We would like to thank Xiaoqin Mai, Jianhui Wu, and two anonymous reviewers for their very helpful and constructive comments in improving earlier versions of this manuscript. Address reprint requests to: Yue-jia Luo, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China. E-mail:
[email protected] 961
962 measure for the objective investigation of outcome evaluation. Recently, Foti and Hajcak (2009) discovered that the amplitude of the FN was inversely related to level of depression as well as stress reactivity. These findings suggest that the FN may be influenced by individual differences in affect. In the present study, we were interested in ERP as an objective measure in exploring the relationship between anxiety and outcome evaluation. A variant of Gehring and Willoughby’s (2002) monetary gambling task was used, as well as ERP recordings, to investigate whether the amplitude of FN in response to outcome would be associated with level of anxiety. In this simple task, which has been shown to consistently elicit FN, participants were asked to gamble for rewards, and their choices were followed by feedback events. We hypothesized that (a) the amplitude of the FN would be significantly higher for high-anxious participants than low-anxious participants after they received a negative outcome during the task, because the former would evaluate negative outcomes as particularly bad, and (b) the amplitude of the FN would be significantly higher for high-anxious participants compared to low-anxious participants after they received an ambiguous outcome, because the former are more likely to interpret the ambiguous outcomes as negative.
Methods Participants Seventy-nine college students participated in a mass screening with the Chinese version of Spielberger’s State-Trait Anxiety Inventory (STAI). This scale has demonstrated good internal consistency as well as convergent and discriminant validity (Shek, 1993; Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983). Participants who scored high in trait anxiety (in the upper 25% of the distribution) were assigned to the high-trait anxiety (HTA) group, whereas the participants who scored low (in the lower 25% of the distribution) were assigned to the low-trait anxiety (LTA) group. As a result, 33 participants (19 women; mean age 5 23.61 ! 1.85 years) were included in the study, 16 in the HTA group (8 women) and 17 in the LTA group (11 women). A t test showed that the two groups differed significantly in traitanxious scores (58.31 vs. 30.18, po.001) but not in age (24.06 vs. 23.18, p 5 .429). All participants denied regular use of medication or other nonmedical substances with the potential to affect the central nervous system, all had normal vision (with correction), and none had a history of neurological disease. All participants were right-handed. They signed the informed consent before the experiment and were paid according to their performance in the task described below. Procedure The participant sat comfortably in an electrically shielded room, approximately 80 cm in front of a computer screen. Each trial began with the presentation of a central fixation point, adjoined on either side by two rectangles (2.51 " 2.51, white against a black background). After 500 ms, the numbers 5 or 25 (indicating the score) were individually presented in either of the two rectangles. The participant then selected one of the two alternatives by pressing the F or J keys on the keyboard with their left or right index finger (F for the alternative on the left and J for the one on the right). The alternatives remained on the screen until the participant made a choice, which was then highlighted by a
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Figure 1. The sequence of events within a single trial of the monetary decision-making task. On each trial, the participants were presented with a choice of two alternatives, one of which they were asked to select using their left or right index finger. Their choice was highlighted for 500 ms. After a subsequent interval of 1000–1500 ms, the participants received feedback, lasting 1500 ms, which indicated the score they had gained or lost. After an additional 800–1200 ms, they were presented with the next trial. RT: Response time.
thickening of the red outline of the chosen rectangle for 500 ms. All stimuli but the fixation point then disappeared for a short interval of random duration between 1000 and 1500 ms; then the point was replaced by the result of the participant’s choice with the 1 or # symbols, indicating the valence of the outcome (see Figure 1). There were five possible outcomes: 15, # 5, 125, # 25, and ?. The ? symbol indicated an ambiguous outcome. When the participant received the ambiguous outcome, he had to guess whether he won or lost in this trial (in fact, the computer counted the ? as zero score). The feedback display remained visible for 1500 ms, and then a black screen was presented for a short interval that varied randomly between 800 and 1200 ms. The formal task consisted of five blocks of 90 trials each. At the end of each block, the participant was informed of the total score that had been earned to that point. Stimulus display and behavioral data acquisition were conducted using E-Prime software (Version 1.1, Psychology Software Tools, Inc., Pittsburgh, PA). Before the experiment, the participants were instructed about the meaning of the feedback display. Specifically, they were encouraged to guess the valence of ambiguous outcomes by themselves. They were told to respond in a way that would maximize the total score amount. The higher the score they earned, the more bonus money they would receive at the end of the experiment. Furthermore, they were asked to choose any strategy to achieve their goal. Unbeknownst to the participants, the probability of receiving positive, negative, or ambiguous outcomes on any given trial was equal. Thus, it was not possible for the subject to devise any meaningful strategy to learn.
Questionnaire After the experiment, the participants were asked to complete a brief questionnaire that contained four main items. First, the participants were asked whether they thought there was a rule that could determine the probability of a reward, with their response to be given on a 5-point scale from 1 (definitely not) to 5 (pretty sure).
Anxiety and feedback negativity Second, participants were asked to rate the probability that choosing each of the alternatives (5 or 25), would yield a reward and to indicate their response on a 7-point scale from 1 (not at all likely) to 7 (extremely likely). This was intended to test whether the HTA group judged the positive outcome as less likely to happen than did the LTA group. Third, participants were asked to rate the subjective feeling when they received negative outcomes ( ! 5 or ! 25) and to indicate their response on a 7-point scale from 1 (very unhappy) to 7 (very happy). This was intended to test whether the HTA group evaluated negative outcomes as particularly bad. Finally, participants were asked to answer the meaning of ambiguous outcomes on a 5-point scale from 1 (always means loss) to 5 (always means win). This was intended to test whether the HTA group were more likely to interpret the ambiguous outcomes as negative ones. (We had tried to use a 7-point scale in the preliminary experiment, but some participants told us that it was a little too delicate to answer the meaning of ambiguous outcomes on a 7-point scale.) Behavioral Analyses We defined the choice of 5 to be the risk-avoidant choice in our experiment, predicting that participants would make this choice to avoid the possibility of a large loss ( ! 25). However, by making this choice, they also gave up the opportunity to receive the larger reward (125). In contrast, the choice of 25 was defined as the risky choice. To provide a basis for comparison with previous research, we tested the average level of risk-avoidant choice made by each participant, as well as the average time each participant took to decide between groups. The data were subjected to the independent-samples T test, with the participants’ choice (5 or 25) and the average response time as two test variables and the level of trait anxiety as the grouping variable. In addition, we collected the participants’ answers to our questionnaire. The response to the first item was used to measure their subjective confidence as to whether they could maximize the bonus money by deducing a rule. The answer to the second item was used to measure the subjective probability of positive outcome. The answer to the third item was used to measure the subjective cost of negative outcome. The answer to the fourth item was used to measure the subjective evaluation of ambiguous outcome. ERP Data Recording and Analyses The electroencephalogram (EEG) was recorded from 64 scalp sites using tin electrodes mounted in an elastic cap (NeuroScan Inc., Herndon, VA) with the reference on the right mastoid. Horizontal electrooculogram (HEOG) was recorded from electrodes placed at the outer canthi of both eyes. Vertical electrooculogram (VEOG) was recorded from electrodes placed above and below the left eye. Electrode impedances were kept below 5 kO. The EEG and EOG were amplified using a 0.05–100-Hz bandpass and continuously sampled at 1000 Hz/channel for offline analysis. All the EEG data were re-referenced off-line to averaged mastoid reference by subtracting from each sample of data recorded at each channel one half the activity recorded at the right mastoid. Ocular artifacts were identified and corrected with an eye-movement correction algorithm. Any trials in which EEG voltages exceeded a threshold of " 100 mV during the recording epoch were excluded from the analysis. The data were baseline
963 corrected by subtracting from each sample the average activity of that channel during the baseline period. For each feedback stimulus, a 1000-ms epoch of data was extracted from the continuous data files for analysis (from 200 ms before the feedback onset to 800 ms after the feedback onset). To minimize overlap between the FN and other ERP components, the amplitude of the FN was measured as the maximum difference wave between different conditions in a window of 200–400 ms following the feedback presentation, relative to a 200ms prestimulus baseline. We followed the methods of Holroyd and Krigolson (2007) and created two difference waves by subtracting the ERP on positive outcome trials from the ERP on negative outcome trials (called ‘‘negative–positive’’ difference wave), and the ERP on positive outcome trials from the ERP on ambiguous outcome trials (called ‘‘ambiguous–positive’’ difference wave). Results Behavioral Results The average number of risk-avoidant choices made by members of the HTA group was slightly higher (249.47 times, SD 5 52.82) than that of the LTA group (231.82 times, SD 5 58.81), but the difference was not statistically significant, t(31) 5 1.268, p 5 .499. The average time taken for decision making by the LTA group (973.71 ms, SD 5 433.35) was shorter than for the HTA group (1158.39 ms, SD 5 677.96), but this result just failed to reached significance, t(31) 5 0.938, p 5 .057. According to the results of our questionnaire, the subjective confidence about the existence of a rule was high (4.18 on a 5-point scale, SD 5 0.88). The subjective probability of positive outcome rated by the HTA group was slightly less than that of the LTA group, but the T score did not reach significance. The HTA group estimated the probability of winning to be 3.94 on a 7-point scale when they chose 5, whereas the LTA group rated this same probability as 4.00, t(31) 5 ! 0.2, p 5 .621. When choosing 25, the HTA group judged the probability of winning to be 3.44, and the LTA group 3.71, t(31) 5 0.85, p 5 .274. The subjective cost of negative outcome revealed a significant group difference when the outcome was ! 5 (HTA group: 3.06, LTA group: 3.82), t(31) 5 ! 2.324, p 5 .027. When the outcome was ! 25, the T score did not reach significance (HTA group: 2.19, LTA group: 2.59), t(31) 5 ! 1.293, p 5 .206. The results of the questionnaire revealed a significant group difference in the interpretation of the ambiguous outcomes. When choosing 5, the HTA group interpreted the meaning of ambiguous outcomes to be 2.88, and the LTA group 3.41, t(31) 5 ! 2.304, p 5 .028. When choosing 25, the HTA group interpreted the meaning of the ambiguous outcomes to be 2.81, and the LTA group 3.24, t(31) 5 ! 2.193, p 5 .036. ERP Results The scalp distribution of the negative–positive difference wave was maximal at frontal-central areas of the scalp, at electrode position FCz ( ! 6.21 mV), 304.79 " 8.20 ms following the onset of the feedback. The scalp distribution of the ambiguous–positive difference wave was also maximal at FCz ( ! 10.85 mV), 290.30 " 6.12 ms following the onset of the feedback (see Figure 2). A one-sample t test revealed the negative–positive difference wave to be significantly less than zero, SD 5 4.07, t(32) 5 ! 8.77, po.001, confirming that the gambling task used
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Figure 2. (a) Grand-average event-related potential evoked by the presentation of feedback at the FCz recording site. Positive, negative, ambiguous: valence of the outcome. (b) Difference waves associated with FCz site and the scalp distribution of difference waves.
in the present study reliably elicited an enhanced FN for monetary loss compared to gain. The result of paired t test showed that there was a significant difference in the amplitude between two kinds of difference waves, t(32) 5 5.70, po.001. But the difference in the latency was not significant, t(32) 5 1.16, p 5 .255. To confirm that the amplitude of the FN was not confounded by overlap with the P300, we followed the method of Holroyd and Krigolson (2007) and carried out paired t tests on the amplitude of difference wave at FCz and Pz (where the peak of the P300 is usually localized). Both kinds of difference waves were significantly larger at FCz than Pz (negative–positive: ! 6.21 mV versus ! 3.94 mV, t[32] 5 6.66, po.001; ambiguous–positive: ! 10.85 mV versus ! 8.63 mV, t[32] 5 4.11, po.001). ERP amplitude of both kinds of difference waves at electrode position FCz were entered into a 2 (outcome magnitude) " 2 (trait anxiety group) analysis of variance (ANOVA), using the trait anxiety group as the between-subjects factor. The Greenhouse–Geisser correction for repeated measures was applied to within-subject comparison. The analysis showed a reliable main effect of trait anxiety group, F(1,31) 5 9.325, p 5 .005, on the amplitude of the negative–positive difference wave. This result indicated that the FN was significantly larger in LTA group ( ! 8.06 mV) than HTA group ( ! 4.21 mV), consistent with our hypothesis a. The main effect of outcome magnitude did not reach significance, F(1,31) 5 1.100, p 5 .302. That was consistent with previous
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Figure 3. Negative minus positive difference wave (a) and ambiguous minus positive difference wave (b) of HTA and LTA groups at the FCz site. The gray shaded areas indicate the 200–400-ms analysis window in which the FN was quantified.
results (Hajcak, Moser, Holroyd, & Simons, 2006; Yeung & Sanfey, 2004). The Group " Magnitude interaction did not reach significance, F(1,31) 5 0.036, p 5 .851, which means that the group effect could not be influenced by outcome magnitude. The main effect of group factor, F(1,31) 5 0.794, p 5 .380, did not reach significance on the amplitude of the ambiguous– positive difference wave (Figure 3). But the main effect of outcome magnitude, F(1,31) 5 10.297, p 5 .003, was significant, indicating that the ambiguous–positive difference wave was larger when participants chose 25 ( ! 12.15 mV) than when they chose 5 ( ! 10.11 mV). The Group " Magnitude interaction was not significant, F(1,31) 5 0.378, p 5 .543.
Discussion Consistent with previous research, the present study found classic feedback negativity as a negative deflection in the ERP difference wave in a decision-making task. Notably, the amplitude of the FN (measured as the negative–positive difference wave) was significantly larger for LTA than for HTA participants. The present results extend prior findings (Foti & Hajcak, 2009) that individual personality traits can significantly influence the features of FN. We had hypothesized (hypothesis a) that the FN amplitude elicited by a negative outcome would be higher for the HTA than for the LTA group, because the former would evaluate a negative outcome as particularly severe. However, the ERP results are inconsistent with our prediction. It is possible that our results
Anxiety and feedback negativity reflect a difference in expected rather than actual outcome evaluation. Previously it has been shown that the FN can be profoundly influenced by outcome expectation. For example, the FN has been found to be greater for unexpected negative outcomes than expected ones (Bellebaum & Daum, 2008; Hajcak, Moser, Holroyd, & Simons, 2007; Yasuda, Sato, Miyawaki, Kumano, & Kuboki, 2004). In our opinion, our results are consistent with previous findings that higher levels of anxiety are associated with the expectancy (not actual outcome) of a more negative outcome in risk-taking tasks (Eisenberg et al., 1998; Lauriola & Levin, 2001; Mitte, 2007; Shepperd, Grace, Cole, & Klein, 2005). However, the behavioral results showed no correlation between trait anxiety level and subjective probability of outcome, thus limiting the validity of our above explanation. In one study (Hajcak et al., 2007), it was suggested that outcome expectations may differ on a trial-by-trial basis. Participants were asked to indicate during each trial whether they expected to receive a reward. A relationship between outcome expectation and the amplitude of the FN was found. In the present study, subjective probability of outcome was collected at the termination of the experiment. This timing may not have been optimal in obtaining a reliable measure of actual expectations during the experiment. In other words, our results may reflect a retrospective analysis of expectation and thus may be only loosely tied to the participants’ real expectations during the task. As an alternative, these unexpected results may be better accounted for by the influence of depression. Foti and Hajcak (2009) suggested that the association between FN and depression scores reflected the relationship between depression and reduced sensitivity to rewards. It is possible that the ERP result in the present study reflect the close association between depression and anxiety (see Beuke, Fischer, & McDowall, 2003). Furthermore, Foti and Hajcak found that the FN was sensitive to depression and stress reactivity, but only the relationship between the FN and stress remained significant after controlling for other psychological variables. Considering the close relationship between anxiety and stress (see Thapar, Kerr, & Harold, 2009), it is possible that our findings actually reflect the modulatory role of stress on the dopaminergic system (for the effect of stress on dopaminergic neurons, see Pani, Porcella, & Gessa, 2000). Future research into this area would be of interest. The second hypothesis of our study was that the FN would be larger for the HTA compared to the LTA group when participants were presented with an ambiguous outcome. This hypothesis was based on our presumption that the HTA group would be more prone to interpret ambiguous outcomes as negative. However, there was no significant difference in the FN elicited by ambiguous outcomes between the two groups whereas the results of the questionnaire revealed a significant group difference in the interpretation of the ambiguous outcomes. Our findings are, therefore, surprising. It is feasible that the processing of ambiguous outcomes consumed more cognitive resources than definite outcomes. Whereas definite outcomes are received passively, ambiguous outcomes must be actively processed. In accordance with the above theory, some researchers suggest that interpreting ambiguous situations may need more top-down control (Voss, Rothermund, & Brandtstadter, 2008). An fMRI study found that ambiguous affective stimuli activated regions implicated in conflict monitoring and cognitive control, including the dorsal anterior cingulated cortex (Simmons, Stein, Matthews, Feinstein, & Paulus, 2006). In our opinion, the
965 particularly high amplitude of FN in the ambiguous condition reflected a more complex process of outcome evaluation. Therefore, this kind of the FN, in response to ambiguous information, was more complicated than the classic (well-studied) FN response. In a recent study, Hirsh and Inzlicht (2008) reported that uncertain feedback, but not negative feedback, elicited a larger FN in neurotic individuals compared to nonneurotic individuals. These results are interesting in light of the close relationship between neuroticism and anxiety (cf. Miller & Pilkonis, 2006). Because neuroticism is correlated with self-reported intolerance of uncertainty (Berenbaum, Bredemeier, & Thompson, 2008), it is plausible to expect that participants with high neuroticism would be more willing to guess the meaning of uncertain feedback than others even when not instructed to do so. As a result, Hirsh and Inzlicht discovered that the uncertain feedback elicited a larger FN in the high neuroticism group, whereas this kind of FN appeared in both groups in our study. Further investigation into the more complex FN in response to ambiguous outcomes is needed. Our results, along with other studies (Foti & Hajcak, 2009; see also Holroyd et al., 2006), suggest that some aspects of RL-ERN theory may need to be reconsidered, especially the idea that the FN is functionally identical to the ERN, defined as a response-locked ERP component elicited when subjects commit errors. It has been proposed by RL-ERN theory that both the FN and ERN are manifestations of the same cognitive and neural processes (Holroyd, Coles, & Nieuwenhuis, 2002; Nieuwenhuis et al., 2004). However, important differences between the ERN and the FN have recently been discovered. Hajcak, McDonald, and Simons (2003) found that ERN activity was generally enhanced in a high anxiety group following both correct and incorrect (error) responses. Comparing the above research with our study, it is clear that the FN and the ERN relate in opposite ways to anxiety measures. Similarly, whereas an increased ERN has been linked with clinical depression (Chiu & Deldin, 2007), Foti and Hajcak found that a decreased FN was linked to depression. Furthermore, Gehring and Willoughby (2004) have shown that the two components exhibit divergent scalp distribution. Taken together, it is more likely that the ERN and the FN should be considered as distinct phenomena, although the functional underpinnings of the two components are still under debate. It will be worthwhile to investigate whether there is a mediating variable in the relationship between anxiety and the FN amplitude. A high level of uncertainty over an outcome and low control over a situation are mind sets underlying anxiety as well as other types of fearlike emotions (cf. Raghunathan & Pham, 1999). According to some research, the perception that an event is uncontrollable can increase apprehension of undesirable outcomes, linking anxiety to pessimistic outcome expectancy (Maner & Schmidt, 2006). In this case, the amplitude of FN should be inversely correlated with measures of sense of control, such as Shapiro Control Inventory (SCI) scores (see Surgenor, Horn, & Hudson, 2002). Further research on this issue would lead to a better understanding about how anxiety influences cognitive functions. Additionally, although the relationship between nonclinical anxiety and the amplitude of FN has been revealed, it remains to be discovered whether this relationship extends to anxiety spectrum disorders, such as obsessive-compulsive disorder (OCD). Because it has been demonstrated that the ERN was significantly enhanced in patients with OCD
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(Gehring, Himle, & Nisenson, 2000), it will be interesting to investigate whether the amplitude of FN would be inversely related to OCD symptom severity. Research in this direction will help examine the continuum between normal and pathological anxiety (cf. Muris & van der Heiden, 2006). In summary, the current ERP results provide evidence that the FN is significantly different between HTA and LTA individuals. We propose that this finding is consistent with research linking anxiety to pessimistic outcome expectancy, but further
proof is still needed. Clinical and nonclinical studies on this issue may prove beneficial to the understanding of the underlying features of anxiety. Along with studies on depression (Foti & Hajcak, 2009), our findings suggest that the amplitude of the FN is related to individual differences in negative affect. Additionally, the proposition that the FN and ERN relate in opposite ways to anxiety (as well as depression) is in contrast to the premise provided by RL-ERN theory that the two components are functionally identical. This area warrants further research.
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Holroyd, C. B., & Krigolson, O. E. (2007). Reward prediction error signals associated with a modified time estimation task. Psychophysiology, 44, 913–917. Krain, A. L., Gotimer, K., Hefton, S., Ernst, M., Castellanos, F. X., Pine, D. S., et al. (2008). A functional magnetic resonance imaging investigation of uncertainty in adolescents with anxiety disorders. Biological Psychiatry, 63, 563–568. Kverno, K. S. (2000). Trait anxiety influences on judgments of frequency and recall. Personality and Individual Differences, 29, 395–404. Lauriola, M., & Levin, I. P. (2001). Personality traits and risky decisionmaking in a controlled experimental task: An exploratory study. Personality and Individual Differences, 31, 215–226. Maner, J. K., & Schmidt, N. B. (2006). The role of risk avoidance in anxiety. Behavior Therapy, 37, 181–189. Miller, J. D., & Pilkonis, P. A. (2006). Neuroticism and affective instability: The same or different? American Journal of Psychiatry, 163, 839–845. Mitte, K. (2007). Anxiety and risk decision-making: The role of subjective probability and subjective cost of negative events. Personality and Individual Differences, 43, 243–253. Muris, P., & van der Heiden, S. (2006). Anxiety, depression, and judgments about the probability of future negative and positive events in children. Journal of Anxiety Disorders, 20, 252–261. Nieuwenhuis, S., Holroyd, C. B., Mol, N., & Coles, M. G. H. (2004). Reinforcement-related brain potentials from medial frontal cortex: Origins and functional significance. Neuroscience and Biobehavioral Reviews, 28, 441–448. Pani, L., Porcella, A., & Gessa, G. L. (2000). The role of stress in the pathophysiology of the dopaminergic system. Molecular Psychiatry, 5, 14–21. Paulus, M. P. (2005). Neurobiology of decision-making: Quo vadis? Cognitive Brain Research, 23, 2–10. Paulus, M. P., Feinstein, J. S., Simmons, A., & Stein, M. B. (2004). Anterior cingulate activation in high trait anxious subjects is related to altered error processing during decision making. Biological Psychiatry, 55, 1179–1187. Raghunathan, R., & Pham, M. T. (1999). All negative moods are not equal: Motivational influences of anxiety and sadness on decision making. Organizational Behavior and Human Decision Processes, 79, 56–77. Richards, A., French, C. C., Calder, A. J., Webb, B., Fox, R., & Young, A. W. (2002). Anxiety-related bias in the classification of emotionally ambiguous facial expressions. Emotion, 2, 273–287. Shek, D. T. (1993). The Chinese version of the State-Trait Anxiety Inventory: Its relationship to different measures of psychological wellbeing. Journal of Clinical Psychology, 49, 349–358. Shepperd, J. A., Grace, J., Cole, L. J., & Klein, C. (2005). Anxiety and outcome predictions. Personality and Social Psychology Bulletin, 31, 267–275. Simmons, A., Stein, M. B., Matthews, S. C., Feinstein, J. S., & Paulus, M. P. (2006). Affective ambiguity for a group recruits ventromedial prefrontal cortex. NeuroImage, 29, 655–661. Spielberger, C. D., Gorsuch, R. L., Lushene, R., Vagg, P. R., & Jacobs, G. A. (1983). Manual for the state-trait anxiety inventory. Palo Alto, CA: Consulting Psychologist Press. Surgenor, L. J., Horn, J., & Hudson, S. M. (2002). Links between psychological sense of control and disturbed eating behavior in women with diabetes mellitus: Implications for predictors of metabolic control. Journal of Psychosomatic Research, 52, 121–128.
Anxiety and feedback negativity Thapar, A., Kerr, M., & Harold, G. (2009). Stress, anxiety, depression, and epilepsy: Investigating the relationship between psychological factors and seizures. Epilepsy & Behavior, 14, 134–140. Voss, A., Rothermund, K., & Brandtstadter, J. (2008). Interpreting ambiguous stimuli: Separating perceptual and judgmental biases. Journal of Experimental Social Psychology, 44, 1048–1056. Wray, L. D., & Stone, E. R. (2005). The role of self-esteem and anxiety in decision making for self versus others in relationships. Journal of Behavioral Decision Making, 18, 125–144.
967 Yasuda, A., Sato, A., Miyawaki, K., Kumano, H., & Kuboki, T. (2004). Error-related negativity reflects detection of negative reward prediction error. NeuroReport, 15, 2561–2565. Yeung, N., & Sanfey, A. G. (2004). Independent coding of reward magnitude and valence in the human brain. Journal of Neuroscience, 24, 6258–6264. (Received April 17, 2009; Accepted October 1, 2009)
Psychophysiology, 47 (2010), 968–978. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.00998.x
Dimension-based attention modulates early visual processing
KLAUS GRAMANN,a,b THOMAS TO¨LLNER,b and HERMANN J. MU¨LLERb,c a Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California, San Diego, La Jolla, CA b Department of Psychology, Ludwig-Maximilians-University, Munich, Germany c Department of Psychology, Birkbeck College, London, UK
Abstract Target selection can be based on spatial or dimensional/featural mechanisms operating in a location-independent manner. We investigated whether dimension-based attention affects processing in early visual stages. Subjects searched for a singleton target among an 8-item array, with the search display preceded by an identical cue array with a dimensionally non-predictive, but spatially predictive singleton. Reaction times (RTs) were increased for changes in the target-defining dimension but not for featural changes within a dimension. This RTeffect was mirrored by modulations of the P1 and anterior transition N2 (tN2). Current density reconstructions revealed increased activity in dorsal occipital cortex and decreased activity in left frontopolar cortex owing to repeated dimensional pop-out identities. These findings strengthen dimension-based theories of visual attention by indicating dimension-, rather than feature-, specific influences within the first 110 ms of visual processing. Descriptors: Attention, EEG, P1, N2
Weighting of Visual Dimensions Feature-based attention plays an important role in current theories of visual search, which assume that target-relevant feature information is encoded selectively in order to guide the allocation of focal attention to the target (Treisman & Sato, 1990; Wolfe, Cave, & Franzel, 1989). This emphasis on the feature specificity of attentional processes in the guidance of visual search has been challenged by Mu¨ller and colleagues (Found & Mu¨ller, 1996; Mu¨ller, Heller, & Ziegler, 1995), who instead proposed a dimension-based, or ‘dimension-weighting,’ account (DWA) of search guidance. This account assumes that target detection is influenced by a ‘pre-attentive’ mechanism of (spatially parallel) search guidance that modulates saliency coding by allocating limited ‘selection weight’ to the various dimensions that potentially define the target. Dimensions are assigned weight largely automatically, in bottom-up manner, with a larger weight allocated to the dimension defining the target on the current trial, implicitly ‘predicting’ that the next target will also be defined in this dimension. Thus, when the next target is indeed defined in this dimension, whether by the same or a different feature relative to the preceding target, target detection is expedited compared to when there is a dimension change (Found & Mu¨ller, 1996). Note that the absence of an effect of feature change/repetition within a repeated dimension is a strong criterion for the dimension specificity of this inter-trial ‘priming’ effect. Since this effect is observed when observers have no explicit knowledge of the targetdefining dimension (Mu¨ller, Krummenacher, & Heller, 2004), it is mainly bottom-up driven. Interestingly, when the task requires observers to explicitly encode the target-defining dimension (or
Visual attention can be oriented to spatial locations without overt gaze shifts (Posner, 1980). Electrophysiologically, the covert orientation of spatial attention is reflected by early sensory evoked potentials (Eimer, 1994; Hillyard & Mangun, 1987; Mangun & Hillyard, 1988; Rugg, Milner, Lines, & Phalp, 1987), with the visual C1 component being the earliest marker (Kelly, Gomez-Ramirez, & Foxe, 2008). When observers are provided with prior information about the upcoming target location (e.g., by spatial pre-cueing), the amplitudes of the (visual C1 and the subsequent) P1 component are enhanced for targets occurring at the attended (as compared to unattended) location(s). Traditionally, P1 amplitude modulations have been taken to reflect a sensory ‘gain control’ mechanism that increases the signal gain at the attended location, thereby enhancing perceptual processing (Eimer, 1994; Hillyard, Vogel, & Luck, 1998; Luck, Woodman, & Vogel, 2000). More recently, however, it has been shown that attention can also be allocated to non-spatial features that define the target in a location-independent manner (Hopf, Boelmans, Schoenfeld, Luck, & Heinze, 2004; Valdes-Sosa, Bobes, Rodriguez, & Pinilla, 1998). Moreover, feature-based attention has been found to influence early stages of processing, reflected in modulations of the visually evoked P1 (Han, Liu, Yund, & Woods, 2000; Mouchetant-Rostaing, Giard, Delpuech, Echallier, & Pernier, 2000; Taylor, 2002). Address reprint requests to: Klaus Gramann, Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California, San Diego, La Jolla, CA 92093-0961. E-mail:
[email protected] 968
Dimension-based early attention feature), the inter-trial effect is enhanced, suggesting that intertrial priming is top-down modulable. To examine this possibility more directly, Mu¨ller, Reimann, and Krummenacher (2003) conducted a dimensional-cueing study in which the likely defining dimension of the upcoming target on a trial was pre-cued by symbolic (as well as direct) cues. The results revealed both a reliable cueing effect (i.e., faster reaction times [RTs] for valid compared to neutral and invalidly cued targets) and a modulation of the inter-trial effect by cue validity: the inter-trial effect was reduced on valid- and invalid-cue trials compared to neutralcue trials. On valid-cue trials, this reduction was due to a reduced disadvantage for a change compared to a repetition of the targetdefining dimensionFdue to observers being able to top-down set themselves to the new (cued) dimension in advance of target onset. That is, the dimensional set established by the end of a trial can be top-down adapted, at least to some extent, in response to the cue.1 Based on this evidence, the DWA assumes that one common ‘attentional weight’ resource is allocated toFand modulates the relative processing efficiency inFthe various visual dimensions based on both bottom-up and top-down factors. This resource is ‘attentional’ in the sense that there is a limit to the total amount of weight available for allocation, so that, if the weight is increased for one dimension, it must be decreased for other dimensions. For this reason, the DWA is essentially an account of dimension ‘weighting,’ rather than ‘priming’ (passive priming is, in principle, cost-free). Concerning the ‘control’ of the weight allocation, Pollmann, Weidner, Mu¨ller and von Cramon (2000), Pollmann, Weidner, Mu¨ller, Maertens, and von Cramon (2006), and Weidner, Pollman, Mu¨ller, and von Cramon (2002) have shown that the (re-) weighting of dimensions involves a whole fronto-posterior network of brain areas, including frontopolar and, respectively, fronto-median areas. Neuropsychological findings suggest that left frontopolar mechanisms are causally involved in dimension switching (e.g., Pollmann, Mahn, Reimann, Weidner, Tittgemeyer et al., 2007), though these mechanisms mediate only implicit switching processes; by contrast, fronto-median mechanisms come into play when weight switching requires more explicit, top-down control (Weidner at al., 2002). However, while the switch signals are likely to be generated by frontal brain mechanisms, the weighting itself modulates processing in posterior, dimension-specific brain areas. Thus, investigating crossdimensional search for pop-out targets unpredictably defined by either color or motion, Pollmann et al. (2000, 2006) found increased activations in occipital areas depending on the dimensional identity of the target (V4 and, respectively, human MT; see also Schoenfeld, Hopf, Martinez, Mai, Sattler, et al., 2007). This pattern of hemodynamic activations is consistent with one fundamental postulate of the DWA, namely, that early (dimensionally organized) visual analyzer modules are modulated depending on the dimensional nature of the preceding target event. However, due to the sluggish nature of hemodynamic re-
1 These inter-trial effects demonstrated by Mu¨ller and colleagues are analogous to Maljkokic and Nakayama’s (1994) demonstration of ‘feature-based priming’ in singleton search, except that they are dimensionspecific in nature and subject to top-down modulation (Maljkovic & Nakayama characterized their feature-specific priming effect as being top-down impenetrable). Also, under comparable conditions, dimensionbased effects are larger than feature-based effects (see Olivers & Meeter, 2006, for a systematic comparison).
969 sponses, imaging studies are inappropriate to further specify the time course of dimensional weighting mechanisms. Rationale of the Present Study By means of event-related brain potentials, the present study was designed to test whether early visual processing can indeed be modulated dependent on the dimensional identity of the preceding sensory (pop-out) event. This was systematically assessed by introducing a visual search task in which the search display that contained the response-relevant target singleton was preceded by a response-irrelevant cue display, which also contained one singleton element (the cue) among the same number of homogeneous items. For instance, the cue could be an odd-colored (or an odd-shaped) item and the subsequent target singleton, to which observers had to respond, was also either a color (or a shape) singleton. Note that, although such cues are direct ‘indicators’ of the singleton dimension triggering an automatic dimensional orienting response, their effect is largely dimension-specific (Mu¨ller et al., 2003)2 and may be top-down enhanced if the task requires the cues to be encoded explicitly (Mu¨ller et al., 2004). The latter was the case in the present study, because observers had to discern the dimension defining the target singleton in the subsequent target display. In addition, the (900-ms) SOA between the cue and target was so long that automatic-activation processes triggered by the cue would have largely subsided by the onset of the target display (see, e.g., Mu¨ller & Rabbitt, 1989). In the Experiment, the cue was non-predictive as to the defining dimension/feature of the upcoming target, but predictive as to its location. In order to capture dimension-based influences occurring at early sensory stages, we primarily focused on the P1 and N1 components. In addition, we expected the anterior transition N2 (tN2) to be modulated by the dimensional identity of the previous sensory event (Gramann, To¨llner, Krummenacher, Eimer, & Mu¨ller, 2007), reflecting the control of dimensional weight setting (see also Pollmann et al., 2007). To further specify the time course of dimensional weighting mechanism, the present investigation used a spatio-temporal coupled distributed source reconstruction algorithm (current density reconstruction, stCDR) (Darvas, Schmitt, Louis, Fuchs, Knoll, & Buchner, 2001) to reconstruct the cortical sources of activity associated with task-specific modulations of scalp potentials. In contrast to other reconstruction approaches (i.e., equivalent dipole reconstructions), this class of algorithms does not specify the number of active sources to explain the measured scalp potentials at any given time. This is an advantage when the exact number of underlying cortical sources is unknown and no complementary information is available from imaging methods (e.g., fMRI). Using the LORETA algorithm (Pascual-Marqui & Biscay-Lirio, 1993), this approach supports spatially smoothed solutions based on the assumption that the underlying patch of active cortex is spread over a certain cortical area rather than being a point source. This assumption is supported by highly correlated activity in neighboring neuronal populations (Haalman & Vaadia, 1997). In addition to the spatial filtering, we used the L2-Norm with temporal coupling (Darvas et al., 2001). Based on the physiologically plausible assumption that neural 2 In more detail, Mu¨ller et al. (2003) found a dimension-specific cueing effect even when a specific target feature, such as ‘red,’ was directly precued by a central red element (rather than a centrally verbal cue)Fthat is, in this case, even invalid color targets, say ‘blue’ ones, were detected more efficiently than equally unlikely targets defined in a different dimension.
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activity develops over time, the temporal coupling acts as a filter preferring relatively smooth, rather than abrupt, changes in the time course of activity. Simulation studies demonstrated that the introduction of temporal constraints to existing CDRs leads to significant improvements in spatial and temporal accuracy due to the additional information provided by the time-dependent model constraint (smoothness in time) (Darvas et al., 2001). This advantage over non-coupled current density reconstructions was most pronounced in the case of noisy data because of the lowpass filter properties of the temporal coupling, but also in the ability to separate sources. However, as any other source reconstruction, the inverse solution is ill-posed and the results should be considered as an approximation of underlying cortical source locations, rather than an exact anatomical description of the contributing structures. Method Participants Twelve observers took part in the Experiment (2 female; age range 21–25 years). All were right-handed, had normal or corrected-to-normal vision, and reported no history of neurological disorder. Observers were either paid or received course credit for participating. All observers provided written informed consent, and the experimental procedure was approved by the ethics committee of the Department of Psychology, University of Munich, in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki). One participant had to be excluded from the analyses, due to excessive artifacts in the electroencephalogram (EEG) recordings. Stimuli and Procedure Observers were seated in a dimly lit experimental chamber. Stimuli were projected by a beamer (Sanyo PLC-XU47, Osaka, Japan), situated 60 cm above the observer’s head, on a 150 cm ! 150 cm white screen. The observer viewed the screen from a distance of 130 cm, with the centre of the display adjusted to the individual straight-ahead line of view. Successively presented cue and target displays each consisted of a circular array of eight colored stimuli on a black background. The stimuli were equidistant (3.91 of visual angle) from a white fixation cross in the screen center (see Figure 1). Each stimulus array contained one singleton, which was equally likely defined in either the color or the shape dimension (red or green circle, of diameter 2.41; blue diamond or triangle, 2.11 ! 2.11 and, respectively, 2.81 ! 3.21 in size), among seven identical distracters (blue circles, 2.41 in diameter). All stimuli were matched in luminance. The singleton could appear randomly at one of the six lateral array positions; however, its location was always the same in the cue and the subsequent target display. Observers were instructed to maintain central fixation throughout a trial and to indicate the dimension of the singleton target, using their left- or right-hand index finger to respond ‘color’ or ‘shape,’ respectively. The response buttons were positioned vertically aligned to avoid spatial stimulus-response compatibility effects. Half the observers started with the left index finger on the upper button and the right index finger on the lower button, and vice versa for the other half. For all observers, the response button assignment was reversed in the second half of the experiment. One experimental session consisted of eighteen blocks of 72 trials each. A trial started with a white fixation cross for 500 ms,
Figure 1. Example of the stimulus array used for the (preceding) cue and the (subsequent) target displays on a trial, with the singleton being defined in the shape dimension (the only triangle amongst circles). The arrays consisted of a circular arrangement of eight stimuli presented against a black background, with a white fixation cross in the center. Distractors were blue circles, and targets were defined in either the color (red or green circle) or the shape dimension (blue triangle or diamond). Participants were asked to discriminate the dimension of the singleton target as fast and accurately as possible.
followed by the cue display for 200 ms. After a constant cuetarget interval of 900 ms (during which only the fixation cross was visible), the target display was presented for 200 ms. The trial was terminated by the observer’s response or after a maximum of 1000 ms. During the inter-trial interval, a black screen was shown for 1000 ms. The feature defining the singleton in the cue display (color: red or green; shape: diamond or triangle) was selected in pseudo-random order. With respect to the singleton feature in the cue display, the target display could contain (at the same position) a singleton defined by the same feature (same Dimension same Feature, sDsF), by a different feature in the same dimension (same Dimension different Feature, sDdF), or by a feature in a different dimension (different Dimension, dD), each with a probability of 1/3. On trials with targets defined in a different dimension, each of the two alternative features was equally likely. EEG recording and data analysis. The EEG was recorded continuously, at a sampling rate of 500 Hz, using 64 Ag/AgCl electrodes, including those corresponding to the 10–10 system (American Electroencephalographic Society, 1994). The electrodes were mounted on an elastic cap (Easy Cap, Falk Minow Services, Munich, Germany). Horizontal and vertical eye movements were monitored by electrodes placed at the outer canthi of the eyes and the superior and inferior orbits, respectively. EEG signals were amplified using a 0.1–100-Hz bandpass filter via BrainAmps (BrainProducts, Munich, Germany) and filtered offline using a 1–40-Hz bandpass (24 dB/Oct). All electrodes were referenced to Cz and re-referenced off-line to linked mastoids.
Dimension-based early attention Event-related potentials (ERPs) were averaged off-line over an 800-ms epoch relative to a 200-ms pre-stimulus baseline. Eye movement artifacts were corrected for by means of independent component analyses (ICA) implemented in the Brain Vision Analyzer software (Brain Products). Following the elimination of artifacts and trials with an incorrect response, the latencies of the P1, N1, and N2 components were determined individually as the maximum deflection within the respective time windows (P1: 80–140 ms; N1: 130–190 ms; N2: 230–300 ms) derived by visual inspection of the grand average potentials. Amplitudes were calculated using five sample points before and after the maximum peak deflection. Amplitudes and latencies of the P1 and N1 components were analyzed by repeated-measures analyses of variance (ANOVAs) with the factors cue-to-target ‘Transition’ (sDsF, sDdF, dD), target ‘Dimension’ (color, shape), target ‘Side’ (left, right), and ‘Electrode Position’ (left, right recording position) at electrode sites revealing the strongest effect of the experimental factor cue-to-target Transition (PO7 and PO8). Amplitudes and latencies of the anterior N2 component were analyzed using a repeated-measures ANOVA with the factors ‘Transition’ (sF, dF, dD), ‘Electrode Site’ (frontal, fronto-central, central), and ‘Electrode Position’ (left, midline, right). Since the present study was primarily designed to provide insight into the neural mechanisms underlying dimensional cueing effects, only the main effects and interactions involving the factor ‘Transition’ will be reported for the electrophysiological data. Whenever required, significant main effects and interactions were further examined using Tukey HSD posthoc contrasts.
Spatio-temporal current density reconstruction (stCDR). A spatio-temporal coupled reconstruction algorithm (as implemented in the EaSI software package; Electro-anatomical Source Imaging, Brain Products), based on the LORETA algorithm (Pascual-Marqui & Biscay-Lirio, 1993), using a L2Norm with temporal coupling (Darvas et al., 2001), was used for source reconstruction. Details of the models can be found in Darvas and colleagues (2001). To identify neural sources underlying dimension-specific P1 and tN2 effects, CDRs were based on separate averages for the three experimental conditions (sDsF, sDdF, dD) over the time window 0–400 ms relative to a ! 100 to 0-ms baseline. By computing source activity for the different experimental conditions in one combined computational step, activation strength of all data sets was standardized by the maximum source activation in one of the three conditions. In the second step, clusters of sources were identified using the clustering algorithm implemented in EaSI. Here, the strength of each source was computed and local maxima for each point in the respective time range were determined. This was followed by the computation of a matrix representing the distances between all maxima separately for each observer and data set. All sources located within a distance of 30 mm were combined into one cluster, yielding a mean location for the various clusters and mean source magnitude within a cluster taking into account the hemisphere of cluster location. Cluster locations were specified using the Talairach daemon (http:// www.talairach.org), and the activations of all reconstructed clusters for the respective P1 and tN2 time ranges were subjected to repeated-measures ANOVAs with the factor cue-totarget Transition. Only clusters that exhibited a significant effect of this factor are reported in the Results.
971 Results Behavioral Data On 2.7% of all trials, observers reacted faster than 100 ms or slower than 1000 ms (sDsF 2.7%, sDdF 2.4%, and dD 2.9%). In addition, observers reacted incorrectly on 4.0% of all trials. The distribution of errors was shifted toward dD (different Dimension) trials, with 6.6% incorrect reactions as compared to 2.7% for sDsF (same Dimension same Feature) and 2.7% for sDdF (same Dimension different Feature) trials. A repeated-measures ANOVA with the factors ‘Dimension’ (color, shape) and ‘Transition’ (sDsF, sDdF, dD) revealed this difference to be significant [main effect of Transition, F(2,20) 5 7.09, po.019; Z2 5 0.415]. The two-way interaction was also significant [F(2,20) 5 4.41, po.026; Z2 5 0.306]: for validly cued dimensions, the percentages of errors were comparable between trials with and without a change in the target-defining feature (color: 2.7% and 2.6% for sDdF and sDsF; form: 2.7% and 2.8% for sDdF and sDsF). However, invalid-dimension cues were associated with significantly more errors when the target was defined by shape rather than color (5.4% vs. 7.8%). RTs on correct trials were analyzed using the same ANOVA, which revealed only the main effect of Transition [F(2,20) 5 13.79, po.001; Z2 5 0.580] to be significant [main effect of Dimension: F(2,20) 5 2.91, po.119; Z2 5 0.225; interaction: F(2,20) 5 1.25, po.31; Z2 5 0.111]. Figure 2 presents the RTs dependent on the cue-target transition aggregated over color- and shape-defined targets. The pattern of cue-target transition effects replicates the pattern of inter-trial effects described by Found and Mu¨ller (1996): there was a significant RT cost for invalidly cued, relative to validly cued, dimensions (43.3-ms cost for dD vs. sDsF, po.001, and 37.1-ms cost for dD vs. sDdF, po.003), while there was no significant cost for invalidly cued, relative to validly cued, features within a dimension (6.3-ms cost for sDdF vs. sDsF, po.76). Electrophysiology P1. The ANOVA of P1 amplitudes revealed a significant main effect of Transition [F(2,20) 5 8.94, po0.002; Z2 5 0.472], with stronger P1 deflections when the target dimension was val-
Figure 2. Mean reaction times (in milliseconds), and associated error rates (in percent), for target singletons, dependent on the identity of the singleton in the cue display: same dimension same feature (sF), same dimension different feature (dF), and different dimension (dD). Bars represent errors and the line reaction times.
972 idly cued (4.30 mV for sDsF and 4.23 mV for sDdF), rather than invalidly cued (3.97 mV for dD); this effect was strongest at electrode locations PO7 and PO8. The identical pattern of effects was observed for electrode pairs P3/P4 (Z2 5 .455), P7/P8 (Z2 5 .293), PO3/PO4 (Z2 5 .315), and O1/O2 (Z2 5 .349). With valid dimension cues, the P1 amplitudes were unaffected by whether or not there was a feature change between the cue and the target (post-hoc contrast sDdF vs. sDsF: po0.70). And invalid dimension cues led to less positive deflections compared to both valid-cue conditions (dD vs. sDsF: po0.002; dD vs. sDdF: po0.012). The interaction Target Side ! Electrode Position was also significant [F(1,10) 5 11.69, po0.007; Z2 5 0.539], with larger P1 amplitudes contralateral to the target hemifield (see Figure 3). After normalizing the data as suggested by McCarthy and Wood (1985), the interaction of Target Side ! Electrode Position failed to reach significance [F(1,10) 5 1.03, po0.754; Z2 5 0.010]. However, the absence of any interaction of the two factors Target Side and Electrode Location with the factor Transition [F(2,20) 5 0.004, po0.996; Z2 5 0.000] underscores that P1 amplitudes were enhanced for dimensionally validly cued
K. Gramann et al. targets, irrespective of the hemifield in which the target was presented. No effects were revealed for P1 latencies. N1. The identical analysis of N1 amplitudes revealed the main effects of Transition [F(2,20) 5 8.79, po0.002; Z2 5 0.468] and Dimension [F(1,10) 5 6.007, po0.034; Z2 5 0.375], as well as the interaction Target Side ! Transition [F(2,20) 5 3.82, po0.039; Z2 5 0.276] to be significant. Post-hoc contrasts revealed more pronounced N1 amplitudes for dimensionally invalidly cued targets as compared to validly cued targets (dD vs. sDsF: po0.001; dD vs. sDdF: po0.04), without an overall difference dependent on the featural validity of the cue (sDdF vs. sDsF: po0.29). The Target Side ! Transition interaction was due to the fact that this dimension-specific pattern was observed only for targets in the left hemifield: N1 amplitudes were significantly more negative-going for invalid dimension cues (pso0.03 for dD vs. sDsF and dD vs. sDdF), with similar amplitudes for valid dimension cues irrespective of the featural validity of the cue (sDsF vs. sDdF: po0.99). In contrast, there was a more feature-specific pattern for targets in the right hemifield, with
Figure 3. Grand-averaged ERP waveforms elicited over early visual areas at electrode positions PO7/PO8 contra- and ipsilateral to (A) left-hemifield targets and (B) right-hemifield targets in a 300-ms interval following stimulus onset. Black lines represent feature repetitions between the cue and target singletons (sDsF), dark gray lines intra-dimensional feature changes (sDdF), and red lines dimension changes (dD). The middle column displays scalp maps for the visual evoked P1 component (upper row) and the N1 component (lower row) for feature repetitions (sDsF), intra-dimensional feature changes (sDdF), and dimension changes (dD). (C) presents scalp maps for the difference waveform, computed by subtracting different-dimension (dD) from same-dimension trials (sD). Scalp maps are displayed for 4 distinct time windows, as described in Hillyard & Anllo-Vento, 1998, for comparison with the typical topography of the selection negativity (SN).
Dimension-based early attention valid-dimension invalid-feature cues producing comparable N1 amplitudes to those for invalid-dimension cues (sDdF vs. dD: po0.57), but significantly more negative-going amplitudes than those for valid-dimension valid-feature cues (sDdF vs. sDsF: po0.02). Neither the Target Side ! Electrode Position [F(1,10) 5 0.68, po0.800; Z2 5 0.007] nor the Target Side ! Electrode Position ! Transition interaction [F(2,20) 5 2.65, po0.95; Z2 5 0.210] reached significance. Analysis of N1 latencies revealed the four-way interaction to be significant [F(2,20) 5 4.09, po0.032; Z2 5 0.290]. However, post-hoc contrasts did not substantiate any of the differences in onset latencies to be reliable. N2. An ANOVA of the N2 amplitudes (see Figure 4) revealed the factor Transition to interact with both Electrode Site [F(4,40) 5 5.09, po0.002; Z2 5 0.337] and Electrode Position [F(4,40) 5 3.87, po0.009; Z2 5 0.279]. Both effects were replicated after normalizing the data before statistical analysis (p’so0.003) (McCarthy & Wood, 1985). No other interactions involving the factor Transition reached significance; this was the case also after normalization (McCarthy & Wood, 1985). Posthoc contrasts revealed reliable Transition effects at right frontal, midline, right fronto-central, and central electrodes. Importantly, these effects were purely dimension-specific (po.001), with no difference between sDsF and sDdF conditions (po.531). In summary, a change of the singleton-defining dimension was associated with enlarged tN2 amplitudes, with a slight right-lateralization largest over fronto-central electrode positions. An identical ANOVA for N2 latencies revealed a significant Transition ! Electrode Site interaction [F(4,40) 5 4.47, po0.004; Z2 5 0.309], due to prolonged latencies for dD conditions at
973 frontal compared to fronto-central and central electrodes (po0.038). Current density reconstruction. As N1 amplitudes were subject to higher-order interactions involving the factor Transition, source reconstruction was restricted to the P1 and N2 components. For the time window of the P1, differential activations for dimensionally validly and invalidly cued targets were revealed for clusters with centroids located in or near left lateral occipital cortex [BA18: F(2,16) 5 3.59, po.050; Z2 5 0.310], right lateral occipital cortex [BA18: F(2,16) 5 5.29, po.017; Z2 5 0.398], and the right cuneus [BA17: F(2,10) 5 6.16, po.018; Z2 5 0.552]. The bilateral middle occipital gyrus (BA 19) showed a strong tendency toward significance in both hemispheres, but failed to reach significance (BA 19 left, po.114; Z2 5 0.214, and BA 19 right po.092; Z2 5 0.233) (see Table 1). For the time window of the tN2, clusters with centroids located in or near the left anterior cingulate cortex [BA 24: F(2,20) 5 12.26, po.001; Z2 5 0.551], left middle frontal gyrus [BA 10: F(2,10) 5 4.80, po.035; Z2 5 0.490], and the left frontopolar cortex [BA 9: F(2,12) 5 3.723, po.055; Z2 5 0.383] displayed dimension-based modulations. In the right hemisphere, the anterior cingulate cortex [BA 24: F(2,16) 5 7.53, po.005; Z2 5 0.458] and the right middle frontal gyrus [BA10: F(2,10) 5 4.50, po.040; Z2 5 0.474] revealed significantly increased activity for dimension change trials (see Figure 5). In summary, for posterior (reconstructed) clusters, a similar pattern of activation was revealed: dimensionally validly cued targets were associated with increased activity as compared to invalidly cued targets, irrespective of the featural validity of the cue. In contrast, clusters located in or near prefrontal cortical
Figure 4. Grand-averaged ERP waveforms elicited over fronto-central electrode positions in the 500-ms interval following stimulus onset, relative to a 200-ms pre-stimulus baseline. Dark gray lines represent feature repetitions between the cue and target singleton (sF), dotted lines intra-dimensional feature changes (dF), and light gray lines dimension changes (dD).
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Table 1. Brain Areas Exhibiting Significant Effects of Visual Dimension Changes Based on stCDR Region P1 (80–140 ms) Left occipital lobe, cuneus Right occipital lobe, cuneus Right occipital lobe, cuneus N2 (240–300 ms) Left anterior cingulate Left superior frontal gyrus Left middle frontal gyrus Right anterior cingulate
x
y
z
Brodmann area
partial eta2
! 13 10 17
! 80 ! 90 ! 85
15 6 12
BA 18 BA 17 BA 18
0.310n 0.552n 0.398n
!6 ! 29 ! 29 5
28 43 22 27
16 1 28 20
BA 24 BA 10 BA 9 BA 24
0.551nn 0.490n 0.383n 0.458nn
N subjects
11/11 6/11 7/11 9/11
Note: Displayed coordinates (x, y, z) represent mean values averaged across observers. Effect sizes (partial eta2 as estimated from repeated measures ANOVA for main effect of transition (sF, dF, dD). Significant effects are marked for values of po0.05 and po0.01 with n and nn, respectively.
areas exhibited increased activity with dimensionally invalidly cued targets. Discussion The aim of the present study was to identify electro-cortical parameters associated with dimensional cueing effects. Behaviorally,
such effects are manifest in faster RTs to targets defined in the same, as compared to a different, visual dimension as the cue. Here we show that, when the upcoming target location is indicated in advance, dimensional information has a significant influence on early visual evoked potentials. As expected, the RT data confirmed previous findings (Mu¨ller et al., 2003) of faster reactions when the target single-
Figure 5. Spatio-temporal coupled current density reconstruction for same and different dimension trials. Left and right columns display current source activation for the visual P1 component and the tN2 component, for (A) same dimension same feature (sDsF), (B) same dimension different feature (sDdF), and (C) different dimension (dD) trials, respectively. Source activity was clipped to 30% of maximum source strength, displaying the strongest 70% of sources active during the reconstructed time period. Note that the source activity shown is based on grand average ERPs and does not represent single-subject reconstructions. Last row (D) displays regions revealing significant dimension-based modulations. Clusters are selected based on current density reconstruction for the time windows 80 to 140 ms for the P1 component and 240 to 300 ms for the tN2, respectively. Cluster centroids for the P1 were located in or near the left occipital ( ! 13, ! 80, 15; BA 18) and the right occipital lobe (10, ! 90, 6; BA 17 and 17, ! 85, 12; BA 18). Cluster centroids based on source reconstruction for the tN2 were located in or near the left lateralized frontopolar cortex ( ! 29, 22, 28; BA 9) and the left anterior cingulate cortex ( ! 6, 28, 16; BA 24).
Dimension-based early attention ton was defined in the same dimension as the preceding cue (e.g., color ! color), compared to when the dimension changed (e.g., shape ! color). Importantly, this same-dimension advantage was independent of whether or not the precise target feature within the repeated dimension matched that of the cue (e.g., red ! red vs. red ! green)Fin other words, despite the cue consisting of a particular feature, its effect was dimension-specific in nature. This pattern points to a special role of visual dimensions in search guidance. Recall that, in the present experiment, the observer’s task was not just to detect the presence of a target, but to respond to its defining dimensionFso that one might ask whether the task requirements were responsible for the dimension-specificity of the effects. However, Found and Mu¨ller (1996) and Mu¨ller et al. (2004) had shown that such tasks produce essentially the same pattern of dimension-based inter-trial effects as a simple detection (target-absent/present) task or a task in which observers respond to the specific target-defining feature (see also Mu¨ller et al., 2003, who found a dimension-specific cueing effect even when a specific target feature, such as ‘red,’ was pre-cued). The latter finding is important, in that it shows that even when specific features are task-relevant, the effects are largely dependent on the broader target dimension. On this basis, it is unlikely that the present dimension-based RT effects were obtained simply because of the use of a dimension discrimination task. Early Sensory Activations of Dimensional Cueing With matching cue and target positions, P1 amplitudes were enhanced for targets defined in the same dimension as the cue, and this enhancement was independent of whether the target was defined by the same or a different feature relative to the cue. In accordance with dimension-based modulations of the P1 component, spatio-temporal coupled source reconstruction revealed effects of cue-target transition on source activity in or near primary and secondary visual areas. While targets defined in the cued dimension were associated with comparable source activity whether or not the target feature matched that of the cue, targets defined in a different dimension as the cue were associated with decreased activity in these same areas. This is in agreement with the assumption that visual information processing is enhanced in neural populations that process dimensionally attended information, and attenuated in areas that process unattended informationFin line with the ‘gain control’ interpretation of the P1 (Luck et al., 2000). However, the absence of a baseline condition does not allow for a direct test of this hypothesis. This pattern of effects is closely in line with the DWA, according to which dimensionally organized modules of visual analyzer units are (implicitly) weighted on a given (‘cue’) sensory event, thus expediting the emergence of the target’s saliency signal at the level of the attention-guiding overall-saliency map on the next (‘target’) event. Enhanced P1 amplitudes might thus reflect the correct weighting of early visual input modules, facilitating the sensory coding of attributes singling out the target amongst nontargets. That is, when the cue appears in one dimension, say color, attentional weight resources are allocated to this dimension, thus enhancing the saliency of all kinds of singleton defined in the same dimension (whether or not they match the cue featurally). This weighting of cortical areas might be associated with a pre-activation of cortical columns, giving rise to enhanced activation when dimensional information fitting the weight set enters the visual system. Note that there was no explicit strategic reason to weight the color over the shape dimension or
975 vice versa, since the cue predicted the upcoming target dimension only at chance level. This points to the largely implicit nature of the processes determining the allocation of attentional weight resources (see, e.g., Mu¨ller et al., 2004). Since the cue was 100% valid with respect to the target position, attention (originally summoned by the exogenous cue) would be endogenously maintained for the cued location (e.g., Mu¨ller & Rabbitt, 1989), which, by way of linked position and dimension expectancies (see, e.g., Kingstone, 1992; To¨llner, Gramann, Mu¨ller, Kiss, & Eimer, 2008), would lead to enhanced P1 amplitudes for targets defined in validly cued dimensions. This suggests that the early visual system uses dimensional information in order to optimize target detection, which further underscores the implicit nature of dimensional weighting processes. Note that, theoretically, this pattern of P1 amplitudes might have been the result of an underlying selection negativity (SN; e.g., Hillyard & Anllo-Vento, 1998). However, neither the time course nor the scalp topography of the difference wave (‘attended targets’ [sD] minus ‘unattended targets’ [dD]) show the typical negative process underlying the P1 and N1 components. In contrast to the typical SN waveform, the dimension-based modulation in the present investigation was characterized by an ongoing positivity in the difference wave lasting until approximately 270 ms post stimulus. A possible explanation for this may be that the experimental design fostered an SN-like process with onset of the cue stimulus; that is, dimension repetitions from cue to target display might have been accompanied by a priming of target discrimination in the cued dimension. This bias of selection toward a specific dimension might have lead to a relative positivity compatible with enhanced P1 amplitudes after target display onset. However, two points weaken this assumption. First, if some kind of selection negativity initiated with onset of the cue was the underlying factor for the observed P1 modulation, it should have been sensitive to not only dimensional, but also featural changes from cue to target display. This was clearly not the case in the present study. Second, any SN associated with onset of cue displays should be manifested in a negative shift for attended features, that is, valid dimension cues should have revealed a negative shift as compared to invalid cues in some time interval between cue and target display. Again, this was not the case. Further support for a dimension-specific modulation of the P1 stems from a recent study by Schoenfeld and colleagues (2007), which reported dimensional selection to begin around 90 ms post-stimulus, based on combined electrophysiological, magneto-encephalographic, and hemodynamic measures of brain activity. In this study, observers were symbolically cued to attend to either the motion or color of an initially stationary array of gray dots and respond with a simple button press to one particular change in the cued dimension, but not in the non-cued dimension. Using a design broadly comparable to the present experiment, the authors noted the absence of any selection negativity. Even though several factors differed between the designs (symbolic dimension cues vs. direct feature cues, block-wise vs. trial-wise cueing, global-change targets vs. local singleton targets), essentially both studies required observers to tell apart the dimension of the target (Schoenfeld et al., 2007, p. 2475, refer to this as ‘between-feature selection [motion vs. color]’)Fwhich stands in marked contrast to earlier studies that investigated the SN using intra-dimensional feature discrimination (Anllo-Vento, Luck, & Hillyard, 1998; Lange, Wijers, Mulder, & Mulder, 1998). This lends further support to the conclusion that the P1
976 modulation observed in the present study reflects dimension-, rather than feature-, based attentional processing.3 A somewhat different pattern of amplitude modulations was observed for the N1 component. The amplitudes of the N1 were comparable for left hemifield targets pre-cued by valid-dimension cues (irrespective of whether or not the cued feature was valid), which were less pronounced than those for targets preceded by invalid-dimension cues (dimension-specific effect pattern); in contrast, for right hemifield targets, enhanced negative amplitudes were evident not only for invalid-dimension cues, but also for valid-dimension invalid-feature cues, with less marked amplitudes only for valid-dimension valid-feature cues (featurespecific pattern). This hemispheric difference may be indicative of distinct roles of left and right visual areas in the attentional processing of target attributes. Previous studies of spatial attention have demonstrated an N1 modulation reflecting facilitated processing of targets that appear at the attended location (Luck, 1995; Mangun, 1995). While the design of the Experiment does not allow for any direct comparison of stimuli at attended versus unattended locations, these findings support the idea that the visual evoked N1 reflects the operation of a discriminative mechanism at attended locations (Hopf, Vogel, Woodman, Heinze, & Luck, 2002; Vogel & Luck, 2000). Importantly, hemispheric differences seem to play a significant role in the type of discrimination (dimension- vs. feature-based) involved. Other studies investigating hemispheric differences underlying the processing of hierarchically organized patterns (Lamb, Robertson, & Knight, 1990; Robertson, Lamb, & Knight, 1988) demonstrated that the processing of global aspects of a pattern is more pronounced within the right posterior superior temporal (PST) area while the left PST is dominantly associated with processing of local aspects. With respect to the observed asymmetry in N1 amplitudes, this account might explain the observed hemispheric differences in N1-deflections: increased negative deflections over the left posterior cortex for invalidly cued dimensions would reflect the necessary discrimination process, i.e., the local processing of target identity, while increased negative deflections over the right posterior cortex would reflect the global aspect of attentional processing, i.e., the processing of an overall change from cue to target display (within or across dimensions). The advantage of such a hemispheric specialization would ‘‘provide a means for local and global levels of structure to be processed in parallel . . .’’ (Lamb, Robertson, & Knight, 1990). Prefrontal Activations of Dimensional Cueing Besides the dimension-based modulations of P1-amplitudes, dimensional cueing was found to further influence the amplitude of the transition N2, with the strongest modulation observed over fronto-central electrode positions. This tN2 effect occurred irrespective of intra-dimensional feature changes/repetitions of the target relative to the cueFdemonstrating that the enlarged amplitudes of the tN2 originate from processes purely related to the (change in the) dimensional identity of the target relative to the cue, similar to the visual P1 component. The tN2 pattern observed in the present study replicates that described by Gramann et al. (2007), suggesting that similar processes are 3 This is in line with Schoenfeld et al. (2007), who concluded: ‘‘When a color feature is to be selected from another feature such as motion, the enhanced processing in V4v begins very early (90–110 ms), whereas if one color is to be selected from another color the enhancement begins somewhat later (130–180 ms). . . . A similar finding was obtained for selection of motion information in area hMT’’ (p. 2476).
K. Gramann et al. associated with visual dimension weighting in cross-dimensional cueing as well as in cross-dimensional search tasks. That is, a change of the singleton-defining dimension from the cue to the target display (but not a change in the defining feature within a repeated dimension) was reflected in enhanced amplitudes, with a slight right-lateralization largest over fronto-central electrode positions. The topography of the tN2 points to generators in or near the anterior cingulate and/or the anterior frontal cortex (see Figure 5), and its latency corresponds to negative components accompanying perceptual mismatch, cognitive conflict, and response inhibition (Kiefer, Marzinzik, Weisbrod, Scherg, & Spitzer, 1998; Pritchard, Shappell, & Brandt, 1991; Wang, Cui, Wang, Tian, & Zhang, 2004). This systematic pattern of N2 effects provides further evidence for a role of frontal processes in the shifting of limited attentional (‘weight’) resources from the old, cue-defining to the new, target-defining dimension. The results of our current density reconstruction are in line with previous fMRI studies of dimension weighting (Pollmann et al., 2000, 2006). Stronger source activations were evident for conditions in which the critical visual dimension was changed, a pattern that mirrors the amplitude variation observed for the tN2 component. Pollmann and colleagues demonstrated that activation in left lateral frontopolar cortex is associated with stimulusdriven dimension changes (Pollmann et al., 2000) and that patients with lesion in this brain area show increased reaction times for dimension change trials as compared to healthy controls (Pollmann et al., 2007). In contrast, top-down controlled dimension changes in singleton conjunction search was shown to be accompanied by increased activity in pregenual paracingulate cortex (Weidner et al., 2002). The present investigation revealed dimension-based modulation of activity in brain areas that closely match the results reported by Pollmann and colleagues. Both sources in or near the left lateralized frontopolar and anterior cingulate cortex demonstrated increased activity for targets defined in invalidly cued dimensions, but no differences in activation for targets in validly cued dimensions (irrespective of the featural validity). The fact that both anterior sources demonstrated the same dimension-based modulation can be explained by the cueing paradigm used in the present study: stimulus-driven changes from the cue to target display would parallel increased frontopolar activity in singleton feature search, while increased activity in anterior cingulate cortex might reflect top-down processes accompanying cue-related attention. However, due to the low spatial resolution of any EEG-based source reconstruction, the present results have to be considered tentative until complementary evidence from imaging studies is available.
Conclusion In summary, the presentFERP and source reconstructionFfindings provide further evidence for the existence of dimension-specific weighting mechanisms as proposed by the DWA. The close resemblance of the source locations revealed in the present study with the results of fMRI studies (Pollmann et al., 2000, 2006; Weidner et al., 2002) supports the proposal that left frontopolar and anterior cingulate regions play a critical role in dimensional weight setting that modulates sensory coding of (non-spatial) stimulus attributes in dorsal occipital regions. Our results suggest that the tN2 is likely to reflect the detection of a change in the target-defining dimension and the initiation of
Dimension-based early attention
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a corresponding weight shifting (Gramann et al., 2007). Most importantly, coding of targets defined in a correctly weighted dimension (and appearing at an attended location) is associated
with increased P1 amplitudes, demonstrating dimension-based gain modulations to accompany spatial-attentional modulations within the first 110 ms of visual processing.
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(Received March 31, 2009; Accepted October 11, 2009)
Psychophysiology, 47 (2010), 979–983. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.00999.x
BRIEF REPORT
Line bisection as a neural marker of approach motivation
KYLE NASH,a IAN MCGREGOR,a and MICHAEL INZLICHTb a
Department of Psychology, York University, Toronto, Ontario, Canada Department of Psychology, University of Toronto Scarborough, Toronto, Ontario, Canada
b
Abstract Approach motivation has been reliably associated with relative left prefrontal brain activity as measured with electroencephalography (EEG). Motivation researchers have increasingly used the line bisection task, a behavioral measure of relative cerebral asymmetry, as a neural index of approach motivation-related processes. Despite its wide adoption, however, the line bisection task has not been confirmed as a valid measure of the precise pattern of activity linked to approach motivation. In two studies, we demonstrate that line bisection bias is specifically related to baseline, approach-related, prefrontal EEG alpha asymmetry (Study 1) and is heightened by the same situational factors that heighten the same approach-related prefrontal EEG alpha asymmetry (Study 2). Results support the line bisection task as an efficient and unobtrusive behavioral neuroscience measure of approach motivation. Descriptors: Line bisection, Approach motivation, Frontal alpha asymmetry
ever, in light of recent work implicating the dorsolateral prefrontal cortex in both visual processing and approach motivation, the present research was conducted to provide much needed validation for the line bisection task as an index of prefrontal asymmetry.
The line bisection task is a widely used behavioral measure of relative cerebral hemisphericity (Jewell & McCourt, 2000). In this task, participants are asked to indicate the perceived midpoint of a number of horizontal lines. Tendencies toward rightward versus leftward errors in estimating the actual midpoints are taken to reflect relative primacy of right versus left visual fields, respectively, and neural activity in the contralateral hemisphere (Milner, Brechmann, & Pagliarini, 1992). Although developed as a clinical measure of visual neglect, researchers have increasingly used it as a general marker of dispositional or situational hemisphericity. Relative left prefrontal activity, as assessed by electroencephalography (EEG), is reliably associated with approach-motivation (Harmon-Jones, 2003; Harmon-Jones & Allen, 1998), thus the line bisection task has often been used to index phenomena related to approach-motivation. For example, action-related emotions (Drake & Myers, 2006), writing about cherished values (Shrira & Martin, 2005), monosemantic processing (Baumann, Kuhl, & Kaze´n, 2005), and narrowed attention (Fo¨rster, Liberman, & Kuschel, 2008; see Gable & Harmon-Jones, 2008) are all approach-related phenomena that have been associated with a rightward line bisection bias, indicative of left hemisphericity. Surprisingly, however, this task has never been validated as a measure of prefrontal asymmetry associated with approach motivation. That is, despite its wide adoption by motivation researchers, the link between EEG prefrontal activity and line bisection bias has yet to be directly demonstrated. In fact, previous research suggests line bisection bias may be more a marker of parietal than prefrontal function (Vallar & Perani, 1986). How-
Visual Processing and Frontal Cortex Parietal and frontal brain areas in the monkey share extensive and direct linkages (Morecraft, Geula, & Mesulam, 1993) and in humans these areas are quite often coactive during various visual tasks, as measured with fMRI (Husain & Nachev, 2007). In a recent study, parietal-frontal pathways were directly manipulated through electrical stimulation that deactivates discrete brain areas during brain tumor surgery in two conscious patients with righthemisphere gliomas (de Schotten, Urbanski, Duffau, Volle, Le´vy, et al., 2005). During removal of the tumor, researchers found the most rightward line bisection bias after deactivation of a subcortical parietal-frontal pathway (de Schotten et al., 2005), which originates in the inferior parietal lobe and the occipito-parietal area and connects to the dorsolateral prefrontal cortex (this pathway is identified as the human homologue of the second branch of the superior longitudinal fasciculus described in the monkey brain, see Schmahmann et al., 2007). These results demonstrate that parietal-frontal interaction is necessary for symmetrical visual processing and the frontal component can be identified as the dorsolateral prefrontal cortex (de Schotten et al., 2005). Importantly, the dorsolateral prefrontal cortex is the brain area specifically linked to approach motivation-related frontal asymmetry. Frontal EEG Asymmetry and Approach Motivation A substantial literature demonstrates frontal EEG asymmetry as an important neural marker of various emotional, motivational,
Address reprint requests to: Kyle Nash, Behavioural Sciences Building, York University, 4700 Keele Street, Toronto, ON, M3J 1P3, Canada. E-mail:
[email protected] 979
980 and psychopathological constructs (Elliot, 2008). Researchers have consequently sought to characterize the differential role that the right and the left prefrontal cortex may play in these experiences, and the emergent literature now strongly supports a link between greater left prefrontal activity and approach motivation. For example, greater left-than-right frontal baseline activity has been related to (a) dispositional measures of approach motivation, such as behavioral activation system and promotion focus orientations (Amodio, Shah, Sigelman, Brazy, & HarmonJones, 2004; Harmon-Jones & Allen, 1997); (b) measures of approach-related emotion, such as positive affect and anger (Harmon-Jones & Allen, 1998; Tomarken, Davidson, Wheeler, & Doss, 1992); and (c) psychopathologies related to approach motivation, such as manic phases of bipolar disorder (HarmonJones, Abramason, Siegelman, Bohlig, Hogan, et al., 2002). Further supporting this association between left prefrontal activity and approach motivation, frontal asymmetry is thought to arise due to different signalling strength from the reward-related dopaminergic pathway to the frontal cortices (Berridge, Espana, & Stalnaker, 2003). Moreover, source localization has found that frontal EEG asymmetry linked to approach motivation specifically indicates dorsolateral prefrontal cortex activity (Pizzagalli, Sherwood, Henriques, & Davidson, 2005). Current Studies Line bisection bias and approach motivation implicate the same cortical area; namely, the dorsolateral prefrontal cortex. This suggests that line bisection bias could potentially index approach-related brain activity. The line bisection task would have a considerable pragmatic advantage over EEG if demonstrated as a valid marker of left prefrontal asymmetry. It is far less invasive and total assessment time is a few minutes as compared to an hour or more. In addition to bringing pragmatic benefits for future research, EEG corroboration of the line bisection task would retrospectively validate the large body of motivationrelated line bisection research previously conducted. The goal of the present research, therefore, is to validate the line bisection task based on EEG asymmetry. In Study 1, we assess dispositional links between line bisection bias and relative left prefrontal EEG neural activity. In Study 2, we directly replicate previous research on state EEG asymmetry using the line bisection task. Study 1 In a single session, we measured both line bisection bias and relative asymmetry, as assessed by EEG, at all homologous EEG sites. We hypothesized that line bisection bias would be related to relative left prefrontal activity from the frontal homologous nodes F7 and F8, which sit over dorsolateral left and right prefrontal cortices, respectively. Method EEG Recording and Processing Thirty-six right-handed, University of Toronto Scarborough psychology students were recorded for eight 1-min intervals of continuous EEG (4 eyes open, 4 eyes closed). Data from 29 (21 female) remained for analyses after exclusions due to malfunctioning equipment (n 5 2), EEG outliers (n 5 3), or excessive artefacts (n 5 2). EEG and right-eye vertical electrooculogram (VEOG) was digitized at 560 Hz with average ear reference and forehead ground. Recordings were collected from 32 electrode sites according to the 10–20 system with a bandpass filter at 0.1–
K. Nash et al. 100 Hz and a notch filter at 60 Hz. Electrode impedances were kept below 5 kO. The continuous EEG recordings were corrected off-line for eye-blinks using the VEOG channel and the second order blind identification (SOBI) procedure, which is a signal processing method for isolating and removing ocular artifacts (Tang, Liu, & Sutherland, 2005), and movement artifacts were automatically detected with a ! 75 mV and 175 mV threshold. Contiguous artefact-free epochs of 2.048 s from each 1-min interval were extracted through a Hamming window and overlapped by 75% to minimize data loss. Power spectra were calculated via fast Fourier transform. Power values (in mV2) were averaged across epochs within each interval. Total power within the alpha band (8–13 Hz), an inverse indication of cortical activity, was logarithmically transformed, and asymmetry scores were calculated as right-site minus homologous left-site log alpha power for all homologous pairs (F8-F7, F4-F3, P8-P7, P4-P3, FP2-FP1, FC2-FC1, FC6-FC5, C4-C3, CP6-CP5, CP2-CP1, T8-T7, O2-O1). Higher scores indicate relatively greater leftthan-right cortical activation. The eight minute intervals for the F8-F7 alpha score were entered in a reliability analysis and demonstrated a satisfactory Cronbach alpha coefficient of .91. Line Bisection After EEG recording, participants completed the line bisection task by marking the perceived center point of 14 staggered horizontal lines, each approximately 23 centimeters long, presented on a landscaped-view sheet of paper. The distance from each line’s true midpoint was measured in millimeters and leftward errors were scored as negative values. A mean line bisection score was calculated by averaging the scores across the 14 lines. Positive values indicated relatively greater left-than-right hemispheric activation. Reliability analysis of the 14 lines also demonstrated an acceptable Cronbach alpha coefficient of .84. Results and Discussion Consistent with the bulk of previous research on neurologically normal, right-handed individuals (Jewell & McCourt, 2000), the participants of this study also demonstrated a slight overall tendency towards leftward line bisection (M 5 ! 1.45, SD 5 4.82). Thus, any associations between line bisection bias and EEG asymmetry would be difficult to attribute to potential population irregularities. To test our hypothesis that bisection bias would be specifically related to relative left prefrontal activity, participants’ line bisection scores were then correlated separately with the alpha asymmetry scores at each electrode site. As seen in Table 1 and Figure 1, bisection bias was positively related to relative left prefrontal activity, as measured with the F8-F7 alpha EEG score (M 5 .10, SD 5 .20), r 5 .38, po.05; the greater the left prefrontal activity at F8-F7, the greater the rightward bias on the line-bisection task. None of the correlations at the other electrodes sites were significant (see Table 1, ps4.16). However, the next strongest correlation with bisection bias, the P8-P7 alpha EEG score (M 5 .22, SD 5 .64), r 5 .26, was in the same direction as the F8-F7 score. Although not significant, this is consistent with parietal-frontal interaction in visual processing outlined above. In sum, results indicate that the specific pattern of left prefrontal activity that has reliably been associated with approach motivation (e.g., Harmon-Jones, 2003) is also significantly related to line bisection bias. However, this study reflects dispositional evidence. Study 2 was thus conducted to examine situational approach motivation.
Line bisection as neural marker of approach motivation
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Table 1. Correlations Between Line Bisection and Relative Left EEG Scores F8-F7 F4-F3 P8-P7 P4-P3 FP2-FP1 FC2-FC1 FC6-FC5 C4-C3 CP6-CP5 CP2-CP1 T8-T7 O2-O1 Line bisection: (left hemisphericity)
0.38n
0.04
0.26
0.13
" 0.10
" 0.07
0.22
0.13
0.19
" 0.02
0.20
" 0.19
Note: npoo.05.
Study 2 In previous research, we demonstrated that high self-esteem (HSE) individuals are inclined towards approach motivation and respond to a challenging event with situational approach motivation, including the F8-F7 alpha EEG score from Study 1 (McGregor, Gailliot, Vasquez, & Nash, 2007; McGregor, Nash, & Inzlicht, 2009). Directly replicating this prior EEG finding, but with the line bisection task, would provide a rigorous demonstration that line bisection bias is sensitive to state neural approach motivation. Based on Study 1, which demonstrated that rightward bisection bias is related to F8-F7 alpha EEG activity, we hypothesized that HSE individuals should respond to a similar challenge manipulation with a shift in rightward bisection bias (i.e., left hemisphericity). Method Self-Esteem, Challenge Manipulation, and Line Bisection Twenty-nine participants (25 female) from a York University undergraduate psychology class first filled out a 10-item selfesteem scale (Rosenberg, 1965) that included items such as, ‘‘I take a positive attitude toward myself,’’ ‘‘I am able to do things as well as most other people,’’ and ‘‘I feel I do not have much to be proud of’’ (reverse scored). They then completed a pre-challenge line bisection task, using the same materials as in Study 1. This measure was used as a covariate to reduce error variance in the main analyses. Next, participants were randomly assigned to either the Challenge or No-Challenge conditions. In the Challenge condi-
tion, participants nominated and described a complex academic dilemma that they currently faced. The No-Challenge condition involved the same task except about a friend’s academic dilemma with no bearing on the participant’s own situation. The Challenge condition has caused HSEs to respond with approach-motivated conviction (McGregor & Marigold, 2003) and is conceptually similar to the academic challenge manipulation that caused HSEs to respond with increased F8-F7 alpha EEG score (McGregor et al., 2009). Finally, the post-challenge line bisection task was administered. For both the pre- and post-challenge line bisections, the same scoring method was used as in Study 1, Reliability analyses of both pre- and post-challenge line bisection again demonstrated acceptable Cronbach alpha coefficients of .81 and .82, respectively.
Results and Discussion Regression of bisection bias on Challenge, Self-Esteem, and the Challenge ! Self-Esteem interaction (with baseline bisection bias as a covariate) demonstrated the predicted interaction effect, t(24) 5 2.21, po.05, with the highest bisection bias among those in the Challenge condition with HSE (see Figure 2). Simple effect analyses showed that rightward bisection bias was highest at HSE (11 SD) in the Challenge condition (y 0 5 2.17)Fsignificantly higher than in the No-Challenge condition (y 0 5 " 1.65), t(24) 5 3.05, po.01. This study shows that only HSE individuals in the Challenge condition responded with increased rightward bisection bias. These results mirror previous results from the same study design but with F8-F7 alpha EEG activity (McGregor et al., 2009), supporting our contention that the line bisection can index situational changes in approach-related neural activity.
Line Bisection Bias (mm)
2.5 2
Challenge No-Challenge
1.5 1 0.5 0 –0.5 –1 –1.5 –2 Low Self-Esteem
Figure 1. Correlations between line bisection bias (left hemisphericity) and relative left EEG activity.
High Self-Esteem
Figure 2. Line bisection bias (left hemisphericity) as a function of SelfEsteem and Challenge.
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K. Nash et al.
Conclusion Although use of the line-bisection as a marker of approach motivation has been increasing in recent years (e.g., Drake & Myers, 2006; Friedman & Fo¨rster, 2005), we believe that Study 1 provided the first direct evidence that this task actually predicts resting left prefrontal alpha asymmetry. Study 2 replicated previous state-related EEG findings but with the line bisection task, demonstrating this task is also sensitive to situational approachrelated neural activity. Importantly, the F7/F8 nodes used to compute EEG alpha asymmetry lie directly over the dorsolateral prefrontal cortex, a cortical area implicated in both symmetrical visual processing and approach motivation. Thus, these results support the simple and non-invasive line bisection task as a neural index of approach motivation and asymmetrical activity related to the dorsolateral prefrontal cortex. We do note certain limitations. Resting EEG alpha asymmetry is known to partially reflect state-related factors, not just
dispositional asymmetry (see Hagemann, 2004). Additionally, the moderate effect in Study 1, while an important observation, suggests the line bisection task may not unerringly index variance in left dorsolateral prefrontal cortex activity. We encourage direct manipulations of approach motivation and measures of EEG and line bisection in subsequent research to bolster the findings presented here. However, in settings where EEG may not be feasible, we conclude that line bisection holds further promise as an efficient and unobtrusive alternative for motivational researchers. A wide array of approach motivation-related phenomenaFsuch as promotion-focus (Amodio et al., 2004; Higgins, 1997), positive affect (Tomarken et al., 1992), power (Keltner, Gruenfeld, & Anderson, 2003), conviction (McGregor, 2006), anger and dissonance reduction (Harmon-Jones, 2004; HarmonJones, Peterson, Gable, & Harmon-Jones, 2008)Fcould potentially be more readily researched with the simple line bisection task.
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983 dimensions of emotion. Journal of Personality and Social Psychology, 62, 676–687. Vallar, G., & Perani, D. (1986). The anatomy of unilateral neglect after right-hemisphere stroke lesions. A clinical/CT-scan correlation study in man. Neuropsychologia, 24, 609–622. (Received April 9, 2009; Accepted October 5, 2009)
Psychophysiology, 47 (2010), 984–988. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.01002.x
BRIEF REPORT
An event-related potential paradigm for identifying (rare negative) attitude stimuli that people intentionally misreport
STEPHEN L. CRITES, Jr., ANDREW J. MOJICA, GUADALUPE CORRAL, and JENNIFER H. TAYLOR Department of Psychology, University of Texas at El Paso, El Paso, Texas, USA
Abstract This experiment explored whether a late positive potential (LPP) of the event-related brain potential is useful for examining attitudes that people attempt to conceal. Participants identified a set of liked, neutral, and disliked people and viewed sequences consisting of either names or pictures of these people. Disliked people appeared rarely among liked people, and participants either: (1) always accurately reported their negative attitudes toward the people; (2) misreported negative attitudes as positive when they saw a picture of a disliked person; or (3) misreported negative attitudes as positive when they saw a name of a disliked person. Rare negative stimuli evoked a larger-amplitude LPP than frequent positive stimuli. Misreporting attitudes significantly reduced the amplitude difference between rare negative and frequent positive stimuli, though it remained significant. Descriptors: Attitudes, Evaluation, Deception, ERPs, Late positive potential, Social neuroscience
concerning a specific past event (e.g., crime) or is feigning memory loss. Researchers using the LPP to explore concealed attitudes create stimulus contexts in which a target is contextually significant only when the person has a certain attitude toward it (Crites, Cacioppo, Gardner, & Berntson, 1995). This permits assessments about attitudes that might predispose a person to future behaviors (e.g., negative attitudes toward the United States). Although considerable research has explored the utility and limitations of using the LPP to assess concealed knowledge (e.g., Allen et al., 1992; Farwell & Donchin, 1991; Johnson et al., 2003; Mertens & Allen, 2008; Rosenfeld et al., 1991, 2006; Rosenfeld, Soskins, Bosh, & Ryan, 2004), there is less research on concealed attitudes (Crites et al., 1995; Johnson, Henkell, Simon, & Zhu, 2008). Crites and colleagues (1995) used the LPP to explore attitudes people misreport by having people view positive (e.g., honest), neutral (e.g., quiet), or negative (e.g., deceitful) trait adjectives. Traits were presented in short sequences with targets being either evaluatively consistent (frequent) or inconsistent (rare) with the contextual traits. Rare traits evoked a larger LPP, broadly distributed but largest over the midline central-parietal areas, than frequent traits even when people misreported their attitude to the rare traits. This rare/frequent effect was comparable for both positive and negative traitsFrare positives and rare negatives both evoked large LPPs. These findings are suggestive but limited because attitudes toward traits are simple compared to those toward objects, issues, and people. An attitude toward a person, for instance, can be based on a large set of traits, behaviors, and memories; and these elements can conflict
Event-related brain potentials (ERPs) can be used to assess whether a stimulus is noteworthy to an individual even if she attempts to conceal this (Allen, Iacono, & Danielson, 1992; Farwell & Donchin, 1991; Johnson, Barnhardt, & Zhu, 2003; Rosenfeld, Angell, Johnson, & Qian, 1991; Rosenfeld, Biroschak, & Furedy, 2006). Although the theoretical underpinnings of this research differ, all rely on a late positive potential (LPP) of the ERP, which has been referred to as the LPP, P3, P300, or late positive component. Two LPP properties allow it to be used for assessing concealed informationF(1) its amplitude varies depending on the contextual significance of a stimulus, and (2) it is relatively unaffected by a person’s response to the stimulus (Goldstein, Spencer, & Donchin, 2002; Johnson, 1986; Nieuwenhuis, Aston-Jones, & Cohen, 2005; Picton, 1992). Researchers using the LPP to explore concealed knowledge create stimulus contexts in which a target is contextually significant only when a person has critical knowledge about it (e.g., Allen et al., 1992; Farwell & Donchin, 1991; Rosenfeld et al., 1991). This permits assessments about whether a person has knowledge A preliminary report based on these data was presented at the 18th annual meeting of the Association for Psychological Science. The authors would like to thank Chris Kazanjian, Naomi Nieto, Lizette Orozco, Erica Peralta, Jessica Rodriguez, and Isabel Villalobos for their assistance with data collection and David Herring, Chris Meissner, and Katherine White for their comments on this paper. Address reprint requests to: Stephen Crites, Department of Psychology, University of Texas at El Paso, El Paso, TX 79968. E-mail:
[email protected] 984
Identifying rare negative attitude stimuli in their evaluative implications (e.g., may like President Clinton’s policies but dislike his extra-marital relationship). Complex attitudes can be hard to measure because the context can alter the observed attitude (Schwarz, 2007). Another problem with assessing concealed information using the LPP is that cognitive processes necessary for deception reduce LPP amplitude. Johnson and colleagues (Johnson et al., 2003; Johnson, Barnhardt, & Zhu, 2004, 2005; Johnson et al., 2008) demonstrated that (1) the LPP amplitude to misreported stimuli is less than to truthfully reported stimuli, and (2) the act of misreporting leads to an increased response-locked ERP component that may indicate response conflict. Thus, deception may require cognitive resources that selectively reduce the amplitude of the LPP to misreported stimuli (see also Mertens & Allen, 2008; Rosenfeld et al., 2004). Because Johnson and colleagues did not use the oddball paradigm, it is not clear whether the amount of LPP reduction associated with deception is significant in the oddball paradigm. There are two objectives of this research. First, to demonstrate that the LPP evoked in the oddball paradigm can be used to distinguish complex attitudes that are misreported. We used attitudes toward people because (1) they are relatively complex compared to attitudes toward traits, and (2) we could examine whether any effect of misreporting varied depending on whether the stimulus was a picture or word. Second, to extend findings of Johnson and colleagues (Johnson et al., 2003, 2008) by demonstrating that misreporting reduces the amplitude of the LPP to an evaluatively rare stimulus in an oddball paradigm. We hypothesized that (1) rare negative stimuli would evoke a larger LPP than frequent positive stimuli even when misreported; and (2) misreporting rare negative stimuli would significantly reduce the amplitude of the LPP relative to rare negative stimuli that are accurately reported. Because the rare/frequent effect occurs regardless of whether positives or negatives are rare (Crites et al., 1995), we focused just on rare negative stimuli.1
Method Participants Data were included from 38 participants (14 male) after data from 10 were excluded (1 due to electrical artifacts and 9 for not following instructions, see below). The age of participants ranged from 17 to 54 (mean of 23). Procedure Participants first came to a pre-session. After providing informed consent, they viewed pictures and names of 353 well-known people (celebrities, politicians, etc.) and indicated either (1) that they did not know the person, or (2) their attitude toward the person on a 7-point bipolar scale ( ! 3 to 13). Before this task, the experimenter explained that the attitude ratings would be used to select a stimulus set for the experimental session. A set of 108 stimuli was selected for each participant: 72 positive (13 or12), 18 neutral (0), and 18 negative ( ! 2 or –3). If this was not possible (e.g., participant did not dislike 18 people), a set of 54 (36 positives, 9 neutrals, and 9 negatives) was selected. People 1 It was difficult to examine rare positives because a preexisting set of celebrity pictures was required (to compare truthful versus deceptive responses across pictures/names) and most people did not dislike enough celebrities in this set to create a frequent negative context.
985 rated with intermediate values ( ! 1 and 11) were not selected. Including neutral stimuli was essential because, if only positive and negative stimuli were used, participants misreporting negatives as positive could press ‘‘positive’’ to every stimulus without evaluating its meaning. Participants returned for the ERP session an average of 8 days (range 1 to 20) after the pre-session. Each participant was prepared for electroencephalogram (EEG) recording and informed that stimuli would appear in sequences of six with a prompt (‘‘Name’’ or ‘‘Picture’’) preceding each sequence to indicate the type of stimuli in that sequence. Participants pressed a key after each stimulus to report their attitude as either negative, neutral, or positive. The experimenter introduced a between-subjects manipulation by instructing participants to either accurately report all negative attitudes (n 5 14), misreport negative attitudes to pictures (n 5 12), or misreport negative attitudes to names (n 5 12). Participants initiated each 6-stimulus sequence when the ‘‘Name/Picture’’ prompt appeared. Each stimulus appeared for 1000 ms and was followed by an attitude report prompt for 800 ms. Participants were instructed to only respond once the attitude report prompt appeared. Nine participants did not follow these instructions and thus had a high rate of non-responses (responses were not recorded during the stimulus presentation). Because the analyses included only correct responses, these participants could not be used. Follow-up analyses including these nine participants (using all trials irrespective of whether the response was correct, incorrect, or missing) produced results comparable to those reported. There were 63 picture and 63 name sequences. Twenty-one of each type consisted of 5 positive stimuli and a negative oddball in either position 4, 5, or 6. Twenty-one consisted of 5 positive stimuli and a neutral oddball in either position 4, 5, or 6. Twentyone consisted of 6 stimuli all of the same valenceFseven all positive, seven all negative, and seven all neutral (ERPs were recorded from positions 4, 5, and 6 in these sequences to obtain 21 trials). Because neutral stimuli were not misreported and are thus not relevant to the hypotheses, data associated with neutral stimuli were not examined. Data Acquisition and Reduction Bioelectrical activity was recorded using Ag/AgCl electrodes from 29 scalp locations (referenced to right mastoid), the left mastoid, supraorbital and infraorbital electrodes (VEOG), and outside the outer canthi of the eyes (HEOG). Neuroscan SynAmps (Compumedics USA, Charlotte, NC) were used to amplify, filter (bandpass of 0.05–30 Hz), and digitize (500 Hz) the signals. EEG data were re-referenced to a digital, linked-mastoids reference (Hagemann, Naumann, & Thayer, 2001). Epochs associated with each target stimulus (0.1-s prestimulus & 1.0-s stimulus) were extracted and baseline corrected. VEOG artifacts were removed (Semlitsch, Anderer, Schuster, & Presslich, 1986). Epochs containing extreme activity ( " 60 mV) were excluded. ERPs were computed by averaging EEG recordings within each experimental condition (average of 17.1 EEG recordings in each ERP). Analyses and Results We digitally filtered (0.15–5 Hz, 48 dB/octave) the ERP data before quantifying the LPP as the maximum positive amplitude between 500 and 900 ms. We explored the LPP scalp distribution across the 29 scalp sites. LPP amplitude and the rare/frequent effect were (1) broadly distributed across the scalp, (2) larger over
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the midline than lateral sites, and (3) larger over the central/ parietal sites. Analyses focused on nine central/parietal locations (Cz, CPz, Pz, C3, CP3, P3, C4, CP4, P4) where the LPP was largest. To simplify analyses and presentation, we created a virtual electrode by averaging the peak amplitude of these nine sites. We first examined whether stimulus type (picture/word) and accurately reporting/misreporting attitudes influenced the LPP amplitude associated with frequent positive and frequent negative
stimuli. There was no significant amplitude difference to positives appearing in a sequence of all positives (M 5 5.63; SD 5 3.72) and negatives appearing in a sequence of all negatives (M 5 6.13; SD 5 4.12), F(1,35) 5 1.50, MSE 5 6.30, p 5 .23 We then explored the effects of stimulus frequency/rarity and stimulus type on LPPs for the 14 participants who always correctly reported their attitudes. Rare negative stimuli evoked a larger LPP than frequent positive stimuli, F(1,13) 5 13.07, MSE 5 10.94, p 5 .003 (see Figures 1 and 2). Planned compar-
Always Truthful
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Names
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Misreporting Negative Pictures
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Figure 1. LeftFERP waveforms at CPz associated with frequent positive and rare negative pictures and names. RightFScalp distribution at the maximal LPP amplitude for each waveform.
Identifying rare negative attitude stimuli
987 Rare Negative
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13 12 11 10 Microvolts
9 8 7 6 5 4 3 2
Pictures
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Figure 2. LPP amplitudes and standard errors to rare negative and frequent positive pictures/name in the three attitude report/misreport conditions.
isons revealed that (1) this difference was significant for both pictures and names, and (2) the magnitude of the difference was equal for both pictures (Diff 5 3.15 mV) and names (Diff 5 3.25 mV). Next, we examined the effects of stimulus frequency/rarity and stimulus type on LPPs for the 12 participants who misreported negative pictures (and correctly reported names) and the 12 who misreported negative names (and correctly reported pictures). Rare negative stimuli evoked a larger LPP (M 5 9.33; SD 5 4.21) than frequent positive stimuli (M 5 5.04; SD 5 3.95) F(1,22) 5 35.97, MSE 5 12.27, po.001. Pictures evoked a larger LPP (M 5 8.08; SD 5 4.27) than names (M 5 6.29; SD 5 3.57), F(1,22) 5 11.43, MSE 5 6.71, p 5 .003. The hypothesized three-way interaction between stimulus frequency/ rarity, stimulus type, and report instructions, F(1,22) 5 10.05, MSE 5 5.82, p 5 .004, was significant (see Figures 1 and 2). We conducted planned contrasts to examine this interaction. The rare-negative/frequent-positive difference in LPP amplitude to pictures was smaller for people who misreported pictures (Diff 5 3.58 mV) relative to those who accurately reported pictures (Diff 5 5.96 mV). Similarly, the difference to names was smaller for people who misreported names (Diff 5 1.88 mV) relative to those who accurately reported names (Diff 5 5.74 mV). Thus, the act of misreporting attitudes reduced the LPP amplitude difference to misreported rare stimuli relative to correctly reported rare stimuli. Although the rare/frequent LPP difference to misreported stimuli was reduced, it was still significant for misreported pictures (Diff 5 3.58 mV), F(1,11) 5 7.60, MSE 5 10.12, p 5 .019, and names (Diff 5 1.88 mV), F(1,11) 5 5.80, MSE 5 3.63, p 5 .035. Finally, although the rare/frequent difference associated with misreporting was less for misreported pictures than for misreported names, this difference was not significant, F(1,22) 5 1.27, MSE 5 13.75, p 5 .272.
Discussion The findings reveal that the amplitude of the LPP evoked in an evaluative oddball paradigm is significantly larger to evaluative oddball stimuli than to frequent stimuli that are consistent with the context, even when the oddball is misreported. That is, when a disliked person appeared in a context of liked people and participants reported that they liked the person, the LPP to this misreported disliked person was significantly larger than the LPP to a liked person. This is a significant advancement over previous work that relied on trait adjectives as proxies for attitudes (Crites et al., 1995) because it demonstrates that the evaluative oddball effect occurs with attitudes toward people and regardless of whether the eliciting stimulus is verbal or pictorial. The present findings also replicate and extend recent research demonstrating that the LPP amplitude is reduced when people attempt to conceal information (Johnson et al., 2003, 2008) by demonstrating that the act of misreporting significantly reduces the amplitude of the LPP in the oddball paradigm. The LPP amplitude reduction associated with deception was statistically equivalent for verbal and pictorial stimuli, though a non-significant trend suggests the reduction associated with verbal stimuli may be greater. The objective of the present research was not to demonstrate that the LPP can be used to assess attitudes, but to establish a foundation for future research by exploring issues that might facilitate or hinder assessment techniques. The present study made significant advancements by demonstrating that the LPP technique can differentiate relatively complex attitudes and the act of misreporting makes it harder to assess misreported attitudes. Future research on assessment would be well served by developing procedures that diminish the impact of misreporting on the LPP and/or identifying other indicators (e.g., ERP components) that signal cognitive loss or response conflict associated
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with active deception. If the LPP could be used in conjunction with another indicator, this would allow stronger inferences
about concealed attitudes/knowledge (Cacioppo & Tassinary, 1990).
REFERENCES Allen, J. J., Iacono, W. G., & Danielson, K. D. (1992). The identification of concealed memories using the event-related potential and implicit behavioral measures: A methodology for prediction in the face of individual differences. Psychophysiology, 29, 504–522. Cacioppo, J. T., & Tassinary, L. G. (1990). Inferring psychological significance from physiological signals. American Psychologist, 45, 16– 28. Crites, S. L. Jr., Cacioppo, J. T., Gardner, W. L., & Berntson, G. G. (1995). Bioelectrical echoes from evaluative categorization: II. A late positive brain potential that varies as a function of attitude registration rather than attitude report. Journal of Personality & Social Psychology, 68, 997–1013. Farwell, L. A., & Donchin, E. (1991). The truth will out: Interrogative polygraphy (‘lie detection’) with event-related brain potentials. Psychophysiology, 28, 531–547. Goldstein, A., Spencer, K. M., & Donchin, E. (2002). The influence of stimulus deviance and novelty on the P300 and novelty P3. Psychophysiology, 39, 781–790. Hagemann, D., Naumann, E., & Thayer, J. F. (2001). The quest for the EEG reference revisited: A glance from brain asymmetry research. Psychophysiology, 38, 847–857. Johnson, R. Jr. (1986). A triarchic model of P300 amplitude. Psychophysiology, 23, 367–384. Johnson, R. Jr., Barnhardt, J., & Zhu, J. (2003). The deceptive response: Effects of response conflict and strategic monitoring on the late positive component and episodic memory-related brain activity. Biological Psychology, 64, 217–253. Johnson, R. Jr., Barnhardt, J., & Zhu, J. (2004). The contribution of executive processes to deceptive responding. Neuropsychologia, 42, 878–901. Johnson, R. Jr., Barnhardt, J., & Zhu, J. (2005). Differential effects of practice on the executive processes used for truthful and deceptive
responses: An event-related brain potential study. Cognitive Brain Research, 24, 386–404. Johnson, R. Jr., Henkell, H., Simon, E., & Zhu, J. (2008). The self in conflict: The role of executive processes during truthful and deceptive responses about attitudes. NeuroImage, 39, 469–482. Mertens, R., & Allen, J. J. B. (2008). The role of psychophysiology in forensic assessments: Deception detection, ERPs, and virtual reality mock crime scenarios. Psychophysiology, 45, 286–298. Nieuwenhuis, S., Aston-Jones, G., & Cohen, J. D. (2005). Decision making, the P3, and the locus coeruleus-norepinephrine system. Psychological Bulletin, 131, 510–532. Picton, T. W. (1992). The P300 wave of the human event-related potential. Journal of Clinical Neurophysiology, 9, 456–479. Rosenfeld, J. P., Angell, A., Johnson, M., & Qian, J.-H. (1991). An ERPbased, control-question lie detector analog: Algorithms for discriminating effects within individuals’ average waveforms. Psychophysiology, 28, 319–335. Rosenfeld, J. P., Biroschak, J. R., & Furedy, J. J. (2006). P300-based detection of concealed autobiographical versus incidentally acquired information in target and non-target paradigms. International Journal of Psychophysiology, 60, 251–259. Rosenfeld, J. P., Soskins, M., Bosh, G., & Ryan, A. (2004). Simple, effective countermeasures to P300-based tests of detection of concealed information. Psychophysiology, 41, 205–219. Schwarz, N. (2007). Attitude construction: Evaluation in context. Social Cognition, 25, 638–656. Semlitsch, H. V., Anderer, P., Schuster, P., & Presslich, O. (1986). A solution for reliable and valid reduction of ocular artifacts, applied to the P300 ERP. Psychophysiology, 23, 695–703. (Received December 16, 2009; Accepted October 13, 2009)
Psychophysiology, 47 (2010), 989–993. Wiley Periodicals, Inc. Printed in the USA. Copyright r 2010 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2010.01003.x
BRIEF REPORT
Objective and continuous measurement of piloerection
MATHIAS BENEDEK,a,b BARBARA WILFLING,b REINGARD LUKAS-WOLFBAUER,b BJO¨RN H. KATZUR,a and CHRISTIAN KAERNBACHa a
Institut fu¨r Psychologie, Christian-Albrechts-Universita¨t zu Kiel, Kiel, Germany Institut fu¨r Psychologie, Karl-Franzens-Universita¨t Graz, Graz, Austria
b
Abstract Increasing attention is drawn to the investigation of piloerection (or goose bumps) in response to strong emotional experiences. This research is complicated by the need to rely on self-report measures. This article proposes an efficient method for the objective and continuous measurement of piloerection. It is based on an optical recording device combined with a discrete Fourier transform analysis quantifying the frequency power related to visible piloerection. The validity of the method was demonstrated in a proof-of-principle experiment involving a person with the exceptional ability to control the erection of his hair. The method reliably mapped all occurrences of visible piloerection and provided insight into the temporal dynamics of the underlying physiological process. It thus proved suitable for future experimental investigation of human piloerection. Descriptors: Piloerection, Goose bumps, Chills, Discrete Fourier transform
Piloerection (also referred to as horripilation, pilomotor reflex, or goose bumps) denotes the visible erection of body hair in humans or animals. This action is achieved by the musculi arrectores pilorum, which are attached at one end to the hair follicles and at the other to the inner surface of the basal layer of the epidermis. Because neither of these attachments is to rigid structures, a contraction produces a twofold effect: The hairs stand up and the epidermis buckles so that ‘goose bumps’ appear (Hellman, 1963). The piloerector muscles are activated by the sympathetic nervous system. Although this is naturally an autonomous reaction, there is at least one report of a person who was able to voluntarily evoke piloerection (Lindsley & Sassaman, 1938). Piloerection is known as a reaction to cold but also to strong emotional experiences (Ja¨nig, 2006). For the last three decades, increasing attention has been drawn to piloerection and the sensation of chills (i.e., thrills or shivers down the spine) as indicators of intense pleasurable experiences especially in response to music (e.g., Blood & Zatorre, 2001; Craig, 2005; Goldstein, 1980; Grewe, Nagel, Kopiez, & Altenmu¨ller, 2007; Panksepp, 1995; Sloboda, 1991; Zatorre, 2005). In the majority of these experiments, participants listened to pieces of music and were asked to continuously report on their experience of piloerection (or chills) by pressing a button or by giving a hand signal. However, the usage of self-report entails, at least, two methodological issues. First of all, it does not allow for
the degree of objectivity commonly given for physiological measures. Secondly, it requires the participants to monitor their bodily sensations, which may draw off attention from the experimental stimuli. This is why researches have claimed that ‘‘[f]uture work must seek to use more objective measures to analyze this phenomenon’’ (Panksepp, 1995, p. 192). So far, only two studies are known in literature employing more objective methods. In the single-case study by Lindsley and Sassaman (1938), a motion picture recording from the skin was analyzed offline. For single frames, the erection of hair was quantified by measuring the distance of the tip of any hair from the surface of the skin. In a study by Craig (2005), participants were asked to place their arm through a hole in a curtain while listening to music stimuli. An experimenter sitting on the opposite side of the curtain observed the arm and noted the occurrence of visible piloerection at 2- to 3-s intervals. Although these approaches represent valuable efforts allowing for increased objectivity, they are time-consuming and may not fully resolve the aforementioned issues, as human judges are still required. The aim of this article is to propose an efficient method for the objective and continuous measurement of piloerection, which is based upon an optical recording device combined with an automated analysis of spatial frequency.
The authors are grateful to the scientific workshop of the Department of Experimental Physics of the University of Graz, Austria for the assistance in the construction of the recording device. Address reprint requests to: Christian Kaernbach, Institut fu¨r Psychologie, Christian-Albrechts-Universita¨t zu Kiel, Olshausenstraße 62, 24118 Kiel, Germany. E-mail:
[email protected] Participant A 35-year-old right-handed male served as the participant. He was recruited in the course of a campaign looking for people with the ability to voluntarily evoke piloerection. In an informal interview, the participant explained that the elicitation is brought
Methods
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990 about by flexing his facial muscles and focusing on a location in his neck from which a shiver and, eventually, piloerection spread down his spine and into his arms. Eight years ago, he realized that he was able to provoke piloerection while being exposed to the vibrations of an electrical hair clipper. This experience fascinated him and motivated further training of this ability. He agreed to participate in an experimental session and gave written informed consent. Apparatus An optical recording device was constructed in order to enable video recordings from the skin surface under standardized conditions. The central body of this device is a hollow aluminum bar (160 ! 40 ! 40 mm) which can be placed lengthwise on a limb (e.g., the forearm) and attached firmly by means of three elastic straps. On one side of the bar, a customary webcam (QuickCam Express; Logitech, Morges, Switzerland) is fixated in an adequate cut-out. It points vertically towards a second cut-out (30 ! 50 mm) on the opposite side of the bar, which opens to the skin below the device. The camera lens holds a stable position 46 mm above the skin surface and captures a skin area of 26.5 ! 32.4 mm. The camera records 24 Bit color images at a resolution of 288 ! 352 pixelsFone millimeter of the skin thus corresponds to 10.9 pixels. Inside the recording device, three white LEDs are placed at a height of 20 mm and a distance of 50 mm from the cut-out, thus illuminating the skin from an average angle of 151. The acute angle of the light incidence causes unilateral illumination of any elevation of the skin (such as goose bumps). An additional green LED serves as a marker signal. It is positioned in a way that a green spot will be visible in one corner of the recorded images as soon as the marker light is turned on. The recording device is connected to a PC via USB. The USB connection manages data transfer but also provides power supply for the camera and the white LEDs. The marker LED obtains
M. Benedek et al. power from a 16 Bit digital I/O device (RedLab 1208LS; Meilhaus Electronic, Puchheim, Germany), which is controlled by Matlab (The Mathworks, Natick, MA) via a USB connection. The video acquisition was carried out by the video capturing software VirtualDub 1.7.7 (available at www.virtualdub.org) using a sampling rate of 10 frames per second. The recording device was constructed using commercially available components costing less than 40 Euro in total; however, technical assistance may be required to ensure stabile fixation of all components.
Quantification of Piloerection Intensity The intensity of piloerection was quantified frame by frame for the entire recorded video. A single video frame corresponded to a 288 ! 352 pixel color image (see Figure 1A). Initial preprocessing involved the selection of a maximal quadratic image section (i.e., 288 ! 288 pixel capturing 26.5 ! 26.5 mm or 7.02 cm2), conversion to gray scale, and application of a high-pass filter (cut-off at 4 cycles/picture) in order to correct for trends in illumination strength due to the natural curvature of the skin (see Figure 1B). Based on the preprocessed image, a two-dimensional discrete Fourier transform (DFT) was computed. The DFT result was corrected for the 1/f2 characteristics of natural images (Field, 1987) and arranged so that the zero-frequency component was located at the centre of the two-dimensional frequency spectrum (see Figure 1C). As directional information of frequency components were of no interest, angular averaging was performed resulting in a one-dimensional spectrum of radial frequency power (see Figure 1D). Finally, the intensity of piloerection was quantified as the maximum amplitude within a restricted frequency range of 0.23 to 0.75 mm " 1 (i.e., 6 to 20 cycles/picture). This range appeared plausible, since the density of hair follicles in the forearm averages 18/cm2 (Otberg, Richter, Schaefer, Blume-Peytavi, Sterry, & Lademann, 2004), from
Figure 1. Procedure of piloerection quantification compared for images (26.5 ! 26.5 mm) without and with visible piloerection. The raw image (A) is transformed into a high-pass filtered gray image (B). Based on this, a two-dimensional discrete Fourier transform is computed (C, shown for frequency range of # 1.13 mm " 1), which is converted to a one-dimensional spectrum of spatial frequency by means of angular averaging (D, shown for frequency range of 0.04–5.43 mm " 1 corresponding to 1–144 cycles/picture). The maximum spectral power in the 0.23 to 0.75 mm " 1 spatial frequency band (darkened section) is considered as a correlate of piloerection intensity.
Objective measurement of piloerection which a one-dimensional frequency of 0.42/mm can roughly be derived assuming equal distribution. All steps of the analysis were performed by means of selfdeveloped Matlab routines compiled in analysis software called Gooselab. The software and additional information and pictures of the software and the recording device are available at www.goosecam.de or via the Software Repository of the Society of Psychophysiological Research (www.sprweb.org).
Experimental Task and Procedure A solid rectangle of changing color was presented over a white background. When the rectangle was black, the participant should just sit and relax (rest condition); when it was blue, the participant should engage in evoking piloerection (activation condition). Both conditions lasted for 30 s and alternated continuously. The session started with a rest condition which was followed by ten pairs of activation and rest conditions. The experiment took place in a soundproof cabin. The participant was seated in a comfortable chair with his arms placed on broad, flat arm rests. The optical recording device was attached to the left dorsal forearm, which was identified as one of the most common places to experience piloerection (Craig, 2005; Goldstein, 1980). The participant was told to find a comfortable seating position and to avoid any unnecessary movements during the experiment. The experimenter left the cabin and the stimuli were presented on a 20’’ TFT screen, which was located 2 feet in front of the head of the participant. The experiment took about 30 min.
Statistical Analysis Based on the piloerection intensity data, onset and offset latencies of piloerection were evaluated relative to the onset time of activation and rest phases. The baseline was defined as the average amplitude in the first rest condition (300 frames). Onset of piloerection was identified at the time when the measure exceeded a certain threshold, which was defined as the baseline plus 10% of the response range (i.e., total maximum amplitude minus baseline); the offset was identified at the time when the measure went below the threshold. For univariate repeated-measures ANOVA, degrees of freedom were corrected by means of the Greenhouse-Geisser method where appropriate, and Bonferroni post-hoc tests were used for pair-wise comparison of means.
991 Results Visual inspection of the recorded video by four independent judges indicated that the participant was able to voluntarily evoke and stop piloerection, following the predefined schedule, ten consecutive times. The dominant spatial frequency for frames within the activation phases was 0.40 mm ! 1 (SD 5 0.06). Figure 2 shows the course of the measure of piloerection for the experimental session (solid line). Before the first activation phase, the measure reflects a stable baseline (M 5 44.01; SD 5 1.06). In the activation phases, the measure shows a steep initial rise, which soon flattens but will continue to rise until the end of the activation phase for most trials. As an activation phase ends and a rest phase begins, the measure initially shows a steep decline, which soon passes into a slow approach towards the baseline level. In the activation phases, the maximal level attained ranged from 127.90 to 169.96 (M 5 150.55, SD 5 12.90). The course of rise and decline appears to resemble an exponential approach towards a maximum level or towards the baseline, respectively. If this was the case, deconvolution of the intensity measure with an appropriate exponential function should result in a driver function with approximately vertical gradients for incline and decline (more details on the employment of deconvolution on physiological data can be found in Benedek & Kaernbach, in press). As shown in Figure 2 (dotted line), the driver function resulting from deconvolution with an exponential function (a time constant of 0.4 s was the maximum that did not give oscillations) shows nearly instantaneous declines of piloerection activity. The time course of the rise in piloerection is, however, not significantly rectified by this deconvolution. The onset and offset latencies of piloerection, based on the intensity measure and the derived driver signal, were compared with the evaluations of four independent judges, who reported observable onset or offset of piloerection in the course of a thorough frame-by-frame inspection of the recorded video. Piloerection onset was judged to occur 5.15 s (SD 5 1.17) after onset of the activation condition, and offset was judged to occur 10.80 s (SD 5 1.98) after onset of the rest condition. The estimated onset latency was significantly shorter for evaluations based on the intensity measure (M 5 2.32, SD 5 0.95) or the driver signal (M 5 1.09, SD 5 0.69; F[2,18] 5 62.11, po.001, e 5 .52, Z2 5 .87). Piloerection offset latency based on the intensity measure did not differ from visible inspection (M 5 10.3, SD 5 2.11), but was markedly shorter if identification was based on the driver signal (M 5 3.35, SD 5 0.79; F[2,18] 5 79.66, po.001, e 5 .85, Z2 5 .90).
Figure 2. The course of piloerection (quantified by means of the dominant spatial frequency power; solid line) and the derived driver (after deconvolution with an adequate exponential function; dotted line) displayed for the whole experimental session including eleven rest phases (R) and ten activation phases (A) of 30 s each.
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Discussion A method for the objective and continuous measurement of human piloerection was validated in a single-case experiment involving a male with voluntary control of his pilomotor muscles. The participant succeeded in evoking visible piloerection in ten consecutive trials, which was reliably mapped by the DFT-based analysis of the video recording of the skin surface of the forearm. The analysis of images displaying piloerection yielded a dominant frequency of 0.40 mm ! 1. Assuming virtually equal distribution of hair follicles over the skin of the forearm, this conforms to a density of about 16 hair follicles per cm2, which is in line with the density of 18/cm2 reported by Otberg et al. (2004). The measure of piloerection showed stable nonzero baseline intensity. A stable baseline should generally be easy to obtain, since piloerection usually does not occur in the absence of specific stimulation. In the case of anesthetized cats, the pilomotor neurons supplying the tail were found to be silent under thermoneutral conditions (Grosse & Ja¨nig, 1976). In the course of further data processing, one could thus consider subtracting the initial baseline value from the data in order to establish a zero baseline. The participant was able to evoke piloerection within less than 3 s. Piloerection intensity then showed a steep initial rise, which soon flattened but continued for the whole activation phase (i.e., 30 s) in most cases. We assumed that the biomechanical processes underlying hair erection (and lying down) may be described by an exponential time course. Deconvolution of the data, using an adequate exponential function, revealed that the driver function that underlies piloerection shows a gradual rise, but a virtually instantaneous decline. This indicates that the cessation of piloerection is indeed driven by biomechanical processes with an exponential time course, whereas the persistent increase of piloerection over time reflects a gradual increase of activation. Human judges confirmed that the DFT measure is indeed due to visible piloerection. However, they appeared to be less sensitive in the detection of piloerection onset or offset than the thresholdanalysis based upon objective measures. The low sensitivity of human judges may be ascribed to change blindness (e.g., Simons & Rensink, 2005), a common effect for tasks involving the detection of minute visual changes over time. The seemingly late reaction (of all measures) to the offset instruction is due to the threshold criterion: Whereas only little activation of piloerection is needed to pass the threshold on onset, most of the activation must have been gone before the activation measure falls below the threshold at offset. The identified onset and offset latencies of the driver most closely matched the times predefined by the schedule of the experiment (1 and 3 s after respective condition onset). This suggests that the driver represents the physiological activation of pilomotor neurons underlying visible piloerection. The use of this measure would be especially beneficial for the precise identification of onset and offset of pilomotor activity. Some limitations of this proof-of-concept study should be discussed. First and foremost, the assessment of piloerection was
performed only for one single subject and only at one single location of the skin, namely, the dorsal part of the forearm. This position is known as a common location for experiencing piloerection (Craig, 2005; Goldstein, 1980) and also proved appropriate in the present study. However, differences in hair density may be expected due to inter-individual differences and for the assessment at different locations. Otberg et al. (2004) reported comparable densities for forearm, thorax, thigh, and calf (18, 22, 17, and 14/cm2), but densities up to twice as high for the back and the upper arm (29 and 32/cm2). For a density twice as high, the dominant frequency would increase by the factor square root of 2 (i.e., from 0.40 to around 0.57 mm ! 1). The frequency range used in the present study (i.e., 0.23 to 0.75 mm ! 1) would be adequate for even higher differences in the dominant frequency and thus can be expected to manage typical variations in hair density. Differences in tone or other characteristics of the skin (e.g., ratio of terminal and vellus hair) may affect the absolute amplitude of the DFT but do not affect the dominant frequency. Again, these differences should not entail changes of the analysis parameters. The method thus is expected to be quite robust to typical variation of skin characteristics. As another limitation, piloerection was studied as a voluntary but not as an emotional response. The investigation of voluntary piloerection allowed studying the basic response behavior under controlled conditions by means of predefined activation phases. Piloerection in response to emotional stimuli is assumed to obey the same physiological principles as for voluntary piloerection and thus to follow the same response constraints (e.g., onset latency). However, different emotional experiences are expected to result in more variation with respect to intensity and duration. This should be especially true since piloerection is elicited more effectively by dynamic stimuli (e.g., music pieces or tactile stimulation) rather than static ones (e.g., emotional pictures; Grewe, Katzur, Kopiez, & Altenmu¨ller, in press). The availability of an objective and continuous measure of piloerection allows for a more powerful investigation of this phenomenon. It provides the adequate means to study the relationship of visible piloerection to the experience of chills. Moreover, it facilitates the unbiased study of psychophysiological correlates of piloerection. One of the most promising avenues for future research is the study of the emotional quality of experiences and states accompanied by piloerection. The absence of spontaneous activity, and its unresponsiveness to small changes in basic arousal, suggest a certain specificity of this psychophysiological response. These investigations might, therefore, bear interesting implications for the ongoing debate on dimensional versus modular models of emotion. In conclusion, a valid method for the objective and continuous measurement of piloerection in humans was presented. The measurement of piloerection thus advances to meet the methodological standards generally claimed for the empirical assessment of any psychophysiological measure. This should facilitate the study of emotional piloerection and, more generally, expand the scope of psychophysiological research.
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Objective measurement of piloerection Craig, D. (2005). An exploratory study of physiological changes during ‘chills’ induced by music. Musicae Scientiae, 9, 273–287. Field, D. J. (1987). Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America A, 4, 2379–2394. Goldstein, A. (1980). Thrills in response to music and other stimuli. Physiological Psychology, 8, 126–129. Grewe, O., Katzur, B., Kopiez, R., & Altenmu¨ller, E. (in press). Chills in different sensory domainsFFrisson elicited by acoustical, visual, tactile and gustatory stimuli. Psychology of Music, in press. Grewe, O., Nagel, F., Kopiez, R., & Altenmu¨ller, E. (2007). Emotions over time: Synchronicity and development of subjective, physiological, and facial affective reactions to music. Emotion, 7, 774–788. Grosse, M., & Ja¨nig, W. (1976). Vasoconstrictor and pilomotor fibres in skin nerves to the cat’s tail. Pflu¨gers ArchivFEuropean Journal Physiology, 361, 221–229. Hellman, K. (1963). The isolated pilomotor muscles as an in vitro preparation. Journal of Physiology, 169, 603–620.
993 Ja¨nig, W. (2006). The integrative action of the autonomic nervous system. Cambridge: Cambridge University Press. Lindsley, D. B., & Sassaman, W. H. (1938). Autonomic activity and brain potentials associated with ‘voluntary’ control of the pilomotors. Journal of Neurophysiology, 1, 342–349. Otberg, N., Richter, H., Schaefer, H., Blume-Peytavi, U., Sterry, W., & Lademann, J. (2004). Variations of hair follicle size and distribution in different body sites. Journal of Investigative Dermatology, 122, 14–19. Panksepp, J. (1995). The emotional sources of ‘chills’ induced by music. Music Perception, 13, 171–207. Simons, D. J., & Rensink, R. A. (2005). Change blindness: Past, present, and future. Trends in Cognitive Sciences, 9, 16–20. Sloboda, J. A. (1991). Music structure and emotional response: Some empirical findings. Psychology of Music, 19, 110–120. Zatorre, R. J. (2005). Music, the food of neuroscience? Nature, 434, 312–315. (Received August 3, 2009; Accepted October 23, 2009)
