Trends in Cognitive Sciences. January, 2011 Volume 15, Issue 1

January 2011 Volume 15, Number 1 pp. 1–46 Editor Stavroula Kousta Executive Editor, Neuroscience Katja Brose

Editorial

1

Journal Manager Rolf van der Sanden Journal Administrator Myarca Bonsink Advisory Editorial Board R. Adolphs, Caltech, CA, USA R. Baillargeon, U. Illinois, IL, USA N. Chater, University College, London, UK P. Dayan, University College London, UK S. Dehaene, INSERM, France D. Dennett, Tufts U., MA, USA J. Driver, University College, London, UK Y. Dudai, Weizmann Institute, Israel A.K. Engel, Hamburg University, Germany M. Farah, U. Pennsylvania, PA, USA S. Fiske, Princeton U., NJ, USA A.D. Friederici, MPI, Leipzig, Germany O. Hikosaka, NIH, MD, USA R. Jackendoff, Tufts U., MA, USA P. Johnson-Laird, Princeton U., NJ, USA N. Kanwisher, MIT, MA, USA C. Koch, Caltech, CA, USA M. Kutas, UCSD, CA, USA N.K. Logothetis, MPI, Tübingen, Germany J.L. McClelland, Stanford U., CA, USA E.K. Miller, MIT, MA, USA E. Phelps, New York U., NY, USA R. Poldrack, U. Texas Austin, TX, USA M.E. Raichle, Washington U., MO, USA T.W. Robbins, U. Cambridge, UK A. Wagner, Stanford U., CA, USA V. Walsh, University College, London, UK

TiCS evolution

Stavroula Kousta

Update Letters

2

What causes dyslexia?: comment on Goswami

Mark S. Seidenberg

Opinion

3

A temporal sampling framework for developmental dyslexia

Usha Goswami

Review

11

Mind the gap: bridging economic and naturalistic risk-taking with cognitive neuroscience

Tom Schonberg, Craig R. Fox and Russell A. Poldrack

20

The critical role of retrieval practice in long-term retention

Henry L. Roediger III and Andrew C. Butler

28

Cognitive enhancement by drugs in health and disease

Masud Husain and Mitul A. Mehta

37

Reward, dopamine and the control of food intake: implications for obesity

Nora D. Volkow, Gene-Jack Wang and Ruben D. Baler

Editorial Enquiries Trends in Cognitive Sciences

Cell Press 600 Technology Square Cambridge, MA 02139, USA Tel: +1 617 397 2817 Fax: +1 617 397 2810 E-mail: [email protected]

Forthcoming articles Emotional processing in anterior cingulate and medial prefrontal cortex Amit Etkin, Tobias Egner and Raffael Kalisch

Visual search in scenes involves selective and non-selective pathways Jeremy Wolfe, Melissa L Vo, Karla K Evans and Michelle R. Greene

Cognitive Culture: Theoretical and empirical insights into social learning strategies Luke Rendell, Laurel Fogarty, William JE Hoppitt, Thomas JH Morgan, Mike M Webster and Kevin N Laland Cover: Obesity has reached epidemic proportions in several countries, having profound societal and health care implications. On pages 37–46, Nora D. Volkow, Gene-Jack Wang and Ruben D. Baler overview the neural bases of obesity and the failure to resist the urge to eat, and conclude that, much as in addiction, obesity is associated with enhanced sensitivity of reward-related circuits to conditioned stimuli linked to energy-dense food, coupled with impaired function of the executive control network that regulates the urge to eat. Understanding the neural (as well as genetic and environmental) bases of obesity undoubtedly holds the key to curbing the current obesity epidemic. Cover image: Tooga/Digital Vision/ Getty Images.

Editorial

TiCS evolution Stavroula Kousta Editor, Trends in Cognitive Sciences

This month there is an additional reason why you should take a look at the masthead of the print issue of the journal. Or, if you are accessing the issue online through the TiCS website, I encourage you to take a look at the journal’s home page. With the new year, the Advisory Editorial Board of the journal has been extensively revised and expanded to reflect more fully the continuously evolving landscape of the cognitive sciences. I would like to take this opportunity to thank the retiring members for their invaluable contributions to the journal over the years. Their input to, feedback on and ambassadorship for TiCS in the scientific community helped to shape the content and scope of the journal, as well as the way it is perceived in the community. At the same time, I extend a very warm welcome to the members who are now joining the Board and thank them for their enthusiastic undertaking of this new role and their commitment to help TiCS to keep abreast of novel exciting developments in the field. In keeping with the interdisciplinary mission of the journal, the expertise of these 13 new members spans diverse fields (from cognitive neuroscience and neurology to philosophy and linguistics), and thus will strengthen and extend the scope of the journal in areas that have seen rapid growth in recent years and have contributed significantly to the developing landscape of the cognitive sciences. When TiCS was first launched in 1997, its mission was to provide a platform for interaction of the disciplines making up the cognitive sciences and to feature engaging and timely overviews of the most exciting research and insights for scientists, students and teachers who want to keep up with the latest developments in the field. This remains the mission of the journal today: to be inclusive of any discipline that contributes to and furthers our understanding of the way in which the mind/brain achieves the

amazing cognitive feats that it does; to encourage interdisciplinary contributions and perspectives; to publish authoritative, insightful and thought-provoking review and opinion pieces that are accessible to a broad audience; and to provide a forum for discussion, debate and commentary. At the same time, and as a direct reflection of the growing effort in the scientific community to raise awareness, explore and discuss the links between scientific research and the world outside the laboratory, we launched a new article type last year devoted to issues at the interface between Science and Society. We also introduced longer, more comprehensive Feature Reviews that address broad topic areas, covering them in more depth, to complement our more focused standard mini-reviews. The online presence of TiCS (www.cell.com/trends/cognitive-sciences) is also stronger than ever before: the journal pages feature not only the current issue, archive and online-first articles, but also thematically organized collections of articles, free access to a review or opinion piece from the current issue on a monthly basis, articles of interest in other Cell Press journals and much, much more. As part of the Neuroscience portfolio in the Cell Press family of journals, the TiCS pages are hosted alongside the pages of its sister journals, Neuron and Trends in Neurosciences. And all other Cell Press titles that carry content relevant to our readership, such as Current Biology and Trends in Ecology and Evolution, are also just a click away. With its Impact Factor reaching 11.667 in 2009 (Journal Citation Reports, Thomson Reuters) and a growing readership, TiCS is the premier monthly review journal for cognitive science. The journal is committed to catering in the best possible way for the needs of the community it serves, so this editorial is also a call to you, the readers, to contact us at [email protected] with your thoughts, comments and suggestions on how we can make TiCS better.

1364-6613/$ – see front matter ß 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.tics.2010.11.003 Trends in Cognitive Sciences, January 2011, Vol. 15, No. 1

1

Update Letters

What causes dyslexia?: comment on Goswami Mark S. Seidenberg Department of Psychology, University of Wisconsin-Madison, 1202 West Johnson Street, Madison, WI 53706, USA

Finding an underlying deficit that links the disparate impairments associated with dyslexia would be major breakthrough. In a recent article in TiCS, Goswami [1] offers a viable candidate for such a deficit – and has done a remarkable job of finding links that are plausible if still mostly circumstantial. Her stimulating article raises numerous directions for future research. The epidemiology of dyslexia is poorly documented. Although many deficits have been reported [2], how often they occur and co-occur at different ages in different languages is still unknown. Goswami’s descriptions of what is found in dyslexia should be read as what was found in some studies. More facts are needed; for example, with respect to the greater sensitivity of dyslexics to allophonic variation (which is crucial to Goswami’s account of phoneme-level difficulties), many have sought this effect, and some have found it. The null results, however, get filed away. Studies testing the Temporal Sampling Framework (TSF) need to be conducted more widely and with a variety of methods, together with tests of other putative deficits. The challenge is not only to establish closer mechanistic, causal connections between the hypothesized deficit and diverse behavioral impairments, but also to explain the distribution of impairments. The theory also needs to accommodate strong evidence for mainly left-hemisphere subcortical anomalies in dyslexia [3,4]. Short of a large-scale international epidemiological study, researchers (both of brain and behavior) need to test the same subjects using each other’s measures, and post all results, both positive and negative. An online archive in which researchers could deposit their stimulus materials would make this possible. Perhaps a major funding agency could see the value in this undertaking. Because reading is a complex task drawing on numerous capacities, it is unsurprising that multiple genetic polymorphisms are apparently involved. The TSF would be stronger if there were a reason why various genetic anomalies converge on low-frequency oscillation in the right hemisphere. I would be inclined to search for anomalies in brain development that have, as one highly salient consequence, the deficit that Goswami has identified. I present the following speculative sketch to illustrate the type of multilevel theory to which we might aspire. (i) Prominent candidate genes for dyslexia are implicated in cell migration [5]. (ii) Disorders of brain development often involve disturbances of interneuron migration and integration [6].

Corresponding author: Seidenberg, M.S. ([email protected]).

2

(iii) Anomalies in the migration of GABAergic (inhibitory) interneurons may underlie a variety of developmental disorders. Such anomalies can be regional rather than global [7]. (iv) GABAergic interneuron pathology impairs lateral inhibition, affecting discrimination of competing types of sensory information [8]. (v) Auditory processing at multiple time and frequency scales in parallel requires resolution of such competing information, and similarly for vision. (vi) For some unknown reason, the processing of lower temporal frequency auditory information is particularly vulnerable, (vii) From which deficits on tasks that rely on this information follow. Therefore, rather than the auditory-processing deficit causing associated impairments in vision, motor performance, attention, learning, memory, and so on, all these impairments arise from a common source: an attested type of neurodevelopmental anomaly, caused by multiple genes, creating the observed variability in the phenotypic outcome. Processing of low temporal frequency auditory signals might be especially affected, with multiple downstream consequences. Goswami’s article is an interesting addition to the literature and her theory will stimulate much valuable research. However, much remains to be learned. References 1 Goswami, U. (2010) A temporal sampling framework for developmental dyslexia. Trends Cogn. Sci. 15, 3–10 2 Pennington, B.F. and Bishop, D.V.M. (2009) Relations among speech, language, and reading disorders. Annu. Rev. Psychol. 60, 283–306 3 Deutsch, G.K. et al. (2005) Children’s reading performance is correlated with white matter structure as measured by diffusion tensor imaging. Cortex 41, 354–363 4 Preston, J.L. et al. (2010) Early and late talkers: school-age language, literacy and neurolinguistic differences. Brain 133, 2185–2195 5 Harold, D. et al. (2006) Further evidence that the KIAA0319 gene confers susceptibility to developmental dyslexia. Mol. Psychiatry 11, 1085–1091 6 Haydar, T.F. (2005) Advanced microscopic imaging methods to investigate cortical development and the etiology of mental retardation. Ment. Retard. Dev. Disabil. Res. Rev. 11, 303–316 7 Levitt, P. et al. (2004) Regulation of neocortical interneuron development and the implications for neurodevelopmental disorders. Trends Neurosci. 7, 400–406 8 Casanova, M.F. et al. (2002) Minicolumnar pathology in autism. Neurology 58, 428–432 1364-6613/$ – see front matter ß 2010 Published by Elsevier Ltd. doi:10.1016/j.tics.2010.10.003 Trends in Cognitive Sciences, January 2011, Vol. 15, No. 1

Opinion

A temporal sampling framework for developmental dyslexia Usha Goswami Centre for Neuroscience in Education, University of Cambridge, Downing St, Cambridge, UK, CB2 3EB

Neural coding by brain oscillations is a major focus in neuroscience, with important implications for dyslexia research. Here, I argue that an oscillatory ‘temporal sampling’ framework enables diverse data from developmental dyslexia to be drawn into an integrated theoretical framework. The core deficit in dyslexia is phonological. Temporal sampling of speech by neuroelectric oscillations that encode incoming information at different frequencies could explain the perceptual and phonological difficulties with syllables, rhymes and phonemes found in individuals with dyslexia. A conceptual framework based on oscillations that entrain to sensory input also has implications for other sensory theories of dyslexia, offering opportunities for integrating a diverse and confusing experimental literature. Dyslexia and auditory neuroscience Developmental dyslexia affects 7% of children and is defined as a specific learning difficulty affecting reading and spelling that is not due to low intelligence, poor educational opportunities or overt sensory or neurological damage [1]. Across languages, children with dyslexia have poor phonological processing skills, leading to the dominant phonological core deficit [2] model of this heterogeneous disorder. Here, I propose a novel causal framework for developmental dyslexia, the temporal sampling framework (TSF), which has this phonological model as its focus. Temporal coding is an important aspect of information coding in the brain [3,4], and temporal coding via the synchronous activity of oscillating networks of neurons at different frequency bands (e.g. Delta, 1.5–4 Hz; Theta, 4–10 Hz; and Gamma, 30–80 Hz [3]) is crucial in the perceptual processing of speech [5]. For example, stimulusinduced modulation (‘phase locking’) of inherent neural oscillations at specific frequencies is important for syllabic perception (Theta) [5,6] and for prosodic perception (Delta) [7]. The acoustic speech signal can be considered as a summation of several frequency bands fluctuating in intensity (amplitude) over time (the ‘amplitude envelope’, AE). Neurally, the auditory system codes amplitude modulation in natural sounds both across different frequency channels and on different timescales [8]. The AE can be analysed in terms of its constituent temporal modulation frequencies. The dominant modulation frequencies are 4–6 Hz, irrespective of the audio frequency band, type of speech or the speaker, reflecting the sequential rate of words and syllables [9]. In auditory cognitive neuroscience, Corresponding author: Goswami, U. ([email protected]).

these insights are exploited in multi-time resolution speech-processing models (Box 1) [5,6]. The framework proposed here integrates difficulties in processing the rate of change of amplitude (rise time) at AE onset (found in dyslexia across languages [10–15]) with impaired temporal sampling of input by low-frequency Theta and Delta oscillatory mechanisms. Rise time difficulties suggest impairments in distinguishing the different modulation frequency ranges in speech, perhaps arising Glossary Allophone: acoustically different forms of the same phoneme; for example, the sound corresponding to the letter P in the spoken syllables ‘spin’ and ‘pin’ is acoustically different, the sound in ‘spin’ being more like /b/, but both sounds are treated in English as the phoneme /p/. Amplitude: volume of sound (intensity). Amplitude envelope (AE): the summation over time of the intensity fluctuations (amplitude modulations) in the different frequency channels in the speech signal. Formant: a concentration of acoustic energy within a narrow frequency band in the speech signal. Formant transition duration: the time taken from the mouth obstruction that forms a consonant to the steady position marking the succeeding vowel (usually rapid, green. When compared with lean subjects, obese subjects had higher baseline metabolism in the somatosensory areas where the mouth, lips and tongue are represented and which are involved with processing food palatability. Modified, with permission, from [22] (a–c) and [68] (d,e).

food (reflected in the activation of OFC and ACC) in normal-weight individuals, which is lost in obesity. Surprisingly, obese individuals, when compared with lean individuals, experienced less activation of reward circuits from the actual food consumption (consummatory food reward), whereas they showed greater activation of somatosensory cortical regions that process palatability when they anticipated consumption [67] (Figure 4). The latter finding is consistent with a study that reported increased baseline glucose metabolic activity (a marker of brain function) in somatosensory regions that process palatability, including insula, in obese as compared with lean subjects [68] (Figure 3d,e). An enhanced activity of

[()TD$FIG]

(a)

(b) 3.5

2 1 0

PE (caudate)

3

5 4 3 2 1 0 23 -1 -2 -3 -4

R2=0.2496

28

33

BMI TRENDS in Cognitive Sciences

Figure 4. Obese subjects have a decreased response in DA target regions when given food compared with that recorded in lean subjects. (a) Coronal section of weaker activation in the left caudate nucleus in response to receiving a milkshake versus a tasteless solution; (b) Correlation between the difference in activation and BMI of the subjects. Modified, with permission, from [67].

42

regions that process palatability could make obese subjects favor food over other natural reinforcers, whereas decreased activation of dopaminergic targets by the actual food consumption might lead to overconsumption as a means to compensate for the weak DA signals [69]. These imaging findings are consistent with an enhanced sensitivity of the reward circuitry to conditioned stimuli (viewing high-calorie food) that predict reward, but a decreased sensitivity to the rewarding effects of actual food consumption in dopaminergic pathways in obesity. We hypothesize that, to the extent that there is a mismatch between the expected reward and a delivery that does not fulfill this expectation, this will promote compulsive eating as an attempt to achieve the expected level of reward. Although the failure of an expected reward to arrive is accompanied by a decrease in DA cell firing in laboratory animals [70], the behavioral significance of such a decrease (when a food reward is smaller than expected) has, to our knowledge, not been investigated. In parallel to these activation changes in the reward circuitry in obese subjects, imaging studies have also documented consistent decreases in the reactivity of the hypothalamus to satiety signals in obese subjects [71,72]. Evidence of cognitive disruption in overweight and obese individuals There is increasing evidence that obesity is associated with impairment on certain cognitive functions, such as executive function, attention and memory [73–75]. Indeed, the ability to inhibit the urges to eat desirable food varies

Review among individuals and might be one of the factors that contribute to their vulnerability for overeating [34]. The adverse influence of obesity on cognition is also reflected in the higher prevalence of attention deficit hyperactivity disorder (ADHD) [76], Alzheimer disease and other dementias [77], cortical atrophy [78] and white matter disease [79] in obese subjects. Although co-morbid medical conditions (e.g. cerebrovascular pathology, hypertension and diabetes) are known to affect cognition adversely, there is also evidence that high BMI, by itself, might impair various cognitive domains, particularly executive function [75]. In spite of some inconsistencies among studies, brainimaging data have also provided evidence of structural and functional changes associated with high BMI in otherwise healthy controls. For example, an MRI study done in elderly females using voxel-wise morphometry showed a negative correlation between BMI and gray matter volumes (including frontal regions), which, in the OFC, was associated with impaired executive function [80]. Using positron emission tomography (PET) to measure brain glucose metabolism in healthy controls, a negative correlation was also shown between BMI and metabolic activity in PFC (dorsolateral and OFC) and in ACC. In this study, the metabolic activity in PFC predicted the subjects’ performance in tests of executive function [81]. Similarly, an NMR spectroscopic study of healthy middle age and elderly controls showed that BMI was negatively associated with the levels of N-acetyl-aspartate (a marker of neuronal integrity) in frontal cortex and ACC [79,82]. Brain-imaging studies comparing obese and lean individuals have also reported lower gray matter density in frontal regions (frontal operculum and middle frontal gyrus) and in post-central gyrus and putamen [83]. Another study, which found no differences in gray matter volumes between obese and lean subjects, did report a positive correlation between white matter volume in basal brain structures and waist:hip ratio; a trend that was partially reversed by dieting [84]. Finally, the role of DA in inhibitory control is well recognized and its disruption might contribute to behavioral disorders of discontrol, such as obesity. A negative correlation between BMI and striatal D2R has been reported in obese [58] as well as in overweight subjects [85]. As discussed above, the lower-than-normal availability of D2R in the striatum of obese individuals was associated with reduced metabolic activity in PFC and ACC [60]. These findings implicate neuroadaptations in DA signaling as contributors to the disruption of frontal cortical regions associated with overweight and obesity. A better understanding of these disruptions might help guide strategies to ameliorate, or perhaps even reverse, specific impairments in crucial cognitive domains. For example, delay discounting, which is the tendency to devalue a reward as a function of the temporal delay of its delivery, is one of the most extensively investigated cognitive operations in relation to disorders associated with impulsivity and compulsivity. Delay discounting has been most comprehensively investigated in drug abusers who prefer small-but-immediate over large-but-delayed rewards [86]. The few studies done in obese individuals have also shown that these individuals display preference for high,

Trends in Cognitive Sciences

January 2011, Vol. 15, No. 1

immediate rewards, despite an increased chance of suffering higher future losses [87,88]. Moreover, a positive correlation between BMI and hyperbolic discounting, whereby future negative payoffs are discounted less than are future positive payoffs, was recently reported [89]. Delay discounting seems to depend on the function of ventral striatum (where NAc is located) [90,91] and of the PFC, including OFC [92], and is sensitive to DA manipulations [93]. Interestingly, lesions of the OFC in animals can either increase or decrease the preference for immediate small rewards over delayed larger rewards [94,95]. This apparently paradoxical behavioral effect is likely to reflect the fact that at least two operations are processed through the OFC; one is salience attribution, through which a reinforcer acquires incentive motivational value, and the other is control over pre-potent urges [96]. Dysfunction of the OFC is associated with an impaired ability to modify the incentive motivational value of a reinforcer as a function of the context in which it occurs (i.e. decrease the incentive value of food with satiety), which can result in compulsive food consumption [97]. If the stimulus is highly reinforcing (such as food and food cues for an obese subject) the enhanced saliency value of the reinforcer will result in an enhanced motivation to procure it, which could appear as a willingness to delay gratification (such as spending time in long lines to buy ice cream). However, in contexts where food is readily available, the same enhanced saliency can trigger impulsive behaviors (such as buying and eating the chocolate located next to the cashier even without previous awareness of the desire of such item). Dysfunction of the OFC (and of the ACC) impairs the ability to rein in pre-potent urges, resulting in impulsivity and an exaggerated delayed discount rate. Food for thought It would appear, from the collected evidence presented here, that a substantial fraction of obese individuals exhibit an imbalance between an enhanced sensitivity of the reward circuitry to conditioned stimuli linked to energydense food and impaired function of the executive control circuitry that weakens inhibitory control over appetitive Box 3. Future basic research directions  A better understanding of the interaction at the molecular, cellular, and circuit levels between the homeostatic and reward processes that regulate food intake.  Understanding the role of genes in modulating the homeostatic and the reward responses to food.  A better understanding of the involvement of other neurotransmitters, such as cannabinoids, opioids, glutamate, serotonin and GABA, in the long-lasting changes that occur in obesity.  Investigating the developmental aspects of the neurobiology underlying food intake (homeostatic and rewarding) and its sensitivity to environmental food exposure.  Understanding the epigenetic modifications in neuronal circuits involved with the homeostatic and rewarding control of food intake in the fetal brain in response to exposure to food excess and food deprivation during pregnancy.  Investigating neuroplastic adaptations in homeostatic and reward circuits associated with chronic exposure to highly palatable foods and/or to high quantities of calorie-dense food.  Investigating the relationship between homeostatic and hedonic processes regulating food intake and physical activity.

43

Review Box 4. Future clinical research directions  Studies to ascertain whether the greater activation of rewardassociated areas in response to food-related cues in obese individuals underlies their vulnerability for overeating or reflects a secondary neuroadaptation to overeating.  It is suggested that enhanced dopaminergic neurotransmission contributes to improved eating behavior through optimization and/or strengthening of cognitive control mechanisms mediated in part through the PFC; however, further research is needed into the currently ill-defined mechanisms involved.  Diet alone is seldom a path to successful (i.e. sustainable) weight loss. It would be instructive to address whether: (i) dieting can trigger a withdrawal syndrome that increases the risk of relapse; and (ii) the decreased leptin levels associated with diet-induced weight loss lead to hyperactivation of reward circuitry and compensatory food seeking behaviors.  Research to determine the neurobiology that underlies decreases in food craving and hunger following bariatric surgery.

behaviors. Regardless of whether this imbalance causes, or is caused by, pathological overeating, the phenomenon is reminiscent of the conflict between the reward, conditioning and motivation circuits and the inhibitory control circuit that has been reported in addiction [98]. Knowledge accumulated during the past two decades of the genetic, neural and environmental bases of obesity leaves no doubt that the current crisis has sprouted from the disconnect between the neurobiology that drives food consumption in our species and the richness and diversity of food stimuli driven by our social and economic systems. The good news is that understanding the deep-seated behavioral constructs that sustain the obesity epidemic holds the key to its eventual resolution (see also Boxes 3 and 4). References 1 Ogden, C.L. et al. (2006) Prevalence of overweight and obesity in the United States, 1999 to 2004. JAMA 295, 1549–1555 2 Flegal, K.M. et al. (2010) Prevalence and trends in obesity among US adults, 1999-2008. JAMA 303, 235–241 3 Finkelstein, E.A. et al. (2009) Annual medical spending attributable to obesity: payer-and service-specific estimates. Health Aff. 28, w822– w831 4 Baessler, A. et al. (2005) Genetic linkage and association of the growth hormone secretagogue receptor (ghrelin receptor) gene in human obesity. Diabetes 54, 259–267 5 Silventoinen, K. and Kaprio, J. (2009) Genetics of tracking of body mass index from birth to late middle age: evidence from twin and family studies. Obes. Facts 2, 196–202 6 Speliotes, E. et al. (2010) Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 7 Thorleifsson, G. et al. (2009) Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat. Genet. 41, 18–24 8 Naukkarinen, J. et al. (2010) Use of genome-wide expression data to mine the ‘Gray Zone’ of GWA studies leads to novel candidate obesity genes. PLoS Genet. 6, e1000976 9 Gosnell, B. and Levine, A. (2009) Reward systems and food intake: role of opioids. Int. J. Obes. 33 (Suppl. 2), S54–S58 10 van Vliet-Ostaptchouk, J.V. et al. (2009) Genetic variation in the hypothalamic pathways and its role on obesity. Obes. Rev. 10, 593– 609 11 Blouet, C. and Schwartz, G.J. (2010) Hypothalamic nutrient sensing in the control of energy homeostasis. Behav. Brain Res. 209, 1–12 12 Coll, A.P. et al. (2007) The hormonal control of food intake. Cell 129, 251–262 13 Dietrich, M. and Horvath, T. (2009) Feeding signals and brain circuitry. Eur. J. Neurosci. 30, 1688–1696

44

Trends in Cognitive Sciences January 2011, Vol. 15, No. 1 14 Belgardt, B. et al. (2009) Hormone and glucose signalling in POMC and AgRP neurons. J. Physiol. 587 (Pt 22), 5305–5314 15 Goldstone, A.P. (2006) The hypothalamus, hormones, and hunger: alterations in human obesity and illness. Prog. Brain Res. 153, 57–73 16 Rolls, E. (2005) Taste, olfactory, and food texture reward processing in the brain and obesity. Int. J. Obes. 85, 45–56 17 Rolls, E.T. (2008) Functions of the orbitofrontal and pregenual cingulate cortex in taste, olfaction, appetite and emotion. Acta Physiol. Hung. 95, 131–164 18 Petrovich, G.D. et al. (2005) Amygdalar and prefrontal pathways to the lateral hypothalamus are activated by a learned cue that stimulates eating. J. Neurosci. 25, 8295–8302 19 Farooqi, I.S. et al. (2007) Leptin regulates striatal regions and human eating behavior. Science 317, 1355 20 Baicy, K. et al. (2007) Leptin replacement alters brain response to food cues in genetically leptin-deficient adults. Proc. Natl. Acad. Sci. U. S. A. 104, 18276–18279 21 Passamonti, L. et al. (2009) Personality predicts the brain’s response to viewing appetizing foods: the neural basis of a risk factor for overeating. J. Neurosci. 29, 43–51 22 Volkow, N.D. et al. (2008) Overlapping neuronal circuits in addiction and obesity: evidence of systems pathology. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 363, 3191–3200 23 Lenoir, M. et al. (2007) Intense sweetness surpasses cocaine reward. PLoS One 2, e698 24 Cason, A.M. et al. (2010) Role of orexin/hypocretin in reward-seeking and addiction: implications for obesity. Physiol. Behav. 100, 419– 428 25 Cota, D. et al. (2006) Cannabinoids, opioids and eating behavior: the molecular face of hedonism? Brain Res. Rev. 51, 85–107 26 Atkinson, T. (2008) Central and peripheral neuroendocrine peptides and signalling in appetite regulation: considerations for obesity pharmacotherapy. Obes. Rev. 9, 108–120 27 Wise, R. (2006) Role of brain dopamine in food reward and reinforcement. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 361, 1149– 1158 28 Small, D.M. et al. (2003) Feeding-induced dopamine release in dorsal striatum correlates with meal pleasantness ratings in healthy human volunteers. Neuroimage 19, 1709–1715 29 Norgren, R. et al. (2006) Gustatory reward and the nucleus accumbens. Physiol. Behav. 89, 531–535 30 Epstein, L. et al. (2009) Habituation as a determinant of human food intake. Psychol. Rev. 116, 384–407 31 Schultz, W. (2010) Dopamine signals for reward value and risk: basic and recent data. Behav. Brain Funct. 6, 24 32 Geisler, S. and Wise, R. (2008) Functional implications of glutamatergic projections to the ventral tegmental area. Rev. Neurosci. 19, 227–244 33 Petrovich, G. Forebrain circuits and control of feeding by learned cues. Neurobiol. Learn. Mem. 2010 Oct 19. [Epub ahead of print] 34 Wang, G.J. et al. (2009) Evidence of gender differences in the ability to inhibit brain activation elicited by food stimulation. Proc. Natl. Acad. Sci. U. S. A. 106, 1249–1254 35 Volkow, N.D. et al. (2002) ‘Nonhedonic’ food motivation in humans involves dopamine in the dorsal striatum and methylphenidate amplifies this effect. Synapse 44, 175–180 36 Berridge, K. (2009) ‘Liking’ and ‘wanting’ food rewards: brain substrates and roles in eating disorders. Physiol. Behav. 97, 537–550 37 Szczypka, M.S. et al. (2001) Dopamine production in the caudate putamen restores feeding in dopamine-deficient mice. Neuron 30, 819–828 38 Faure, A. et al. (2008) Mesolimbic dopamine in desire and dread: enabling motivation to be generated by localized glutamate disruptions in nucleus accumbens. J. Neurosci. 28, 7148–7192 39 Saddoris, M. et al. (2009) Associatively learned representations of taste outcomes activate taste-encoding neural ensembles in gustatory cortex. J. Neurosci. 29, 15386–15396 40 Smith, K.S. and Berridge, K.C. (2007) Opioid limbic circuit for reward: interaction between hedonic hotspots of nucleus accumbens and ventral pallidum. J. Neurosci. 27, 1594–1605 41 Wassum, K.M. et al. (2009) Distinct opioid circuits determine the palatability and the desirability of rewarding events. Proc. Natl. Acad. Sci. U. S. A. 106, 12512–12517

Review 42 Avena, N.M. et al. (2008) Evidence for sugar addiction: behavioral and neurochemical effects of intermittent, excessive sugar intake. Neurosci. Biobehav. Rev. 32, 20–39 43 Graillon, A. et al. (1997) Differential response to intraoral sucrose, quinine and corn oil in crying human newborns. Physiol. Behav. 62, 317–325 44 Richard, D. et al. (2009) The brain endocannabinoid system in the regulation of energy balance. Best Pract. Res. Clin. Endocrinol. Metab. 23, 17–32 45 Di Marzo, V. et al. (2009) The endocannabinoid system as a link between homoeostatic and hedonic pathways involved in energy balance regulation. Int. J. Obes. 33 (Suppl. 2), S18–S24 46 Matias, I. and Di Marzo, V. (2007) Endocannabinoids and the control of energy balance. Trends Endocrinol. Metab. 18, 27–37 47 Garfield, A. and Heisler, L. (2009) Pharmacological targeting of the serotonergic system for the treatment of obesity. J. Physiol. 587, 48– 60 48 Halford, J. et al. (2010) Pharmacological management of appetite expression in obesity. Nat. Rev. Endocrinol. 6, 255–269 49 Lam, D. et al. (2010) Brain serotonin system in the coordination of food intake and body weight. Pharmacol. Biochem. Behav. 97, 84–91 50 Lattemann, D. (2008) Endocrine links between food reward and caloric homeostasis. Appetite 51, 452–455 51 Rosenbaum, M. et al. (2008) Leptin reverses weight loss-induced changes in regional neural activity responses to visual food stimuli. J. Clin. Invest. 118, 2583–2591 52 Guthoff, M. et al. (2010) Insulin modulates food-related activity in the central nervous system. J. Clin. Endocrinol. Metab. 95, 748– 755 53 Chechlacz, M. et al. (2009) Diabetes dietary management alters responses to food pictures in brain regions associated with motivation and emotion: a functional magnetic resonance imaging study. Diabetologia 52, 524–533 54 Batterham, R.L. et al. (2007) PYY modulation of cortical and hypothalamic brain areas predicts feeding behaviour in humans. Nature 450, 106–109 55 Malik, S. et al. (2008) Ghrelin modulates brain activity in areas that control appetitive behavior. Cell Metab. 7, 400–409 56 Fulton, S. et al. (2006) Leptin regulation of the mesoaccumbens dopamine pathway. Neuron 51, 811–822 57 Geiger, B.M. et al. (2009) Deficits of mesolimbic dopamine neurotransmission in rat dietary obesity. Neuroscience 159, 1193– 1199 58 Wang, G.J. et al. (2001) Brain dopamine and obesity. Lancet 357, 354– 357 59 Johnson, P.M. and Kenny, P.J. (2010) Dopamine D2 receptors in addiction-like reward dysfunction and compulsive eating in obese rats. Nat. Neurosci. 13, 635–641 60 Volkow, N.D. et al. (2008) Low dopamine striatal D2 receptors are associated with prefrontal metabolism in obese subjects: possible contributing factors. Neuroimage 42, 1537–1543 61 Fineberg, N.A. et al. (2010) Probing compulsive and impulsive behaviors, from animal models to endophenotypes: a narrative review. Neuropsychopharmacology 35, 591–604 62 Davis, L.M. et al. (2009) Bromocriptine administration reduces hyperphagia and adiposity and differentially affects dopamine D2 receptor and transporter binding in leptin-receptor-deficient Zucker rats and rats with diet-induced obesity. Neuroendocrinology 89, 152– 162 63 Geiger, B.M. et al. (2008) Evidence for defective mesolimbic dopamine exocytosis in obesity-prone rats. FASEB J. 22, 2740–2746 64 Cordeira, J.W. et al. (2010) Brain-derived neurotrophic factor regulates hedonic feeding by acting on the mesolimbic dopamine system. J. Neurosci. 30, 2533–2541 65 Stoeckel, L. et al. (2008) Widespread reward-system activation in obese women in response to pictures of high-calorie foods. Neuroimage 41, 636–647 66 Killgore, W. and Yurgelun-Todd, D. (2005) Body mass predicts orbitofrontal activity during visual presentations of high-calorie foods. Neuroreport 31, 859–863 67 Stice, E. et al. (2008) Relation of reward from food intake and anticipated food intake to obesity: a functional magnetic resonance imaging study. J. Abnorm. Psychol. 117, 924–935

Trends in Cognitive Sciences

January 2011, Vol. 15, No. 1

68 Wang, G. et al. (2002) Enhanced resting activity of the oral somatosensory cortex in obese subjects. Neuroreport 13, 1151–1155 69 Stice, E. et al. (2008) Relation between obesity and blunted striatal response to food is moderated by TaqIA A1 allele. Science 322, 449– 452 70 Schultz, W. (2002) Getting formal with dopamine and reward. Neuron 36, 241–263 71 Cornier, M.A. et al. (2009) The effects of overfeeding on the neuronal response to visual food cues in thin and reduced-obese individuals. PLoS One 4, e6310 72 Matsuda, M. et al. (1999) Altered hypothalamic function in response to glucose ingestion in obese humans. Diabetes 48, 1801–1806 73 Bruce-Keller, A.J. et al. (2009) Obesity and vulnerability of the CNS. Biochim. Biophys. Acta. 1792, 395–400 74 Bruehl, H. et al. (2009) Modifiers of cognitive function and brain structure in middle-aged and elderly individuals with type 2 diabetes mellitus. Brain Res. 1280, 186–194 75 Gunstad, J. et al. (2007) Elevated body mass index is associated with executive dysfunction in otherwise healthy adults. Compr. Psychiatry 48, 57–61 76 Cortese, S. et al. (2008) Attention-deficit/hyperactivity disorder (ADHD) and obesity: a systematic review of the literature. Crit. Rev. Food Sci. Nutr. 48, 524–537 77 Fotuhi, M. et al. (2009) Changing perspectives regarding late-life dementia. Nat. Rev. Neurol. 5, 649–658 78 Raji, C.A. et al. (2010) Brain structure and obesity. Hum. Brain Mapp. 31, 353–364 79 Gazdzinski, S. et al. (2008) Body mass index and magnetic resonance markers of brain integrity in adults. Ann. Neurol. 63, 652–657 80 Walther, K. et al. (2010) Structural brain differences and cognitive functioning related to body mass index in older females. Hum. Brain Mapp. 31, 1052–1064 81 Volkow, N.D. et al. (2008) Inverse association between BMI and prefrontal metabolic activity in healthy adults. Obesity 17, 60–65 82 Gazdzinski, S. et al. (2009) BMI and neuronal integrity in healthy, cognitively normal elderly: a proton magnetic resonance spectroscopy study. Obesity 18, 743–748 83 Pannacciulli, N. et al. (2006) Brain abnormalities in human obesity: a voxel-based morphometric study. Neuroimage 31, 1419–1425 84 Haltia, L.T. et al. (2007) Brain white matter expansion in human obesity and the recovering effect of dieting. J. Clin. Endocrinol. Metab. 92, 3278–3284 85 Haltia, L.T. et al. (2007) Effects of intravenous glucose on dopaminergic function in the human brain in vivo. Synapse 61, 748–756 86 Bickel, W.K. et al. (2007) Behavioral and neuroeconomics of drug addiction: competing neural systems and temporal discounting processes. Drug Alcohol. Depend. 90 (Suppl. 1), S85–S91 87 Brogan, A. et al. (2010) Anorexia, bulimia, and obesity: shared decision making deficits on the Iowa Gambling Task (IGT). J. Int. Neuropsychol. Soc. 1–5 88 Weller, R.E. et al. (2008) Obese women show greater delay discounting than healthy-weight women. Appetite 51, 563–569 89 Ikeda, S. et al. (2010) Hyperbolic discounting, the sign effect, and the body mass index. J. Health Econ. 29, 268–284 90 Cardinal, R.N. (2006) Neural systems implicated in delayed and probabilistic reinforcement. Neural Netw. 19, 1277–1301 91 Gregorios-Pippas, L. et al. (2009) Short-term temporal discounting of reward value in human ventral striatum. J. Neurophysiol. 101, 1507– 1523 92 Bjork, J.M. et al. (2009) Delay discounting correlates with proportional lateral frontal cortex volumes. Biol. Psychiatry 65, 710–713 93 Pine, A. et al. (2010) Dopamine, time, and impulsivity in humans. J. Neurosci. 30, 8888–8896 94 Mobini, S. et al. (2002) Effects of lesions of the orbitofrontal cortex on sensitivity to delayed and probabilistic reinforcement. Psychopharmacology 160, 290–298 95 Roesch, M.R. et al. (2007) Should I stay or should I go? Transformation of time-discounted rewards in orbitofrontal cortex and associated brain circuits. Ann. N. Y. Acad. Sci. 1104, 21–34 96 Schoenbaum, G. et al. (2009) A new perspective on the role of the orbitofrontal cortex in adaptive behaviour. Nat. Rev. Neurosci. 10, 885–892

45

Review 97 Schilman, E.A. et al. (2010) The role of the striatum in compulsive behavior in intact and orbitofrontal-cortex-lesioned rats: possible involvement of the serotonergic system. Neuropsychopharmacology 35, 1026–1039 98 Volkow, N.D. et al. (2009) Imaging dopamine’s role in drug abuse and addiction. Neuropharmacology 56 (Suppl. 1), 3–8 99 Davidson, T. et al. (2009) Contributions of the hippocampus and medial prefrontal cortex to energy and body weight regulation. Hippocampus 19, 235–252 100 Forloni, G. et al. (1986) Role of the hippocampus in the sex-dependent regulation of eating behavior: studies with kainic acid. Physiol. Behav. 38, 321–326 101 Haase, L. et al. (2009) Cortical activation in response to pure taste stimuli during the physiological states of hunger and satiety. Neuroimage 44, 1008–1021

46

Trends in Cognitive Sciences January 2011, Vol. 15, No. 1 102 Massa, F. et al. (2010) Alterations in the hippocampal endocannabinoid system in diet-induced obese mice. J. Neurosci. 30, 6273–6281 103 McNay, E.C. (2007) Insulin and ghrelin: peripheral hormones modulating memory and hippocampal function. Curr. Opin. Pharmacol. 7, 628–632 104 Bragulat, V. et al. (2010) Food-related odor probes of brain reward circuits during hunger: a pilot FMRI study. Obesity 18, 1566– 1571 105 Benarroch, E. (2010) Neural control of feeding behavior: overview and clinical correlations. Neurology 74, 1643–1650 106 Olszewski, P. et al. (2008) Analysis of the network of feeding neuroregulators using the Allen Brain Atlas. Neurosci. Biobehav. Rev. 32, 945–956