HANDBOOK OF CLINICAL NEUROLOGY Series Editors
MICHAEL J. AMINOFF, FRANC¸OIS BOLLER, AND DICK F. SWAAB VOLUME 98
EDINBURGH LONDON NEW YORK OXFORD PHILADELPHIA ST LOUIS SYDNEY TORONTO 2011
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Handbook of Clinical Neurology 3rd Series Available titles Vol. 79, The human hypothalamus: basic and clinical aspects, Part I, D.F. Swaab ISBN 0444513574 Vol. 80, The human hypothalamus: basic and clinical aspects, Part II, D.F. Swaab ISBN 0444514902 Vol. 81, Pain, F. Cervero and T.S. Jensen, eds. ISBN 0444519017 Vol. 82, Motor neurone disorders and related diseases, A.A. Eisen and P.J. Shaw, eds. ISBN 0444518940 Vol. 83, Parkinson’s disease and related disorders, Part I, W.C. Koller and E. Melamed, eds. ISBN 9780444519009 Vol. 84, Parkinson’s disease and related disorders, Part II, W.C. Koller and E. Melamed, eds. ISBN 9780444528933 Vol. 85, HIV/AIDS and the nervous system, P. Portegies and J. Berger, eds. ISBN 9780444520104 Vol. 86, Myopathies, F.L. Mastaglia and D. Hilton Jones, eds. ISBN 9780444518966 Vol. 87, Malformations of the nervous system, H.B. Sarnat and P. Curatolo, eds. ISBN 9780444518965 Vol. 88, Neuropsychology and behavioural neurology, G. Goldenberg and B.C. Miller, eds. ISBN 9780444518972 Vol. 89, Dementias, C. Duyckaerts and I. Litvan, eds. ISBN 9780444518989 Vol. 90, Disorders of Consciousness, G.B. Young and E.F.M. Wijdicks, eds. ISBN 9780444518958 Vol. 91, Neuromuscular Junction Disorders, A.G. Engel, ed. ISBN 9780444520081 Vol. 92, Stroke – Part I: Basic and epidemiological aspects, M. Fisher, ed. ISBN 9780444520036 Vol. 93, Stroke – Part II: Clinical manifestations and pathogenesis, M. Fisher, ed. ISBN 9780444520043 Vol. 94, Stroke – Part III: Investigations and management, M. Fisher, ed. ISBN 9780444520050 Vol. 95, History of Neurology, S. Finger, F. Boller and K.L. Tyler, eds. ISBN 9780444520081 Vol. 96, Bacterial Infections of the Central Nervous System, K.L. Roos and A.R. Tunkel, eds. ISBN 9780444520159 Vol. 97, Headache, G. Nappi and M.A. Moskowitz, eds. ISBN 9780444521392
Foreword
We spend about one-third of our life either sleeping or attempting to do so. Sleep is not only comforting, but is also essential for our normal cognitive functioning and for our survival. Yet sleep can be disturbed or abnormal in up to one-quarter of the US population. The field of sleep medicine has developed dramatically in the past few years. To reflect these advances, we are proud to introduce the present two volumes, which are a novelty in several respects. It is the first time that two Handbook volumes have been dedicated entirely to sleep and its disorders. Readers will find in these two volumes considerable emphasis on recent developments in the field. There is a new focus on diagnostic techniques, particularly imaging. Fresh attention is given to genetics and clinical aspects of sleep. Finally, there is extensive coverage of management and of new therapeutic strategies for sleep disorders. The volumes were edited by Pasquale Montagna and Sudhansu Chokroverty. As series editors, we reviewed all the chapters and made suggestions for improvement, but we are delighted that the volume editors and chapter authors produced such scholarly and comprehensive accounts of different aspects of sleep and its disorders. Hence we hope that these volumes will appeal to clinicians and neuroscientists alike. Significant new advances, particularly in terms of diagnosis and therapy, lead to new insights that demand a critical appraisal. Our goal is to provide basic researchers with the foundations for new approaches to the study of these disorders, and clinicians with a state-of-the-art reference that summarizes the clinical features and management of the many neurological manifestations of sleep disorders. In addition to the print form, the Handbook series is now available electronically on Elsevier’s Science Direct site. This should make it even more accessible to readers and should facilitate searches for specific information. We are grateful to the two volume editors and to the numerous authors who contributed their time and expertise to summarize developments in their field and helped put together these outstanding volumes. As always, we are grateful to the team at Elsevier and in particular to Mr. Michael Parkinson, Ms. Caroline Cockrell, and Mr. Timothy Horne for their unfailing and expert assistance in the development and production of these volumes. Michael J. Aminoff Franc¸ois Boller Dick F. Swaab
Preface
Sleep has been mentioned in art, literature, religion, and philosophy since antiquity, but a long period of ignorance and a lack of interest paralyzed the scientific community until recently. There has been an explosion of information about sleep medicine and sleep research in the past three decades, making it difficult to keep abreast of progress. There is therefore a need for a comprehensive book on sleep medicine and sleep science. Sleep researchers have made remarkable progress in the last century in unraveling the mysteries of sleep, including its molecular neurobiology and functional neuroanatomy. The 1930s to 1950s was an active period for sleep research, and, since the late 1990s, there has been a resurgence of interest in the neurobiology of sleep. The twenty-first century is witnessing the continuation of such progress. Advances have occurred in basic science, clinical aspects, laboratory techniques, and therapy. Advances in basic science include new understanding of the neurobiology of sleep–wakefulness, including new models of rapid eye movement (REM) sleep mechanisms; controversy about sleep states, stages, and memory consolidations; advances in the understanding of sleep–wake-dependent genes, gene products, and the circadian clock, and the role of sleep duration in mortality and morbidity; and fascinating noninvasive neuroimaging studies (particularly positron emission tomographic and single photon emission computed tomographic scans) visualizing marked changes in function in cortical and subcortical neuronal networks in different sleep states. Advances in clinical science include new understanding of the neurobiology of narcolepsy-cataplexy, restless legs syndrome, REM behavior disorders, and fatal familial insomnia. Further clinical advances have been made in our understanding of sleep apnea and heart failure, and nocturnal paroxysmal dystonia (now known as nocturnal frontal lobe epilepsy), and in describing new parasomnias and acquiring new knowledge about the genetics of sleep disorders. These clinical advances required revision of the International Classification of Sleep Disorders in 2005. New laboratory techniques (e.g., actigraphy, cyclic alternating pattern recognition and scoring in the electroencephalogram, peripheral arterial tonometry, and pulse transit time), in addition to the gold-standard techniques of polysomnography, with advances in ambulatory recordings, multiple sleep latency, and maintenance of wakefulness tests, expanded the horizon of the field of sleep medicine. Publication of the American Academy of Sleep Medicine (AASM) Manual for Scoring of Sleep and Associated Events in 2007 was a step towards standardization of the techniques. Finally, significant advances have been made in therapy, with the addition of new drugs for treating narcolepsycataplexy, insomnia, and restless legs syndrome. Considerable improvement has been made in treating central and upper-airway obstructive sleep apnea syndrome with the addition of bi-level positive airway pressure, flexible positive airway pressure, autotitrating continuous positive airway pressure, assisted servo-ventilation, and intermittent positive pressure ventilation for treating sleep-disordered breathing in neuromuscular disorders. Application of appropriately timed bright light therapy for circadian rhythm sleep disorders is also a significant therapeutic contribution of modern sleep science. It is therefore an opportune moment to produce a comprehensive volume on sleep disorders, addressing all these recent advances in basic, technical, clinical, and therapeutic issues. When we first drafted a preliminary list of topics, it immediately became obvious that a single volume, as originally conceived, would not be enough to cover the topic in the Handbook of Clinical Neurology (HCN) series. This series is widely regarded as the ultimate reference work of clinical neurology and it is found in every medical library. However, the previous two series of the HCN were organized by disease, and neither in the first nor second series was any volume specifically dedicated to sleep disorders. This absence was probably due to inadequate knowledge and awareness about sleep disorders within the context of classic neurological diseases at that time.
x
PREFACE
Despite all the progress, two vexing questions remain: What is sleep and why do we sleep? What happens if we are sleep-deprived? In animal experiments Rechtschaffen’s rats on carousel (“disk over water”), deprived of REM and non-REM sleep, lost weight despite eating excessively and died. REM-deprived rats survived longer than nonREM-deprived rats. In later experiments by other investigators using different sleep deprivation techniques, rats did not show a similar syndrome. Furthermore, adult and newborn dolphins survive with no ill effects after long periods (weeks) without sustained sleep. Awareness of the importance of sleep leads to an acceptance of sleep medicine as an independent specialty. There are new guidelines for practicing sleep medicine developed by the AASM and European Sleep Research Society. Other countries are also in the process of developing guidelines independently or in collaboration with the World Association of Sleep Medicine and other national and international organizations. In these two volumes devoted to sleep disorders, nationally and internationally known scholars, researchers, clinicians, and educators address various aspects of sleep disorders medicine to keep sleep clinicians and researchers, and all those interested in sleep, abreast of recent developments. We, the editors, owe these authors an enormous amount of gratitude for their excellent contributions, which we hope will make these two volumes authoritative reference books. They will be useful to those practicing neurology and internal medicine, especially those in pulmonary, cardiovascular, gastrointestinal, renal and endocrine specialties, and to family physicians, psychiatrists, otolaryngologists, pediatricians, dentists, psychologists, and to neurosurgeons and neuroscientists, as well as technologists, nurses, respiratory therapists, and other paraprofessionals with an interest and curiosity about the mysteries of sleep. Pasquale Montagna Sudhansu Chokroverty
Acknowledgments
We thank all of the authors for their scholarly contributions and patience in waiting to see these two volumes finally in production after a long and protracted period (beyond our control). We also thank all the authors, editors, and publishers who have granted us permission to reproduce illustrations that were published in other books and journals. We must thank Mike Parkinson, development editor for the Handbook of Clinical Neurology, for his dedication and professionalism, and the editorial and production staff at Elsevier B.V. Dr. Montagna would like to express his gratitude and love to his family and in particular to his wife, Flavia Valentini, for her continued support throughout the long time it took to edit the books, and especially for her unfailing assistance in a time of severe personal adversities. Dr. Chokroverty wishes to thank Annabella Drennan, the editorial assistant to the journal Sleep Medicine, for assisting in proofreading and corrections of many of the chapters, and, his wife, Manisha Chokroverty, MD, for her love, patience, tolerance, and continued support throughout the long period of editing and proofreading during the production of these volumes.
List of contributors
Sonia Ancoli-Israel Department of Psychiatry, University of California, San Diego, CA, USA
Chiara Cirelli Department of Psychiatry, University of Wisconsin, – Madison, WI, USA
Laurent Argaud Emergency and Intensive Care Department, Edouard Herriot Hospital, Lyon, France
Deirdre A. Conroy University of Michigan Addiction Research Center, Ann Arbor, MI, USA
Veronique Bach Laboratory DMAG-INERIS (EA 3901), Faculty of Medicine, University of Picardy Jules Verne, Amiens, France
Jana R. Cooke Division of Pulmonary and Critical Care Medicine, University of California, San Diego, CA, USA
Alexander A. Borbe´ly Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland Kirk J. Brower University of Michigan Addiction Research Center, Ann Arbor, MI, USA Peter R. Buchanan Woolcock Institute of Medical Research, University of Sydney, Department of Respiratory Medicine, Liverpool Hospital and Sleep Medicine Consultative Service, St. Vincent’s Clinic, Sydney, Australia Virginie Cardot Laboratory DMAG-INERIS (EA 3901), Faculty of Medicine, University of Picardy Jules Verne, Amiens, France
Thanh Dang-Vu Cyclotron Research Centre, University of Lie`ge, Lie`ge, Belgium Virginia de los Reyes Stanford University Sleep Medicine Program, Stanford, CA, USA Martin Desseilles Cyclotron Research Centre, University of Lie`ge, Belgium F. Dijoud Department of Pathology, Hoˆpital Femme-Me`re-Enfant, Universite´ Lyon 1, Lyon, France Alan S. Eiser Department of Neurology and Department of Psychiatry, University of Michigan Medical Center, Ann Arbor, MI, USA
Karen Chardon Laboratory DMAG-INERIS (EA 3901), Faculty of Medicine, University of Picardy Jules Verne, Amiens, France
John A. Fleetham Department of Medicine, University of British Columbia, Vancouver, Canada
Ronald D. Chervin Department of Neurology, University of Michigan, Ann Arbor, MI, USA
Nancy Foldvary-Schaefer Sleep Disorders Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
xiv LIST OF CONTRIBUTORS Patrice Fort A. Kahn (deceased) UMR5167 CNRS, Institut Fe´de´ratif des Neurosciences Pediatric Sleep Unit, Children’s University Hospital, de Lyon (IFR 19), Universite´ Claude Bernard Lyon I, Free University of Brussels, Brussels, Belgium Lyon, France Ineko Kato P. Franco Department of Pediatrics, Nagoya City University Pediatric Sleep Unit, Hoˆpital Femme-Me`re-Enfant, Medical School, Nagoya, Japan SIDS Reference Center of Lyon & INSERM-628, Universite´ Lyon 1, Lyon, France Douglas B. Kirsch David Gozal Department of Pediatrics, Comer Children’s Hospital, University of Chicago, Chicago, IL, USA J. Groswasser Pediatric Sleep Unit, Children’s University Hospital, Free University of Brussels, Brussels, Belgium Ronald R. Grunstein Woolcock Institute of Medical Research, University of Sydney and Sleep Investigation Unit, Department of Respiratory and Sleep Medicine, Royal Prince Alfred Hospital, and Sleep Medicine Consultative Service, St Vincent’s Clinic, Sydney, Australia Christian Guilleminault Stanford University Sleep Medicine Program, Stanford, CA, USA Viktor Hanak Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA Kristyna M. Hartse Sonno Sleep Center, El Paso, TX, USA Max Hirshkowitz Department of Medicine & Menninger Department of Psychiatry, Baylor College of Medicine, and Michael E. DeBakey VAMC Sleep Center, Houston, TX, USA Shahrokh Javaheri University of Cincinnati College of Medicine, and Sleepcare Diagnostics, Cincinnati, OH, USA
Department of Neurology, University of Michigan, Ann Arbor, MI, USA James M. Krueger Department of Veterinary and Comparative Anatomy, Pharmacology and Physiology, Washington State University, Pullman, WA, USA B. Kugener Pediatric Sleep Unit, Hoˆpital Femme-Me`re-Enfant, SIDS Reference Center of Lyon & INSERM-628, Universite´ Lyon 1, Lyon, France Carol A. Landis Department of Biobehavioral Nursing and Health Systems, University of Washington, Seattle, WA, USA Peretz Lavie Sleep Medicine Center, Rambam Hospital and Lloyd Rigler Sleep Apnea Research Laboratory, Ruth and Bruce Rappaport Faculty of Medicine, Technion- Israel Institute of Technology, Haifa, Israel P. Le´vy Pulmonary Function Test and Sleep Laboratory, Department of Rehabilitation and Physiology and HP2 Laboratory, INSERM-ERI 17, University Hospital, Grenoble, France Jean- Pierre Libert Laboratory DMAG-INERIS (EA 3901), Faculty of Medicine, University of Picardy Jules Verne, Amiens, France
Erin A. Johnson Division of Sleep Medicine, Brigham and Women’s Hospital, Boston, MA, USA
J.S. Lin INSERM-628, Institut Fe´de´ratif des Neurosciences de Lyon (IFR 19), Universite´ Lyon 1, Lyon, France
Barbara E. Jones Department of Neurology and Neurosurgery, McGill University, Montreal Neurological Institute, Montreal, Quebec, Canada
Pierre-Herve´ Luppi UMR5167 CNRS, Institut Fe´de´ratif des Neurosciences de Lyon (IFR 19), Universite´ Claude Bernard Lyon I, Lyon, France
LIST OF CONTRIBUTORS xv Jeannine A. Majde J.L. Pe´pin Department of Veterinary and Comparative Anatomy, Pulmonary Function Test and Sleep Laboratory, Pharmacology and Physiology, Washington State Department of Rehabilitation and Physiology and HP2 University, Pullman, WA, USA Laboratory, INSERM-ERI 17, University Hospital, Grenoble, France M. Mahmood Kevin R. Peters Kaiser Permanente South San Francisco Medical Department of Psychology, Trent University, Center, South San Francisco, CA, USA Peterborough, Canada Beth Malow Department of Neurology and Sleep Disorders Program, Vanderbilt University Medical Center, Nashville, TN, USA Pierre Maquet Cyclotron Research Centre, University of Lie`ge, Belgium Robert W. McCarley Neuroscience Laboratory and Harvard Department of Psychiatry, VA Boston Healthcare System, Brockton, MA, USA Enza Montemitro Department of Paediatric, Sleep Disease Centre, University of Rome “La Sapienza”-S Andrea Hospital, Rome, Italy Hawley E. Montgomery-Downs Departments of Psychology and Pediatrics, West Virginia University, Morgantown, WV, USA Tryggve Neve´us Uppsala University Children’s Hospital, Uppsala, Sweden William C. Orr Lynn Health Science Institute and Oklahoma University Health Sciences Center, Oklahoma City, OK, USA Allan I. Pack Division of Sleep Medicine and Center for Sleep and Respiratory Neurobiology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA Markku Partinen Helsinki Sleep Clinic, Vitalmed Research Center, and Department of Neurology, University of Helsinki, Helsinki, Finland.
Giora Pillar Sleep Medicine Center, Rambam Hospital and Lloyd Rigler Sleep Apnea Research Laboratory, Ruth and Bruce Rappaport Faculty of Medicine, Technion- Israel Institute of Technology, Haifa, Israel A. Raoux Pediatric Sleep Unit, Hoˆpital Femme-Me`re-Enfant, SIDS Reference Center of Lyon & INSERM-628, Universite´ Lyon 1, Lyon, France David M. Rector Department of Veterinary and Comparative Anatomy, Pharmacology and Physiology, Washington State University, Pullman, WA, USA Lisa M. Richards Division of Sleep Medicine, Brigham and Women’s Hospital, Boston, MA, USA Dominique Robert Emergency and Intensive Care Department, Edouard Herriot Hospital, Lyon, France V.S. Rotenberg Department of Psychiatry, Tel Aviv University, Tel Aviv, Israel S. Scaillet Pediatric Sleep Unit, Children’s University Hospital, Free University of Brussels, Brussels, Belgium Thorsten Scha¨ffer Medical Faculty, Ruhr-University Bochum, and Institute of Clinical Physiology, Helios Klinik Hagen-Ambrock, Germany Mark S. Scher Division of Pediatric Neurology, Rainbow Babies and Children’s Hospital, University Hospitals of Cleveland, Case-Western Reserve University, Cleveland, OH, USA
xvi LIST OF CONTRIBUTORS Amir Sharafkhaneh Irene Tobler Department of Medicine, Baylor College of Medicine, Institute of Pharmacology and Toxicology, University Michael E. DeBakey VAMC Sleep Center and of Zurich, Zurich, Switzerland Methodist Hospital Sleep Diagnostic Laboratory, Houston, TX, USA Giulio Tononi Department of Psychiatry, University of Wisconsin – Carlyle Smith Madison, Madison, WI, USA Department of Psychology, Trent University, Peterborough, Canada Pierre Tourneux Laboratory DMAG-INERIS (EA 3901), Faculty of Medicine, University of Picardy Jules Verne, Amiens, Mark Solms France Department of Psychology, University of Cape Town, Rondebosch, South Africa Eus J.W. van Someren Netherlands Institute for Neuroscience, an Institute of Virend K. Somers the Royal Netherlands Society of Arts and Sciences; Division of Cardiovascular Diseases, Mayo Clinic, Department of Integrative Neurophysiology, VU Rochester, MN, USA University and Leiden Institute for the Clinical and Experimental Neuroscience of Sleep, Leiden University Alex Steiger Medical Center, The Netherlands Max Planck Institute of Psychiatry, Munich, Germany R. Tamisier Pulmonary Function Test and Sleep Laboratory, Department of Rehabilitation and Physiology and HP2 Laboratory, INSERM-ERI 17, University Hospital, Grenoble, France Frederic Telliez Laboratory DMAG-INERIS (EA 3901), Faculty of Medicine, University of Picardy Jules Verne, Amiens, France Michael J. Thorpy Sleep–Wake Disorders Center, Montefiore Medical Center, New York, NY, USA
Joyce A. Walsleben Division of Pulmonary, Critical Care and Sleep Medicine, New York University School of Medicine, New York, NY, USA John W. Winkelman Harvard Medical School and Division of Sleep Medicine, Brigham and Women’s Hospital, Boston, MA, USA
Handbook of Clinical Neurology, Vol. 98 (3rd series) Sleep Disorders, Part 1 P. Montagna and S. Chokroverty, Editors # 2011 Elsevier B.V. All rights reserved
Chapter 1
History of sleep medicine MICHAEL J. THORPY * Sleep—Wake Disorders Center, Montefiore Medical Center, and Albert Einstein College of Medicine, New York, NY, USA
Sleep; King of all the gods and of all mortals, hearken now, prithee, to my word; and if ever before thou didst listen, obey me now, and I will ever be grateful to thee all my days (Homer, 14th book of the Iliad: Mueller, 1984). Only a few physiological conditions have received as much attention from poets, novelists, scholars, and scientists as sleep. Writers from Aristotle and Ovid to Shakespeare and Dante have been fascinated by sleep and its impact upon our emotions, behavior, and health. Causes and reasons for sleep have been pondered by some of the world’s greatest minds. Regardless of what the reason is, it is likely that sleep and dreams developed in animals because they were of some evolutionary benefit. Not only has sleep evolved through the ages but the environment for sleep has also undergone a change. From communal sleeping rooms with beds of twigs, straw, or skins, the bedroom has changed in the 21st century into a private place with electronic equipment, including remote-controlled television, DVD players, internet access, and even exercise equipment. The size of bedrooms has enlarged over the years. A rudimentary understanding of insomnia and sleepiness was known in ancient times, but specific sleep disorders, such as narcolepsy, began to be recognized only in the late 19th century. Differentiation between causes of sleepiness and insomnia has reached a peak within the last 50 years since the development of sophisticated technology for the investigation of sleep. Although most sleep disorders have probably been present since humans evolved, modern society has inadvertently produced several new disorders. The electric light bulb, developed by Thomas Edison, has allowed the light of day to be extended into night so that shift work can now occur around the clock, but at the expense of circadian rhythm disruption and
*
sleep disturbance. Similarly, international jet travel has enabled the rapid crossing of time zones, which also can lead to a disruption of circadian rhythms and to sleep disturbance. Scientific investigation has produced more information on the physiology and pathophysiology of sleep in recent years than ever before. This rapid advance in sleep research and the development of sleep disorders medicine are producing answers to questions that date from antiquity.
SLEEP IN PREHISTORIC AND ANCIENT TIMES Sleep’s the only medicine that gives ease (Sophocles, Philoctetes: Lloyd Jones, 1994). The sleep patterns and sleep disorders of prehistoric humans are unknown, and therefore we must speculate from the comparative physiology of animals and from evidence of other behaviors and illnesses. Theories on the phylogenetic development of sleep stages in mammals have been developed from information available on the mammal-like reptiles. The earliest form of life developed about 600 million years ago in the pre-Cambrian period, and mammal-like reptiles evolved approximately 250 million years ago. The monotremes (egg-laying mammals) evolved as a separate line from the therian (live-bearing) mammals about 180 million years ago. It is about this time when it is believed that slow-wave sleep appeared; rapid eye movement (REM) sleep (paradoxical sleep) appeared about 50 million years later. Recent sleep research on one of the three surviving monotremes, the Australian short-nosed echidna and platypus, has provided some of the evidence for the evolution of sleep stages. The monotremes have high-voltage REM sleep, which suggests that REM sleep may have had its origin in reptilian ancestors (Karmanova, 1982; Siegel et al., 1998).
Correspondence to: Michael J. Thorpy, M.D., Sleep–Wake Disorders Center, Montefiore Medical Center, 111 East 210th Street, Bronx, NY 10467, USA. Tel: 718-920-4841, Fax: 718-798-4352, E-mail:
[email protected] 4
M.J. THORPY
The pattern of sleep and waking behavior in prehistoric humans can be deduced from studies of animal groups phylogenetically closest to humans, namely nonhuman primates, such as apes and Old World monkeys. Sleep–wake patterns in nonhuman primates consist mainly of polyphasic episodes of rest and activity with frequent (up to 12) cycles of wakeful activity throughout the 24-hour day. Humans have the most developed monophasic pattern, with one episode of consolidated sleep and one main episode of wakefulness. Some animals, e.g., the chimpanzee, have a biphasic sleep–wake pattern, with a nap taken during the daytime. The chimpanzee has a rather prolonged sleep episode from dusk to dawn of approximately 10 hours; however, during this time there are frequent, brief awakenings. The daytime is characterized by two long episodes of wakefulness and an approximately 5-hour midday nap, which also includes frequent, brief wakefulness episodes. This type of sleep pattern may have the advantage of providing some security from predators. Extrapolating from nonhuman primate studies, it seems likely that a similar polyphasic sleep pattern was likely to have been present in earliest humans (prior to the Neolithic period), particularly if they also attempted to sleep between dusk and dawn. There would have been frequent awakenings during the major sleep episode, as a single sleep episode of more than 10 hours appears unlikely. The monophasic sleep–wake pattern probably began in the latter part of the Neolithic period (since 10 000 BC). Neanderthal humans (70 000–40 000 BC) may well have been in a transitional stage between a polyphasic sleep pattern and the monophasic pattern seen today. Prehistoric humans may have attempted to treat sleep disturbances, but how early they would have done this is unknown. Therapy probably resembled that utilized by sick animals, such as the removal of infective agents, eating various plants to induce emesis, and possibly even bloodletting. Bloodletting became an increasingly frequent therapeutic means for treating disease, including sleep disorders, in more advanced ancient civilizations. Primitive societies, even today, consider many illnesses and diseases to be caused by gods, magic, and spirits, and therefore various forms of divination, such as the casting of bones, moving of beads, charms, fetishes, chanting or the use of elaborate ceremonies, are invoked for therapeutic reasons. For disturbances of sleep and wakefulness, prehistoric humans probably applied similar forms of treatment.
Ancient Egypt
which was written around 1350 BC, contains information on the interpretation of dreams. Dreams were regarded as being contrary predictions; for example, a dream of death meant a long life. However, an extensive text on a variety of medical subjects, including treatment, the Georg Ebers papyrus (1600 BC), has not been reported to contain any information on sleep disturbances. Ancient Egyptian medical practice consisted largely of praying to the gods and invoking the help of these divine healers. Thoth, who was a physician to the gods, and Imhotep were both important gods of healing at that time. The ancient Egyptians were known for their attention to hygiene and cleanliness, and it is likely that such attention was also paid to sleeping habits. Medical opinion at the time held that the body was made up of a system of channels (Metu), which conveyed air to all parts of the body. Because they believed that bodily fluids could enter this system of channels, the ancient Egyptians were particularly concerned about feces entering the Metu. Hence, purging and enemas were the treatment modalities of many illnesses of that time, which included infective illnesses, such as malaria, parasitic infections, smallpox, and leprosy. Wine and other mildly alcoholic drinks (as compared to distilled alcoholic products) were consumed in large amounts and were probably the earliest treatments for insomnia but also may have been important in its development. Medicinal plants were utilized, particularly the product of the opium poppy (Papaver somniferum), and hyoscyamine and scopolamine, derived from belladonna and nightshade (Gunther, 1959). The word “opium” is derived from the Greek word for “juice,” as the drug is derived from the juice of the poppy. Papaver somniferum was coined at a much later date; somniferum was derived from the Latin word Somnus (the Roman god of sleep). In subsequent periods in history opium (laudanum) was widely used as a treatment for insomnia, and it is likely that it was used as far back as the Sumerian age. Accordingly, opium may have been the first hypnotic medication used. Another common treatment performed by the ancient Egyptians for a variety of ailments and illnesses was bloodletting. This was likely to have been used for sleep disorders, particularly for those disorders that produced excessive sleepiness or stupor. Medical treatment by physicians was widely available during this time. In fact, the names of several hundred physicians have been documented in ancient Egypt. Herodotus (fifth century BC) wrote of the Egyptians:
Most of our current knowledge of ancient Egyptian medicine derives from the ancient medical papyruses of Egypt (Ebbell, 1937). The Chester Beatty papyrus,
Medicine with them is distributed in the following way: every physician is for one disease and not for several, and the whole country is full of
HISTORY OF SLEEP MEDICINE physicians for the eyes; others of the head; others of the teeth; others of the belly, and others of obscure diseases (Grene, 1987). It appears likely that some physicians specialized in insomnia, and possibly even in disorders that produced excessive sleepiness. There certainly were physicians who specialized in dream interpretation, for example Artemidorus of Daldis, who wrote the major work on dreams, Oneirocritica (White, 1975).
Ancient India Other civilizations, such as those of ancient India and China, developed around the same time. In India, as in Egypt, infective illnesses were common, and therefore physicians, who were largely from the Brahman or priestly caste, were viewed with great importance. Medical practice mainly consisted of magical and religious practices but also featured soundly based, rational treatments. Over 700 Indian vegetable medicines have been documented from ancient times and include the plant called Rauwolfia serpentina (reserpine). Rauwolfia was used for the treatment of anxiety (and is currently being used for hypertension in some parts of the world) among other disorders, and is likely to have been used to treat insomnia (its side-effects include drowsiness).
Ancient China The ancient Chinese viewed sleep as a state of unity with the universe: everything is one; during sleep the soul undistracted, is absorbed into the unity; when awake, distracted it sees the different beings (Chinese philosopher Chuang Tzu, 300 BC: Palmer et al., 2006). The ancient Chinese believed in the importance of the universe and environment in producing all things, including behavior and health. The basic principles of life were thought to derive from the interplay of two basic elements in nature, the active, light, dry, warm, positive, masculine yang, and the passive, dark, cold, moist, negative yin. The proportions of yin and yang determined the Tao (the way), which determined right and wrong, good and bad, health or illness. The basic yin–yang symbol is attributed to Fu Hsi (c. 2900 BC), who originated the concept of eight interacting conditions, the “Pa kua.” The yin–yang has since become the symbol for sleep and wakefulness. (This yin–yang symbol has been adopted by the American Academy of Sleep Medicine as its emblem.) Chinese views on physiology were similar to those of the ancient Greeks;
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they also believed in a humoral system of physiology. The palpation of the pulse was important in the diagnosis of disease, as were the patient’s symptoms, the patient’s social and economic status, the weather, and particularly the patient’s dreams, as well as the dreams of other family members. These were all taken into consideration to determine whether a patient had upset the Tao. The most important medical compendium of the time was that produced by Yu Hsiung (c. 2600 BC), the Nei Ching (Canon of Medicine). There is a great deal of controversy over the authorship of this text. It mentioned five important methods of treatment: curing the spirit, nourishing the body, the administration of medications, treating the whole body, and the use of acupuncture and moxibustion (counterirritation by moxa, a combustible substance that is burned on the skin). Acupuncture and a modified form of moxibustion are used today for the treatment of insomnia and other sleep disturbances in traditional Chinese medicine. When these therapies were first established (at least since c. 2600 BC), it follows that they were most likely applied to sleep disorders as well. Massage and breathing exercises were also commonly employed for various reasons, in a manner similar to yoga – these are therapies regularly used today for the treatment of some types of insomnia. In addition to acupuncture, moxibustion, massage, and breathing exercises, the ancient Chinese had a plethora of herbal medicines. Herbal medicines consisted of extracts of virtually anything available, including minerals and metals, animal-derived products, and waste products (Gunther, 1959). Two of these herbal remedies are worth noting. One was ephedra (Ma Huang), which is believed to have been used for over 4000 years. It is a stimulant that contains ephedrine, derived from the horsetail plant and first described by the Red Emperor, Shen Nung (c. 2800 BC). Ancient Chinese physicians used it for the treatment of asthma, hayfever, and nasal and chest congestion. It is reasonable to believe that it may have been used for the treatment of other breathing disorders of sleep as well. The second common medicinal herb was ginseng (a man-shaped root), which was used for a variety of ailments, including pulmonary problems and gastrointestinal disorders. It is also thought to heighten vitality and reduce fatigue and sedation (its role in excessive somnolence due to many causes including sleep disorders is thus apparent). Acupuncture was widespread and is believed to have been developed by the Yellow Emperor (Huang Ti) around 2600 BC (Veith, 1949). Acupuncture and moxibustion were used for treating virtually every illness and symptom and therefore may well have been administered for sleep disorders.
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Ancient Greece Much of what we know about early Greek medicine is derived from the Iliad and Odyssey of Homer, a collection of traditions, legends, and epic poems. Homer (c. 900 BC) based his epic works on the life of the ancient Greeks in the days of the Mycenaean Citadel of about 1200 BC. The Mycenaeans, who came from mainland Greece about 1600 BC, conquered the Minoans, who had established a well-developed civilization in Crete at Knossus. This civilization was the setting for Homer’s epics, which concerned an earlier period, but his writings included medical details that were probably derived from his own era. However, Homer’s view of medicine in early Greece, called homeric medicine, is the best representation of early Greek medical practices. The quotation from the Iliad stated at the beginning of this chapter reflects the importance that Homer ascribed to good-quality sleep. The god of sleep, Hypnos, from whom the terms hypnotic and hypnotism have derived, was first reported in the 14th book of the Iliad by Homer, and was mentioned again in the Theogony of Hesiod (c. 700 BC) about two centuries later (Wittern, 1989). Also mentioned in Homer was the chieftain Asclepios and his two sons, Machaon, who in subsequent centuries became known as the father of surgery, and Podalirios, the father of internal medicine (Figure 1.1). In subsequent years, Asclepios became known as the god of healing, and temples were erected in his honor, the first being established about the sixth century BC in Thessaly or Epidauros. The Asclepieian temples were a collection of several buildings that in many cases were very elaborate and ornate. They consisted of a tholos, a round building that contained water for purification, and a main temple, which were separated by a building called the abaton. The abaton was a most important structure as it was the site where ill patients were placed for a cure. The cure consisted of an “incubation” ceremony in which the cure took place in each worshipper’s dreams. The medical ceremony began at dusk and the ill patient lay on a bed of skins to await a visit by Asclepios, the god of healing. During the night the priest would visit each patient and administer a treatment, which often consisted of medicines derived from animals such as snakes and geese. Upon awakening the next morning after dreaming of Asclepios, the patient was expected to have been cured. This treatment was clearly the forerunner of sleep therapy, which has been practiced through the ages until the present day, particularly in eastern countries. Although Asclepieian medicine was used to treat any type of illness, it was most effective for those of a psychological nature. Much of the healing was probably related to
Fig. 1.1. Asclepios.
the impressive ceremony and the relaxation that occurred in conjunction with the setting. The priestphysicians instilled faith in the cures, not only to their patients but also to themselves. However, many attempted cures were in the realm of magic and fantasy. A more rational style of medicine developed around the fifth century BC largely due to the influence of the Greek scientist-philosophers. Alcmaeon (fifth century BC), of the Crotona school of medical thought, concentrated on humans, and his basic belief was that health was harmony and disease was a disturbance of harmony (Freeman, 1966). He considered the brain essential for memory and thought, a notion that Aristotle, who believed that the mind resided in the heart, would reject 100 years later. Alcmaeon proposed what was probably the first theory on the cause of sleep, when he postulated that sleep occurred when the blood moved away from the surface of the body to the deeper vessels, including those going to the brain; withdrawal of blood from the brain and inner vessels was associated with waking. However, his major contribution to medicine
HISTORY OF SLEEP MEDICINE was the detailed description of the optic pathways at the base of brain. His much more rational concepts of medicine have led some to consider him the first true medical scientist. Around the time of Alcmaeon, a center of medicine was established in Sicily, and Empedocles (c. 493–443 BC) was credited with the original concept that all things are composed of four basic elements: water, air, fire, and earth (the importance of these four elements had been established earlier: Freeman, 1966). Empedocles believed that sleep occurs when the fire in the blood cools, thus separating one of the four elements from the others. He believed that illnesses were due either to separation of the four elements or to alterations in their balance. The principle of the balance of body humors, known as humoralism, became established medical doctrine around this time. Humoralism considered health to be due to the balance of four body fluids: blood, phlegm (water), yellow bile (“choler,” secreted by the liver) and black bile (“gall,” secreted by the spleen and kidneys). These fluids were usually seen during severe illnesses and disappeared when the crises were over. In whatever disease sleep is laborious, it is a deadly symptom (Hippocrates, Aphorisms II: Adams, 1891). Hippocrates (460–370 BC), born on the island of Cos, has had more influence upon medicine than any other individual in history. He produced many of the basic tenets that underlie the practice of modem medicine. Hippocrates produced numerous works that are gathered under the title Corpus Hippocraticum, which comprises not only his own writings but also the writings of others of the time (Chadwick et al., 1978). His approximately 72 books covered all aspects of medicine, including medical ethics, and are most widely known for the hippocratic oath. In his writings, Hippocrates discussed not only his theory of the cause of sleep, but also made suggestions on the cause of dreams, which he considered to be of “medical” origin. Hippocrates stated that: “sleep is due to blood going from the limbs to the inner regions of the body.” This statement was based upon the recognition of the importance of the blood being warmed by the inner part of the body in order to produce sleep. Hippocrates also alluded to some diseases of sleep of the time when he spoke of epilepsy (which appear to be descriptive of sleep apnea and the non-REM arousal disorders): I have known many persons in sleep groaning and crying out, some in a state of suffocation, some jumping up and fleeing out of doors, and deprived of their reason until they awaken, and
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afterward becoming well and rational as before, although they be pale and weak; and this will happen not once but frequently (Adams, 1891). Hippocrates believed that narcotics derived from the opium poppy could be useful in treatment; therefore, they were most likely applied to treat insomnia at that time. Other philosophers, such as Diogenes (c. 480 BC) and Heraclitus (c. 450 BC), believed sleep was an incomplete “humidification of the bodily soul” and death was the complete humidification. Following these philosophers, Aristotle (384–322 BC), had an important influence upon medicine. He believed that dreams were important predictors of the future but proposed a theory of sleep based upon the effect of food ingestion (Hett, 1964; Wijsenbeek-Wijler, 1978). He proposed that food, once eaten, induced fumes that were taken into the blood vessels and then transferred into the brain where they induced sleepiness. The fumes subsequently cooled and returned to the lower parts of the body, taking heat away from the brain, thereby causing sleep onset. The sleep process continued as long as food was being digested.
Ancient Rome Greek medicine began to develop in Rome around the time of Hippocrates. Atomism, the concept that all physical objects are comprised of atoms in an infinite number that undergo random motion, was first developed by Democritus of Abdera (c. 420 BC) and Leucippus of Miletus (c. 430 BC). Leucippus regarded sleep as a state caused by the partial or complete splitting-off of atoms. Democritus considered insomnia to be the result of an unhealthy diet and daytime sleeping as being a sign of ill health. Epicurus (c. 300 BC) revived the theory of atomism and wrote extensively on sleep and dreams, although his own works have been lost. The Roman poet Titus Lucretius Carus (c. 50 BC) wrote of the teachings of Epicurus on atomism, sleep, and dreams in a poem entitled “De rerum natura.” In this poem, the loss of central control that leads to loss of peripheral muscle control and relaxation forms the foundation of a neural theory of sleep that took 2000 years to be expanded upon: And so, when the motions are changed, sense withdraws deep within. And since there is nothing which can, as it were, support the limbs, the body grows feeble, and all the limbs are slackened; arms and eyelids droop, and the hams, even as you lie down, often give way, and relax their strength (On the Nature of the Universe: Lucretius, 1994). Asclepiades of Bithynia (c. 120–70 BC), another figure in Roman medicine, believed that the physician was
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more important in curing disease than was nature. He used the term “phrenitis” for mental illness and invoked treatment that consisted of hygiene, opium, and wine. He was also the first to popularize the tracheostomy as a treatment for upper-airway obstruction resulting in apnea. The Greek philosopher and physician Galen (AD 129–c. 200) had a great impact on the development of medicine. Galen’s detailed writings substantially contributed to the knowledge of anatomy and he also outlined the important elements of diagnosis and treatment (Siegel, 1973, 1976). He believed bloodletting was important in the treatment of many illnesses, but he also encouraged conservative treatments, such as diet, rest, and exercise. He also spoke about dream interpretations and utilized many herbal medicines, e.g. valerian for the treatment of insomnia. In both ancient Rome and ancient Greece the similarity between death and sleep was often emphasized. Sleep and death, who are twin brothers (Homer, Iliad, c. 850 BC: Mueller, 1984). What else is sleep but the image of chill death? (Ovid, Amores 11, 43 BC–AD 17: Simpson, 2001).
Sleep in the Bible Similarly, in the Bible, death was described as being similar to sleep in that it was God who caused people to awaken from sleep; without Him they would never wake (Psalms 76:6). However, death was also contrasted with sleep in the example of a dead girl, about whom Christ said, “the little girl did not die but she is sleeping” (Matthew 9:24; Mark 5:39; Luke 8:52). This may have referred to the fact that she could be resurrected from death as one is awakened from sleep. The Bible also contains numerous references to sleep and dreams, which were largely regarded as being predictors of the future (but less significant than in previous eras) (Mackenzie, 1965). Dreams also played an important part in the Bible as a means of communication between God and mankind. The first book of the Bible, Genesis (28: 10–16), reports communication between Jacob and God: And Jacob went out from Beresheeba, and went toward Haran. And he lighted upon a certain place, and tarried there all night, because the sun was set; and he took one of the stones of that place and put them for his pillows, and laid down in that place to sleep.
And he dreamed, and behold a ladder set up on the earth, and the top of it reached to heaven: and behold the angels of God ascending and descending on it . . . And Jacob awaked out of his sleep, and he said, surely the Lord is in this place; and I knew it not. Many other examples of dreams are presented in the Bible, such as Joseph’s dream to take Mary as his wife, his dream to flee to Egypt with his family, the dream that it was safe to return home, and the dream of the Magi. Excessive sleeping was regarded as being unacceptable as it produced laziness and could subsequently lead to poverty. Laziness causes a deep sleep to fall (Proverbs 6:9–11, 10:5, 19:15, 20:13, 24:33–34).
SLEEP IN THE MIDDLE AGES AND THE RENAISSANCE Long sleep at after-noones by stirring fumes Breeds Slouth, and Agues Aking heads and Rheumes (School at Salerno, Regimen Sanitatis Salernitanum, 1095–1224: McVaugh, 1980). The time from the fall of Rome in AD 476 until the fall of Constantinople in AD 1453 is often referred to as the Middle Ages, the first 500 years being the Dark Ages. Both Ages comprise the medieval period, the Age of Faith, a time when medicine was greatly influenced by the rise of Christianity. With the spread of the word of Christianity, people were convinced that the day of judgement was about to come, and disease was considered to be God’s punishment. Prayer and good deeds were considered to be important for cures and to prevent illness. Concern for “thy neighbor” led to the establishment of facilities for the care of the ill, most of which were run with religious motives. Medicine involved strong religious mysticism, and there was a loss of the rational, clinical observation and management of disease that had begun to develop in earlier years. Although superstition and magic swept the western world, some physicians such as Avicenna, with skill in observation and deduction, slowly advanced medical knowledge. In the Muslim world, there was a similar religious approach to medicine. Although in Islam, disease was regarded as a punishment by Allah, hospitals in Muslim countries were very much better than those in the west because of their improved sanitation and better and more spacious facilities. At that time physicians
HISTORY OF SLEEP MEDICINE were largely of the Christian and Jewish faiths, but Muslim practitioners gradually helped spread medicine in the east. The Persian Razi (850–c. 923), also known as Rhazes in the west, wrote more than 200 books on many topics, including medicine (Ranking, 1914). Avicenna (980–1037 AD), who also contributed to medical understanding, was regarded both in Islam and Christendom as being of equal importance to Galen (Gruner, 1930, 1970). Included among his many contributions to medicine are the associations between epilepsy and insomnia and sleep deprivation. In the latter part of this era, Moses ben Maimon (1135–1204 AD), also known as Maimonides, emerged as the most influential Jewish physician in Arabic medicine. He appeared to combine the thoughts of Hippocrates, Galen, and Avicenna but his primary focus was on philosophy. Maimonides had his own view of how much and when a person should sleep: The day and night consist of 24 hours. It is sufficient for a person to sleep one third thereof which is eight hours. These should [preferably] be at the end of the night so that from the beginning of sleep until the rising of the sun will be eight hours. Thus he will arise from his bed before the sun rises (Mishneh Torah, Hilchoth De’oth, ch. IV, no. 4). In the 10th century AD, several medical schools came into prominence. Perhaps the first was that established at Salerno, not far from Monte Cassino. The school at Salerno developed a practical scientific approach to medicine, eschewing its neighbors’ concentration on philosophy and religious mysticism. Several universities in France, including those at Montepellier and Paris, were also highly regarded. At Paris, the school had a medical rather than a surgical bias, being more influenced by the church. At Montpellier, Greek practices were more in evidence. By AD 1000, at the end of the Dark Ages, monastic medicine began to decline as the influence of the universities increased. Many hospitals developed that are well known today, such as St. Thomas’s and St. Bartholomew’s in England and the Hoˆtel-Dieu in Paris. Diet was regarded as an important form of treatment, as were medications, particularly those derived from plant materials. One of the most commonly used medications at this time was theriac, which had been developed in the first century AD; it consisted of many substances derived from plants and animals, including snake flesh. Theriac would have been used for the treatment of a variety of sleep disorders, particularly those thought to be caused by poisons. Mysticism and astrology were important elements of medicine in the Middle Ages. Often the most important treatment to be considered
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was exorcism; however, purgatives and bloodletting were treatments that were still commonly employed. In the 15th and 16th centuries, the works of Hippocrates were revived. Paracelsus (1493–1541), known as the father of pharmacology, began using metals in treatment, often producing some outstanding cures (Pachter, 1951). Although illnesses such as leprosy and the plague had largely disappeared, venereal diseases such as gonorrhea and syphilis were rampant. Paracelsus created a remedy that he believed to be “superior to all other heroic remedies” which he called laudanum. Laudanum was originally an extract of opium with brandy combined with other seemingly random ingredients, such as frogspawn. Among other purposes, this potion was used to induce sleep. Art and medicine became allied, as evidenced in the anatomical drawings of Michelangelo Buonarroti (1475–1564) and Albrecht Du¨rer (1471–1528). Andreas Vesalius (1514–1564) produced one of the greatest medical books in history, De Humani Corporis Fabrica (O’Malley, 1965). Its detailed anatomical drawings surpassed those of Galen and Fabricius, and it became the anatomical cornerstone of scientific medicine in the centuries to come.
SLEEP IN THE 17TH AND 18TH CENTURIES Methought I heard a voice cry, “Sleep no more! Macbeth does murder sleep,” the innocent sleep, Sleep that knits up the ravell’d sleeve of care, The death of each day’s life, sore labour’s bath, Balm of hurt minds, great nature’s second course, Chief nourisher in life’s feast (Shakespeare: Macbeth, Act II, c.1605: Coursen, 1997). In the 17th century, medicine underwent a major change from the doctrines that had influenced it up to that time, such as aristotelianism, galenism, and paracelsianism, to more scientifically directed theories, with the underlying teleological desire to accumulate knowledge on the way things work. This time was known as the Age of Scientific Revolution and included the major medical developments of Francis Bacon, William Harvey, and Marcello Malpighi. Medicine in general was now being viewed as an advancement in our control over nature and was more soundly based on scientific principles. However, it was still a time to be speculative and philosophical about medicine: He sleeps well who knows not that he sleeps ill (Francis Bacon, Omamenta Rationalia, IV; quote from Publilius Syrus, Sententiae: Wight Duff and Duff Arnold, 1994).
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M.J. THORPY The scientific revolution began with the theories of when being abed, they betake themselves to Rene´ Descartes (1596–1650), who rejected Aristotle’s sleep, presently in the arms and legs, leapings doctrines and developed theories based on mechanisms and contractions of the tendons, and so great a (Descartes, 1632). In this regard he was similar to restlessness and tossings of their members ensue, Francis Bacon (1561–1626), who espoused experimentathat the diseased are no more able to sleep, than tion and utilitarianism. Descartes developed a hydrauif they were in a place of the greatest torture lic model of sleep, which considered that the pineal (Willis, 1684). gland maintained fullness of the cerebral ventricles Willis also discovered that laudanum, a solution of for the maintenance of alertness. The loss of “animal powdered opium, was effective in treating the spirits” from the pineal causes the ventricles to colrestless-legs syndrome. lapse, thereby inducing sleep. Despite some setbacks, a scientific approach to The chemical principles of Paracelsus were advanced medicine continued with the works of Linnaeus and in the 17th century, and medicines, including the use of von Haller. Karl von Linne (1707–1778), called Linnaeus, mercurials, began to take over from treatments such as made important contributions to the classifications of purging and bloodletting. Illness was now considered botany, zoology, and medicine (Linnaeus, 1751). He to be something that attacked the body in a distinct emphasized the importance of cyclical changes in botmanner, and the galenic and earlier concepts that disease any, which was nowhere more clearly presented than was a derangement of humors, the essential elements of in his flower clock. The flower clock was developed the body, were starting to fade. Atomism, which had upon the principle that different species of flowers been proposed by Democritus, Leucippus, and Epicurus open their leaves at various times of the day. Thereseveral centuries before the time of Christ, underwent a fore, a garden of flowers arranged in a circular patrevival in the 17th century and was supported by the findtern could give an estimate of the time of day by ings of Jan Baptista van Helmont (1577–1644), who the pattern of flower and leaf openings and closings. coined the term “gas” and recognized that air was comLinnaeus’ finding was an important early milestone in posed of a variety of gases. Robert Boyle (1627–1691) the development of the science of biological rhythms demonstrated the importance of air for life and the in plants and animals. As far back as ancient Greece effect of gases under pressure, which led to the discovthere had been some recognition of variation in the ery that the reddening of venous blood occurred because behavior of plants and animals, not only on a seaof exposure of blood to gases contained in the air. Howsonal basis but also on a daily basis. ever, the major discovery of the 17th century was that of One of the first chronobiological experiments was William Harvey (1578–1657), who was the first to demthat of Sanctorious (c. 1657), who measured the cyclionstrate that blood was pumped around the body by cal pattern of change in a number of his own physiothe heart. logical variables. His experimental apparatus has been It was against this background that the great neurolregarded as the first “laboratory for chronobiology.” ogists, Thomas Willis (1621–1675) and Thomas SydenSubsequently the intrinsic pattern of circadian activity ham (1624–1689), developed the principles and was demonstrated in the experiment performed by practice of clinical neurology. Willis made a number Jacques de Mairan in 1729, which was reported by of contributions to the knowledge of various disorders M. Marchant (de Mairan, 1729). De Mairan placed a in sleep, including restless-legs syndrome, nightmares, heliotrope plant in a darkened closet and observed and insomnia. He recognized that a component that the leaves continued to open in darkness, at the contained in coffee could prevent sleep and that sleep same time of day as they had in sunlight. This experiwas not a disease but primarily a symptom of underlyment illustrated the presence of an intrinsic circadian ing causes. His book The Practice of Physick devoted rhythm in the absence of environmental lighting confour chapters to disorders producing sleepiness and ditions. De Mairan also recognized the importance insomnia (Willis, 1684). Like Descartes, he considered of this observation for understanding the behavior of that the animal spirits contained within the body patients: undergo rest during sleep. However, he believed that those animal spirits residing in the cerebellum became this seems to be related to the sensitivity of a active during sleep to maintain a control over physiolgreat number of bed-ridden sick people, who, ogy. He believed that some of the “animal spirits” in their confinement, are aware of the differbecame intermittently unrestrained, leading to the ences of day and night. development of dreams. He also described restless-legs syndrome, which he considered to be an escape of the During the 17th and 18th centuries, medical schools animal humors into the nerves supplying the limbs: had rapidly expanded throughout Europe, with those
HISTORY OF SLEEP MEDICINE north of the French–Italian Alps beginning to gain in prominence. The Swiss-born scientist Albrecht von Haller (1708–1777), a pupil of Boerhaave of the University of Leiden, an important medical center in Europe, made major contributions to many scientific topics, including medicine. Von Haller performed numerous experiments on the nervous system and demonstrated the sensitivity of nerve and the irritability of muscle; in doing so he dispelled much of the mysticism of previous eras. Von Haller produced a major work entitled Elementa Physiologiae in which he devoted 36 pages to the physiology of sleep and proposed a theory for its cause (von Haller, 1766). In a vascular concept, similar to that of Alcmaeon in the fifth century BC, von Haller believed that sleep was caused by the flow of blood to the head, which induced pressure on the brain, thereby inducing sleep by cutting off the “animal spirits.” Von Haller derived his beliefs from the views of his mentor Hermann Boerhaave (1667–1738). Von Haller’s theory was expanded in the 19th century into the congestion theory of the causes of sleep, a theory that was still believed into the early part of the 20th century. He also considered dreams to be a symptom of disease, “a stimulating cause, by which the perfect tranquility of the sensorium is interrupted.” The late 17th century was also the time of the discovery of oxygen by Karl Scheele (1742–1786) and Joseph Priestley (1733–1804), but it was Antoine-Laurent Lavoisier (1743–1794) who coined the name “oxygen” and recognized its importance in the maintenance of living tissue. Despite the important advances in clinical medicine that occurred in the 17th century, there were very few therapeutic advances. Medications still consisted of potions developed from plant and animal tissues, and opium was still the main form of sedation, in a common formulation called “Hoffmann’s anodyne of opium.” The ancient practices of bleeding and purging continued to be widely prescribed throughout the 18th century. It was not until the late 1700s that the greatest advance was made in the development of sleep medicine. It occurred in Bologna with Luigi Galvani’s (1737–1798) demonstration of electrical activity of the nervous system. His findings led to the subsequent development of the field of electrophysiology, and the gradual destruction of the humoralist theory of nervous activity. With the development of the scientific approach to medicine, the discovery of atomism, animal electrophysiology, the advances in respiratory and cardiovascular physiology, as well as treatment advances, such as quinine for malaria and digitalis for heart disease, medicine was about to enter its modern era, the 19th century.
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SLEEP IN THE 19TH CENTURY What probing deep Has ever solved the mystery of sleep? (Thomas Aldrich (1836–1907), Human Ignorance: Aldrich, 1876). The 19th century could be regarded as the “age of sleep theories” as some of the greatest physicians, psychologists, and physiologists turned their attention to explanations of the cause of sleep. Advances were made in the clinical recognition of sleep disorders, particularly the causes of daytime sleepiness, and several comprehensive books were written entirely on the physiological and clinical aspects of sleep. Much of what was known about insomnia and its causes, however, was only a slight expansion of earlier knowledge. There were major advances in understanding the cause of sleep, and in the latter half of the century a number of specific sleep disorders were recognized. The anatomy of sleep and wakefulness was partially revealed through the animal experiments of two outstanding neuroanatomists of the time, Luigi Rolando (1773–1831) and Marie Jean Pierre Flourens (1794–1867). Rolando in 1809 demonstrated that a state of sleepiness occurred when the cerebral hemispheres of birds were removed. His experiments were replicated by Flourens in 1824 with the ablation of the cerebral hemispheres of pigeons: Just imagine an animal which has been condemned to be permanently asleep, one that has been devoid even of the ability to dream during sleep; this is more or less the situation of the pigeon in which I had ablated the cerebral hemispheres (Flourens, 1824). The theories of the cause of sleep can be placed into four main groups: vascular (mechanical, anemic, congestive), chemical (humoral), neural (histological) and a fourth group, which explains the reason for sleep rather than the physiological cause of sleep, the behavioral (psychological, biological) theories. The vascular theories of sleep were those most widely disputed in the early part of the 19th century. They were based upon the first rational theory for the cause of sleep, proposed by Alcmaeon in ancient Greece in the fifth century BC (Wittern, 1989). Alcmaeon believed that sleep is caused by blood filling the brain and waking associated with the return of blood to the rest of the body, a concept consistent with the notions of ancient times, when it was recognized that brain disorders such as apoplexy were associated with stupor (karos). Hippocrates had an alternative theory; he believed that sleep is due to blood flowing in the opposite direction, from the limbs to the central part of the body
12 M.J. THORPY (Chadwick et al., 1978, p. 8). Von Haller, in the 18th Alexander Fleming supported the anemia theory after century, agreed with Alcmaeon’s concept and prohe performed an experiment in which he occluded the posed that blood going to the head causes the brain carotid arteries and induced a sleep-like state. One of to be pressed against the skull, thereby inducing sleep the strongest advocates for the anemia theory was by cutting off the “animal spirits.” Von Haller derived Frans Cornelius Donders (1818–1889), a professor at his beliefs from the views of his mentor, Hermann Utrecht in Holland, who carefully observed the cereBoerhaave (1667–1738), who had presented a similar bral circulation in animals through windows placed in theory a few years earlier, in 1750. These theories the skull (Donders, 1849). Donders and subsequently described the cause of sleep to be related to the blood Angelo Mosso (1826–1910), who observed the cerebral vessels, either congestion (pressure of blood) in the circulation in humans with skull defects, believed that brain or anemia (lack of blood) in the brain. Johann at sleep onset blood passed from the brain to the skin Fredreich Blumenbach (1752–1840), professor at (Mosso, 1880). Arthur Edward Durham (1833–1895), Go¨ttingen, who is regarded as the founder of modern who wrote extensively on the topic in 1860, believed anthropology, was the first to observe the brain of a that the blood passed from the brain during sleep not sleeping subject in 1795 (Blumenbach, 1795). He noted only to supply the skin but also to supply the internal that the surface of the brain was pale during sleep organs (Durham, 1860). compared with wakefulness; contrary to earlier theOne of the final advocates for the anemia theory of ories, he proposed that sleep was caused by the lack sleep was the physiologist William Henry Howell of blood in the brain. It was against this background (1860–1945). Howell believed that the change in arterial of early sleep theories that the 19th-century researchers blood pressure at the base of the brain was responsible looked for the cause of sleep. for cerebral anemia (Howell, 1897). Sir Leonard The theory that sleep was due to congestion of the Erskine Hill (1866–1952) extensively studied the cerebrain was the most accepted vascular theory in the first bral circulation, and in 1896 reported the absence of a half of the 19th century. Robert MacNish wrote a semchange in cerebral blood pressure during sleep (Hill, inal volume on sleep and its disorders, entitled 1896). He believed that the brain did not become aneThe Philosophy of Sleep (MacNish, 1830). MacNish mic or congested during sleep, and showed that intrasupported the previous concept that sleep was due cranial pressure was normal during sleep compared to pressure on the brain by blood. In 1846 Johannes with during wakefulness. By the end of the 19th cenEvangelista Purkinje (1787–1869), an outstanding neurotury the vascular sleep theories, based on congestion anatomist and professor of physiology and pathology at or anemia of the brain, were less enthusiastically supBreslau (Wroclaw, in modern Poland), proposed a ported. Subsequent research showed that changes durslightly different theory for the cause of sleep that was ing sleep of both cerebral blood flow and intracranial consistent with the congestive concept (Purkinje, 1846; pressure do occur, but it was no longer believed that Kruta, 1967). Purkinje proposed that the brain pathways these changes were responsible for the cause of sleep. (corona radiata) become compressed by blood congesThe neural theories for the cause of sleep were tion of the cell masses of the brain (basal ganglia), based upon mid-19th-century developments in the histhereby severing neural transmission and inducing tological understanding of the central nervous system. sleep. James Cappie in 1872 wrote in detail about the Camillo Golgi (1843–1926) demonstrated the first clear circulation of the brain, and was one of the last supporpicture of the nerve cell and its processes. His studies ters of the congestion theory. This theory was finally were extended by Heinrich Waldeyer (1837–1921), who contradicted by the findings of the outstanding clinical first named the nerve cell – the neuron – and demonneurologist John Hughlings Jackson (1835–1911). In strated an afferent axon and efferent dendrites. In 1863 Jackson observed the optic fundi during sleep and 1890, Rabl-Ruckhardt developed a hypothesis, called reported that the retinal arteries became pale during “neurospongium,” stating his belief that during sleep sleep, which was consistent with Blumenbach’s earlier there was a partial paralysis of the neuron prolongafindings. He therefore reasoned that brain congestion tions, which prevented communication with adjacent was not a cause of sleep. nerve cells. Subsequently, Raphael Jacques Lepine The main alternative to the congestion theory was (1840–1919) of Paris in 1894 and Marie Mathias Duval that sleep was due to insufficient blood in the brain (1844–1907) in 1895 proposed similar theories, agreeing (anemia). William Alexander Hammond (1828–1900), that sleep was produced by retraction of ameboid prothe noted American physician, in 1854 was the first in cesses of the nerve cell (Lepine, 1894; Duval, 1895). the 19th century to direct attention to the anemia theThe outstanding histologist Santiago Ramo´n y Cajal ory, after observing the brain of a patient who had a (1852–1934) proposed that small cells termed neuroglia traumatic skull injury (Hammond, 1873, p. 31). In 1855, interacted between neurons and were able to promote,
HISTORY OF SLEEP MEDICINE or inhibit, the transfer of information from one cell to another. Cajal, who in 1906 was awarded the Nobel prize along with Golgi for his work on neurohistology, suggested that the alteration in the transference of information by neuroglia could explain not only sleep but also the effect of hypnotic medications (Cajal, 1895, 1952). Ernesto Lugaro proposed an alternative histological theory that sleep was due to expansion of the neuron processes (Lugaro, 1898). He believed that neural impulses inducing sleep passed through expanded processes (gemmules) to allow transmission between cells. (In the early 20th century, the theories relating movements to parts of the neuron were largely discredited and theories based upon synaptic transmission of neurotransmitters became the prominent neural explanation for changes of sleep and wakefulness.) The chemical theories of sleep originated with Aristotle who believed that sleep was due to the effects of “fumes” taken into the blood vessels following the ingestion of food. Wilhelm Sommer in 1868 proposed that sleep was due to the lack of oxygen. Sommer’s theory (quoted in de Manace´ı¨ne, 1897) was developed from the work of Carl von Voit (1831–1908) and Max Pettenkofer (1818–1901, who had shown in 1867 that the body absorbed more oxygen during sleep than during the day. Eduard Friedrich Wilhelm Pflu¨ger (1829– 1910) became the main advocate for the oxygen hypothesis in 1875 (Pflu¨ger, 1875). Thierry Wilhelm Preyer (1841–1897) in 1877 believed that the accumulation of lactic acid during daytime fatigue led to a deficiency of oxygen in the brain at night, thereby causing hypoxemia and subsequent sleep (Preyer, 1877). This theory led to several others on the accumulation of toxic substances, which included cholesterol and other toxic waste products. Perhaps the most widely disseminated theory was that of Leo Errera of Brussels. Errera believed that the accumulation of poisonous substances called “leucomaines” induced sleep by passing from the blood to the brain (Errera, 1891). The leucomaines were believed to be gradually broken down during sleep, thereby leading to subsequent wakefulness. Raymond Emil Dubois (1818–1896) in 1895 proposed that sleep was a result of carbon dioxide toxicity, which in small amounts during wakefulness led to sleep (Dubois, 1895). Abel Bouchard (1833– 1899) in 1886 proposed that sleep was due to toxic agents, excreted in the urine during sleep, that he called “urotoxins”; he also believed that diurnally produced urine contained toxic agents that produced wakefulness. The chemical theories continued to be popular at the end of the 19th century. The behavioral theories of sleep developed from those of ancient times when general explanations were given for sleep. Although many behavioral theories
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were proposed over the years, the inhibition theory was the most popular. This theory, first alluded to in 1889 by Charles Edouard Brown-Se´quard (1817–1894), and later expanded upon by Heubel and Ivan Pavlov, explained sleep as a process resulting from something being removed or inhibited in the brain. Brown-Se´quard, an outstanding clinical neurologist and physiologist, who believed that most glands had secretions that pass into the blood stream, is also known as the father of endocrinology. Based upon the previous work of Rolando (1809) and Flourens (1822), who had demonstrated that the removal of the cerebral cortex was accompanied by a sleep-like state (Flourens, 1824), Brown-Se´quard (1852, 1889) proposed that sleep was due to an inhibitory reflex. The inhibitory theory of sleep was advanced with the experiment of Heubel, of Kiev University in Russia, who proposed that sleep was due to the loss of peripheral sensory stimulation, which was essential for the maintenance of alertness (Heubel, 1876). Subsequently, the inhibitory theory of sleep was greatly expanded by the work of Ivan Pavlov in the early 20th century (Pavlov, 1923, 1927). Marie de Manace´¨ıne in 1897, in his book entitled Sleep: Its Physiology, Pathology, Hygiene, and Psychology, regarded sleep as being the “resting state of consciousness,” which was an appealing truism, although it provided little information on the mechanism of sleep (de Manace´ı¨ne, 1897) (Figure 1.2). A few researchers believed that a specific site in the body was capable of inducing sleep. The thyroid had been considered to be a sleep-inducing gland, until it was recognized that removal of the thyroid was not associated with insomnia. Jonathon Osborne in 1849 proposed that the choroid plexus was the “organ of sleep.” He reasoned that congestion of the choroid kept the ventricles distended to produce sleep, and that contraction of the choroid was associated with wakefulness. In the latter part of the 19th century two neurologists, Maurice Edouard Marie Gayet and Ludwig Mauthner, reported clinical findings that eventually led to the discovery of the brainstem’s role in sleep and wakefulness. In 1875 Gayet presented a patient with lethargy and associated eye movement paralysis who had upper brainstem pathology at autopsy, which led Gayet to believe that the lethargy was due to a thalamic defect that produced impaired transmission from the brainstem to the cerebral hemispheres (Gayet, 1875). Mauthner in 1890 reported a similar association between an eye movement disorder and sleepiness but placed the site of the deficit at the brainstem level. These findings received little attention at the turn of the century because of the more popular vascular and chemical sleep theories.
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M.J. THORPY
Fig. 1.2. Title page of Sleep: Its Physiology, Pathology, Hygiene, and Psychology by Marie de Manace´¨ıne (1897).
The science of chronobiology made a few advances in the 19th century, largely through the studies of plant biologists, such as Augustin Pyramus de Candolle (1778– 1841), who demonstrated in 1832 that plants in constant conditions had a rhythm that differed slightly from 24 hours (de Candolle, 1832). Wilhelm Friedrich Phillip Pfeffer (1845–1920) in 1875 confirmed de Mairan’s finding that plants had their own intrinsic rhythm when devoid of environmental influences. In 1845 James George Davey (1813–1895) reported circadian rhythms of his own core body temperature (Davey, 1858), and in 1866 William Ogle performed similar experiments: There is a rise in the early morning while we are still asleep, and a fall in the evening while we are still awake, which cannot be explained by reference to any of the hitherto mentioned influences. They are not due to variations in light; they are probably produced by periodic variations in the activity of the organic functions. Although the theories regarding the cause of sleep were the focus of attention in the second half of the
19th century, important contributions were made to sleep disorders medicine. In 1869, Hammond, who was well known for his contributions to medicine during the Civil War, wrote a book based on his series of publications on insomnia, entitled Sleep and its Derangements (Hammond, 1873). Silas Weir Mitchell (1829–1914), a well-known and influential neurologist in America, wrote a number of clinical articles discussing the recognition of abnormal respiration during sleep, night terrors, nocturnal epilepsy, and the effect of stimulants on insomnia (Weir Mitchell, 1890). Perhaps the greatest clinical contribution in the field of sleep disorders medicine was the first description in 1880 of narcolepsy by Jean Baptiste Edouard Ge´lineau (1828–1906), who derived “narcolepsy” from the Greek words narkosis (a benumbing) and lepsis (to overtake) (Ge´lineau, 1880). The term “cataplexy,” for the emotionally induced muscle weakness (a prominent symptom of narcolepsy), was subsequently coined in 1916 by Richard Henneberg. Although Ge´lineau was the first to describe the clinical manifestations of narcolepsy clearly, several patients had previously been described by Caffe in 1862, Carl Friedrich Otto Westphal (1833–1890) in 1877 (Westphal, 1877), and Franz Fischer in 1878 (Fischer, 1878). The leading sleep disorder of the 20th century, obstructive sleep apnea syndrome, was described in 1836, not by a clinician but by the novelist Charles Dickens (1812–1870). Dickens published a series of papers entitled The Posthumous Papers of the Pickwick Club in which he described an obese boy named Joe who was excessively somnolent, a loud snorer, and who probably had right-sided heart failure (thus earning the nickname “young dropsy”: Dickens, 1836) (Figure 1.3). Mr. Lowton hurried to the door. . . The object that presented itself to the eyes of the astonished clerk was a boy – a wonderfully fat boy standing upright on the mat, with his eyes closed as if in sleep. He had never seen such a fat boy, in or out of a traveling caravan; and this, coupled with the utter calmness and repose of his appearance, so very different from what was reasonably to have been expected of the inflicter of such knocks, smote him with wonder. “What’s the matter?” inquired the clerk. The extraordinary boy replied not a word; but he nodded once, and seemed, to the clerk’s imagination, to snore feebly. “Where do you come from?” inquired the clerk. The boy made no sign. He breathed heavily, but in all other respects was motionless.
HISTORY OF SLEEP MEDICINE
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publications. William Wadd, surgeon to the King of England, in 1816 wrote about the relationship between obesity and sleepiness. George Catlin, a lawyer, in 1872 described the breathing habits of the American Indian in his book entitled The Breath of Life; he graphically portrayed the effects of obstructed breathing during sleep (Figure 1.4). William Henry Broadbent (1835–1907) in 1871 was the first physician to report the clinical features of the obstructive sleep apnea syndrome, and William Hill in 1889 observed that upper-airway obstruction contributed to “stupidity” in children. The most notable description was by William Hughes Wells (1854–1919) in 1878; he cured several patients of sleepiness by treatment of upper-airway obstruction (Wells, 1878).
SLEEP IN THE 20TH CENTURY The interpretation of dreams is the royal road to a knowledge of the part the unconscious plays in the mental life (Freud, 1958).
Fig. 1.3. Joe, the fat boy from The Posthumous Papers of the Pickwick Club by Charles Dickens (1836).
The clerk repeated the question thrice, and receiving no answer, prepared to shut the door, when the boy suddenly opened his eyes, winked several times, sneezed once, and raised his hand as if to repeat the knocking. Finding the door open, he stared about him with astonishment, and at length fixed his eyes on Mr. Lowton’s face.
Sleep medicine advances in the 20th century were greatly affected by the development of new diagnostic means and the innovations in surgery. For the first time objective diagnostic procedures complemented the physician’s skill. X-rays were discovered in 1895 by Wilhelm Konrad Roentgen (1845–1923) and the first clinical application was reported in 1896. Widespread routine use of X-ray procedures began in the early 20th century; sophisticated brain imaging techniques such as computed axial tomography and nuclear magnetic resonance scanning began in the second half of the century. The vascular theories of the cause of sleep were no longer popular, and although the chemical theories
“What the devil do you knock in that way for?” inquired the clerk, angrily. “Which way?” said the boy, in a slow, sleepy voice. “Why, like forty hackney-coachmen,” replied the clerk. “Because master said I wasn’t to leave off knocking till they opened the door, for fear I should go to sleep” said the boy. More than 100 years followed Charles Dickens’ description before the obstructive sleep apnea syndrome became a well-recognized clinical entity. However, a number of writers in the 19th century did allude to some of the features of sleep apnea in their
Fig. 1.4. Obstructed breathing during sleep. (Reproduced from Catlin (1872).)
16
M.J. THORPY were briefly of interest due to the findings of Rene´ eye movements. Sigmund Freud in 1895, before the Legendre and Henri Pieron in 1907 (Legendre and publication of his first book on dreams in 1900, Pieron, 1907; Pieron, 1913), they were overshadowed recognized that paralysis of skeletal muscles during largely by the behavioral theory of Ivan Petrovitch dream sleep prevented the dreamer from acting out Pavlov (1849–1936). Pavlov, who is regarded as one of dreams (Freud, 1958). the greatest physiologists of all time, published his initial Sleep research, both basic and clinical, had its greatlectures on conditional reflexes in 1927 (Pavlov, 1927). est period of growth during the second half of the 20th There he expressed a belief that sleep was due to widecentury. The advances in neurochemistry, electrophysispread cortical inhibition: ology, neurophysiology, chronobiology, pathology of sleep, and sleep disorders medicine and the developSleep. . . is an inhibition which has spread over ment of sleep societies are too numerous to list, but the great section of the cerebrum, over the entire a summary is given below. hemispheres and even into the lower lying midbrain.
Neurochemistry
Pavlov’s studies on dogs showed that a continuous and monotonous stimulus would be followed by drowsiness and sleep. He reasoned that the continuous stimulus acts at a certain point of the central nervous system and leads to inhibition with resulting sleepiness. Although Pavlov’s theories on conditioning were interesting, they held little information on physiological mechanisms. Vladimir Michailovich Bekhterev (1857– 1927) published his findings on human reflexology and sleep in 1894 (translated into English in 1932). Bekhterev also believed that sleep was a general inhibition due to a loss of higher-level reflexes: [Sleep is] a reflex which has been biologically evolved for the purpose of protecting the brain from further poisoning by the products of metabolism, and which may be evoked, as an association reflex, and the conditions of fatigue. Bekhterev’s theory, similar to that of Edouard Clapare`de, who in 1905 viewed sleep as an “instinct,” was subsequently influenced by the work of Legendre and Pieron; it proposed that the biochemical processes leading to the inhibition of the brain were “hypnotoxins.” Since that time, electrophysiological studies have demonstrated that the passive, cortical inhibition proposed by Pavlov and Bekhterev does not occur; instead, the brain maintains its activity during sleep, particularly during REM sleep.
Dichotomy of sleep Since the days of ancient Greece, it had been recognized that sleep consisted of two different states, one associated with dreaming and the other with quiet sleep. Willis in the 17th century had noticed the difference, and believed that dream sleep was associated with release of the “animal spirits” from the cerebellum. However, the physiological changes of dreaming sleep were not reported until 1868 when Wilhelm Griesinger (1816–1868) noted the associated
Our studies have established that the accumulation of the hypnotoxin produces an increasing need for sleep (Pieron, 1913). Although attempts to replicate the work of Legendre and Pieron on hypnotoxin were often unsuccessful, in 1967 John Pappenheimer and colleagues induced sleep with cerebrospinal fluid obtained from sleep-deprived goats. The transmissible chemical, called “factor S,” was subsequently identified as a muramyl peptide in 1982 and is thought to act via the leukocyte monokine interleukin-1. Finding alternative sleep factors has met with mixed success; the number of putative sleep factors has grown enormously in the last 20 years. However, in 1988 Osamu Hayaishi discovered that prostaglandin PGD2, found in the preoptic muclei, was capable of inducing sleep in rats, leading to the speculation that the preoptic nucleus is the site of the perennial and elusive “sleep center.” Hypocretin/orexin was discovered independently in 1998 by two separate groups of researchers. Luis de Lecea and Thomas Kilduff and colleagues from San Diego identified two peptides derived from a single gene in the hypothalamus that had a sequence homology to secretin that they called hypocretin (de Lecea et al., 1998). At the same time, Takeshi Sakurai and Akira Amemiya and colleagues also isolated these same peptides in Texas and named them orexin (Greek for appetite) (Sakurai et al., 1998). Both groups were not investigating sleep, but were searching for novel obesity treatments. Chemelli et al. further studied hypocretin/orexin in 1999 and discovered that loss of hypocretin produced symptoms in rodents that were similar to that of cataplexy and sleep attacks as seen in humans. Emmanuel Mignot and colleagues in 1999 determined that dogs with narcolepsy had a loss of hypocretin and subsequently it was shown that most human patients with narcolepsy and cataplexy had reduced or absent cerebrospinal fluid levels of hypocretin (Lin et al., 1999).
HISTORY OF SLEEP MEDICINE
Electrophysiology Feeble currents of varying direction pass through the multiplier when electrodes are placed on two points of the external surface [of the brain] (Caton, 1875). The most useful objective diagnostic means for sleep disorders has proven to be electrophysiological techniques. Following Galvani’s demonstration of the electrical activity of the nervous system in the late 18th century, Richard Caton (1842–1926) in 1875 demonstrated action potentials in the brains of animals, an important step in the development of the electroencephalograph (Caton, 1875). In 1929, Johannes (Hans) Berger (1873–1941), the first to record electrical activity of the human brain, demonstrated differences in activity between wakefulness and sleep. Berger’s discovery led to the development of the electroencephalograph as a clinical tool for the diagnosis of brain disease. The electroencephalograph was applied to determine different sleep states in 1937, when Alfred L. Loomis, E. Newton Harvey (1887–1959), and Garret Hobart were able to classify sleep into five stages, from A to E (Loomis et al., 1935). Dream sleep was characterized in 1953 by Eugene Aserinsky and Nathaniel Kleitman, who demonstrated the occurrence of rapid eye movements during the dreaming stage of sleep, that they called “rapid eye movement (REM) sleep.” The traditional manner of producing sleep studies by using polysomnographs that used ink and paper was rapidly replaced by digital systems after the year 2000. The death knell to paper systems came at the end of 2006 when the Grass P78 polysomnograph recording paper became unavailable. In 1957 Kleitman and William Dement discovered a recurring pattern of REM sleep and non-REM sleep during overnight electroencephalographic monitoring – a finding that made it clear that sleep no longer could be regarded as a homogeneous state (Dement and Kleitman, 1957). In 1968, Allan Rechtschaffen and Anthony Kales developed a scoring manual, A Manual of Standardized Terminology, Techniques, and Scoring System for Sleep Stages of Human Subjects, which has become the standard in the field (Rechtschaffen and Kales, 1968). In 2007, a major revision of the traditional sleep-staging rules was developed by the American Academy of Sleep Medicine (Iber et al., 2007). The first report of an effective measure of daytime alertness was by Gary Richardson et al., in 1978. This study compared narcoleptics with normal individuals by applying the Multiple Sleep Latency Test (MSLT) that had been conceived and developed by Mary Carskadon, working with William Dement at Stanford University:
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analysis of hypnogenic mechanisms has thus underlined the paramount importance of inhibition and disinhibition in the deterrnination of sleep onset and maintenance – a striking illustration of Sherrington’s visionary concepts (Bremer, 1977).
Neurophysiology In the early part of the 20th century, two schools of thought emerged regarding the neurophysiological basis of sleep and wakefulness. One characterized sleep as due to disinhibition with release of an active “sleep center,” and the other as due to a passive event, the result of inhibition of a “waking center.” The theories, proposed at the end of the 18th century by Mauthner and others, assumed an interruption of peripheral sensory stimulation, thereby allowing the cerebral cortex to produce sleep. This “deafferentation” theory had been suggested first by Purkinje in 1846. The notion of a specific sleep center did not receive much support, as illustrated by the comment of the prominent clinical neurologist Jacques Jean Lhermitte (1877–1959) in 1910 (Lhermitte, 1910): We absolutely object to the thought of the existence of a nerve center attributed to the function of sleep. The conception of a center for sleep is erroneous, as it disavows the most simple principles of physiology. Lhermitte was supported in 1914 by a pioneer of brain localization, Joseph Jules Dejerine, who said, “Sleep cannot be localized” (Dejerine and DejerineKlumpke, 1914). However, in 1929, Constantin von Economo (1876–1931) proposed a “center for regulation of sleep” based on anatomical and clinical studies of “encephalitis lethargica” at the Psychiatric Clinic of Wagner von Jauregg in Vienna (von Economo, 1923, 1929a). Viral encephalitis reached epidemic proportions between 1916 and 1920, and von Economo had the opportunity to correlate the clinical features of sleep disturbance with the central nervous system pathology. His studies demonstrated inflammatory lesions in the posterior hypothalamus in patients with excessive sleepiness and lesions in the preoptic area and anterior hypothalamus in patients with insomnia (von Economo, 1929b). Von Economo, influenced by the studies by Pieron and Pavlov, suggested that the “sleep-regulating center” was controlled by substances circulating in the blood. These substances caused the sleep center to exert an inhibitory influence on the cerebral cortex, thereby leading to sleep. The same year in Zurich, Walter Rudolph Hess (1881–1973), who was awarded the Nobel prize with Egas Moniz for his work in neuroanatomy,
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M.J. THORPY
confirmed von Economo’s findings by demonstrating that stimulation of the central gray matter in the region of the thalamus induced sleep (Hess, 1944). Kleitman in 1939 regarded the cerebral cortex as being the source of wakefulness, and believed that sleep due to inactivity of the central nervous system was brought about by a reduction in peripheral stimulation because of fatigue. His hypothesis conformed to the “deafferentation” theory. Steven Walter Ranson (1880–1942) in 1932 demonstrated that lesions placed at the top of the brainstem produced sleepiness; experimentally, this was consistent with von Economo’s findings (Ranson and Ingram, 1932). In 1935, Fre´de´ric Bremer, of the University of Brussels, experimentally gave support to the deafferentation theory. Bremer completely transected the midbrain, producing the “cerveau isole´” preparation, an isolation of the cerebrum, and was able to show characteristic sleep patterns on the electroencephalogram. The studies up until this time were consistent with the concept that a lesion that prevented transmission of peripheral stimulation was important in the production of sleep. However, Ranson in 1939 showed that lesions of the lateral hypothalamus, in the absence of upper-brainstem lesions, were associated with sleep due to a loss of the “waking center.” A few years later, Walle Jetz Harinx Nauta demonstrated that posterior hypothalamic lesions produced sleepiness whereas anterior hypothalamic lesions produced insomnia, thereby supporting the concept of a waking center in the posterior hypothalamus and a sleep center in the anterior hypothalamus (Nauta, 1946). According to Nauta: Whereas Ranson and his collaborators held that periods of sleep were caused by more or less intrinsic periodic decreases in activity of the waking center, we are inclined to attribute these decreases to the inhibitory influence of a sleep center. Horace W. Magoun and Ruth Rhines, at the Northwestem University Medical School in Chicago, demonstrated in 1946 that the lower portion of the brainstem reticular formation was responsible for inhibiting skeletal muscle tone (Magoun and Rhines, 1946). This function of the lower brainstem had earlier been alluded to by the clinical studies of Jackson in 1898. That the lower reticular formation could have an inhibitory function through descending pathways led to Guiseppe Moruzzi and Magoun’s finding in 1949 that the brainstem reticular formation also had ascending pathways (Moruzzi and Magoun, 1949; Moruzzi, 1964). This resulted in the discovery of the “ascending reticular activating system,” which led to a new emphasis in the physiological investigation of sleep. Stimulation of
the ascending reticular activating system produced electroencephalographic patterns of wakefulness. It was now recognized that the brainstem transection studies did not produce sleep because of “deafferentation” of peripheral sensory input, but because of the loss of the wakefulness stimulus from the ascending reticular activating system. As a result, sleep became regarded as a passive phenomenon. At the beginning of the second half of the 20th century, research concentrated on determining the neurophysiological basis for non-REM and REM sleep. Following the electrophysiological documentation of REM sleep, Michel Jouvet and colleagues in 1959 demonstrated REM sleep-related muscle atonia, and in 1965 demonstrated that the brainstem serotonincontaining neurons of the raphe nuclei were important in sleep and wakefulness (Jouvet and Delorme, 1965). Subsequently, Jouvet demonstrated that the rostral raphe nucleus was important for non-REM sleep, whereas the caudal raphe nucleus was important in the maintenance of REM sleep. In 1975, Robert William McCarley and J. Allan Hobson proposed a reciprocal interaction model of REM and non-REM sleep, with rostral REM “on” cells and caudal REM “off’ cells (McCarley and Hobson, 1975). In 1996 a small group of cells, called the ventrolateral preoptic nucleus, was discovered by Sherin to be an important sleep-generating nucleus that comes as close as any cell group to being a major “sleep center” (Sherin et al., 1996).
Chronobiology Despite the multiplicity of its constituents, the circadian system often behaves like one unit which is characterized by the durability of its oscillations and its internal temporal order (Aschoff, 1981). Auguste Henri Forel (1848–1931), a Swiss physician, is credited with stimulating the investigation of circadian rhythms as important time-measuring systems. His studies in 1910 on the accurate timing system of bees were consistent with those of de Mairan in the 18th century on the opening of the flower petals at a given time of day. The circadian behavior of rodents was first reported by Curt P. Richter in his Ph.D. thesis in 1922; and Erwin Bunning in 1935 was able to demonstrate the genetic origin of circadian rhythms in plants and subsequently developed a concept of “biological clocks.” In the early 1960s Richter searched for the biological clock in extensive studies that culminated with the report in 1965 that lesions placed in the anterior ventral hypothalamus produced disruption of circadian rhythms (Richter, 1965). Two groups acting independently in 1972, Robert Y. Moore and Victor
HISTORY OF SLEEP MEDICINE 19 B. Eichler, and F.K. Stephan and Irving Zucker, discovpsychiatry. Freud’s book The Interpretation of Dreams ered the “clock” to be two small, bilateral nuclei in the (1958) led to the development of psychoanalysis, which anterior hypothalamus, which were subsequently called was applied to the treatment of insomnia. the suprachiasmatic nuclei (Moore and Eichler, 1972; Psychoactive medications became widely used with Stephen and Zucker, 1972). the introduction of the phenothiazines in the 1950s, but Jules Aschoff and Kurt Wever investigated human hypnotic medications, particularly the barbiturates, had circadian rhythms in the absence of environmental time been in common usage since barbital was introduced in cues in 1962 in an underground laboratory in Munich. 1903. The 1960s saw the introduction of the benzodiazeThey demonstrated a free-running pattern of sleep pine hypnotics, which largely replaced the barbiturates and wakefulness with a period length of greater than in the late 1970s. However, the 1980s saw a decline in 24 hours. the use of hypnotics with increased physician and public A similar free-running pattern was demonstrated in awareness of the disadvantages of chronic hypnotic field experiments (1964) by the speleologist Michel use. Insomnia became recognized as a symptom rather Siffre, who lived for 3 months in the absence of time than a diagnosis, and treatment was directed to the cues on an ice glacier deep in the Franco-Italian moununderlying physical or psychological causes. tains (Siffre, 1964). Many human biological rhythms Several books on sleep had a major influence on the have recently been discovered, such as the 24-hour epidevelopment of sleep disorders medicine. Pieron’s Le sodic secretory pattern of cortisol that was reported by problème physiologique du sommeil in 1913 summarElliot David Weitzman (1929–1983) in 1966 (Weitzman ized the scientific sleep literature at that time. A simiet al., 1966). lar approach was taken by Kleitman, who produced In 1980, Weitzman et al. demonstrated the internal his monumental treatise, Sleep and Wakefulness, in organization of temperature, neuroendocrine rhythms, 1939 (updated in 1963 to contain 4337 references) and the sleep–wake cycle in subjects who were moni(Kleitman, 1963). The Association of Sleep Disorder tored in an environment free of time cues for periods Centers classification committee, chaired by Howard of up to 6 months. Sutherland Simpson (1863–1926) Roffwarg, produced the Diagnostic Classification of and J.J. Galbraith in 1906 had demonstrated that the Sleep and Arousal Disorders in 1979; it ushered in light–dark cycle could influence mammal behavior. the modern era of sleep diagnoses and became the first However, it was not until the 1980s that Czeisler and classification to be widely used. The Principles and colleagues demonstrated the importance of the light– Practice of Sleep Disorders Medicine, edited by Meir dark cycle in the entrainment of human circadian Kryger, Thomas Roth, and William Dement in 1989, rhythms. The genetic basis for the control of circadian was the first comprehensive textbook on basic sleep rhythms was established in 1971 by R. Konopka, initiresearch and clinical sleep medicine (Kryger et al., 1989). ally in fruit flies but subsequently in humans (Konopka Increased knowledge about sleep and sleep disorand Benzer, 1971). The recognition by K. Toh in 2001 ders in general has resulted from the research of a that advanced sleep phase syndrome was associated few core sleep disorders, which include narcolepsy, with a genetic mutation of the human period gene obstructive sleep apnea syndrome, and the insomnias. two (hPer2) led to the recognition that alterations in Following Ge´lineau’s description in the late 19th centiming of the sleep–wake pattern could be controlled tury, narcolepsy was brought to general recognition in by genetic factors (Toh et al., 2001). 1926 by the Australian-born neurologist William John Adie (1886–1935) (Adie, 1926), and stimulants were first Pathology of sleep used for treatment by Otakar Janota (1898–1969) and A. Skala in 1930. In 1941 John Burton Dynes and Knox Five billion people go through the cycle of sleep H. Finley applied the electroencephalograph to the diagand wakefulness every day, and relatively few of nosis of narcolepsy (Dynes and Finley, 1941), and the them know the joy of being fully rested and fully characteristic sleep-onset REM period of night sleep alert all day long (William Dement 1988). was discovered in 1960 by Gerald Vogel. Dement and colSleep disorders were poorly described at the turn of the leagues at Stanford University developed a narcoleptic century, and, other than narcolepsy and sleeping dog colony in the 1970s, which advanced the understandsickness (African trypanosomiasis), few specific sleep ing of the biochemical and neuroanatomical bases of the disorders were recognized. In addition to general disorder. The Multiple Sleep Latency Test was applied to medical illness, environmental effects and anxiety were the diagnosis by Richardson et al. in 1978, and the docuviewed as the main causes of sleep disturbance. mentation of a strong association between the histocomHowever, a gradual recognition of the multiplicity of patability antigen HLA-DR2 and narcolepsy was made sleep diagnoses began to parallel progress in by Yutaka Honda and colleagues in 1984 (Juji et al., 1984).
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Following the reports of snoring, sleepiness, and obesity in the 19th century, Sir William Osler (1849– 1919) referred in 1906 to Dickens’ description of Joe (Osler, 1906): “An extraordinary phenomenon in excessively fat young persons is an uncontrollable tendency to sleep – like the fat boy in Pickwick.” Charles Sidney Burwell in 1956 brought general recognition to obstructive sleep apnea syndrome, which he called the “pickwickian syndrome” (Burwell et al., 1956), and the first objective documentation of polysomnographic features was simultaneously reported by Henri Gastaut and Jung in 1965 (Gastaut et al., 1965; Jung and Kuhlo, 1965). Although the tracheotomy had been performed since the time of Asclepiades (first century BC), Wolfgang Kuhlo and Erich Doll in 1972 reported that it provided an effective treatment of the obstructive sleep apnea syndrome. Tanenosuke Ikematsu in 1964 popularized uvulopalatopharyngoplasty surgery for the treatment of snoring, which was subsequently applied to the obstructive sleep apnea syndrome by Shiro Fujita in 1981 (Fujita et al., 1981). The same year, nasal continuous positive airway pressure treatment was described by Colin Sullivan and subsequently became the treatment of choice (Sullivan et al., 1981). Another sleep-related breathing disorder called “Ondine’s curse” was first reported by John W. Severinghaus and Robert A. Mitchell in 1962. Named after the water nymph in Jean Giraudoux’s play Ondine (1954), this disorder was characterized by the failure of automatic ventilation that could lead to fatal apnea during sleep: Live! It’s easy to say. If at least I could work up a little interest in living – but I’m too tired to make the effort. Since you left me, Ondine, all the things my body once did by itself, it now only does by special order. . . I have to supervise five senses, two hundred bones, a thousand muscles. A single moment of inattention, and I forget to breathe. He died, they will say, because it was a nuisance to breathe (Giraudoux, 1954, Act III). Insomnia received more interest in earlier centuries than in the first half of the 20th century, probably because of the availability of effective hypnotic medications. Frederick Snyder in the 1960s recognized and promoted the importance of psychiatric disorders in sleep medicine, especially depression: “Troubled minds have troubled sleep, and troubled sleep causes troubled minds” (Snyder, 1969). The polysomnograph was applied to the investigation of patients with insomnia following the discovery of obstructive sleep apnea in 1965, and objective
measures of hypnotic effectiveness were developed by Kales et al. in 1969. The concept of a conditioned insomnia (psychophysiological insomnia) was first presented in the Diagnostic Classification of Sleep and Arousal Disorders (Association of Sleep Disorder Centers, 1979), and subsequently became recognized as a common form of primary insomnia. The behavioral technique “stimulus control” developed by Richard Bootzin in 1972 was an effective treatment of insomnia, as was “sleep restriction therapy,” developed by Arthur Spielman in 1987 (Spielman et al., 1987). Circadian rhythm sleep disorders were recognized in the late 1970s, partly due to recognition of the chronobiological features of “jet lag” and “shift work.” Thomas A. Edison, who was responsible for the development of the electric light bulb, which stimulated the development of shift work, had his own views on sleep: In my opinion sleep is a habit, acquired by the environment. Like all habits it is generally carried to extremes. The man that sleeps four hours soundly is better off than a dreamy sleeper of eight hours (Baldwin, 1995). The atypical, sleep-onset insomnia called the “delayed sleep phase syndrome,” discovered by Weitzman and colleagues in 1981, led to a radically different form of treatment called “chronotherapy,” which was based on chronobiological principles (Czeisler et al., 1981; Weitzman et al., 1981). Many other sleep disorders have been discovered in the 20th century, including REM sleep behavior disorder by Carlos Schenck et al. in 1986; paroxysmal nocturnal dystonia in 1981 by Lugaresi & Cirignotta and fatal familial insomnia in 1986 by Lugaresi et al.; and food allergy insomnia by Andre Kahn et al. in 1985. General and medical awareness of sleep disorders has dramatically increased since the 1970s through the contributions of sleep disorders clinicians and the sleep societies. In addition to those mentioned, a few of the many who have contributed to this recognition include: Roger Broughton, Michel Billiard, Christian Guilleminault, Peter Hauri, J. David Parkes, the late Pierre Passouant, and Bedrich Roth.
Sleep disorders medicine we have created a new clinical specialty, sleep disorders medicine! whose task is to watch over all of us while we are asleep (William Dement 1985). Organized sleep disorders medicine in the USA began with the founding of the Association for the
HISTORY OF SLEEP MEDICINE 21 Psychophysiological Study of Sleep in 1961, an associamainly psychologists but also physicians, in behavioral tion comprised of sleep researchers, many with clinical sleep medicine. interests. Sleep research led to the investigation of In 2005, a fellowship training program was sleep disorders, which resulted in the establishment in approved by the American College of Graduate Medithe early 1970s of clinical sleep disorder centers for cal Education for eligibility to take a board certificathe diagnosis and treatment of patients. In 1976, the tion examination in sleep medicine. The first Association of Sleep Disorder Centers (ASDC) was examination was held in 2007. This examination of founded. The first sleep disorder center to be engaged the Board on Internal Medicine is open to physicians in active patient evaluations and treatment was that who have been board-certified by one of the specialty established at Stanford University in California by boards of the American Board of Psychiatry and Dement. An accreditation process for sleep disorders Neurology, the Board of Internal Medicine, the centers was established by the ASDC, and the first to American Board of Pediatrics, or the American Board be accredited in 1977 was the Sleep–Wake Disorders of Otolaryngology, and who have completed 1 year Unit, headed by Weitzman, at Montefiore Medical of sleep medicine fellowship training. Until 2011 Center in New York. physicians trained in sleep medicine who meet certain In 1978, the Association of Polysomnographic Techcriteria of training are eligible to sit the Board of nologists, founded by Peter Anderson McGregor, set Sleep Medicine examination despite not having standards of practice for polysomnographic technolocompleted 1 year of American College of Graduate gists. In 2007 the association changed its name to the Medical Education-certified training. American Association of Sleep Technologists. A sleep-related foundation, the National Sleep In 1983 the Association for the Psychophysiology Foundation, was created in 1990 by the American Sleep Study of Sleep was renamed the Sleep Research SociDisorders Association, and subsequently became indeety and in 1984 the Clinical Sleep Society was founded pendent of the association. The National Sleep Foundaas the membership branch of the Association of Sleep tion is an independent nonprofit organization Disorder Centers. In 1986, the Association of Sleep dedicated to improving public health and safety by Disorder Centers, the Clinical Sleep Society, the achieving understanding of sleep and sleep disorders, Sleep Research Society, and the Association of Polyand by supporting sleep-related education, research, somnographic Technologists formed a federation and advocacy. The National Sleep Foundation relies called the Association of Professional Sleep Societies. on voluntary contributions, including grants from The Association of Sleep Disorder Centers was foundations, corporations, government agencies, and renamed as the American Sleep Disorders Association other organizations to support its programs. Another in 1987. Subsequently the name was again changed in foundation, the American Sleep Medicine Foundation 1999 to the American Academy of Sleep Medicine. (formerly the Sleep Medicine Education and Research In 1978, the medical journal Sleep was created to Foundation) was established by the American Academy present research and clinical articles on sleep, and in of Sleep Medicine Board of Directors in March 1998 to 1979 a complete issue was devoted to the diagnostic promote education and fund research. classification of sleep and arousal disorders (AssociaWith the increased recognition of the importance of tion of Sleep Disorder Centers, 1979). The Internasleep disorders medicine many international sleep tional Classification of Sleep Disorders manual was societies have been founded, beginning with the Europroduced in 1990 and a second edition was produced pean Sleep Research Society in 1971, the Japanese Sociin 1997 (American Sleep Disorders Association, 1990, ety for Sleep Research in 1978, the Belgian Association 1997). Several other sleep journals were created, includfor the Study of Sleep in 1982, the Scandinavian Sleep ing Sleep Medicine Reviews (1997), Sleep Medicine Research Society in 1985, the Latin American Sleep (2000), Behavioral Sleep Medicine (2003), and the Society in 1986, the Sleep Society of Canada in 1986, Clinical Journal of Sleep Medicine (2005), the official and the British Sleep Society in 1989. Sleep medicine journal of the American Academy of Sleep Medicine. has become a major branch of medicine with practiClinicians who were trained in sleep medicine were tioners in nearly every country of the world. eligible to take a certification examination that was first held in 1978. The examination was open to both The woods are lovely, dark, and deep, physicians and other doctoral clinicians. With the recBut I have promises to keep, ognition of the importance of behavioral treatments And miles to go before I sleep, in sleep medicine, especially in insomnia, a board And miles to go before I sleep. examination was developed in 2003 for clinicians, (Robert Frost, 1923).
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Handbook of Clinical Neurology, Vol. 98 (3rd series) Sleep Disorders, Part 1 P. Montagna and S. Chokroverty, Editors # 2011 Elsevier B.V. All rights reserved
Chapter 2
Normal sleep-recording and scoring techniques MAX HIRSHKOWITZ 1 * AND AMIR SHARAFKHANEH 2 Department of Medicine & Menninger Department of Psychiatry, Baylor College of Medicine and Michael E. DeBakey VAMC Sleep Center, Houston, TX, USA
1
2
Department of Medicine, Baylor College of Medicine, Michael E. DeBakey VAMC Sleep Center and Methodist Hospital Sleep Diagnostic Laboratory, Houston, TX, USA
EEG AND EOG CORRELATES OF NORMAL HUMAN SLEEP Overview Sleep is a state associated with inactivity and decreased responsiveness to environmental stimuli. Unlike coma, sleep is rapidly reversible. Furthermore, sleep is an active process and not a passive consequence of brainstem and cortical metabolic depression (Hirshkowitz and Sharafkhaneh, 2005). Humans characteristically sleep during the dark photoperiod and the timing of sleep onset generally coincides with declining core body temperature. Sleep is often conceptualized as a brain process. Furthermore, our tendency to dichotomize the world would erroneously consider sleep as a single process contrasting with wakefulness. Sleep is actually composed of several distinct processes that are different both quantitatively and qualitatively. Each type of sleep has its own unique characteristics, regulatory mechanisms, and mental correlates. Deprivation of one particular sleep process will lead to selective rebound of that type of sleep when the individual is subsequently allowed to sleep ad lib. Because sleep is a brain process, an electroencephalographic (EEG) technique for recording brain activity was adopted for sleep research soon after its discovery at the beginning of the 20th century (Berger, 1930). Hans Berger, the father of EEG, himself made the first EEG sleep recording. He noted that the alpha rhythm (a 8–13 cycle per second (cps) waveform), prominent during eyes-closed relaxed wakefulness, would disappear and be replaced by low-voltage, mixed-frequency activity when an individual fell asleep. Nearly a century later, this EEG correlate is still used as a marker for *
sleep onset. Closer scrutiny of EEG reveals that alpha rhythm frequency may slow slightly and amplitude increase just before sleep onset. Furthermore, blinking and saccadic eye movements disappear and may be replaced by slow, rolling eye movements.
Traditional recording technique The traditional EEG sleep-recording technique employed high-gain, analog, differential bioamplifiers to record continuous ink pen tracings on fan-fold paper driven by a mechanical chart drive. The bioamplifiers would magnify the voltage difference between two gold or silver disk electrodes attached to the surface of the skin or scalp (Gibbs and Gibbs, 1950). Electrode placement varied from laboratory to laboratory until a standardized technique was developed (Jasper, 1958). An ad hoc committee was formed by the Sleep Research Society and an amazing collection of content-expert thought leaders began meeting under the chairmanship of Drs. Allan Rechtschaffen and Anthony Kales in the late 1960s. Differences of opinion were hashed out (in some cases after raucous disagreement, shouting, and recrimination, as I’ve been told by committee members in attendance) and consensus was finally reached. Someone once told me that Dr. Rechtschaffen physically barred the exit door at one point and threatened: “no one leaves until we all agree.” I wrote about the incident as an example of the mythos that develops surrounding pivotal events in the history of any field, but in this case Dr. Rechtschaffen phoned me after reading my article and said that it was true and really happened. As
Correspondence to: Max Hirshkowitz, Ph.D., Michael E. DeBakey VAMC Sleep Center 111 i, 2002 Holcombe Blvd, Building 100, Suite 6C-344, Houston 77030, USA. Tel: (713) 794-7562, Fax: (713) 794-7558, E-mail:
[email protected] 30
M. HIRSHKOWITZ AND A. SHARAFKHANEH
realized by the chairman, reaching consensus was really the key issue. It was the single most important element that allowed A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects (Rechtshaffen and Kales, 1968) to succeed and endure for over four decades. R&K (as it is commonly called, after the chairmen’s initials) was not particularly innovative nor did it depart much from already-established techniques. Its genius was in the consensus achieved. If everyone had returned to their respective laboratories and continued doing things their own way, the project would have failed and it is unlikely that sleep research would have advanced as rapidly with a common set of terms, recording techniques, and data reduction procedure. R&K firmly established making a so-called monopolar recording from a central EEG derivation. By referencing an electrode at either C3 or C4 to a (relatively) inactive site (the earlobe or mastoid), the amplified potential difference between the two sites would reflect the electrical activity over the brain’s central lobes on the scalp. Furthermore, the signal is conditioned using high- and low-pass analog filters that reduce (roll off) amplitude by a known percentage per octave. This could be described in terms of the frequency at which some fraction (e.g. one-half) of the amplitude was attenuated. Another way of looking at analog filtering relates to the fact that EEG amplifiers are alternating current (AC)-coupled. This simply means that the amplifier signal output gravitates to ground (0 V) at a set rate. That is, the instantaneous potential difference between two electrodes is sensed, amplified, sent to the pen’s electromagnets, and produces a deflection that is recorded on a moving paper strip. Within a set period of time, that pen point will fall to 0 V and the rate at which it falls amounts to filtering (expressed as a fall time constant). For
example, a rapid fall time constant would attenuate the ability to display slow waves. Aserisnky and Kleitman’s (1953) discovery of “regularly occurring periods of eye motility” during somnolence made recording eye movements during overnight sleep studies de rigueur. The same recording principles used for EEG apply to making eye movement recordings. Traditional technique recommended making two monopolar recordings displayed on separate channels. Electrodes placed just outside the outer canthus of each eye were referenced to the earlobe or mastoid. In this manner, horizontal eye movements would produce deflections in opposite directions on the two channels as the positive corneal potentials moved toward the electrode on one eye and away from the electrode on the other. In order to detect vertical eye movements, the electrodes at the outer canthi were displaced vertically 1 cm above on one eye’s midline and 1 cm below midline on the other. Filtering could be constricted to a tighter bandwidth because the range of relevant activity is narrower for electro-oculograms (EOG) than for EEG. However, filters were set to preserve low frequencies because many sleep researchers used EOG to visualize slow-wave activity from eye leads because they are proximal to the frontal lobe. Jouvet’s description of near electromyographic (EMG) atonia in skeletal muscles during paradoxical (rapid eye movement (REM)) sleep completed our outline of major bioelectrical correlates during sleep (Jouvet et al., 1959). Consequently, a submentalis electromyogram (i.e., chin EMG) was added to many investigators’ routine recording montage. Thus, the standard recording technique described in R&K required a minimum of four channels (Table 2.1) for overnight sleep recordings. The term “polysomnogram” emerged to designate recordings made in this manner and the recording process itself became known as “polysomnography”
Table 2.1 Traditional sleep-scoring technique parameters* Label
Derivation
Specification origin
Reference
EEGC EOGL EOGR EMGSM EEGO
C3–A2 or C4–A1 LOC-A2 or LOC-M2 ROC-A2 or ROC-M2 Chin EMG O3–A2 or O4–A1
Rechtshaffen and Kales Rechtshaffen and Kales Rechtshaffen and Kales Rechtshaffen and Kales Bonnet et al. (1992)
EEGF
F3–A2 or F4–A1
R&K R&K R&K R&K American Sleep Disorders Association (ASDA) American Academy of Sleep Medicine (AASM)
C:Central, L:Left, R:Right, S:Submental, O:Occipital, F:Frontal.
Iber et al. (2007)
(1968) (1968) (1968) (1968)
NORMAL SLEEP-RECORDING AND SCORING TECHNIQUES (Keenan 2009). This amalgam of Greek and Latin terminology (painful to the ears of some) quickly became standard parlance (preferred over the linguistically purer polyhypnogram or multisomnoscript).
Waveforms OVERVIEW EEG activity contains both ongoing background activity and specific events that stand out from the background. As a continually oscillating voltage fluctuation, one way to categorize EEG is based on its frequency. Fairly consistent bandwidths of activity occur across individuals and are designated with Greek letters (Table 2.2). Another way to label EEG activity is based on morphology (Niedermeyer and Lopes da Silva, 1987). Particular waveforms are usually given fairly descriptive names (Table 2.2).
Table 2.2 Normal electroencephalogram waveforms in humans Designation
Description
a (alpha)
8–13 cps rhythm associated with relaxed wakefulness when eyes are closed. Alpha activity is normally most prominent in occipital leads. Bursts of alpha lasting 3 seconds, or longer, are used to define arousal from nonrapid eye movement sleep. Alpha activity may be intermixed with slow wave in patients suffering from or experiencing pain >13 cps waveform occurring both during alert, vigilant wakefulness and to a lesser extent during sleep. Sometimes during sleep it will be seen as bursts or “brushes” riding in or on other activity. Increased beta activity is known to occur during sleep in patients with major depressive disorders and in individuals taking certain drugs (e.g., barbiturates) 4–8 cps activity usually most prominent in central and temporal leads. A unique variant commonly occurs during rapid eye movement sleep and is called sawtooth theta, owing to its notched appearance, reminiscent of a buzz saw’s blade 3.5 cps activity that is usually highamplitude. Delta activity at the lower end of the spectrum (75 mV) must neither equal nor exceed 6 seconds during an epoch. Under normal circumstances, neither rapid nor slow eye movements accompany N2 (Figure 2.4, panel B).
Table 2.3 Electroencephalographic (EEG), electro-oculographic, and electromyographic (EMG) characteristics of each sleep stage Brain wave activity
Delta
Theta
Alpha
Beta
Spindle
K complex
EMs
EMG
Mentation
W
15-second a activity in a 30-second epoch 75 mV delta activity Low-voltage mixedfrequency activity with saw-tooth theta activity
þþ
þ
Slow and rapid
"
Thoughts
þ
þ
Slow
#
Hypnagogic
þ
þ
þþ
þþ
None
#
-
þþ
þ
þ
None
#
-
þ
þ
þ
Rapid
Dreams
N1
N2
N3
R
NORMAL SLEEP-RECORDING AND SCORING TECHNIQUES
Stage
EMs, eye movements.
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36
M. HIRSHKOWITZ AND A. SHARAFKHANEH E1-M2 E2-M2 EMGSM F4-M1 C4-M1 O2-M1
Fig. 2.3. Stage W. E1 (left outer canthus); E2 (right outer canthus); M1 (left mastoid); M2 (right mastoid); EMGSM (electromyogram: submentalis); F4 (right frontal); C4 (right central); O2 (right occipital).
E1-M2 E2-M2 EMGSM F4-M1 C4-M1
A
O2-M1 E1-M2 E2-M2 EMGSM F4-M1 C4-M1
B
O2-M1 E1-M2 E2-M2 EMGSM F4-M1 C4-M1 O2-M1
C Fig. 2.4. Stages N1 (panel A), N2 (panel B), and N3 (panel C). E1 (left outer canthus); E2 (right outer canthus); M1 (left mastoid); M2 (right mastoid); EMGSM (electromyogram: submentalis); F4 (right frontal); C4 (right central); O2 (right occipital).
Stage N3, previously called either stage 3 or stage 4, is scored when an epoch contains 6 seconds or more of greater than 75 mV delta or slow waves in the EEG derived from frontal sites. It should be noted, however, that the AASM manual does allow for an alternative to monopolar frontal lead recording and the specified bipolar derivation (Fz–Cz) will produce lower-amplitude signals (Figure 2.4, panel C). Unfortunately, the current version of the new system does not indicate how amplitude
criteria should be adjusted to avoid staging differences produced as an artifact of differences in recording technique.
REM
SLEEP
Stage R, previously called REM sleep, is scored when REM and muscle atonia accompanying an N1-like EEG pattern (Figure 2.5). Stage R is accompanied by
NORMAL SLEEP-RECORDING AND SCORING TECHNIQUES
37
E1-M2 E2-M2 EMGSM F4-M1 C4-M1
A
O2-M1
E1-M2 E2-M2 EMGSM F4-M1 C4-M1 O2-M1
B Fig. 2.5. Stage R. Examples of both phasic (panel A) and tonic (panel B) rapid eye movement sleep are illustrated. E1 (left outer canthus); E2 (right outer canthus); M1 (left mastoid); M2 (right mastoid); EMGSM (electromyogram: submentalis); F4 (right frontal); C4 (right central); O2 (right occipital).
Patterns across the night GENERAL
DESCRIPTION
Normal sleep stage architecture across the night is fairly consistent between individuals (Figure 2.6). A healthy young adult good sleeper will spend 7–8 hours in bed and sleep 85–90% of that time (Williams et al., 1974; Hirshkowitz et al., 1992). Normal entry into sleep
W Sleep stage
low-voltage, mixed-frequency EEG, low chin EMG levels, and saccadic eye movements. Not every epoch of stage R must contain eye movement activity. Once stage R has commenced, it continues regardless of the presence of eye movements until: (1) stage W occurs; (2) stage N3 occurs; (3) chin EMG increases and criteria for N1 are met; (4) an arousal or large body movement occurs, followed by N1-like EEG and slow eye movements; and (5) a sleep spindle or K complex occurs in the first 15 seconds of an epoch that does not contain subsequent REM. Researchers have sometimes distinguished stage R epochs with concomitant eye movement bursts from epochs lacking eye movements with the terms phasic REM sleep versus tonic REM sleep, although neither of these designations is sanctioned by the AASM scoring system.
R N1 N2 N3 0
1
2
3 4 5 6 7 Total recording time (in hours)
8
9
Fig. 2.6. Full night histogram showing sleep macroarchitecture.
for an adult may take 5–15 minutes and wakefulness usually gives way to stage N1. After a few minutes, N2 commences and it in turn is followed by N3. N3 continues either continuously or punctuated by N2 over the next hour and finally relents with the onset of stage R (occurring approximately 90 minutes from the initial sleep onset). The first stage R episode is usually brief, lasting 5–15 minutes. The end of the first stage R episode completes the first N–R (NREM–REM) cycle. Young adults will usually go back into N2 and N3 for the next 90 minutes, with stage R recurring to finalize the second N–R cycle. The second stage R episode is usually of longer duration than the first but the cycle’s N3 duration is decreased. As the sleep period progresses, succeeding N–R cycles generally have less stage N3, more N2, and longer stage R durations.
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M. HIRSHKOWITZ AND A. SHARAFKHANEH
Table 2.4 Generalizations about normal sleep stage architecture 1 2
3 4 5 6 7 8 9 10
We enter sleep through stage N1 or N2 Latency to sleep onset is about 5–15 minutes; however, it may be much longer when sleeping in the laboratory, especially when sleeping there for the first time Stage N1 occupies usually 15) were observed in patients with narcolepsy, idiopathic hypersomnolence, or moderate/severe obstructive sleep apnea (Johns, 1991). In a population-based sample, subjects with intermediate and high ESS scores, in comparison to those with low scores, had only a 30% and 69% increased risk, respectively, for sleep onset during the MSLT (Punjabi et al., 2003). In a study of 237 referred patients with suspected or confirmed sleep-disordered breathing, the ESS score did not reflect mean sleep latency on the MSLT or severity of sleep apnea as measured by the apnea–hypopnea index or minimum oxygen saturation (Chervin and
Table 3.3 The Epworth Sleepiness Scale Name: ____________________________________ Today’s date: ________ Your age (years): ________ Your sex (male ¼ M; female ¼ F): _______________ How likely are you to doze off or fall asleep in the following situations, in contrast to feeling just tired? This refers to your usual way of life in recent times. Even if you have not done some of these things recently try to work out how they would have affected you. Use the following scale to choose the most appropriate number for each situation: 0 1 2 3
¼ ¼ ¼ ¼
would never doze slight chance of dozing moderate change of dozing high chance of dozing
Situation Sitting and reading Watching TV Sitting, inactive in a public place (e.g. a theater or a meeting) As a passenger in a car for an hour without a break Lying down to rest in the afternoon when circumstances permit Sitting and talking to someone Sitting quietly after a lunch without alcohol In a car, while stopped for a few minutes in the traffic
Chance of dozing ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________
Thank you for your cooperation
Aldrich, 1999). Another study of 102 patients at a sleep disorders center also found no significant association between ESS score and mean sleep latency on MSLT (Benbadis et al., 1999). Some of the discrepancy may arise from underestimation or lack of awareness of sleep propensity: in one study nearly 20% of the subjects underestimated their risk of dozing off (Reyner and Horne, 1998). These results suggest that, in practice, the ESS is best used to assess subjective sleepiness in a standardized manner, and to complement rather than replace other neurophysiological measures. Use of ESS scores longitudinally for individual patients may prove helpful in tracking symptom evolution or treatment response. The Stanford Sleepiness Scale provides a validated, subjective measure of instantaneous sleepiness on a seven-point scale (Hoddes et al., 1972; Herscovitch and Broughton, 1981). This scale, in contrast to the ESS, can be used by the same patient many times in one day. Similarly, the older Karolinska scale and a more recent cartoon face scale are alternative methods
ASSESSMENT OF DAYTIME SLEEPINESS 49 of tracking the acute level of sleepiness in a given and increased sleep efficiency. In a study of 147 patient (Akerstedt and Gillberg, 1990; Maldonado referred patients, the only factor among demographic et al., 2004). information, polysomnographic data, and subjective The Sleep–Wake Activity Inventory (SWAI) is a assessments that was found to correlate significantly multidimensional self-report of sleepiness. The EDS with mean sleep latency on the MSLT was sleep factor was demonstrated to be a valid predictor of latency on overnight polysomnography (Chervin et al., mean sleep latency on an MSLT and also appeared to 1995). Arousals or hypoxia from sleep apnea or, less separate pathological levels of sleepiness from normal often, periodic leg movements during sleep may be alertness. The EDS factor improved in patients with an indicator of the root cause of the EDS. However, sleep-disordered breathing who were effectively treapolysomnography is not generally used as an objective ted (Rosenthal et al., 1993). When a large population measure of EDS. sample was analyzed, the SWAI results appeared to demonstrate a “natural break” in EDS scores. Scores MULTIPLE SLEEP LATENCY TEST were below 10 (less sleepy) when nocturnal sleep time The MSLT is considered the standard, for objective was at or above 7 hours and above 10 when less sleep assessment of EDS, to which all other measures are was obtained (Johnson et al., 1999). compared. Developed by Carskadon & Dement in the The impact of EDS on activities of daily living can 1970s at Stanford University, this test was described be assessed by the Functional Outcomes of Sleep Quesas a test of physiological sleep tendency. The MSLT tionnaire. Initially, this questionnaire was validated to measures the speed with which a patient is able to fall discriminate between normal subjects and those seekasleep in a controlled environment at time points ing medical attention for a sleep problem (Weaver spread throughout the day (Carskadon and Dement, et al., 1997). In an older sample, significant reductions 1977). The guidelines for the tests were later revised in functioning, for a broad range of activities, were in 1986 and some pretest conditions have been evalunoted among sleepier patients, particularly those with ated recently. In January 2005, the American Academy several medical conditions or more than four medicaof Sleep Medicine published practice parameters for tions (Gooneratne et al., 2003). the clinical use of the MSLT (Carskadon et al., 1986; Adult questionnaires are unlikely to be optimal for Thorpy, 1992; Bonnet and Arand, 1998; Littner et al., use with children and adolescents. For these age groups, 2005). one of the first parental EDS assessments to be validated The technical guidelines for recording an MSLT are is contained within the Pediatric Sleep Questionnaire similar to those for nocturnal polysomnography, and at (PSQ). The PSQ and four-item sleepiness subscale have minimum utilize the Rechtschaffen and Kales (1968) proved useful in research conducted in general pediatric recording montage required to stage sleep. Included waiting rooms (Chervin et al., 2000; Archbold et al., in the montage are a referential EEG from a central 2002). Another instrument also used primarily for (C3 or C4) location, two horizontal electro-oculograms research is the Children’s Sleep Habits Questionnaire, (left and right) at the outer canthi, and a mental or subwhich screens children aged 4–10 years for sleep promental electromyogram. One or two occipital EEG blems and includes a subscale on daytime sleepiness leads are often helpful to determine sleep onset, as (Owens et al., 2000). The Pediatric Daytime Sleepiness reflected by loss of alpha activity. Other helpful leads Scale is a 13-question survey suitable for assessment of include an electrocardiogram, a microphone for respiEDS in middle-school-age children (Drake et al., 2003). ratory noise, a measure of nasal–oral airflow, and belts Finally, the ESS also has been modified and used in chilfor the detection of chest and abdominal movement. dren for research purposes (Melendres et al., 2004). None Measures of airflow and chest movement may, for of these questionnaires have been validated against example, reveal that sleep-disordered breathing interMSLT results, except for the PSQ sleepiness subscale feres with sleep onset. (Chervin et al., 2006). This subscale correlates with Preparation for the MSLT ideally begins with a 1- or MSLT findings to a limited extent, similar to that gener2-week sleep log kept by the patient, to assist with ally observed between subjective and objective sleepiness interpretation of the study. It has been recommended measures in adults. that the sleep–wake cycle be standardized for at least 7 days before the test (American Academy of Sleep NOCTURNAL POLYSOMNOGRAPHY Medicine, 2005). Medications that affect sleep, rapid The overnight polysomnogram often plays a central eye movement (REM) sleep, or sleepiness should be role in the diagnostic process. Findings that may help discontinued, if safe to do so, at least 15 days or five assess the severity of EDS include a short sleep latency half-lives before testing (American Academy of Sleep
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D.B. KIRSCH AND R.D. CHERVIN
Medicine, 2005). Records of other substances that may change sleep, such as caffeine products, alcohol, or illegal drugs, should also be obtained, and these agents should not be used during the test (though withdrawal from higher doses of caffeine may also modify test results) (Carskadon et al., 1986). Optimally, a nocturnal polysomnogram should be performed during the patient’s usual hours of sleep, before beginning the MSLT. This allows interpretation of MSLT results with full understanding of the quality and amount of sleep on the previous night. The nap attempts should take place in a bedroom that is dark and quiet, at the patient’s desired temperature. The subject wears regular street clothes. Four or five nap opportunities begin 1.5–3 hours after the end of the polysomnogram and continue at 2-hour intervals. Prior to each attempt, the subject lies in bed and is told to “allow yourself to fall asleep” or “not to resist falling asleep.” No sleeping is allowed between the tests, nicotine use is avoided 30 minutes before each test, and vigorous activity is suspended 15 minutes before each trial (Carskadon et al., 1986). If sleep occurs at any time during one of the 20-minute trials, 15 additional minutes of recording time are allowed to see whether “sleep-onset” REM sleep occurs. If no sleep occurs, the nap attempt is terminated and a sleep latency of 20 minutes is recorded for the trial. The sleep latencies from all four or five nap attempts are averaged to obtain the mean sleep latency. Interpretation of the MSLT result must take place within the clinical context. A mean sleep latency of less than 8 minutes in adults is generally considered to reflect severe, pathological sleepiness. Values of 8 minutes or less are commonly found in patients with disorders that cause EDS (American Academy of Sleep Medicine, 2005). In a recent meta-analysis, narcoleptics had a mean sleep latency of 3.1 2.9 minutes, and patients with idiopathic hypersomnia had a mean sleep latency of 6.2 3.0 minutes (Littner et al., 2005). Normal adults typically have sleep latencies between 10 and 20 minutes. Roehrs and Roth (1992) suggest that a sleep latency of 9 minutes or more may be normal, based on studies of older patients. Other studies have also demonstrated changes in sleep latency normative values based on age: college students had a mean latency of 9.9 minutes, adults 18–29 years old 11.1 minutes, and adults 30–80 years old 12.5 minutes (Levine et al., 1988). Children tend to have substantially longer sleep latencies; for instance, a preadolescent may have an average sleep latency of 19 minutes (Hoban and Chervin, 2001). The MSLT has been used to demonstrate EDS in several types of sleep disorders, most notably narcolepsy, obstructive sleep apnea, and experimentally induced sleep deprivation (Carskadon and Dement, 1981; Mitler
et al., 1987). In addition to shortened sleep latency, many patients with narcolepsy (at least 80%) show REM sleep on two or more MSLT naps (van den Hoed et al., 1981). However, other chronic sleep disorders and especially obstructive sleep apnea can also cause similar sleep-onset REM periods (Chervin and Aldrich, 2000). Therefore, clinical information and the preceding polysomnogram are essential to an interpretation of MSLT results. The MSLT also may be used to assess response to treatment of EDS, though the utility of this measurement is uncertain, as patients may have a better clinical response than improvement in objective sleep latency (Thorpy, 1992). In clinical practice, the MSLT is useful to confirm narcolepsy with cataplexy; essential to make a diagnosis of narcolepsy without cataplexy; sometimes useful to identify narcolepsy secondary to another medical condition; and often useful to distinguish idiopathic hypersomnolence from narcolepsy. The MSLT is not routinely indicated in the assessment of patients for obstructive sleep apnea or its response to treatment, insomnia, circadian rhythm disorders, or medical or neurological causes of sleepiness (Littner et al., 2005).
MAINTENANCE OF WAKEFULNESS TEST The Maintenance of Wakefulness Test (MWT) is a variation of the MSLT designed to assess an individual’s ability to remain awake during sleep-inducing circumstances. The patient, seated in bed within a dimly lit and quiet room, is told to “sit still and remain awake” rather than to “try to fall asleep,” as in an MSLT. As initially described, the subject is monitored for sleep onset during five sessions, each lasting 20 or 40 minutes, scheduled at 2-hour intervals, beginning 2 hours after awakening from a prior polysomnogram. The recording guidelines from a polysomnographic perspective resemble those for an MSLT. More recent recommendations have included a light source of 0.1 lux, room temperature adjusted for patient comfort, and light meals 1 hour before the first nap and immediately after the noon nap. Patients should not be allowed to use extraordinary measures to stay awake, such as slapping their own face (Mitler et al., 1982; Doghramji et al., 1997). When MWT sleep latencies are compared to MSLT latencies in control subjects, the MWT sleep latencies are longer by approximately 300%. Patients with narcolepsy have shorter sleep latencies than control patients. Though fewer data exist for the MWT in comparison to the MSLT, evidence supports use of the MWT to monitor treatment effects in some sleep disorders (Mitler et al., 1982; Poceta et al., 1992). Initially, controversy existed due to the wide variety of protocols used (nap length, number of naps) and the lack of normative data.
ASSESSMENT OF DAYTIME SLEEPINESS However, some normative data were published by Doghramji et al. (1997) for both 20- and 40-minute nap length. The lower limit for normal sleep latency (first epoch of sleep scored) was considered 2 standard deviations below the mean: for a 20-minute MWT, the limit is 10.9 minutes (mean ¼ 18.1 3.6 minutes); for a 40-minute MWT, the lower limit is 12.9 minutes (mean 32.6 9.9 minutes). These data showed no clear evidence that age or sex affected MWT sleep latency (Mitler et al., 2000). One advantage of the MWT is that it has face value as a measure of the ability to stay awake under sedentary circumstances, which could have relevance for safety, concentration, or job performance. The MWT has been used by the Federal Aviation Administration to aid in determination of whether pilots with treated sleep apnea are alert enough to fly (Office of Aerospace Medicine, 2003). Some have suggested that patients should not be allowed to drive if their MWT is less than 15 minutes (1 standard deviation below the mean for sleep apnea patients in one series) (Poceta et al., 1992). However, no clear standard has been set for the MWT sleep latency for operators of any vehicle type, including drivers involved with public transportation (Poceta et al., 1992). Furthermore, neither the MWT nor the MSLT has been validated prospectively as an effective predictor of motor vehicle crashes or other accidents related to sleepiness.
OTHER TESTING Pupillometry measures the spontaneous variation of the pupil diameter and the pupillary light reflex. Sleepiness-related alterations in spontaneous pupil behavior in a dark environment were described by Lowenstein & Loewenfeld in 1958. Later, Yoss et al. (1969) discovered pupillary changes in patients with narcolepsy. More recent studies have confirmed these earlier reports, and have attempted to increase the objectivity of pupillometry (Wilhelm et al., 1998). Increase in spontaneous pupil variation has been observed in patients with hypersomnolence, including those with narcolepsy, sleep apnea, and increased sleep disruption (Cluydts et al., 2002). Many pupillometric variables correlate with mean sleep latency on MSLT, but not to the extent that they can be used effectively to predict the mean sleep latency (McLaren et al., 2002). Pupillometry remains expensive, complex, and rarely performed for assessment of sleepiness. Vigilance testing examines subjects’ performance on a monotonous task, which may be particularly relevant to sedentary work over long periods. One example of a practical vigilance test is a driving simulator; patients with narcolepsy and sleep apnea perform worse on a 30-minute test than do subjects without sleep disorders (Findley et al., 1995). Combination of a driving simulator
51
with a simultaneous cognitive task may be even more sensitive for sleepiness (Rupp et al., 2004). The Performance Vigilance Test, a monotonous, simple reaction time test, is sensitive to insufficient sleep and also improves after treatment for obstructive sleep apnea (Kribbs et al., 1993; Dinges et al., 1997). The Oxford Sleep Resistance Test monitors similar responses to a repeating stimulus and is used to point at which responses cease to determine sleep latency. Subjects with severe untreated sleep apnea clearly perform worse than normal controls (Bennett et al., 1997). Many other cognitive tests, including those of attention and memory, also may differentiate patients with sleep disorders from healthy subjects (Fulda and Schulz, 2001). Evoked potentials have been investigated for potential use in the assessment of sleepiness. Brainstem auditory evoked responses measured during sleep in apneic patients and narcoleptics are normal. Long-latency cortical potentials, both auditory and sensory, appear more sensitive to sleepiness, but have large individual variability, causing difficulty with interpretation. Event-related potentials, most commonly the auditory P300, have been used to distinguish between subjects with sleep apnea from normal controls, based on wave amplitude and latency. However, the wave alterations generally remain within physiological limits, and treatment of sleep apnea with positive airway pressure may not shorten this mildly prolonged latency (Bastuji and Garcia-Larrea, 1999). In narcolepsy, some studies have suggested that the amplitude of the auditory P300 is diminished, though other authors have not found this abnormality (Broughton et al., 1988; Sangal and Sangal, 1995).
PRACTICAL APPLICATIONS Although most patients referred to a sleep disorders clinic have a chief complaint of EDS, assessment of this problem by a careful history is usually sufficient to estimate the severity and impact. Most of the diagnostic challenge centers on the determination of underlying causes. An ESS can provide a quick, inexpensive, and repeatable quantification of subjective sleepiness. A nocturnal polysomnogram is often indicated to identify root causes for abnormal sleepiness, when such causes are not readily apparent from the history. In practice, most polysomnograms are obtained to identify or quantify any underlying sleep-disordered breathing. An MSLT is required when cataplexy is absent but narcolepsy is still in the differential diagnosis. Clinicians sometimes obtain an MSLT when an objective measure of sleepiness may help to resolve conflicting historical reports (for example, from family members and the patient); motivate treatment of sleepdisordered breathing that by history may not be severe;
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distinguish mood disorder-related sleepiness, often associated with normal sleep latencies, from other causes; or provide objective support for consequential decisions or recommendations (relating to surgery, for example). An MWT may be useful when the main clinical question concerns the ability of the patient to maintain wakefulness during sedentary situations. However, despite intuitive appeal and reasonable demonstrations of validity, neither the MSLT nor other tests of sleepiness have been adequately demonstrated to predict future motor vehicle crashes or other morbidity. Confounds, such as anxiety, can affect test results, which therefore tend to be more useful when they confirm or detect severe sleepiness than when they fail to demonstrate short sleep latencies. Negative MSLT findings generally should not be used to discount reliable subjective reports of excessive sleepiness. No single test can provide a complete assessment of excessive sleepiness, which often must involve integration of clinical, subjective, and objective laboratory data.
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Carskadon MA, Dement WC (1977). Sleep tendency: an objective measure of sleep loss. Sleep Res 6: 200. Carskadon MA, Dement WC (1981). Cumulative effects of sleep restriction on daytime sleepiness. Psychophysiology 18: 107–113. Carskadon MA, Dement WC, Mitler MM et al. (1986). Guidelines for the multiple sleep latency test (MSLT): a standard measure of sleepiness. Sleep 9: 519–524. Chervin RD (2000). Sleepiness, fatigue, and lack of energy in obstructive sleep apnea. Chest 118: 372–379. Chervin RD (2003). Assessment of daytime sleepiness. In: S Chokroverty, WA Hening, AS Walters (Eds.), Sleep and Movement Disorders. Elsevier, Philadelphia, pp. 132–143. Chervin RD, Aldrich MS (1999). The Epworth sleepiness scale may not reflect objective measures of sleepiness or sleep apnea. Neurology 52: 125–131. Chervin RD, Aldrich MS (2000). Sleep onset REM periods during multiple sleep latency tests in patients evaluated for sleep apnea. Am J Respir Crit Care Med 161 (2 Pt 1): 426–431. Chervin RD, Kraemer HC, Guilleminault C (1995). Correlates of sleep latency on the multiple sleep latency test in a clinical population. Electroencephalogr Clin Neurophysiol 95 (3): 147–153. Chervin RD, Hedger K, Dillon JE et al. (2000). Pediatric sleep questionnaire (PSQ): validity and reliability of scales for sleep-disordered breathing, snoring, sleepiness, and behavioral problems. Sleep Med 1: 21–32. Chervin RD, Weatherly RA, Ruzicka DL et al. (2006). Subjective sleepiness and polysomnographic correlates in children scheduled for adenotonsillectomy vs. other surgical care. Sleep 29: 495–503. Chokroverty S (2009). An overview of sleep. In: S Chokroverty (Ed.), Sleep Disorders Medicine. 2nd edn. Saunders/ Elsevier, Philadelphia, pp. 5–21. Cluydts R, De Valck E, Verstaeten E et al. (2002). Daytime sleepiness and its evaluation. Sleep Med 6 (2): 83–96. Dinges DF, Pack F, Williams K et al. (1997). Cumulative sleepiness, mood disturbance, and psychomotor vigilance performance decrements during a week of sleep restriction to 4–5 hours per night. Sleep 20 (4): 267–270. Dittner AJ, Wessely SC, Brown RG (2004). The assessment of fatigue: a practical guide for clinicians and researchers. J Psychosom Res 56: 157–170. Doghramji K, Mitler MM, Sangal RB et al. (1997). A normative study of the maintenance of wakefulness test. Electroencephalogr Clin Neurophysiol 103: 554–562. Drake C, Nickel C, Burduvali E et al. (2003). The pediatric daytime sleepiness scale (PDSS): sleep habits and school outcomes in middle-school children. Sleep 26 (4): 455–458. Fava M (2004). Daytime sleepiness and insomnia as correlates of depression. J Clin Psychiatry 65 (Suppl. 16): 27–32. Findley L, Unverzagt M, Guchu R et al. (1995). Vigilance and automobile accidents in patients with sleep apnea or narcolepsy. Chest 108: 619–624.
ASSESSMENT OF DAYTIME SLEEPINESS Fulda S, Schulz H (2001). Cognitive dysfunction in sleep disorders. Sleep Med Rev 5 (6): 423–445. Gibbs JW 3rd, Ciafaloni E, Radtke RA (2002). Excessive daytime somnolence and increased rapid eye movement pressure in myotonic dystrophy. Sleep 25: 672–675. Gooneratne NS, Weaver TE, Cater JR et al. (2003). Functional outcomes of excessive daytime sleepiness in older adults. J Am Geriatr Soc 51 (5): 642–649. Herscovitch J, Broughton R (1981). Sensitivity of the Stanford sleepiness scale to the effects of cumulative partial sleep deprivation and recovery oversleeping. Sleep 4: 83–91. Hoban TF, Chervin RD (2001). Assessment of sleepiness in children. Semin Pediatr Neurol 8 (4): 216–228. Hoddes E, Dement W, Zarcone V (1972). The development and use of the Stanford sleepiness scale (SSS). Psychophysiology 9: 150. Johns MW (1991). A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep 13: 540–545. Johnson EO, Breslau N, Roth T et al. (1999). Psychometric evaluation of daytime sleepiness and nocturnal sleep onset scales in a representative community sample. Biol Psychiatry 45 (6): 764–770. Kapur VK, Redline S, Nieto FJ et al. (2002). The relationship between chronically disrupted sleep and healthcare use. Sleep 25: 289–296. Kribbs NB, Pack AI, Kline LR et al. (1993). Effects of one night without nasal CPAP treatment on sleep and sleepiness in patients with obstructive sleep apnea. Am Rev Respir Dis 147 (5): 1162–1168. Levine B, Roehrs T, Zorick F et al. (1988). Daytime sleepiness in young adults. Sleep 11: 39–46. Littner MR, Kushida C, Wise M et al. (2005). Practice parameters for clinical use of the multiple sleep latency test and the maintenance of wakefulness test. Sleep 28: 113–121. Lowenstein O, Loewenfeld IE (1958). Electronic pupillography: a new instrument and some clinical applications. Arch Ophthalmol 59: 352–363. Lyznicki JM, Doege TC, Davis RM et al. (1998). Sleepiness, driving, and motor vehicle crashes. JAMA 279: 1908–1913. McLaren JW, Hauri PJ, Lin SC et al. (2002). Pupillometry in clinically sleepy patients. Sleep Med 3: 347–352. Maldonado CC, Bentley AJ, Mitchell D (2004). A pictorial sleepiness scale based on cartoon faces. Sleep 27 (3): 541–548. Melendres MC, Lutz JM, Rubin ED et al. (2004). Daytime sleepiness and hyperactivity in children with suspected sleep-disordered breathing. Pediatrics 114 (3): 768–775. Mignot E (1998). Genetic and familial aspects of narcolepsy. Neurology 50 (2): S16–S22. Mitler MM, Gujavarty KS, Browman CP (1982). Maintenance of wakefulness test: a polysomnographic technique for evaluation of treatment efficacy in patients with excessive somnolence. Electroencephalogr Clin Neurophysiol 53: 658–661. Mitler MM, Nelson S, Hajdukovic R (1987). Narcolepsy: diagnosis, treatment, and management. Psychiatr Clin North Am 11: 307–317.
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Mitler MM, Doghramji K, Shapiro C (2000). The maintenance of wakefulness test: normative data by age. J Psychosom Res 49: 363–365. National Sleep Foundation (2002). Sleep in America Poll. NSF, Washington, DC. Office of Aerospace Medicine (2003). Guide for Aviation Medical Examiners. Available online at: http://www.faa.gov Owens JA, Spirito A, McGuinn M (2000). The children’s sleep habits questionnaire (CSHQ): psychometric properties of a survey instrument for school-aged children. Sleep 23 (8): 1–9. Pigeon W, Sateia M, Ferguson R (2003). Distinguishing between excessive daytime sleepiness and fatigue: toward improved detection and treatment. J Psychosom Res 54: 61–69. Poceta JS, Timms RM, Jeong DU et al. (1992). Maintenance of wakefulness test in obstructive sleep apnea syndrome. Chest 101: 893–897. Punjabi NM, Bandeen-Roche K, Young T (2003). Predictors of objective sleep tendency in the general population. Sleep 26 (6): 678–683. Rechtschaffen A, Kales A (1968). A Manual of Standardized Terminology, Techniques, and Scoring System for Sleep Stages of Human Subjects. Brain Information Service/ Brain Research Institute, Los Angeles. Reyner LA, Horne JA (1998). Falling asleep whilst driving: are drivers aware of prior sleepiness? Int J Legal Med 111: 120–123. Rinaldi R, Vignatelli L, D’Alessandro R et al. (2001). Validation of symptoms related to excessive daytime sleepiness. Neuroepidemiology 20: 248–256. Roehrs T, Roth T (1992). Multiple sleep latency test: technical aspects and normal values. J Clin Neurophysiol 9 (1): 63–67. Roehrs T, Carskadon M, Dement W et al. (2000). Daytime sleepiness and alertness. In: M Kryger, T Roth, W Dement (Eds.), Principles and Practice of Sleep Medicine. 3rd edn. W.B. Saunders, Philadelphia, pp. 43–52. Rosen GM, Bendel AE, Neglia JP (2003). Sleep in children with neoplasms of the central nervous system: case review of 14 children. Pediatrics 112 (1): 46–54. Rosenthal L, Roehrs TA, Roth T (1993). The sleep–wake activity inventory: a self-report measure of daytime sleepiness. Biol Psychiatry 34 (11): 810–820. Rupp T, Arnett JT, Acebo C et al. (2004). Performance on a dual driving simulation and subtraction task following sleep restriction. Percept Mot Skills 99: 739–753. Sangal RB, Sangal JM (1995). P300 latency: abnormal in sleep apnea with somnolence and idiopathic hypersomnia, but normal in narcolepsy. Clin Electroencephalogr 26: 146–153. Taheri S (2004). The genetics of sleep disorders. Minerva Med 95 (3): 203–212. Thorpy MJ (1992). The clinical use of the multiple sleep latency test: the Standards of Practice Committee of the American Sleep Disorders Association. Sleep 15 (3): 268–276. Torsvall L, Akerstedt T, Gillander K et al. (1989). Sleep on the night shift: 24h EEG monitoring of spontaneous sleep/wake behavior. Psychophysiology 26: 352–358.
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van den Hoed J, Kraemer H, Guilleminault C et al. (1981). Disorders of excessive daytime somnolence: polygraphic and clinical data for 100 patients. Sleep 4: 23–37. Weaver TE, Laizner AM, Evans LK et al. (1997). An instrument to measure functional status outcomes for disorders of excessive sleepiness. Sleep 20 (10): 835–843.
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Handbook of Clinical Neurology, Vol. 98 (3rd series) Sleep Disorders, Part 1 P. Montagna and S. Chokroverty, Editors # 2011 Elsevier B.V. All rights reserved
Chapter 4
Actigraphic monitoring of sleep and circadian rhythms EUS J.W. VAN SOMEREN * Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Society of Arts and Sciences; Department of Integrative Neurophysiology, VU University and Leiden Institute for the Clinical and Experimental Neuroscience of Sleep, Leiden University Medical Center, The Netherlands
INTRODUCTION Although polysomnography, the continuous monitoring of multiple physiological parameters during sleep, as described in Chapter 2, is the golden standard for the objective assessment of sleep and its disturbances, there may be circumstances that ask for a different approach. For example, one may want to evaluate a large number of nights, or subjects who comply poorly with wearing electrodes for hours, as may be the case in children, or in dementia. Actigraphy provides a cost-effective method of estimating the occurrence of periods of sleep and wakefulness from information on the timing, duration, and intensity of movements for multiple days, weeks, or even months. Actigraphy is the continuous long-term assessment of activity-induced acceleration by means of a small solid-state recorder. Technical progress has enabled the integration of an acceleration sensor, amplifier, filter, microprocessor, and digital memory into a case the size of a wristwatch. Like a wristwatch, these so-called actigraphs are usually worn on the wrist. After the first report on the relation of wrist movement to sleep (Kupfer et al., 1974), the first actigraphs were soon described (Colburn et al., 1976; McPartland et al., 1976) and validated for use in sleep research (Kripke et al., 1978; Mullaney et al., 1980; Webster et al., 1982). Since then, actigraphs of decreasing size and increasing capacity have become commercially available, of which an example is shown in Figure 4.1. The present chapter discusses their application in clinical
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and experimental research on sleep and its day–night rhythm.
APPLICATIONS Actigraphy has been applied in a variety of clinical and research fields which include sleep disorders, obesity, depression, hyperactivity, and movement disorders, including periodic leg movements during sleep (reviewed in Tryon, 1991). The most extensive use has been in sleep research in healthy subjects, where it has even been suggested as an alternative for the costly and time-consuming gold standard of polysomnography. The reliability of actigraphy in the clinical evaluation of sleep disorders is a matter of debate, mostly focusing on the question whether actigraphy can replace polysomnography (Pollak et al., 2001; Tryon, 2004). There is no doubt, however, that actigraphic recordings can give valuable insights into a patient’s sleep and sleep–wake rhythms, whether or not a further investigation with polysomnography is required. Practice parameters for the use of actigraphy in the clinical assessment of sleep disorders have been published by the Board of Directors of the American Academy of Sleep Medicine in 1995 (Sadeh et al., 1995). In 2003, the practice parameters were updated (Littner et al., 2003), with an accompanying review paper on the role of actigraphy in the study of sleep and circadian rhythms (Ancoli-Israel et al., 2003). The present chapter focuses on the use of actigraphy in estimating sleep parameters and in obtaining the rest– activity rhythm over multiple days.
Correspondence to: Prof. Eus J.W. Van Someren, Head Dept. Sleep and Cognition, Netherlands Institute for Neuroscience, Meibergdreef 33, 1105 AZ Amsterdam, The Netherlands. Tel: þ 31 20 566 5500, Fax: þ 31 20 6961006, E-mail: e.van.
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Fig. 4.1. Example of an actigraph worn on the wrist (Actiwatch, Cambridge Neurotechnology, Cambridge, UK).
THE ACCELERATION SIGNAL: MOVEMENT AND ARTIFACT The movement-induced signal that actigraphs utilize is picked up by a piezoelectric element, which generates small voltages if accelerations occur. It is important to realize that actigraphy data may contain artifacts. Artifacts that may affect the signal mostly during wakefulness include externally imposed movement from riding in vehicles (Ancoli-Israel et al., 1997; Pollak et al., 2001). An artifact that may be of more importance during sleep is that very sensitive accelerometers can pick up chest movements associated with breathing, if the wrist is positioned on the chest. In addition to these artifactual signalgenerating events, there may be artifactual absence of signal if an actigraph has (temporarily) not been worn. The artifacts mentioned above generate faults in the presence or absence of activity. In addition, there is the risk of an artifact that strongly affects the strength, i.e., amplitude, of the movement-induced acceleration signal. This artifact is due to the earth’s gravitational field. More specifically, the mere rotation of the wrist from upwards to downwards will induce an acceleration signal change of 2 g. This signal is a strong overestimate of the energy involved in the arm movement, because it would take much more muscle effort to induce a signal of 2 g with a wrist movement that does not change the orientation of the accelerometer in the gravitational field. The frequency range that is most affected by such gravitational artifacts depends on the speed of rotation of the wrist. Detailed investigations have demonstrated that most of these artifacts occur in the frequency range below 0.5 Hz (Van Someren et al., 1996b). These artifacts have led early studies to suggest that most of the activity-induced accelerations occur around 0.25 Hz (Redmond and Hegge, 1985), which resulted in low-pass filtering at 2 Hz in early actigraphs. However, later work demonstrated that frequency components of up to about 11 Hz are prominently present in movementinduced acceleration signals, while relatively few truly movement-induced accelerations occur below 0.5 Hz
(Van Someren et al., 1996b). Thus, although it is not possible to prevent gravitational artifacts completely with single-site accelerometer signal, a band-pass filter of 0.5–11 Hz is presently advocated and will yield a more acceptable estimate than the early filter settings of 0.25–2 Hz. After filtering, a data reduction step is necessary to allow for storage of long-term activity data in the limited memory of actigraphs. This may be accomplished in several ways, and some actigraphs leave the choice to the user. The following methods have been applied. First, one may reduce the data by measuring the time that the acceleration signal exceeds a certain threshold just above the noise floor of the device, generating a “time above threshold” number, to be stored in every 30-second or 1-minute epoch. Longer epochs are not advocated for reliable sleep detection. Alternatively, one may integrate the acceleration signal over the time it exceeds the threshold, generating a so-called area under the curve. Yet another approach is to count the number of threshold crossings, often referred to as “zero crossings.” For the remainder of this chapter we will refer to any such output as “activity level.” Depending on the mode of recording, it may be necessary to fine-tune the algorithms used to derive estimates of sleep and wakefulness from the 30-second or 1-minute epochs of activity levels.
PLACEMENT OF THE ACTIGRAPH Actigraphs have mostly been placed on the nondominant wrist, but may also be placed on the dominant wrist, the ankles, or the trunk. During active daytime wakefulness, the dominant wrist shows most motor activity (Middelkoop et al., 1997). The effect of using the dominant versus nondominant wrist on the validity of the nocturnal sleep–wake estimates is equivocal (Van Hilten et al., 1993; Nagels et al., 1996). Assessment from other places on the body generally gives results that differ only marginally from wrist-assessed movements (Meijer et al., 1992; Middelkoop et al., 1997). However, in sleep–wake rhythm research, the dominant wrist may be the preferred site in subjects who are virtually nonambulatory and sedentary, as is the case in some demented elderly patients. In conclusion, placement on the wrist is recommended, and whereas the effect of placement on the dominant or nondominant site is equivocal, it should be standardized for all subject groups within a study.
ESTIMATING SLEEP^WAKE STATE AND SLEEP PARAMETERS During sleep, the activity level is low and periods of immobility last much longer than during quiet wakefulness. Based on these simple premises, algorithms have
ACTIGRAPHIC MONITORING OF SLEEP AND CIRCADIAN RHYTHMS been developed to estimate from a time series of activity counts whether a subject is awake or asleep (Cole et al., 1992; Sadeh et al., 1994). The algorithms require storage of activity level in 30-second or 1-minute intervals, and do not work well if the data have been acquired and stored with a lower time resolution, i.e., aggregated over longer time intervals. In general, the classification of an epoch as representing “sleep” or “wakefulness” is based on a weighted sum of the activity level in the current epoch and of the activity levels and their standard deviation in a time window of a few minutes surrounding the current epoch. If this sum exceeds a certain threshold, the epoch is scored as wakefulness, and if not, as sleep. This results in a sequence of sleep and wake epochs for each recorded night, from which parameters like sleep latency, sleep duration, wakefulness after sleep onset, sleep efficiency, and several fragmentation indexes can be derived. An example of how such algorithms translate activity levels into sleep estimates is shown in Figure 4.2. Several studies (Pilsworth et al., 2001) investigated the reliability of actigraphic sleep estimate by means of a one-to-one comparison of actigraphy epochs classified as sleep versus wakefulness and equivalent polysomnography epochs classified as sleep versus wakefulness by the gold standards of Rechtschaffen
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and Kales (1968). In healthy subjects, actigraphy is a sensitive method: nearly 100% of the epochs classified as sleep by polysomnography are also identified as sleep by actigraphy. The specificity, however, is poor: actigraphy correctly identifies only about 40% of the epochs classified as wakefulness using polysomnography. Because healthy subjects have only a limited amount of wakefulness during their major sleep period, the overall accuracy is still high: about 90% of the epochs obtain the same classification from actigraphy and polysomnography. The reliability and validity of the actigraphy-derived sleep parameters are a matter of debate. As a result of the low sensitivity for wakefulness during the nocturnal sleep period, actigraphy tends to underestimate intermittent wakefulness and overestimate the total sleep time and sleep efficiency (Mullaney et al., 1980; Cole et al., 1992; De Souza et al., 2003). The precision of the sleep parameter estimates, and especially the precision of the sleep onset latency estimate, is very sensitive to even small deviations in the reported times of lights out and getting up, because these times have to be entered into the sleep-scoring software and determine the start and stop time of the analysis. Usually, these times are obtained from a sleep–wake diary the subject is asked to fill out daily. However, even healthy subjects may make considerable mistakes, and a
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Fig. 4.2. Example of the steps taken to derive the sleep–wake state and sleep parameters. The upper panel shows the first 2 days of a typical activity recording, where every bar represents the activity level in 1 minute. The gray part of the time series is zoomed in on in the middle panel, to provide more detail in the alternating periods of activity and rest. Based on a sleep– wake diary, the times of lights out (23:53 hours) and getting out of bed (6:47 hours) have to be entered into the software. They are shown as small dark gray bars just below the second panel. Subsequently an algorithm is run to estimate sleep onset (23:58 hours) and offset (6:47 hours), shown as small light gray bars just below the middle panel, as well as the momentary sleep– wake state over the night, which is shown in the third (thin) panel as alternating gray (wakefulness) and white (sleep) periods. Sleep parameters can be calculated from this sequence. Note, in the present example, that the subject appears to sleep soundly for the first sleep cycle of about 80 minutes, then experiences much wakefulness for more than an hour, after which sleep is once more rather sound. For the example given, a total sleep time of 5:47 hours results, and a sleep efficiency of 84%. Usually, such sleep parameters are calculated and averaged over multiple nights. (Sleep Analysis software, Cambridge Neurotechnology, Cambridge, UK.)
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Fig. 4.3. A bedside monitoring system, consisting of a miniature logger equipped with a light sensor, a pressure-sensitive mat switch, and a software algorithm can be used to determine automatically bedtime, lights-out time, and rise time. The precision of these times determines the precision of actigraphic sleep estimates. Sleep diary times are prone to contain errors (Krahn et al., 1997; Usui et al., 1998; Eissa et al., 2001), which is not surprising because subjects are required to memorize precisely clock times at the very times when their cognitive abilities suffer from high sleep pressure or sleep inertia. The figure gives an example of a 24-hour (ordinate) actigraphic recording combined with the automated bedtime detection system. Black columns represent minute-by-minute activity counts (abscissa, arbitrary units). The light grey area indicates the period during which the subject is in bed and the dark grey area indicates the lights-out period.
precise sleep–wake diary may not even be feasible at all in very young subjects and patients with motor disabilities or limited cognitive capabilities. In these groups, actigraphic sleep estimates may become feasible only by combining actigraphy with a bedside monitor which records bedtimes, lights-out times and get-up times. Such a system has recently been developed in our group (Figure 4.3), allowing for sleep parameter estimates in these subjects as well as for improved reliability of sleep parameter estimates in healthy subjects.
COMPARISON WITH POLYSOMNOGRAPHY Actigraphy has some disadvantages as compared to polysomnography. Although a reasonable estimate of being awake or asleep is feasible from actigraphy recordings in healthy subjects under normal conditions, the reliability in, for example, insomniac and elderly patients may be worse: these subjects show an increase in the number of epochs where no movements are made, yet wakefulness is present (Hauri and Wisbey, 1992). Also, actigraphy cannot discriminate between sleep stages. In case of screening for sleep apnea, polysomnography can easily be extended to include sensors obtaining respiratory effort, oronasal airflow, and blood oxygen desaturation. Obviously, this is not within the scope of actigraphy. On the other hand, actigraphy also has a number of advantages as compared to polysomnography. Actigraphy is cost-effective, easily applied, less demanding for the subject, and allows several nights of recording continuously. This makes sleep studies feasible in a larger number of clinical and experimental investigations. For example, whereas polysomnography may be difficult
to attain in demented elderly individuals, actigraphy is usually well tolerated. The advantage of being able to record for several nights continuously deserves attention. It is known that two polysomnographic recordings obtained over subsequent nights may show considerable differences. This has been referred to as a “first-night effect.” In a study on the first-night effect in actigraphic recordings, no systematic difference for the first night could be found (Van Hilten et al., 1993). However, there was a considerable within-subject variation over the six nights recorded. This indicates that, in addition to systematic first-night effects, there may also be a considerable variability in sleep parameters as obtained over several nights. Acebo and colleagues (1999) have provided estimates of the reliability of sleep scores based on 1–7 nights in children. We have recently investigated the day-today variability in a systematic empirical way in elderly insomniacs and demented elderly subjects: the reliability of sleep parameter estimates continues to increase if the number of recorded nights is extended, even up to 10 nights of sleep (Van Someren, 2007). Thus, an advantage of actigraphy over polysomnography is that it is much more feasible to do such long-term investigations that allow for improved sleep parameter estimates as well as insight into the variability of the sleep parameters. This advantage has not yet been fully exploited, since clinicians and researchers have often relied on three nights of recording, the minimum advised in the practice parameters for the use of actigraphy in the clinical assessment of sleep disorders, as published by the Board of Directors of the American Academy of Sleep Medicine (Littner et al., 2003). Figure 4.4 shows how the reliability of an actigraphic estimate of the percentage of wakefulness after sleep
Absolute difference (mean±s.e.m.) of two estimates of % wake after sleep onset (WASO)
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Fig. 4.4. The reliability of an actigraphic estimate of the percentage of wakefulness after sleep onset (WASO) in a group of 12 demented elderly individuals improves with the number of recorded days. Subjects were actigraphically recorded for 20 days continuously, and actigraphic WASO estimates were derived in pairs from the day 1–10 period and from the day 11–20 period. Pairs resulted from calculating WASO twice for a single day (day 1 and day 11), twice over a period of 2 days (days 1–2 and 11–12), twice over 3 days (days 1–3 and 11–13), up to twice over 10 days (days 1–10 and 11–20). The resulting WASO estimates were averaged over the number of days. The figure shows how the average ( SEM, abscissa) absolute difference between two separate actigraphic estimates of WASO, derived from assessments only 10 days apart, decreases with the number of days (ordinate) included to obtain the estimate.
onset (WASO) in a group of 12 demented elderly individuals improves with the number of recorded days.
CIRCADIAN AND DIURNAL RHYTHMS Circadian rhythms, i.e., rhythms with a period of about 24 hours, are present in most physiological and behavioral parameters, including the vigilance state (sleep versus wakefulness) and activity level. Such rhythms are usually described in terms of the phase, period, and amplitude of a sinusoidal curve fitted to the data. In experimental protocols, the functionality of the circadian timing system is usually evaluated by measuring alterations in the period, phase, and/or amplitude of this curve after imposing shifts in the environmental light–dark cycle, or by putting animals or human subjects in a constantly lit environment without any time cues for a period of up to weeks or months. In the latter situation a rhythm that may deviate from 24 hours emerges, and this is called the free-running rhythm. The majority of actigraphic studies, however, are obtained under unrestrained conditions in the subject’s normal environment. Yet, information on the
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functionality of the circadian timing system can be extracted from actigraphic recordings assessed in the subject’s usual environment. When the actigraphic data are plotted as a time series, a clear circadian rhythm can be seen, and several variables can be calculated for a quantitative description of the rhythm. A traditional way of quantifying circadian rhythms is by fitting a single or dual harmonic cosine function on the data, thus summarizing it in a mesor (a measure for the mean of a circular function), the phase of the peak, the amplitude, and the period of the rhythm. This ‘cosinor’ method of data reduction has successfully been applied to quantify the specific time course of body temperature and hormone levels. However, because the rest–activity rhythm is far from sinusoidal, the goodness of fit of such functions is usually unacceptable for application to activity data. Nonparametric methods to describe the activity time series have therefore been proposed. They outperformed several frequently used parametric variables in a comparative study on their sensitivity to the effect of bright daylight – the primary input to the biological clock of the brain – on the circadian rest–activity rhythm (Van Someren et al., 1999), and appeared sensitive as well as in other treatment studies (Van Someren et al., 1998). In addition to nonparametric equivalents of the timing and level of the peak and trough of the rest–activity rhythms, and the amplitude that results from their difference, two variables deserve some additional description. First, in most healthy subjects, the activity profiles from different 24-hour periods resemble each other to a reasonable extent. In some diseases, notably in Alzheimer patients, subsequent days may lose any such typical pattern (Figure 4.5). This phenomenon can be quantified using the interdaily stability (IS) value (Van Someren et al., 1999; Carvalho-Bos et al., 2007), essentially a normalized 24-hour value from a periodogram (Sokolove and Bushell, 1978). IS gives an indication of the strength of coupling between the rest– activity rhythm and supposedly stable environmental cues with a 24-hour pattern, also known as Zeitgebers. Second, in most healthy subjects, sleep and wakefulness are both confined to one major period of time each. If one takes a nap during the daytime, sleep and wakefulness both occur in two instead of one periods of time during 24 hours. In some diseases, notably in Alzheimer patients, periods of high and low vigilance, and consequently high and low amounts of activity, may alternate even more frequently, resulting in a fragmented rhythm (Figure 4.5). The nonparametric variable intradaily variability (IV) (Van Someren et al., 1999) gives an indication of the fragmentation of the rhythm, i.e., the frequency and extent of transitions between rest and activity.
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Fig. 4.5. Examples of 7-day activity plots in Alzheimer patients. Each bar represents the activity counts in 1 hour. The top panel (A) shows a rhythm that does not significantly differ from rhythms of control subjects. Panel B shows the rhythm of a patient with a low interdaily stability (IS), panel C a patient with a high intradaily variability (IV), and the bottom panel (D) a patient with both low IS and high IV. (Reproduced from Van Someren et al. (1996a), with permission.)
It should be noted that, under the conditions of everyday life, the measured rest–activity rhythm does not strictly represent the function of the endogenous biological clock of the brain, located in the hypothalamic suprachiasmatic nucleus (SCN). The measurements in fact at best represent the interaction of the endogenous biological clock with the environmental 24-hour time structure, which includes social demands and the light– dark cycle – the primary input to the SCN. Such conditions are referred to as entrained conditions. Rhythms obtained under such conditions are usually referred to as diurnal rhythms, whereas rhythms obtained under experimental
conditions in the absence of any time cues are referred to as circadian rhythms. If one wants to obtain an indication of clock function in the absence of entrainment, one needs to apply dedicated laboratory protocols like constant routine and enforced sleep–wake cycles of considerably shorter or longer duration than 24 hours (ultrashort sleep–wake cycles, forced desynchrony).
PERSPECTIVES The presently available actigraphs and the accompanying software are useful tools to provide clinicians
ACTIGRAPHIC MONITORING OF SLEEP AND CIRCADIAN RHYTHMS and researchers with objective indices of sleep. They should not be regarded as a replacement for polysomnography. As has been described, actigraphy has both shortcomings and advantages as compared to polysomnography. This final section discusses a number of recent and ongoing developments that promise a further improvement of actigraphic estimates of sleep parameters. First, optimization of the estimates may be accomplished by adapting the algorithm for sleep estimates to the specific group of subjects under study. For example, lowering the activity threshold that should be surpassed in order to score wakefulness may improve sleep estimates in elderly subjects (Colling et al., 2000). While lying awake in bed elderly subjects may move less than young subjects do. Important for clinical neurology, sleep recordings in patients suffering from Parkinson’s disease may require even more significant adaptations. Thresholds may have to be lowered even more than is already the case in their matching healthy elderly control subjects, because movement-induced accelerations are of a lower amplitude (Eichhorn et al., 1996). In addition, if patients show tremor, it is important to discriminate high levels of activity due to “healthy” movements from those resulting from tremor. An actigraph doing just this has recently been developed and validated for tremor recording (Van Someren et al., 2006). Such devices are likely to provide more detailed insight into activity rhythms originating from the alteration of periods of sleep and wakefulness, and those associated with fluctuating amounts of tremor. A common characteristic of the present generation of actigraphs is that the accompanying sleep analysis software utilizes only one activity measure, be it time above threshold, area under the curve, or zero crossings. However, movements related to wakefulness and sleep, possibly even sleep stages, may differ in more than one signal dimension. Movements may differ in frequency, vigor, fragmentation, and duration. It has been noted, for example, that limb movements during rapid eye movement (REM) sleep are brief, rapid, and jerky (Chase and Morales, 1990) and Aserinsky (1986) has shown that the acceleration characteristics of eye movements are different in REM sleep and wakefulness, suggesting that twitches resulting in wrist movements associated with REM sleep might have a different acceleration profile than the awake wrist movement acceleration profile. A recent advance in the online data reduction algorithm and storage capacity of an actigraph has made it possible to obtain multiple dimensions of the acceleration signal simultaneously, i.e., the amplitude, duration, and repetitiveness (Van Someren et al., 2006). Although this novel
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actigraph has been developed to allow for online discrimination of pathological tremor from normal movements in Parkinson’s disease, for example (Van Someren et al., 1993), it would be of considerable interest to evaluate how the different dimensions of the acceleration signal vary across wakefulness and sleep stages and could be of value in improving their discrimination. An unpublished study indeed found that amplitude, frequency, number, average duration, and total duration of movements differed significantly across wake and sleep stages. Related to the single activity measure mentioned above is the fact that actigraphy utilizes only one type of signal (activity) to estimate sleep and wakefulness, whereas the gold standard of polysomnography utilizes multiple signals. Since the multiple signals of polysomnography are not redundant, it is somewhat unlikely a priori that sleep parameter estimates derived from the single signal of actigraphy could ever reach complete agreement with polysomnographic sleep parameter estimates (Tryon, 2004). Actigraphs may be used to obtain movement signals on other sites, and process them in different ways. The most successful example of this approach is the use of actigraphs to assess periodic leg movements during sleep (King et al., 2005). Alternatively, actigraphs have been developed to obtain other measurements in addition to movement signals, e.g., heart rate and skin temperature. Heart rate variability measures may improve estimates of sleep depth (Otzenberger et al., 1997) and support the screening for obstructive sleep apnea (Roche et al., 1999) and periodic limb movements during sleep (Winkelman, 1999). A development that goes beyond actigraphy is that several research programs are presently utilizing the ongoing miniaturization of sensors and microelectronics to integrate measurement systems within the bedding of the subject under study. These developments will ultimately allow for unobtrusive assessment of signals reflecting heart rate, breathing, gross movements, and skin temperature, which together are likely to provide even better and more detailed estimates of sleep.
REFERENCES Acebo C, Sadeh A, Seifer R et al. (1999). Estimating sleep patterns with activity monitoring in children and adolescents: how many nights are necessary for reliable measures? Sleep 22: 95–103. Ancoli-Israel S, Clopton P, Klauber MR et al. (1997). Use of wrist activity for monitoring sleep/wake in demented nursing-home patients. Sleep 20: 24–27. Ancoli-Israel S, Cole R, Alessi C et al. (2003). The role of actigraphy in the study of sleep and circadian rhythms. Sleep 26: 342–392.
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Aserinsky E (1986). Proportional jerk: a new measure of motion as applied to eye movements in sleep and waking. Psychophysiology 23: 340–347. Carvalho-Bos S, Riemersma-van der Lek RF, Waterhouse J et al. (2007). Strong association of the rest–activity rhythm with well-being in demented elderly women. Am J Geriatr Psychiatry 15: 92–100. Chase MH, Morales FR (1990). The atonia and myoclonia of active (REM) sleep. Annu Rev Psychol 41: 557–584. Colburn TR, Smith BM, Guarini JJ et al. (1976). An ambulatory activity monitor with solid state memory. Biomed Sci Instrum 12: 117–122. Cole RJ, Kripke DF, Gruen W et al. (1992). Automatic sleep/ wake identification from wrist activity. Sleep 15: 461–469. Colling E, Wright M, Lahr S et al. (2000). A comparison of wrist actigraphy with polysomnography as an instrument of sleep detection in elderly persons. Sleep 23: A378. De Souza L, Benedito-Silva AA, Pires ML et al. (2003). Further validation of actigraphy for sleep studies. Sleep 26: 81–85. Eichhorn TE, Gasser T, Mai N et al. (1996). Computational analysis of open loop handwriting movements in Parkinson’s disease – a rapid method to detect dopamimetic effects. Mov Disord 11: 289–297. Eissa MA, Poffenbarger T, Portman RJ (2001). Comparison of the actigraph versus patients’ diary information in defining circadian time periods for analyzing ambulatory blood pressure monitoring data. Blood Press Monit 6: 21–25. Hauri PJ, Wisbey J (1992). Wrist actigraphy in insomnia. Sleep 15: 293–301. King MA, Jaffre MO, Morrish E et al. (2005). The validation of a new actigraphy system for the measurement of periodic leg movements in sleep. Sleep Med 6: 507–513. Krahn LE, Lin SC, Wisbey J et al. (1997). Assessing sleep in psychiatric inpatients: nurse and patient reports versus wrist actigraphy. Ann Clin Psychiatry 9: 203–210. Kripke DF, Mullaney DJ, Messin S et al. (1978). Wrist actigraphic measures of sleep and rhythms. Electroencephalogr Clin Neurophysiol 44: 674–676. Kupfer DJ, Weiss BL, Foster G et al. (1974). Psychomotor activity in affective states. Arch Gen Psychiatry 30: 765–768. Littner M, Kushida CA, Anderson WM et al. (2003). Practice parameters for the role of actigraphy in the study of sleep and circadian rhythms: an update for 2002. Sleep 26: 337–341. McPartland RJ, Foster FG, Kupfer DJ et al. (1976). Activity sensors for use in psychiatric evaluation. IEEE Trans Biomed Eng 23: 175–178. Meijer GA, Westerterp KR, van Hulsel AM et al. (1992). Physical activity and energy expenditure in lean and obese adult human subjects. Eur J Appl Physiol 65: 525–528. Middelkoop HAM, Van Dam EM, Smilde-Van Den Doel DA et al. (1997). 45-hour continuous quintuple-site actimetry: relations between trunk and limb movements and effects of circadian sleep–wake rhythmicity. Psychophysiology 34: 199–203.
Mullaney DJ, Kripke DF, Messin S (1980). Wrist actigraphic estimation of sleep time. Sleep 3: 83–92. Nagels G, Marion P, Pickut BA et al. (1996). Actigraphic evaluation of handedness. Electroencephalogr Clin Neurophysiol 101: 226–232. Otzenberger H, Simon C, Gronfier C et al. (1997). Temporal relationship between dynamic heart rate variability and electroencephalographic activity during sleep in man. Neurosci Lett 229: 173–176. Pilsworth SN, King MA, Shneerson JM et al. (2001). A comparison between measurements of sleep efficiency and sleep latency measured by polysomnography. Sleep 24: S106. Pollak CP, Tryon WW, Nagaraja H et al. (2001). How accurately does wrist actigraphy identify the states of sleep and wakefulness? Sleep 24: 957–965. Rechtschaffen A, Kales A (1968). A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. United States Department of Health, Education and Welfare, Bethesda. Redmond DP, Hegge FW (1985). Observations on the design and specification of a wrist-worn activity monitoring system. Behav Res Methods Instrum Comput 17: 659–669. Roche F, Gaspoz JM, Court-Fortune I et al. (1999). Screening of obstructive sleep apnea syndrome by heart rate variability analysis. Circulation 100: 1411–1415. Sadeh A, Sharkey KM, Carskadon MA (1994). Activitybased sleep–wake identification: an empirical test of methodological issues. Sleep 17: 201–207. Sadeh A, Hauri PJ, Kripke DF et al. (1995). The role of actigraphy in the evaluation of sleep disorders. Sleep 18: 288–302. Sokolove PG, Bushell WN (1978). The chi square periodogram: its utility for analysis of circadian rhythms. J Theor Biol 72: 131–160. Tryon WW (1991). Activity Measurement in Psychology and Medicine. Plenum Press, New York. Tryon WW (2004). Issues of validity in actigraphic sleep assessment. Sleep 27: 158–165. Usui A, Ishizuka Y, Obinata I et al. (1998). Validity of sleep log compared with actigraphic sleep–wake state. Psychiatry Clin Neurosci 52: 161–163. Van Hilten JJ, Braat EA, van der Velde EA et al. (1993). Ambulatory activity monitoring during sleep: an evaluation of internight and intrasubject variability in healthy persons aged 50–98 years. Sleep 16: 146–150. Van Someren EJW (2007). Improving actigraphic sleep estimates: how many nights? J Sleep Res 16: 269–275. Van Someren EJW, Van Gool WA, Vonk BFM et al. (1993). Ambulatory monitoring of tremor and other movements before and after thalamotomy: a new quantitative technique. J Neurol Sci 117: 16–23. Van Someren EJW, Hagebeuk EEO, Lijzenga C et al. (1996a). Circadian rest–activity rhythm disturbances in Alzheimer’s disease. Biol Psychiatry 40: 259–270. Van Someren EJW, Lazeron RHC, Vonk BFM et al. (1996b). Gravitational artefact in frequency spectra of movement acceleration: implications for actigraphy in young and elderly subjects. J Neurosci Methods 65: 55–62.
ACTIGRAPHIC MONITORING OF SLEEP AND CIRCADIAN RHYTHMS Van Someren EJW, Scherder EJA, Swaab DF (1998). Transcutaneous electrical nerve stimulation (TENS) improves circadian rhythm disturbances in Alzheimer’s disease. Alzheimer Dis Assoc Disord 12: 114–118. Van Someren EJW, Swaab DF, Colenda CC et al. (1999). Bright light therapy: improved sensitivity to its effects on rest–activity rhythms in Alzheimer patients by application of nonparametric methods. Chronobiol Int 16: 505–518.
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Van Someren EJW, Pticek MD, Speelman JD et al. (2006). A new actigraph for long-term tremor recording. Mov Disord 21: 1136–1143. Webster JB, Kripke DF, Messin S et al. (1982). An activitybased sleep monitor system for ambulatory use. Sleep 5: 389–399. Winkelman JW (1999). The evoked heart rate response to periodic leg movements of sleep. Sleep 22: 575–580.
Handbook of Clinical Neurology, Vol. 98 (3rd series) Sleep Disorders, Part 1 P. Montagna and S. Chokroverty, Editors # 2011 Elsevier B.V. All rights reserved
Chapter 5
Video recordings and video polysomnography NANCY FOLDVARY-SCHAEFER 1 * AND BETH MALOW 2 Sleep Disorders Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
1 2
Department of Neurology and Sleep Disorders Program, Vanderbilt University Medical Center, Nashville, TN, USA
INTRODUCTION
METHODOLOGY
The differentiation of nocturnal seizures, parasomnias, arousals, and other nonepileptic sleep-related behaviors can be challenging during polysomnography (PSG). While a limited number of electroencephalogram (EEG) channels is adequate for sleep staging in routine PSG (Iber et al., 2007), the identification of seizures requires more extensive EEG monitoring (American Electroencephalographic Society, 1994a). Video PSG (VPSG) combines simultaneous PSG and video-EEG to evaluate patients with nocturnal events. This technique has several advantages over routine PSG, including the ability to analyze behavior, correlate behavior with neurophysiologic parameters, and detect epileptiform activity.
EEG/electrode placement
INDICATIONS The American Academy of Sleep Medicine Standards of Practice indications for PSG guidelines (1987) recommend the use of VPSG in patients with nocturnal behaviors in whom seizures are suspected when the clinical history and routine EEG are inconclusive. Other indications for VPSG include the evaluation of sleep-related events that are violent or potentially injurious, parasomnias with unusual or atypical features, and presumed parasomnias or seizure disorders that fail to respond to conventional therapy. The differential diagnosis of abnormal sleep-related behaviors includes epileptic seizures, nonrapid eye movement (NREM) arousal disorders, REM sleep behavior disorder, rhythmic movement disorder, and psychiatric disorders such as panic and dissociative disorder.
*
The EEG is generated by inhibitory and excitatory postsynaptic potentials of cortical neurons. EEG activity is a reflection of the summation of the potentials generated by the underlying cortex and its interactions with subcortical structures. The 10/20 system of the International Federation of Societies for EEG and Clinical Neurophysiology is the method of electrode placement used in conventional EEG (Jasper, 1958). The system is based on measurements of 10% and 20% of the distance between standard cranial landmarks. Each electrode site is identified by a letter, representing the underlying region of the brain, and a number indicating a specific position in that region, with odd numbers indicating the left hemisphere and even numbers indicating the right hemisphere (Figure 5.1). Each recording channel represents the difference in electrical potential between a pair of electrodes. Several pairs of electrodes are combined to form a montage. Additional rows of electrodes in between the coronal and sagittal rows of the 10/20 system may be placed for more precise localization of epileptiform activity. Known as the 10/10 system, this method of electrode placement is used primarily during long-term monitoring (American Electroencephalographic Society, 1994b). Additional, closely spaced scalp electrodes increase the detection of epileptiform activity (Morris et al., 1986).
Montages Montages may be either referential, in which one of the electrodes in each pair is connected to a common electrode, or bipolar, in which there is no common
Correspondence to: Nancy Foldvary-Schaefer, D.O., Sleep Disorders Center, Neurological Institute, Cleveland Clinic, FA-20, 9500 Euclid Avenue, Cleveland, OH 44195, USA. Tel: (216) 445-2990, Fax: (216) 445-6205, E-mail:
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Fig. 5.1. Schematic of the 10-20 system of electrode placement.
electrode. Bipolar montages are usually arranged in a chain with a common electrode in adjacent derivations. The EEG is typically viewed on an anterior–posterior bipolar montage, although any configuration of electrodes may be used (Figure 5.2). The American Academy of Sleep Medicine (1987) guidelines recommend the use of an extended EEG montage in the evaluation of patients with nocturnal spells. The American Electroencephalographic Society (1994b) Guideline Thirteen recommends the use of
at least six EEG channels to evaluate patients with suspected seizures. Electrode placements FP1, FP2 (or other frontal placements), C3, C4, O1, O2, T7, and T8 are suggested. When limited to a few EEG channels, attempts should be made to design a montage that best addresses the clinical question. Unfortunately, too often the clinical history does not provide such detail and a limited number of EEG channels fails to exclude the diagnosis of epilepsy. Additional electromyogram (EMG) monitoring may be indicated, such as in patients with suspected REM sleep behavior disorder (Mahowald and Schenck, 1994). The optimal number and placement of EEG electrodes depend on a variety of factors, including the location, size, and characteristics of the epileptogenic focus. Commonly chosen as a reference in PSG, the auricular and mastoid electrodes (A1/A2 or M1/M2) may be active in temporal lobe seizures, making localization difficult or misleading. In a study comparing abbreviated EEG montages with a standard 18-channel bipolar montage, seizures were more readily distinguished from arousals using seven- and 18-EEG channel montages as compared to four-channel recordings (Foldvary et al., 2000). Accuracy increased progressively with the number of channels. Seven- and 18-channel montages provided significantly better accuracy for identifying temporal seizures than the four-channel montage. However, the 18-channel montage was not superior to a seven-channel montage incorporating anterior temporal electrode placements. More extensive EEG
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Fig. 5.2. Bilateral tonic seizure in sleep in a 6-year-old female with left frontal lobe epilepsy depicted using an abbreviated electroencephalogram (EEG) montage in (A) 30-second and Continued
VIDEO RECORDINGS AND VIDEO POLYSOMNOGRAPHY
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Clinical onset C3–TP10 200 uV
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B Clinical onset Fp1–F7 200 uV F7–T7 T7–P7 P7–O1 Fp2–F8 F8–T8 T8–P8 P8–O2 Fp1–F3 F3–C3 C3–P3 P3–O1 Fp2–F4 F4–C4 C4–P4 P4–O2 Fz–Cz Cz–Pz
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C Fig. 5.2. Cont’d. (B) 10-second epochs, and (C) an 18-channel montage in a 10-second epoch. While altering the epoch length facilitates interpretation significantly, the seizure becomes readily apparent only on the expanded EEG montage.
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montages did not increase the accuracy of frontal lobe seizure or arousal detection. In a subsequent study comparing an abbreviated montage using the recommended electrode placements of the American Electroencephalographic Society (1994a), the same investigators found an 18-channel montage superior in differentiating seizures from nonepileptic events (Foldvary-Schaefer et al., 2005). This appeared to be particularly true in frontal lobe epilepsy (Figure 5.2). Few have investigated the value of extensive EEG montages in the sleep laboratory. Among 122 patients with suspected parasomnias, VPSG provided a definite diagnosis in 35% of cases, with epilepsy being most common (Aldrich and Jahnke, 1991). In another 30% of cases, VPSG provided supportive evidence of sleep terrors or epilepsy. Sleep disorders were identified in five of 36 patients with known epilepsy. Only 34% of studies were inconclusive.
Viewing and reformatting Virtually all sleep laboratories have made the transition from analog recordings to computerized digital systems. Digital systems provide a more efficient means of data analysis, review, and storage. For the evaluation of sleep-related behaviors, current technology allows the reader to view an event in a variety of ways by altering the paper speed, filter setting, sensitivity, and montage. Readily apparent on conventional EEG paper speed (30 mm/second), isolated epileptiform discharges are virtually impossible to identify on 30-second PSG epochs (10 mm/second). In digital recordings, a sampling rate of 200 Hz is recommended to identify epileptiform discharges of short duration.
Video Video recordings are a necessary component of VPSG. Tonic-clonic motor activity, automatisms, and versive head movements are readily recognized by physicians with experience in the diagnosis of epilepsy. Minor motor features, such as brief generalized or focal tonic posturing and myoclonus, are more difficult to characterize using the clinical history alone. Negative motor activity, including behavior arrest, staring, and subtle loss of postural tone, may not be recognized even by experienced observers without video recordings and patient interaction. Sleep rooms should be equipped with adjustable cameras so that the patient is in view at all times.
The sleep technologist Technologists performing VPSG must have the skills to assess and manage patients with unexplained nocturnal behaviors. Technologists should be trained to identify behaviors that are likely to be epileptic in nature and
interact with the patient to determine level of consciousness. The degree of unresponsiveness, recollection of dream content, and presence of lateralizing signs, including postictal language deficits and Todd’s paralysis, during and immediately following nocturnal events, should be ascertained. Technologists should be capable of administering first aid to patients with generalized motor seizures, knowledgeable on the management of postictal violent or aggressive behavior, and able to recognize potentially injurious situations, including prolonged seizures and complications such as aspiration.
INTERPRETATION The interpretation of VPSG requires knowledge of the clinical and electrographic manifestations of seizures and nonepileptic parasomnias. Epileptic seizures are classified as focal or generalized based on their clinical and electrographic features. Epilepsy syndromes are constellations of specific signs and symptoms that can be used to predict the natural history of a disorder.
Generalized epilepsy Generalized epilepsy syndromes are characterized by seizures with initial activation of neurons involving both cerebral hemispheres. Most of the generalized epilepsies are characterized by interictal epileptiform discharges having a generalized, bianterior maximal distribution (F3, F4 or FP1, FP2) with progressive amplitude decay posteriorly. These discharges are typically detected when recording from frontal and central electrode placements. Consequently, they may be apparent on routine PSG. During generalized seizures, the EEG typically shows diffuse rhythmic activity or repetitive epileptiform activity, reflecting initial involvement of both cerebral hemispheres. Tonic and atonic seizures are characterized by a sudden appearance of generalized low-voltage fast activity, suppression, spike–wave complexes or rhythmic activity. Generalized polyspikes interrupted by slow waves characterize clonic seizures, producing a characteristic pattern of myogenic artifact that is relatively easy to identify even at a paper speed of 10 mm/second.
Focal epilepsy Focal or localization-related epilepsies are characterized by focal (partial) seizures that originate from a localized cortical region. The electrographic manifestations of focal epilepsy depend upon a variety of factors, including the size and location of the ictal generator, location and number of recording electrodes, and the attenuating characteristics of the skull and other intervening tissues (Jayakar et al., 1991).
VIDEO RECORDINGS AND VIDEO POLYSOMNOGRAPHY In many cases, the EEG shows interictal epileptiform discharges from the region harboring the epileptogenic lesion. The EEG may be normal in patients with epileptogenic lesions arising from deep or midline regions or show generalized epileptiform activity due to rapid propagation to the contralateral hemisphere. Most focal seizures are characterized by rhythmic activity that evolves in frequency, distribution (field), and amplitude (Sharbrough, 1993). Repetitive spikes or sharp waves and sudden attenuation of activity over one region or cerebral hemisphere are also observed. Seizures characterized by excessive motor activity may be obscured by muscle artifact, rendering the EEG uninterpretable. This is most commonly observed in patients with frontal lobe epilepsy in whom parasomnias or psychogenic seizures may be erroneously diagnosed due to the apparent lack of an EEG correlate. The EEG may be normal even during a seizure if the event is brief and the epileptogenic focus is distant from the recording electrodes, another feature of frontal lobe epilepsy. Similarly, EEG changes may not be apparent when a focal seizure remains relatively confined to a limited area. While temporal lobe
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epilepsy is the most common of the focal epilepsies in adolescents and adults, frontal lobe epilepsy more often presents with seizures during sleep that can be difficult to differentiate from other types of nocturnal behaviors. The absence of epileptiform abnormalities does not definitively exclude the diagnosis of epilepsy.
Effect of sleep stage The stage of sleep from which nocturnal spells emerge and the time of the spell relative to sleep onset provide useful diagnostic information, particularly when evaluating patients with nonepileptic events in sleep. Sleeprelated seizures usually arise from NREM sleep. NREM arousal disorders usually arise from slow-wave sleep in the first third of the sleep period. Included in the category are somnambulism, sleep terrors, and confusional arousals (Figure 5.3). REM sleep behavior disorder typically presents in the last third of the sleep period, when REM sleep predominates. Affected individuals have vigorous or violent behavior in sleep associated with vivid dream imagery and augmented EMG activity of the chin and/or extremities. Rhythmic
Fp1–F7 50 uV F7–T7 T7–P7 P7–O1 Fp2–F8 F8–T8 T8–P8 P8–O2 Fp1–F3 F3–C3 C3–P3 P3–O1 Fp2–F4 F4–C4 C4–P4 P4–O4 LOC–Pz 100 uV ROC–Pz Air Flow–REF2 20 uV Thoracic–REF2 100 uV Abdomina–REF2 50 uv Chin–EMG1 5 uV LANT–RANT 20 uv ECG+–ECG– 100 uV
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Fig. 5.3. Video polysomnography recording from a 29-year-old woman with nocturnal wandering spells. The patient suddenly awoke from slow-wave sleep (arrow) and began to get out of bed without awareness.
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movements associated with rhythmic movement disorder usually occur during sleep–wake transitions. Dissociative episodes emerge from wakefulness. Nocturnal panic attacks occur from NREM sleep, usually at the transition of stage 2 from stage 3.
Artifacts Artifacts are commonly seen in recordings of patients with nocturnal spells and must be distinguished from epileptiform activity and the EEG changes associated with parasomnias. Although artifacts may obscure the EEG, their sterotyped presentation may be supportive of the diagnosis in question. Examples of this include the EMG artifact of a tonic-clonic seizure, head or body rocking artifact in rhythmic movement disorder, and the rhythmic bitemporal myogenic artifact of bruxism. Other types of artifact that may mimic epileptiform activity include that produced by head tremor, eye movements, and tongue movements (glossokinetic artifact). Normal patterns that are occasionally misinterpreted as epileptic include positive occipital sharp transients of sleep, repetitive vertex waves of young patients, small sharp spikes, wicket spikes, and rhythmic temporal theta of drowsiness.
CONCLUSIONS VPSG combines video EEG and PSG for the evaluation of unexplained behaviors in sleep. The differential diagnosis most commonly includes epileptic seizures and parasomnias. Additional time is required for electrode placement and data analysis and more space is required on storage media. However, misdiagnosis can lead to unnecessary treatment with medications that may produce significant adverse effects and failure to make an accurate diagnosis may lead to serious, potentially fatal accidents and injuries. When VPSG fails to clarify the diagnosis, long-term video EEG monitoring should be considered.
REFERENCES Aldrich MS, Jahnke B (1991). Diagnostic value of videoEEG polysomnography. Neurology 41: 1060–1066. American Electroencephalographic Society (1994a). Guideline Fifteen. Guidelines for polygraphic assessment of sleep-related disorders (polysomnography). J Clin Neurophysiol 11: 116–124. American Electroencephalographic Society (1994b). Guideline Thirteen. Guidelines for standard electrode position nomenclature. J Clin Neurophysiol 11: 111–113. American Sleep Disorders Association Standards of Practice Committee (1987). Practice parameters for the indications for polysomnography and related procedures. Sleep 20: 406–422. Foldvary N, Caruso AC, Mascha E et al. (2000). Identifying montages that best detect electrographic seizure activity during polysomnography. Sleep 23: 221–229. Foldvary-Schaefer N, De Ocampo J, Mascha E et al. (2005). Accuracy of seizure detection using abbreviated EEG during polysomnography. J Clin Neurophysiol 23: 68c–71c. Iber C, Ancoli-Israel S, Chesson A et al. (2007). The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. American Academy of Sleep Medicine, Westchester, IL. Jasper HH (1958). The 10-20 electrode system of the International Federation. Electroencephalogr Clin Neurophysiol 10: 370–375. Jayakar P, Duchowny M, Resnick TJ (1991). Localization of seizure foci: pitfalls and caveats. J Clin Neurophysiol 8: 414–431. Mahowald M, Schenck CH (1994). REM sleep behavior disorder. In: MH Kryger, T Roth, WC Dement (Eds.), Principles and Practice of Sleep Medicine. 2nd edn. W.B. Saunders, Philadelphia, pp. 574–588. Morris HH, Lu¨ders H, Lesser RP et al. (1986). The value of closely spaced scalp electrodes in the localization of epileptiform foci: a study of 26 patients with complex partial seizures. Electroencephalogr Clin Neurophysiol 63: 107–111. Sharbrough FW (1993). Scalp-recorded ictal patterns in focal epilepsy. J Clin Neurophysiol 10 (3): 262–267.
Handbook of Clinical Neurology, Vol. 98 (3rd series) Sleep Disorders, Part 1 P. Montagna and S. Chokroverty, Editors # 2011 Elsevier B.V. All rights reserved
Chapter 6
Functional neuroimaging in sleep, sleep deprivation, and sleep disorders MARTIN DESSEILLES *, THANH DANG-VU, AND PIERRE MAQUET Cyclotron Research Centre, University of Lige, Belgium
INTRODUCTION The optimal management of patients with sleep disorders would require a comprehensive understanding of the underlying specific pathological mechanisms, but also an exact appreciation of the consequences of ensuing sleep disruption. The latter objective is hampered by our incomplete knowledge of normal sleep. During the last 50 years, considerable progress has been made in the understanding of the neural mechanisms by which sleep is induced, maintained, and regulated (McCarley et al., 1983; Buzsaki, 1998; Kryger et al., 2000; Saper et al., 2001; Steriade and Timofeev, 2003). Yet, at present, our understanding remains fragmentary and we are still striving for a comprehensive description of sleep mechanisms. Likewise, the functions of sleep are not yet undisputedly specified, although several hypotheses have been proposed (Maquet et al., 2003). Consequently, the effect of sleep on cerebral and bodily functions (Stickgold and Walker, 2007), as well as the consequences of sleep deprivation or fragmentation (Chee and Chuah, 2008), are not yet fully understood at the different levels of description. Neuroimaging studies conducted in sleep disorders have suffered from this fragmentary knowledge of normal sleep. For instance, they often have not been able to tease apart the pathological mechanisms of a given disorder from the consequences of the ensuing sleep disruption. Nevertheless, impressive advances have been made in some sleep disorders. In this section, our aim is to describe the present state of the art and hopefully exemplify the limitations of the available neuroimaging literature. The review begins with a short account of neuroimaging studies conducted
*
during normal sleep, because they nicely introduce the subsequent pathological sections.
NEUROIMAGING IN NORMAL HUMANS Introduction Studies using positron emission tomography (PET), single photon emission computed tomography (SPECT) or functional magnetic resonance imaging (fMRI) reviewed in this section have shown that global and regional patterns of brain activity during sleep are outstandingly different from wakefulness. These studies also demonstrated the persistence of brain responses to external stimuli during sleep as well as plastic changes in brain activity related to previous waking experience.
Nonrapid eye movement (NREM) sleep NREM sleep, when compared to wakefulness or REM sleep (Maquet et al., 1997; Maquet, 2000), is characterized by a global decrease in cerebral blood flow (CBF), and by a regional deactivation in the dorsal pons, mesencephalon, cerebellum, thalami, basal ganglia, basal forebrain and anterior hypothalamus, prefrontal cortex, anterior cingulate cortex and precuneus. This distribution of brain activity could be at least partially explained by NREM sleep generation mechanisms in mammals (Maquet et al., 1990). Taking into account that PET measurements average cerebral activity over 90 seconds to 45 minutes, decreases in CBF and metabolism during NREM are thought to reflect a change in firing pattern, characterized by synchronized bursting activity followed by long hyperpolarization periods, more than a decrease in the average neuronal firing rate (Maquet, 2000). Accordingly, as compared to wakefulness, the average
Corrrespondence to: Martin Desseilles, MD, PhD, Cyclotron Research Centre, University of Lie`ge, Baˆtiment B30, 8, alle´e du 6 Aouˆt – B-4000 Lie`ge (Belgium). Tel: þ32 4 366 23 16, Fax: þ32 4 366 29 46, E-mail:
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cerebral metabolism and blood flow levels begin to decrease in light (stage 1 and 2) NREM sleep (Madsen et al., 1991b; Maquet et al., 1992; Kjaer et al., 2002), and are the lowest during deep (stage 3 and 4) NREM sleep, also named slow-wave sleep (SWS) (Maquet et al., 1990; Madsen et al., 1991a). NREM sleep rhythms (spindles, delta, and slow oscillations) are generated by a cascade of events occurring in thalamoneocortical networks, initially induced by a decreased activation from the brainstem tegmentum (Steriade and Amzica, 1998). Accordingly, in humans, brainstem blood flow is decreased during light NREM sleep (Kajimura et al., 1999) as well as during SWS (Braun et al., 1997; Maquet et al., 1997; Kajimura et al., 1999; Nofzinger et al., 2002). However, during light NREM sleep, the pontine tegmentum appears specifically deactivated whereas the mesencephalon seems to retain an activity that is not significantly different from wakefulness (Kajimura et al., 1999). In SWS, both
pontine and mesencephalic tegmenta are deactivated (Maquet et al., 1997). The thalamus occupies a central position in the generation of NREM sleep rhythms like spindles and delta waves, due to the intrinsic oscillating properties of its neurons and to the intrathalamic and thalamocortico-thalamic connectivity. As expected, in humans, regional CBF decreases have been found in the thalamus during both light and deep NREM sleep (Braun et al., 1997; Maquet et al., 1997; Kajimura et al., 1999), and also in proportion to the power density of the electroencephalogram (EEG) signal in the spindle frequency range (Hofle et al., 1997). However, in a recent study, regional CBF was not correlated with delta activity in the thalamus (Dang-Vu et al., 2005), suggesting the potential active participation of the cortex in the generation of the delta rhythm recorded on the scalp (Figure 6.1). The role of the cortex in the generation of NREM sleep oscillations (e.g., slow cortical rhythm) begins to
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Fig. 6.1. Regional cerebral blood flow (rCBF) decrease as a function of delta power during nonrapid eye movement (NREM) sleep. Left panel: rCBF decreases as a function of delta power during NREM sleep. Image sections are centered on the ventromedial prefrontal cortex. The color scale indicates the range of Z values for the relevant voxels. Right panel: Plot of the adjusted rCBF responses (arbitrary units) in the ventromedial prefrontal cortex in relation to the adjusted delta power values (mV2) during NREM sleep corresponding to left panel pictures: rCBF activity decreases when delta power increases. Each circle/cross represents one scan: green circles are stage 2 scans, red crosses are stage 3–4 scans. The blue line is the linear regression. (Reprinted from Dang-Vu et al., 2005; copyright (2005). Reprinted with permission from Elsevier.)
FUNCTIONAL NEUROIMAGING IN SLEEP, SLEEP DEPRIVATION, AND SLEEP DISORDERS 73 be understood at the microscopic system level (Steriade precuneus, posterior cingulate cortex, and parahippoet al., 1993). Their neural correlates at the macroscopic campal gyrus (Dang-Vu et al., 2008). Beside identifying system levels are less well characterized. EEG power the brain structures involved in the generation, propagadensity maps have revealed a relatively typical pretion, or modulation of NREM sleep oscillations, these dominance of the delta frequency band in the frontal studies emphasize that NREM sleep is not a state of regions whereas sigma power predominated over the brain quiescence characterized by persistent decrease vertex (Finelli et al., 2001). Human PET data similarly in brain activity, but a state during which brain activity showed that the pattern of cortical deactivation was is temporally organized in specific oscillations. not homogeneously distributed throughout the cortex. As compared to wakefulness, the least active areas in REM sleep SWS were observed in various associative cortices of In contrast to NREM sleep, REM sleep is characterthe frontal (in particular in the dorsolateral and orbital ized by a sustained neuronal activity (Steriade and prefrontal cortex), parietal, and to a lesser extent in the McCarley, 1990; Jones, 1991) and, correspondingly, by temporal and insular, lobes (Braun et al., 1997; Maquet high cerebral requirements (Maquet et al., 1990) and et al., 1997; Andersson et al., 1998; Kajimura et al., blood flow (Madsen et al., 1991a; Lenzi et al., 1999) 1999). In contrast the primary cortices were the least (Figure 6.2). In this active but sleeping brain, some deactivated cortical areas (Braun et al., 1997). A linear areas are particularly active, even more than during (inverse) relationship between delta waves and rCBF is wakefulness, while others have lower than average also found in ventromedial prefrontal regions during regional activity. NREM sleep (Dang-Vu et al., 2005). The reasons for During REM sleep, significant rCBF increases have this heterogeneous cortical distribution remain unclear. been found in the pontine tegmentum, thalamic nuclei, One hypothesis is that since polymodal association limbic and paralimbic areas (amygdaloid complexes cortices are the most active cerebral areas during (Maquet et al., 1996; Nofzinger et al., 1997), hippocampal wakefulness, and because sleep intensity is homeostatiformation (Braun et al., 1997; Nofzinger et al., 1997), and cally related to prior waking activity at the regional anterior cingulate cortex (Maquet et al., 1996; Braun level (Borbely, 2001), these cortices might be more et al., 1997; Nofzinger et al., 1997)). Posterior cortices in profoundly influenced by SWS rhythms than primary temporo-occipital areas were also found to be activated cortices (Maquet, 2000). (Braun et al., 1997; Wehrle et al., 2005), although less The predominance of slow oscillation-related rCBF consistently. In contrast, the inferior and middle dorsodecreases in ventromedial prefrontal regions may be lateral prefrontal gyri and the inferior parietal cortex functionally important since these cortical regions, were the least active brain regions (Maquet et al., 1996, known to deteriorate after a short sleep deprivation 2005; Braun et al., 1997). (Horne, 1988, 1993; Pilcher and Huffcutt, 1996; Harrison Regional brain activity in mesopontine, occipital, and and Horne, 1998, 1999), are involved in mood regulation thalamic regions during human REM sleep (Maquet and in various cognitive functions (such as planning or et al., 1996; Braun et al., 1997; Nofzinger et al., 1997; probability matching) (Harrison and Horne, 1999) that Wehrle et al., 2005) is in keeping with our current help in adapting individual behavior. Studies of the delunderstanding of sleep generation in animals. REM eterious effects of sleep deprivation on human cognisleep is generated by neuronal populations of the mesotion also pointed to an exquisite sensitivity of these pontine reticular formation that activate the thalamic association cortices to sleep deprivation (see below). nuclei which in turn activate the cortex (Steriade and Recent studies have used simultaneous EEG/fMRI McCarley, 1990). recordings during NREM sleep to characterize the neuThe activation of limbic and paralimbic structures, ral correlates of NREM sleep oscillations in healthy including amygdaloid complexes, hippocampal formahumans. In contrast to PET studies that described the tion, and anterior cingulate cortex, is constantly reported patterns of brain activity during the different sleep (Maquet et al., 1996; Braun et al., 1997; Nofzinger et al., stages or correlated with values of EEG spectral power, 1997). Animal data show that the amygdala plays a role in the better temporal resolution of fMRI allows assessREM sleep modulation. For example, ponto-geniculoment of the brain activity changes directly related to occipital (PGO) waves, a major component of REM sleep brief events such as spindles and delta waves. Spindles phasic endogenous activity, were increased in cats by have been associated with increases of brain activity in electrical stimulation of the central nucleus of amygdathe thalamus, anterior cingulate cortex, insula, and supeloid complexes (Calvo et al., 1987), while carbachol (chorior temporal gyrus (Schabus et al., 2007). Delta waves linergic agonist) injections in the same nucleus enhanced have been associated with increases of brain activity both REM sleep and PGO activity (Calvo et al., 1996). in the inferior frontal gyrus, brainstem, cerebellum,
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Fig. 6.2. Cerebral glucose metabolism (CMRGlu) and regional cerebral blood flow (rCBF) during rapid eye movement (REM) sleep (first column), deep non-REM (NREM) sleep or slow-wave sleep (SWS) (second column), and wakefulness (third column). Row A: CMRGlu quantified in the same individual at 1-week interval, using 18F-fluorodeoxyglucose and positron emission tomography (PET). The three images are displayed at the same brain level using the same color scale. The average CMRGlu during deep NREM sleep (versus wakefulness) is significantly decreased. During REM sleep the CMRGlu is as high as during wakefulness. Row B1: Distribution of the highest regional brain activity, as assessed by CBF measurement using PET, during wakefulness and REM sleep. The most active regions during wakefulness are located in the polymodal associative cortices in the prefrontal and parietal lobes (both on the medial wall and convexity). During REM sleep, the most active areas are located in the pontine tegmentum, the thalami, the amygdaloid complexes, and the anterior cingulate cortex. Other data (not shown) have shown a large activity in the occipital cortices, the insula, and the hippocampus (Braun et al., 1997). Row B2: Distribution of the lowest regional brain activity, as assessed by CBF measurement using PET, during NREM and REM sleep. In both sleep stages, the least active regions are located in the polymodal associative cortices in the prefrontal and parietal lobes (convexity). During NREM sleep, the brainstem and thalami are also particularly deactivated.
Likewise, the rebound of REM sleep induced by microinjections of gamma aminobutyric acid (GABA) agonist into the periaqueductal gray matter elicited a significant increase in c-fos labeling in the amygdala (Sastre et al., 2000). The activated temporo-occipital areas during REM sleep (Braun et al., 1997) include inferior temporal cortex and fusiform gyrus, which are extrastriate cortices belonging to the ventral visual stream. Functional connectivity of these areas is also modified during REM sleep. The functional relationship between striate and extrastriate cortices, usually excitatory during wakefulness, is reversed during REM sleep (Braun et al., 1997, 1998). Likewise, the functional relationship between the amygdala and the temporal and occipital cortices is different during REM sleep than during wakefulness
or NREM sleep (Maquet and Phillips, 1998). This pattern suggests that not only the functional neuroanatomy but also the functional interactions between neuronal populations are different during REM sleep than during wakefulness. Pontine waves or PGO waves are also primary features of REM sleep. In rats, the generator of the pontine waves projects to a set of brain areas shown to be active in human REM sleep: the occipital cortex, the enthorinal cortex, the hippocampus, and the amygdala, as well as brainstem structures participating in the generation of REM sleep (Datta et al., 1998). In cats, although most easily recorded in the pons (Jouvet, 1967), the lateral geniculate bodies (Mikiten et al., 1961), and the occipital cortex (Mouret et al., 1963), PGO waves are observed in many parts of the brain,
FUNCTIONAL NEUROIMAGING IN SLEEP, SLEEP DEPRIVATION, AND SLEEP DISORDERS including limbic areas (amygdala, hippocampus, cingulate gyrus) (Hobson, 1964). Using PET, regional CBF in the lateral geniculate bodies and the occipital cortex was shown to be more tightly coupled to spontaneous eye movements during REM sleep than during wakefulness (Peigneux et al., 2001). These data are in keeping with other pieces of evidence suggesting the existence of pontine waves in humans, and have been more recently corroborated by an fMRI study (Wehrle et al., 2005). In epileptic patients, direct intracerebral recordings in the striate cortex showed monophasic or diphasic potentials during REM sleep, isolated or in bursts (Salzarulo et al., 1975). In normal subjects, surface EEG revealed transient occipital and/or parietal potentials time-locked to the REMs (McCarley et al., 1983). Source dipoles of magnetoencephalography signal were localized in the brainstem, thalamus, hippocampus and occipital cortex during REM sleep (Inoue et al., 1999; Ioannides et al., 2004).
The brain remains reactive to external stimulation during sleep Available functional neuroimaging data globally suggest that the processing of external stimuli persists during NREM sleep. A pioneering fMRI study found that during NREM sleep, as during wakefulness, several areas continue to be activated by external auditory stimulation: the thalamic nuclei, the auditory cortices, and the caudate nucleus (Portas et al., 2000). Moreover, the left amygdala and the left prefontal cortex were found to be more activated by subjects’ own name than by pure tones, and more so during sleep than during wakefulness, suggesting the persistence during sleep of specific responses for meaningful or emotionally loaded stimuli. In contrast, other groups observed that response to auditory stimulation was decreased during sleep as compared to wakefulness (Czisch et al., 2002). Intriguingly, the brain activation pattern of visual stimulation during SWS in adults showed a decrease in activity in the rostromedial occipital cortex (Born et al., 2002). This decrease was more rostral and dorsal compared to the relative regional CBF increase along the calcarine sulcus found during visual stimulation in the awake state. The origin of this negative blood oxygenation level is still unclear despite replication (Czisch et al., 2004).
NEUROIMAGING IN SLEEP DISORDERS Introduction In this section, we will mainly focus on primary sleep disorders. We will include several types of dyssomnia related to intrinsic sleep disorders (e.g., idiopathic
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insomnia, narcolepsy, and obstructive sleep apnea), abnormal motor behavior during sleep (e.g., periodic limb movement disorder and REM sleep behavior disorder (RBD)). Sleep may also be secondarily disrupted in a number of conditions ranging from environmental causes (e.g., jet lag, shift work, noisy environment) to medical diseases (e.g., endocrine disorders, chronic pain, brain lesions) and psychiatric disorders (e.g., anxiety, depression, schizophrenia). From the latter conditions, we will only consider the sleep disorders secondary to depression. Single case reports of brain functional imaging like in recurrent hypersomnia (Nose et al., 2002) and in sleepwalking (Bassetti et al., 2000) as well as rare disorders such as fatal familial insomnia, Landau– Kleffner syndrome and the syndrome of continuous spike-and-wave discharges during slow wave sleep are not reviewed.
Idiopathic insomnia Idiopathic insomnia is a lifelong inability to obtain adequate sleep that is presumably due to an abnormality of the neurological control of the sleep–wake system (AASM, 2001). This disorder is thought to reflect an imbalance between the arousal system and the various sleep-promoting systems (AASM, 2001). In particular, hyperactivity within the arousal system is presently believed to be the final common pathway of the disorder (AASM, 2001). For instance, several studies have reported increased alertness on the multiple sleep latency test, increased heart rate during the sleep period, increased anxiety on rating scales, and increased tension during wakefulness (Stepanski et al., 1988; Bonnet and Arand, 1995, 1997). In addition, poor sleep leads to altered mood and motivation, decreased attention and vigilance, low levels of energy and concentration, and increased daytime fatigue (AASM, 2001). Quantitative EEG recordings suggest an overall cortical hyperarousal in insomnia (Perlis et al., 2001). However, it should be noticed that hyperarousal in primary insomnia was characterized by an increase in beta/gamma activity at sleep onset, followed by a decline leading to a brief period of hypoarousal (Perlis et al., 2001). Accordingly, some neuroimaging studies show a cortical hyperarousal pattern in insomnia while others report a decrease in cortical functions. In the latter, decreased metabolism might originate from the time window coincidence of the cortical hypoarousal period with neuroimaging acquisition, and therefore does not discard the hyperarousal hypothesis of primary insomnia (Smith et al., 2002). Only a small number of studies tried to characterize the functional neuroanatomy of idiopathic insomnia
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disorder (referred to as primary insomnia in these reports). Using technetium-99m-hexamethylene-propyleneamine Oxime (99mTC-HMPAO), a gamma-emitting radionuclide imaging agent, regional CBF was estimated in 5 insomniacs and 4 normal sleepers during NREM sleep. Patients with insomnia revealed major rCBF decreases in the basal ganglia, frontal medial, occipital, and parietal cortices. These results suggest that idiopathic insomnia is associated with an abnormal pattern of regional brain function during NREM sleep that particularly involves basal ganglia (Smith et al., 2002). More recently, regional cerebral glucose metabolism (CMRglu) was measured using 18F-fluorodeoxyglucose (18FDG) PET in 7 patients with idiopathic insomnia and 20 healthy age- and gender-matched subjects during waking and NREM sleep (Nofzinger et al., 2004a). Insomniac patients showed increased global CMRglu during sleep as compared to healthy subjects, suggesting an overall cortical hyperarousal in insomnia. In addition insomniac patients had a smaller decline, related to healthy subjects, in CMRglu from waking to sleep states in the ascending reticular activating system, hypothalamus, thalamus, insular cortex, amygdala, hippocampus, anterior cingulate, and medial prefrontal cortices. During wakefulness, reduced metabolism, as compared to healthy subjects, was detected in the prefrontal cortex bilaterally, in the left superior temporal, parietal, and occipital cortices and in the thalamus, hypothalamus, and brainstem reticular formation. Taken together, these findings confirm that regional brain activity does not normally progress from waking to sleep states in patients with
insomnia. Moreover, it was proposed that daytime fatigue resulting from inefficient sleep may be reflected by decreased activity in the prefrontal cortex (Nofzinger et al., 2004a) (Figure 6.3). Interestingly, 4 of the insomnia patients from the Smith’s study were rescanned after cognitive behavioral therapy (Smith et al., 2005). Sleep latency was reduced by at least 43% and there was a global 24% increase in CBF, with significant increases in the basal ganglia after this psychotherapeutic treatment. Such an increase in brain activity has been proposed to reflect the normalization of sleep homeostatic processes. These promising results will certainly inspire further investigations on the effects of psychotherapy on brain functioning in insomnia.
Depression The most common primary diagnosis in patients presenting with a complaint of insomnia is depression (Benca, 2000). Depression is a subclass of mood disorders, which are psychiatric disorders characterized by either one or more episodes of depression, or partial or full manic or hypomanic episodes. Depressive disorders include major depressive disorder, diagnosed in people who have experienced at least one major depressive episode. The Diagnostic and Statistical Manual of Mental Disorders (DSM-IV: American Psychiatric Association, 1994) provides diagnostic criteria for major depression. At least five symptoms must be present for the same 2-week period, nearly every day, and at least one symptom must be either depressed
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Fig. 6.3. Regional cerebral glucose metabolism (CMRGlu) in patients with insomnia assessed during both waking and nonrapid eye movement sleep states by using 18F-fluorodeoxyglucose positron emission tomography. Panel A: Brain structures that did not show decreased glucose metabolic rate from wakefulness to sleep states in patients with insomnia. Panel B: Brain structures where relative glucose metabolism during wakefulness was higher in healthy subjects than in patients with insomnia. (Reproduced from Nofzinger et al. (2004a), with permission from the American Journal of Psychiatry, Copyright 2004. American Psychiatric Association.)
FUNCTIONAL NEUROIMAGING IN SLEEP, SLEEP DEPRIVATION, AND SLEEP DISORDERS 77 mood or loss of interest or pleasure. Other symptoms suggest an increased arousal in depressed patients of major depressive episodes include insomnia or (Clark and Watson, 1991; Joiner et al., 1999), a hypothhypersomnia, significant weight loss or weight gain, esis that finds support in functional neuroimaging psychomotor activity or retardation, fatigue, feelings data. Beta activity is proposed as an EEG marker of worthlessness or excessive or inappropriate guilt, of arousal during sleep. In an 18FDG PET study (Nofzinger et al., 2000) beta power was negatively poor concentration, recurrent thoughts of death, and correlated with subjective sleep quality, in both normal recurrent suicidal ideation. The disease is classified as and depressed subjects, although depressed patients dysthymic when the full criteria for major depression exhibited increased beta activity during the night comare not met and when individuals are chronically pared to normal controls. Interestingly, beta power depressed for at least 2 years. The association between was correlated with glucose metabolism levels in the typical features of depression, insomnia, and, more ventromedial prefrontal cortex, a region amongst the rarely, excessive sleepiness (AASM, 2001) remains not most deactivated during consolidated SWS (see above) clearly understood. (Nofzinger et al., 2000). In depressed patients, modifications of sleep archiThese clinical, electrophysiological, and neuroimagtecture are characterized by reduced SWS, early onset ing studies provide some evidence in keeping with the of the first episode of REM sleep, and increased phahypothesis of increased hyperarousal in depressed sic REM sleep (Thase, 1998). patients. Nevertheless, pathophysiological mechanisms In the following sections, we will present studies linking hyperarousal with depression as well as insomconducted in depressed patients during wakefulness, nia with depression remain to be established. after sleep deprivation, during NREM, and during The physiological mechanisms underpinning the benREM sleep. eficial effects of sleep deprivation are complex and not WAKEFULNESS NEUROIMAGING IN DEPRESSION completely understood yet. It has been hypothesized that REM sleep pressure is enhanced in depressed patients. Neuroimaging studies in depressed patients during In depressed patients responding favorably to sleep depwakefulness indicate that dysfunction of the prefrontal rivation, as compared to nonresponders, baseline brain cortical and striatal systems, which normally modulate activity during wakefulness was reported to be higher limbic and brainstem structures, play an important role in the anterior cingulate cortex (Wu et al., 1992; Clark in the pathogenesis of depressive symptoms (Mayberg, et al., 2001) and/or the nearby mesial frontal cortex 1997; Drevets, 2001). Abnormalities within orbital and (Ebert et al., 1991, 1994b; Wu et al., 1999; Clark et al., medial prefrontal cortex areas persist following symp2001), then to decrease significantly after sleep deprivatom remission (Drevets, 2000). These findings involve tion as compared to wakefulness. A similar pattern of interconnected neural circuits in which dysfunction of brain activity was observed in elderly depressed patients, neurotransmission may result in the depressive sympincluding normalization after total sleep deprivation toms (Drevets, 2000, 2001). associated with antidepressant treatment (Smith et al., The Hamilton Depression Rating Scale (HDRS) is 1999). In addition, the normalization of anterior cinguwidely used to measure the severity of depression in late metabolism persisted even after recovery sleep mood disorders. Voxelwise correlation maps have (Smith et al., 1999). Interestingly, it was also shown that shown that total HDRS score correlates with metabosleep deprivation responders, as compared to non18 lism as measured by F-FDG PET during wakefulness responders, exhibit a significant decrease in relative in a large set of cerebral areas, including limbic strucbasal ganglia D2 receptor occupancy after sleep depritures, thalamus, and basal ganglia. Moreover, sleep vation (Ebert et al., 1994a). These results suggest that disturbance, a distinct symptom cluster included in the antidepressant benefits of sleep deprivation are the HDRS, correlated positively with glucose metabocorrelated with enhanced endogenous dopamine release lism in limbic structures and basal ganglia (Milak in responders, as compared to nonresponders. These et al., 2005). results corroborate previous hypotheses of dopaminergic participation in the therapeutic action of sleep depriSLEEP DEPRIVATION IN DEPRESSION vation, and indirectly support a dopamine hypothesis of Intriguingly, sleep deprivation has rapid beneficial depression (Ebert et al., 1994a). effects in about 60% of depressed patients (WirzRecently, a preliminary work studied the effect Justice and Van den Hoofdakker, 1999). Responders of concomitant sleep deprivation and antidepressant to sleep deprivation are usually patients with high medication in 6 depressed patients (Wu et al., 2008). behavioral activation and low levels of tiredness (Szuba They were administered the serotonergic antidepreset al., 1991; Bouhuys et al., 1995). These findings sant sertraline for a week and then underwent FDG
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PET before and after total sleep deprivation. Glucose metabolism decreased in the inferior frontal gyrus and inferior frontal/orbital frontal cortex and increased in the dorsolateral prefrontal cortex, in correlation with reduced score of HDRS.
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It was shown that whole-brain absolute CMRglu during NREM sleep is higher in depressed patients than in normal subjects (Ho et al., 1996). The greatest increases were observed in the posterior cingulate, the amygdala, the hippocampus, and the occipital and temporal cortex. Significant reductions of relative CMRglu were found in the prefrontal and anterior cingulate cortices, caudate nucleus, and medial thalamus. More recently, depressed patients showed smaller decreases than controls in relative regional CMRglu from presleep wakefulness to NREM sleep in the left and right laterodorsal frontal gyri, right medial prefrontal cortex, right superior and middle temporal gyri, insula, right posterior cingulate cortex, lingual gyrus, striate cortex, cerebellar vermis, and left thalamus (Germain et al., 2004b). These results suggest that transition from wakefulness to NREM sleep in depressed patients is characterized by persistent “elevated” activity in frontoparietal regions and thalamus. Intuitively, it is as if the low frontal metabolism during wakefulness could not be further decreased during NREM sleep, as is the case for normal subjects. These findings suggest that abnormal thalamocortical network function may underpin sleep abnormalities and nonrestorative sleep complaints in depressed patients (Germain et al., 2004b).
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Anterior paralimbic areas (anterior cingulate cortex, right insula, right parahippocampal gyrus) were shown to be less active in depressed patients than in normal subjects, during REM sleep, as compared to wakefulness (Nofzinger et al., 1999). The spatial extent of paralimbic activation from waking to REM sleep was shown to be greater in the depressed patients as compared to healthy controls (Nofzinger et al., 2004b). Moreover, from waking to REM sleep, depressed patients showed greater activation in bilateral dorsolateral prefrontal, left premotor, primary sensorimotor, and left parietal cortices, as well as in the midbrain reticular formation (Nofzinger et al., 2004b) and in the tectal area, inferior temporal cortex, amygdala, and subicular complex (Nofzinger et al., 1999). The density of REM (number of REMs per minute of REM sleep) has been correlated with the severity of the depression (Thase et al., 1997; Buysse et al., 1999).
Average REM count (an automated analog of REM density) was positively correlated with regional CMRglu bilaterally in the striate cortex, the posterior parietal cortices, and in the medial and ventrolateral prefrontal cortices in depressed patients compared to healthy controls. Moreover, it was negatively correlated with regional CMRglu in areas corresponding bilaterally to the lateral occipital cortex, cuneus, temporal cortices, and parahippocampal gyri (Germain et al., 2004a). For the authors, these results suggest that average REM count may be a marker of hypofrontality during REM sleep in depressed patients. Bupropion (an antidepressant drug) increases CMRglu in anterior cingulate, medial prefrontal cortex, and right anterior insula from waking to REM sleep. After analysis, this effect was linked to a reduction in waking relative metabolism in these structures following treatment in the absence of a significant effect on REM sleep relative metabolism (Nofzinger et al., 2001).
SUMMARY Taken together, these data suggest a close link between mood alteration and activity in limbic and paralimbic structures. Especially, it suggests that hyperactivity in the anterior cingulate cortex of depressed patients during wakefulness may hinder further increases in REM sleep. From this perspective, sleep deprivation would alleviate depression symptoms in decreasing abnormally elevated activity in the anterior cingulate cortex during wakefulness. However, available data remain limited and further studies using more detailed designs are needed to understand the causes and consequences of these mesial frontal metabolic disturbances. Overall, relationships between sleep, insomnia, and depression open a neurobiological window to the understanding of the pathophysiological mechanisms of depression which should be extensively exploited in the future.
Narcolepsy Narcolepsy is a disorder which is characterized by excessive sleepiness that is typically associated with cataplexy, sleep paralysis, and hypnagogic hallucinations (AASM, 2001). To the best of our knowledge, in narcoleptic patients, the voxelwise functional neuroanatomy of waking state, REM sleep, or SWS is not yet fully described. Nor are the neural correlates characterized of other characteristic symptoms such as cataplexy, hypnopompic/ hypnagogic hallucinations, or sleep paralysis. Early observations using 133Xe inhalation showed that, during wakefulness, brainstem and cerebellar
FUNCTIONAL NEUROIMAGING IN SLEEP, SLEEP DEPRIVATION, AND SLEEP DISORDERS blood flow was lower in narcoleptic patients than in normal subjects (Meyer et al., 1980). In contrast, after sleep onset (3 out of 13 in REM sleep), the CBF increased in all areas, and particularly in temporoparietal regions. This pattern was supposedly attributed to dreaming activity, in line with prior reports showing that regional blood flow was increased in temporoparietal areas during visual dreaming and hypnagogic hallucinations (Meyer et al., 1980, 1987). In another study, 6 narcoleptic patients underwent 99 mTC-HMPAO SPECT and showed similar HMPAO uptake in waking state and REM sleep (Asenbaum et al., 1995), suggesting a similar overall cortical activity. An activation of parietal regions during REM sleep was shown with data analysis by regions of interest (Asenbaum et al., 1995). The latter result is intriguing given the parietal deactivation usually observed by PET studies during normal REM sleep (Maquet, 2000). Overall, further studies are needed to confirm these results on a broader population. Data describing the neural correlates of cataplexy in narcoleptic patients are very scarce. One SPECT study was conducted on 2 patients during a cataplexy episode compared to REM sleep or baseline waking period (Hong et al., 2006). During cataplexy, perfusion increased in limbic areas (including amygdala) and basal ganglia, thalami, premotor cortices, sensorimotor cortices, and brainstem, whereas perfusion decreased in prefrontal cortex and occipital lobe. Increased cingulate and amygdala activity may relate to concomitant emotional processing that is usually reported as a powerful trigger of cataplexy. However, such hyperperfusion in the pons, thalami, and amygdaloid complexes was not found in a recent single case report (Chabas et al., 2007). A very recent event-related fMRI study was performed on narcoleptic patients and controls while they watched sequences of humorous pictures. This study is based on the clinical observation that cataplexy episodes are often triggered by positive emotions (e.g., hearing or telling jokes). A group comparison revealed that humorous pictures elicited reduced hypothalamic response together with enhanced amygdala response in the narcoleptic patients. These results suggest that hypothalamic hypocretin activity physiologically modulates the processing of emotional inputs within the amygdala, and that suprapontine mechanisms of cataplexy might involve a dysfunction of hypothalamic– amygdala interactions triggered by positive emotions (Schwartz et al., 2008). Another fMRI study confirmed an increase of activity in the emotional network in narcoleptic patients as compared to controls while viewing humorous cartoons (Reiss et al., 2008). Increased activity was also observed in the right inferior frontal gyri,
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an area involved in inhibition (Aron et al., 2004). In addition a reduction in hypothalamic activity was shown in 1 subject experiencing a cataplectic attack. For authors, these findings suggest an overdrive of the emotional circuitry and possible compensatory suppression by cortical inhibitory regions in cataplexy (Reiss et al., 2008). Given the role of acetylcholine as an important neurotransmitter in the generation of REM sleep (see above), cholinergic dysfunction was hypothesized to underlie narcolepsy. However, at present, the available PET data did not show any change in muscarinic cholinergic receptors in narcoleptic patients (Sudo et al., 1998). Similarly, the dopamine system has been probed by PET in narcoleptic patients because increased dopamine D2 binding was shown in the brain of deceased narcoleptic patients (Aldrich et al., 1992; Kish et al., 1992). Results remain controversial. One SPECT study has shown that D2 receptor binding in the striatal dopaminergic system was elevated and correlates with the frequency of cataplectic and sleep attacks in 7 patients with narcolepsy (Eisensehr et al., 2003a). However, this finding was not confirmed by other PET (Rinne et al., 1995, 1996; MacFarlane et al., 1997) or SPECT (Hublin et al., 1994; Staedt et al., 1996) studies. This discrepancy might be related to the drug treatment of narcoleptic patients. Indeed, considerable increase in the uptake of 11C-raclopride, a specific D2 receptor ligand, was observed in the putamen of narcoleptic subjects older than 31 years who had undergone prolonged treatment (Khan et al., 1994). Likewise, despite the fact that the binding of iodobenzamide (IBZM, a highly selective central nervous system dopamine D2 receptor ligand) was similar in narcoleptic patients and normal controls, treatment by stimulants and/or antidepressants for 3 months significantly changed the ligand uptake in 4 out of 5 patients (Staedt et al., 1996). Collectively, these neuroimaging results suggest that the reported postmortem increase in dopamine binding might be due to the long-term effect of prior treatment rather than intrinsic modifications. Two fMRI studies assessed the effects of stimulant drugs on cerebral function in narcoleptic patients. The first one tested the effect of modafinil, a wakefulnesspromoting drug (Ellis et al., 1999). In normal subjects, larger brain responses to a multiplexed visual and auditory stimulation paradigm were found at 10.00 hours than at 15.00 hours in visual areas, but not in auditory areas, suggesting time-of-day influences. Surprisingly, the reverse pattern of activity was observed in a group of 12 narcoleptic patients, with higher activity at 15.00 hours than at 10.00 hours. Additionally, modafinil administration did not modify the average level of activation in either normal subjects or narcoleptics
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(n ¼ 8), but postdrug activation level was inversely proportional to the predrug activation level. These findings are not easy to interpret but at least suggest that modafinil can modulate brain activation to external stimuli. The second study used fMRI and assessed the effects of amphetamines in a small sample of patients with narcoleptic syndrome (n ¼ 2) (Howard et al., 1996). As compared to 3 normal control subjects, the extent of the brain response to auditory and visual stimulation decreased after amphetamine administration in normal subjects. The reverse pattern was observed in narcoleptic patients. Once again, data are very scarce, these findings remain difficult to interpret, and larger samples should be studied before any generalization can be made. Interestingly, using SPECT in 21 healthy volunteers, modafinil has been shown to increase wakefulness and regional CBF in the arousal-related systems and in brain areas related to emotion and executive function (including thalami, dorsal pons, frontopolar, orbitofrontal, superior frontal and middle frontal gyri, insular gyri, cingulate gyrus, inferior temporal gyri, and parahippocampal gyrus) (Joo et al., 2008). Despite these results, it is not clear if activity elicited by wake-promoting drug is underpinned by the same network in narcoleptic patients and healthy controls. Finally, narcolepsy has been linked to a loss of hypothalamic neurons producing orexin (hypocretin), a neuropeptide implicated in arousal systems (Lin et al., 1999). Hypocretin neurons are localized in the lateral hypothalamus and have widespread excitatory projections throughout the brainstem, basal forebrain, and spinal cord. Hypocretin neurons receive in turn inputs from excitatory (glutaminergic) and inhibitory (noradrenergic, serotonergic, and GABAergic) neurons (Baumann and Bassetti, 2005). Hypocretin neurons are hypothesized to be implicated in maintaining wakefulness (Sakurai, 2005) and regulating motor functions (locomotion, muscle tone), energy expenditure (Sakurai, 2005), and sympathetic activity (Baumann and Bassetti, 2005). In humans, postmortem autopsy studies showed a loss of hypocretin mRNA and absence of hypocretin peptides in the hypothalami of narcoleptic patients (Peyron et al., 2000; Thannickal et al., 2003). Low cerebrospinal fluid (CSF) hypocretin-1 levels are usual findings in narcolepsy with definite cataplexy (Mignot et al., 2002). In contrast, in most patients with narcolepsy without cataplexy and in other primary sleep–wake disorders (such as insomnia or restless-legs syndrome (RLS)), CSF hypocretin-1 levels are normal (Baumann and Bassetti, 2005). Moreover, the CSF hypocretin-1 levels have been found to be low in several neurological disorders, irrespective of sleep habits (see, for instance, in advanced Parkinson’s disease, Drouot et al. (2003)).
These elements suggest that hypocretin deficiency may represent in specific clinical contexts a marker of hypothalamic dysfunction rather than an immediate cause of sleep–wake disturbance (Baumann and Bassetti, 2005). Differences in brain morphology that are not identifiable in routine structural MRI can be investigated using the technique of voxel-based morphometry (VBM) that compares the brain structure of patients and controls assessed by high-quality MRI (Ashburner and Friston, 2000, 2001). At present, VBM studies have reported equivocal results in narcoleptic patients. A first study did not show any structural change in brains of patients with hypocretin-deficient narcolepsy (Overeem et al., 2003). These authors suggested that narcolepsy is either associated with microscopic changes untractable by VBM or that functional abnormalities of hypocretin neurons are not associated with structural correlates (Overeem et al., 2003). In another VBM study, however, narcoleptic patients exhibited bilateral cortical graymatter reductions predominantly in inferior temporal and inferior frontal brain regions (Kaufmann et al., 2002). Relative global gray-matter loss was independent of disease duration or medication history and there were no significant subcortical gray-matter alterations. Still another VBM study detected a significant bilateral decrease in hypothalamic gray-matter concentration in narcoleptic patients related to unaffected healthy controls (Draganski et al., 2002). Decreased gray-matter concentration was also observed in the vermis, the superior temporal gyrus, and the right nucleus accumbens. Given the major projection sites of hypocretin-1 (the hypothalamus among others) and hypocretin-2 (the nucleus accumbens among others), the decrease in gray matter was thought to reflect the secondary neuronal loss due to the destruction of specific hypocretin projections (Draganski et al., 2002). This study was corroborated by another VBM study (Buskova et al., 2006). Another VBM study found significant gray-matter loss in the right prefrontal and frontomesial cortex of patients with narcolepsy (Brenneis et al., 2005). For the authors, the volume reduction of gray matter in narcoleptic patients could indicate a disease-related atrophy. Several factors can explain these controversial results, such as possible bias due to inhomogeneous patient groups, prestatistical image processing, or history of treatment (Brenneis et al., 2005). VBM studies with large sample of drug-naive patients should be performed to advance further in this very complex physiopathology. Proton magnetic resonance spectroscopy (1H-MRS) was used in order to assess the N-acetylaspartate (NAA) content in the ventral pontine areas (Ellis et al., 1998) and the hypothalamus of narcoleptic
FUNCTIONAL NEUROIMAGING IN SLEEP, SLEEP DEPRIVATION, AND SLEEP DISORDERS patients (Lodi et al., 2004). In both studies, an analysis of spectral peak area ratios revealed a decrease in the NAA/creatine-phosphocreatine ratio in narcoleptic patients compared with control subjects. These results were interpreted as a neuronal loss or damage in the ventral pontine area and in the hypothalamus of the narcoleptic patients. Another 1H-MRS study in 17 narcoleptics patients showed a higher GABA concentration in the medial prefrontal cortex, which was more prominent in patients without nocturnal sleep disturbance (Kim et al., 2008). The authors suggest it might be a compensatory mechanism to reduce nocturnal sleep disturbances in narcolepsy. The results of the Lodi study (Lodi et al., 2004) were confirmed by an 18FDG PET study that was used to measure relative difference between CMRGlu of 24 narcoleptic patients and 24 normal controls during wakefulness (Joo et al., 2004) (Figure 6.4). Narcoleptic patients had reduced CMRGlu in bilateral precuneus, bilateral posterior hypothalami, and mediodorsal thalamic nuclei (Joo et al., 2004). This study prevails over a SPECT study that was subsequently conducted (Yeon Joo et al., 2005).
Obstructive sleep apnea syndrome Obstructive sleep apnea syndrome (OSAS) is characterized by repetitive episodes of upper-airway obstruction that occur during sleep, generally associated with a reduction in blood oxygen saturation (AASM, 2001). Population-based epidemiologic studies revealed a high prevalence (1–5% of adult men) of OSAS. These studies also associate OSAS with significant morbidity, such as hypertension, cardiovascular disease, stroke, or motor vehicle accidents (Young et al., 2002). OSAS has a complex pathophysiology which is not yet completely understood. Several studies suggest that
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OSAS in all age groups is due to a combination of both anatomic airway narrowing and abnormal upperairway neuromotor tone. Besides the known anatomic factors, such as craniofacial anomalies, obesity, and adenotonsillar hypertrophy, that contribute to OSAS, clear anatomical contributing factors cannot always be identified (AASM, 2001). This suggests that alterations in upper-airway neuromuscular tone also play an important role in the etiology of OSAS (Arens and Marcus, 2004). The pathophysiology of OSAS also includes enhanced chemoreflex sensitivity and an exaggerated sympathetic response during hypoxemic episodes (Caples et al., 2005). Furthemore, it is still a matter of debate whether the cognitive consequences of OSAS are reversible or not (Aloia et al., 2004; Brown, 2005). Functional impairments are often associated with neuropsychological deficits which are often thought to be reversible with appropriate treatment (Aloia et al., 2004; Brown, 2005). In contrast, structural alterations may indicate irreversible cerebral changes and would underpin permanent cognitive impairments (Alchanatis et al., 2004), although this proposal remains a matter of debate in the literature (Gale and Hopkins, 2004). In addition, the specific consequences of sleep fragmentation and hypoxia on cognition and brain function have still to be teased apart and thoroughly characterized. We will present successively an overview of cognitive alterations, changes in brain structure and function, and finally neuroimaging studies exploring ventilatory control in OSAS.
OVERVIEW
OF COGNITIVE ALTERATIONS
Alterations of mental process, behavior, and interpersonal relations are a common observation in OSAS patients (Brown, 2005). Moreover OSAS has been associated with distinct cognitive alterations in various
Fig. 6.4. Cerebral glucose metabolism (CMRGlu) in the hypothalamus and thalamus in narcoleptic patients during wakefulness. Bilateral posterior hypothalami and mediodorsolateral thalamic nuclei show hypometabolism in narcoleptic patients compared to controls. (Reproduced with permission from Joo et al. (2004), Copyright 2004. Wiley-Liss, Inc., A Wiley Company.)
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domains. Both fragmented sleep and hypoxemia are proposed as the main factors leading to neurocognitive impairment during wakefulness (Berry et al., 1986; Findley et al., 1986, 1995; Bedard et al., 1991; Cheshire et al., 1992; Bonnet, 1993; George et al., 1996; Young et al., 1997). Several studies emphasized the deterioration of executive functions in OSAS patients, including the inability to initiate new mental processes (Naegele et al., 1995; Feuerstein et al., 1997), deficits in working memory (Greenberg et al., 1987; Naegele et al., 1995), contextual memory (Harrison et al., 2000), selective attention (Kotterba et al., 1998), continuous attention (Kotterba et al., 1998), and analysis and synthesis (Greenberg et al., 1987; Naegele et al., 1995). A metaanalysis showed that untreated patients with OSAS had a negligible impairment of intellectual and verbal functioning but a substantial impairment of vigilance and executive functioning (Beebe et al., 2003). In addition, a “cognitive reserve” could be protective against OSAS-related cognitive decline (Alchanatis et al., 2005). Most studies suggest that cognitive impairments improve with nasal continuous positive airway pressure (nCPAP) treatment but evidence suggests that some changes may be permanent (Aloia et al., 2004; Brown, 2005). For instance, after nCPAP, OSAS patients improved attention / vigilance in most studies and did not improve constructional abilities or psychomotor functioning (Aloia et al., 2004). Intrinsic neural dysfunction related to these deleterious factors would add to daytime sleepiness to explain the neuropsychological deterioration of OSAS patients (Beebe and Gozal, 2002). Interestingly, several studies have linked OSAS and depression (Schroder and O’Hara, 2005). Moreover, several authors have demonstrated improvement in depression scores and overall psychopathology by using nCPAP therapy (Engleman et al., 1997).
STRUCTURAL
CHANGES
Using VBM in 21 patients with OSAS and in 21 control subjects, structural changes in brain morphology were assessed (Macey et al., 2002). Diminished regional and often unilateral gray-matter loss was apparent in patients with OSAS in multiple brain sites involved in motor regulation of the upper airway as well as in various cognitive functions, including the frontal and parietal cortex, temporal lobe, anterior cingulate, hippocampus, and cerebellum. Another VBM study conducted in 7 OSAS patients and 7 controls showed a significantly lower gray-matter concentration solely within the left hippocampus in the OSAS patients (Morrell et al., 2003). There was no difference in total gray-matter volume between the two groups. In
a more recent VBM study (27 OSAS patients and 24 controls), it has been found that there are no graymatter volume deficits or focal structural changes in severe OSAS patients. Whole-brain volume decreases without focal changes after 6 months of cPAP treatment (O’Donoghue et al., 2005). Another study compared both neuropathological and neuropsychological effects of hypoxia in patients with either carbon monoxide poisoning or OSAS (Gale and Hopkins, 2004). Brain imaging showed a hippocampal atrophy in both groups even though a linear relationship between hippocampal volume and memory performance was found for only a subset of selected tests (the delayed recall or the Rey–Osterrieth Complex Figure Design and Trial 6 of the Rey Auditory Verbal Learning Test, among others), and only in the OSAS group. Hippocampal volume was related to performance on nonverbal information processing (Wechsler Adult Intelligence Scale – Revised Block Design). Further data will be necessary to delineate better the specificity and contribution of hippocampal atrophy in OSAS.
CHANGES
IN BRAIN FUNCTION
As described earlier, cognitive executive functions, associated with specific prefrontal-subcortical brain circuits, are dysfunctional in OSAS patients (Alchanatis et al., 2004). Another study, using single-voxel 1HMRS, attempted to demonstrate that OSAS can induce axonal loss or dysfunction and myelin metabolism impairment in the frontal periventricular white matter. Magnetic resonance spectra were obtained from prefrontal cortex, parieto-occipital and frontal periventricular white matter. NAA-to-creatine and cholineto-creatine ratios were significantly lower in the frontal white matter of OSAS patients when compared to controls. Absolute concentrations of NAA and choline were also significantly reduced in the frontal white matter of OSAS patients (Alchanatis et al., 2004). These findings may offer an explanation for the sometimes irreversible cognitive deficits associated with OSAS. Despite these results, which suggest an implication of frontal-lobe white-matter lesion in daytime cognitive dysfunction, it still lacks a direct relationship between frontal dysfunction and cognitive impairments. Likewise, some clarification is needed to show the respective roles (in cognitive alterations supposed to be frontal) of hypoxia, sleep fragmentation, or sleep deprivation which occur during OSAS. Another 1H-MRS study in OSAS patients showed that, in the left hippocampal area, the N-acetyl-containing/ creatine-containing compounds ratio was significantly increased (Bartlett et al., 2004). Analysis indicated that this was probably due to a decrease in creatine-containing
FUNCTIONAL NEUROIMAGING IN SLEEP, SLEEP DEPRIVATION, AND SLEEP DISORDERS 83 compounds which was correlated with worse OSAS the estimates of cardiovascular variability (Kryger severity and neurocognitive performance. Authors suget al., 2000). Several important regulatory mechanisms gest that the metabolic changes in the hippocampal area in cardiovascular homeostasis seem to be impaired in represent adjustments to brain bioenergetics and may OSAS patients. Specific chemoreceptors seem to be reflect the different susceptibility of this tissue to hypimplicated in the pathophysiology of OSAS (Mateika oxic damage in OSAS, as in ischemic preconditioning. and Ellythy, 2003). For instance, the ventilatory An earlier and less reflective 1H-MRS study in 23 response to carbon dioxide is elevated in OSAS OSAS patients showed that the NAA-to-choline ratio patients (Mateika and Ellythy, 2003). The partial presin cerebral white matter was significantly lower in sure of carbon dioxide that delimits the carbon dioxide patients with moderate to severe OSAS than in patients ventilatory recruitment threshold is elevated in patients with mild OSAS and healthy subjects (Kamba et al., with OSAS (Mateika and Ellythy, 2003). An altered 1997). This finding suggests the presence of cerebral autonomic balance has been suggested as one possible damage, probably caused by repeated apneic episodes. pathogenic factor. This autonomic dysfunction has In addition, a study by Halbower et al. (2006) showed been thought to be implicated in the subsequent devela decrease in the NAA-to-choline ratio in the left opment of cardiovascular diseases in patients with hippocampus and in the right frontal cortex using the OSAS. Several fMRI studies have been conducted in same technique in a pediatric population with OSAS. OSAS patients to characterize the neural correlates of Together VBM and spectroscopy studies point to integrated afferent airway signals with autonomic outan atrophy and/or dysfunction of hippocampal regions flow and airway motor response (Harper et al., 2003; in OSAS. Henderson et al., 2003; Macey et al., 2003, 2006). For Long-term consequences of OSAS have been more instance, altered neuronal response after Valsalva rarely assessed after nCPAP treatment. An early maneuver was shown in cerebellar, limbic, and motor 99 mTC-HMPAO SPECT study in 14 adult OSAS areas involved in the control of diaphragmatic and patients (Ficker et al., 1997) reported a marked frontal upper-airway muscles (Figure 6.5). Enhanced sympahyperperfusion in 5 patients. In distinction, regional thetic outflow after a forehead cold pressor challenge analysis showed a reduced perfusion in the left parietal results in both diminished and exaggerated responses region. It is noteworthy that all these changes were in limbic area, cerebellar, frontal cortex, and thalamus. completely reversed by effective nCPAP therapy, sugAn fMRI study evaluated the brain activity changes gesting that the main deleterious effects of OSAS on during baseline and expiratory loading conditions in 9 brain activity are reversible. The authors suggest that OSAS patients and 16 controls (Macey et al., 2003). there might be an apnea-associated effect of local vasReduced neural signals emerged in OSAS patients cular autoregulation mechanisms acting to compensate within the frontal cortex, anterior cingulate, cerebellar systemic blood flow alterations or blood gas changes dentate nucleus, dorsal pons, anterior insula, and lentiin OSAS. Using 1H-MRS, a study showed that NAA in form nuclei. Signal increases in OSAS over control the parietal-occipital cortex was significantly reduced subjects developed in the dorsal midbrain, hippocammore in 14 OSAS patients than in controls, but this pus, quadrangular cerebellar lobule, ventral midbrain, reduction persisted after nCPAP therapy despite clinical, and ventral pons. Fastigial nuclei and the amygdala neuropsychological, and neurophysiological normalizashowed substantially increased variability in OSAS tion (Tonon et al., 2007). In addition, mandibular subjects. No group differences were found in the thaladvancement led to decreased fMRI response in the amus. Both groups developed similar expiratory loadleft cingulate gyrus and the bilateral prefrontal cortices ing pressures, but appropriate autonomic responses in 12 healthy subjects during induced respiratory stress did not emerge in OSAS patients. A more recent fMRI (Hashimoto et al., 2006). Simultaneously, the subjective study evaluated the brain activity changes during baseeffects of this treatment were assessed by a visual line and inspiratory loading in 7 OSAS patients and analog scale and confirmed successful reduction of 11 controls (Macey et al., 2006). A number of cortical respiratory stress. and subcortical areas mediating sensory and autonomic processes, and motor timing were affected. Altered signals appeared in primary sensory thalamus CHANGES IN VENTILATORY CONTROL and sensory cortex, supplementary motor cortex, cerIn OSAS patients, apnea has considerable hemodyebellar cortex and deep nuclei, cingulate, medial temnamic consequences that are mediated by a complex poral, and insular cortices, right hippocampus, and cascade of physiological events. Repetitive episodes midbrain (Macey et al., 2006). of apnea trigger marked fluctuations in both blood These altered brain activation patterns, during pressure and heart rate, with consequent effects on waking, could reflect neural dysfunctions that mediate
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M. DESSEILLES ET AL. Inferior parietal cortex Right view
Precentral gyrus
Left view
Superior temporal gyrus Cerebellar cortex
Superior frontal gyrus
Front view
Fig. 6.5. Neural response (cerebral blood flow) during Valsalva maneuvers in obstructive sleep apnea syndrome using functional magnetic resonance imaging. There are areas of significant difference in signal intensity between controls and obstructive sleep apnea syndrome (OSAS) patients during Valsalva maneuver in cortical regions. Voxels are color-coded for depth from the surface and rendered on to the cortex of an average anatomical image set of all control and OSAS patients. (Reproduced from Henderson et al. (2003), with permission from the Journal of Applied Physiology, Copyright 2003. American Physiological Society.)
the prominently diminished upper-airway tone which occurs in OSAS patients during sleep.
SUMMARY Altogether, these findings suggest that neuropsychological damage in OSAS is brought about by various alterations in prefrontal cortex, hippocampal and parietal cortex. Even if abnormal brain activations are reversible under nCPAP, several studies have suggested that not all neuropsychological damage disappears after nCPAP (Bedard et al., 1993; Feuerstein et al., 1997; Naegele et al., 1998). Accordingly, structural brain changes have been reported in OSAS patients. Although the basic pathophysiological mechanisms are not completely understood, a dysregulation in the autonomic regulation seems to have an important role in these mechanisms. However, it is important to notice that peripheral factors may confound the deficits observed in studies focused on OSAS patients, including exaggerated body mass index and
motivational problems (Tasali and Van Cauter, 2002; Spiegel et al., 2004).
Abnormal motor behaviors during sleep Abnormal motor behaviors during sleep include the periodic limb movements and RBD, a specific parasomnia syndrome associated with REM sleep. Abnormal motor behaviors are a common cause of sleep disturbance and the understanding of the underlying physiopathology should be useful in the management (diagnostic and prognostic information) of insomnia (Montplaisir et al., 1994).
PERIODIC
LIMB MOVEMENTS
Periodic limb movement disorder during sleep (PLMS) and RLS are distinct but overlapping syndromes. PLMS is characterized by periodic episodes of repetitive and highly stereotyped limb movements that occur during sleep (ASDA, 1990). RLS is a disorder
FUNCTIONAL NEUROIMAGING IN SLEEP, SLEEP DEPRIVATION, AND SLEEP DISORDERS characterized by uncomfortable leg sensations, usually prior to sleep onset, that cause an almost irresistible urge to move the legs (ASDA, 1990). The diagnosis of PLMS requires the presence of PLMS on polysomnography as well as an associated sleep complaint. RLS, however, is essentially made on clinical grounds. Moreover, PLMS are themselves nonspecific, occurring both with RLS and with other sleep disorders (e.g., narcolepsy, sleep apnea syndrome, RBD) as well as in normal individuals (Tan and Ondo, 2000). Thus, the diagnosis of PLMS requires the exclusion of other potential causes for the associated sleep complaint (Lesage and Hening, 2004). Structural cerebral abnormalities have been reported in patients with idiopathic RLS (Etgen et al., 2005). High-resolution T1-weighted MRI of 51 patients and 51 controls analyzed using VBM revealed a bilateral gray-matter increase in the pulvinar in patients with idiopathic RLS. These authors suggest that changes in thalamic structures are either involved in the pathogenesis of RLS or may reflect a consequence of chronic increase in afferent input of behaviorally relevant information. Finally, an fMRI study also attempted to localize some cerebral generators of leg discomfort and periodic limb movements in RLS (Bucher et al., 1997). The leg discomfort study showed a bilateral activation of the cerebellum and contralateral activation of the thalamus in patients. During a second condition, combining periodic limb movements and sensory leg discomfort, patients also showed activity in the cerebellum and thalamus with additional activation in the red nuclei and brainstem close to the reticular formation. Interestingly, when subjects were asked to imitate PLMS voluntarily, there was no activation in the brainstem, but rather additional activation in the globus pallidus and motor cortex. These results suggest an involuntary mechanism of induction and a subcortical origin for RLS. In addition, a recent VBM study examining 14 patients with idiopathic RLS detected a slightly increased gray-matter density in the ventral hippocampus and in the middle orbitofrontal gyrus (Hornyak et al., 2007). Recently, 45 idiopathic RLS patients and 30 healthy controls were studied using quantitative whole-brainbased diffusion tensor imaging (Unrath et al., 2008). In the RLS group, regional fractional anisotropy used as a quantitative marker of white-matter integrity was reduced in several subcortical areas, including areas in the proximity of motor and somatosensory cortices, the right hemispheric thalamus (posterior ventral lateral nucleus), in motor projectional fibers, and adjacent to the left anterior cingulum. In addition, high-resolution three-dimensional MRI was performed in 63 idiopathic RLS patients using optimized VBM (Unrath et al.,
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2007). As compared to controls, regional decreases of gray-matter volume were shown in primary somatosensory cortex and primary motor areas. Clusters in both areas correlated with the severity of RLS symptoms and with disease duration. Together these results show a neocortical and subcortical network of area involving sensorimotor impairment. Incongruent results might be due to differences in populations examined, such as treatment-induced effects on cerebral morphology in RLS, duration of the illness, or methodological issues (size of the samples). A suprasegmental release of inhibition of descending inhibitory pathways implicating dopaminergic, adrenergic, and opiate systems is thought to be involved in PLMS pathogenesis (Wetter and Pollmacher, 1997). This is supported by the observation of PLMS during spinal anaesthesia (Watanabe et al., 1987), for instance. Patients’ condition worsens when dopamine antagonists are given (Akpinar, 1982), whereas dopaminergic drugs have been shown to relieve PLMS (Brodeur et al., 1988; Montplaisir et al., 1991, 2000). Staedt et al. have tested the hypothesis of decreased dopaminergic activity in PLMS patients. In a series of SPECT studies, they report a decreased IBZM striatal uptake, indicating a lower D2 receptor occupancy in PLMS patients (Staedt et al., 1993, 1995a, b; Happe et al., 2003). Treating patients with dopamine replacement therapy increased the IBZM binding and improved the sleep quality in these patients (Staedt et al., 1995a). One study evaluated the striatal pre- and postsynaptic dopamine status in 10 drug-naive patients suffering from both RLS and PLMS and 10 age-matched controls, by means of 123I methyl 3 beta-(4-iodophenyl) tropane-2 beta-carboxylate (123I beta-CIT), a ligand of dopamine transporter, and 123I-IBZM SPECT respectively (Michaud et al., 2002). There was no difference in DA transporter (123I-beta-CIT) binding between RLS-PLMS patients and controls. The study of the striatal D2 receptor binding (123I-IBZM) revealed again a significantly lower binding in patients as compared with controls. Numerous mechanisms may be responsible for this decrease in D2 receptor binding. Since 123 I-beta-CIT binding is normal, a decreased number of D2 receptors or a decreased affinity of D2 receptors for 123I-IBZM is more likely than a downregulation of D2 receptors due to an increased level of synaptic dopamine (Michaud et al., 2002). Fourteen patients with idiopathic RLS and PLMS successfully treated by dopaminergic (e.g., ropinirole) and nondopaminergic (e.g., gabapentin) treatment were investigated while off medication by using 123I-IBZM and SPECT (Tribl et al., 2004). They were compared to 10 healthy sex- and age-matched control subjects. The patients presented with sleep disturbances, severe
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PLMS, and severe RLS symptoms during the period of scanning while off medication and did not show any significant differences in striatal to frontal 123I-IBZM binding to D2 receptors compared to controls, in contrast to the previous study. The authors suggest that the dopaminergic system in these patients might be affected elsewhere, possibly in the diencephalospinal part of the dopaminergic system (Tribl et al., 2004). These studies support the hypothesis that a central dopamine dysfunction is involved in the physiopathology of RLS-PLMS, although more recent studies specifically implicate the cerebral metabolism of iron (Allen, 2004). Iron and the dopaminergic system are linked since iron is an important cofactor for tyrosine hydroxylase, the step-limiting enzyme in dopamine synthesis, and also plays a major role in the functioning of postsynaptic D2 receptors (Kryger et al., 2000). A neuropathologic study (7 RLS brain and 5 normal brain) has shown a marked decrease in H-ferritin (ferritin heavy chain) and iron staining in RLS subtantia nigra. Transferrin receptor staining on neuromelanincontaining cells was decreased in RLS brains compared to normal brains, whereas transferrin staining in these cells was increased (Connor et al., 2003). Using a special MRI measurement (R2*), Allen et al. (2001) assessed regional brain iron concentrations in 10 subjects (5 with RLS, 5 controls). R2* was significantly decreased in the substantia nigra, and somewhat less significantly in the putamen, both in proportion to RLS severity. These results show that this R2* MRI measurement may prove useful in the management of RLS, and also indicate that brain iron insufficiency may occur in RLS patients in some brain regions. In addition, another study found diminished iron concentration across 10 brain regions in early-onset RLS but not in late-onset RLS when compared to controls (Earley et al., 2006). These convergent observations seem to show that RLS may be a functional disorder resulting from impaired iron metabolism (i.e., impaired regulation of transferring receptors) (Connor et al., 2003). Interestingly, altered iron metabolism in lymphocytes was shown in 24 subjects with RLS as compared with controls. Lymphocytes showed an increase in ferroportin (a transmembrane protein that transports iron from the inside to the outside of a cell), implying increased cellular iron excretion, in the face of increased iron need (Earley et al., 2008).
REM
SLEEP BEHAVIOR DISORDER
RBD is characterized by brisk movements of the body associated with dream mentation that usually disturbs sleep continuity (Schenck et al., 1986). During the nocturnal spells, patients behave as if they are acting out
their dream (ASDA, 1997). This disease may be idiopathic (up to 60%) or associated with other neurologic disorders. A sizeable proportion of patients with RBD will develop extrapyramidal disorders (Schenck et al., 1996; Gagnon et al., 2002, 2004), Lewy body dementia (Fantini et al., 2005), and multiple system atrophy (Plazzi et al., 1997; Gilman et al., 2003). More recently, a strong association between RBD and alpha-synucleinopathies has been observed, with the parasomnia often preceding the clinical onset of the neurodegenerative disease (Fantini et al., 2005; Boeve et al., 2007). Worthy of note, lesions in the mesopontine tegmentum of cats can lead to the disappearance of muscle atonia during REM sleep together with dream enactment behavior (Sakai et al., 1979). A study combining MRI and 123I-IMP SPECT in 20 RBD patients and 7 healthy controls during REM sleep reported significantly decreased blood flow in the upper portion of both sides of the frontal lobe and pons in patients with RBD, in comparison with normal elderly subjects (Shirakawa et al., 2002). Another SPECT study in 8 RBD patients during waking rest showed decreased activity in frontal and temporoparietal cortices but found increased activity in the pons, putamen, and right hippocampus (Mazza et al., 2006). In addition, brainstem function was evaluated by 1H-MRS in a 69-year-old man with idiopathic RBD. An analysis of spectral peak area ratios revealed an increase in the choline/creatine ratio. This change suggests that brainstem neurons have functional impairment at the cell membrane level (Miyamoto et al., 2000). In contrast, one group using 1H-MRS in 15 patients with idiopathic RBD and 15 matched control subjects failed to reveal any difference in metabolic peaks of NAA/creatine, choline/creatine and myoinositol/creatine ratios in the pontine tegmentum and the midbrain (Iranzo et al., 2002). This result does not support the hypothesis of marked mesopontine neuronal loss or 1 H-MRS detectable metabolic disturbances in idiopathic RBD. Despite these equivocal results, 1H-MRS may provide for noninvasive metabolic evaluation of brainstem neuronal function in RBD and find application in the differentiation of secondary RBD with neurodegenerative disorders from idiopathic disorders. Using SPECT and (N)-(3-iodopropene-2-yl)-2betacarbomethoxy-3beta-(4-chlorophenyl) tropane labeled with iodine-123 (IPT), a ligand of striatal presynaptic dopamine transporters), IPT binding in RBD patients (n ¼ 5) during wakefulness was found to be lower than in normal controls but higher than in Parkinson patients (n ¼ 14) (Eisensehr et al., 2000, 2003b). These results suggest that the number of presynaptic dopamine transporters is decreased in both Parkinson and RBD patients. Other studies probed the density of striatal dopaminergic terminals using PET and 11C-dihydrotetrabenazine (11C-DTBZ, a monoamine vesicular transporter
FUNCTIONAL NEUROIMAGING IN SLEEP, SLEEP DEPRIVATION, AND SLEEP DISORDERS inhibitor used as an in vivo marker for dopamine nerve terminals). Significant reductions in striatal 11C-DTBZ binding characterized 6 elderly subjects with chronic idiopathic RBD, as compared to 19 age-matched controls, particularly in the posterior putamen (Albin et al., 2000). Likewise 11C-DTBZ binding in the striatum was decreased in 13 patients with multiple-system atrophy (MSA) (Gilman et al., 2003). Striatal 11C-DTBZ uptake was inversely correlated with the severity of symptoms in this MSA group. Moreover 123I-iodobenzovesamiol (123I-IBVM) binding was reduced in the thalamus in this MSA population. 123I-IBVM is a radiotracer that selectively binds to the intraneuronal storage vesicles of cholinergic nerve endings, and is used as a highly specific marker for cerebral cholinergic neurons. It remains to be shown whether these alterations play a causal role in the pathophysiology of RBD or reflect functional consequences and adaptations to the pathological conditions. Although there is evidence that some Parkinson patients do show excessive nocturnal movements (Trenkwalder, 1998; Happe et al., 2003), it is interesting that only a small percentage of Parkinson patients develop full-blown RBD. This suggests that modifications of other systems of neurotransmission are probably necessary for full-blown RBD to occur.
CONCLUSIONS Brain functional imaging provides unprecedented possibilities to explore brain function during normal and pathological sleep. Nevertheless, brain functional imaging in sleep is still in its infancy, at present mostly restricted to research purposes. As shown in this review, brain functional imaging in patients affected by sleep disorders may address different kinds of issues. The first topic is the characterization of the cerebral aftermath of sleep disruption due to intrinsic sleep disorders or to extrinsic environmental or medical causes. The second, more ambitious, aim would be to characterize better the primary physiopathological mechanisms of sleep disorders, or at least their cerebral correlates. This attempt is hampered by several factors. Scanning patients during their sleep is not at all easy, for practical and methodological reasons. It requires some adjustment in the imaging environment and it is never guaranteed that the participant will sleep during data acquisition opportunities. Clinical manifestations in sleep disorders are often unpredictable and transient (e.g., sleepwalking, RBD); thus one cannot predict whether the pathological event will occur during the scanning period. In the same manner, most clinical manifestations induce large movements. These pathological movements during sleep may lead to image
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artifacts and misinterpretation of brain activation, making their study in functional neuroimaging very difficult. In this respect, SPECT is probably the most appropriate procedure for the reason that the radiotracer can be simply administered during the clinical events, well before the brain images are acquired. A example of such a study pertains to sleepwalking (Bassetti et al., 2000). Finally, and not least, the theoretical framework necessary for designing the protocol of clinical neuroimaging studies is not necessarily available for all sleep disorders. For instance, the discovery of the hypocretin system and its role in narcolepsy in the late 1990s (Lin et al., 1999) has indubitably changed how experimental designs should be run in neuroimaging in narcoleptic patients. Nevertheless, alternative approaches are available, as the functional and structural consequences of these sleep disorders can also be assessed during wakefulness, as seen above. A third area of interest is the establishment of a nosography of sleep disorders. For instance, neuroimaging could help classify different subtypes of insomnia in terms of their underlying characteristic patterns of regional brain activity, an approach that may prove complementary to the clinical observation. Finally, functional neuroimaging can also be used to assess the effects of hypnotic drugs on regional brain function. This may enlighten our understanding of their effects, assuming that hypnotic medications inducing typical patterns of brain activation might rely on cellular mechanisms similar to those prevailing in normal sleep. Although substantial progress in methodology has been made, a large research effort is still needed to characterize better pathophysiological mechanisms of sleep disorders, teasing apart their causes from their consequences. Optimally, brain functional imaging should be helpful in order to assess, in an individual patient, the functional and structural consequences of long-term sleep disruption. These considerations argue for closer collaboration and partnership between basic neuroscientist sleep researchers, sleep clinicians, and neuroimagers in designing and conducting more informative (multimodal) experiments in a large number of sleep disorders.
ACKNOWLEDGMENTS The authors are supported by the Fonds National de la Recherche Scientifique (FNRS) (Belgium; grant number 3.4516.05 to Martin Desseilles). This work was additionally supported by the research funds of the University of Lie`ge, the Queen Elisabeth Medical Foundation, and the Interuniversity Attraction Pole program.
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Handbook of Clinical Neurology, Vol. 98 (3rd series) Sleep Disorders, Part 1 P. Montagna and S. Chokroverty, Editors # 2011 Elsevier B.V. All rights reserved
Chapter 7
The phylogeny of sleep KRISTYNA M. HARTSE * Sonno Sleep Center, El Paso, TX, USA
INTRODUCTION Why do we sleep? Despite a voluminous body of scientific and clinical literature, the definitive answer to this fundamental question has yet to be found. To the insomnia patient with unrelenting chronic sleeplessness, the answer is painfully and viscerally obvious. Sleep prevents feelings of sleepiness and dysphoria during the day. To the scientist and clinician, however, this answer, although responsive to the universally acknowledged effects of sleep loss, does not address the specific biological or functional reasons for sleep (Rechtschaffen, 1998). All mammals and birds studied to date exhibit unambiguous signs of sleep. Furthermore, an array of specific human sleep disorders, including sleep apnea, narcolepsy, periodic limb movements, restless legs, and insomnia, correlate with deficits in health and well-being. These consequences of disturbed sleep in conjunction with the universality of sleep in mammalian organisms imply that sleep serves an important life-enhancing or even life-sustaining function. Correlations, however, do not prove causality. Whether the function of sleep can be discovered by studying human sleep disorders or more generally by studying neurologically and biochemically complex mammalian species is questionable. A different approach to discovering the origins and functions of sleep would be through the study of nonmammalian organisms which have remained relatively unchanged from their ancient fossil ancestors and which may provide clues about the origins of sleep. The presence of behavioral and electrophysiological signs of sleep in living mammals and birds suggests that sleep has been perpetuated in evolution from ancient origins. A behavior such as sleep, of course, does not leave a fossil record, which severely limits
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the extent to which definitive statements can be made about the origin of sleep. However, by studying living, phylogenetically ancient organisms such as insects, fish, amphibians, reptiles, and primitive mammals, clues to the function of sleep might be revealed. This phylogenetic approach to investigating the origins of sleep has not been without controversy, and there is disagreement in the literature about the presence or absence of sleep in nonmammals. There is general agreement that most nonmammalian organisms exhibit behavioral sleep or rest. However, the electrophysiological signs of sleep in nonmammalian organisms may be very different from that of mammals. This observation has led some authors to conclude that, by definition, nonmammalian species do not sleep because mammalian electrophysiological correlates of sleep are not present (Walker and Berger, 1973). These issues will be reviewed as we examine evidence for the origins of sleep.
THE DEFINITION OF SLEEP Sleep is defined by both behavioral and electrophysiological criteria. The well-established behavioral criteria include: (1) a species-specific posture; (2) behavioral quiescence; (3) elevated arousal thresholds; and (4) rapid state reversibility with moderately intense stimulation to distinguish sleep from hypothermia, torpor, or coma (Flanigan et al., 1974). Sleep homeostasis, the compensatory rebound in sleep after deprivation of quiescent states, is an additional feature in the definition of sleep (Tobler, 2005). In mammals and birds, there are distinctive electrophysiological correlates that accompany behavioral sleep. As a result of the close relationship between behavior and electrophysiology, electrophysiological correlates are almost universally substituted for behavioral observation to define the
Correspondence to: Kristyna M. Hartse, Ph.D., Clinical Director, Sonno Sleep Center, 1400 George Dieter, Suite 210, El Paso TX 79936, USA. Tel: 915-533-8499, E-mail:
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K.M. HARTSE
presence of sleep. Slow-wave sleep (SWS) is marked by high-amplitude neocortical slow waves. Cyclically alternating with SWS is rapid eye movement (REM) sleep (also called paradoxical sleep), characterized by lowvoltage brain activity similar to that of waking, skeletal muscle inhibition, and REMs. Although the distribution and amounts of non-REM (NREM) and REM sleep vary widely among mammals and birds (Zeplin et al., 2005), the electrophysiology of these two states is well established except in cetaceans (whales and dolphins) and a monotreme, the echidna, a primitive egg-laying mammal (Mukhametov, 1987; Siegel et al., 1996; Lyamin et al., 2002, 2004, 2005). The electrophysiological correlates associated with nonmammalian behavioral sleep have received considerable attention. No change in brain activity during behavioral quiescence, slow waves during waking which diminish with behavioral sleep, both the presence and absence of SWS, and both the presence and absence of REM sleep have all been reported. However, some of the most rigorous studies, particularly in reptiles, have revealed the presence of a high-voltage spike which is prominent during behavioral sleep and minimally present during behavioral wakefulness (Flanigan, 1973, 1974; Flanigan et al., 1973, 1974). The spikes increase in a homeostatic response following enforced wakefulness, and both spikes in reptiles and SWS in mammals respond similarly to pharmacological agents (Hartse and Rechtschaffen, 1974, 1982). These
findings have suggested that the spikes are a nonmammalian electrophysiological correlate of SWS. Persuasive evidence for REM sleep in nonmammalian organisms is not strong. Because the appearance of the reptilian spike is substantially different from the neocortical slow waves recorded in mammals (Figure 7.1), these findings have led some investigators to conclude that the spike is not an electrophysiological marker of sleep (Walker and Berger, 1973). Further studies in the rat and cat, however, have revealed the presence of a spike recorded from the ventral hippocampus (VH) during SWS which is similar to the reptilian spikes (Metz and Rechtschaffen, 1976; Hartse et al., 1979). VH spikes and reptilian spikes have a similar morphology: they both show a rebound following enforced wakefulness, and they both respond similarly to pharmacological agents. Additional support for a relationship between hippocampal spikes and neocortical slow waves is the finding that hippocampal sharp waves are modulated by neocortical activity during SWS (Sirota et al., 2003). The generation of slow waves requires neocortical development, and slow-wave activity recorded from brain surface electrodes is easily observed in mammals that have extensive neocortical development. However, the rudimentary neocortex of fish, amphibians, and reptiles in comparison to mammals would seem to preclude on anatomical grounds the observation of slow waves in these species (Nieuwenhuys, 1994). In addition, it has recently been convincingly argued that the
CAT HIPPOCAMPUS 100 mv HIPPOCAMPUS –INTEGRATION
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Fig. 7.1. Comparison of mammalian ventral hippocampus spikes in the cat with reptilian spikes in the tortoise. In each recording the upper tracing shows the raw, unfiltered record. The lower tracing shows the signal after it has been passed through a bandpass filter set for 30–1000 Hz, a 60-Hz notchfilter, and a Beckman 9852 integrator coupler. Both spikes are shown at slow and fast speeds. (Reprinted with permission from Hartse and Rechtschaffen, 1982.)
THE PHYLOGENY OF SLEEP presence of the mammalian neocortex per se is not necessarily the most critical element in the electrophysiological expression of slow waves, but rather it is the advanced development of palliopallial connectivity in mammals and birds which accounts for the presence of slow waves in these species (Rattenborg, 2006a). This position has also spurred debate (Rattenborg, 2007; Rial et al., 2007b). In contrast to the findings of SWS associated with mammalian and avian quiescence, some investigators have reported the presence of slow waves during reptilian waking which diminish during behavioral sleep. This observation has been interpreted as suggesting that reptilian wakefulness, and not reptilian sleep, is the precursor of mammalian SWS (De Vera et al., 1994; Rial et al., 2007a). This position, however, has not been widely adopted based upon the preponderance of evidence (Rattenborg et al., 2007). As we can see, the task of identifying sleep in nonmammalian organisms has not been a straightforward one. Besides the imposition of mammalian criteria for sleep on nonmammalian organisms, additional constraints in studying nonmammalian species include technical difficulties in evaluating organisms which inhabit unusual arboreal or aquatic environments not conducive to electrophysiological recording, an absence of stereotaxic atlases to assure comparable electrode placements between species, and a sparsity of data which establish homologous brain structures between mammals and nonmammals (Hartse, 1994). Nonetheless, even given these constraints as well as the contradictory findings of nonmammalian studies, the most general conclusion that can be made is that behavioral quiescence is a universal phenomenon in living organisms.
INVERTEBRATES In comparison to the many studies on sleep in vertebrates, the literature on sleep behavior and electrophysiology in invertebrates, organisms without backbones, is exceedingly sparse. The exception, as we shall see, is a growing body of work in insects, specifically the fruit fly, Drosophila melanogaster, which suggests that these organisms may serve as a model for studying the molecular basis for mammalian sleep (Hendricks et al., 2000b). Invertebrates which have been studied to date meet the behavioral criteria for a sleep-like state. The sea slug (Aplysia californica) exhibits periods of nocturnal behavioral quiescence and decreased motor activity (Strumwasser, 1971). In addition a rise in 5-hydroxytryptophan (5-HT) secretion in the hemolymph during the dark portion of the 24-hour cycle, corresponding to periods of decreased locomotor activity, suggests that behavioral sleep is accompanied by mammalian-like
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neurochemical changes (Levenson et al., 1999). Using time lapse video, preliminary studies in the pond snail, Lymnaea stagnalis, have identified a resting state which is associated with reduced responsiveness to an appetitive stimulus and an increase in quiescence following rest deprivation (R. Stephenson, personal communication). Two ocean-dwelling gastropods, the cuttlefish (Sepia pharonis) and octopus (Octopus vulgaris), meet the criteria for behavioral sleep, and both exhibit rebounds in behavioral sleep following periods of enforced wakefulness (Duntley et al., 2002; Brown et al., 2006). Electrophysiological recordings from above the vertical lobe in the brain of the octopus have revealed trains of high-amplitude spikes associated with behavioral quiescence, suggesting a correspondence to the spikes observed during reptilian behavioral quiescence (Flanigan, 1973, 1974). A recent study in the crayfish (Procambarus clarkii) also documented clear signs of behavioral sleep as well as a rebound in recovery sleep following enforced wakefulness (Ramon et al., 2004; Mendoza-Angeles et al., 2007). However, in contrast to findings in the octopus, continuous fast spikes occurred during behavioral wakefulness. With the onset of distinctive quiescent postures, “continuous slow waves” in the 15–20-Hz bandwidth were observed. This frequency range is substantially higher than the 0.5–4.0-Hz frequency range typically associated with mammalian SWS. Thus, although these recent studies are in agreement about the presence of behavioral sleep, there is significant divergence in the electrophysiological findings.
INSECTS Some of the most promising clues to the function of sleep have come from a new and rapidly growing body of work on sleep in insects, specifically D. melanogaster (for a recent review, see, Hartse, 2009). In early observational studies both in the field and in the laboratory, wasps, bees, flies, butterflies, and moths were observed to exhibit state-reversible behavioral sleep associated with species-specific postures as well as increased arousal thresholds (Rau and Rau, 1916; Andersen, 1968). The honey bee (Apis mellifera) exhibits distinctive antennae and head postures correlated with behavioral sleep and waking (Figure 7.2). During behavioral sleep decreases in locomotor activity, decreases in thoracic temperature, decreases in neck muscle activity, and increases in thresholds to infrared stimuli are present (Kaiser, 1988). Following 12 hours of sleep deprivation, a significant increase in antenna immobility in sleep-deprived as compared to control bees is present in a homeostatic response to deprivation (Sauer et al., 2004). The electrophysiological
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Fig. 7.2. A quiescent bee. (Courtesy of Cheryl Moorehead.)
correlates of sleep in the bee are unknown, but single cell recordings from directionally sensitive optomotor interneurons reveal a circadian rhythmicity with decreased sensitivity to a pattern stimulus at night, corresponding to times of decreased locomotor activity and behavioral sleep (Kaiser and Steiner-Kaiser, 1983). Delivery of a puff of air reversed this decreased sensitivity, indicating neuronal state reversibility. Three species of scorpion (Tobler and Stadler, 1988) as well as the cockroach (Tobler, 1983; Tobler and Neuner-Jehle, 1992) all meet the behavioral criteria for sleep. A recent study in the cockroach (Stephenson et al., 2007) has elegantly demonstrated a similarity to the results in mammals following sleep deprivation. Deprivation of cockroach behavioral quiescence led to both an increased metabolic rate without a change in body mass and increased mortality in comparison to controls. These results are remarkably similar to the findings of increased metabolic rate and increased mortality in the rat following sustained sleep deprivation (Rechtschaffen and Bergmann, 2002). The fruit fly, D. melanogaster, has been proposed as a model for the study of mammalian sleep, and the similarities between characteristics of sleep in the fruit fly and in mammalian species are striking (Hendricks et al., 2000b; Cirelli, 2006). The well-studied genetic mapping, the short life cycle which permits rapid assessment of genetic manipulations in subsequent generations, and the relative ease of maintaining large experimental colonies make the fruit fly an excellent candidate for studying the molecular and genetic aspects of sleep. Initial studies provided convincing evidence that behavioral rest in Drosophila is analogous to mammalian sleep (Hendricks et al., 2000a; Shaw et al., 2000). Similar to mammals, Drosophila exhibits increased arousal thresholds and an increase in quiescence following enforced wakefulness that is independent of the circadian clock. Behavioral wakefulness occurs in
response to the administration of caffeine, metamphetamine, and modafinil whereas behavioral quiescence is induced following antihistamine administration (Shaw et al., 2000; Hendricks et al., 2003; Andretic et al., 2005). There are age-related changes across the lifespan with increased quiescence in immature organisms and a decline in quiescence as well as a fragmentation of rest periods with age (Shaw et al., 2000; Koh et al., 2006). A decrease in spike-like local field potentials with quiescence suggests an electrophysiological correlate of behavioral sleep (Nitz et al., 2002), and chemical lesioning as well as stimulation of the mushroom bodies have identified a specific neuroanatomical locus for the control of sleep in the fly brain (Joiner et al., 2006; Pitman et al., 2006). One of the major advantages in utilizing Drosophila is its well-known genetic profile and the identification of mutant strains with specific alterations in normal patterns of quiescence (Cirelli, 2003; Kume et al., 2005). A mutation in the dopamine transporter gene has been identified in a near-sleepless mutant ( fumin) (Kume et al., 2005). Significantly increased motor activity across the light–dark cycle independent of the circadian clock, decreased arousal thresholds, and an absence of rebound in response to rest deprivation were associated with normal lifespans and an absence of obvious morphological, reproductive, or developmental abnormalities. Other mutant strains exhibiting reduced sleep amounts, however, also have decreased lifespans (Cirelli et al., 2005). These findings not only suggest an important role for dopamine in the control of sleep expression, but also that some forms of sleeplessness may have a genetic basis which is not necessarily associated with adverse consequences for survival. Recent studies of specific protein manipulations also suggest that Drosophila may provide new insights into the functional molecular basis of sleep (Foltenyi et al., 2007; Naidoo et al., 2007). In summary, quiescent states, which meet the criteria for behavioral sleep, are present in invertebrate species. The electrophysiology of invertebrate sleep is not well known, although there is evidence that distinctive electrophysiology may exist. In addition, studies in Drosophila are rapidly advancing understanding of the detailed molecular and genetic structure of sleep mechanisms that may provide fruitful clues to sleep function in mammals.
FISH AND AMPHIBIANS Behavioral sleep has been described in both fish and amphibians. Very few electrophysiological studies have been conducted in these organisms primarily as the result of significant technical problems in obtaining
THE PHYLOGENY OF SLEEP 101 reliable recordings in an aquatic environment. Several plays a major role in the regulation of mammalian species of Bermuda reef fish and freshwater fish sleep and wakefulness, and hypocretin deficits are (Peyrethon and Dusan-Peyrethon, 1967; Siegmund, 1969; now well known to be the major neurochemical defect Tauber and Weitzman, 1969; Shapiro and Hepburn, in patients with narcolepsy (for a review see Mignot, 1976; Tobler and Borbely, 1985) exhibit behavioral sleep. 2005). In contrast to narcoleptic patients who exhibit Eye movements have been observed during periods of excessive daytime sleepiness and cataplexy (a sudden behavioral quiescence in Bermuda reef fish, suggesting loss of muscle tone in response to emotional stimuli), the presence of REM sleep (Tauber and Weitzman, mutant zebrafish lacking the hypocretin receptor did 1969). Other studies, however, have failed to replicate not demonstrate an increase in daytime sleep or the this finding (Peyrethon and Dusan-Peyrethon, 1967; development of cataplexy-like behaviors. They did, Shapiro and Hepburn, 1976), leading these authors to however, exhibit an increase in nocturnal sleep–wake conclude that REM sleep is not present in these species. transitions and an increase in nocturnal sleep fragmenThere is evidence for a rebound in resting behavior foltation. Although zebrafish hypocretin receptors were lowing enforced behavioral wakefulness in the perch not found in proximity to the monoaminergic neuro(Cichlosoma nigrofasciatum) and goldfish (Carassius transmitter systems regulating mammalian sleep and auratus), indicating the presence of homeostatic regulatwaking, these receptors did colocalize with GABAergic ing mechanisms, similar to those in mammals (Tobler neurons in the anterior hypothalamus, suggesting that and Borbely, 1985). these neurons rather than monoaminergic systems are An emerging body of work has suggested that the important for sleep regulation in zebrafish. This study zebrafish, Danio rerio, may also serve as a model is of significance not only because it suggests that the organism for understanding the genetic and molecular zebrafish may be an important organism for underregulation of sleep. Zebrafish meet the criteria for standing the underlying molecular basis of sleep, but behavioral sleep, and in addition, quiescent states are also that nonmammalian species must be meticulously induced by sleep-promoting substances such as melatoevaluated, using rigorous methodology, since there nin, diazepam, and sodium pentobarbital (Zhdanova may be important and significant differences which et al., 2001). Genetic and immunohistochemistry studlimit the generalization of findings to mammals. ies demonstrate that the cholinergic, aminergic, and The evidence for electrophysiological correlates of orexin/hypocretin systems of the zebrafish show striking sleep in fish is meager. Recordings in the tench (Tinca similarities to mammals (Zhdanova et al., 2001; Kaslin tinca) did not reveal distinctive electrophysiology assoet al., 2004; Prober et al., 2006). However, a recent ciated with behavioral sleep (Peyrethon and Dusandetailed study also suggests important differences Peyrethon, 1967). Slow waves with superimposed between sleep in zebrafish and sleep in mammals spike-like activity occurring during behavioral sleep (Yokogawa et al., 2007). The behavioral characteristics have been observed in the catfish (Karmanova and of sleep in zebrafish were observed in this study, but Lazarev, 1978). Neither of these studies reported the homeostatic response to sleep deprivation exhibited electrophysiological evidence for REM sleep. striking differences in comparison to mammals. FollowAlso of note are descriptions of “sleep swimming” ing 6 hours of sleep deprivation induced by electrical zooplanktivorous fish which increase the frequency stimulation, there was an expected rebound in quiesof nocturnal dorsal, pectoral, and caudal fin strokes cence when fish were released into darkness at the while maintaining a stereotypic nocturnal position in end of the deprivation period. However, sleep-deprived the coral reef (Goldshmid et al., 2004). The measurable fish which were released into light following deprivabeneficial effects to the coral reef resulting from this tion did not exhibit a homeostatic sleep rebound. Also behavior include enhanced water replenishment and of note is that there was virtually a complete suppresincreased oxygenation. It could be argued that, by defsion of sleep by maintaining the zebrafish under condiinition, behavioral sleep is not present in these fish tions of constant illumination. No sleep rebound since they are obviously not quiescent. On the other occurred following this light-induced sleep suppreshand, the increased activity of nocturnal fin movesion, but sleep gradually reemerged after several days. ments occurring with a stereotypic body posture may This suppression of sleep under conditions of constant be a unique variation on quiescent behavior. Such illumination may be similar to the marked decrease in unique manifestations of “nonquiescent sleep” could sleep which occurs in migrating birds (Rattenborg also exist in other organisms which have been judged et al., 2004; Rattenborg, 2006b). In contrast to mamas not exhibiting sleep by current definitions. mals, there was not a close localization between either The amphibians are of interest because they reprelarval or adult zebrafish hypocretin receptors and the sent the basal stock from which land vertebrates monoaminergic and cholinergic systems. Hypocretin developed (Romer, 1966). Detailed sleep studies in
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Fig. 7.3. A sleeping frog. (Courtesy of Roy Smith.)
amphibians are sparse, and reports of electrophysiological correlates associated with sleep-like states have, once again, been variable. Clear signs of behavioral sleep were not observed in either the bullfrog, Rana catesbiana, or the western toad, Bufo boreas (Hobson, 1967; Huntley et al., 1978) (Figure 7.3). However, the tree frog, Hyla squirella and H. cinerea, exhibited signs of behavioral rest, but distinctive electrophysiology associated with this behavioral rest was not present (Hobson et al., 1968). Slow waves in the South American toad and spike-like activity in the frog R. temporaria during behavioral sleep have both been reported (Segura, 1966; Lazarev, 1978). Similar to some studies in frogs, no distinctive electrophysiological correlates of behavioral sleep have been found in the salamander, and variations in heart rate did not correlate with activity and inactivity (McGinty, 1972). However, spectral analysis did reveal electroencephalogram frequency increases during arousal (Lucas et al., 1969). In summary, the data from amphibian studies are quite variable, but support the existence of behavioral sleep with possible electrophysiological correlates.
Fig. 7.4. An American alligator dozing in late afternoon. (Courtesy of Kristyna M. Hartse.)
these findings, no signs of behavioral or electrophysiological sleep, independent of ambient temperature, were found in the American alligator (Alligator mississipiensis) (Van Twyver, 1973) (Figure 7.4). SWS, but not paradoxical sleep, has been reported to occur in the caiman (Warner and Huggins, 1978; Meglasson and Huggins, 1979). Although spikes and sharp waves were observed in this study, this electrophysiology was not correlated with degrees of progressive postural relaxation. Behavioral quiescence associated with highamplitude electrical activity that disappeared with behavioral waking has been reported in a snake, Python saebe (Peyrethon and Dusan-Peyrethon, 1969). Findings in other reptiles have been equally diverse. SWS has not been reported in lizards (Figure 7.5), but two studies have suggested the presence of paradoxical sleep alternating with periods of quiet sleep not marked by slow waves (Tauber et al., 1968; AyalaGuerrero and Mexicano, 2008). In iguanas, spikes and
REPTILES The first stem reptiles from which modern reptiles originated are seen in the fossil record during the carboniferous period approximately 30 million years ago (Romer, 1966). Reptilian sleep has been more extensively studied than sleep in most other nonmammalian species. Although there is general agreement that reptiles exhibit signs of behavioral sleep and wakefulness, the electrophysiological findings and their interpretation have been variable and often in sharp disagreement. In the caiman (Caiman sclerops), behavioral quiescence accompanied by high-voltage spiking, which disappeared during behavioral waking, was first reported by Flanigan et al. (1973). There was no convincing evidence for SWS or paradoxical sleep. In contrast to
Fig. 7.5. A monitor lizard sleeps on a branch. (Courtesy of Kathleen Andersen.)
THE PHYLOGENY OF SLEEP 103 sharp waves occurring during behavioral quiescence and (Hartse, 1994). Some may be due to true species differdisappearing during behavioral waking have been docuences. Some may be due to variations in the meticulousmented (Flanigan et al., 1973). Turtles and tortoises also ness and consistency with which recording procedures exhibit spikes during behavioral sleep which disappear were performed. Some may be due to the biased impoduring behavioral wakefulness. A rebound in spikes sition of mammalian criteria for sleep on nonmammaoccurred following enforced wakefulness. There was lian organisms which have a very different palette of no convincing evidence, however, for the presence of electrophysiology and behavior from that of mammals. either SWS or paradoxical sleep in turtles and tortoises However, the most persuasive evidence supports the (Flanigan, 1974; Flanigan et al., 1974). Further supporting presence of behavioral sleep with an electrophysiological the position that reptiles do not exhibit REM sleep are correlate, the high-amplitude spike, in reptiles. single-unit studies in the brainstem of freely moving turtles (Eiland et al., 2001). Bursting patterns characterBIRDS AND MAMMALS istic of reticular formation neurons in the mammalian In contrast to invertebrates and nonmammalian vertebrainstem were not observed during behavioral quiesbrates, sleep in birds and mammals has been studied cence, nor were cyclically occurring periods of eye extensively (for reviews, see Amlander and Ball, movements and phasic muscle bursts typical of mam1994; Zeplin et al., 2005). Birds exhibit both SWS and malian REM sleep. paradoxical sleep, although paradoxical sleep differs Pharmacological studies in the tortoise, demonstrating from mammalian paradoxical sleep in that it occurs a similar response of the reptilian spike and the cat VH as short bouts lasting from a few seconds to a few spike to amphetamine, Nembutal, parachlorophenylalaminutes in duration. It is also well known that, unlike nine, and alpha methyl-tyrosine do, however, indicate a most mammals, with a few exceptions described similarity between the reptilian spikes and mammalian below, birds exhibit unihemispheric SWS, i.e., one SWS (Hartse and Rechtschaffen, 1982). In another study hemisphere shows clear SWS and the other hemisphere high-voltage spike activity was associated with behavioral exhibits clear waking with eye closure contralateral to quiescence in the tortoise, but the lack of the spike’s assothe sleeping hemisphere. It has been proposed that uniciation with elevated arousal thresholds in this study as hemispheric sleep may have evolved in response to the well as the modulating effect of temperature upon the risk of predation by allowing parts of the cerebrum to presence of the spike led these investigators to conclude be differentially alert (Lima et al., 2005). that the spikes are not an electrophysiological manifestaThe question of whether migrating birds sleep has tion of “true” sleep (Walker and Berger, 1973). In conrecently received attention (Rattenborg, 2006b). Migratrast, only mammalian-like SWS, but not paradoxical tions occurring over a period of several days suggest sleep, has been reported in the tortoise, Testudo margineither that birds sleep in flight or that sleep requirements ata (Hermann et al., 1964), and both SWS and paradoxical are drastically reduced during this time. No electrophyssleep have been reported in the European pond turtle, iological recordings of sleep have been made during Emys orbicularis, as well as in the tortoise, Gopherus flaactual migration. However, in the laboratory, the white vomarginatus (Vasilescu, 1970; Ayala-Guerrero et al., crowned sparrow, a migrating songbird, spends 63% 1988). No electrophysiological correlates of behavioral less time sleeping during the migratory season as comsleep and wakefulness as well as an absence of a homeopared to the no-migratory season (Rattenborg et al., static response to enforced waking in the sea turtle, Car2004). As pointed out by Rattenborg (2006b), by strict etta caretta L., led to the conclusion that this species does definition birds in flight do not sleep because they are not sleep (Susic, 1972). Finally, observations of highnot quiescent, even though it seems unlikely that sleep amplitude slow-wave activity during waking in the lizard does not occur for several days. Avian unihemispheric have prompted one group of investigators to conclude SWS may allow some sleep in flight, but it seems that reptilian waking is the precursor to mammalian unlikely that REM sleep, which is typically sensitive to SWS (Rial et al., 2007a). environmental disruption, occurs under these conditions. The often contradictory findings in the reptile literThe finding that sleep amounts are drastically reduced ature have raised an important issue. If REM sleep is during the migratory season suggests that birds may absent in reptiles, this suggests that REM may not have have a periodically reduced sleep need in response to been present in stem reptiles ancestral to birds and the demand of migration (Rattenborg, 2006b). Further mammals and as a result REM sleep may have evolved research with new technologies that permit in-flight independently in birds and mammals rather than being electrophysiological recordings is clearly required to perpetuated from a common ancient ancestor. There resolve the question of whether or not birds sleep in are a number of parameters that could account for flight (Figures 7.6 and 7.7). the differences in findings from the reptile studies
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Fig. 7.6. A sleeping water fowl. (Courtesy of Berit Watkin.)
Fig. 7.7. Dozing flamingos. (Courtesy of Kathleen Andersen.)
The features of sleep in mammals are well known through the use of a wide variety of electrophysiological and neurochemical techniques (for a review, see Zeplin et al., 2005). Like birds, mammals, with the exception of some cetaceans (whales and dolphins), exhibit both NREM and REM sleep in a predictable cyclically alternating fashion (Figure 7.8). The presence
Fig. 7.8. A sleeping lion. (Courtesy of Kathleen Andersen.)
of NREM and REM sleep in both birds and mammals is of interest since the absence of REM sleep in reptiles would suggest that REM sleep is a more recent development in the phylogenetic history of land-dwelling organisms. Utilizing data primarily from mammals, several different theories have been advanced to explain the function of sleep. Some of the most persuasive data support an energy conservation hypothesis, and there is a positive correlation between basal metabolic rate (BMR) and total sleep time (Zeplin and Rechtschaffen, 1974; Zeplin et al., 2005). That is, animals with higher metabolic rates spend more time asleep. However, recent path model analyses have found a significant negative correlation between BMR and total sleep time in mammals (Lesku et al., 2006). In contrast to the mammalian data, similar path model analyses in birds have not revealed a relationship between BMR and either SWS or REM sleep. The only statistically significant relationship in avian species was an inverse relationship between SWS time and risk of predation, suggesting different, independently evolved functions for sleep in mammals and birds (Roth et al., 2006). It has also recently been proposed that “mammalian sleep has no function apart from the rest of simple organisms” (Rial et al., 2007a). Although the simplicity of this theory is attractive, there is meager support for this position when the totality of data from phylogenetic studies is examined (Rattenborg et al., 2007). Furthermore, a recent metabolic study in the desert iguana, Dipsosaurus dorsalis, supports the position that sleep contributes to energy conservation, even in poikilothermic organisms (Revell and Dunbar, 2007). Under controlled laboratory conditions, the mean metabolic rate of sleeping iguanas was 27.6% less in comparison to waking across temperature ranges of 20–40 C. However, a larger metabolic saving accrued during wakefulness at cooler temperatures than during sleep at warmer temperatures, suggesting that the energy conservation function of sleep in poikilotherms may be less significant than the impact of behavioral thermoregulation upon energy conservation. One group of animals which may shed light upon the origins of REM sleep are the living monotremes, primitive egg-laying mammals representing an early branch in mammalian evolution (Figure 7.9). The first study in the short-beaked echidna, Tachyglossus aculeatus, revealed the presence of NREM sleep, but unambiguous REM sleep could not be conclusively identified (Allison et al., 1972). More recent studies have utilized single-cell recordings from the echidna midbrain reticular formation and pons, structures known to have a distinctive bursting pattern of activity during REM sleep, to clarify whether or not REM
THE PHYLOGENY OF SLEEP
Fig. 7.9. The short-beaked echidna in its Australian habitat. (Courtesy of Ian Michael Thomas.)
sleep is present in these organisms (Siegel et al., 1996). A unique electrophysiological pattern of brainstem unit discharge variability accompanied by highamplitude forebrain slow waves was observed (Siegel et al., 1996). This brainstem single-unit activity was not typical of the bursting pattern present in the mammalian reticular formation during REM sleep, and phasic motor activity or eye movements did not occur in concert with this unit activity. These findings may be interpreted to support the hypothesis that sleep in the echidna is an amalgam of cortex-synchronized NREM and brainstemactivated REM sleep which was subsequently differentiated during evolution into separate NREM and REM states (Siegel et al., 1998). A subsequent study in the echidna has identified unambiguous REM sleep based upon the usual mammalian criteria for this stage (Nichol et al., 2000). In contrast to these findings, the platypus (Ornithorhynchus anatinus), another monotreme, exhibits abundant amounts of REM sleep characterized by muscle atonia, eye movements, and phasic twitching. Similar to the echidna, these elements of REM sleep occurred in the presence of high-voltage slow waves (Siegel et al., 1999). Differences between the echidna and the platypus in the expression of REM sleep may be the result of adaptation to the vulnerability of their sleeping environments (Siegel et al., 1998). The only group of mammals in which REM sleep has not been clearly identified is the cetaceans (whales and dolphins). Like birds, these mammals exhibit unihemispheric SWS with eye closure contralateral to the sleeping hemisphere (Mukhametov, 1987; Lyamin et al., 2000, 2002, 2004). No arousal threshold studies have been performed, and a variable rebound in unihemispheric sleep following unihemispheric sleep deprivation has been reported in one study of the dolphin (Oleksenko et al., 1992). No evidence for unambiguous
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REM sleep has been revealed in cetaceans, although jerking movements similar to the phasic twitches of mammalian REM sleep have been observed in the gray whale (Lyamin et al., 2000). It should not necessarily be concluded from these studies, however, that aquatic mammals do not have REM sleep. Current techniques for the detection of REM sleep in cetaceans may not be adequate or, alternatively, REM sleep in the aquatic environment may be present in a form different from that observed in terrestrial environments. Not only is the questionable absence of REM sleep in cetaceans different from sleep in land-dwelling mammals, the pattern of behavioral sleep and wakefulness in newborn cetacean calves is also different from the young of land-dwelling mammals (Lyamin et al., 2005). Unlike most mammalian infants which spend significant periods of the 24-hour-day sleeping, dolphin and killer whale neonates exhibited virtually no periods of behavioral rest or eyelid closure, which is correlated with the presence of sleep, for several months after birth. In concert with their infants, mothers also exhibited almost no resting behavior for several months postpartum. These findings challenge the concept that a basal amount of sleep, as it is currently defined by electrophysiological and behavioral criteria, is necessary for normal growth and development in all mammals.
THE PHYLOGENY OF SLEEP AND HUMAN SLEEP DISORDERS Phylogenetic sleep studies unquestionably provide clues to our understanding of human sleep mechanisms, and more importantly, these data form a nexus of evidence for ultimately understanding the functionality of sleep in humans. How does the study of sleep in organisms as diverse as the fruit fly and the whale contribute to our understanding of human sleep?
Assessing the effects of sleep loss Although the insomnia patient is frequently advised that lack of sleep is not harmful or life-threatening, studies in Drosophila demonstrate that sleep loss can affect lifespan, aging, and gene expression (Cirelli, 2006; Koh et al., 2006). By extension, these findings imply that the impact of sleep loss in humans may have greater physiological significance than has been previously appreciated. Epidemiological data support a correlation between shortened as well as extended sleep and decreased lifespan, indicating that less-thanoptimal sleep amounts are likely to be deleterious to longevity in humans (Kripke et al., 2002; Hublin et al., 2007). There is also evidence that some strains of genetically short-sleeping Drosophila, akin to the
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normal human “short sleeper,” do not experience deleterious effects of sleep loss such as decreased longevity (Kume et al., 2005), although other studies have demonstrated decreased longevity in short-sleeping flies (Cirelli, 2006). By using the fruit fly as a model for the study of sleep, the relationship between sleep duration, lifespan, and aging can be dissected with greater precision.
Models for treatment If the molecular consequences of reduced sleep can be assessed by using the Drosophila model, then additional precision may be gained in assessing the molecular effects of pharmacological treatments for insomnia or excessive daytime sleepiness. Caffeine, amphetamine, and antihistaminics have already been demonstrated to have effects on behavioral sleep and waking in Drosophila which are similar to the effects on sleep in humans (Shaw et al., 2000). Modafinil, a treatment for human narcolepsy, produces a similar alerting response in fruit flies and in humans (Hendricks et al., 2003). Recently a specific biomarker for sleepiness in humans, salivary amylase, has been identified directly as the result of work in Drosophila (Seugnet et al., 2006). Ideally, the future development of effective new compounds to enhance sleepiness and/or alertness in humans could be potentially assessed on a molecular level in a model such as Drosophila for efficacy, safety, and side-effects.
The genetics of sleep The studies in nonmammalian organisms, specifically Drosophila and zebrafish, have tremendously advanced our understanding of sleep genetics. Although the genetics of humans sleep disorders are not well understood, delayed sleep phase syndrome (Toh et al., 2001), narcolepsy (Mignot, 2005), and periodic limb movements (Stefansson et al., 2007) have all recently been identified as having a genetic basis. The identification of mutant strains of Drosophila, for example shortsleeping flies, may shed light on the mechanisms for the origins and perpetuation of some sleep disorders in humans.
CONCLUSIONS Our original question, “Why do we sleep?”, has not been answered by this review. However, simple behavioral observations as well as correlations between surface brain activity and behavior in unstudied, interesting nonmammalian organisms are no longer adequate to advance our understanding of the function of sleep from a phylogenetic perspective. The same
rigorous and innovative neurophysiological and molecular methodologies which have been applied to the study of mammalian sleep must also be applied to nonmammalian species. Conversely, the application of molecular techniques developed in Drosophila to the study of sleep in mammalian organisms may provide further insight into the function or functions of sleep in humans and other mammals. Clearly, there are many fruitful avenues of research to pursue which have the potential to unravel the complex relationships between sleep function and species survival.
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Handbook of Clinical Neurology, Vol. 98 (3rd series) Sleep Disorders, Part 1 P. Montagna and S. Chokroverty, Editors # 2011 Elsevier B.V. All rights reserved
Chapter 8
Ontogeny of EEG sleep from neonatal through infancy periods MARK S. SCHER* Division of Pediatric Neurology, Rainbow Babies and Children’s Hospital, University Hospitals of Cleveland, Case-Western Reserve University, Cleveland, OH, USA
Electrographic and polygraphic recordings of newborns and infants have been performed for almost a half-century. Pioneering studies by multiple researchers worldwide offer neurophysiologic information concerning the developing central nervous system (Ellingson, 1964; Anders et al., 1971; Parmelee and Stern, 1972; Prechtl, 1974; Dreyfus-Brisac, 1979; Lombroso, 1989; Hrachovy et al., 1990; Pope et al., 1992). Earlier investigations predated the creation of the modern neonatal intensive care unit (NICU); however, these seminal works described for the first time electrographic patterns and physiologic behaviors which define the rudimentary state of the preterm neonate. Given the higher rate of neonatal mortality, particularly in the premature infant, the clinical neurophysiologist had a more limited consultative role in the neurologic care of the sick neonate. With the creation of the modern-day tertiary care NICU, the sophistication of medical care, including technological improvements in physiologic recordings, now offers the neurological consultant a more active role in neonatal neurophysiological assessments for medical care. The decline in neonatal morbidity and mortality has concentrated renewed attention on the neurological performance during both the acute and convalescent periods in the days to weeks after birth for the highrisk newborn. Given the immature clinical repertoire of the newborn and infant, as well as limited access to neonates in a busy intensive care setting, electroencephalographic (EEG) polygraphic studies can extend the clinician’s abilities to document functional brain maturation, as well as the presence and severity of encephalopathic states. Serial EEG sleep analyses have a significant impact on documenting aberrant functional
brain maturation (i.e., dysmaturity) (Scher et al., 2003a). Quantitative estimates of brain dysmaturity using computer analyses are being refined as research tools to develop objective measures for detecting subtle expressions of encephalopathy as well as predicting outcome. Neonatal survivors also require close supervision after discharge over successive stages in brain maturation during infancy and later childhood. Maturation of neonatal and infant behavior requires careful evaluation of both waking and sleep behaviors. Combined neurophysiological monitoring with systematic behavioral assessments can better evaluate functional brain maturation. The clinician can apply knowledge of sleep ontogeny to the evaluation of different pediatric populations who are at risk for developmental delay, as suggested by altered behaviors during sleep or wakefulness. Computer-assisted analysis tools will extend our abilities to examine physiologic relationships between cerebral and noncerebral measures, and explore associations with selected outcome measures (AjmoneMarsan, 1986; Scher et al., 1990, 2005b).
CAVEATS CONCERNING NEUROPHYSIOLOGIC INTERPRETATION OF STATE A number of caveats will assist the neurophysiologist in an understanding of the application of sleep interpretation from the neonatal through infancy periods. Maturational changes of EEG polygraphic patterns emerge at successively older postmaturational ages (PMA): neurophysiologic maturity of a neonate can
*Correspondence to: Mark S. Scher, M.D., Rainbow Babies and Children’s Hospital, 11100 Euclid Ave., M/S 6090, Cleveland, OH 44106-6090, USA. Tel: (216) 844-3691, Fax: (216) 844-8444, E-mail:
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be estimated within 2 weeks for the preterm infant (i.e., < 37 weeks PMA) and 1 week for the full-term infant, reflecting the PMA of the infant independent of birth weight. Temporal coincidence or concordance among physiologic sleep behaviors emerges with increasing maturity, similar to fetal behavioral states documented by abdominal sonography. Significant functional reorganization of state occurs at 30, 36, and 48 weeks’ PMA, reflecting cortical-subcortical neuronal networks that subserve sleep. Finally, serial neurophysiologic studies rather than a single recording more accurately document normal ontogeny or the evolution of delayed or abnormal changes reflective of a brain disorder. Subsequent developmental stages also occur during infancy regarding sleep reorganization principally after 3, 9, and 12 months of age. The clinician needs to develop a confident style of neurophysiologic pattern recognition and clinical correlation by repetitive experiences with a wide variety of EEG polygraphic recordings. Before an accurate interpretation can be offered to the referring clinician, knowledge of the child’s PMA as well as the range of behavioral phenomena that are anticipated at that age during the recording are needed; this requires close communication between the electrodiagnostic technologist and the neurophysiologist. Ongoing discussion with the neonatologist results in continual re-evaluation of the neurophysiologic interpretations within the clinical context.
GENERAL COMMENTS ON RECORDING TECHNIQUES AND INSTRUMENTATION FOR NEONATES AND INFANTS Appropriate recording techniques will yield highquality EEG polygraphic studies. The neurophysiologist should apply a minimum of 10 EEG electrodes in addition to a full complement of noncerebral polygraphic electrodes, given that specific regional and hemispheric electrographic patterns need to be correlated with other noncerebral physiologic behaviors. Placement of electrodes by either paste or collodion must be achieved with ease and efficiency by the technologist who must always be cognizant of the fragile state of the neonate within the busy NICU environment. While double interelectrode distances may be preferred for the infant 40–1000 Hz) which have been studied primarily in adult populations. Spectral analyses have also been performed involving sleep studies for non-EEG physiologic parameters, particularly cardiorespiratory measures, as discussed under the section on ontogeny of autonomic behavior during sleep. Changes in the balance between sympathetic and parasympathetic influences during sleep can be assessed by the spectral analysis of HRV (Villa et al., 2000). Few studies extend these evaluations up through infancy. Most studies dealing with maturation of cardiorespiratory behavior do not include ages beyond 6 months of age.
Sleep ontogenesis and neural plasticity Advances in developmental neuroscience over the last 15 years have expanded our knowledge base regarding the sequential steps in brain maturation. Third-trimester and early postnatal developmental stages of brain maturation encompass extensive remodeling or resculpting. This process of experience or activity-dependent development signifies how signaling at the molecular level influences both individual cell types as well as neuronal networks of interconnecting cell groups which subserve more complex functions. Use or disuse of specific neuronal populations or networks will lead to pruning and remodeling of the brain’s neuronal circuitry. During the last trimester of pregnancy and
ONTOGENY OF EEG SLEEP FROM NEONATAL THROUGH INFANCY PERIODS into the first year of life, dendritic arborization, synaptogenesis, myelinization, and neurotransmitter development rapidly evolve in the immature brain (Goldman-Rakic, 1987). Apoptosis or programmed cell death also contributes to modifying brain structure or function during both prenatal and postnatal periods (Bredesen, 1995; Hughes et al., 1999). Adverse conditions of prematurity (i.e., during both prenatal and postnatal time periods) from medical illnesses and environmental stresses collectively alter this process of activity-dependent development and apoptosis, changing neuronal circuitry relative to the stage of maturation. Given that remodeling of neuronal connectivity is ultimately required for the expression of complex neurobehaviors of sleep, cognition, emotion, and social skills at older ages (Caviness, 1989), aberrant remodeling will alternatively contribute to neurocognitive and neurobehavioral deficits. Automated neurophysiologic methodologies can assess brain organization and maturation in the newborn, offering a surrogate marker for activity-dependent development of the fetal and neonatal brain. Computational algorithms applied to selected physiologic measures of neonatal sleep can provide insights into the process by which neuronal networks change and adapt over longer periods of time during extrauterine life under adverse medical and socioeconomic conditions, and in the context of genetic endowment. Applications and methods of nonlinear dynamics to experiments in neurobiology will help characterize better the biologic process of neuroplasticity (Arabanel and Rabinovich, 2001; Stam, 2005). Computational analyses of complex physiologic behaviors reflect changes in neuronal circuitry and can enhance our understanding of the encoding and transmission of information by neuronal networks that subserve human performance ranging from sleep to cognition. The application of these processing techniques in both neonatal intensive care and pediatric sleep laboratory settings will permit better assessment of EEG sleep state organization and maturation through computational neuroscience.
SUMMARY Serial neonatal and infant electroencephalographic/ polysomnographic studies document the ontogeny of cerebral and noncerebral physiologic behaviors, based on visual inspection or computer analyses. EEG patterns and other physiologic relationships serve as templates for normal brain maturation, and also help distinguish intrauterine from extrauterine development. Such strategies will ultimately improve our diagnostic skills for the care of the high-risk fetus, neonate, and infant.
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EEG sleep studies remain the only bedside neurodiagnostic procedure which provides a continuous record of cerebral function over long periods of time. While other advanced methods of anatomical or functional inquiry, such as volumetric and functional magnetic resonance imaging, report brief snapshots of cerebral anatomy and function, neurophysiologic studies provide a time- and frequency-dependent functional perspective into brain ontogeny. Sleep ontogenesis in neonates and infants can document expected patterns of brain maturation, to anticipate better deviations from these biologically programmed processes under stressful and/or pathological conditions.
ACKNOWLEDGMENT This study was supported in part by grants NS01110, NS26793, NS34508, NR09814, NR04926, and HL07193.
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Handbook of Clinical Neurology, Vol. 98 (3rd series) Sleep Disorders, Part 1 P. Montagna and S. Chokroverty, Editors # 2011 Elsevier B.V. All rights reserved
Chapter 9
Neurobiology of waking and sleeping BARBARA E. JONES * Department of Neurology and Neurosurgery, McGill University, Montreal Neurological Institute, Montreal, Quebec, Canada
HISTORICAL BACKGROUND Over the course of the 20th century, concepts evolved as evidence accumulated concerning the existence and delineation of intrinsic neural systems controlling waking and sleeping. Waking was once thought to be maintained by sensory inputs and sleep to result from the cessation of sensory inputs to the brain (Bremer, 1929; Kleitman, 1939). Yet, from variable alterations of waking and sleeping that occurred with cerebral lesions clinically in humans or experimentally in animals, both waking and sleeping were found to be generated actively by intrinsic neural systems. Based upon analyses of human brains following death from encephalitis lethargica, von Economo (1930) was among the first to propose that waking and sleeping systems were localized in different regions of the forebrain since hypersomnolence was associated with lesions of the posterior hypothalamus whereas insomnia was associated with lesions of the anterior hypothalamus and preoptic area (Figure 9.1). Moruzzi and Magoun (1949) went on to show that the brainstem reticular formation together with the posterior hypothalamus were both necessary and sufficient for the maintenance of a waking state (Figure 9.1).
Cortical activation and deactivation In the early studies, it was established that lesions in the rostral pontine and mesencephalic reticular formation, extending into the posterior hypothalamus, resulted in a loss of fast electroencephalographic (EEG) activity typical of cortical activation of the wake state (Lindsley et al., 1950). In contrast, lesions of the ascending sensory pathways or even complete sensory deafferentation did not diminish the amount of cortical activation (Vital-Durand and Michel, 1969). The absence of waking signs in the experimental animals was similar to that in humans diagnosed as comatose *
and found to have lesions of the rostral brainstem and posterior diencephalon (Plum and Posner, 1980). Electrical stimulation of the brainstem reticular formation in an anesthetized animal evoked cortical activation which was conducted along two major pathways into the forebrain to reach the cortex (Starzl et al., 1951) (Figure 9.1). The first route was into the thalamus from where impulses were in turn conveyed in a relatively widespread manner to the cerebral cortex, particularly the frontal regions. The second route was ventral to the thalamus extending through the hypothalamus up to the basal forebrain from where impulses were also conveyed in a widespread manner to the cortex. This ventral extrathalamic route was found to be sufficient, since cortical activation could still be attained following total ablation of the thalamus. Neuroanatomical studies revealed that the neurons within the netlike, or reticular, core of the brainstem were characterized by long radiating dendrites which received collateral inputs from multiple sensory modalities and by long branching axons which ascended from the rostral brainstem into the thalamus and/or into the hypothalamus and up to the basal forebrain (Nauta and Kuypers, 1958; Scheibel and Scheibel, 1958). The ascending reticular activating system thus had the capacity to respond to multiple sensory inputs and to transmit in turn impulses to widely distributed areas of the cortex, through neurons in the diffuse thalamocortical projection system and a basalocortical projection system. Electrical stimulation of the thalamus could elicit widespread cortical activation. However, the effect of the stimulation depended upon its frequency. Whereas high-frequency stimulation elicited fast cortical activity, low-frequency stimulation recruited spindle-like or slow wave-like activity on the cerebral cortex, which
Correspondence to: Dr. Barbara E. Jones, Montreal Neurological Institute, 3801 University Street, Montreal, Quebec, H3A 2B4, Canada. Tel: þ1 514-398-1913, Fax: þ1 514-398-5871, E-mail:
[email protected] 132
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Cx
Th BF PH POA
TM
Mes DR VTA
RF LDT LC
CB
Pons
Cortical activation (W/REM):
Glu Ach
Cortical de-activation (SWS):
GABA
Behavioral arousal (W):
Glu NA/DA Ser HA Orx
Behavioral quiescence (SWS/REM):
GABA
RF Medulla
Fig. 9.1. Sleep–wake state substrates. Sagittal schematic view of the human brain depicting neurons with their chemical neurotransmitters and pathways by which they influence cortical activity or behavior across the sleep–wake cycle. Neurons which are active during waking (red symbols) include cells with ascending projections toward the cortex, which stimulate cortical activation, and cells with descending projections toward the spinal cord, which stimulate behavioral arousal with postural muscle tone. Those with predominantly ascending projections discharge in association with fast, gamma electroencephalogram (EEG) activity and cease firing with slow, delta activity to be active during both wakefulness and rapid eye movement sleep (W/REM, filled red symbols); they include neurons which release glutamate (Glu, diamonds) or acetylcholine (ACh, circles) (see Figures 9.2 and 9.3). Those with more diffuse or descending projections discharge in association with behavioral arousal and electromyogram (EMG) activity and cease firing with muscle atonia to be active during W and silent during REM (W, empty red symbols); they include neurons which release glutamate (Glu, diamonds), noradrenaline (norepinephrine) (NA, square), serotonin (Ser, star), histamine (HA, cross) or orexin (Orx, asterisk) (see Figure 9.4). Neurons which are active during sleep (blue or aqua symbols) include cells with ascending projections toward the cortex, which dampen fast cortical activity, and those with descending projections toward the hypothalamus, brainstem, or spinal cord, which diminish behavioral arousal and muscle tone. Those with projections to the cortex or local area discharge in association with slow EEG activity during slow-wave sleep (SWS, blue triangle; see Figure 9.3); those with descending projections discharge in association with decreasing muscle tone and EMG (SWS/REM, aqua triangles; see Figure 9.3). They include particular GABAergic neurons in the basal forebrain and preoptic area that bear a2-adrenergic receptors and are thereby inhibited by NA. Also shown are GABAergic neurons in the pontomesencephalic tegmentum, which can inhibit local reticular or monoaminergic neurons, and GABAergic neurons (together with glycinergic neurons, not shown) in the ventral medullary reticular formation that project directly to the spinal cord where they can inhibit neck and other motor neurons during sleep. BF, basal forebrain; CB, cerebellum; Cx, cortex; DR, dorsal raphe; GABA, gamma-aminobutyric acid; LC, locus coeruleus nucleus; LDT, laterodorsal tegmental nucleus; Mes, mesencephalon; PH, posterior hypothalamus; POA, preoptic area; RF, reticular formation; SC, spinal cord; Th, thalamus; TM, tuberomammillary nucleus; VTA, ventral tegmental area. (Adapted from Jones (2005).)
NEUROBIOLOGY OF WAKING AND SLEEPING 133 resembled the EEG activity of sleep (Akert et al., 1952). There are also a small number of GABAergic neuSimilarly, stimulation in the preoptic area and basal rons distributed through the reticular formation which forebrain could activate the cortex, yet depending upon would have the capacity to inhibit other neurons in the precise location and frequency, could also elicit the region (Figure 9.1). These could correspond to a slow-wave activity and a state of sleep (Hess, 1957; small number of neurons which actually increase their Sterman and Clemente, 1962a, b). It thus appeared rate of firing during sleep (Steriade et al., 1982). In that, within the same regions of the forebrain, differstudies using c-Fos expression as a reflection of neural ent patterns of discharge by the same or different neuactivity, a number of GABAergic neurons in the reticrons could stimulate either cortical activation and ular formation did appear to be active during sleep waking or cortical slow-wave activity and sleeping. (Maloney et al., 1999, 2000).
Behavioral arousal and quiescence Neurons of the reticular formation were also seen to send descending projections into the spinal cord (Scheibel and Scheibel, 1958) (Figure 9.1). As evident from electrical stimulation, reticulospinal neurons could stimulate movement and enhance postural muscle tone, recorded on the electromyogram (EMG), as typical of behavioral arousal (Sprague and Chambers, 1954). Yet, depending upon the condition of the animal, such stimulation in the medulla could also inhibit muscle tone, reflexes, and movement (Magoun and Rhines, 1946). Thus presumably different neurons in the brainstem evoked behavioral arousal with postural muscle tone or behavioral quiescence with muscle atonia.
THE RETICULAR ACTIVATING SYSTEM Forebrain projecting reticular neurons The neurons of the reticular formation with ascending projections are concentrated in the oral pontine and mesencephalic reticular formation, although they are present in smaller numbers in the caudal pontine and medullary reticular formation (Jones and Yang, 1985). They project rostrally to the midline and intralaminar thalamic nuclei which form the nonspecific thalamocortical projection system that project in turn in a widespread manner to the cerebral cortex (Figure 9.1). The reticular neurons also project through the hypothalamus up to the level of the basal forebrain. In the mesencephalon, they discharge at their highest rate in association with fast cortical activity that occurs during both wakefulness and rapid eye movement (REM) sleep (Steriade et al., 1982). Considerable evidence indicates that neurons of the ascending reticular activating system utilize the neurotransmitter glutamate (Glu) and thus excite through multiple Glu receptors (a-amino-3-hydroxyl5-methyl-4-isoxazole-propionate (AMPA), kainate, N-methyl-D-aspartic acid (NMDA) or metabotropic) their target neurons in the thalamus, hypothalamus, and/or basal forebrain (Kaneko et al., 1989, 2002; McCormick, 1992; Jones, 1995).
Spinal projecting reticular neurons Neurons through the reticular formation project to the spinal cord, though in greatest numbers from the caudal pontine and medullary fields, and terminate variably in the dorsal horn, intermediate zone or ventral horn (Jones and Yang, 1985). The vast majority of pontine and medullary reticular neurons discharge at their highest rate during waking in association with movements (Siegel et al., 1977, 1979). They decrease or cease firing with slow-wave sleep (SWS). Many fire in association with phasic activity during REM sleep. Considerable evidence indicates that the large, thus presumably reticulospinal neurons of the pontine and medullary reticular formation utilize the neurotransmitter Glu (Kaneko et al., 1989, 2002; Jones, 1995) (Figure 9.1). A large number of smaller neurons through the reticular formation and a small number of spinally projecting medium-sized neurons in the medullary reticular formation synthesize gamma-aminobutyric acid (GABA) (Jones et al., 1991). In addition, these or other medium-sized reticulospinal neurons utilize the inhibitory neurotransmitter glycine (Fort et al., 1993). Such GABAergic or glycinergic reticular or reticulospinal neurons could exert an inhibitory influence upon other excitatory reticulospinal neurons or brainstem and spinal motor neurons (Holstege and Bongers, 1991). They could represent the small percentage of reticular neurons that increase their discharge rate with quiet waking and sleep, relative to active waking and discharge maximally with muscle atonia during REM sleep (Sakai et al., 1981). They could also correspond to the small number of medullary reticular neurons that discharge in association with loss of muscle tone which occurs in narcolepsy with cataplexy (Siegel et al., 1991). Indeed, many medullary reticular neurons which are GABAergic express c-Fos with REM sleep, as evident during rebound following deprivation (Maloney et al., 1999, 2000). In any event, both excitatory and inhibitory reticulospinal neurons can influence movement and muscle tone such as to stimulate behavioral arousal or reciprocally promote behavioral quiescence and different states (Figure 9.1).
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The cholinergic pontomesencephalic neurons Neurons which utilize acetylcholine (ACh) as a neurotransmitter were proposed to form a major contingent of the reticular formation based upon histochemical staining for its catabolic enzyme, acetylcholinesterase (AChE), by Shute and Lewis (1967) (Figure 9.1). The cholinergic contingent of the activating system was considered by Shute & Lewis to be preeminent, thus leading them to designate the entire system as the “cholinergic reticular activating system.” With application of immunohistochemical staining for the synthetic enzyme choline acetyltransferase (ChAT), the cholinergic neurons were later found to be more limited in their distribution and localized to two major cell groups in the brainstem, the laterodorsal tegmental nucleus and pedunculopontine tegmental nucleus (Mesulam et al., 1983b). Like other neurons of the reticular formation, nonetheless, these cholinergic neurons project forward into the forebrain. They project prominently to the thalamus, including most densely to the medial and lateral geniculate nuclei and the midline and intralaminar nuclei. They can thus excite both specific and nonspecific thalamocortical projection systems. Acting through both nicotinic and muscarinic receptors, indeed, ACh depolarizes and excites the thalamic projection neurons and evokes tonic firing by them to stimulate thalamocortical activation and prevent slow-wave activity (McCormick, 1992). The pontomesencephalic cholinergic neurons also project through the extrathalamic ventral ascending pathway into the posterior hypothalamus where they influence wake-promoting neurons (see below) and to a lesser degree up to the basal forebrain (Jones and Cuello, 1989; Ford et al., 1995). Although cholinergic neurons have not yet been unequivocally identified in the pontomesencephalic tegmentum in recording studies, neurons considered to be “possibly” cholinergic were recorded in the region of those cells in the cat and shown to discharge in association with cortical activation during both wake (W) and REM sleep (W/REM) (El Mansari et al., 1989) (Figure 9.1). In addition, however, some “possibly” cholinergic neurons were found to discharge only during REM sleep and in association with the muscle atonia of that state (El Mansari et al., 1989; Kayama et al., 1992). It is thus possible that particular cholinergic neurons, which project to the pontomedullary reticular formation (Mitani et al., 1988; Jones, 1990; Semba et al., 1990), might generate REM sleep with muscle atonia. Injection of the cholinergic agonist, carbachol, into the pontomesencephalic tegmentum induces cortical activation with muscle atonia and other signs of REM sleep (George et al., 1964; Baghdoyan et al., 1984). Such action might be possible through different
effects of ACh upon different neurons mediated by muscarinic type 1 (M1) and 2 (M2) receptors. ACh could inhibit (through M2 receptors) glutamatergic reticular neurons involved in facilitating activity and muscle tonus and excite (through M1 receptors) particular GABAergic neurons involved in inhibiting activity and muscle tone (Figure 9.1).
FOREBRAIN RELAYS OF THE ACTIVATING SYSTEM The nonspecific thalamocortical projection system The midline and intralaminar nuclei of the thalamus, unlike the sensory and motor relay nuclei, project to multiple regions of the cerebral cortex in a thus nonspecific manner, often in highest density to frontal regions, though for some nuclei in a truly diffuse manner in high density to all regions (Herkenham, 1986) (Figure 9.1). They discharge at their highest rate in association with cortical activation during waking and REM sleep. Their discharge is generally tonic and relatively fast during these states (Glenn and Steriade, 1982). Indeed, they can attain frequencies in the gamma range (40 Hz) in association with similar gamma EEG activity, which they may accordingly stimulate (Steriade et al., 1993a). They utilize Glu as a neurotransmitter, as proven recently by their content of vesicular Glu transporter 2 (VGluT2) (Fremeau et al., 2001; Kaneko et al., 2002; Hur and Zaborszky, 2005). Like other thalamocortical projection neurons, those of the midline and intralaminar nuclei change both their rate and mode of discharge during SWS (Steriade et al., 1993a). Due to intrinsic properties, all thalamic projection neurons have two modes of firing, tonic and bursting, the latter mediated by a calcium lowthreshold spike (LTS), which is activated when the neurons are hyperpolarized (Steriade and Llinas, 1988). This hyperpolarization occurs when the thalamic neurons are released from excitatory influences from the brainstem-activating systems. Moreover, the reticular thalamic neurons which surround and innervate the thalamic relay neurons begin first to discharge in bursts when removed from this depolarizing influence. The reticular thalamic neurons utilize GABA as a neurotransmitter and thereby further hyperpolarize the thalamocortical projection neurons in an active and punctual manner. They accordingly entrain the projection neurons in rhythmic bursting, which occurs first at a spindle frequency (12–14 Hz) and then at a delta frequency (1–4 Hz), as well as a slower oscillation (0.1–1 Hz). These patterns of SWS are thus transmitted through thalamo-cortico-thalamic loops as a product of
NEUROBIOLOGY OF WAKING AND SLEEPING the intrinsic properties of the neurons within those circuits (Steriade et al., 1993b). During these slow patterns, thalamocortical transmission of sensory inputs is virtually blocked and consciousness is lost.
The basalocortical projection system First identified by immunohistochemical staining for AChE, the innervation of the cerebral cortex by cholinergic fibers from the basal forebrain was originally proposed by Shute and Lewis (1967) to represent the important relay of the brain reticular activating system to the cerebral cortex within the “cholinergic reticular activating system” (Figure 9.1). Moreover, a potent excitatory effect of ACh upon cortical neurons was demonstrated by Krnjevic and Phillis (1963) and proposed to underlie the fast cortical activity that characterized activation. Indeed, pharmacological enhancement of ACh with physostigmine, the AChE inhibitor, or administration of muscarinic or nicotinic agonists stimulated cortical activation with waking (Domino et al., 1968). Blocking muscarinic receptors with atropine led to deactivation of the cortex with predominant slowwave activity, despite continued behavioral arousal, and thus disassociation between cortical activity and behavior (Longo, 1966). It was also found by Jasper and Tessier (1971) that ACh release from the cerebral cortex was maximal in association with cortical activation during both waking and REM sleep (Celesia and Jasper, 1966). These early studies thus indicated that ACh and cholinergic neurons played an important, if not critical, role in cortical activation which occurs during waking and REM sleep and thus in cortical activation, irrespective of behavioral arousal. As identified and delineated by ChAT immunohistochemistry, the cholinergic neurons are distributed across the basal forebrain from rostral to caudal in the medial septum (MS), nuclei of the diagonal band of Broca (DBB), magnocellular preoptic nucleus (MCPO), substantia innominata (SI) and globus pallidus (GP), as described in the rat brain (Mesulam et al., 1983b) and corresponding largely to what was originally called the nucleus basalis magnocellularis of Meynert in primates (Mesulam et al., 1983a). Collectively these cell groups provide a rich cholinergic innervation to the hippocampus and paleocortex (predominantly from MS-DBB) and to the entire neocortex (predominantly from MCPO-SI-GP). In the cortex, cholinergic fibers innervate both interneurons and pyramidal cells across all layers (Beaulieu and Somogyi, 1991). ACh exerts excitatory influences upon both cell types, predominantly through muscarinic (M1) receptors and also inhibitory influences upon some interneurons (through M2 receptors) (McCormick, 1993). The influence of the basal
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forebrain cholinergic neurons upon cortical neurons prevents slow-wave cortical activity and promotes fast cortical activity, particularly in a gamma range (30–60 Hz) (Metherate et al., 1992; Cape and Jones, 2000). As recently determined by juxtacellular labeling and immunohistochemical identification of recorded neurons in rats, the cholinergic basal forebrain neurons discharge maximally in association with cortical activation during waking and REM sleep or, as it is more appropriately called in rats, paradoxical sleep (PS) (Lee et al., 2005b) (Figure 9.2). Moreover, they fire in high-frequency spike bursts with gamma and theta activity during active, attentive waking and during PS (Figure 9.2, expanded traces). As typical of W/PSactive (or W/REM, as represented in the human brain in Figure 9.1), their discharge is positively correlated with high-frequency gamma EEG activity and negatively correlated with slow delta EEG activity, and it is not correlated with EMG amplitude (Figure 9.3). In contrast to thalamocortical neurons of the nonspecific thalamocortical projection system, the cholinergic cells cease firing prior to and during SWS. Since ACh stimulates cortical activation, the cessation of discharge by the cholinergic cells is likely a determinant in the natural onset of SWS, including spindle, delta, and slow oscillations in the cortex. In addition to cholinergic neurons, other neurons, including glutamatergic and GABAergic neurons, are distributed through the basal forebrain and give rise to cortical, local, or descending projections (Jones, 2004, 2005; Henny and Jones, 2008). These noncholinergic cells are heterogeneous in their response to different neurotransmitters, in their activity profile across sleep–wake states, and in their role in modulating cortical activity and sleep–wake states, as will be elaborated below. Some presumed glutamatergic neurons, likely having cortical projections, discharge like the cholinergic basalocortical projection neurons, maximally in association with cortical activation during waking and REM sleep (Figure 9.3). Other presumed glutamatergic neurons, likely having descending projections, discharge maximally with behavioral arousal during waking. Some GABAergic neurons discharge also in parallel with the cholinergic cells; yet another important contingent discharges in an inverse manner to the cholinergic and presumed glutamatergic neurons and could thus promote sleep (see below) (Figure 9.3).
DIFFUSELY PROJECTING AROUSAL SYSTEMS The reticular formation and cholinergic pontomesencephalic tegmental neurons have the capacity to influence widespread areas of the forebrain and cortex
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Cholinergic basal forebrain unit aW
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Fig. 9.2. Discharge of a cholinergic basal forebrain neuron across sleep–wake states. Record of a neuron labeled by juxtacellular technique with Neurobiotin (Nb) and identified by immunohistochemistry for choline acetyltransferase (ChAT) as cholinergic in the magnocellular preoptic nucleus (MCPO) of the rat. As evident in 10-second traces (above), the unit fired during aW, virtually ceased firing during SWS, resumed firing during tPS, and discharged maximally during PS. As evident in expanded 0.5-second traces (below), the unit discharged in rhythmic bursts of spikes with theta EEG activity that was present intermittently during periods of aW, toward the end of tPS, and continuously during PS. aW, active wake; EMG, electromyogram; EEG, electroencephalogram; PF, prefrontal cortex; RS, retrosplenial cortex; SWS, slow-wave sleep; tPS, transition to paradoxical sleep; PS, paradoxical sleep. Bar for horizontal scale: 1 second. Bar for vertical scales: 1 mV for EEG/EMG and 1.5 mV for unit. (Reprinted with permission from Lee et al. (2005b).)
through their projections to the major subcortical relay stations. Some also give rise to branching axons with descending as well as ascending projections of some distance, thus allowing simultaneous influence upon forebrain and spinal cord systems (Jones and Yang, 1985). It is thus likely that some neurons of the reticular formation can simultaneously stimulate cortical activation and behavioral arousal with enhanced muscle tone and/or motor activity. In the case of the cholinergic neurons, some may actually stimulate cortical activation while dampening behavioral arousal and diminishing muscle tone in the generation of REM sleep. Such widespread influence can thus determine the state of the brain and organism. Following the development of histofluorescent techniques in the 1960s, other cell groups were revealed within the brainstem which contained monoamines and which gave rise to highly diffuse projections through the entire central nervous system (Dahlstrom and Fuxe, 1964; Ungerstedt, 1971b). Moreover, they acted as neuromodulators able to influence
the activity of other neurons or actions of other neurotransmitters on those neurons in a relatively subtle, slow, and prolonged manner. As proposed by Jouvet (1969), the monoamines and their neural systems appeared ideally suited to influence – if not determine – sleep–wake states. Most notable of these, the locus coeruleus nucleus neurons were found to contain noradrenaline (NA) (norepinephrine) and to give rise to varicose axons which branched and sent collaterals through the entire nervous system, such as potentially to permit from one neuron the simultaneous release of NA throughout the brain and spinal cord (Jones and Moore, 1977; Jones and Yang, 1985). Indeed, this small cluster of neurons in the brain resembles a central sympathetic ganglion, sending fibers to broad regions and releasing NA from the varicosities along its axons to influence its multiple target cells in a nonsynaptic manner (Descarries et al., 1977). The locus coeruleus noradrenergic neurons thus appeared to represent an ideal substrate stimulating arousal.
NEUROBIOLOGY OF WAKING AND SLEEPING Slow EEG: Delta
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Fig. 9.3. Sleep–wake-related electroencephalogram (EEG)/electromyogram (EMG) and unit activity of basal forebrain neurons in the rat. Normalized average gamma power (A), delta power (B), and EMG amplitude (C) across all sleep–wake stages in the rat. (D–G) Normalized average unit spike rate for basal forebrain cell groups. In D, waking (W)/paradoxical sleep (PS)-active cells, whose discharge is positively correlated with gamma EEG activity and negatively correlated with delta EEG activity (including putative cholinergic cells represented in Figure 9.1 in the human brain as W/REM cells, circles). E shows W-active cells whose discharge is positively correlated with EMG amplitude and which fire maximally during W (including putative glutamatergic cells represented in Figure 9.1, diamonds). F shows SWS-active cells whose discharge is negatively correlated with gamma and positively correlated with delta EEG activity and which fire maximally during SWS (including putative GABAergic neurons represented in Figure 9.1, triangles). G shows SWS/PS-active cells whose discharge is negatively correlated with EMG amplitude and which fire at progressively higher rates during SWS through PS (including putative GABAergic neurons represented in Figure 9.1 as SWS/REM cells, triangles). aW, active wake; qW, quiet wake; tSWS, transition to slow-wave sleep; SWS, slow-wave sleep; tPS, transition to paradoxical sleep; PS, paradoxical sleep. (Reprinted with permission from Jones, (2005).)
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Early pharmacological studies had indicated a potent influence of the catecholamines, NA and dopamine (DA), in stimulating waking with behavioral arousal (Jouvet, 1972). Amphetamine, which releases NA and DA, evoked a prolonged waking state characterized by fast cortical activity and pronounced behavioral arousal. Depletion of NA and DA by inhibition of catecholamine synthesis (with a-methyl-para-tyrosine, AMPT) resulted in decreases in waking and increases in sleep.
Noradrenergic locus coeruleus neurons The locus coeruleus neurons project along the same ascending pathways as the neurons of the reticular formation; however, while innervating the relay stations in the thalamus, hypothalamus and basal forebrain, they send axons further along to innervate the entire cerebral cortex directly (Jones and Yang, 1985) (Figure 9.1). Other neurons send axons through the brainstem to innervate neurons therein, yet extend their fibers into the entire spinal cord, and a certain number innervate through bifurcating axons both the forebrain and the spinal cord. Through these regions, NA exerts different effects upon different neurons through different receptors. In the thalamus, NA serves mainly to depolarize and excite both specific and nonspecific thalamocortical projection neurons by acting primarily upon a1-adrenergic receptors and thus stimulating fast tonic discharge and preventing slow bursting discharge of the thalamic neurons to promote cortical activation (McCormick, 1992). In the posterior hypothalamus, NA also excites wake-promoting neurons (Bayer et al., 2005) (see below). In the basal forebrain, the cholinergic neurons are similarly excited by NA through a1-adrenergic receptors (Fort et al., 1995). NA also excites motor systems and exerts a direct excitatory influence upon motor neurons in the spinal cord (Sqalli-Houssaini and Cazalets, 2000). Indeed, the excitatory influence of NA upon motor neurons and their activity is also evident in brainstem motor neurons as an important tonic influence that determines their activity and tonus during waking (Fenik et al., 2005). It is notable that NA inhibits certain neurons through a2-adrenergic receptors; indeed, sleep-promoting neurons in the forebrain appear to be inhibited by NA (see below). According to their projections and the effects of NA released by their diffusely projecting fibers, the locus coeruleus noradrenergic neurons thus have the capacity simultaneously to stimulate cortical activation and behavioral arousal of waking and to prevent sleep. As established many years ago without the need to identify recorded neurons as NA-containing in the
locus coeruleus, given the very compact and homogeneous aggregation of these cells in the rat brain, locus coeruleus noradrenergic neurons discharge selectively during waking, diminish firing during SWS, and cease firing altogether during PS (Aston-Jones and Bloom, 1981) as W-active cells (W, Figure. 9.1). Their discharge during waking is maximal in response to sensory stimuli and situations that are associated with high behavioral arousal, stress, and activation of the peripheral sympathetic nervous system (Jacobs et al., 1991). Their discharge would thus be associated with behavioral arousal and incompatible with SWS and PS. Indeed, as was formally proposed by McCarley and Hobson (1975) many years ago, locus coeruleus noradrenergic neurons could prevent the occurrence of PS through an inhibitory influence upon cholinergic PS promoting neurons in the pontomesencephalic tegmentum. They can also prevent the muscle atonia of PS by their excitatory influence upon motor neurons (Fenik et al., 2005).
Dopaminergic mesencephalic neurons The DA-containing neurons are located in the mesencephalic tegmentum concentrated within the substantia nigra and ventral tegmental area. Although the dopaminergic neurons do not project in the diffuse manner of the noradrenergic neurons, they nonetheless reach broad areas of the forebrain, particularly the dorsal striatum from the substantia nigra and the ventral striatum and cortex from the ventral tegmental area (Moore and Bloom, 1979). They also project to the thalamus (Sanchez-Gonzalez et al., 2005) and on to cholinergic basal forebrain neurons (Jones and Cuello, 1989; Gaykema and Zaborszky, 1996), similar to noradrenergic neurons. They influence target neurons in differing manners through D1 or D2 receptors. From early lesion studies, DA neurons appeared to influence behavioral arousal more than cortical activity, since their destruction in animals resulted in akinesia with little change in cortical activation, as in Parkinson patients (Ungerstedt, 1971a; Jones et al., 1973). Yet, evidence subsequently indicated that these neurons can also facilitate cortical activation by enhancing gamma EEG activity along with attentive behavior (Montaron et al., 1982). Recordings from identified DA-containing neurons have not yet been realized in naturally sleeping–waking animals. Early studies described the activity of possibly dopaminergic neurons, which particularly in the ventral tegmental area are intermingled with a vast majority of nondopaminergic neurons, across the sleep–waking cycle, and concluded that they did not change their average firing rate across this cycle (Miller et al.,
NEUROBIOLOGY OF WAKING AND SLEEPING 1983), a very surprising finding. On the other hand, studies employing c-Fos as an indicator of activity presented evidence that dopaminergic neurons of the ventral tegmental area are more active during waking and REM sleep (W/REM) than SWS (Maloney et al., 2002). A study employing electrophysiological properties as a marker for dopaminergic neurons found that possibly DA-containing neurons of the ventral tegmental area discharged in bursts of spikes during aroused waking and during PS (Dahan et al., 2007). It is thus possible that dopaminergic neurons are more similar to cholinergic neurons, from which they receive input and to which they project, and thereby discharge maximally during both W and PS. Considering the important role of DA in the limbic system, such activity could mediate the emotive aspects of particular waking and dream states. Given, however, that the drugs employed in the prevention of hypersomnolence including narcolepsy with cataplexy act prominently upon both NA and DA release (Wisor et al., 2001), it is currently not clear whether dopaminergic neurons in the ventral mesencephalon (or perhaps diencephalon) function to promote behavioral arousal and prevent sleep-like noradrenergic neurons (Figure 9.1) or might be active during REM sleep to stimulate cortical activation without stimulating behavioral arousal. In any event, it is likely that the enhanced release of both NA and DA is important for the antinarcoleptic action of amphetamines and modafinil (Lin et al., 1992).
Serotonergic raphe neurons The influence of serotonin (Ser, also 5-hydroxytryptamine, 5-HT) upon EEG activity and sleep–wake states is different from that of the catecholamines. Indeed, it is so different that the early pharmacological and lesion studies indicated to Jouvet (1972) that the serotonergic raphe neurons generated SWS. Inhibition of Ser synthesis (with para-chlorophenylalanine, PCPA) and lesions of serotonergic raphe neurons both produced insomnia. Yet, upon recording from possibly serotonergic raphe neurons, it was surprisingly discovered that the presumed serotonergic cells discharged during waking, diminished firing during SWS, and ceased firing during REM sleep (McGinty and Harper, 1976). They are, thus, like noradrenergic neurons, W-active cells (W, Figure 9.1). In contrast to noradrenergic locus coeruleus neurons, however, the presumed serotonergic raphe neurons do not discharge during response and orientation to sensory stimuli and do not fire under conditions of physiological stress when the sympathetic nervous system is activated (Jacobs and Fornal, 1999). They do fire during motor activity and
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particularly during rhythmic motor patterns, such as grooming or locomotion. Ser is known to facilitate locomotor activity and directly excite motor neurons, particularly through 5-HT2 receptors (Barbeau and Rossignol, 1990; Kjaerulff and Kiehn, 2001). In that serotonergic raphe neurons can also attenuate sensory inputs (Fields and Basbaum, 1978), it is possible that their activity prevents sensory inputs from disrupting rhythmic motor activity during locomotion or grooming (Jacobs and Fornal, 1999). The major serotonergic projections into the spinal cord dorsal and ventral horns derive from medullary raphe nuclei (magnus, pallidus, and obscurus). Serotonergic neurons also project into the forebrain (particularly from the dorsal and central superior raphe nuclei) along the major ascending brainstem pathways, and like the noradrenergic neurons, also beyond to reach directly the cerebral cortex. Ser can inhibit many thalamic neurons, including the intralaminar nuclei, through 5-HT1 receptors, though exciting others through 5-HT2 receptors (Monckton and McCormick, 2002). Ser inhibits cholinergic basal forebrain neurons through 5-HT1 receptors and thereby diminishes gamma EEG activity (Khateb et al., 1993; Cape and Jones, 1998). Serotonergic raphe neurons would thus promote waking and behavioral arousal along with rhythmic motor activity but not cortical activation with sensory responsiveness, which would be attenuated. Promotion of such rhythmic pattern generation that would underlie behaviors such as grooming might favor a more relaxed waking state from which sleep would follow more easily than from a highly attentive state. Ser does nonetheless facilitate muscle tone and antagonize the hyperpolarization of motor neurons that occurs with the muscle atonia of REM sleep (Kubin et al., 1992; Fenik et al., 2005). It can also prevent REM sleep initiation by inhibiting cholinergic pontomesencephalic neurons (Luebke et al., 1992).
Histaminergic tuberomammilary neurons Histamine (HA) was long thought to have a wakepromoting influence since antihistaminergic drugs, used for the treatment of allergies, were associated with somnolence (Lin et al., 1988; Schwartz et al., 1991). It was subsequently discovered that, like the noradrenergic locus coeruleus neurons, the histaminergic neurons give rise to a highly diffuse innervation of the brain and spinal cord. The histaminergic neurons are also relatively tightly clustered in the posterior hypothalamus concentrated in the tuberomammillary nucleus (Figure 9.1). They excite target neurons in the brain, including thalamocortical projection neurons, cholinergic basal forebrain neurons, and cortical neurons
140 B.E. JONES predominantly through H1 receptors, by which HA also partially redundant; although, as discussed above, each appears to stimulate fast cortical activity (McCormick, plays a slightly different role and is invoked by a 1992; Reiner and Kamondi, 1994; Khateb et al., 1995). slightly different condition. On the other hand, it was Recently identified histaminergic neurons have quite surprising to learn in recent years that one particbeen recorded in the tuberomammillary nucleus of the ular peptide, its receptors and the neurons that release mouse across natural sleep–waking states (Takahashi it, appeared to be critical for the maintenance of et al., 2006). Like other monoaminergic neurons, these waking and behavioral arousal, since in its absence narcells discharge during waking and cease firing during colepsy with cataplexy occurs (Chemelli et al., 1999; sleep as W-active, or even wake-specific, cells suppoLin et al., 1999; Peyron et al., 2000; Thannickal et al., sedly not discharging at all during SWS or PS (W, Fig2000). This peptide is orexin (Orx, also called ure 9.1). Their discharge was particularly elevated hypocretin). during attentive waking, more so than during waking with movement. They would appear to differ in this Orexinergic posterior hypothalamic neurons way from both the noradrenergic and serotonergic In the 1990s, two groups simultaneously discovered a neurons and have been postulated to play a particularly new set of peptides in the hypothalamus, Sakurai and important role in attention. Such a role was supported his colleagues (1998) called them orexins (Orx A and by a diminished arousal response to novel stimuli seen B), meaning peptides that would stimulate appetite in mice with knockout of the gene for histidine decarand eating; de Lecea and his colleagues (1998) called boxylase, the synthetic enzyme for HA (Parmentier them hypocretins (Hcrt 1 and 2), meaning peptides that et al., 2002). It is also noteworthy that in narcoleptic are contained in hypothalamic neurons and have simidogs, presumed histaminergic neurons continued to larities with the gut hormone, secretin. Just 1 year later, fire, in contrast to noradrenergic locus coeruleus neuit was discovered by Yanagisawa and his collaborators rons, during episodes of cataplexy (John et al., 2004). that knockout of the gene for Orx in mice resulted in Such discharge seemingly did not affect the immobility narcolepsy and cataplexy (Chemelli et al., 1999) and or muscle atonia of the abnormal state and could be by Mignot and his collaborators (2002) that it was the partly responsible for the state of alertness and congene for the Hrct 2 (Orx 2) receptor that was lacking scious awareness that can persist during cataplectic in dogs with narcolepsy-cataplexy (Lin et al., 1999). episodes in dogs and humans. Histaminergic neurons Indeed, humans having suffered from narcolepsy with are nonetheless normally active during attentive and cataplexy were subsequently found to have mutations aroused waking when, as W-active cells, they would in Orx (Hcrt) genes, low levels of Orx (Hcrt) in cerepromote cortical activation and attention. brospinal fluid and/or loss of Orx-containing neurons Early lesion studies, employing particularly large in the hypothalamus (Peyron et al., 2000; Thannickal electrolytic or thermolytic lesions, of each of the actiet al., 2000). Clearly, orexinergic hypothalamic neurons vating or arousal systems, including the reticular forplay a critical role in maintaining waking. mation with the posterior hypothalamus (Lindsley The Orx neurons are located in the posterior portion et al., 1950) and the catecholaminergic neurons (Jones of the hypothalamus, where they are distributed across et al., 1973), revealed major deficits or elimination of the lateral hypothalamus, perifornical area, and dorcortical activation, behavioral arousal and the waking somedial nucleus (Peyron et al., 1998) (Figure 9.1). Like state in experimental animals, corroborating observathe noradrenergic locus coeruleus neurons, they give tions in human cases of coma following large brainrise to highly diffuse projections extending through stem lesions (Plum and Posner, 1980; Parvizi and the forebrain to reach the subcortical relays of the actiDamasio, 2003). Yet, when using more refined technivating systems in the thalamus and basal forebrain and ques for performing lesions and particularly using neuto continue up to the cerebral cortex. They also project rotoxins selective for cell bodies and neurons through the hypothalamus, the brainstem, and into the containing particular neurotransmitters or bearing parspinal cord. According to orexin’s effects following ticular receptors, no long-lasting deficits in waking or intracerebroventricular administration and to the cortical activation were apparent (Jones et al., 1977; effects of elimination of Orx or Orx neurons in knockWebster and Jones, 1988; Denoyer et al., 1991; Holmes out mice, the orexinergic system facilitates cortical and Jones, 1994; Blanco-Centurion et al., 2004, 2006, activation and arousal, stimulates the hypothalamo2007). These results, emerging over many years now, pituitary-adrenal and hypothalamo-pituitary-thyroid have indicated that no one neural system or neuroaxis and excites both sympathetic and motor systems transmitter is critical for generating a waking state, (Lubkin and Stricker-Krongrad, 1998; Shirasaka et al., although any one might be sufficient. The activating 1999; Hara et al., 2001; Espana et al., 2002; Yamanaka and arousal systems are thus multiple, parallel, and
NEUROBIOLOGY OF WAKING AND SLEEPING et al., 2003). Orx neurons thus stimulate arousal while activating neuroendocrine, sympathetic, and motor systems to support and sustain activity through the physiological changes associated with increased energy metabolism. This influence occurs through the excitatory action of Orx on Orx-1 or Orx-2 receptors upon multiple neurons, including cortical neurons, midline thalamocortical projection neurons, cholinergic basal forebrain neurons, histaminergic neurons, cholinergic pontomesencephalic neurons, noradrenergic locus coeruleus neurons, and motor neurons (Horvath et al., 1999; Bayer et al., 2001, 2004; Eggermann et al., 2001; Burlet et al., 2002; Yamuy et al., 2004). Interestingly, no inhibitory actions of Orx have been found, even upon the sleep-promoting neurons which are inhibited by NA (see below) (Bayer et al., 2002). The Orx neurons can thus play a central role in stimulating arousal by exciting all other arousal systems while activating neuroendocrine, sympathetic, and motor systems that support arousal and activity. Studies utilizing c-Fos expression or release of Orx indicated that Orx neurons are active and release their peptide in association with waking and arousal during the active period of the day (Kiyashchenko et al., 2002; Zeitzer et al., 2003). Yet, it remained uncertain whether they became silent during sleep and particularly REM sleep until recording from identified Orx neurons was achieved by juxtacellular labeling in the rat (Lee et al., 2005a; Mileykovskiy et al., 2005). Orx neurons were thus found to discharge during waking and virtually cease firing during SWS and PS (Figure 9.4). Their discharge occurred during active waking and was correlated with postural muscle tone recorded on the nuccal EMG (Figure 9.4, expanded traces). The Orx neurons were thus like other neurons whose discharge was positively correlated with EMG (Figure 9.3) and whose profile could be typified as W-on and PS-off (W, Figure 9.1). In their case, however, in contrast to other known cell groups, including the noradrenergic locus coeruleus neurons, their discharge is necessary to maintain active waking, since it is in their absence that narcolepsy with cataplexy occurs. Since cataplexy is often elicited by an emotional stimulus or also in animals by food, it appears to be triggered by activation of systems which act in an opposite manner to the Orx neurons during those conditions. The cholinergic neurons, which are active during both waking and REM sleep, and ACh or its agonists, which can evoke cortical activation with muscle atonia or a REM sleep-like state, could exert this opposing influence. Indeed, this influence could be exerted from both the basal forebrain and brainstem to result in cortical activation associated with a loss of muscle tonus (Reid et al., 1994a, b; Nishino et al., 1995;
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Cape et al., 2000) during conditions when orexin release is absent, such as during natural REM sleep or narcoleptic attacks occurring in the absence of orexinergic transmission, which would otherwise override the cholinergic influence to excite motor and sympathetic systems.
SLEEP-PROMOTING SYSTEMS Although it is clear that thalamic neurons play an important role in shaping the activity of the cortex across waking and sleeping, their influence depends upon their pattern of discharge, which is tonic and fast during waking and becomes bursting and slow during sleep. In contrast, there are neurons in the forebrain and brainstem which are selectively active during sleep and thus appear to play a specific role in promoting sleep.
Preoptic region and basal forebrain From early studies involving lesions or stimulation, the preoptic region and basal forebrain were known to have the capacity to exert a sleep-promoting influence. Early lesions produced insomnia (McGinty and Sterman, 1968). Stimulation produced a predominance of parasympathetic responses, including decreased heart rate, blood pressure, respiration, and temperature along with decreased activity (Hess, 1957) and sleep (Sterman and Clemente, 1962a, b). Single-unit recording studies revealed neurons in the preoptic area and basal forebrain that discharged maximally during sleep (Szymusiak and McGinty, 1986; Alam et al., 1996; Szymusiak et al., 1998). In both these regions, however, such cells are intermingled with cells which discharge maximally during waking and PS in association with cortical activation (above) or less commonly during waking alone (above) (Koyama and Hayaishi, 1994; Lee et al., 2005b). Sleepactive neurons are of two types, one which discharges in association with cortical slow-wave, delta activity during SWS and another which discharges in association with progressively decreasing muscle tonus during SWS and PS (Figure 9.3). The SWS cell group could influence cortical deactivation or slow-wave activity by ascending projections to the cortex or local projections on to basalocortical cholinergic neurons. The SWS/PS cell group could influence muscle tone and behavioral quiescence by descending projections to the posterior hypothalamus and brainstem (Figure 9.1).
GABAergic neurons Sleep-active neurons have also been revealed by c-Fos expression during sleep recovery following deprivation (Sherin et al., 1996). By this technique, the majority of
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Orexinergic lateral hypothalamic unit
B
W
SWS
C
tPS
D
PS
Unit EEG : RS EEG : PF EMG
A
aW
Fig. 9.4. Discharge of an Orx neuron across sleep–wake states. Record of a neuron labeled by juxtacellular technique with Neurobiotin (Nb) and identified by immunohistochemistry for Orx in the rat. As evident in 10-second traces (above), the unit fired during wakefulness (A) and was virtually silent during slow-wave sleep (B), transition to paradoxical sleep (C), and paradoxical sleep (D). As evident in an expanded trace (of approximately 4 seconds, below), the unit discharged during active wake (aW) and increased firing phasically in association with increases in muscle tone seen on the EMG. aW, active wake; EEG, electroencephalogram; EMG. electromyogram; PF, prefrontal cortex; PS, paradoxical sleep; RS, retrosplenial cortex; SWS, slow-wave sleep; tPS, transition to paradoxical sleep; W, wake. Horizontal scale bars: 1 second. Vertical scale bar: 1 mV for EEG, 0.5 mV for EMG, and 2 mV for unit. (Reprinted with permission from Lee et al. (2005a).)
sleep-active cells in the preoptic area and basal forebrain have been found to contain the synthetic enzyme for GABA (glutamic acid decarboxylase, GAD) (Gong et al., 2004; Modirrousta et al., 2004). Yet, many GABAergic neurons are active during waking and cortical activation, as evident from both c-Fos and juxtacellular recording studies (Manns et al., 2000; Modirrousta et al., 2004). The sleep-active GABAergic cells must then be different in other ways from the Wactive or W/PS-active GABAergic cells. From in vitro pharmacological studies performed first in the basal forebrain and then in the ventrolateral preoptic area (VLPO), it was discovered that, whereas cholinergic neurons were depolarized and excited by NA through a1-adrenergic receptors, a small contingent
of cells, which were identified as GABAergic in the VLPO, were hyperpolarized and inhibited by NA through a2-adrenergic receptors (Fort et al., 1995, 1998; Gallopin et al., 2000). Moreover, following juxtacellular recording and labeling of neurons that discharge maximally with slow-wave activity, it was found that a large proportion of these were GABAergic and that these particular GABAergic cells bear a2-adrenergic receptors (Manns et al., 2003). An important contingent of sleep-promoting neurons would thus be composed of GABAergic neurons in the basal forebrain and preoptic area which are inhibited by NA and would thus be disinhibited when NA release declines as locus coeruleus neurons cease discharge with decreasing arousal (Jones, 2005). Reciprocally, by
NEUROBIOLOGY OF WAKING AND SLEEPING releasing GABA, the sleep-promoting neurons can inhibit cortical or subcortical systems promoting cortical activation or behavioral arousal, including noradrenergic, histaminergic, and orexinergic neurons (Sherin et al., 1998; Steininger et al., 2001; Henny and Jones, 2006). It should also be mentioned that there are GABAergic neurons through the hypothalamus and brainstem, which can also function to inhibit arousal-promoting neurons. Notably, GABAergic neurons in the pontomesencephalic tegmentum and medulla appear to be active with sleep and PS recovery during which they may inhibit local monoaminergic cells, other reticular neurons, or motor neurons (Maloney et al., 1999, 2000; Fenik et al., 2005) (see above) (Figure 9.1). Glycinergic neurons also participate in this process (Chase et al., 1989; Boissard et al., 2002), and both GABA and glycine are released in high concentrations in the region of brainstem and spinal cord motor neurons during muscle atonia (Kodama et al., 2003). In addition, the neurons of the thalamic reticular nucleus which shape spindling activity utilize GABA through which they hyperpolarize and pace the relay neurons to induce bursting in thalamo-cortico-thalamic circuits (Steriade et al., 1994) (see above).
GABA and hypnotic drugs Given the important role of GABA in inhibiting neurons of the arousal systems and thus in promoting sleep, it is not surprising that many hypnotic drugs as well as anesthetics act as GABA agonists (Lancel, 1999; Gottesmann, 2002; Mignot et al., 2002; Rudolph and Antkowiak, 2004; Mendelson, 2005). Such drugs act upon the benzodiazepine site of the GABAA receptor (linked to chloride channels) to enhance and prolong GABA’s action or directly upon the GABAA receptor to mimic its action and promote respectively spindling activity or slow-wave activity along with sleep. Some drugs act upon GABAB receptors (linked to potassium channels) and promote slow-wave activity along with minimal muscle tone with sleep.
SUMMARY Waking and sleeping are actively generated by neuronal systems distributed through the brainstem and forebrain with different projections, discharge patterns, neurotransmitters, and receptors. Specific ascending systems stimulate cortical activation, characterized by fast, particularly gamma, activity which occurs during waking and REM sleep. In addition to glutamatergic neurons of the reticular formation and thalamus, cholinergic pontomesencephalic and basal forebrain neurons are
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integral components of the ascending activating system. Discharging during W and REM sleep, cholinergic neurons stimulate cortical activation in the presence or absence of postural muscle tone and behavioral arousal. Comprised by glutamatergic reticulospinal and other neurons, specific descending systems stimulate behavioral arousal, characterized by postural muscle tone along with motor activity during waking. Diffusely projecting systems give rise to both ascending and descending projections and thus simultaneously facilitate both cortical activation and behavioral arousal. These include the neurons containing the modulatory neurotransmitters NA, DA, Ser, HA, and Orx. Commonly discharging during waking and ceasing to discharge during SWS and REM sleep, these systems excite through particular receptors other neurons of the activating and arousal systems to promote waking and prevent sleep. Largely parallel in their projections and actions, they are partially redundant and thus not individually necessary for the generation of waking and arousal. On the other hand, Orx is necessary for the maintenance of waking since in its absence narcolepsy with cataplexy occurs in humans and animals. These neurons excite all other activating and arousal systems along with neuroendocrine, sympathetic, and motor systems to support activity, arousal, and muscle tone. Sleeping is initiated by inhibition of the activating and arousal systems. This inhibition is effected at multiple levels through particular GABAergic neurons which become active during sleep. Neurons in the preoptic area and basal forebrain play a particularly important role in this process. Some become active during SWS, promoting deactivation and slow-wave activity in the cerebral cortex. Others discharge at progressively increasing rates during SWS and REM sleep, promoting behavioral quiescence and diminishing muscle tone. Through their projections and inhibitory neurotransmitter, they have the capacity to inhibit the monoaminergic neurons and Orx neurons in the brainstem and hypothalamus. They are in turn inhibited by NA through a2-adrenergic receptors. During REM sleep, cholinergic systems become active and stimulate cortical activation, while the monoaminergic and orexinergic systems are inhibited, leaving motor and other neurons devoid of their excitatory influence. The selective inhibition of these systems and additional direct inhibition of motor neurons by GABA (and glycine) produces a loss of behavioral responsiveness and postural muscle tone, despite maintained activation of the cerebral cortex, which characterizes this “paradoxical” state of sleep as well as its pathological manifestation in narcolepsy with cataplexy.
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ACKNOWLEDGMENTS Most of the recent research of the author presented in this article was funded by grants from the Canadian Institutes of Health Research and US National Institutes of Health and performed at the Montreal Neurological Institute by Maan Gee Lee, Ian Manns, Oum Hassani, Mandana Modirrousta, Pablo Henny, Frederic Brischoux, and Lynda Mainville, to whom I am most grateful. I am also thankful to my collaborators Michel Muhlethaler and colleagues at the Centre Me´dicale Universitaire in Geneva, whose work is also mentioned. I also thank Napoleon Soberanis for his assistance with the schematic figures.
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Handbook of Clinical Neurology, Vol. 98 (3rd series) Sleep Disorders, Part 1 P. Montagna and S. Chokroverty, Editors # 2011 Elsevier B.V. All rights reserved
Chapter 10
Neurobiology of REM sleep ROBERT W. MCCARLEY * Neuroscience Laboratory and Harvard Department of Psychiatry, VA Boston Healthcare System, Brockton, MA, USA
INTRODUCTION This chapter presents an overview of the current “state of the art” of knowledge of the neurophysiology and cellular pharmacology of sleep mechanisms. It is written from the perspective that recent years have seen a remarkable development of knowledge about sleep mechanisms, due to the capability of current cellular neurophysiological, pharmacological, and molecular techniques to provide focused, detailed, and replicable studies that have enriched and informed the knowledge of sleep phenomenology and pathology derived from electroencephalogram (EEG) analysis. This chapter has a cellular and neurophysiological/neuropharmacological focus, with most of the emphasis on mechanisms relevant to rapid eye movement (REM) sleep. With respect to a detailed historical introduction to the topics of this chapter, this is available in Steriade and McCarley (2005). For the reader interested in an update on the terminology and techniques of cellular physiology, one of the standard neurobiology texts could be consulted (Kandel et al., 2000). Overviews of REM sleep physiology are also available (McCarley, 2004; Steriade and McCarley, 2005), and the present chapter draws on these accounts for the text. We begin this chapter with brief and elementary overviews of sleep architecture and phylogeny/ontogeny so as to provide a basis for the later mechanistic discussions. We then move to a discussion of REM sleep and the relevant anatomy and physiology, then comment very briefly on the role of hypocretin/orexin in REM sleep control. Sleep may be divided into two phases. REM sleep is most often associated with vivid dreaming and a high level of brain activity. The other phase of sleep, called non-REM sleep or slow-wave sleep (SWS), is usually associated with reduced neuronal activity; thought
*
content during this state in humans is, unlike dreams, usually nonvisual and consisting of ruminative thoughts. As one goes to sleep the low-voltage fast EEG of waking gradually gives way to a slowing of frequency and, as sleep moves toward the deepest stages, there is an abundance of delta waves, EEG waves with a frequency of 0.5 to < 4 Hz and of high amplitude. The first REM period usually occurs about 70 minutes after the onset of sleep. REM sleep in humans is defined by the presence of low-voltage fast EEG activity, suppression of muscle tone (usually measured in the chin muscles) and the presence, of course, of REMs. The first REM sleep episode in humans is short. After the first REM sleep episode, the sleep cycle repeats itself with the appearance of non-REM sleep and then about 90 minutes after the start of the first REM period, another REM sleep episode occurs. This rhythmic cycling persists throughout the night. The REM sleep cycle length is 90 minutes in humans and the duration of each REM sleep episode after the first is approximately 30 minutes. While EEG staging of REM sleep in humans usually shows a fairly abrupt transition from non-REM to REM sleep, recording of neuronal activity in animals presents a quite a different picture. Neuronal activity begins to change long before the EEG signs of REM sleep are present. To introduce this concept, Figure 10.1 shows a schematic of the time course of neuronal activity relative to EEG definitions of REM sleep. Later portions of this chapter will elaborate on the activity depicted in this figure. Over the course of the night delta wave activity tends to diminish and non-REM sleep has waves of higher frequencies and lower amplitude. REM sleep is present in all mammals, and recent data suggest this includes the egg-laying mammals
Correspondence to: Robert W. McCarley, M.D., Director, Neuroscience Laboratory, Professor and Head, Harvard Department of Psychiatry, VA Boston Healthcare System, 940 Belmont Street, Brockton, Massachusetts 02301, USA. Tel: (508) 583-4500 x63723, E-mail:
[email protected] 152
R.W. MCCARLEY Time course of REM sleep and sleep neurotransmitter rhythms: REM-on neurons, , acetylcholine REM-off neurons, , norepinephrine, serotonin 4
Relative level
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Fig. 10.1. Schematic of a night’s course of rapid eye movement (REM) sleep in humans showing the occurrence and intensity of REM sleep as dependent upon the activity of populations of “REM-on” ( ¼ REM-promoting neurons), indicated by the solid line. As the REM-promoting neuronal activity reaches a certain threshold, the full set of REM signs occurs (dark areas under curve indicate REM sleep). Note, however that, unlike the step-like electroencephalographic diagnosis of stage, the underlying neuronal activity is a continuous function. The neurotransmitter acetylcholine is thought to be important in REM sleep production, acting to excite populations of brainstem reticular formation neurons to produce the set of REM signs. Other neuronal populations utilizing the monoamine neurotransmitters serotonin and norepinephrine are likely REM-suppressive; the time course of their activity is sketched by the dotted line. The terms REM-on and REM-off generally apply to other neuronal populations important in REM sleep, including those utilizing the neurotransmitter gamma-aminobutyric acid. (These curves mimic actual time courses of neuronal activity, as recorded in animals, and were generated by a mathematical model of REM sleep in humans, the limit cycle reciprocal interaction model of McCarley and Massaquoi (1986a)).
(monotremes), such as the echidna (spiny anteater) and the duckbill platypus. Birds have very brief bouts of REM sleep. REM sleep cycles vary in duration according to the size of the animal, with elephants having the longest cycle and smaller animals having shorter cycles. For example, the cat has a sleep cycle of approximately 22 minutes, while the rat cycle is about 12 minutes. In utero, mammals spend a large percentage of time in REM sleep, ranging from 50% to 80% of a 24-hour day. At birth, animals born with immature nervous systems have a much higher percentage of REM sleep than do the adults of the same species. For example, sleep in the human newborn occupies two-thirds of the time, with REM sleep occupying one-half of the total sleep time, or about one-third of the entire 24-hour period. The percentage of REM sleep declines rapidly in early childhood so that by approximately age 10 the adult percentage of REM sleep is reached, 20% of total sleep time. The predominance of REM sleep in the young suggests an important function in promoting nervous system growth and development. Delta sleep is minimally present in the newborn but increases over the first years of life, reaching a maximum at about age 10 and declining thereafter. Feinberg and coworkers (1990) have noted the first three decades of this delta activity time course can be fit by a
gamma probability distribution and that approximately the same time course obtains for synaptic density and positron emission tomography measurements of metabolic rate in human frontal cortex. They speculate that the reduction in these three variables may reflect a pruning of redundant cortical synapses that is a key factor in cognitive maturation, allowing greater specialization and sustained problem-solving.
REM SLEEP PHYSIOLOGY AND RELEVANT BRAIN ANATOMY REM-promoting systems TRANSECTION
STUDIES
Lesion studies performed by Jouvet and co-workers in France demonstrated that the brainstem contains the neural machinery of the REM sleep rhythm (reviewed in Steriade and McCarley, 2005). As illustrated in Figure 10.2, a transection made just above the junction of the pons and midbrain produced a state in which periodic occurrence of REM sleep was found in recordings made in the isolated brainstem while, in contrast, recordings in the isolated forebrain showed no signs of REM sleep. Thus, while forebrain mechanisms (including those related to circadian rhythms) modulate REM sleep, the fundamental rhythmic
NEUROBIOLOGY OF REM SLEEP Cortex
Plane of pontine transection
Cerebellum LDT/PPT
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Thalamus DRN MRF
LC
PRF BRF Brainstem
Fig. 10.2. Schematic of a sagittal section of a mammalian brain (cat) showing the location of nuclei especially important for rapid eye movement (REM) sleep. BRF, PRF, and MRF, bulbar, pontine, and mesencephalic reticular formation; LDT/PPT, laterodorsal and pedunculopontine tegmental nuclei, the principal site of cholinergic (acetylcholinecontaining) neurons important for REM sleep and electroencephalogram desynchronization. LC, locus coeruleus, where most norepinephrine-containing neurons are located; DRN, dorsal raphe nucleus, the site of many serotonin-containing neurons. The oblique line is the plane of transection that Jouvet (1962) found preserves REM sleep signs caudal to the transection but abolishes them rostral to the transection.
generating machinery is in the brainstem, and it is here that anatomical and physiological studies have focused. The anatomical sketch provided by Figure 10.2 also shows the cell groups important in REM sleep; the attention of the reader is called to the cholinergic neurons, which act as promoters of REM phenomena, and to the monoaminergic neurons, which may act to suppress most components of REM sleep. Note that Figure 10.2 shows that the Jouvet transection spared these essential brainstem zones.
EFFECTOR NEURONS FOR DIFFERENT COMPONENTS OF REM SLEEP: BRAINSTEM RETICULAR FORMATION IS PRINCIPAL LOCATION
By effector neurons we mean those neurons directly in the neural pathways leading to the production of different REM components, such as the REMs. A series of physiological investigations over the past 30 years have shown that the “behavioral state” of REM sleep in nonhuman mammals is dissociable into different components under control of different mechanisms and different anatomical loci. The reader familiar with pathology associated with human REM sleep will find this concept easy to understand, since much pathology consists of inappropriate expression or suppression of individual components of REM sleep. As in humans, the cardinal signs of REM sleep in nonhuman mammals
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are muscle atonia, EEG activation (low-voltage fast pattern, sometimes termed an activated or desynchronized pattern), and REMs. PGO waves are another important component of REM sleep found in recordings from deep brain structures in many animals (they are visible in the cat recording of Figure 10.3). PGO waves are spiky EEG waves that arise in the pons and are transmitted to the thalamic lateral geniculate nucleus (a visual system nucleus) and to the visual occipital cortex, hence the name PGO waves. There is suggestive evidence that PGO waves are present in humans but the depth recordings necessary to establish their existence have not been done. PGO waves are EEG signs of neural activation; they index an important mode of brainstem activation of the forebrain during REM sleep. It is worth noting that they are also present in nonvisual thalamic nuclei, although their timing is linked to eye movements, with the first wave of the usual burst of 3–5 waves occurring just before an eye movement. Most of the physiological events of REM sleep have effector neurons located in the brainstem reticular formation, with important neurons especially concentrated in the pontine reticular formation (PRF). Thus PRF neuronal recordings are of special interest for information on mechanisms of production of these events. Intracellular recordings of PRF neurons (Figure 10.3) show that these effector neurons have relatively hyperpolarized membrane potentials and generate almost no action potentials during non-REM sleep. As illustrated in Figure 10.3, PRF neurons begin to depolarize even before the occurrence of the first EEG sign of the approach of REM sleep, the PGO waves that occur 30–60 seconds before the onset of the rest of the EEG signs of REM sleep. As PRF neuronal depolarization proceeds and the threshold for action potential production is reached, these neurons begin to discharge (generate action potentials). Their discharge rate increases as REM sleep is approached and the high level of discharge is maintained throughout REM sleep, due to the maintenance of this membrane depolarization. Throughout the entire REM sleep episode almost the entire population of PRF neurons remains depolarized. The resultant increased action potential activity leads to the production of those REM sleep components which have their physiological bases in activity of PRF neurons. PRF neurons are important for the REMs (the generator for saccades is in PRF), the PGO waves (a different group of neurons) and a group of dorsolateral PRF neurons controls the muscle atonia of REM sleep (these neurons become active just before the onset of muscle atonia). Neurons in midbrain reticular formation (MRF, see location in Figure 10.2) are
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R.W. MCCARLEY EMG
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0.5 SEC
Fig. 10.3. Changes in the membrane potential (MP) of a medial pontine reticular formation neuron over a sleep–wake cycle. The five traces (from electromyogram (EMG) through MP) in (A) are a set of inkwriter recordings defining behavioral states in relationship to the MP level. Note that the inkwriter sensitivity is not high enough to trace individual action potentials (MP trace). (B) Oscilloscope photographs detail changes in the frequency of action potentials together with the MP level. The first trace of (A) is EMG from the deep nuchal muscles. The second trace is EEG from the frontal cortex. The third trace of lateral geniculate nucleus (LGN) activity shows PGO waves, which consist of high-amplitude pre-REM waves in T, irregular, highfrequency waves during REM sleep, and rather high-amplitude waves near the end of REM sleep. The fourth trace is an electro-oculogram (EOG) from the lateral rectus extraocular muscles. The fifth trace is the inkwriter MP record in which the many single spike-like deflections on the trace are prominent excitatory postsynaptic potentials (EPSPs) or compounds of EPSPs and actual action potentials. The MP records in (B) are eight photographs of the oscilloscope display of the tape-recorded MP. The labels indicate the corresponding segment on the inkwriter MP trace (double arrows). See also text description. (Adapted from Ito et al. (2002).)
especially important for EEG activation, for the lowvoltage fast EEG pattern. These neurons were originally described as making up the ascending reticular activating system (ARAS), the set of neurons responsible for EEG activation. Subsequent work has enlarged this original ARAS concept to include cholinergic neurons, with contributions in waking to EEG activation also coming from monoaminergic systems,
neurons utilizing serotonin and norepinephrine (NE) as neurotransmitters.
REM-on neurons and REM promotion Current data suggest that cholinergic influences act by increasing the excitability of brainstem reticular neurons important as effectors in REM sleep either
NEUROBIOLOGY OF REM SLEEP
155
directly or indirectly by disinhibition, inhibiting GABAergic neurons which are inhibitory to reticular formation neurons. The essential data supporting cholinergic mechanisms are summarized below.
PRODUCTION
OF A
REM-LIKE
IC
STATE BY DIRECT
Cnf
INJECTION OF ACETYLCHOLINE AGONISTS INTO
LDT
THE PONTINE RETICULAR FORMATION
It has been known since the mid-1960s that cholinergic agonist injection into the PRF produces a state that very closely mimics natural REM sleep (for review and detailed literature citations for this section, see Steriade and McCarley, 2005). The latency to onset and duration are dose-dependent; within PRF, most workers have found the shortest latencies to come from injections in dorsorostral pontine reticular sites. Muscarinic cholinergic receptors appear to be of major importance, with nicotinic receptors playing a lesser role. Of note, most of the in vivo cholinergic data has come from felines. In rats and mice a similar REM induction effect can be induced, although it often is less robust in these species, perhaps as a result of difficulty in localization of applications in the smaller brains and interaction with circadian control (reviewed in Steriade and McCarley, 2005), as well as perhaps a different localization of GABAergic neurons inhibited by carbachol (see below). However, as described below, the in vitro evidence for carbachol excitatory effects on reticular formation neurons in the rat is undisputed. The precise site where in vivo carbachol is most effective in inducing REM or muscle atonia in the rat is disputed but appears to be within the pontine oralis nucleus slightly rostral to the subcoeruleus or in an area neighboring the superior cerebral peduncle (ventral tegmental nucleus) (Gnadt and Pegram, 1986; Taguchi et al., 1992; Bourgin et al., 1995; Deurveilher et al., 1997; Marks and Birabil, 1998). Experiments using the acetylcholinesterase inhibitor neostigmine in the mouse suggest that the pontine oralis nucleus is also an effective REM-inducing site in the mouse (Coleman et al., 2004a, b), although these findings have been disputed (Pollack and Mistlberger, 2005). Of note also are the REM-reducing effects of muscarinic knockouts (Goutagny et al., 2005).
LDT/PPT CHOLINERGIC PROJECTIONS TO RETICULAR FORMATION NEURONS
Cholinergic projections in brainstem and to brainstem sites arise from two nuclei at the pons–midbrain junction that contain cholinergic neurons, the laterodorsal tegmental nucleus (LDT) and the pedunculopontine tegmental nucleus (PPT). A sagittal schematic of their location is shown in Figure 10.1, and Figure 10.4, a
SCP PPT PPT PFTG
Fig. 10.4. Coronal section of the brainstem at the pons– midbrain junction showing the location of the acetylcholine-containing neurons most important for rapid eye movement sleep in laterodorsal tegmental nucleus (LDT)/ pedunculopontine tegmental nucleus (PPT), and a schematic of projections of LDT to pontine reticular formation. (PFTG is an abbreviation of one component of PRF.) IC, inferior colliculus; Cnf, cuneiform nucleus; SCP, superior cerebellar peduncle. (Adapted from Mitani et al., 1988.)
coronal view, shows their projections to critical PRF zones, as first shown by Mitani et al. (1988) and repeatedly confirmed. A similar series of studies has documented the extensive rostral projections of cholinergic neurons to thalamus and basal forebrain, where their actions are important for EEG activation – a topic to be discussed below.
DIRECT
EXCITATION OF PONTINE RETICULAR
FORMATION NEURONS BY CHOLINERGIC AGONISTS
In vitro pontine brainstem slice preparations offer the ability to apply agonists/antagonists in physiological concentrations, which are usually in the low micromolar range, whereas effective in vivo injections use concentrations that are a thousandfold greater, in the millimolar range, and thus raise the possibility of mediation of effects by nonphysiological mechanisms. Application of micromolar amounts of cholinergic agonists in vitro produces an excitation of a majority (about two-thirds) of medial PRF neurons. Another advantage of the in vitro preparation is the ability to use a sodium-dependent action potential blocker, tetrodotoxin; these experiments show that the excitatory effects of cholinergic agonists on PRF neurons in the
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rat in vitro are direct (Greene et al., 1989). Furthermore the depolarizing, excitatory effects of cholinergic agonist mimic the changes seen in PRF neurons during natural REM sleep (Figure 10.3).
discharges during both wakefulness and REM sleep, are important for the EEG activation of both REM sleep and waking (see extensive discussion in Steriade and McCarley, 2005).
LDT/PPT
OTHER
LESION AND STIMULATION EFFECTS
Extensive destruction of the cell bodies of LDT/PPT neurons by local injections of excitatory amino acids leads to a marked reduction of REM sleep (Webster and Jones, 1988). Low-level (10 mA) electrical stimulation of LDT increases REM sleep (Thakkar et al., 1996).
DISCHARGE ACROSS THE
ACTIVITY OF
REM
LDT/PPT
NEURONS
CYCLE
A subset of these neurons has been shown to discharge selectively during REM sleep, and with the onset of increased discharges occurring before the onset of REM sleep (El Mansari et al., 1990; Steriade et al., 1990; Kayama et al., 1992), as schematized in Figure 10.1. This LDT/PPT discharge pattern and the presence of excitatory projections to the PRF suggest that LDT/PPT cholinergic neurons may be important in producing the depolarization of reticular effector neurons, leading to production of the events characterizing REM sleep. The group of LDT/PPT and reticular formation neurons that become active in REM sleep are often referred to as REM-on neurons. Subgroups of PRF neurons may show discharges during waking motoric activity, either somatic or oculomotor, but a sustained depolarization throughout almost all of the population occurs only during REM sleep. Studies of the immediate early gene cFos expression have shown activation of choline acetyltransferase-positive neurons in REM rebound in the rat following deprivation (Merchant-Nancy et al., 1995; Maloney et al., 1999). Verret et al. (2005) have questioned these two Jones laboratory studies’ findings; Jones (personal communication, October 2005) notes that the 72-hour-long duration of deprivation in the Verret et al. (2005) study may have produced anomalous findings. It must be emphasized that cFos expression, although useful, does not offer a 1:1 isomorphism with action potential occurrence (Fields et al., 1997). Of particular note, rat single-unit in vivo studies strongly support cholinergic activation during REM sleep (Steriade and McCarley, 2005).
CHOLINERGIC
NEURONS
Cholinergic neurons are important in the production of the low-voltage fast or “desynchronized” EEG pattern of both REM sleep and waking. Rostral projections of a subgroup of LDT/PPT neurons, those with
NEUROTRANSMITTERS AND PONTINE
RETICULAR FORMATION NEURONS
Peptides colocalized with acetylcholine. There are many peptides that are colocalized with the neurotransmitter acetylcholine in LDT/PPT neurons; this colocalization likely also means they are synaptically coreleased with acetylcholine. The peptide substance P is found in about 40% of LDT/PPT neurons and, overall, more than 15 different colocalized peptides have been described. The role of these peptides in modulating acetylcholine activity relevant to wakefulness and sleep remains to be elucidated, but it should be emphasized that the colocalized vasoactive intestinal peptide has been reported by several different investigators to enhance REM sleep when it is injected intraventricularly. A later section of this chapter will discuss GABAergic influences, as well as the role of GABAergic reticular formation neurons.
REM
MUSCLE ATONIA
This is an important REM feature from a clinical point of view because disorders of this system are present in many patients who present to sleep disorder clinicians. This topic is covered in detail in another chapter in this volume, so we here very briefly summarize. Work by Chase and collaborators and by Segal and collaborators (reviewed in Steriade and McCarley, 2005) suggests three important zones for atonia, which we list according to their projections: PRF ! bulbar reticular formation ! motoneurons. We here discuss only the PRF portion of the atonia circuitry. Pontine reticular formation ventral to locus coeruleus. Jouvet and colleagues in Lyon, France, reported that bilateral lesions of the pontine reticular region just ventral to the locus coeruleus (LC), termed by this group the peri-LC alpha, and its descending pathway to the bulbar reticular formation abolished the muscle atonia of REM sleep (Jouvet, 1979; Sastre and Jouvet, 1979). It is to be emphasized that this zone is a reticular zone, not one containing noradrenergic neurons like the LC proper, and that the name refers only to proximity to LC. The Lyon group also reported that not only was the nuchal muscle atonia of REM suppressed, but that cats so lesioned exhibited “oneiric behavior,” including locomotion, attack behavior, and behavior with head raised and with horizontal and vertical movements “as if watching something.” Morrison and collaborators (Hendricks et al., 1982) confirmed the basic finding of REM without atonia with bilateral
NEUROBIOLOGY OF REM SLEEP
REM-SUPPRESSIVE SYSTEMS: REM-OFF NEURONS The neurons described in the previous section that increase discharge rate with the advent of REM have been termed “REM-on neurons.” In contrast, groups of other neurons radically decrease and may nearly arrest discharge activity with the approach and onset of REM; these are often termed “REM-off” neurons. The typical discharge activity profile is for discharge rates to be highest in waking, then decrease in synchronized sleep and with near-cessation of discharge in REM sleep. REM-off neurons are distinctive both because they are in the minority in the brain and also because they are recorded in zones with neurons that use biogenic amines as neurotransmitters. The loci include a midline zone of the brainstem raphe nuclei, and a more lateral band-like zone in the rostral pons/ midbrain junction that includes the nucleus LC, a reticular zone and the peribrachial zone. Figure 10.5 provides an illustration of the time course of REM-on and REM-off neurons over the sleep cycle, as recorded in animals; these data are the basis for the REM time course over the night presented in Figure 10.1.
Raphe nuclei Neurons with a REM-off discharge profile were first described by McGinty and Harper (1976) in the dorsal raphe nucleus (DRN), a finding confirmed by other workers (Trulson and Jacobs, 1979; Hobson et al., 1983a; Lydic et al., 1987a, b). Neurons with the same REM-off discharge pattern have been found in the other raphe nuclei, including nucleus linearis centralis (McCarley, 1978; Hobson et al., 1983a), centralis superior (Rasmussen et al., 1984), raphe magnus (Cespuglio et al., 1981; Fornal et al., 1985), and in raphe pallidus (Sakai et al., 1983). Identification of these extracellularly recorded neurons with serotonin-containing neurons was made on the basis of recording site location in the vicinity of histochemically identified serotonin neurons and the similarity of the extracellularly recorded slow, regular discharge pattern to that of histochemically identified serotonergic neurons in vitro. Nonserotonergic neurons in the raphe system have
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15 Discharge rate (impulse/sec)
pontine tegmental lesions but reported that lesions extending beyond the LC alpha region and its efferent pathway to bulb were necessary for more than a minimal release of muscle tone and to produce the elaborate “oneiric behaviors.” The exact location and numbers of inhibitory pathways are still a matter of some controversy, with all investigators agreeing on the important, if not exclusive, role of the peri-LC alpha, or, as it is often termed, the subcoeruleus.
10
5
0 0
20 40 60 80 Percentage of cycle completed
100
Fig. 10.5. Time course of rapid eye movement (REM)-on neurons (solid lines) and REM-off neurons (dotted lines) over the sleep cycle. The cycle begins with the end of one REM period (0%) and ends with the end of the next REM period (100% complete). The data in bins are from averaging of the time course of a REM-on reticular neuron over many cycles and the solid smooth line is the reciprocal interaction mathematical model fit. The arrow marks the bin at which an electrographically defined REM sleep episode is most likely to begin. The REM-off data were similarly derived from locus coeruleus recordings (empirical data not shown here, and discharge rate is not to the same scale as REM-on neurons). (Adapted from McCarley and Hobson, 1975.)
been found to have different discharge pattern characteristics. While this extracellular identification methodology does not approach the “gold standard” of intracellular recording and labeling, the circumstantial evidence that the raphe REM-off neurons are serotonergic appears strong.
Locus coeruleus The second major locus of REM-off neurons is the LC, as described in cat (Hobson et al., 1973, 1975), rat (Aston-Jones and Bloom, 1981a, b), and monkey (Foote et al., 1980). The argument that these extracellularly recorded discharges are from NE-containing neurons parallels that for the putative serotonergic REM-off neurons. Extracellularly recorded neurons that are putatively noradrenergic have the same slow, regular discharge pattern as NE-containing neurons identified in vitro and have the proper anatomical localization of recording sites, including recording sites in the compact LC in the rat, where the NE-containing neurons are rather discretely localized. Thus, while the evidence that these REM-off neurons are NE-containing is indirect and circumstantial, it nonetheless appears quite strong.
158 R.W. MCCARLEY Finally, the remaining groups of REM-off neurons activity over the sleep–wake cycle was very clear: are principally localized to the anterior pontine tegmenwaking > non-REM > REM sleep. There was also a tum/midbrain junction either in the peribrachial zone, clear inverse relationship between PGO waves and dorsal or in a more medial extension of it, recording sites that raphe discharge, and a premonitory increase in dorsal correspond to the presence of aminergic neurons scatraphe activity prior to the end of the REM sleep episode, tered through this zone. The “stray” REM-off neurons a phenomenon also observed and commented upon by in other reticular locations also correspond to dispersed Trulson and Jacobs (1979). adrenergic neuronal groups, although adrenergic idenEvidence that dorsal raphe serotonergic activity tification in this case is much less secure. At this point inhibited REM sleep also came from in vivo pharmacowe note that putatively dopaminergic neurons in sublogical experiments (Ruch-Monachon et al., 1976) and stantia nigra and midbrain do not alter their discharge dorsal raphe cooling by Cespuglio et al. (1979). Hobson rate or pattern over the sleep–wake cycle (Steinfels and McCarley (Hobson et al., 1975; McCarley and et al., 1983), and thus are unlikely to play important Hobson, 1975) originally proposed that monoaminergic roles in sleep–wake cycle control. neurons might inhibit REM-on cholinergic REMpromoting neurons, now known to be in LDT/PPT. This Do REM-off neurons play a permissive, postulate of monoaminergic inhibition of cholinergic disinhibitory role in REM sleep genesis neurons was originally regarded as extremely controby interacting with cholinergic versial. However, interest was quickened in the 1990s REM-on neurons? by: (1) documentation of serotonergic projections from the dorsal raphe to the mesopontine cholinergic neuThe intriguing reciprocity of the discharge time course rons in the laterodorsal (LDT) and pedunculopontine of REM-off and REM-on neurons led to the initial (PPT) tegmental nuclei that are implicated in the prohypothesis of interaction of these two groups, as origiduction of REM sleep (Aston-Jones and Bloom, nally proposed for the REM-off adrenergic neurons 1981a; Semba and Fibiger, 1992; Honda and Semba, (Hobson et al., 1973, 1975; McCarley, 1973; McCarley 1995; Steininger et al., 1997); (2) in vitro demonstration and Hobson, 1975). The phenomenological, behavioral, of serotonergic inhibition of mesopontine cholinergic and cellular data have been sufficiently strong so that neurons (Luebke et al., 1992; Leonard and Llina´s, diverse groups of investigators have proposed that 1994); and (3) the report that microinjection of a serothe REM-off neurons, as a complete or partial set, tonergic 5-HT1A agonist into the PPT inhibits REM act in a permissive, disinhibitory way on some or all sleep (Sanford et al., 1994). It was also demonstrated of the components of REM sleep, and we will here that the level of serotonin release in the cat DRN parsummarize these postulates, as well as presenting the allels the time course of presumptively serotonergic phenomenology on which they are based. Many of neuronal activity: waking (W) > SWS > REM sleep these theories arose in the mid-1970s, as increased tech(Portas and McCarley, 1994), suggesting that this nical capability led to extracellular recordings of REMwould also be true at axonal release sites in the LDT/ off neurons. PPT, since serotonin levels at distant DRN projection sites had the same behavioral state ordering of levels as DORSAL RAPHE SEROTONERGIC NEURONS those in the DRN: W > SWS > REM sleep (Auerbach The possibility that the dorsal raphe serotonergic neuet al., 1989; Imeri et al., 1994) in rats and in cats (Wilkinrons act to suppress one of the major phenomena of son et al., 1991). REM sleep, PGO waves, was explicitly proposed by Since axon collaterals of DRN serotonergic neurons Simon et al. (1973), on the basis of lesion data, and inhibit this same DRN population via somatodendritic in vivo pharmacological experiments using reserpine 5-HT1A receptors (Sprouse and Aghajanian, 1987), it (Brooks et al., 1972), which depleted brainstem serotofollowed that the introduction of a selective 5-HT1A nin and simultaneously produced nearly continuous receptor agonist in the DRN via microdialysis perfuPGO-like waves. The study of McGinty and Harper sion should produce strong inhibition of serotonergic (1976) was the first of many to document the inverse neural activity, which would be indicated by a reduction relationship between the discharge activity of extracelof 5-HT release in the DRN. Moreover, if the hypothesis lularly recorded dorsal raphe neurons and REM sleep. of serotonergic inhibition of REM-promoting neurons With respect to REM sleep onset, the decrease in diswere correct, the inhibition of DRN serotonergic activity charge activity of presumptively serotonergic raphe neushould disinhibit REM-promoting neurons, producing rons is remarkably consistent. Using a cycle-averaging an increase in REM sleep concomitant with the changes technique, Lydic et al. (1983), found the time course in DRN extracellular serotonin. Portas et al. (1996) of presumptively serotonergic dorsal raphe neuronal tested the effects of microdialysis perfusion of
NEUROBIOLOGY OF REM SLEEP 8-hydroxy-2-(di-n-propylamino)tetralin (8-OH-DPAT), a selective 5-HT1A receptor agonist, in freely moving cats. In perfusions during W, DRN perfusion of 8-OH-DPAT decreased 5-HT levels by 50% compared with artificial cerebrospinal fluid (Figure 10.6), presumptively through 5-HT1A autoreceptor-mediated inhibition of serotonergic neural activity. Concomitantly the 8-OH-DPAT perfusion produced a short latency, approximately threefold increase in REM sleep, from a mean of 10.6% baseline to 30.6% (P < 0.05, n ¼ 5 animals), although waking was not significantly affected (Figure 10.6). In contrast, and suggesting DRN specificity, 8-OH-DPAT delivery through a probe in the aqueduct did not increase REM sleep but rather tended to increase waking and decrease SWS. These data in the cat were confirmed in the rat. Bjorvatn et al. (1997) used microdialysis to perfuse 8-OH-DPAT (10 mmol/l) into the DRN of rats and
159
found a fourfold increase in REM sleep compared to control perfusion with artificial cerebrospinal fluid, while the other vigilance states were not significantly altered. Sakai and Crochet (2001) failed to replicate the findings of Portas et al. (1996) in the cat and Bjorvatn et al. (1997) in the rat, perhaps due to technical differences (McCarley, 2004).
IN
VIVO AND IN VITRO EVIDENCE OF SEROTONERGIC
INHIBITION OF
LDT/PPT
NEURONS
The data of Portas et al. (1996), however, did not directly demonstrate serotonergic inhibition of neurons in the cholinergic LDT/PPT. Moreover the presence of some neurons with REM-on and other neurons with wake/REM-on activity in LDT/PPT was a puzzle in terms of the global changes in monoaminergic inhibition. McCarley et al. (1995) postulated that, while
Microdialysis delivery of 8-OH DPAT to dorsal raphe Effects on 5HT release & REM sleep
5HT release (fmoles/sample)
4
W SWS REM
3
2
1
0
Behavioral state
REM
SWS
W ACSF control 0
1
8-OH DPAT 2
3 Time in Hours
4
5
Fig. 10.6. Time course of 5-hydroxytryptamine (5-HT) levels (top portion of figure) and behavioral state (bottom portion of figure) during control dorsal raphe nucleus (DRN) artificial cerebrospinal fluid perfusion (interrupted horizontal line) and during DRN 8-hydroxy-2-(di-n-propylamino)tetralin (8-OH-DPAT) perfusion (solid horizontal line) in a typical experiment. Note that, prior to perfusion, waking DRN 5-HT levels (circles) are higher than those in slow-wave sleep (SWS: squares) and rapid eye movement (REM) sleep (stars). Each 5-HT value is expressed in fmol per 7.5 ml sample, and was obtained during an uninterrupted 5-minute sequence of the behavioral state. Upon the onset of 10 mM 8-OH-DPAT perfusion (arrow) the 5-HT level dropped quickly to levels as low as those normally present in SWS or REM. Behaviorally, 8-OH-DPAT administration markedly increased REM sleep (black bars in the hypnogram). (Adapted from Portas et al. (1996).)
160
R.W. MCCARLEY
monoamines might inhibit REM-on cholinergic neurons, wake/REM-on neurons might not be inhibited, thus explaining their continued activity in waking – since serotonergic activity is highest during wakefulness, the observed high discharge rate of wake/ REM-on neurons during wakefulness would not be consistent with a high level of serotonergic inhibition from a high level of DRN activity. In vitro data were also consistent with a subset, not the entire population, of LDT/PPT cholinergic neurons inhibited by serotonin acting at 5-HT1A receptors (Luebke et al., 1992; Leonard and Llina´s, 1994). Thakkar and collaborators (1998) developed a novel methodology allowing both extracellular single-cell recording and local perfusion of neuropharmacological agents via an adjacent microdialysis probe in freely behaving cats to test this hypothesis of differential serotonergic inhibition as an explanation of the different staterelated discharge activity. Discharge activity of REM-on neurons was almost completely suppressed by local microdialysis perfusion of the selective 5-HT1A agonist 8-OH-DPAT, while this agonist had minimal or no effect on the wake/REM-on neurons, as illustrated in Figure 10.7. Of note, the ordering of 5-HT concentrations in the cholinergic PPT is wake > non-REM > REM, consistent with the unit discharge data and, moreover, application of the
5-HT1A agonist 8-OH DPAT to the PPT suppressed REM sleep and increased wakefulness (Strecker et al., 1999, and unpublished data; Figure 10.8). The finding that only a subpopulation of the recorded LDT/PPT cells was inhibited by 8-OH-DPAT is consistent with rat pontine slice data, where, in combined intracellular recording and labeling to confirm the recorded cell’s cholinergic identity, some, but not all, of the cholinergic neurons in the LDT/PPT were inhibited by serotonin (Luebke et al., 1992). The different percentages of LDT/PPT neurons that are inhibited by serotonin or serotonin agonists in vitro (64%) compared with the in vivo findings (36.4%) of Thakkar et al. (1998) may be due to anatomical differences between species (rat versus cat) and/or different concentrations of agents at the receptors.
LOCUS
COERULEUS AND
SLEEP PHENOMENA
Lesion studies. Lesion studies furnish an unclear picture of the role of the LC in REM sleep. Bilateral electrolytic lesions of LC in cat by Jones et al. (1977) led these workers to conclude that the LC was not necessary for REM sleep. In the REM-like sleep state following the lesion there was a twofold reduction of PGO spikes while the number in deep synchronized sleep increased approximately threefold, so that the total number of spikes
REM-on neurons
8
REM
Wake/REM-on neurons 4
ACSF
ACSF
8-OH-DPAT
8-OH-DPAT
6
Spikes/sec
Spikes/sec
3 4
2 2
0
A
1 AW
QW
SWS
REM–
Behavioral state
REM+
B
AW
QW
SWS
REM– REM+
Behavioral state
Fig. 10.7. State-related activity of units in the cholinergic laterodorsal tegmental nucleus (LDT) and pedunculopontine tegmental nucleus (PPT) and the effects of a serotonin 1A agonist applied by microdialysis. (A) Rapid eye movement (REM)-on units (n ¼ 9): grand mean (SEM) of discharge rate in each behavioral state before (open circle, artificial cerebrospinal fluid) and after (closed circle) 10 mmol/l 8-hydroxy-2-(di-n-propylamino)tetralin (8-OH-DPAT) was added to the perfusate. Note suppression of activity (highly statistically significant). (B) Wake/REM-on units (n ¼ 25): grand mean ( SEM) of discharge rate before (open circle, artificial cerebrospinal fluid) and after (closed circle) 10 mmol/l 8-OH-DPAT was added to the perfusate. Note the minimal effect of 8-OH-DPAT, not statistically significant. AW, active wake; QW, quiet wake; SWS, slow-wave sleep. (Adapted from Thakkar et al. (1998).)
NEUROBIOLOGY OF REM SLEEP (Mean +/− SEM) 5-HT fmoles/sample
2.0
5HT concentrations in PPT vary with behavioral state
1.5 1.0 0.5 0.0
A
Wake 350
REM
Control 1 µM 8-OH-DPAT (n=5)
300 250 % Control
Non REM
5HT agonist in PPT increases wakefulness and decreases REM sleep
200 150 100 50
B
0 Wake
Non REM
REM
Fig. 10.8. (A) Microdialysis measurements of 5-hydroxytryptamine (5-HT) concentrations in the cholinergic pedunculopontine tegmental nucleus (PPT) parallel the behavioral state discharge rate ordering of dorsal raphe neurons. 8-OH-DPAT, 8-hydroxy2-(di-n-propylamino)tetralin. (Adapted from Strecker et al. (1999).) (B) Microdialysis-applied 5-HT1A agonist in the PPT suppresses rapid eye movement (REM) sleep and increases wakefulness. (From Strecker et al., unpublished paper.)
remained approximately the same – a picture much like that following acute raphe lesions. Over time the total number of PGO spikes declined and the percentage of a REM sleeplike state increased from about 5% to 10% versus a control value of 15%. We use the term “REM sleep-like” because muscle atonia was abolished and there was in fact motor activity like that described in the previous chapter for the “REM sleep without atonia” state following tegmental lesions; this syndrome likely resulted from spread of the lesion to the reticular area subserving atonia. Other lesion effects included loss of spontaneous micturition and defecation, a rise in mean temperature from 37.1 to 38.3 C, loss of grooming, and a loss of coordination and balance. The picture following unilateral LC lesions was quite strikingly different. Caballero and De Andres (1986) found a 50% increase in the percentage of REM sleep (P < 0.001) following unilateral electrolytic lesions of LC in cats; cats with lesions in neighboring tegmentum and sham-operated controls showed no change. The postoperative condition of animals with unilateral lesions was much better than after bilateral lesions; in only one unilaterally LC-lesioned animal was there urinary retention, and this was transient and no “alteration in any other vegetative function was observed.” Accordingly, Caballero and DeAndres (1986) attributed the differences between their study and that of Jones et al.
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(1977) to nonspecific effects of the larger lesions that, as with almost any central nervous system insult, may have led to a REM sleep reduction. Locus coeruleus cooling induces REM sleep. Cespuglio et al. (1982) performed unilateral and bilateral cooling of the LC in felines, using the same methodology as for the dorsal raphe cooling. In repeated cooling trials REM sleep was repetitively induced, and the percentage of REM sleep increased by 120% over control periods. This raises the general point that nonspecific effects of destructive lesions always decrease REM, as do other central nervous system insults. Satinoff’s comment (1988) about nonspecific effects is that, “One might also say that rendering an animal unconscious by a blow to the head eliminates REM sleep. In a sense it does, but that sense is completely trivial.” It is consequently hard to draw definitive and interpretable conclusions about destructive lesions, especially those that do not enhance REM sleep. Jones and colleagues (1977), for example, concluded that her lesions showed the LC was not necessary for REM sleep, in the sense of being a region actively promoting REM, although later studies in the Jones laboratory were consistent with a disinhibition hypothesis (Maloney et al., 1999). In summary, many nonspecific factors decrease REM and few, if any, increase it; consequently lesions or manipulations that increase REM are always more directly interpretable.
SITE(S)
OF
REM-OFF
AND
REM-ON
INTERACTION
The model for REM sleep control proposed here discusses REM-off suppression of REM-on neurons. It must be emphasized that there are several, nonmutually exclusive possible sites of interaction. These include direct acetylcholine (Ach)-NE interactions in the LDT and PPT. For example, there is now evidence that choline acetyltransferase-labeled fibers are present in LC and it has long been known that the NE-containing LC neurons also stain intensely for the presence of acetylcholinesterase (see review of NE–ACh anatomical interrelationship in McCarley, 2004). NE varicosities are present throughout the reticular formation and the LDT and the peribrachial area that is the site of choline acetyltransferase-positive neurons. Thus adrenergic–cholinergic interactions may take place directly between these two species of neurons and/or may take place at reticular neurons.
GABAERGIC INFLUENCES AND REM SLEEP In addition to the monoamines and acetylcholine as modulators and controllers of the sleep cycle, there is accumulating evidence that GABAergic influences
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may play an important role. Defining the role of gamma-aminobutyric acid (GABA) with certainty is difficult, however. Since GABA is a ubiquitous inhibitory neurotransmitter, purely pharmacological experiments using agents that increase or decrease GABA do not answer a key question, namely whether the results so obtained were representative of the increases or decreases in GABA that occur naturally in the course of the sleep cycle, or were simply and trivially the result of a pharmacological manipulation of GABA systems not naturally playing a role in sleep cycle control. Microdialysis is potentially a very useful way of sampling naturally occurring changes in GABA levels over the sleep cycle, but is often limited in sensitivity and hence in time resolution of when the changes occur in the sleep cycle. This section surveys GABA data from dorsal raphe, LC, and PRF that are relevant to sleep–wakefulness control. From the standpoint of sleep cycle control, one of the most puzzling aspects has been defining what causes the “REM-off” neurons in the LC and DRN to slow and cease discharge as REM sleep is approached and entered. The reciprocal interaction model (see below) hypothesized that a recurrent inhibition of LC/DRN might account for this. While recurrent inhibition is present, there is no clear evidence that it might be the causal agent in REM-off neurons turning off. Thus, the prospect that a GABAergic mechanism might be involved is of great intrinsic interest.
Dorsal raphe nucleus MICRODIALYSIS
IN DORSAL RAPHE NUCLEUS
Nitz and Siegel (1997b) obtained in vivo microdialysis samples from the DRN in naturally sleeping cats, noting that “cessation of firing of serotonergic dorsal raphe neurons is a key controlling event of rapid eye movement (REM) sleep.” This study is the single extant microdialysis study of GABA release in DRN, and reported a significant increase in GABA levels in REM sleep (0.072 pmol/ml or 72 fmol/ml) compared with wakefulness (0.042 pmol/ml), while SWS (0.049 pmol/l) did not significantly differ from wakefulness. Glutamate and glycine release did not change over the sleep cycle. Further supporting a GABA role in REM control via inhibition of serotonergic neurons was the 67% increase in REM sleep observed with microinjections of the GABA agonist muscimol into the DRN and the observation that reverse microdialysis of the GABA antagonist picrotoxin completely abolished REM sleep. For comparative purposes we note that the approximately threefold increase in REM sleep observed with microdialysis application of the 5-HT1A agonist 8-OHDPAT to DRN by Portas et al. (1996) was greater,
suggesting that factors other than GABA might influence serotonergic neurons. Although the data did not directly support GABAergic inhibition as a mechanism of the slowing of serotonergic unit discharge in the passage from wakefulness to SWS, Nitz & Siegel noted the possibility that a small increase in the release of GABA, possibly beyond the resolution of the microdialysis technique, might be sufficient to reduce DRN unit discharge in SWS, a suggestion indirectly supported by data from (Levine and Jacobs, 1992).
MICROIONTOPHORESIS
OF
DRN
NEURONS
Gervasoni et al. (2000) reported that, in the unanesthetized but head-restrained rat, the iontophoretic application of bicuculline on rodent DRN serotonergic neurons, identified by their discharge characteristics, induced a tonic discharge during SWS and REM and an increase of discharge rate during quiet waking. They postulated that an increase of a GABAergic inhibitory tone present during wakefulness was responsible for the decrease of activity of the DRN serotonergic cells during SWS and REM sleep. In addition, by combining retrograde tracing with cholera toxin B subunit and glutamic acid decarboxylase immunohistochemistry, they provided evidence that the GABAergic innervation of the DRN arose from multiple distant sources and not only from interneurons, as classically accepted. Among these afferents, they suggested GABAergic neurons located in the lateral preoptic area and the pontine ventral periaqueductal gray (PAG), including the DRN itself, could be responsible for the reduction of activity of the DRN serotonergic neurons during SWS and REM sleep, respectively. However a report from the same laboratory in the same year described results at variance with these, in that Sakai and Crochet (2000) were unable to block the cessation in vivo of extracellular discharge of presumed serotonergic DRN neurons during REM sleep by either bicuculline or picrotoxin application via a nearby microdialysis probe in felines. While it is entirely possible that GABA pharmacological actions could differ radically in the cat and rat, the most parsimonious interpretation is that the two series of experiments had technical differences. The argument for a different pharmacology in the cat and rat is weakened also by the results of Nitz and Siegel (1997b), which agree with the Gervasoni et al. (2000) rat data.
Locus coeruleus MICRODIALYSIS
IN LOCUS COERULEUS
The single published study on sleep–wake analysis of GABA release in the LC region placed microdialysis probes on the border of LC or in the peri-LC region in the cat (Nitz and Siegel, 1997a). GABA release was
NEUROBIOLOGY OF REM SLEEP found to increase during REM sleep (1.9 fmol/ml) as compared to both waking values (1.2 fmol/ml) and SWS (1.6 fmol/ml). GABA release during SWS showed a trend-level significance (P < 0.06) when compared with waking. The concentration of glutamate and glycine in microdialysis samples was unchanged across sleep and wake states. These data, because of the SWS differences, appear to offer more direct support for LC than for DRN neurons for the hypothesis of GABA-induced inhibition causing the reduction in LC/DRN discharge in SWS and virtual cessation of firing in REM sleep. Incidentally, the authors did not explicitly comment on the reason for their finding a 35-fold greater GABA concentration in the DRN than in the LC during waking; this may have been due to various methodological differences, thus calling to attention the difficulty in measuring GABA.
MICROIONTOPHORESIS
OF
LC
NEURONS
Gervasoni et al. (1998) applied their methodology of microiontophoresis and single-unit extracellular recordings in the LC of unanesthetized, head-restrained rats. Bicuculline, a GABA-A receptor antagonist, was able to restore tonic firing in the LC noradrenergic neurons during both REM sleep (in contrast to its effects in the DRN) and SWS. Application of bicuculline during wakefulness increased discharge rate. These data, combined with those of Nitz and Siegel (1997a), are thus consistent with GABAergic inhibition in the LC during REM and SWS.
Source of state-related GABAergic input to DRN and LC Overall, the DRN and LC studies just surveyed are consistent with, but do not prove, the hypothesis that increased GABAergic inhibition leads to REM-off cells turning off. The increased GABAergic tone could simply be a consequence of other state-related changes without causing these changes. Here, as with other neurotransmitters, it would be helpful to have unit recordings of GABAergic neurons with inputs to LC/DRN. One could see if these neurons had the requisite lead times and state-related discharge time course to cause the changes. Where might these neurons be located?
PERIAQUEDUCTAL
GRAY?
The Gervasoni et al. (2000) study on DRN pointed to the PAG as a possible source of the GABAergic input proposed to inhibit DRN neurons. In accord with this hypothesis, both ventrolateral (vl) PAG lesions (Petitjean et al., 1975) and muscimol injections (Sastre et al., 1996) produced a large increase in REM sleep.
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Thakkar and colleagues (2002) thus decided to record vlPAG unit activity in freely behaving cats to determine if neurons selectively increased their tonic discharge activity before and during REM sleep, and hence might furnish GABAergic inhibition of monoaminergic neurons. Several types of state-specific neuronal populations were found in the PAG, but none of the 33 neurons showed a tonic discharge increase before and during REM, but rather were phasic in pattern and increased discharge rate too late in the cycle to be a cause of the DRN SWS suppression. These data thus suggest that, although vlPAG neurons may regulate phasic components of REM sleep, they do not have the requisite tonic pre-REM and REM activity to be a source of GABAergic tone to monoaminergic neurons responsible for their REM-off discharge pattern. The negative findings would suggest that, at a minimum, neurons with the requisite activity are not abundant in the vlPAG.
VENTROLATERAL
PREOPTIC AREA
(VLPO)?
This forebrain site was retrogradely labeled by Gervasoni et al. (2000) as projecting to the DRN. Forebrain influences on REM sleep are discussed in the next chapter, but the Jouvet transection experiments suggest, however, these are not essential for the basic REM cyclicity found in the pontine cat.
GABA and the pontine reticular formation: disinhibition and REM sleep PHARMACOLOGICAL
STUDIES IN CATS ON THE
BEHAVIORAL STATE EFFECTS OF
GABA
AGENTS
Xi et al. (1999, 2001) have provided pharmacological evidence of GABA suppression of REM using agents injected into the nucleus pontis oralis, in a region about 2 mm lateral to the midline and more than 1 mm ventral to LC, a region where carbachol induced a short-latency (