Neural Correlates of Human Non-REM Sleep Oscillations
Centre de Recherches du Cyclotron et Service de Neurologie Université de Liège, Faculté de Médecine
Promoteurs : Prs Pierre MAQUET et Gustave MOONEN Thèse présentée en vue de l’obtention du grade de
Thien Thanh DANG-VU A Multimodal
Functional Neuroimaging
Approach
Cover : adapted from The Nightmare
(Johann Heinrich Füssli; 1781)
To my parents.
For their loving and constant support.
Faculté de Médecine
Neural correlates of human non-REM sleep oscillations A multimodal functional neuroimaging approach
Thien Thanh DANG-VU
Centre de Recherches du Cyclotron et Service de Neurologie Université et CHU de Liège
Promoteurs : Prs Pierre MAQUET et Gustave MOONEN Thèse présentée en vue de l’obtention du grade de Docteur en sciences biomédicales et pharmaceutiques
2007-2008
Table of contents
ABBREVIATIONS... 5
ACKNOWLEDGEMENTS ... 7
LIST OF PUBLICATIONS ... 9
SUMMARY ... 13
Neural correlates of NREM sleep oscillations assessed by EEG / PET... 13
Neural correlates of NREM sleep oscillations assessed by EEG / fMRI... 14
RÉSUMÉ ... 17
Corrélats cérébraux des rythmes du sommeil lent en EEG / PET... 17
Corrélats cérébraux des rythmes du sommeil lent en EEG / fMRI... 18
1. INTRODUCTION ... 21
1.1. Why should we study sleep?... 23
Summary ... 24
Introduction ... 25
Sleep organization and stages ... 25
Sleep and brain plasticity during neural development ... 27
REM sleep ... 27
NREM sleep ... 31
Summary and further considerations... 32
Brain plasticity in adulthood: The role for sleep in learning and memory ... 34
Behavioural level... 34
Brain system level: reactivation of neuronal ensembles during sleep ... 40
Cellular and molecular level ... 50
Conclusions ... 55
1.2. Why should we study NREM sleep oscillations ?... 57
Cellular mechanisms and Coalescence of NREM sleep oscillations ... 59
Spindles ... 59
Delta waves ... 61
Slow oscillations and coalescence of NREM sleep rhythms ... 62
EEG descriptive features and Homeostatic regulation... 66
NREM sleep oscillations and Memory reprocessing ... 68
Brain reactivity to Sensory stimulation... 69
1.3. What has neuroimaging taught us about sleep and sleep disorders?... 70
Summary ... 71
Introduction ... 72
Neuroimaging in normal human sleep ... 73
Functional neuroanatomy of sleep stages... 73
Neuroimaging and dreams ... 79
Sleep and memory... 81
Neuroimaging in sleep disorders... 82
Sleep deprivation... 82
Primary insomnia ... 88
Sleep-related breathing disorders ... 90
Neurological Disorders ... 95
Parasomnias... 103
Psychiatric disorders: sleep brain imaging in depression ... 107
Conclusions ... 109
2. METHODOLOGICAL CONSIDERATIONS... 113
2.1. Positron emission tomography... 116
2.2. Functional magnetic resonance imaging... 118
2.3. Processing the EEG signal recorded during fMRI acquisitions ... 121
Gradient artifacts ... 121
Ballistocardiogram (BCG) artifacts ... 123
2.4. Statistical analysis of functional brain imaging data ... 124
Spatial preprocessing... 125
General linear model, design matrix and regressors ... 126
Volterra series ... 127
Fixed effects – Random effects... 129
Inferences ... 129
3. NEURAL CORRELATES OF NREM SLEEP OSCILLATIONS ASSESSED BY EEG / PET ... 131
3.1. Spindles ... 134
3.2. Delta and Slow waves... 135
Summary ... 136
Introduction ... 137
Results ... 138
Discussion ... 141
Thalamic versus cortical delta oscillations... 142
Neocortical correlates of delta waves: a role for ventromedial prefrontal areas ? ... 144
Other negative correlations of rCBF with delta ... 145
Conclusions ... 147
Methods... 148
Subjects and experimental protocol ... 148
EEG acquisition... 148
PET acquisition ... 149
PET and EEG analysis ... 149
EEG / FMRI ... 151
4.1. Spindles ... 155
Summary ... 155
Introduction ... 156
Results ... 157
Discussion ... 163
Common activation pattern ... 164
Difference in cortical activity associated with slow and fast spindles... 164
Activity associated with slow and fast spindles in thalami and other subcortical structures ... 166
Methodological issues ... 167
Conclusions ... 168
Methods... 168
Population... 168
EEG acquisition and analysis... 169
fMRI data acquisition and analysis ... 169
4.2. Delta and Slow waves... 172
Summary ... 172
Introduction ... 173
Results ... 175
Discussion ... 182
Comparison with earlier PET and fMRI studies ... 182
Functional segregation of brain responses associated with SWS oscillations ... 183
Slow and delta waves, common or distinct processes ?... 185
Resting state networks during SWS ... 186
Conclusions ... 188
Methods... 189
Subjects ... 189
EEG acquisition and analysis... 189
fMRI data acquisition and analysis. ... 191
5. CONCLUSIONS AND PERSPECTIVES ... 193
Perspectives in physiology ... 197
Information processing during NREM sleep oscillations ... 198
Perspectives in pathology... 203
APPENDIX : OTHER RELATED ARTICLES... 205
Dreaming: A Neuroimaging View ... 207
Summary ... 207
Introduction ... 208
Functional Neuroanatomy of Normal Human REM Sleep ... 210
Towards the Integration of Canonical Maps of REM Sleep and Predominant Dream Features ... 216
Conclusions ... 221
Neuroimaging of REM sleep and dreaming ... 223
Summary ... 223
Introduction ... 224
REM sleep physiology viewed from a neuroimaging perspective... 227
Dreaming viewed from a neuroimaging perspective : integration of REM sleep cerebral mapping and major dream features ... 231
Conclusions ... 234
Sleep and sleep states: PET activation patterns ... 236
Summary ... 236
Introduction ... 236
Sleep stages and sleep oscillations ... 237
NREM sleep ... 237
REM sleep ... 240
Neuroimaging and dreams ... 243
Sleep and memory... 245
Conclusions ... 246
Sleep: Dreaming and Consciousness ... 248
Summary ... 248
Introduction ... 249
Sleep : definitions and mechanisms ... 251
NREM sleep ... 251
REM sleep ... 252
Sleep and Dreaming ... 255
Dreaming : definitions, properties and content ... 255
Neurobiological correlates of dreaming... 257
Lucid dreaming ... 263
Sleep and Consciousness... 265
Consciousness in sleep ... 265
Sleep in altered states of consciousness ... 267
Conclusions ... 273
A prominent role for amygdala in the Variability in Heart Rate during REM sleep.... 275
Summary ... 275
Introduction ... 276
Methods... 278
Results ... 282
Discussion ... 287
REFERENCES ... 291
Abbreviations
ACh acetylcholine
BA Brodmann area
BOLD blood oxygen level dependent
CBF cerebral blood flow
DLPF dorsolateral prefrontal cortex
EEG electroencephalography
EKG electrocardiography
EMG electromyography
EOG electro-oculography
EPSP excitatory post-synaptic potential
ERP event-related potential
18
FDG [
18F] fluorodeoxyglucose
fMRI functional magnetic resonance imaging
FOV field of view
FWHM full width half maximum
H
215O [
15O]-labeled water
IPSP inhibitory post-synaptic potential
LDT laterodorsal tegmentum
LTD long-term depression
LTP long-term potentiation
mCi millicurie
MRI magnetic resonance imaging
NMDA N-methyl D-aspartate
NREM non rapid eye movement
OSA obstructive sleep apnea
PET positron emission tomography
PGO ponto-geniculo-occipital waves
PLMS periodic leg movements during sleep
PLMW periodic leg movements during wakefulness
PPT pedunculopontine tegmental nucleus
RBD REM sleep behaviour disorder
rCBF regional cerebral blood flow
RE thalamic reticular neurons
REM rapid eye movement
RLS restless legs syndrome
SD standard deviation
SMA supplementary motor area
SPECT single-photon emission computed tomography
SPM statistical parametric mapping
SWA slow wave activity
SWS slow wave sleep
TC thalamocortical neurons
TE echo time
TR repetition time
VBM voxel-based morphometry
VMPF ventromedial prefrontal cortex
Acknowledgements
Studying sleep is paradoxically not a restful job, and even less when applying novel and unusual techniques. This work was made possible by the collaboration of many persons, from the development of protocols to the numerous and long (sleepless) nights of recording and finally the writing of results. I wish here to express my deepest gratitude to all the people who guided, helped and encouraged me in my first steps in scientific research.
My first and warm thanks go to Pr Gustave Moonen, head of the department of Neurology and dean of the faculty of Medicine, an exceptional teacher in physiology then in neurology, who gave me the opportunity to work in his neuroscience laboratory and later sent me to the Cyclotron Research Centre (CRC). There I was welcomed by Pr Pierre Maquet, an exceptional scientific father, who introduced me to the exciting fields of sleep research and functional neuroimaging. I’m greatly indebted to him for his constant availibility, perspicacious advice and scientific meticulousness. I also thank Pr André Luxen, director of the CRC, for his repeated support and encouragement, and Pr Philippe Peigneux, now head of the Neuropsychology unit at the ULB, who patiently guided me through my first analyses of functional brain imaging data.
I thank the members of the jury, Pr Vincent Seutin (president), Pr Robert Poirrier, Dr Steven Laureys, Pr Jean Schoenen, Dr Dominique Dive, Pr Serge Goldman (ULB) and Pr Marcello Massimini (Milan), for accepting to assess my thesis.
Working at the CRC has been an exceptional experience and the opportunity to meet
exceptional colleagues and friends from various backgrounds and countries. Many thanks to
Dr Manuel Schabus (Salzburg), with whom I enjoyed working tirelessly day and night and
who greatly contributed to this thesis. I also thank the other post-doctoral researchers and
visiting scientists for sharing their expertise and time : Dr Annabelle Darsaud (Grenoble), Dr
Géraldine Rauchs (Caen) (special thanks for her insightful comments on a preliminary version
of the thesis), Dr Steffen Gais (Lübeck), Dr Gilberte Tinguely (Zürich) and Pr Julie Carrier
(Montreal). This work would not have been possible without the technical contribution of
physicist Dr Evelyne Balteau and engineers Dr Christophe Phillips and Christan Degueldre; I
thank them for their help. I’m grateful to the other PhD students, who began working at the CRC a few years ago like me, for their precious help but mostly for the nice times spent inside and outside the lab : Martin Desseilles (also a friend for now many years, with whom I started medicine and research), Geneviève Albouy, Mélanie Boly, Maxime Bonjean, Pierre Orban, Frédéric Peters, Christina Schmidt, Caroline Schnakers, Virginie Sterpenich and Gilles Vandewalle. The CRC team has progressively grown up since then and I’ve been happy to start working with new PhD students Pierre Boveroux, Marie-Aurélie Bruno, Victor Cologan, Dorothée Feyers, Ariane Foret, Yves Leclercq, Laura Mascetti, Luca Matarazzo and Audrey Vanhaudenhuyse. I also thank other members of the CRC team with whom I shared interesting discussions on various occasions, Dr Steven Laureys, Pr Eric Salmon, Dr Gaëtan Garraux, Dr Christine Bastin, Dr Quentin Noirhomme and Dr Fabienne Collette. Finally, I thank secretaries Annick Claes, Brigitte Herbillon and Annette Konings (retired) for their helpful assistance.
In parallel with the preparation of this thesis, I have pursued my residency in neurology at Liege University Hospital. I would like to thank the senior physicians of the department for their enlightened guidance in the ordinary clinical practice as well as in complex neurological cases : Pr Gustave Moonen, Pr Bernard Sadzot, Pr Robert Poirrier, Pr Eric Salmon, Pr Bernard Rogister, Dr Dominique Dive, Dr Shibeshih Belachew, Dr Christophe Hotermans, Dr Isabelle Hansen and Dr Gaëtan Garraux. I have also appreciated working with my fellow residents during these long days (and evenings) on the wards (Mélanie Boly, Elisabeth Bruls, Marie-Laure Cuvelier, Christian de Fays, Candice Delcourt, Isabelle Lievens, Delphine Magis, Estelle Rikir), not forgetting the paramedical staff and secretary Lucienne Arena.
During these years, I had the opportunity to participate to scientific meetings, visit other research laboratories, and meet international experts in the field. In this regard, I would like to express my gratitude to Pr Jacques Montplaisir (Montreal) and his team (especially Dr Dominique Petit, Régine Denesle and Mireille Charron) who warmly welcomed me in their sleep research lab for two months of fulfilling learning and experiences.
All those not acknowledged here, please forgive me. I deeply thank people, relatives and friends who contributed to all aspects of my personal advancement. In particular, my final thanks go to my loving parents to whom this work is dedicated.
List of publications
Publications reported in the thesis
Dang-Vu TT, Desseilles M, Peigneux P, Maquet P. A Role for Sleep in Brain Plasticity;
Pediatric Rehabilitation, 9(2), 2006, 98-118 ……… p. 24-56.
Dang-Vu TT, Desseilles M, Petit D, Mazza S, Montplaisir J, Maquet P. Neuroimaging in Sleep Medicine; Sleep Medicine, 8, 2007, 350-373 ………... p. 71-111.
Dang-Vu TT, Desseilles M, Laureys S, Degueldre C, Perrin F, Philips C, Maquet P,
Peigneux P. Cerebral correlates of delta waves during non-REM sleep revisited; Neuroimage, 28 (1), 2005, 14-21 ………... p. 136-150.
Schabus M, Dang-Vu TT, Albouy G, Balteau E, Boly M, Carrier J, Darsaud A, Degueldre C, Desseilles M, Gais S, Phillips C, Rauchs G, Schnakers C, Sterpenich V, Vandewalle G, Luxen A, Maquet P. Hemodynamic cerebral correlates of sleep spindles during human non-
rapid eye movement sleep; Proceedings of the National Academy of Sciences of the UnitedStates of America, 104(32), 2007, 13164-9 ……….. p. 155-171.
Dang-Vu TT, Schabus M, Desseilles M, Albouy G, Boly M, Darsaud A, Gais S, Rauchs G,
Sterpenich V, Vandewalle G, Carrier J, Moonen G, Balteau E, Degueldre C, Luxen A, Phillips C, Maquet P. Spontaneous Neural Activity during Human Slow Wave Sleep : an EEG
/ fMRI study; submitted ………. p. 172-192.Dang-Vu TT, Desseilles M, Albouy G, Darsaud A, Gais S, Rauchs G, Schabus M, Sterpenich
V, Vandewalle G, Schwartz S, Maquet P. Dreaming: A Neuroimaging View; Swiss Archives of Neurology and Psychiatry, 156(8), 2005, 415-425 ……….. p. 207-222.
Dang-Vu TT, Schabus M, Desseilles M, Schwartz S, Maquet P. Neuroimaging of REM sleep and dreaming; In Patrick McNamara and Deirdre Barrett (Eds.) The New Science of Dreaming, Praeger Publishers, Greenwood Press, 2007, Vol. 1, 95-113 ………… p. 223-235.
Dang-Vu TT, Desseilles M, Peigneux P, Laureys S and Maquet P. Sleep – Definition and Description: PET activation patterns; In Larry R. Squire (Ed.) New Encyclopaedia of Neuroscience, Elsevier, Oxford (UK), 2008 (in press) ……… p. 236-247.
Dang-Vu TT, Schabus M, Cologan V, Maquet P. Sleep : Dreaming and Consciousness; In
William Banks (Ed.) Encyclopaedia of Consciousness. Elsevier, Oxford (UK), 2009 (in press)
………... p. 248-274.
Desseilles M, Dang-Vu T, Laureys S, Peigneux P, Degueldre C, Phillips C, Maquet P. A
prominent role for amygdaloid complexes in the Variability in Heart Rate (VHR) during Rapid Eye Movement (REM) sleep relative to wakefulness; Neuroimage, 32(3), 2006, 1008-1015 ……….. p. 275-290.
Other publications
Gais S, Albouy G, Boly M, Dang-Vu TT, Darsaud A, Desseilles M, Rauchs G, Schabus M, Sterpenich V, Vandewalle G, Maquet P, Peigneux P. Sleep transforms the cerebral trace of
declarative memories; Proceedings of the National Academy of Sciences of the United Statesof America, 104(47), 2007, 18778-83.
Sterpenich V, Albouy G, Boly M, Vandewalle G, Darsaud A, Balteau E, Dang-Vu TT, Desseilles M, D’Argembeau A, Gais S, Rauchs G, Schabus M, Degueldre C, Luxen A, Collette F, Maquet P. Sleep-Related Hippocampo-Cortical Interplay during Emotional
Memory Recollection; PloS Biology, 5(11), 2007, e282.Dauvilliers Y, Pennestri M-H, Petit D, Dang-Vu T, Lavigne G, Montplaisir J. Periodic leg
movements during sleep and wakefulness in narcolepsy; Journal of Sleep Research, 16(3),2007, 333-9.
Vandewalle G, Balteau E, Phillips C, Degueldre C, Moreau V, Sterpenich V, Albouy G,
Darsaud A, Desseilles M, Dang-Vu TT, Peigneux P, Luxen A, Dijk DJ, Maquet P. Daytime
light exposure dynamically enhances brain responses; Current Biology, 16(16), 2006, 1616-
21.
Maquet P, Ruby P, Maudoux A, Albouy G, Sterpenich V, Dang-Vu T, Desseilles M, Boly M, Perrin F, Peigneux P, Laureys S. Human cognition during REM sleep and the activity profile
within frontal and parietal cortices. A reappraisal of functional neuroimaging data; Progressin Brain Research, 150, 2005, 219-27.
Schwartz S, Dang-Vu TT, Ponz A, Duhoux S, Maquet P. Dreaming: A Neuropsychological
View; Swiss Archives of Neurology and Psychiatry, 156(8), 2005, 426-439.Peigneux P, Melchior G, Schmidt C, Dang-Vu T, Boly M, Laureys S, Maquet P. Memory
processing during sleep: mechanisms and evidence from neuroimaging studies; PsychologicaBelgica, 44-1/2, 2004, 121-142
Maquet P, Sterpenich V, Albouy G, Dang-Vu T, Desseilles M, Boly M, Ruby P, Laureys S, Peigneux P. Brain imaging on passing to sleep; In Parmeggiani and Vellutti (Ed.) The
Physiological Nature of Sleep. Imperial College Press, London (UK), 2005, 123-137.Maquet P, Ruby P, Schwartz S, Laureys S, Albouy G, Dang-Vu T, Desseilles M, Boly M, Peigneux P. Regional organisation of brain activity during paradoxical sleep (PS); Archives Italiennes de Biologie 142 (4), 2004, 413-9
Maquet P, Peigneux P, Laureys S, Boly M, Dang-Vu T, Desseilles M and Cleeremans A.
Memory processing during human sleep as assessed by functional neuroimaging; Revue
Neurologique, 159 (11 suppl), 2003, 6S27-6S29.
Maquet P, Laureys S, Perrin F, Ruby P, Melchior G, Boly M, Dang-Vu T, Desseilles M and Peigneux P. Festina Lente:
Evidences for fast and slow learning processes and a role for sleep in human motor skill learning; Learning and Memory, 10 (4), 2003, 237-239.Maquet P, Peigneux P, Laureys S, Desseilles M, Boly M, Dang-Vu T. Off-line processing of
memory traces during human sleep; Sleep and Biological Rhythms, 1, 2003, 75-83.Summary
Non Rapid Eye Movement (NREM) sleep in humans is defined by spontaneous neural activities organized by specific rhythms or oscillations. The aim of this thesis is to characterize, by means of neuroimaging techniques, the shaping of brain function by these physiological rhythms.
The studied oscillations are sleep spindles, delta waves and slow oscillation, representing the main identifiable neurophysiological events of human NREM sleep. Sleep spindles are a hallmark of light NREM sleep. They are commonly described on electroencephalographic (EEG) recordings as 11-15 Hz oscillations, lasting more than 0.5 sec and with a typical waxing-and-waning waveform. During deeper stages of NREM sleep, spindles are progressively replaced by a slow wave activity (SWA; 0.5-4 Hz), which encompasses delta waves (1-4 Hz) and slow oscillations (0.5-1 Hz).
In combination with EEG, we studied these rhythms using two different functional brain imaging techniques : positron emission tomography (PET) and functional magnetic resonance imaging (fMRI).
These studies originally contribute to the understanding of the generating mechanisms and functional roles of NREM sleep oscillations, which are a hallmark of sleep architecture in healthy humans.
Neural correlates of NREM sleep oscillations assessed by EEG / PET
In this section, we report the analyses of PET data devoted to the study of NREM sleep
oscillations. We characterized the brain areas in which activity, measured in terms of regional
cerebral blood flow (rCBF), was correlated with EEG spectral power in the spindle (11-15
Hz), delta waves (1-4 Hz) and slow oscillation (0.5-1 Hz) frequency bands, in 23 non-sleep-
deprived young healthy volunteers.
EEG activity in the spindle frequency band was negatively correlated with rCBF in the thalamus. This result was in agreement with data suggesting the generation of spindles within cortico-thalamo-cortical loops (Steriade, 2006).
Spectral power in the delta band was negatively correlated with rCBF in the medial prefrontal cortex, striatum, insula, anterior cingulate cortex, precuneus and basal forebrain, which are structures potentially involved in the modulation of cortical delta waves (Dang-Vu et al., 2005b). The functional brain mapping of slow oscillations was highly similar to the one of delta waves, in keeping with the hypothesis that both types of oscillations share common physiological mechanisms.
These results consisted in negative correlations, which means that the cerebral blood flow in these areas was lower when the power in the corresponding frequency band was higher. The different rhythms of NREM sleep are synchronized by the slow oscillation, which alternates a hyperpolarization phase during which cortical neurons remain silent, and a depolarization phase associated with important neuronal firing. The prominent effect of hyperpolarization phases could account for the decrease in blood flow found in PET studies. Indeed, PET has a limited temporal resolution, around one minute, and therefore averages brain activity over relatively long periods, during which hyperpolarization phases predominate. Thus PET imaging does not allow to directly study brief events, lasting one second or so, such as NREM sleep oscillations. Besides, the spectral power values used in PET studies are just an indirect reflection of the appearance of these rhythms during sleep. These considerations justify the use of fMRI because, together with improved spatial resolution, its temporal resolution around one second allows to assess brain responses associated to the occurrence of NREM sleep oscillations, taken as identifiable events.
Neural correlates of NREM sleep oscillations assessed by EEG / fMRI
The largest section of the thesis is devoted to the use of fMRI in the study of NREM sleep
oscillations. We characterized the brain areas in which activity, measured in terms of blood
oxygen level dependent (BOLD) signal, was correlated with the occurrence of NREM sleep
oscillations. Compared to EEG with PET, EEG recording with simultaneous fMRI was technically much more challenging. In particular, the analysis of EEG data acquired simultaneously with fMRI required a complex signal processing in order to remove all artefacts induced during the scanning procedure. After clean EEG data had been obtained, automatic detection of spindles (Molle et al., 2002), delta waves and slow oscillations (Massimini et al., 2004) was performed according to published criteria, and provided the series of events to be used as regressors in the statistical analysis of fMRI data. The latter assessed the main effects of spindles, delta waves and slow oscillations on BOLD signal changes across the 14 non-sleep-deprived young healthy volunteers selected for this study.
Spindles were analysed considering 2 potential subtypes. Indeed, in humans, while most spindles are recorded in central and parietal regions and display a frequency around 14 Hz (‘fast’ spindles), others are prominent on frontal derivations with a frequency around 12 Hz (‘slow’ spindles). Previous data also show differences between both subtypes in their modulation by age, circadian and homeostatic factors, menstrual cycle, pregnancy and drugs (De Gennaro and Ferrara, 2003). However, no clear evidence of a distinct neurobiological basis for these two subtypes of spindles has been demonstrated so far. After automatic detection of spindles and their differentiation as ‘fast’ and ‘slow’, we showed that the two subtypes were associated with activation of partially distinct thalamo-cortical networks. These data further support the existence of 2 subtypes of sleep spindles modulated by segregated neural networks (Schabus et al., 2007).
Slow oscillation has initially been described at the cellular level in animals as an oscillation
<1 Hz of membrane potential, alternating a hyperpolarization phase (‘down’) during which
cortical neurons are silent and a depolarization phase (‘up’) associated with intense neuronal
firing (Steriade, 2006). At the macroscopic level, this slow rhythm is found on human EEG
recordings as high amplitude slow waves, defined by a peak-to-peak amplitude of more than
140 µV (Massimini et al., 2004). The slow oscillation also synchronizes other NREM sleep
rhythms such as spindles (Molle et al., 2002) and delta waves (defined here as waves of lower
peak-to-peak amplitude : between 75 and 140 µV). The organization of NREM sleep by the
slow oscillation suggests that NREM sleep should be characterized by increased brain
activities associated with the ‘up’ state of slow oscillation. Indeed, we observed significant
BOLD signal changes in relation to both slow waves and delta waves in specific brain areas
including inferior and medial frontal gyrus, parahippocampal gyrus, precuneus, posterior
cingulate cortex, ponto-mesencephalic tegmentum and cerebellum. All these responses consisted in brain activity increases. These results stand in sharp contrast with earlier sleep studies, in particular PET studies, reporting decreases in brain activity during NREM sleep.
Here we showed that NREM sleep cannot be reduced to a state of global and regional brain activity decrease, but is actually an active state during which phasic increases in brain activity are synchronized to the slow oscillation.
We then compared brain responses to delta and slow waves respectively and found no
significant difference. In agreement with our PET data, this result suggests that slow waves
and delta waves share common neurobiological mechanisms. However, when effects of slow
and delta waves were tested separately, we observed that slow waves were specifically
associated with activation of brainstem and mesio-temporal areas, while delta waves were
associated with activation of inferior and medial frontal areas. This result is important in
regard to the potential role of slow oscillation in memory consolidation during sleep
(Marshall et al., 2006). Indeed, the preferential activation of mesio-temporal areas with high
amplitude slow waves suggests that the amplitude of the wave is a crucial factor in the
recruitment during sleep of brain structures involved in the processing of memory traces.
Résumé
Le sommeil lent de l’homme est défini par la présence d’activités neuronales spontanées, organisées sous forme de rythmes ou oscillations spécifiques. L’objectif des travaux réalisés dans le cadre de cette thèse est de caractériser, par des méthodes de neuroimagerie, le fonctionnement cérébral au cours de ces rythmes physiologiques.
Les oscillations que nous avons étudiées sont les fuseaux du sommeil, les ondes delta et les oscillations lentes, représentant les principales activités neurophysiologiques identifiables chez l’homme au cours du sommeil lent. Les fuseaux du sommeil constituent un élément essentiel du sommeil lent léger. Ils sont communément décrits sur les enregistrements électroencéphalographiques (EEG) comme des oscillations de fréquence comprise entre 11 et 15 Hz, d’une durée d’au moins 0,5 sec, et de morphologie caractéristique d’augmentation puis de diminution d’amplitude. Au cours des stades plus profonds de sommeil lent, les fuseaux sont en grande partie remplacés par une activité d’onde lente (SWA; 0,5-4 Hz) qui recouvre les ondes delta (1-4 Hz) et les oscillations lentes (0,5-1 Hz).
En combinaison à l’EEG, nous avons utilisé deux techniques d’imagerie fonctionnelle différentes pour étudier ces rythmes: la tomographie par émission de positons (PET) et l’imagerie en résonance magnétique fonctionnelle (fMRI). Ces études apportent une contribution originale à notre compréhension du sommeil lent chez l’homme sain, par l’exploration des mécanismes générationnels de ces oscillations, piliers de l’architecture du sommeil.
Corrélats cérébraux des rythmes du sommeil lent en EEG / PET
Dans cette section, nous décrivons l’utilisation de la PET dans l’étude des rythmes du
sommeil lent. Nous avons caractérisé les régions cérébrales dans lesquelles l’activité, mesurée
en terme de débit sanguin cérébral régional (rCBF), était corrélée à la puissance spectrale
EEG dans la bande de fréquence des fuseaux (11-15 Hz), des ondes delta (1-4 Hz) et des
oscillations lentes (0.5-1 Hz), chez 23 jeunes volontaires sains et non privés de sommeil.
L’activité EEG dans la bande des fuseaux était corrélée négativement avec le rCBF dans le thalamus. Ce résultat est en accord avec les données suggérant la genèse des fuseaux par des boucles d’interaction cortico-thalamo-corticale (Steriade, 2006).
La puissance spectrale dans la bande delta était négativement corrélée avec le rCBF au niveau du cortex préfrontal médial, du striatum, de l’insula, du cortex cingulaire antérieur, du précuneus et du télencéphale basal, régions potentiellement impliquées dans la modulation des ondes delta corticales (Dang-Vu et al., 2005b). La carte des oscillations lentes était superposable à celle des ondes delta, ce qui suggère que ces deux types d’oscillations relèvent chez l’homme de mécanismes physiologiques communs.
Ces résultats démontraient donc des corrélations négatives, ce qui signifie que le débit sanguin cérébral dans ces régions était d’autant plus faible que la puissance dans la bande de fréquence correspondante était élevée. L’interprétation de ce phénomène doit intégrer le fait que les différents rythmes du sommeil lent sont sculptés par l’oscillation lente, laquelle alterne une phase d’hyperpolarisation au cours de laquelle les neurones corticaux sont silencieux, et une phase de dépolarisation au cours de laquelle ils déchargent en bouffées.
L’effet prépondérant des phases d’hyperpolarisation pourrait expliquer la baisse de débit cérébral démontrée en PET. En effet, cette dernière présente une résolution temporelle limitée, de l’ordre de la minute, ce qui a pour effet d’intégrer l’activité cérébrale sur des périodes de temps relativement longues, au cours desquelles les phases d’hyperpolarisation corticale prédominent. L’imagerie en PET ne permet pas donc pas d’étudier directement des événements brefs de l’ordre de la seconde, tels que les oscillations du sommeil lent. En outre, les valeurs de puissance spectrale utilisées pour caractériser ces rythmes en PET ne reflètent qu’indirectement leur survenue au cours du sommeil. Ces considérations justifient le recours à l’imagerie en fMRI, dont la résolution temporelle de l’ordre de la seconde permet d’évaluer les réponses cérébrales associées à la survenue des oscillations du sommeil lent, considérées cette fois comme des événements identifiables.
Corrélats cérébraux des rythmes du sommeil lent en EEG / fMRI
Dans cette partie, la plus importante, nous décrivons l’analyse en fMRI des rythmes du
sommeil lent. Nous avons caractérisé les régions cérébrales dont l'activité, mesurée par le
signal BOLD, était corrélée à la survenue des oscillations du sommeil lent. Par rapport à la situation rencontrée en PET, l’enregistrement des données EEG nécessaire à la détection des rythmes du sommeil lent, simultanément à l’acquisition fMRI, a posé des difficultés techniques considérablement plus grandes. En particulier, l’interprétation de l’EEG dans ces conditions a nécessité un traitement précis du signal afin d’en éliminer les éléments artéfactuels qui le contaminent. Ce n’est qu’après ce processus que la détection automatique des fuseaux (Molle et al., 2002), des ondes delta et des oscillations lentes (Massimini et al., 2004) selon des critères publiés a pu s’effectuer, permettant d’obtenir les séries d’événements qui furent entrés comme régresseurs dans l’analyse statistique des données fMRI. Cette dernière évalue l’effet principal des fuseaux, ondes delta et oscillations lentes sur les variations du signal BOLD chez l’ensemble des 14 jeunes volontaires sains et non privés de sommeil sélectionnés pour l’étude.
En ce qui concerne les fuseaux, ils furent subdivisés en 2 sous-types. Chez l’homme en effet, alors que la grande majorité des fuseaux sont enregistrés dans les régions centrales et pariétales, avec une fréquence d’environ 14 Hz (fuseaux ‘rapides’), d’autres fuseaux dits
‘lents’ (environ 12 Hz) prédominent dans les régions frontales. Des données antérieures rapportent également des différences entre ces deux sous-types en ce qui concerne leur modulation par des paramètres comme l’âge, les facteurs circadiens et homéostatiques, la phase du cycle menstruel, la grossesse et certains agents pharmacologiques (De Gennaro and Ferrara, 2003). Cependant, aucune description formelle d’un substrat biologique distinct n’avait encore été établie pour ces 2 sous-types de fuseaux. Après détection automatique des fuseaux et leur ségrégation en fuseaux ‘rapides’ et ‘lents’, nous avons pu démontrer que les 2 sous-types de fuseaux étaient associés à des activations dans des réseaux thalamo-corticaux partiellement distincts. Ces données apportent donc des arguments pour établir l’existence de 2 sous-types biologiquement différenciés de fuseaux du sommeil (Schabus et al., 2007).
L’oscillation lente du sommeil lent a été décrite initialement au niveau cellulaire chez
l’animal comme une oscillation de fréquence <1Hz et qui alterne une phase
d’hyperpolarisation (ou ‘down’), au cours de laquelle les neurones corticaux sont silencieux,
et une phase de dépolarisation (ou ‘up’) qui correspond à une période de décharges neuronales
intenses (Steriade, 2006). Chez l’homme, cette oscillation lente est également retrouvée sur
les enregistrements EEG de surface sous forme d’ondes lentes de haute amplitude, définies
par une amplitude pic-à-pic de plus de 140 µV (Massimini et al., 2004). L’oscillation lente
synchronise aussi d’autres rythmes du sommeil lent tels les fuseaux (Molle et al., 2002) et les ondes delta (définies ici par des ondes de plus basse amplitude pic-à-pic : entre 75 et 140 µV).
L’organisation du sommeil lent par ces oscillations lentes suggère que le sommeil lent devrait être marqué par des activations cérébrales survenant en synchronie avec les phases ‘up’ des oscillations lentes. De fait, nous avons observé des variations significatives de signal BOLD en association avec les ondes lentes et delta dans des régions cérébrales spécifiques incluant le gyrus frontal inférieur et médial, le gyrus parahippocampique, le precuneus, le cortex cingulaire postérieur, le tegmentum ponto-mésencéphalique et le cervelet. Ces variations étaient positives dans toutes les régions mises en évidence, ce qui traduit une augmentation d’activité. Ces résultats sont originaux en ce qu’ils suggèrent que le sommeil lent, contrairement à ce qui était conclu des précédentes études du sommeil chez l’homme (particulièrement en PET), ne se réduit pas à une hypoactivation cérébrale globale et régionale. Au contraire, nos données montrent que le sommeil lent s’accompagne d’une activation cérébrale phasique rythmée par la phase de dépolarisation des oscillations lentes.
Nous avons ensuite comparé les réponses cérébrales aux ondes delta et celles aux ondes
lentes. Aucune région cérébrale ne présentait d’activité significativement différente en
fonction des 2 types d’ondes. En accord avec nos données PET, ce résultat suggère qu’il n’y
a pas de différence formelle sur le plan des mécanismes neurobiologiques entre ondes lentes
et ondes delta. Toutefois, lorsque les effets des ondes lentes et delta furent testés séparément,
nous avons observé que les ondes lentes activaient spécifiquement le tronc cérébral et le
cortex mésio-temporal alors que les ondes delta activaient les aires frontales inférieure et
médiale. Cet résultat est important si l’on considère en particulier le rôle potentiel des
oscillations lentes dans la consolidation des traces mnésiques au cours du sommeil (Marshall
et al., 2006). L’activation préférentielle des aires mésio-temporales avec les ondes lentes de
haute amplitude suggère en effet que l’amplitude de l’onde est un paramètre déterminant dans
le recrutement au cours du sommeil de structures cérébrales impliquées dans le traitement des
traces mnésiques.
1. Introduction
With regard to sleep, the patient should wake during the day and sleep during the night.
If this rule be anywise altered it is so far worse;
But there will be little harm provided he sleeps in the morning for the third part of the day;
But the worst of all is to get no sleep either night or day;
For it follows from this symptom that the insomnolency is connected with sorrow and pains … Hippocrates, The Book of Prognostics (circa 400 BC)
1.1. Why should we study sleep?
For a very long time across history, sleep has been considered as a passive process leading to an ‘abject annihilation of conscioussness’ (Eccles, 1961). But during the last decades, it has been recognized that sleep is not ‘wasted time’. Indeed, sleep occupies about one third of our lifetime and is a life supporting process. Acute sleep deprivation leads to alteration in mood, alertness and performance (Bonnet, 2005). In rodents, total sleep deprivation leads to death within several weeks (Rechtschaffen and Bergmann, 2002), due to a combination of hypothermia, stress, energy expenditure, failure of host defence and malnutrition. Chronic sleep restriction, a highly common situation in modern societies, engenders progressive cognitive deficits and disturbances of various physiologic systems (endocrine, metabolic, immune, cardiovascular, etc.) (Dinges et al., 2005). It is now commonly accepted that sleep deprivation is also a major cause of accidents, for instance accounting for a huge proportion of traffic accidents (Horne and Reyner, 1995; Mitler et al., 1988).
Such an impact on daytime functioning has made sleep a major area of research for both basic scientists and clinicians. The elucidation of the mechanisms and functions of sleep is not only a tremendously exciting scientific quest but also a crucial matter of public health.
Among all the hypotheses on sleep functions (see below), the one that has received the
greatest interest in the last few years postulates that sleep promotes neural plasticity and
memory consolidation. Below we discuss this hypothesis by reviewing animal and human
data, at the different levels of system organization.
From
A Role for Sleep in Brain Plasticity
Dang-Vu TT, Desseilles M, Peigneux P and Maquet P
Pediatric Rehabilitation, 9(2), 2006, p. 98-118Summary
The idea that sleep might be involved in brain plasticity has been investigated for many years through a large number of animal and human studies, but evidence remains fragmentary.
Large amounts of sleep in the early life suggest that sleep may play a role in brain maturation.
In particular, the influence of sleep in the developing visual system has been highlighted. The
current data suggest that both Rapid Eye Movement (REM) and NREM sleep states would be
important for brain development. Such findings stress the need of an optimal paediatric sleep
management. In the adult brain, the role of sleep in learning and memory is emphasized by
studies at behavioural, systems, cellular and molecular levels. First, sleep amounts are
reported to increase following a learning task and sleep deprivation impairs task acquisition
and consolidation. At the systems level, neurophysiological studies suggest possible
mechanisms for the consolidation of memory traces. These imply both thalamocortical and
hippocampo-neocortical networks. Similarly, neuroimaging techniques demonstrated the
experience-dependent changes in cerebral activity during sleep. Finally, recent works show
the modulation during sleep of cerebral protein synthesis and expression of genes involved in
neuronal plasticity.
Introduction
Sleep appears to be essential for the survival and integrity of most living organisms. However its exact functions remain speculative despite our growing understanding of the processes initiating and maintaining sleep. Many non-mutually exclusive roles have been attributed to sleep: brain thermoregulation (McGinty and Szymusiak, 1990), neuronal detoxification (Inoue et al., 1995), energy conservation (Berger and Phillips, 1995), tissue restoration (Adam and Oswald, 1977) and immune defence (Everson, 1993). Another hypothesis has focused much attention for the last years and proposes that sleep might participate in brain plasticity (Maquet et al., 2003b). The latter refers to the ability of the brain to persistently modify its structure and function according to genetic information and environmental changes or to comply with the interaction between these two factors (Kolb and Whishaw, 1998). Links between sleep and brain plasticity have been considered during the early life as well as in the adult organism. It is known that sleep amounts are greater during neonatal periods of rapid brain development than at any other time of life (Frank and Heller, 1997; Jouvet-Mounier et al., 1970; Roffwarg et al., 1966). This suggests that sleep should be important for brain development and synaptic plasticity during the early life. We will first review experimental data testing this relationship, after a brief description of sleep organization and basic physiology. In the second part of this article, we will provide a general description of sleep implication in learning and memory, in adult subjects.
Sleep organization and stages
In homeotherms, sleep is composed of two main stages. REM sleep, also known as
paradoxical sleep (PS) is characterized by ocular saccades (as seen on EOG), muscular atonia
(as seen on EMG), and high-frequency, low-amplitude rhythms on EEG recordings (figure
1.B). NREM sleep is characterized by specific EEG oscillations: spindles, delta and slow
rhythms. In humans, NREM sleep is further categorized in light (stage 2) and deep (stages 3
and 4) stages (figure 1.B). Spindles, a prominent feature of light NREM sleep, are defined as
waxing-and-waning oscillations within the 11-15 Hz (sigma band) frequency range, lasting at
least 0.5 second (Rechtschaffen and Kales, 1968). During deep NREM sleep, also referred to
as slow wave sleep (SWS), the EEG is mainly characterized by a slower oscillation in the delta range (1-4 Hz). A slow rhythm (<1 Hz) occurs both during light and deep NREM sleep and manifests itself respectively as the regular recurrence of spindles every 3-10 seconds or as slow waves below 1 Hz (Achermann and Borbely, 1997; Steriade and Amzica, 1998).
Refinement in the arbitrary categorization of NREM sleep stages varies amongst species.
Subdivided into light and deep SWS in carnivores such as cats or dogs, only one NREM stage is usually defined in rats or mice. The distribution of stages is unequal throughout the night:
SWS is most abundant during the first half of the night in humans, up to 80% of the sleep time in this period, whereas the proportion of REM sleep dramatically increases in the second half of the night (figure 1.A). The duration and intensity of sleep is thought to be regulated by the interaction of homeostatic processes, in which the requirement for sleep builds during waking and is relieved by sleep, and circadian rhythms, which determine the timing of the sleep/wake cycle according to internal (e.g., the suprachiasmatic biological clock) and external (e.g., the light-dark cycle) signalling systems (Borbely and Achermann, 1999b).
Figure 1. Sleep organization and stages.
(A) Hypnogram of a normal sleep in a young healthy adult. This figure illustrates the dynamics of sleep-wake cycles in the course of a normal night of sleep. The black bars represent the time spent in a specific stage of sleep (or wakefulness). The time of the night is indicated on the horizontal axis. Sleep stages are indicated on the vertical axis. Note the progressive decrease in deep NREM sleep (stages 3- 4) duration and the increase in REM sleep periods across the night. M: movement time; W: waking;
R: REM sleep; 1-4: NREM sleep stages 1 to 4. Courtesy of Dr Gilberte Tinguely.
(B) Polygraphic patterns of sleep stages. The six panels show each a 20-s epoch of wakefulness (W), NREM sleep (stages 1-4 : N1-4) and REM sleep (R), respectively, with EEG (upper trace), EOG (middle trace) and EMG (lower trace). Scale bars on the left = 75µV. Courtesy of Dr Gilberte Tinguely.
Sleep and brain plasticity during neural development
REM sleep
Most of the work concerning sleep and brain development has studied the behavioural, morphological and electrophysiological consequences of REM sleep deprivation during critical periods of brain maturation in animals, because REM sleep occupies a large proportion of time during early brain development (Roffwarg et al., 1966). Indeed, REM sleep time seems to correlate with maturity at birth (Mirmiran and Ariagno, 2003; Siegel, 1995). In an altricial mammal, that is an animal born in a relatively immature state, such as the rat, the high amount of REM sleep at birth declines to a low level during the first month of life. On the other hand, in a precocial mammal such as the guinea pig, prenatally high levels of REM sleep decrease to low levels at birth. Thus it appears that the amount of REM sleep declines to a low adult level when the rapid period brain maturation is completed (Jouvet- Mounier et al., 1970). Indeed, in human newborns at birth, more than half of the daily 16-18 hours of sleep are occupied by REM sleep (Anders et al., 1995). Then the decline in REM sleep is much slower in humans and reaches low levels only at preschool years period (Anders et al., 1995; Roffwarg et al., 1966). Globally, the time course of REM sleep development in humans and other mammals corresponds well with the brain maturation period.
During a period in which environmental experiences are very limited, the development of
precise neuronal connections in the mammalian brain requires a high level of endogenous
neuronal activation. REM sleep is characterized by a high endogenous phasic neuronal
activity and a particular neuromodulatory context that favourably influences early neural
development (Mirmiran and Ariagno, 2003). This phasic activity is characterized in the visual
system by ponto-geniculo-occipital (PGO) waves (Davenne and Adrien, 1987; Mouret et al.,
1963). REM sleep is also associated with acetylcholine (ACh) release (Jones, 1991), a
neurotransmitter that influences neural development (Lauder and Schambra, 1999) and
synaptic remodelling (Rasmusson, 2000). The most popular experimental approach to test the
role of REM sleep during brain maturation is to selectively deprive the developing animal of
REM sleep.
a. REM sleep deprivation during brain maturation
One possibility is to deprive the mammal of its normal quota of REM sleep during the critical period of brain development and then to study the consequences of this deprivation on later brain function and plasticity in adulthood. Investigators have used a pharmacological approach, since long-term instrumental deprivation or lesion studies are not feasible (Hilakivi, 1987; Hilakivi et al., 1987; Mirmiran et al., 1981; Vogel et al., 1990a; Vogel et al., 1990b;
Vogel et al., 1990c). REM sleep was therefore suppressed using antidepressant drugs such as clomipramine or antihypertensive molecules like clonidine during the second and third weeks of postnatal development in rats.
Neonatally REM sleep-deprived animals showed long-lasting changes such as anxiety, disturbed sleep, reduced sexual activity, despair behaviour, reduced pleasure seeking and increased alcohol preference (De Boer et al., 1989; Hilakivi and Hilakivi, 1987; Hilakivi et al., 1984; Mirmiran et al., 1983; Mirmiran et al., 1981; Neill et al., 1990; Vogel et al., 2000).
It has been proposed that these behavioural changes could reflect symptoms of endogenous depression (Vogel et al., 1990a). There are several other arguments for this hypothesis, provided by other studies (Neill et al., 1990; Vogel et al., 1990b; Vogel et al., 2000). For example, adult rats neonatally treated with clomipramine displayed reduced shock-induced aggression and enhanced defensive responses. Administration of antidepressant drugs to these animals in adulthood improved some of these behavioural changes. These findings suggest that neonatal REM sleep deprivation induces adult depression (Mirmiran and Ariagno, 2003).
Regional brain measurements in these neonatally REM sleep-deprived rats displayed a significant size reduction of the cerebral cortex and brainstem. A proportional reduction of tissue protein was also found in the affected brain areas (Mirmiran et al., 1983).
Neurotransmitter circuitry was also modified: in the cerebral cortex, the level of the gamma- amino-butyric-acidergic (GABAergic) depression of the glutamate-induced single cortical neurons responses was greater in the neonatally REM sleep-deprived rats as compared to controls (Mirmiran et al., 1990; Mirmiran et al., 1988), while there was a hypersensitivity of the pyramidal cells to noradrenalin in the hippocampus (Gorter et al., 1990).
In rats, environmental enrichment has been demonstrated to increase the size of the cerebral
cortex, the efficacy and number of synapses and the problem-solving ability (Juraska et al.,
1980; Will et al., 1977). But the neonatally REM sleep-deprived rats subjected to this enriched environment did not display any significant plasticity effect after weaning (Mirmiran et al., 1983). Hippocampal plasticity has been studied in rats by using the kindling model, in which kindling causes a prolonged decrease in latency and increase in sensitivity for epileptogenesis by electrical stimulation in the hippocampus. When compared with kindled controls, neonatally REM sleep-deprived kindled rats displayed an increase in latency and a reduced excitability ratio (Gorter et al., 1991).
b. REM sleep and the visual system development
Relationship between sleep and synaptic plasticity during brain maturation have been extensively studied in the developing visual system, since this system has provided a model for much of our understanding of the mechanisms of neural development (Frank and Stryker, 2003). Development of central visual pathways occurs at ages when sleep amounts are very high, or during landmark changes in sleep expression (Jouvet-Mounier et al., 1970); it requires both endogenous and exogenous (visual experience) sources of neuronal activity. The interplay of these two factors has been well documented in the lateral geniculate nucleus of the thalamus (LGN) and primary visual cortex (V1). It has been shown that early development of both LGN and V1 depends upon endogenous or spontaneous neural activity.
For example, in cats, the segregation of retinal afferents in the LGN, which normally occurs between embryonic day 45 (E45) and birth (E65), well before eye opening, was impaired by the infusion, during this period, of tetrodotoxin, which prevents spontaneously generated action potential activity involved in this segregation (Shatz and Stryker, 1988). A similar stage of development is reported in V1, where the segregation of LGN afferents into ocular dominance columns begins well before the onset of visual experience (Horton and Hocking, 1996). Visual experience is required at later critical periods for the maintenance and refinement of selective and strong visual responses and precise columnar structure in the cortex. For instance, while rudimentary orientation selectivity can develop in the absence of patterned visual experience, this response property rapidly deteriorates if visual experience is prevented during a period that begins about 2 weeks after eye opening (Crair et al., 1998;
White et al., 2001).
To test the role of REM sleep in brain development, several works have studied the effects of
REM sleep deprivation or the elimination of the REM sleep PGO waves, on subsequent visual
system development. A first study found that brainstem lesions in kittens that eliminated PGO waves resulted in smaller LGN volumes and reduced LGN soma sizes (Davenne and Adrien, 1984). This result was then confirmed and extended in a second study, in which PGO waves suppression in developing cats produced much slower LGN responses to stimulation of the optic chiasm (Davenne et al., 1989). These morphological and functional changes in LGN cells are consistent with a delayed maturation of the LGN and suggest that REM sleep activity provides a source of endogenous neuronal activity necessary for normal LGN development.
More recent works used various forms of selective REM sleep deprivation or suppression of PGO waves combined with monocular deprivation (MD). One commonly used method to selectively deprive kittens of REM sleep is the “flower pot” or “pedestal” technique, that consists of placing the animal on a platform emerging from water. On this pedestal, the animal can generate NREM sleep but not REM sleep, because at onset of REM sleep, the animal falls in the water due to muscular atonia. Using this technique, Oksenberg et al.
showed that one week of REM sleep deprivation in kittens enhanced the effects of MD on LGN cell morphology: LGN cells receiving input from the occluded eye were smaller when REM sleep deprivation was combined with MD compared to MD alone therefore resulting on greater difference in the size of LGN cells activated by the open and deprived eyes (Oksenberg et al., 1996). A similar increase in LGN cell size disparity has been reported when MD is combined with brainstem lesions that eliminate PGO waves in kittens, and in this case, LGN cells receiving input from the open eye appeared to increase in size (Shaffery et al., 1999). These studies suggest that at least some effects of REM sleep deprivation on LGN cell size are mediated by the phasic processes of REM sleep. Another interesting result showed that REM sleep deprivation for one week decreased immunoreactivity for the calcium binding protein parvalbumin, which has been demonstrated to influence certain forms of neuronal synaptic plasticity (Caillard et al., 2000), in GABAergic interneurons of the developing LGN (Hogan et al., 2001). Therefore all these studies indicate that REM sleep may influence plasticity in the LGN during critical periods of visual system development (Benington and Frank, 2003).
REM sleep has also been reported to modulate the expression of long-term potentiation (LTP)
elicited during the critical period for visual system development (Kirkwood et al., 1995). In
this type of LTP, high-frequency white-matter stimulation in neocortical slices from juvenile
rats (postnatal days (P) 28-30) produces LTP in upper neocortical layers; this effect wanes
with age (P35+), and is no more observed in adult neocortex. Shaffery et al. used a less
stressful version of the “pedestal” technique (“multiple small-platform”) and found that 1 week of REM sleep deprivation extended the critical period for this developmentally regulated form of LTP in visual cortex (Shaffery et al., 2002); this type of LTP was observed in slices of visual cortex from REM sleep deprived rats at ages P34-40, when it is not normally found. This result is in line with the concept of a maturational delay and suggests that REM sleep deprivation impairs or retards normal brain maturation.
NREM sleep
NREM sleep is characterized by events which potentially induce synaptic plasticity, such as synchronized bursting in thalamocortical circuits, transient increases of intracellular calcium, and in some mammals, the release of somatotropins (Cauter and Spiegel, 1999; Steriade, 2000; Steriade and Amzica, 1998). A role for NREM sleep in developmental cortical plasticity is suggested by maturational changes in NREM sleep that coincide with periods of heightened cortical plasticity. In the cat, there is a steep decrease in REM sleep and a sharp increase in NREM sleep amounts near the beginning of the critical period for visual system development (Jouvet-Mounier et al., 1970). The beginning of this period also coincides in rats with the development of NREM sleep homeostasis. Before the fourth postnatal week, NREM sleep EEG does not intensify following sleep deprivation, indicating that the regulatory relationship between wake and NREM sleep matures in parallel with periods of heightened cortical plasticity (Frank et al., 1998).
A more recent study has highlighted a relationship between NREM sleep and developmental
cortical plasticity in vivo (Frank et al., 2001). MD not only induces morphological changes in
the LGN during the critical period for visual system maturation, but also provokes rapid
changes in neocortical responses to visual stimulation at this time (Benington and Frank,
2003). Frank et al. combined MD with periods of ad lib sleep or sleep deprivation. Cats at the
peak of the critical period had one eye sutured shut and were kept awake in an enlightened
environment for 6 hours. This MD period was used as a standard stimulus for the induction of
plasticity. The authors wanted to determine whether the effects of MD would be enhanced by
a period of sleep occurring immediately thereafter. Both optical imaging of intrinsic cortical
signals and extracellular unit recording showed that sleep nearly doubled the effects of MD on
visual cortical responses. In this study, it was not possible to determine the exact contribution
of REM and NREM sleep to this process. However, the enhancement of cortical plasticity
was highly correlated with NREM sleep time and intensity, suggesting an important role for NREM sleep in the rapid cortical synaptic remodelling elicited by MD (Frank et al., 2001;
Frank and Stryker, 2003). Another study has shown that NREM sleep electrical activity itself underwent changes as a consequence of waking experience during a late critical period (P30- 60) in cats and mice (Miyamoto et al., 2003): dark-rearing induced during sleep a huge and reversible decrement of delta activity (1-4 Hz) that was restricted to the visual cortex.
Interestingly, this modulation was impaired by gene-targeted reduction of NMDA receptor function, potentially reflecting that NMDA receptor activation participates in the adjustment of NREM sleep rhythms by sensory experience during a late critical period for visual system development (Miyamoto et al., 2003).
Taken together, these findings could suggest that NREM sleep consolidates waking experience; a process that might begin during critical periods of brain development when the animal is most sensitive to waking experience, and is retained throughout life (Frank and Stryker, 2003).
Summary and further considerations
REM sleep deprivation produces several anatomical and electrophysiological changes in the developing visual system and modulates cortical plasticity. NREM sleep seems to be necessary for the consolidation of visual traces during critical periods of experience- dependent cortical plasticity in vivo. These results suggest that both sleep states may be important for neural development, although the contribution of each state is likely to be different. If the precise role of each state is still unclear, current findings indicate that the relative amounts of REM sleep and NREM sleep during early life both influence brain maturation (Frank and Stryker, 2003). REM sleep is maximally expressed at ages when endogenous neuronal activity is crucial for the establishment of fundamental neuronal circuitry in the visual system. NREM sleep, on the other hand, is present at later stages of development, quickly matures after eye opening (Frank and Heller, 1997; Jouvet-Mounier et al., 1970) and become homeostatically regulated by wake in a way similar to adult NREM sleep during critical periods of experience-dependent synaptic plasticity (Frank et al., 1998).
Therefore it is possible that while REM sleep helps establish early patterns of neural circuitry,
NREM sleep in part consolidates changes in neural circuitry elicited by waking experience
(Frank and Stryker, 2003).
Although the findings discussed above strongly support a role for sleep in neural development, some considerations should be kept in mind (Benington and Frank, 2003; Frank and Stryker, 2003). First, the potential secondary effects of the experimental manipulation used in a study should be considered. For instance, sleep deprivation induces stress and has multiple behavioural or neurochemical effects, that may in turn influence the results of an experiment (Siegel, 2001; Vertes and Eastman, 2000). Another issue is that manipulations performed in one sleep state may also influence neural processing in other vigilance states, making it difficult to determine which vigilance state is responsible for the observed effects.
For example, REM sleep deprivation can alter NREM sleep architecture, increasing sleep fragmentation and suppressing deeper stages of NREM sleep, even when total amounts of NREM sleep are preserved (Beersma et al., 1990; Brunner et al., 1993; Endo et al., 1997).
At present, available experimental evidence strongly suggests a role for sleep in brain
development, but further studies are still needed. In particular, we currently know little about
the cellular and molecular mechanisms by which sleep exerts its effects during neural
maturation. The confirmation of such data might have important public health implications
(Mirmiran and Ariagno, 2003). For example, the use of antidepressants or antihypertensive
drugs such as clonidine during pregnancy and lactation can suppress fetal and neonatal REM
sleep. Moreover, infants born prematurely suffer from sleep disturbances during a long stay in
the neonatal intensive care unit (Mirmiran and Ariagno, 2003). Some behavioural and
physiological consequences in adulthood in these individuals may be caused by sleep
deprivation in early life and may consequently be better prevented by the development of
appropriate clinical interventions, with the aim of improving the neurobehavioral outcome of
high-risk infants.
Brain plasticity in adulthood: The role for sleep in learning and memory
Sleep has also been implicated in the plastic cerebral changes that underlie learning and memory in the adult brain. Three sequential steps may be considered to test this hypothesis:
exposure to a new stimulus, processing of memory traces and performance at retest. In this design, sleep would participate to the consolidation of memory traces (Maquet, 2001).
Consolidation refers to the processing of memory traces during which the traces may be reactivated, analysed and gradually incorporated into long-term memory (Sutherland and McNaughton, 2000). According to this hypothesis, the memory trace stays in a fragile state until the first postexposure sleep period has occurred (Fishbein and Gutwein, 1977). At present, the major debate is whether memory trace consolidation during sleep relies on specific patterns of neuronal activities and their effects at the subcellular level, or on time- dependent factors unrelated to sleep itself (e.g. circadian rhythms, stress hormones, etc.) (Maquet, 2001). In the following lines, we will give a summary review of relevant works, assessing the role of sleep in learning and memory in adult animals and humans, and presented following the different levels of description, from the behavioural to the molecular scale.
Behavioural level
