|Reference : Influence of sleep homeostasis and circadian rhythm on waking EEG oscillations during...|
|Scientific congresses and symposiums : Poster|
|Life sciences : Genetics & genetic processes|
Human health sciences : Neurology
|Influence of sleep homeostasis and circadian rhythm on waking EEG oscillations during a constant routine|
|Muto, Vincenzo [Université de Liège - ULg > > Centre de recherches du cyclotron; Welbio, Belgium > > >]|
|Meyer, Christelle [Université de Liège - ULg > > Centre de recherches du cyclotron; Welbio, Belgium > >]|
|Jaspar, Mathieu [Université de Liège - ULg > Département de Psychologie : cognition et comportement > Neuropsychologie > >]|
|Shaffii, Anahita [Université de Liège - ULg > > Centre de recherches du cyclotron >]|
|Kussé, Caroline [Université de Liège - ULg > > Centre de recherches du cyclotron >]|
|Foret, Ariane [Université de Liège - ULg > > Centre de recherches du cyclotron > >]|
|Chellappa, Sarah Laxhmi [Université de Liège - ULg > > Centre de recherches du cyclotron >]|
|Vandewalle, Gilles [Université de Liège - ULg > > Centre de recherches du cyclotron >]|
|Collette, Fabienne [Université de Liège - ULg > Département de Psychologie : cognition et comportement > Neuropsychologie >]|
|Archer, Simon [Surrey Sleep Research Centre, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK. > > > >]|
|Dijk, Derk-Jan [Surrey Sleep Research Centre, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK > > > >]|
|Maquet, Pierre [Université de Liège - ULg > > Centre de recherches du cyclotron >]|
|21st Congress of the European Sleep Research Society|
|4 - 8 September 2012|
|European Sleep Research Society|
|[en] Sleep ; gene PERIOD3 (PER3) ; circadian rhythms|
|[en] Introduction & Objectives
Human sleep and wake EEG oscillations are modulated by complex non-additive interaction between homeostatic and circadian processes. Quantitative analysis of EEG data, during extended wakefulness, indicate that its frequency-specificity is influenced by both factors, such that low-frequencies (<8Hz) increase with time spent awake (1), thus more homeostatically-driven, while alpha activity undergoes a clear circadian modulation (2).
Interindividual differences in sleep-wake regulation in young volunteers are associated with the variable-number tandem-repeat (VNTR) polymorphism in the coding region of the circadian clock gene PERIOD3 (PER3). Individuals homozygous for the longer allele of PER3 (PER35/5) were reported to generate more slow wave activity during NREM sleep and theta activity during wakefulness, relative to individuals with the shorter allele (PER34/4). However, the phase and amplitude of circadian markers do not differ between these genotypes (3).
Here we tested the hypothesis if fluctuations in the dynamics of waking EEG frequency-specificity are modulated by a polymorphism in the clock gene PER3, under 42h of sustained wakefulness.
Materials and Methods
Population. A total of 400 young men and women were recruited, from whom DNA samples and questionnaire data were collected. On the basis of their PER3 polymorphism, 35 healthy young volunteers (age: 19-26 y; 17 females) were recruited, out of which twelve were PER35/5 and twenty-three PER34/4 homozygotes, and matched by age, gender, level of education, chronotype and IQ at the group level.
Study protocol. The laboratory part of this study began in the evening of day 1 until day 5 (Fig. 1). During the first 2 nights (habituation and baseline), volunteers followed one out of two possible sleep-wake schedules (00:00-08:00 or 01:00-09:00). Thereafter, participants underwent approximately 42 hours of sustained wakefulness under constant routine (CR) conditions (semi-recumbent position, dim light <5 lux, no time-of-day information), and a subsequent recovery sleep episode.
EEG recordings. Continuous EEG measurements with 9 EEG channels (F3, Fz, F4, C3, Cz, C4, Pz, O1, O2) were performed throughout the CR. Waking EEG was recorded every 2-h, during a modified version of the Karolinska Drowsiness Test (KDT) (4). Data presented here pertain to the last 60-sec of KDT, during which subjects were instructed to relax, to fixate a dot displayed on a screen ca. 75cm and to try to suppress blinks. After re-referencing to mean mastoids, recordings were scored using Rechtschaffen criteria. The 1-min EEGs during the KDT were manually and visually scored for artifacts (eye blinks, body movements, and slow eye movements) offline by 2 independent observers. The absolute EEG power density was then calculated for artifact-free 2-s epochs in the frequency range of 0.5 to 20 Hz , overlapping by 1 second using the pwelch function in MATLAB (7.5.0). For data reduction, power density of artifact-free 2-s epochs was averaged over 20-s epochs.
Statistics. Waking EEG delta (0.75-4.5Hz), theta (4.75-7.75Hz) and alpha (8-12.0Hz) power density computed on Central derivation (Cz) were analyzed with a mixed-model analysis of variance (PROC Mixed), with main factors “elapsed time awake” and “genotype” (PER34/4 and PER35/5), and the interaction of these two factors. All p-values derived from r-ANOVAs were based on Huynh-Feldt's (H-F) corrected degrees of freedom (p<0.05). Multiple comparisons were performed using Tukey-Kramer test. Theoretical coefficients for the homeostatic sleep pressure (derived from a quasi-linear function) and the circadian oscillation (24-hour period sine wave) were used in a multiple regression model to predict delta, theta and alpha activity during the CR. Prior to multiple regression analysis, data were normalized according to PROC Transreg, in order to derive the best normalization method for linear and non-linear datasets.
Analysis of delta activity yielded a significant main effect of “elapsed time awake” (F=5.31; p < 0.0001), albeit no significant effects for “genotype” (F=0.01; p = 0.94) nor for the interaction of these factors (F=0.85; p = 0.65). The multiple regression model revealed a significant regression (R² = 0.0433 Adj. R² = 0.0404; F = 15.24; p <0.0001), for the homeostat (p < 0.0001 ) and circadian (p = 0.0006) coefficients.
Analysis of theta activity yielded a significant main effect of “elapsed time awake” (F= 12.2; p < 0.0001), although no significant effects for “genotype” (F= 0.1; p = 0.70) nor for the interaction of these factors (F= 0.67; p = 0.86). The multiple regression model revealed a significant regression (R²= 0.072 Adj. R² =0.069; F= 26.36; p <0.0001), for the homeostat (p < 0.0001 ) and circadian (p < 0.0001 ) coefficients.
Analysis of alpha activity yielded a significant main effect of “elapsed time awake”(F=3.43; p < 0.0001), although no significant effects for “genotype” (F = 0.01; p = 0.92) nor for the interaction of these factors (F= 1.23; p = 0.22). The multiple regression model revealed a significant regression (R²=0.052; Adj. R²=0.05; F =18.63; p <0.0001), for the homeostat (p = 0.0012) and for the circadian (p < 0.0001) coefficients.
Our results indicate that fluctuations in the dynamics of waking EEG activity are modulated by the circadian and homeostatic processes, although the magnitude of these differences are not underlined by a polymorphism in the clock gene PER3.
1. Cajochen C, Brunner DP, Kräuchi K, Graw P, Wirz-Justice A. Power density in theta/alpha frequencies of the waking EEG progressively increases during sustained wakefulness. Sleep. 1995, 10:890-894.
2. Cajochen C, Wyatt JK, Czeisler CA, Dijk DJ.Separation of circadian and wake duration-dependent modulation of EEG activation during wakefulnessNeuroscience. 2002, 114:1047-60.
3. Viola AU, Archer SN, James LM, Groeger JA, Lo JC, Skene DJ, von Schantz M, Dijk DJ PER3 polymorphism predicts sleep structure and waking performance. Curr Biol 2007,17:613–618.
4. Gillberg M, Kecklund G, Akerstedt T. Relations between performance and subjective rating of sleepiness during a night awake. Sleep 1994, 17:236-241.
ACKNOWLEDGEMENTS & SPONSORS
Cyclotron Research Centre (CRC) ; Belgian National Funds of Scientific Research (FNRS) ; Actions de Recherche Concertées (ARC, ULg) – Fondation Médicale Reine Elisabeth (FMRE) ; Walloon Excellence in Lifesciences and Biotechnology (WELBIO) ; Wellcome Trust ; Biotechnology and Biological Sciences Research Council (BBSRC)
|Cyclotron Research Centre, University of Liège, Belgium|
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