References of "Archer, Simon"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailCortical excitability dynamics of during sleep deprivation set PVT performance
Borsu, Chloé; Gaggioni, Giulia ULg; Ly, Julien ULg et al

Poster (2014, September)

Detailed reference viewed: 5 (1 ULg)
Full Text
Peer Reviewed
See detailThe circadian system sets the temporal organization of basic human neuronal function
Chellappa, Sarah Laxhmi ULg; Ly, Julien; Gaggioni, Giulia et al

Conference (2014, June 16)

Detailed reference viewed: 15 (3 ULg)
Full Text
Peer Reviewed
See detailCortical excitability depends on time awake and circadian phase
Ly, Julien; Gaggioni, Giulia; Chellappa, Sarah Laxhmi ULg et al

Conference (2014)

Detailed reference viewed: 15 (8 ULg)
Peer Reviewed
See detailDifference in neural correlates of discrimination during sleep deprivation in PER3 homozygous
Shaffii-Le Bourdiec, Anahita; Muto, Vincenzo ULg; Jaspar, Mathieu ULg et al

Poster (2012, September 07)

Detailed reference viewed: 24 (8 ULg)
See detailInfluence of sleep homeostasis and circadian rhythm on executive discriminative ability during a constant routine
Jaspar, Mathieu ULg; Meyer, Christelle ULg; Muto, Vincenzo ULg et al

Poster (2012, September)

Introduction & Objectives The human brain upholds cognitive performance throughout a waking day due to putative circadian (C) arousal signal (1) which counteracts the increase in homeostatic (H) sleep ... [more ▼]

Introduction & Objectives The human brain upholds cognitive performance throughout a waking day due to putative circadian (C) arousal signal (1) which counteracts the increase in homeostatic (H) sleep pressure associated to the deterioration in brain efficiency. When wakefulness is extended into the circadian night, maintenance of cognitive performance is jeopardized (Fig.1). Some individuals are very vulnerable to the negative effects of sleep loss and circadian misalignment, whereas others are resilient (3). These individuals differences can be readily explained within the conceptual framework of the circadian and homeostatic regulation of performance (4,5) but also by individual genetic differences and notably the PERIOD3 gene polymorphism (6). In this experiment, we investigated the consequences of sleep deprivation on cognitive performance during a working memory task (3-back). Following the signal detection theory, the ability to discriminate target from non-target stimuli is estimated by d prime (d') and criterion (cr). Here we assessed whether d' and cr were modulated by the raising sleep need and the oscillatory circadian signal. We also tested whether the individual vulnerability to sleep loss predicted by the PERIOD3 gene polymorphism influences this cognitive modulation, which is also driven by the sleep/wake regulation. Materials and Methods Population: Thirty-five right-handed healthy young volunteers aged from 19 to 26 (17 females) were recruited on the basis of their PER3 polymorphism. From a sample of about 400 screened volunteers, twelve 5/5 and twenty-three 4/4 homozygotes (matched for age, gender, chronotype, IQ, and level of education at the group level) participated in this study. Study protocol: Participants wore actigraphs for three weeks before the laboratory study. The first two weeks allowed us to determine their habitual sleep/wake schedule. During the third one, a strict sleep schedule adjusted on two possible timetables (00:00-08:00 or 01:00-09:00) was imposed in order to stagger fMRI sessions. Compliance to this schedule was again checked by wrist actigraphy and sleep diaries. The laboratory study began in the evening of day 1 and ran over 5 nights (Fig. 2). During the first 2 nights (habituation and baseline), the volunteers slept according to habitual sleep/wake schedule. Participants remained awake from the morning of day 3 for 42 hours. During this period, they remained in a semi-recumbent position, under dim light conditions (5 lux, eye level), with no information on clock time, in a constant routine protocol (CR). Saliva samples was hourly collected for melatonin analysis. Every 2 hours, volunteers received calibrated isocaloric snacks, behavioral data were collected and waking EEG recorded. During CR, behavioral measures were used to assess subjective (Karolinska Sleepiness Scale, KSS) and objective alertness (psychomotor vigilance task [PVT]). Executive functioning efficiency was assessed using the 3-back (Fig. 3) and SART tasks. During fMRI, participants performed alternating blocks of 0- and 3-back task. D’ and cr (Fig. 4) were analyzed with mixed-model analysis of variance (PROC Mixed), with main factors “session” and “genotype” (PER3 4/4 & PER3 5/5). All p-values derived from r-ANOVAs were based on Huynh-Feldt's (H-F) corrected degrees of freedom (p<0.05). Exploratory analysis assessed theoretical coefficients for the homeostatic sleep pressure (derived from a quasi-linear function) and the circadian oscillation (as a 24-hour period sine wave) were utilized in a multiple regression model to predict d’ and cr performance during the CR. Before these analyses, d’ and cr have been normalized using a z-score transformation. Results. Analyses on d’ 1. MIXED MODEL : Significant effect of sessions (F(12,385) = 17.16, p < 0.0001), but no group effect (F(1,133) = 0.00, p = 0.99) or interaction (F(12,385) = 1.51, p = 0.11). 2. REGRESSION: Significant regression (R² = 0.24, F(2,440) = 69.94, p <0.0001). The two predictors are significant (homeostat: p < 0.0001 ; circadian: p < 0.0001). Analyses on cr 1. MIXED MODEL : Significant effect of sessions (F(12,385) = 4.12, p < 0.0001), but no group effect (F(1,133) = 0.00, p = 0.99) or interaction (F(12,385) = 0.75, p = 0.71). 2. REGRESSION: Significant regression (R² = 0.04, F(2,440) = 9.35 , p = 0.0001). Only one predictor was significant (homeostat: p < 0.0001 ; circadian: p = 0.96). Conclusion These preliminary results show that both sleep homeostatic pressure and circadian factors influence executive discriminative ability during sleep loss, as assessed by signal detection theory (d’). Decision criterion (cr) appears modulated only by homeostatic sleep pressure. The difference between these two parameters could be explained by the theoretical modeling of the circadian oscillation and future analyses will incorporate individual experimentally-derived homeostatic and circadian parameters. Neither discrimination ability (d’) or criterion (cr) seem sensitive measures of individual cognitive vulnerability to sleep loss predicted by PER3 polymorphism. REFERENCES (1) Aston-Jones. Sleep Med. 2005, 6(Suppl 1), S3-7. (2) Dijk & Archer. Sleep Med. Rev. 2010, 14, 151-160.(3) Van Dongen & al. Sleep. 2004, 27, 423-433. (4) Mongrain & al. J. Sleep Res. 2006, 15, 162-166. (5) Van Dongen et al. Sleep. 2007, 30, 1129-1143. (6) Groeger & al. Sleep. 2008, 31, 1159-1167. (7) Vandewalle & al. J. Neuro. 2009, 29, 7948-7956. ACKNOWLEDGEMENTS & SPONSORS Cyclotron Research Centre (CRC) ; Belgian National Funds of Scientific Research (FNRS) ; Actions de Recherches Concertées (ARC, ULg) – Fondation Médicale Reine Elisabeth (FMRE) ; Walloon Excellence in Lifesciences and Biotechnology (WELBIO) ; Wellcome Trust ; Biotechnology and Biological Sciences Research Council (BBSRC) [less ▲]

Detailed reference viewed: 102 (28 ULg)
See detailInfluence of sleep homeostasis and circadian rhythm on waking EEG oscillations during a constant routine
Muto, Vincenzo ULg; Meyer, Christelle ULg; Jaspar, Mathieu ULg et al

Poster (2012, September)

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 ... [more ▼]

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. Results. Delta activity 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. Theta activity 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. Alpha activity 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. Conclusion 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. REFERENCES 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) [less ▲]

Detailed reference viewed: 110 (15 ULg)
Full Text
Peer Reviewed
See detailModulation of fMRI assessed brain responses to blue and green light by sleep homeostasis, circadian phase and PER3 polymorphism
Vandewalle, Gilles ULg; Archer, Simon; Wuillaume, Catherine et al

in Sleep (2009), 32(Suppl. 1),

Detailed reference viewed: 16 (2 ULg)
Full Text
Peer Reviewed
See detailPolymorphism in PERIOD3 predicts fMRI-assessed inter-individual differences in the effects of sleep deprivation
Vandewalle, Gilles ULg; Archer, Simon; Wuillaume, Catherine et al

in Journal of Sleep Research (2008), 17(Suppl. 1),

Detailed reference viewed: 10 (3 ULg)
Full Text
Peer Reviewed
See detailA PERIOD3 polymorphism predicts fMRI assessed brain responses following sleep loss
Vandewalle, Gilles ULg; Archer, Simon; Wuillaume, Catherine et al

in Sleep (2008), 31(Suppl. 1),

Detailed reference viewed: 12 (2 ULg)