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See detailHuman cortical excitability depends on time spent awake and circadian phase
Ly, Julien ULg; Gaggioni, Giulia ULg; Chellappa, Sarah Laxhmi ULg et al

Scientific conference (2014, October 04)

At any point in time, human performance results from the interaction of two main factors: a circadian signal varying with the time of the day and the sleep need accrued throughout the preceding waking ... [more ▼]

At any point in time, human performance results from the interaction of two main factors: a circadian signal varying with the time of the day and the sleep need accrued throughout the preceding waking period. But what’s happen at the cortical cerebral level? We used a novel technique coupling transcranial magnetic stimulation with electroencephalography (TMS/EEG) to assess the influence of time spent awake and circadian phasis on human cortical excitability. Twenty-two healthy young men underwent 8 TMS/EEG sessions during a 28 hour sleep deprivation protocole. We found that cortical excitability depends on both time spent awake and circadian phasis. [less ▲]

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See detailHuman cortical excitability depends on time awake and circadian phase
Ly, Julien ULg; Chellappa, Sarah Laxhmi ULg; Gaggioni, Giulia ULg et al

Conference (2014, September 17)

At any point in time, human performance results from the interaction of two main factors: a circadian signal varying with the time of the day and the sleep need accrued throughout the preceding waking ... [more ▼]

At any point in time, human performance results from the interaction of two main factors: a circadian signal varying with the time of the day and the sleep need accrued throughout the preceding waking period. But what’s happen at the cortical cerebral level? We used a novel technique coupling transcranial magnetic stimulation with electroencephalography (TMS/EEG) to assess the influence of time spent awake and circadian phasis on human cortical excitability. Twenty-two healthy young men underwent 8 TMS/EEG sessions during a 28 hour sleep deprivation protocole. We found that cortical excitability depends on both time spent awake and circadian phasis. [less ▲]

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See detailHuman cortical excitability depends on time spent awake and circadian phase
Ly, Julien ULg; Chellappa, Sarah Laxhmi ULg; Gaggioni, Giulia ULg et al

Conference (2014, September 17)

At any point in time, human performance results from the interaction of two main factors: a circadian signal varying with the time of the day and the sleep need accrued throughout the preceding waking ... [more ▼]

At any point in time, human performance results from the interaction of two main factors: a circadian signal varying with the time of the day and the sleep need accrued throughout the preceding waking period. But what’s happen at the cortical cerebral level? We used a novel technique coupling transcranial magnetic stimulation with electroencephalography (TMS/EEG) to assess the influence of time spent awake and circadian phasis on human cortical excitability. Twenty-two healthy young men underwent 8 TMS/EEG sessions during a 28 hour sleep deprivation protocole. We found that cortical excitability depends on both time spent awake and circadian phasis. [less ▲]

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See detailCortical excitability dynamics of during sleep deprivation set PVT performance
Borsu, Chloé; Gaggioni, Giulia ULg; Ly, Julien ULg et al

Poster (2014, September)

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See detailThe circadian system sets the temporal organization of basic human neuronal function
Chellappa, Sarah Laxhmi ULg; Ly, Julien; Gaggioni, Giulia ULg et al

Conference (2014, June 16)

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See detailNeuroimaging, cognition, light and circadian rhythms
Gaggioni, Giulia ULg; Maquet, Pierre ULg; Schmidt, Christina et al

in Frontiers in Systems Neuroscience [=FNSYS] (2014)

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See detailCortical excitability dynamics of during sleep deprivation set PVT performance
Borsu, Chloé; Gaggioni, Giulia ULg; Ly, Julien et al

Conference (2014)

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See detailCortical excitability depends on time awake and circadian phase
Ly, Julien; Gaggioni, Giulia ULg; Chellappa, Sarah Laxhmi ULg et al

Conference (2014)

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See detailNeuroimaging the effects of light on non-visual brain functions
Vandewalle, Gilles ULg; Dijk, Derk-Jan

in Neuroimaging of Sleep and Sleep Disorders (2013)

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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)

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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 ▲]

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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 ▲]

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See detailInterindividual differences in circadian rhythmicity and sleep homeostasis in older people: effect of a PER3 polymorphism.
Viola, Antoine U.; Chellappa, Sarah Laxhmi ULg; Archer, Simon N. et al

in Neurobiology of Aging (2012), 33(5), 101017-27

Aging is associated with marked changes in the timing, consolidation and structure of sleep. Older people wake up frequently, get up earlier and have less slow wave sleep than young people, although the ... [more ▼]

Aging is associated with marked changes in the timing, consolidation and structure of sleep. Older people wake up frequently, get up earlier and have less slow wave sleep than young people, although the extent of these age-related changes differs considerably between individuals. Interindividual differences in homeostatic sleep regulation in young volunteers are associated with the variable-number, tandem-repeat (VNTR) polymorphism (rs57875989) in the coding region of the circadian clock gene PERIOD3 (PER3). However, predictors of these interindividual differences have yet to be identified in older people. Sleep electroencephalographic (EEG) characteristics and circadian rhythms were assessed in 26 healthy older volunteers (55-75 years) selected on the basis of homozygosity for either the long or short allele of the PER3 polymorphism. Homozygosity for the longer allele (PER3(5/5)) associated with a phase-advance in the circadian melatonin profile and an earlier occurrence of the melatonin peak within the sleep episode. Furthermore, older PER3(5/5) participants accumulated more nocturnal wakefulness, had increased EEG frontal delta activity (0.75-1.50 Hz), and decreased EEG frontal sigma activity (11-13 Hz) during non-rapid eye movement (REM) sleep compared with PER3(4/4) participants. Our results indicate that the polymorphism in the clock gene PER3 may contribute to interindividual differences in sleep and circadian physiology in older people. [less ▲]

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See detailReciprocal interactions between wakefulness and sleep influence global and regional brain activity
Muto, Vincenzo ULg; Mascetti, Laura ULg; Matarazzo, Luca et al

in Current Topics in Medicinal Chemistry (2011), 11(19), 2403-13

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See detailEffects of light on cognitive brain responses depend on circadian phase and sleep homeostasis.
Vandewalle, Gilles ULg; Archer, Simon N; Wuillaume, Catherine et al

in Journal of biological rhythms (2011), 26(3), 249-59

Light is a powerful modulator of cognition through its long-term effects on circadian rhythmicity and direct effects on brain function as identified by neuroimaging. How the direct impact of light on ... [more ▼]

Light is a powerful modulator of cognition through its long-term effects on circadian rhythmicity and direct effects on brain function as identified by neuroimaging. How the direct impact of light on brain function varies with wavelength of light, circadian phase, and sleep homeostasis, and how this differs between individuals, is a largely unexplored area. Using functional MRI, we compared the effects of 1 minute of low-intensity blue (473 nm) and green light (527 nm) exposures on brain responses to an auditory working memory task while varying circadian phase and status of the sleep homeostat. Data were collected in 27 subjects genotyped for the PER3 VNTR (12 PER3(5/5) and 15 PER3(4/4) ) in whom it was previously shown that the brain responses to this task, when conducted in darkness, depend on circadian phase, sleep homeostasis, and genotype. In the morning after sleep, blue light, relative to green light, increased brain responses primarily in the ventrolateral and dorsolateral prefrontal cortex and in the intraparietal sulcus, but only in PER3(4/4) individuals. By contrast, in the morning after sleep loss, blue light increased brain responses in a left thalamofrontoparietal circuit to a larger extent than green light, and only so in PER3(5/5) individuals. In the evening wake maintenance zone following a normal waking day, no differential effect of 1 minute of blue versus green light was observed in either genotype. Comparison of the current results with the findings observed in darkness indicates that light acts as an activating agent particularly under those circumstances in which and in those individuals in whom brain function is jeopardized by an adverse circadian phase and high homeostatic sleep pressure. [less ▲]

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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),

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See detailBrain responses following sleep loss are predicted by a polymorphism in PERIOD3 as assessed in humans using fMRI
Vandewalle, Gilles ULg; Archer, Simon N; Wuillaume, Catherine et al

Poster (2008, May)

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