Un service d’analyse de données EEG de sommeil et d’éveil

Des algorithmes et de l’expertise

Notre service d’analyse réunit des outils puissants d’analyse du sommeil et de l’éveil, et un accompagnement pour vous aider à recueillir des données de qualité et produire des résultats de qualité.

1

Un accompagnement à la réalisation des études, de la phase de design jusqu’à l’utilisation des résultats

  • Aide à la définition des protocoles d’étude,
  • Expertise de la qualité technique des données EEG : expertise des caractéristiques d’enregistrement (fréquence d’échantillonnage, résolution), expertise du bruit environnant, lié aux autres appareils d’enregistrement, à l’environnement électromagnétique…
  • Conseil pour la pose des capteurs pour améliorer la qualité technique des données
  • Monitoring au long cours de la qualité des données sur toute la durée de l’étude pour assurer la continuité des données
  • Support pour l’extraction et l’exploitation des résultats d’analyse

2

Une suite de prétraitements
et d’analyse dédiés à l’EEG et sommeil
et d’éveil

  • Scorage automatique des stades de sommeil : algorithme ASEEGA
  • Scorage automatique de la somnolence : algorithme MEEGAWAKE
  • Analyse automatique de tests de KDT : algorithme MEEGAFIT
  • Analyse spectrale
  • Détection et analyse de spindles
  • Paramètres de sommeil : temps passé dans les différents stades, latences, cycles…
  • Localisation d’artefacts

3

D’autres types de prestations
sont possibles

  • Qualification de la situation d’enregistrement
  • Qualification de l’environnement d’enregistrement
  • Choix d’un matériel adapté aux contraintes de l’étude

Fiabilité

Les algorithmes Physip sont

Itérations

Ils sont reproductibles par design : 2 itérations de nos analyses sur un même enregistrement avec la même version de l’algorithme produiront 2 fois un résultat strictement identique

Pourcents

Ils sont entièrement automatiques : ils proposent une analyse objective, dépourvue de biais

Ans

Ils sont validés cliniquement par un ensemble de publications de référence. Ils sont utilisés depuis plus de 10 ans par les plus grandes équipes de recherche

Pourcents

Les résultats sont systématiquement vérifiés manuellement pour assurer la qualité des résultats

Puissance et flexibilité

Nos analyses ouvrent les possibles de l’analyse du sommeil et de la vigilance.
Ils fournissent des résultats d’analyse à la pointe de l’état actuel des connaissances sur le sommeil : hypnogramme, micro structures, analyse de l’activité alpha…

La puissance de l’analyse automatique permet de traiter de très gros volumes de données, autour de 5000 enregistrements/jour à l’heure actuelle : le sommeil peut entrer dans l’ère du big data.

Tous nos algorithmes sont conçus pour pouvoir réduire le nombre de capteurs nécessaires : ASEEGA et MEEGAWAKE ne nécessitent que 2 capteurs EEG, Cz – Pz préférentiellement ou C4 – O2 possiblement, pour produire un scorage du sommeil et de la somnolence. Cette caractéristique, exigeante pour nous, offre une flexibilité maximale pour vos études :

Données de PSG classique

  • scorage + toutes analyses sur Cz-Pz ou C4 O2
  • scorage sur Cz – Pz ou C4 – O2 + toutes analyses (analyse spectrale, spindles) sur toutes ou partie des dérivations disponibles
  • pas de scorage si Cz – Pz et C4 – O2 sont absentes ou corrompues, toutes analyses (analyse spectrale, spindles) sur toutes ou partie des dérivations disponible

Acquisitions possibles en montage réduit : Cz – Pz + 1 électrode de Ground seulement

  • Temps de montage réduit
  • Utilisation possible d’appareils moins coûteux que des enregistreurs PSG : appareils de polygraphie, appareils EEG réduits
  • Permet les études out-of-the-lab, au domicile, en vie réelle

Contexte

Le scorage du sommeil est expert dépendant. La littérature a démontré la variabilité inter expert et intra expert du scorage : 2 scoreurs qui lisent un même tracé n’en donnent pas la même analyse, 1 même scoreur qui lit 1 même tracé à quelques mois d’intervalles n’en donne pas la même analyse.

Ces variations ne sont pas négligeables. Elles introduisent des biais dans les analyses. Il peut être difficile de commenter des variations dans les résultats, dès lors qu’on n’est pas absolument sûr qu’elles ne viennent pas de variations dans la façon dont les données ont été analysées – inter ou intra expert. L’analyse automatique déterministe telle qu’elle est mise en œuvre dans nos algorithmes annule la variabilité inter et intra expert.

Elle garantit que toutes les données d’une étude sont analysées de façon strictement identique. Elle garantit que les données de différentes études, dans différents labos, sont analysées de façon strictement identiques. Elle permet les comparaisons fines, les études transversales, les études de big data.

Références clients

Questions fréquentes

Est-ce que l’analyse d’ASEEGA® est reproductible ?

Oui, ASEEGA est 100% reproductible : un même tracé analysé deux fois par le logiciel ASEEGA® produira exactement le même résultat.

Quelles règles de scorage sont suivies par le logiciel ASEEGA®

Le logiciel ASEEGA® classifie le sommeil comme l’AASM en 5 stades, 4 stades de sommeil + l’éveil. La définition de l’endormissement, nécessaire pour le calcul de nombreux paramètres de sommeil, suit également les règles de l’AASM. D’autres classifications et d’autres définitions de l’endormissement sont disponibles au besoin.

Pourquoi utiliser CzPz

Notre approche consiste à simplifier l’EEG, d’où le choix de réduire le nombre de capteurs utilisés. Dans cette perspective, et dans la suite des travaux du Dr Odile Benoit, CzPz s’est révélé la voie la plus propice. L’analyse a été développée et validée sur cette dérivation. Utiliser CzPz est la garantie d’obtenir la plus grande fiabilité de l’analyse.

Comment arrivez-vous à distinguer l’éveil et le REM en n’utilisant que l’EEG ?

Grâce à la mise en œuvre de méthodes performantes de traitement du signal, le logiciel ASEEGA® parvient à réaliser cette distinction, sans le concours de l’EOG ni de l’EMG. La concordance entre analyse visuelle et analyse automatique est similaire sur le REM et l’éveil et sur les autres stades de sommeil.

Publications

Applications

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📖 Berthomier C., et al., Looking for a reference for large datasets: relative reliability of visual and automatic sleep scoring, bioRxiv 2019

C. Berthomier, V. Muto, C. Schmidt, G. Vandewalle, M. Jaspar, J. Devillers, G. Gaggioni, S. Chellappa, C. Meyer, C. Phillips, E. Salmon, P. Berthomier, J. Prado, O. Benoit, M. Brandewinder, J. Mattout, P. Maquet, Looking for a reference for large datasets: relative reliability of visual and automatic sleep scoring, BioRxiv doi: https://doi.org/10.1101/576090

Study Objectives: New challenges in sleep science require to describe fine grain phenomena or to deal with large datasets. Beside the human resource challenge of scoring huge datasets, the inter- and intra-expert variability may also reduce the sensitivity of such studies. Searching for a way to disentangle the variability induced by the scoring method from the actual variability in the data, visual and automatic sleep scorings of healthy individuals were examined. Methods: A first dataset (DS1, 4 recordings) scored by 6 experts plus an autoscoring algorithm was used to characterize inter-scoring variability. A second dataset (DS2, 88 recordings) scored a few weeks later was used to investigate intra-expert variability. Percentage agreements and Conger′s kappa were derived from epoch-by-epoch comparisons on pairwise, consensus and majority scorings. Results: On DS1 the number of epochs of agreement decreased when the number of expert increased, in both majority and consensus scoring, where agreement ranged from 86% (pairwise) to 69% (all experts). Adding autoscoring to visual scorings changed the kappa value from 0.81 to 0.79. Agreement between expert consensus and autoscoring was 93%. On DS2 intra-expert variability was evidenced by the kappa systematic decrease between autoscoring and each single expert between datasets (0.75 to 0.70). Conclusions: Visual scoring induces inter- and intra-expert variability, which is difficult to address especially in big data studies. When proven to be reliable and if perfectly reproducible, autoscoring methods can cope with intra-scorer variability making them a sensible option when dealing with large datasets.

> Accéder à l’article Looking for a reference for large datasets: relative reliability of visual and automatic sleep scoring

📃 Chylinski D., et al., Sleep fragmentation is associated with brain tau but not amyloid-β burden in healthy older adults, Front. Neurosci. Conference Abstract 2019

Chylinski D, Rudzik F, Coppieters ‘T Wallant D, Van Egroo M, Muto V, Narbutas J, Villar González P, Besson G, Lambot E, Laloux S, Hagelstein C, Ghaemmaghami P, Degueldre C, Berthomier C, Bethomier P, Brandewinder M, Schmidt C, Maquet P, Salmon E, Phillips C, Bahri M, Bastin C, Collette F and Vandewalle G (2019). Sleep fragmentation is associated with brain tau but not amyloid-β burden in healthy older adults. Front. Neurosci. Conference Abstract: Belgian Brain Congress 2018 — Belgian Brain Council. doi: 10.3389/conf.fnins.2018.95.00054

Belgian Brain Congress 2018

📖 Gaggioni G. et al., Age-related decrease in cortical excitability circadian variations during sleep loss and its links with cognition, Neurobiology of Aging 2019

Giulia Gaggioni, Julien Q.M. Ly, Vincenzo Muto, Sarah L. Chellappa, Mathieu Jaspar, Christelle Meyer, Tillo Delfosse, Amaury Vanvinckenroye, Romain Dumont, Dorothée Coppieters ‘t Wallant, Christian Berthomier, Justinas Narbutas, Maxime Van Egroo, Andé Luxen, Eric Salmon, Fabienne Collette, Christophe Phillips, ChristinanSchmidt, Gilles Vandewalle, Age-related decrease in cortical excitability circadian variations during sleep loss and its links with cognition, Neurobiology of Aging, Volume 78, June 2019

Cortical excitability depends on sleep-wake regulation, is central to cognition, and has been implicated in age-related cognitive decline. The dynamics of cortical excitability during prolonged wakefulness in aging are unknown, however. Here, we repeatedly probed cortical excitability of the frontal cortex using transcranial magnetic stimulation and electroencephalography in 13 young and 12 older healthy participants during sleep deprivation. Although overall cortical excitability did not differ between age groups, the magnitude of cortical excitability variations during prolonged wakefulness was dampened in older individuals. This age-related dampening was associated with mitigated neurobehavioral consequences of sleep loss on executive functions. Furthermore, higher cortical excitability was potentially associated with better and lower executive performance, respectively, in older and younger adults. The dampening of cortical excitability dynamics found in older participants likely arises from a reduced impact of sleep homeostasis and circadian processes. It may reflect reduced brain adaptability underlying reduced cognitive flexibility in aging. Future research should confirm preliminary associations between cortical excitability and behavior and address whether maintaining cortical excitability dynamics can counteract age-related cognitive decline.

> Accéder à l’article complet : Age-related decrease in cortical excitability circadian variations during sleep loss and its links with cognition

📖 Taillard J. et al., Non-REM Sleep Characteristics Predict Early Cognitive Impairment in an Aging Population, Front. Neurol. 2019

Jacques Taillard, Patricia Sagaspe, Christian Berthomier, Marie Brandewinder, Hélène Amieva, Jean-François Dartigues, Muriel Rainfray, Sandrine Harston, Jean-Arthur Micoulaud-Franchi and Pierre Philip, Non-REM Sleep Characteristics Predict Early Cognitive Impairment in an Aging Population, Front. Neurol., 13 March 2019 | https://doi.org/10.3389/fneur.2019.00197

Objective: Recent research suggests that sleep disorders or changes in sleep stages or EEG waveform precede over time the onset of the clinical signs of pathological cognitive impairment (e.g., Alzheimer’s disease). The aim of this study was to identify biomarkers based on EEG power values and spindle characteristics during sleep that occur in the early stages of mild cognitive impairment (MCI) in older adults.
Methods: This study was a case-control cross-sectional study with 1-year follow-up of cases. Patients with isolated subjective cognitive complaints (SCC) or MCI were recruited in the Bordeaux Memory Clinic (MEMENTO cohort). Cognitively normal controls were recruited. All participants were recorded with two successive polysomnography 1 year apart. Delta, theta, and sigma absolute spectral power and spindle characteristics (frequency, density, and amplitude) were analyzed from purified EEG during NREM and REM sleep periods during the entire second night.
Results: Twenty-nine patients (8 males, age = 71 ± 7 years) and 29 controls were recruited at T0. Logistic regression analyses demonstrated that age-related cognitive impairment were associated with a reduced delta power (odds ratio (OR) 0.072, P < 0.05), theta power (OR 0.018, P < 0.01), sigma power (OR 0.033, P < 0.05), and spindle maximal amplitude (OR 0.002, P < 0.05) during NREM sleep. Variables were adjusted on age, gender, body mass index, educational level, and medication use. Seventeen patients were evaluated at 1-year follow-up. Correlations showed that changes in self-reported sleep complaints, sleep consolidation, and spindle characteristics (spectral power, maximal amplitude, duration, and frequency) were associated with cognitive impairment (P < 0.05).
Conclusion: A reduction in slow-wave, theta and sigma activities, and a modification in spindle characteristics during NREM sleep are associated very early with a greater risk of the occurrence of cognitive impairment. Poor sleep consolidation, lower amplitude, and faster frequency of spindles may be early sleep biomarkers of worsening cognitive decline in older adults.

> Accéder à l’article complet : Non-REM Sleep Characteristics Predict Early Cognitive Impairment in an Aging Population

📖 Ghaemmaghami P., et al., The genetic liability for insomnia is associated with lower amount of slow wave sleep in young and healthy individuals. Front. Neurosci. Conference Abstract: Belgian Brain Congress 2018

Ghaemmaghami P, Muto V, Jaspar M, Meyer C, Elansary M, VanEgroo M, Berthomier C, Lambot E, Brandewinder M, Luxen A, Degueldre C, Salmon E, Archer SN, Phillips C, Dijk D, Posthuma D, Van Someren E, Collette F, Georges M, Maquet P and Vandewalle G (2018). The genetic liability for insomnia is associated with lower amount of slow wave sleep in young and healthy individuals. Front. Neurosci. Conference Abstract: Belgian Brain Congress 2018 — Belgian Brain Council. doi: 10.3389/conf.fnins.2018.95.00069

Introduction. Identifying risk factors for insomnia in individuals that are likely to develop insomnia is needed to develop prevention strategies. Novel genetic tools using results of large case-control genome wide association studies (GWAS) allow to predict the liability for complex diseases based on full-genome common genetic variations. Here, we applied such tools to assess the link between the genetic liability of developing insomnia and sleep phenotypes in young and healthy adults not reporting any sleep complaint.

Methods. Electroencephalography was recording during 8h baseline sleep in 360 healthy young male volunteers with normal sleep (aged 22.09 y ± 2.71). Sleep architecture, the percentage and latency of each sleep stage, and the total number, hourly rate and mean duration of awakenings were extracted from automatic sleep scoring (Aseega, Physip). Blood samples were collected in all participants to assess common Single Nucleotide Polymorphisms (SNPs) over the entire genome. Individual liability for insomnia was computed based on whole genome SNPs using the summary-statistics of a case-control GWAS seeking for genetic determinants of insomnia (Hammerschlag et al. Nat Genet 2017;49:1584–1592, https://ctg.cncr.nl/software/summary_statistics).

Results. Generalized linear mixed model reveal significant associations of one’s genetic risk score for insomnia with the percentage of sleep stage 2 (r = 0.13, p <0.05) and the percentage of sleep stage 3 (r = -0.12, p < 0.05). These results suggest that higher liability for insomnia is associated with lower percentage of slow wave sleep while it is associated with higher percentage of lighter sleep. The results remain significant after adjusting for age.

Conclusion. These results show that individual genetic liability for insomnia is linked to sleep lower amount of what is considered most important to dissipate sleep need (i.e. slow wave sleep) in young and healthy individuals. These findings could help identifying novel prevention targets for insomnia.

> Accéder à l’article : The genetic liability for insomnia is associated with lower amount of slow wave sleep in young and healthy individuals.

📃 Escourrou P. J., et al., Respiratory events during stable wake in sleep apnea patients: occurrence and mechanism, European Respiratory Journal 2018

P. J. Escourrou, M. Brandewinder, C. Berthomier, P. Berthomier, G. Baffet, Z. Balekji, N. Puisais, G. Roisman, Respiratory events during stable wake in sleep apnea patients: occurrence and mechanism., European Respiratory Journal 2018 52: PA2509; DOI: 10.1183/13993003.congress-2018.PA2509

📃 Muto V. et al., Inter- And Intra-expert Variability In Sleep Scoring: Comparison Between Visual And Automatic Analysis, Sleep, 2018

V. Muto, C. Berthomier, C. Schmidt, G. Vandewalle, M. Jaspar, J. Devillers, S. Chellappa, C. Meyer, C. Phillips, P. Berthomier, J. Prado, O. Benoit, M. Brandewinder, J. Mattout, P. Maquet, Inter- And Intra-expert Variability In Sleep Scoring: Comparison Between Visual And Automatic Analysis, Sleep, Volume 41, Issue suppl_1, 27 April 2018, Pages A121, https://doi.org/10.1093/sleep/zsy061.314

📃 Peter-Derex L. et al., Evaluation Of A Single-channel Automatic Sleep Analysis Software In Sleep Disorders, Sleep 2018

L. Peter-Derex, C. Berthomier, M. Brandewinder, J. Mattout, P. Berthomier, H. Bastuji, Evaluation Of A Single-channel Automatic Sleep Analysis Software In Sleep Disorders, Sleep, Volume 41, Issue suppl_1, 27 April 2018, Pages A406, https://doi.org/10.1093/sleep/zsy061.1091

📖 Maire M., et al., Human brain patterns underlying vigilant attention: impact of sleep debt, circadian phase and attentional engagement, Sci Rep. 2018

Maire M, Reichert CF, Gabel V, Viola AU, Phillips C, Berthomier C, Borgwardt S, Cajochen C, Schmidt C. Human brain patterns underlying vigilant attention: impact of sleep debt, circadian phase and attentional engagement. Sci Rep. 2018 Jan 17;8(1):970. doi: 10.1038/s41598-017-17022-9.

Sleepiness and cognitive function vary over the 24-h day due to circadian and sleep-wake-dependent mechanisms. However, the underlying cerebral hallmarks associated with these variations remain to be fully established. Using functional magnetic resonance imaging (fMRI), we investigated brain responses associated with circadian and homeostatic sleep-wake-driven dynamics of subjective sleepiness throughout day and night. Healthy volunteers regularly performed a psychomotor vigilance task (PVT) in the MR-scanner during a 40-h sleep deprivation (high sleep pressure) and a 40-h multiple nap protocol (low sleep pressure). When sleep deprived, arousal-promoting thalamic activation during optimal PVT performance paralleled the time course of subjective sleepiness with peaks at night and troughs on the subsequent day. Conversely, task-related cortical activation decreased when sleepiness increased as a consequence of higher sleep debt. Under low sleep pressure, we did not observe any significant temporal association between PVT-related brain activation and subjective sleepiness. Thus, a circadian modulation in brain correlates of vigilant attention was only detectable under high sleep pressure conditions. Our data indicate that circadian and sleep homeostatic processes impact on vigilant attention via specific mechanisms; mirrored in a decline of cortical resources under high sleep pressure, opposed by a subcortical « rescuing » at adverse circadian times.

> Accéder à l’article complet : Human brain patterns underlying vigilant attention: impact of sleep debt, circadian phase and attentional engagement

📖 Dang-Vu T. T., et al., Sleep spindles may predict response to cognitive behavioral therapy for chronic insomnia, Sleep Med., 2017

Dang-Vu TT, Hatch B, Salimi A, Mograss M, Boucetta S, O’Byrne J, Brandewinder M, Berthomier C, Gouin JP., Sleep spindles may predict response to cognitive behavioral therapy for chronic insomnia, Sleep Med. 2017 Nov;39:54-61. doi: 10.1016/j.sleep.2017.08.012.

BACKGROUND: While cognitive-behavioral therapy for insomnia constitutes the first-line treatment for chronic insomnia, only few reports have investigated how sleep architecture relates to response to this treatment. In this pilot study, we aimed to determine whether pre-treatment sleep spindle density predicts treatment response to cognitive-behavioral therapy for insomnia. METHODS: Twenty-four participants with chronic primary insomnia participated in a 6-week cognitive-behavioral therapy for insomnia performed in groups of 4-6 participants. Treatment response was assessed using the Pittsburgh Sleep Quality Index and the Insomnia Severity Index measured at pre- and post-treatment, and at 3- and 12-months’ follow-up assessments. Secondary outcome measures were extracted from sleep diaries over 7 days and overnight polysomnography, obtained at pre- and post-treatment. Spindle density during stage N2-N3 sleep was extracted from polysomnography at pre-treatment. Hierarchical linear modeling analysis assessed whether sleep spindle density predicted response to cognitive-behavioral therapy. RESULTS: After adjusting for age, sex, and education level, lower spindle density at pre-treatment predicted poorer response over the 12-month follow-up, as reflected by a smaller reduction in Pittsburgh Sleep Quality Index over time. Reduced spindle density also predicted lower improvements in sleep diary sleep efficiency and wake after sleep onset immediately after treatment. There were no significant associations between spindle density and changes in the Insomnia Severity Index or polysomnography variables over time. CONCLUSION: These preliminary results suggest that inter-individual differences in sleep spindle density in insomnia may represent an endogenous biomarker predicting responsiveness to cognitive-behavioral therapy. Insomnia with altered spindle activity might constitute an insomnia subtype characterized by a neurophysiological vulnerability to sleep disruption associated with impaired responsiveness to cognitive-behavioral therapy.

> Accéder au résumé : Sleep spindles may predict response to cognitive-behavioral therapy for chronic insomnia.

📖 Reichert C.F., et al., Cognitive brain responses during circadian wake-promotion: evidence for sleep-pressure-dependent hypothalamic activations, Sci Rep. 2017

Reichert CF, Maire M, Gabel V, Viola AU, Götz T, Scheffler K, Klarhöfer M, Berthomier C, Strobel W, Phillips C, Salmon E, Cajochen C, Schmidt C., Cognitive brain responses during circadian wake-promotion: evidence for sleep-pressure-dependent hypothalamic activations, Sci Rep. 2017 Jul 17;7(1):5620. doi: 10.1038/s41598-017-05695-1.

The two-process model of sleep-wake regulation posits that sleep-wake-dependent homeostatic processes interact with the circadian timing system to affect human behavior. The circadian timing system is fundamental to maintaining stable cognitive performance, as it counteracts growing homeostatic sleep pressure during daytime. Using magnetic resonance imaging, we explored brain responses underlying working memory performance during the time of maximal circadian wake-promotion under varying sleep pressure conditions. Circadian wake-promoting strength was derived from the ability to sleep during an evening nap. Hypothalamic BOLD activity was positively linked to circadian wake-promoting strength under normal, but not under disproportionally high or low sleep pressure levels. Furthermore, higher hypothalamic activity under normal sleep pressure levels predicted better performance under sleep loss. Our results reappraise the two-process model by revealing a homeostatic-dose-dependent association between circadian wake-promotion and cognition-related hypothalamic activity.

> Accéder à l’article complet : Cognitive brain responses during circadian wake-promotion: evidence for sleep-pressure-dependent hypothalamic activations

📖 Vallat R., et al., Increased Evoked Potentials to Arousing Auditory Stimuli during Sleep: Implication for the Understanding of Dream Recall, Front Hum Neurosci. 2017

Vallat R, Lajnef T, Eichenlaub JB, Berthomier C, Jerbi K, Morlet D, Ruby PM., Increased Evoked Potentials to Arousing Auditory Stimuli during Sleep: Implication for the Understanding of Dream Recall. Front Hum Neurosci. 2017 Mar 21;11:132. doi: 10.3389/fnhum.2017.00132. eCollection 2017.

High dream recallers (HR) show a larger brain reactivity to auditory stimuli during wakefulness and sleep as compared to low dream recallers (LR) and also more intra-sleep wakefulness (ISW), but no other modification of the sleep macrostructure. To further understand the possible causal link between brain responses, ISW and dream recall, we investigated the sleep microstructure of HR and LR, and tested whether the amplitude of auditory evoked potentials (AEPs) was predictive of arousing reactions during sleep. Participants (18 HR, 18 LR) were presented with sounds during a whole night of sleep in the lab and polysomnographic data were recorded. Sleep microstructure (arousals, rapid eye movements (REMs), muscle twitches (MTs), spindles, KCs) was assessed using visual, semi-automatic and automatic validated methods. AEPs to arousing (awakenings or arousals) and non-arousing stimuli were subsequently computed. No between-group difference in the microstructure of sleep was found. In N2 sleep, auditory arousing stimuli elicited a larger parieto-occipital positivity and an increased late frontal negativity as compared to non-arousing stimuli. As compared to LR, HR showed more arousing stimuli and more long awakenings, regardless of the sleep stage but did not show more numerous or longer arousals. These results suggest that the amplitude of the brain response to stimuli during sleep determine subsequent awakening and that awakening duration (and not arousal) is the critical parameter for dream recall. Notably, our results led us to propose that the minimum necessary duration of an awakening during sleep for a successful encoding of dreams into long-term memory is approximately 2 min.

> Accéder à l’article complet : Increased Evoked Potentials to Arousing Auditory Stimuli during Sleep: Implication for the Understanding of Dream

📖 Dang-Vu T. T., et al., Sleep spindles predict stress-related increases in sleep disturbances, Front. Hum. Neurosci., 2015
Dang-Vu T. T., Salimi A., Boucetta S., Wenzel K., O’Byrne J., M. Brandewinder M., Berthomier C. and Gouin J.-P., Sleep spindles predict stress-related increases in sleep disturbances, Front. Hum. Neurosci., 10 February 2015 | doi: 10.3389/fnhum.2015.00068
 
The aim of this study was to prospectively assess whether spindle density would predict the worsening of sleep disturbances in response to a standardized stressor. We chose to follow a population of undergraduate university students during a period of increasing academic stress. In this context, assessing students at the beginning of the semester, corresponding to a lower stress period, and reevaluating them during a follow-up in the week preceding the final examinations, a period of higher stress, provides a unique opportunity to examine individual differences in the evolution of insomnia symptoms in response to a standardized stressor.
 
📃 O'Byrne J, et al., Spindles and slow waves predict treatment responses to cognitive-behavioural therapy for chronic primary insomnia, J. Sleep Res. 2014

O’Byrne J, Boucetta S, Reed L,Malhi O, Zhang V, Arcelin A, Wenzel K, Brandewinder M, Berthomier C, Gouin J-P, Dang-Vu TT. Spindles and slow waves predict treatment responses to cognitive-behavioural therapy for chronic primary insomnia. J. Sleep Res., 23 Suppl. 1:209, 2014 22nd ESRS Congress, Tallinn, Estonia 16 – 20 September 2014

📃 Sagaspe P, et al., Polysomnographic data in patients with isolated memory complaints or mild cognitive impairment, J. Sleep Res. 2014

Sagaspe P, Taillard J, Chaufton C, Berthomier C, Brandewinder M, Amiéva H, Dartigues J-F, Philip P. Polysomnographic data in patients with isolated memory complaints or mild cognitive impairment. J. Sleep Res., 23 Suppl. 1:240, 2014 22nd ESRS Congress, Tallinn, Estonia 16 – 20 September 2014  

📖 Eichenlaub JB, et al., Brain Reactivity Differentiates Subjects with High and Low Dream Recall Frequencies during Both Sleep and Wakefulness, Cereb. Cortex 2014

Eichenlaub JB, Bertrand O, Morlet D, Ruby P. Brain Reactivity Differentiates Subjects with High and Low Dream Recall Frequencies during Both Sleep and Wakefulness. Cereb. Cortex 24 (5): 1206-1215. 2014

The neurophysiological correlates of dreaming remain unclear. According to the « arousal-retrieval » model, dream encoding depends on intrasleep wakefulness. Consistent with this model, subjects with high and low dream recall frequency (DRF) report differences in intrasleep awakenings. This suggests a possible neurophysiological trait difference between the 2 groups. To test this hypothesis, we compared the brain reactivity (evoked potentials) of subjects with high (HR, N = 18) and low (LR, N = 18) DRF during wakefulness and sleep. During data acquisition, the subjects were presented with sounds to be ignored (first names randomly presented among pure tones) while they were watching a silent movie or sleeping. Brain responses to first names dramatically differed between the 2 groups during both sleep and wakefulness. During wakefulness, the attention-orienting brain response (P3a) and a late parietal response were larger in HR than in LR. During sleep, we also observed between-group differences at the latency of the P3a during N2 and at later latencies during all sleep stages. Our results demonstrate differences in the brain reactivity of HR and LR during both sleep and wakefulness. These results suggest that the ability to recall dreaming is associated with a particular cerebral functional organization, regardless of the state of vigilance.

> Accéder à l’article complet : Brain Reactivity Differentiates Subjects with High and Low Dream Recall Frequencies during Both Sleep and Wakefulness

📖 Ruby P, et al., Alpha Reactivity to Complex Sounds Differs during REM Sleep and Wakefulness, PLoS ONE 2013

Ruby P, Blochet C, Eichenlaub J-B, Bertrand O, Morlet D, Bidet-Caulet A. Alpha Reactivity to Complex Sounds Differs during REM Sleep and Wakefulness. PLoS ONE 8(11): e79989, 2013.

We aimed at better understanding the brain mechanisms involved in the processing of alerting meaningful sounds during sleep, investigating alpha activity. During EEG acquisition, subjects were presented with a passive auditory oddball paradigm including rare complex sounds called Novels (the own first name – OWN, and an unfamiliar first name – OTHER) while they were watching a silent movie in the evening or sleeping at night. During the experimental night, the subjects’ quality of sleep was generally preserved. During wakefulness, the decrease in alpha power (8–12 Hz) induced by Novels was significantly larger for OWN than for OTHER at parietal electrodes, between 600 and 900 ms after stimulus onset. Conversely, during REM sleep, Novels induced an increase in alpha power (from 0 to 1200 ms at all electrodes), significantly larger for OWN than for OTHER at several parietal electrodes between 700 and 1200 ms after stimulus onset. These results show that complex sounds have a different effect on the alpha power during wakefulness (decrease) and during REM sleep (increase) and that OWN induce a specific effect in these two states. The increased alpha power induced by Novels during REM sleep may 1) correspond to a short and transient increase in arousal; in this case, our study provides an objective measure of the greater arousing power of OWN over OTHER, 2) indicate a cortical inhibition associated with sleep protection. These results suggest that alpha modulation could participate in the selection of stimuli to be further processed during sleep.

> Accéder à l’article complet : Alpha Reactivity to Complex Sounds Differs during REM Sleep and Wakefulness

📖 Ruby P, et al., Alpha reactivity to first names differs in subjects with high and low dream recall frequency, Front Psychol 2013

Ruby P, Blochet C, Eichenlaub JB, Bertrand O, Morlet D, Bidet-Caulet A. Alpha reactivity to first names differs in subjects with high and low dream recall frequency. Front Psychol.;4:419, 2013.

Studies in cognitive psychology showed that personality (openness to experience, thin boundaries, absorption), creativity, nocturnal awakenings, and attitude toward dreams are significantly related to dream recall frequency (DRF). These results suggest the possibility of neurophysiological trait differences between subjects with high and low DRF. To test this hypothesis we compared sleep characteristics and alpha reactivity to sounds in subjects with high and low DRF using polysomnographic recordings and electroencephalography (EEG). We acquired EEG from 21 channels in 36 healthy subjects while they were presented with a passive auditory oddball paradigm (frequent standard tones, rare deviant tones and very rare first names) during wakefulness and sleep (intensity, 50 dB above the subject’s hearing level). Subjects were selected as High-recallers (HR, DRF = 4.42 ± 0.25 SEM, dream recalls per week) and Low-recallers (LR, DRF = 0.25 ± 0.02) using a questionnaire and an interview on sleep and dream habits. Despite the disturbing setup, the subjects’ quality of sleep was generally preserved. First names induced a more sustained decrease in alpha activity in HR than in LR at Pz (1000–1200 ms) during wakefulness, but no group difference was found in REM sleep. The current dominant hypothesis proposes that alpha rhythms would be involved in the active inhibition of the brain regions not involved in the ongoing brain operation. According to this hypothesis, a more sustained alpha decrease in HR would reflect a longer release of inhibition, suggesting a deeper processing of complex sounds than in LR during wakefulness. A possibility to explain the absence of group difference during sleep is that increase in alpha power in HR may have resulted in awakenings. Our results support this hypothesis since HR experienced more intra sleep wakefulness than LR (30 ± 4 vs. 14 ± 4 min). As a whole our results support the hypothesis of neurophysiological trait differences in high and low-recallers.

> Accéder à l’article complet : Alpha reactivity to first names differs in subjects with high and low dream recall frequency  

📖 Schmidt C, et al., Circadian and homeostatic modulation of cognition-related cerebral activity in humans, J. Sleep Res. 2013

Schmidt C, Maire M, Reichert C F, Scheffler K, Klarhoefer M, Strobel W, Krebs J, Berthomier P, Berthomier C, Cajochen C. Circadian and homeostatic modulation of cognition-related cerebral activity in humans. J. Sleep Res. 21 (Suppl. 1), 1-371, 2012.

Brain mechanisms involved in the maintenance of wakefulness and associated cognitive processes are affected by inter-individual differences in sleep-wake regulation. For instance, different time-of-day and sleep-wake related modulations in cognition-associated cerebral activity are chronotype and PERIOD3 genotype dependent. However, the respective contributions of circadian and homeostatic processes on neurobehavioral performance and their cerebral correlates throughout the 24-h cycle remain largely unexplored. In a current project, we further investigate the impact of these processes on the cerebral correlates underlying human cognition in a 40-h multiple nap (NP) and sleep deprivation (SD) protocol.
Results:
In this ongoing study we have observed that the circadian and sleep-wake homeostatic modulation in subjective sleepiness and objective vigilance undergoes considerable inter-individual differences. Electrophysiological data report the classical slow wave sleep rebound or decrease observed during the recovery night after the SD and NP conditions respectively. A preliminary analysis of the fMRI data, comparing task-related BOLD activity while performing the psychomotor vigilance task during the biological night (3 h before scheduled wake up time) in the first 11 participants indicated that differential homeostatic sleep pressure levels (SD versus NP) exert an effect on task-related BOLD activity. Globally, cortical responses (e.g. inferior frontal, middle temporal, insula) are higher while performing intermediate reaction time levels on the PVT when sleep pressure is kept low by multiple naps. When looking at BOLD activity underlying optimal PVT performance, at the end of the biological night, the preliminary results indicate that hypothalamic responses as well as several cortical areas (e.g. bilateral insula) are more active under NP as compared to SD conditions. Whether the above mentioned inter-individual variations in neurobehavioral performance are paralleled by differences in cognition-related BOLD activity is currently being analysed.
Conclusion:
Time of day and disproportional homeostatic sleep pressure affect neurobehavioral performance modulation, which is mirrored at the cerebral level. The existence of large inter-individual variability in the vulnerability to circadian and/or homeostatic related detrimental effects on neurobehavioral performance should be taken into account in future analyses.

Symposium SFRMS – Sleep and Neuro-Psychiatric Disorders

📖 Schmidt C, et al., Circadian preference modulates the neural substrate of conflict processing across the day, PLoS One 2012

Schmidt C, Peigneux P, Leclercq Y, Sterpenich V, Vandewalle G, Philips C, Berthomier P, Berthomier C, Tinguely G, Gais S, Schabus M, Desseilles M, Dang-Vu T, Salmon E, Degueldre C, Balteau E, Luxen A, Cajochen C, Maquet P, Collette F. Circadian preference modulates the neural substrate of conflict processing across the day. PLoS One. 2012;7(1):e29658. 2012 Jan 4. PLoS One. 2012;7(1):e29658. 2012 Jan 4.

Human morning and evening chronotypes differ in their preferred timing for sleep and wakefulness, as well as in optimal daytime periods to cope with cognitive challenges. Recent evidence suggests that these preferences are not a simple by-product of socio-professional timing constraints, but can be driven by inter-individual differences in the expression of circadian and homeostatic sleep-wake promoting signals. Chronotypes thus constitute a unique tool to access the interplay between those processes under normally entrained day-night conditions, and to investigate how they impinge onto higher cognitive control processes. Using functional magnetic resonance imaging (fMRI), we assessed the influence of chronotype and time-of-day on conflict processing-related cerebral activity throughout a normal waking day. Sixteen morning and 15 evening types were recorded at two individually adapted time points (1.5 versus 10.5 hours spent awake) while performing the Stroop paradigm. Results show that interference-related hemodynamic responses are maintained or even increased in evening types from the subjective morning to the subjective evening in a set of brain areas playing a pivotal role in successful inhibitory functioning, whereas they decreased in morning types under the same conditions. Furthermore, during the evening hours, activity in a posterior hypothalamic region putatively involved in sleep-wake regulation correlated in a chronotype-specific manner with slow wave activity at the beginning of the night, an index of accumulated homeostatic sleep pressure. These results shed light into the cerebral mechanisms underlying inter-individual differences of higher-order cognitive state maintenance under normally entrained day-night conditions.

> Accéder à l’article complet : Circadian preference modulates the neural substrate of conflict processing across the day

Validation

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📃 Berthomier C. et al., Real-Time Automatic Measure of Drowsiness based on a Single EEG Channel, Journal of Sleep Research, 2008

Berthomier C, Muzet A, Berthomier P, Prado J, Mattout J, Real-Time Automatic Measure of Drowsiness based on a Single EEG Channel, Journal of Sleep Research, Vol 17/suppl.1,P434, 2008

19th Congress of the European Sleep Research Society (ESRS)
September 9-13, 2008, Glasgow, UK

📃 Berthomier C. et al., Real-Time Automatic Wake/Sleep Scoring based on a Single EEG Channel, Sleep 2008

Berthomier C, Herman-Stoïca M, Berthomier P, Drouot X, Prado J, Mattout J, d’Ortho MP, Real-Time Automatic Wake/Sleep Scoring based on a Single EEG Channel, Sleep, Vol 31 (suppl.), A338, 2008 22nd Annual Meeting of the Associated Professional

Sleep Societies, LLC (SLEEP 2008)
June 7-12, 2008, Baltimore, MD, USA

📖 Berthomier C. et al., Automatic Analysis of Single-Channel Sleep EEG: Validation in Healthy Individuals, Sleep, 2007

Berthomier C; Drouot X; Herman-Stoïca M; Berthomier P; Prado J; Bokar-Thire D; Benoit O; Mattout J; d’Ortho MP., Automatic Analysis of Single-Channel Sleep EEG: Validation in Healthy Individuals, Sleep 2007;30(11):1587-1595.

STUDY OBJECTIVE: To assess the performance of automatic sleep scoring software (ASEEGA) based on a single EEG channel comparatively with manual scoring (2 experts) of conventional full polysomnograms. DESIGN: Polysomnograms from 15 healthy individuals were scored by 2 independent experts using conventional R&K rules. The results were compared to those of ASEEGA scoring on an epoch-by-epoch basis. SETTING: Sleep laboratory in the physiology department of a teaching hospital. PARTICIPANTS: Fifteen healthy volunteers. MEASUREMENTS AND RESULTS: The epoch-by-epoch comparison was based on classifying into 2 states (wake/sleep), 3 states (wake/REM/ NREM), 4 states (wake/REM/stages 1-2/SWS), or 5 states (wake/REM/ stage 1/stage 2/SWS). The obtained overall agreements, as quantified by the kappa coefficient, were 0.82, 0.81, 0.75, and 0.72, respectively. Furthermore, obtained agreements between ASEEGA and the expert consensual scoring were 96.0%, 92.1%, 84.9%, and 82.9%, respectively. Finally, when classifying into 5 states, the sensitivity and positive predictive value of ASEEGA regarding wakefulness were 82.5% and 89.7%, respectively. Similarly, sensitivity and positive predictive value regarding REM state were 83.0% and 89.1%. CONCLUSIONS: Our results establish the face validity and convergent validity of ASEEGA for single-channel sleep analysis in healthy individuals. ASEEGA appears as a good candidate for diagnostic aid and automatic ambulant scoring.

> Accéder à l’article complet : Automatic Analysis of Single-Channel Sleep EEG: Validation in Healthy Individuals

📃 Berthomier C. et al., Real-Time Automatic Measurement of Recorded Sleep Time, Chest 2007

Berthomier C, Berthomier P, Herman-Stoïca M, Drouot X, Prado J, Benoit O, Mattout J, d’Ortho MP, Real-Time Automatic Measurement of Recorded Sleep Time, Chest, 132/4 (Suppl.) 649S, 2007

American College of Chest Physicians (ACCP) congress: CHEST.
20-25 octobre 2007, Chicago, Il, USA

📃 Berthomier C. et al., Wake-REM-NREM Automatic Classification based on a Single EEG Channel: Epoch by Epoch Comparison with Human Sleep Scoring in Patients, J. Sleep Res., 2006

Berthomier C, Drouot X, Herman-Stoïca M, Berthomier P, Prado J, Benoit O, Mattout J, d’Ortho MP, Wake-REM-NREM Automatic Classification based on a Single EEG Channel: Epoch by Epoch Comparison with Human Sleep Scoring in Patients,  J. Sleep Res., Vol 15/suppl.1, P295, 2006

18th Congress of the European Sleep Research Society (ESRS)
September 12-16, 2006, Innsbruck, Austria

📃 Berthomier C. et al., A Wake-REM-NREM Automatic Analysis Using a Single EEG Channel: Epoch by Epoch Comparison with Human Sleep Scoring in Healthy Subjects, Sleep Medicine 2005

Berthomier C, Drouot X, Herman-Stoïca M, Berthomier P, Prado J, Mattout J, d’Ortho MP, A Wake-REM-NREM Automatic Analysis Using a Single EEG Channel: Epoch by Epoch Comparison with Human Sleep Scoring in Healthy Subjects, Sleep Medicine, Vol. 6/Suppl.2 (2005), S194

1st Congress of the World Association of Sleep Medicine (WASM)
15-18 October 2005, Berlin, Germany

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