La technologie Physip
La puissance du numérique au service du traitement des données physiologiques
Physip, créée en 2002, est une spin-off de Telecom ParisTech : le projet est issu d’une thèse portant sur le traitement numérique de signaux d’EEG de sommeil. Physip fait partie des pionniers de l’utilisation de technologies du numérique pour l’analyse de signaux EEG.
Physip dispose d’un ensemble de savoir-faire complémentaires : traitement du signal appliqué à l’EEG, méthodes d’apprentissage et de classification, traitement massif de données orientés vers l’analyse numérique de signaux d’électroencéphalogramme (EEG).
Nous avons constitué un portefeuille exceptionnel de technologies développées en interne
- Analyse automatique du sommeil ASEEGA
- Evaluation automatique du niveau de somnolence MEEGAWAKE
- Evaluation de la pression de sommeil MEEGAFIT
- Prédiction du risque de performances dégradées MEEGAPERF
- Une plateforme d’outils d’expertise du signal
L’EEG à 2 capteurs :
une approche unique, une avancée technologique décisive
Dès l’origine, Physip a développé tous ses modules innovants d’analyse de l’EEG en intégrant la contrainte forte de la réduction du nombre de capteurs. Les analyses de Physip n’ont besoin que de 2 capteurs EEG. Pourquoi cette contrainte ? Pour ouvrir les possibles de l’EEG, sortir l’EEG du laboratoire. Réduire le nombre de capteurs, c’est faciliter la pose de l’EEG pour les médecins et les techniciens EEG, c’est faciliter les utilisations ambulatoires, c’est améliorer l’acceptabilité de l’EEG pour des utilisations professionnelles, peut-être des utilisations quotidiennes dans un futur proche. Réduire le nombre de capteurs, c’est aussi réduire le volume de données, pour des applications déportées, pour la télémédecine.
Physip conduit un R&D exigeante. Elle nous permet de faire avancer en permanence les algorithmes d’analyse existants et d’en développer de nouveaux pour répondre à de nouveaux besoins scientifiques et industriels.
Efficacité et Rigueur
La R&D à Physip est essentiellement collaborative. Nous travaillons avec des académiques et des industriels. Nous avons un réseau de collaborations académiques en support de notre R&D dans le domaine du sommeil et de la vigilance, des sciences cognitives, des performances, du traitement du signal. Physip a une importante activité de publication scientifique pour démontrer la fiabilité de ses solutions. Nous travaillons avec des industriels des secteurs des transports, de l’aéronautique, du militaire…
Avec une dizaine de projets de recherche menés à bien, nous avons acquis une expérience unique dans le développement d’algorithmes d’identification de marqueurs EEG, de la phase exploratoire à la validation clinique ou en conditions simulées.
Projets de recherche
Scoal : Sommeil, Cognition et troubles de la mémoire dans la maladie d'Alzheimer, 2011-2015
Objectif L’enjeu du projet SCOAL est de mieux comprendre les troubles du sommeil (changements de l’architecture du sommeil et/ou apparition de troubles du sommeil) avant-coureurs dans les troubles cognitifs légers et leur contribution potentielle à une éventuelle détérioration cognitive progressive. Pour Physip, il s’agit de rechercher dans l’EEG de sommeil des marqueurs diagnostiques et pronostiques de la maladie d’Alzheimer. Partenaires Le projet est piloté par l’unité CNRS Sanpsy, avec Patricia Sagaspe et Jacques Taillard notamment. Il réunit, outre Physip,
Financement Le projet est financé par le programme ANR MALZ. Résultats Des premiers résultats ont été publiés au congrès de l’ESRS 2014 à Tallinn. 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
Meegaperf : Monitoring EEG automatique des PERForances, 2011-2015
Objectif Meegaperf vise à identifier de façon automatique dans l’EEG des marqueurs de risques de performances dégradées au niveau physique et cognitif. Le projet consiste à enregistrer l’EEG de sujets au cours de tâches cognitives et physiques propices aux baisses ou ruptures de performances, pour repérer les signes dans l’activité cérébrales qui permettent d’anticiper ces ruptures de performances. Financement Meegaperf est un projet RAPID, Régime d’Aide aux Projets d’Innovation Duale, financé par la DGA et la DGCIS devenue DGE. Didier Bazalgette, responsable Facteurs Humains de la MRIS (DGA) accompagne ce projet. PartenairesLe travail sur les performances physiques a nécessité d’imaginer un dispositif complexe, permettant l’enregistrement synchronisé de plusieurs signaux physiologiques, rapportés à la performance physique évaluée par la fréquence de pédalage d’un ergocycle. La conception et la réalisation du dispositif d’acquisition ont réuni tous les partenaires et des compétences variées pour aboutir à des données exceptionnelles. Meegaperf, piloté par Physip, réunit :
- l’unité Inserm U1028, du Centre de Recherche Neurologique de Lyon : Jérémie Mattout, Aline Bompas, Emmanuel Maby, Patrick Bouchet, travaillent ou ont travaillé pour le projet pour le volet performances cognitives et l’enregistrement des signaux EEG du volet performances physiques.
- le CRIS, Centre de Recherche sur l’Innovation dans le Sport, Université Lyon I : Christian Collet, Vincent Pialoux, Michel Clémençon, Thomas Creveaux sur les performances physiques.
- le département TSI de Telecom ParisTech : Slim Essid, Cecilia Damon, François Rigaud, Alexis Benichoux pour le travail sur les artefacts EEG lors d’acquisitions en mouvement.
En s’appuyant sur cette objectivation des performances physiques, Physip développe l’algorithmie EEG de détection des marqueurs EEG de rupture de performances.
Objectif Développer une méthode d’analyse de la pression de sommeil mesurée par KDT (Karolinska Drowsiness Test). Partenaires Drowsimeter a été réalisée avec l’équipe Sanpsy, Jacques Taillard en particulier. Soutien et financement Le projet a fait l’objet d’un financement AIMA, BPI – Centre Francilien de l’Innovation. Résultats Une méthode de réjection automatique d’artefact a été développée et validée. Elle permet de réaliser de façon rapide, objective, reproductible, le travail de prétraitement indispensable préalable au calcul de la puissance spectrale dans la bande 4-7Hz. Les résultats ont été présentés en communication au Congrès du Sommeil à Marseille en 2013.
DAM : Drowsiness Automatic Monitoring, 2006-2008
Validation sujets sains, 2003-2005
co-fondateur de Physip, PDG
co-fondateur de Physip, Directeur IT
Ingénieur de recherche
Qu’est-ce que Physip ?
Physip est une société française, située à Paris, dans le 11e arrondissement, qui a développé des méthodes innovantes d’analyse de l’activité cérébrale de sommeil et d’éveil via l’électroencéphalogramme. Nous proposons ces méthodes aux médecins, chercheurs, aux industriels de la pharma et de l’équipement médical pour optimiser leur analyse de données de sommeil et d’éveil.
Que signifie ASEEGA® ?
ASEEGA® signifie Automatic Sleep EEG Analysis. Le logiciel ASEEGA® existe en 3 versions : Essentiel, Lab et Recherche. Trouvez ici quelle version est la plus adaptée à vos besoins et vos usages.
Quelle est la spécificité de votre méthode d’analyse ?
Nos méthodes d’analyse partagent toutes les mêmes caractéristiques : elles sont entièrement automatiques, basées sur l’EEG seul et sur une seule dérivation d’EEG, située préférentiellement en CzPz. Elles atteignent ainsi un compromis unique entre performance et praticité.
Quelle est la fiabilité de l’analyse ASEEGA® ?
Nous disposons d’une base de plus de 500 enregistrements de PSG de nuit, qui nous permettent de contrôler en permanence la rigueur et la fiabilité de notre travail. Les validations d’ASEEGA® ont fait l’objet de publications dans des revues majeures. De grandes équipes de recherche internationales font confiance aux performances d’ASEEGA®.
Quelle est votre expertise dans le domaine de l’analyse du sommeil ?
Nous développons des méthodes d’analyse de l’EEG de sommeil et d’éveil depuis presque 25 ans, sur une expertise internationalement reconnue en traitement du signal appliqué à l’EEG. L’ensemble de nos développements a été réalisé en collaboration étroite avec des médecins et chercheurs spécialistes du domaine.
📖 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.
📃 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.
📃 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
📖 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.
📖 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
📃 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.
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.
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
📃 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