The regional integrity of brain subcortical structures has been implicated in sleep-wake regulation, however, their associations with sleep parameters remain largely unexplored. Here, we assessed association between quantitative Magnetic Resonance Imaging (qMRI)-derived marker of the myelin content of the brainstem and the variability in the sleep electrophysiology in a large sample of 18-to-31 years healthy young men (N = 321; similar to 22 years). Separate Generalized Additive Model for Location, Scale and Shape (GAMLSS) revealed that sleep onset latency and slow wave energy were significantly associated with MTsat estimates in the brainstem (p(corrected) <= 0.03), with overall higher MTsat value associated with values reflecting better sleep quality. The association changed with age, however (MTsat-by-age interaction-p(corrected) <= 0.03), with higher MTsat value linked to better values in the two sleep metrics in the younger individuals of our sample aged similar to 18 to 20 years. Similar associations were detected across different parts of the brainstem (p(corrected) <= 0.03), suggesting that the overall maturation and integrity of the brainstem was associated with both sleep metrics. Our results suggest that myelination of the brainstem nuclei essential to regulation of sleep is associated with inter-individual differences in sleep characteristics during early adulthood. They may have implications for sleep disorders or neurological diseases related to myelin.

Sleep has been suggested to contribute to myelinogenesis and associated structural changes in the brain. As a principal hallmark of sleep, slow-wave activity (SWA) is homeostatically regulated but also differs between individuals. Besides its homeostatic function, SWA topography is suggested to reflect processes of brain maturation. Here, we assessed whether interindividual differences in sleep SWA and its homeostatic response to sleep manipulations are associated with in-vivo myelin estimates in a sample of healthy young men. Two hundred twenty-six participants (18-31 y.) underwent an in-lab protocol in which SWA was assessed at baseline (BAS), after sleep deprivation (high homeostatic sleep pressure, HSP) and after sleep saturation (low homeostatic sleep pressure, LSP). Early-night frontal SWA, the frontal-occipital SWA ratio, as well as the overnight exponential SWA decay were computed over sleep conditions. Semi-quantitative magnetization transfer saturation maps (MTsat), providing markers for myelin content, were acquired during a separate laboratory visit. Early-night frontal SWA was negatively associated with regional myelin estimates in the temporal portion of the inferior longitudinal fasciculus. By contrast, neither the responsiveness of SWA to sleep saturation or deprivation, its overnight dynamics, nor the frontal/occipital SWA ratio were associated with brain structural indices. Our results indicate that frontal SWA generation tracks inter-individual differences in continued structural brain re-organization during early adulthood. This stage of life is not only characterized by ongoing region-specific changes in myelin content, but also by a sharp decrease and a shift towards frontal predominance in SWA generation.

Study Objectives Sleep disturbances and genetic variants have been identified as risk factors for Alzheimer’s disease (AD). Our goal was to assess whether genome-wide polygenic risk scores (PRS) for AD associate with sleep phenotypes in young adults, decades before typical AD symptom onset. Methods We computed whole-genome PRS for AD and extensively phenotyped sleep under different sleep conditions, including baseline sleep, recovery sleep following sleep deprivation, and extended sleep opportunity, in a carefully selected homogenous sample of 363 healthy young men (22.1 years ± 2.7) devoid of sleep and cognitive disorders. Results AD PRS was associated with more slow-wave energy, that is, the cumulated power in the 0.5–4 Hz EEG band, a marker of sleep need, during habitual sleep and following sleep loss, and potentially with larger slow-wave sleep rebound following sleep deprivation. Furthermore, higher AD PRS was correlated with higher habitual daytime sleepiness. Conclusions These results imply that sleep features may be associated with AD liability in young adults, when current AD biomarkers are typically negative, and support the notion that quantifying sleep alterations may be useful in assessing the risk for developing AD.

Sleep studies face new challenges in terms of data, objectives and metrics. This requires reappraising the adequacy of existing analysis methods, including scoring methods. Visual and automatic sleep scoring of healthy individuals were compared in terms of reliability (i.e., accuracy and stability) to find a scoring method capable of giving access to the actual data variability without adding exogenous variability. A first dataset (DS1, four recordings) scored by six 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 explore intra-expert variability. Percentage agreements and Conger's kappa were derived from epoch-by-epoch comparisons on pairwise and consensus scorings. On DS1 the number of epochs of agreement decreased when the number of experts increased, ranging 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 the hypothesis of intra-expert variability was supported by a systematic decrease in kappa scores between autoscoring used as reference and each single expert between datasets (.75-.70). Although visual scoring induces inter- and intra-expert variability, autoscoring methods can cope with intra-scorer variability, making them a sensible option to reduce exogenous variability and give access to the endogenous variability in the data.

A bidirectional detrimental relationship between sleep alteration and Alzheimer’s disease 35 (AD) has been reported in cognitively normal older adults. Here, we tested whether a similar 36 association could be detected in young adults, decades before typical AD symptom onset. We 37 investigated associations between sleep endophenotypes and genome-wide Polygenic Risk 38 Scores (PRS) for AD in 363 young men (22.1±2.7y) devoid of sleep and cognitive disorders. AD 39 PRS was associated with higher slow wave energy, a marker of sleep need, during habitual 40 sleep and following sleep loss, and, potentially, with the relative increase in slow wave energy 41 following sleep deprivation, reflecting sleep homeostasis. Furthermore high AD PRS was 42 correlated with higher daytime sleepiness. These results imply that sleep features may be 43 associated with AD liability in young adults and suggest that, contrary to older adults, denser 44 and/or more intense sleep slow waves are associated with AD risk in early adulthood. 45

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. Statement of Significance We confirmed and extended previous findings highlighting the intra- and inter-expert variability in visual sleep scoring. On large datasets those variability issues cannot be completely addressed by neither practical nor statistical solutions such as group training, majority or consensus scoring. When an automated scoring method can be proven to be as reasonably imperfect as visual scoring but perfectly reproducible, it can serve as a reliable scoring reference for sleep studies. * EEG : – Electroencephalogram EOG : – Electro-oculogram EMG : – Electromyogram PSG : – Polysomnography BAS : – Baseline night EXT : – Extended sleep opportunity BEF : – Night before sleep deprivation REC : – Recovery night after sleep deprivation V : – visual scorer A : – automated analysis Aseega DS1 : – Dataset 1 DS2 : – Dataset 2

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.

Il a été montré que l’analyse visuelle (AV) de données polysomnographiques (PSG) est affectée par une variabilité inter-expert (différences entre les scorages d’un même tracé réalisés par 2 ou plusieurs experts) et intra-expert (différences entre les scorages d’un même expert). L’objectif était de quantifier, chez le sujet sain, la variabilité inter- et intra-expert au sein d’un groupe de 6 experts appartenant au même centre ayant travaillé à homogénéiser leurs scorages, en utilisant l’analyse automatique (AA) Aseega en référence. Quatre tracés (data set 1, DS1) ont été scorés (AASM) par chacun des 6 experts et l’AA, soit 28 scorages. Quatre-vingt-huit autres tracés (DS2) ont scorés quelques semaines après par les mêmes experts et par l’AA, soit 176 scorages. L’accord époque par époque (concordance et coefficient kappa de Conger, K) a été calculé entre les AV et l’AA. La concordance inter-expert sur DS1 est fonction du nombre d’experts comparés et passe de 86 % en moyenne entre 2 experts à 69 % pour 6 experts. K diminue de 0,81 à 0,79 en ajoutant AA au groupe d’AV. Entre DS1 et DS2, toutes les concordances entre AA et chaque AV diminuent (3,7 % en moyenne). Les désaccords entre experts ne se concentrent pas sur une faible proportion d’époques, même dans un groupe d’experts très homogène. La variabilité intra-expert s’observe par la dégradation de la concordance entre AV et AA entre DS1 et DS2 et peut s’interpréter comme une dérive du scorage visuel. L’AA, quand elle est reproductible et fiable, systématise l’analyse de données PSG.

Event Abstract Back to Event The genetic liability for insomnia is associated with lower amount of slow wave sleep in young and healthy individuals Pouya Ghaemmaghami1*, Vincenzo Muto1, 2, Mathieu Jaspar1, 2, 3, Christelle Meyer1, 2, Mahmoud Elansary3, Maxime VanEgroo1, Christian Berthomier4, Eric Lambot1, 2, Marie Brandewinder4, Andre Luxen1, Christian Degueldre1, Eric Salmon1, 3, 5, Simon N. Archer6, Christophe Phillips1, 7, Derk-Jan Dijk6, Danielle Posthuma8, Eus Van Someren8, Fabienne Collette1, 3, Michel Georges3, Pierre Maquet1, 2, 5 and Gilles Vandewalle1, 2 1 GIGA-Cyclotron Research Center In Vivo Imaging, University of Liege, Belgium 2 Walloon Excellence in Life Sciences and Biotechnology (WELBIO), Belgium 3 Université de Liège, Belgium 4 Other 5 Department of Neurology, University Hospital Liège, Belgium 6 Surrey Sleep Research Centre, University of Surrey, United Kingdom 7 GIGA-In silico Medicine, University of Liège, Belgium 8 Netherlands Institute for Neuroscience (KNAW), Netherlands 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. Acknowledgements FNRS, ULiège, ARC, FEDER, Welbio, FMRE, Clerdent Foundation Keywords: insomnia, Sleep, EEG, Genetics, aging Conference: Belgian Brain Congress 2018 — Belgian Brain Council, LIEGE, Belgium, 19 Oct - 19 Oct, 2018. Presentation Type: e-posters Topic: NOVEL STRATEGIES FOR NEUROLOGICAL AND MENTAL DISORDERS: SCIENTIFIC BASIS AND VALUE FOR PATIENT-CENTERED CARE Citation: 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 (2019). 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 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 23 Aug 2018; Published Online: 17 Jan 2019. * Correspondence: Dr. Pouya Ghaemmaghami, GIGA-Cyclotron Research Center In Vivo Imaging, University of Liege, Liège, Liège, 4000, Belgium, ghaemmaghami.pouya@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Pouya Ghaemmaghami Vincenzo Muto Mathieu Jaspar Christelle Meyer Mahmoud Elansary Maxime VanEgroo Christian Berthomier Eric Lambot Marie Brandewinder Andre Luxen Christian Degueldre Eric Salmon Simon N Archer Christophe Phillips Derk-Jan Dijk Danielle Posthuma Eus Van Someren Fabienne Collette Michel Georges Pierre Maquet Gilles Vandewalle Google Pouya Ghaemmaghami Vincenzo Muto Mathieu Jaspar Christelle Meyer Mahmoud Elansary Maxime VanEgroo Christian Berthomier Eric Lambot Marie Brandewinder Andre Luxen Christian Degueldre Eric Salmon Simon N Archer Christophe Phillips Derk-Jan Dijk Danielle Posthuma Eus Van Someren Fabienne Collette Michel Georges Pierre Maquet Gilles Vandewalle Google Scholar Pouya Ghaemmaghami Vincenzo Muto Mathieu Jaspar Christelle Meyer Mahmoud Elansary Maxime VanEgroo Christian Berthomier Eric Lambot Marie Brandewinder Andre Luxen Christian Degueldre Eric Salmon Simon N Archer Christophe Phillips Derk-Jan Dijk Danielle Posthuma Eus Van Someren Fabienne Collette Michel Georges Pierre Maquet Gilles Vandewalle PubMed Pouya Ghaemmaghami Vincenzo Muto Mathieu Jaspar Christelle Meyer Mahmoud Elansary Maxime VanEgroo Christian Berthomier Eric Lambot Marie Brandewinder Andre Luxen Christian Degueldre Eric Salmon Simon N Archer Christophe Phillips Derk-Jan Dijk Danielle Posthuma Eus Van Someren Fabienne Collette Michel Georges Pierre Maquet Gilles Vandewalle Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. 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