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bioRxiv · 10.1101/2020.07.08.193508

NREM sleep stages specifically alter dynamical integration of large-scale brain networks

Abstract

Functional dissociations in the brain observed during non-rapid eye movement (NREM) sleep have been mainly associated with reduced information integration and impaired consciousness that accompany increasing sleep depth. Most of the studies that evaluated this relation have mostly been focused on spatial alterations of brain networks across various vigilance states. Here, we explored the dynamical properties of large-scale functional brain networks derived from transients or moments of activity changes in fMRI using two complementary studies of simultaneous EEG-fMRI recordings of sleep. Our results revealed distinct alterations in the individual network’s dynamical characteristics across wakefulness and NREM sleep. Positive activations of visual-sensory areas simultaneously deactivate focal subcortical regions known to be involved with arousal regulation. The cerebellum is also found to dissociate into posterior and anterior regions, with the former being predominantly present during wakefulness than in the deep sleep. Most surprisingly, we found that global network activity and pair-wise network interactions increased significantly in NREM stage 2 before an abrupt loss of communication is observed in NREM stage 3. Thus, by providing a spatiotemporal and more accurate network-level representation of brain organization, we were able to capture new features of information integration of consciousness during sleep, and provide concrete evidence for the presence of unstable yet distributed global synchronization in NREM stage 2.Competing Interest StatementThe authors have declared no competing interest.View Full Text

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BibTeXRIS

Anjali Tarun, Danyal Wainstein-Andriano, Virginie Sterpenich, Laurence Bayer, Lampros Perogamvros, Mark Solms, Nikolai Axmacher, Sophie Schwartz, Dimitri Van De Ville. 2020-07-09. NREM sleep stages specifically alter dynamical integration of large-scale brain networks. https://doi.org/10.1101/2020.07.08.193508

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