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Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep

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  • A. B.A. Stevner, University of Oxford
  • ,
  • D. Vidaurre
  • J. Cabral
  • K. Rapuano, Department of Psychological and Brain Sciences, Dartmouth College
  • ,
  • S. F.V. Nielsen, Department of Applied Mathematics and Computer Science
  • ,
  • E. Tagliazucchi, Netherlands Institute for Neuroscience NIN - KNAW, University Hospital Schleswig-Holstein, Johann Wolfgang Goethe Universitat Frankfurt am Main
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  • H. Laufs, University Hospital Schleswig-Holstein, Johann Wolfgang Goethe Universitat Frankfurt am Main
  • ,
  • P. Vuust
  • G. Deco, Universitat Pompeu Fabra, Barcelona, Theoretical and Computational Neuroscience Group, Center of Brain and Cognition, Universitat Pompeu Fabra, 08018 Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain., Max Planck Institute for Human Cognitive and Brain Sciences, Monash University
  • ,
  • M. W. Woolrich, University of Oxford
  • ,
  • E. Van Someren, Netherlands Institute for Neuroscience NIN - KNAW, Vrije Universiteit Amsterdam
  • ,
  • M. L. Kringelbach

The modern understanding of sleep is based on the classification of sleep into stages defined by their electroencephalography (EEG) signatures, but the underlying brain dynamics remain unclear. Here we aimed to move significantly beyond the current state-of-the-art description of sleep, and in particular to characterise the spatiotemporal complexity of whole-brain networks and state transitions during sleep. In order to obtain the most unbiased estimate of how whole-brain network states evolve through the human sleep cycle, we used a Markovian data-driven analysis of continuous neuroimaging data from 57 healthy participants falling asleep during simultaneous functional magnetic resonance imaging (fMRI) and EEG. This Hidden Markov Model (HMM) facilitated discovery of the dynamic choreography between different whole-brain networks across the wake-non-REM sleep cycle. Notably, our results reveal key trajectories to switch within and between EEG-based sleep stages, while highlighting the heterogeneities of stage N1 sleep and wakefulness before and after sleep.

Original languageEnglish
Article number1035
JournalNature Communications
Number of pages14
Publication statusPublished - 2019

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