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

ABSTRACT: 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 choreograph

SUBMITTER: Stevner ABA 

PROVIDER: S-EPMC6399232 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature