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

A B A Stevner,H Laufs,K Rapuano,S F V Nielsen,J Cabral,E Tagliazucchi,G Deco,M L Kringelbach,D Vidaurre,M W Woolrich,E Van Someren,P Vuust

doi:10.1038/s41467-019-08934-3

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 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.

Highlights

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
Using a data-driven exploration of large-scale brain networks and associated dynamics from continuous functional magnetic resonance imaging (fMRI) recordings, we have explored the rich dynamical complexity in spatiotemporal patterns of brain activity during the healthy wake-NREM sleep cycle
Comparing the temporal evolution of the Hidden Markov Model (HMM)-derived whole-brain network states with the independently obtained EEG-based PSG scoring, we have discovered a rich repertoire of brain dynamics underpinning the traditional PSG stages

Summary

Introduction

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. 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. On-going brain activity is recorded from a low number of EEG electrodes and typically categorised into wakefulness, rapid-eye movement (REM) sleep and—according to the most recent set of guidelines—three stages of non-REM (NREM) sleep (N1–N3)[2]. The relative lack of correspondence between PSG and subjective experience becomes important in populations with sleep complaints, where PSG is not indicated in the clinical evaluation of insomnia, the most common of all sleep disorders[7,8]

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