Abstract
Human brain dynamics and functional connectivity fluctuate over a range of temporal scales in coordination with internal states and environmental demands. However, the neurobiological significance and consequences of functional connectivity dynamics during rest have not yet been established. We show that the coarse-grained clustering of whole-brain dynamic connectivity measured with magnetic resonance imaging reveals discrete patterns (dynamic connectivity states) associated with wakefulness and sleep. We validate this using EEG in healthy subjects and patients with narcolepsy and by matching our results with previous findings in a large collaborative database. We also show that drowsiness may account for previous reports of metastable connectivity states associated with different levels of functional integration. This implies that future studies of transient functional connectivity must independently monitor wakefulness. We conclude that a possible neurobiological significance of dynamic connectivity states, computed at a sufficiently coarse temporal scale, is that of fluctuations in wakefulness.
Highlights
The dynamics of neural populations in the human brain leads to a continuously changing landscape of interactions with cell assemblies forming and dissolving either spontaneously or in coordination with sensory stimulation[1, 2]
We base our results on the analysis of four different datasets: 1) The sleep dataset, consisting of 58 subjects scanned for 52 minutes with simultaneous EEG-functional magnetic resonance imaging (fMRI) and containing subjects investigated in previous work who exhibit wakefulness as well as sleep during the scanning session[33] (23% N1, 19% N2, and 10% N3 sleep, age 23.5 ± 3.3, 39 females)
We demonstrate that the clustering of dynamic functional connectivity over relatively short temporal windows identifies fluctuations in wakefulness as determined with simultaneous polysomnography, the gold standard for sleep scoring
Summary
The dynamics of neural populations in the human brain leads to a continuously changing landscape of interactions with cell assemblies forming and dissolving either spontaneously or in coordination with sensory stimulation[1, 2]. Arousal[27, 31], a hypothesis supported by the observation that loss of wakefulness during typical resting state experiments induces non-stationarities in whole-brain dynamical connectivity[16] The verification of this hypothesis requires the investigation of fMRI combined with EEG (the gold standard for sleep staging)[32] and has not been performed prior to this report. Our analysis gives researchers at hand a data-driven and non-supervised method for the identification of sleep in resting state fMRI recordings without the need of simultaneous EEG monitoring This more universal approach extends the supervised classifiers introduced in a previous report[33] and allows proper (re-) interpretation of past and future resting state studies in the context of fluctuating degrees of wakefulness
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