Dynamic functional connectivity states characterize NREM sleep and wakefulness

Abstract

According to recent neuroimaging studies, temporal fluctuations in functional connectivity patterns can be clustered into dynamic functional connectivity (DFC) states and correspond to fluctuations in vigilance. However, whether there consistently exist DFC states associated with wakefulness and sleep stages and what are the characteristics and electrophysiological origin of these states remain unclear. The aims of the current study were to investigate the properties of DFC in different sleep stages and to explore the relationship between the characteristics of DFC and slow‐wave activity. We collected both eyes‐closed wakefulness and sleep data from 48 healthy young volunteers with simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. EEG data were employed as the gold standard of sleep stage scoring, and DFC states were estimated based on fMRI data. The results demonstrated that DFC states of the fMRI signals consistently corresponded to wakefulness and nonrapid eye movement sleep stages independent of the number of clusters. Furthermore, the mean dwell time of these states significantly correlated with slow‐wave activity. The inclusion or omission of regression of the global signal and the selection of parcellation schemes exerted minimal effects on the current findings. These results provide strong evidence that DFC states underlying fMRI signals match the fluctuations of vigilance and suggest a possible electrophysiological source of DFC states corresponding to vigilance states.

Variable functional connectivity architecture of the preterm human brain: Impact of developmental cortical expansion and maturation

“…To assess the impact of the state of consciousness on FC variability pattern and its potential bias on estimating developmental changes in FC variability, we performed additional analyses comparing adults scanned while asleep (n = 17) with adults scanned while awake with eyes open (n = 17). Data from subjects scanned while asleep were provided by Zhou and colleagues and have been described in detail previously (19). Data from subjects scanned while awake were collected as part of the Brain Genomics Superstruct Project of Harvard University and the Massachusetts General Hospital (20).…”

Cerebral functional networks during sleep in young and older individuals

“…Age-related differences may be even greater in a sample of older individuals who are more representative of the broader population. Furthermore, investigating dynamic connectivity, which revealed specific recurring connectivity patterns for wakefulness and NREM sleep 48 , will help further understanding of sleep connectivity modifications in aging. Other limitations include the greater connectivity instability in wakefulness after sleep deprivation 49 which possibly reduces the observed differences for comparison including wakefulness.…”

“…Our work, as well as results from others, have indicated that the effects of sleep deprivation differ in young and older individuals. As compared to young individuals, older participants show a similar or smaller impact of sleep deprivation on waking EEG and cognitive performance and a shallower rebound of slow wave sleep during recovery sleep 16 , 48 – 51 . Together, these results suggest that the sleep deprivation may have enhanced age-related differences in functional connectivity between the wakefulness and sleep states.…”

Functional connectivity of the human hypothalamus during wakefulness and nonrapid eye movement sleep

“…Additionally, we showed that the negative connection between the two hypothalamic regions was stronger during N1 and N3 than during N2. The lower negative correlation between these two hypothalamic regions may be due to the more frequent state transitions, leading to less stable connectivity during N2 (Zhou et al, 2019).…”