NREM sleep stages specifically alter dynamical integration of large-scale brain networks

Abstract

Functional dissociations in the brain observed during non-rapid eye movement (NREM) sleep have been associated with reduced information integration and impaired consciousness that accompany increasing sleep depth. Here, we explored the dynamical properties of large-scale functional brain networks derived from transient brain activity using functional magnetic resonance imaging. Spatial brain maps generally display significant modifications in terms of their tendency to occur across wakefulness and NREM sleep. Unexpectedly, almost all networks predominated in activity during NREM stage 2 before an abrupt loss of activity is observed in NREM stage 3. Yet, functional connectivity and mutual dependencies between these networks progressively broke down with increasing sleep depth. Thus, the efficiency of information transfer during NREM stage 2 is low despite the high attempt to communicate. Critically, our approach provides relevant data for evaluating functional brain network integrity and our findings robustly support a significant advance in our neural models of human sleep and consciousness.

An altered balance of integrated and segregated brain activity is a marker of cognitive deficits following sleep deprivation

“…In summary, these results suggest that following prolonged wakefulness, the proportional integration and segregation of brain activity within structured cortical networks appears to be driven from levels in well RW towards those observed during offline states, such as NREM sleep, suggesting that there exists an underlying continuum of functional segregation from RW to NREM sleep. We were not able to robustly measure the differences between NREM2 and NREM3 in this study with the same amount of consecutive data points as in the wake conditions; however, other studies suggest that brain networks are even more segregated during NREM3 compared to NREM2 sleep [ 35 ] and that total integration decreases in proportion to SWA [ 20 ].…”

Hemodynamic Deconvolution Demystified: Sparsity-Driven Regularization at Work

“…Beyond simple averaging, clustering algorithms (e.g., K-means and consensus clustering) can be employed to discern multiple CAPs or iCAPs at the whole-brain level for a large number of subjects. Previous findings based on iCAPs have for instance revealed organizational principles of brain function during rest (Karahanoglu and Ville 2015) and sleep (Tarun et al 2021) in healthy controls, next to alterations in 22q11ds (Zoeller et al 2019) and multiple sclerosis (Bommarito et al in press). Next to CAPs-inspired approaches, dynamic functional connectivity has recently been investigated with the use of co-fluctuations and edge-centric techniques (Faskowitz et al 2020;Esfahlani et al 2020;Jo et al 2021;Sporns et al 2021;van Oort et al 2018).…”

Characterizing pupil dynamics coupling to brain state fluctuation based on lateral hypothalamic activity

“…We also revealed a unique LH-ACC interaction underlying the varied brain state-dependent pupil dynamics. The ACC is an important hub of the default mode network in both humans and rats 37 , and its involvement in the resting-state connectivity has been observed to differ between high and low arousal states, showing a decreased participation of the ACC in the default mode network during the lower arousal state compared to higher state 38,39 . The neural activity in the ACC has been linked to arousal fluctuation and associated with pupil size changes, which can be explained by its bidirectional connection to the noradrenergic LC 4,13,16,40 .…”