Abnormal dynamic functional connectivity after sleep deprivation from temporal variability perspective

Abstract

Sleep deprivation (SD) is very common in modern society and regarded as a potential causal mechanism of several clinical disorders. Previous neuroimaging studies have explored the neural mechanisms of SD using magnetic resonance imaging (MRI) from static (comparing two MRI sessions [one after SD and one after resting wakefulness]) and dynamic (using repeated MRI during one night of SD) perspectives. Recent SD researches have focused on the dynamic functional brain organization during the resting-state scan. Our present study adopted a novel metric (temporal variability), which has been successfully applied to many clinical diseases, to examine the dynamic functional connectivity after SD in 55 normal young subjects. We found that sleep-deprived subjects showed increased regional-level temporal variability in largescale brain regions, and decreased regional-level temporal variability in several thalamus subregions. After SD, participants exhibited enhanced intra-network temporal variability in the default mode network (DMN) and increased inter-network temporal variability in numerous subnetwork pairs. Furthermore, we found that the internetwork temporal variability between visual network and DMN was negative related with the slowest 10% respond speed (β = À.42, p = 5.57 Â 10 À4 ) of the psychomotor vigilance test after SD following the stepwise regression analysis. In conclusion, our findings suggested that sleep-deprived subjects showed abnormal dynamic brain functional configuration, which provides new insights into the neural underpinnings of SD and contributes to our understanding of the pathophysiology of clinical disorders.

Problematic smartphone use is associated with differences in static and dynamic brain functional connectivity in young adults

“…Previous studies have proved that there are unignorable dynamic fluctuations in the human brain's functional organizations (Hutchison et al, 2013a,b), which is closely related to the cognitive (Patil et al, 2021) and emotional (Tobia et al, 2017) processes. Meanwhile, both excessively increased (Long et al, 2020b;Sun et al, 2022) and decreased (Jin et al, 2017;Luo L. et al, 2021;Luo Z. et al, 2021) dynamisms were thought to be reflective of abnormal brain functions. Specially, a decreased dynamism may indicate a disturbance in the information processing across brain regions (Luo Z. et al, 2021).…”

“…The examined dynamic network metrics included the temporal variability for the entire brain network and nodal temporal variability of each node (Zhang et al, 2016;Dong et al, 2019;Long et al, 2020b;Sun et al, 2022). These two metrics quantify the temporal stability of brain dFC patterns at the global and nodal levels, respectively; higher values of temporal variability indicate more fluctuations of the dFC patterns (less stable dFCs) over time.…”

“…These two metrics quantify the temporal stability of brain dFC patterns at the global and nodal levels, respectively; higher values of temporal variability indicate more fluctuations of the dFC patterns (less stable dFCs) over time. More details about the calculations of these two metrics can be found in previous publications (Zhang et al, 2016;Dong et al, 2019;Long et al, 2020b;Sun et al, 2022).…”

“…Besides the global and nodal network metrics, large-scale subnetwork analyses were also performed on both the sFC and dFC architectures strictly following the procedures in previous publications (Dong et al, 2019;Long et al, 2020b;Li L. et al, 2021;Sun et al, 2022). According to prior work (Cole et al, 2013;Mohr et al, 2016;Long et al, 2019Long et al, , 2021, all ROIs in the Power atlas were firstly assigned into nine large-scale subnetworks including the defaultmode, salience, visual, subcortical, auditory, frontoparietal, cinguloopercular, sensorimotor and attention subnetworks (Figure 1A).…”

“…The strengths of within-and between-subnetwork sFC were calculated by averaging the z-transformed sFC values across all involved connections within a specific subnetwork, or between a specific pair of subnetworks (Li L. et al, 2021). Similarly, the temporal variabilities of within-and betweensubnetwork dFC were also obtained by calculating the average variabilities of dFC across all involved connections (Dong et al, 2019;Long et al, 2020b;Sun et al, 2022). This resulted in nine within-subnetwork sFC/dFC measures and 36 betweensubnetwork sFC/dFC measures.…”