Modular slowing of resting-state dynamic functional connectivity as a marker of cognitive dysfunction induced by sleep deprivation.
Adult
Attention
/ physiology
Brain
/ diagnostic imaging
Cognitive Dysfunction
/ diagnostic imaging
Connectome
Humans
Male
Memory, Short-Term
/ physiology
Nerve Net
/ diagnostic imaging
Psychomotor Performance
/ physiology
Sleep Deprivation
/ diagnostic imaging
Time Factors
Visual Perception
/ physiology
Attention
Cognitive challenge model
Cognitive decline
Dynamic functional connectivity
Resting-state
Sleep deprivation
fMRI
Journal
NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515
Informations de publication
Date de publication:
15 11 2020
15 11 2020
Historique:
received:
08
02
2020
revised:
25
05
2020
accepted:
07
07
2020
pubmed:
1
8
2020
medline:
26
2
2021
entrez:
1
8
2020
Statut:
ppublish
Résumé
Dynamic Functional Connectivity (dFC) in the resting state (rs) is considered as a correlate of cognitive processing. Describing dFC as a flow across morphing connectivity configurations, our notion of dFC speed quantifies the rate at which FC networks evolve in time. Here we probe the hypothesis that variations of rs dFC speed and cognitive performance are selectively interrelated within specific functional subnetworks. In particular, we focus on Sleep Deprivation (SD) as a reversible model of cognitive dysfunction. We found that whole-brain level (global) dFC speed significantly slows down after 24h of SD. However, the reduction in global dFC speed does not correlate with variations of cognitive performance in individual tasks, which are subtle and highly heterogeneous. On the contrary, we found strong correlations between performance variations in individual tasks -including Rapid Visual Processing (RVP, assessing sustained visual attention)- and dFC speed quantified at the level of functional sub-networks of interest. Providing a compromise between classic static FC (no time) and global dFC (no space), modular dFC speed analyses allow quantifying a different speed of dFC reconfiguration independently for sub-networks overseeing different tasks. Importantly, we found that RVP performance robustly correlates with the modular dFC speed of a characteristic frontoparietal module.
Identifiants
pubmed: 32736002
pii: S1053-8119(20)30641-8
doi: 10.1016/j.neuroimage.2020.117155
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
117155Informations de copyright
Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.