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Stability of dynamic functional architecture differs between brain networks and states

Authors :
Le Li
Bin Lu
Chao-Gan Yan
Source :
NeuroImage, Vol 216, Iss , Pp 116230- (2020)
Publication Year :
2020
Publisher :
Elsevier, 2020.

Abstract

Stable representation of information in distributed neural connectivity is critical to function effectively in the world. Despite the dynamic nature of the brain’s functional architecture, characterizing its temporal stability within a continuous state has been largely neglected. Here we characterized stability of functional architecture at a dynamic timescale (~1 min) for each brain voxel by measuring the concordance of dynamic functional connectivity (DFC) over time, compared between association and unimodal regions, and established its reliability using test-retest resting-state fMRI data of adults from an open dataset. After the measure of functional stability was established, we further employed another fMRI open dataset which included movie-watching and resting-state data of children and adolescents, to explore how stability was modified by natural viewing from its intrinsic form, with specific focus on the associative and primary visual cortices. The results showed that high-order association regions, especially the default mode network, demonstrated high stability during resting-state scans, while primary sensory-motor cortices revealed relatively lower stability. During movie watching, stability in the primary visual cortex was decreased, which was associated with larger DFC variation with neighboring regions. By contrast, higher-order regions in the ventral and dorsal visual stream demonstrated increased stability. The distribution of functional stability and its modification describes a profile of the brain’s stability property, which may be useful reference for examining distinct mental states and disorders.

Details

Language :
English
ISSN :
10959572
Volume :
216
Issue :
116230-
Database :
Directory of Open Access Journals
Journal :
NeuroImage
Publication Type :
Academic Journal
Accession number :
edsdoj.6b2434f3405142ddbaea674e90aea957
Document Type :
article
Full Text :
https://doi.org/10.1016/j.neuroimage.2019.116230