Literature DB >> 32353459

Decreased Dynamic Segregation but Increased Dynamic Integration of the Resting-state Functional Networks During Normal Aging.

Li He1, Xiaoqin Wang1, Kaixiang Zhuang1, Jiang Qiu2.   

Abstract

A hallmark of the aging process is increased connectivity between networks and decreased connectivity within networks, which to some extent reflects the reorganization of the brain networks during normal aging. Considering the brain as a complex dynamic system, emerging evidence suggests the time-varying connectivity patterns to be more informative of brain functions. However, the age effect on the dynamic reconfiguration of intrinsic resting state networks is still elusive. By tracking the ongoing formation and dissipation of putative functional modules across time and space, we explored the age-related changes of segregation and integration and further elucidated the underlying brain network dynamics mechanism during normal aging. Results showed that aging strongly weakened dynamic global segregation while enhanced dynamic global integration across the whole brain. Aging was associated with decreasing dynamic segregation of most networks (except the cerebellum) while increasing dynamic integration of only a few networks at the large-scale network level. Notably, the fronto-parietal network, the default mode network, the visual network, and a small group of nodes from these networks, whose dynamic segregation and integration, were both modulated by age. These findings provide direct evidence that there are remarkable changes of dynamic network architecture across the human adult lifespan and suggest the age-related modulations of dynamic segregation and integration intuitively reflect the adaptive changes of the functional dedifferentiation and compensation in older adults.
Copyright © 2020 IBRO. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  aging; community detection; dynamic network; functional specificity; graph theory; resting state

Mesh:

Year:  2020        PMID: 32353459     DOI: 10.1016/j.neuroscience.2020.04.030

Source DB:  PubMed          Journal:  Neuroscience        ISSN: 0306-4522            Impact factor:   3.590


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