Functional network reorganization in older adults: Graph-theoretical analyses of age, cognition and sex.
Aging
Cognition
Connectome
Education
Resting-state functional connectivity
Sex
Journal
NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515
Informations de publication
Date de publication:
01 07 2020
01 07 2020
Historique:
received:
20
09
2019
revised:
28
02
2020
accepted:
14
03
2020
pubmed:
24
3
2020
medline:
16
2
2021
entrez:
24
3
2020
Statut:
ppublish
Résumé
Healthy aging has been associated with a decrease in functional network specialization. Importantly, variability of alterations of functional connectivity is especially high across older adults. Whole-brain functional network reorganization, though, and its impact on cognitive performance within particularly the older generation is still a matter of debate. We assessed resting state functional connectivity (RSFC) in 772 older adults (55-85 years, 421 males) using a graph-theoretical approach. Results show overall age-related increases of between- and decreases of within-network RSFC. With similar phenomena observed in young to middle-aged adults, i.e. that RSFC reorganizes towards more pronounced functional network integration, the current results amend such evidence for the old age. The results furthermore indicate that RSFC reorganization in older adults particularly pertain to early sensory networks (e.g. visual and sensorimotor network). Importantly, RSFC differences of these early sensory networks were found to be a relevant mediator in terms of the age-related cognitive performance differences. Further, we found systematic sex-related network differences with females showing patterns of more segregation (i.e. default mode and ventral attention network) and males showing a higher integrated network system (particularly for the sensorimotor network). These findings underpin the notion of sex-related connectivity differences, possibly facilitating sex-related behavioral functioning.
Identifiants
pubmed: 32201326
pii: S1053-8119(20)30243-3
doi: 10.1016/j.neuroimage.2020.116756
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
116756Informations de copyright
Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.