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Differential spatial working memory-related functional network reconfiguration in young and older adults.

Authors :
Yue WL
Ng KK
Liu S
Qian X
Chong JSX
Koh AJ
Ong MQW
Ting SKS
Ng ASL
Kandiah N
Yeo BTT
Zhou JH
Source :
Network neuroscience (Cambridge, Mass.) [Netw Neurosci] 2024 Jul 01; Vol. 8 (2), pp. 395-417. Date of Electronic Publication: 2024 Jul 01 (Print Publication: 2024).
Publication Year :
2024

Abstract

Functional brain networks have preserved architectures in rest and task; nevertheless, previous work consistently demonstrated task-related brain functional reorganization. Efficient rest-to-task functional network reconfiguration is associated with better cognition in young adults. However, aging and cognitive load effects, as well as contributions of intra- and internetwork reconfiguration, remain unclear. We assessed age-related and load-dependent effects on global and network-specific functional reconfiguration between rest and a spatial working memory (SWM) task in young and older adults, then investigated associations between functional reconfiguration and SWM across loads and age groups. Overall, global and network-level functional reconfiguration between rest and task increased with age and load. Importantly, more efficient functional reconfiguration associated with better performance across age groups. However, older adults relied more on internetwork reconfiguration of higher cognitive and task-relevant networks. These reflect the consistent importance of efficient network updating despite recruitment of additional functional networks to offset reduction in neural resources and a change in brain functional topology in older adults. Our findings generalize the association between efficient functional reconfiguration and cognition to aging and demonstrate distinct brain functional reconfiguration patterns associated with SWM in aging, highlighting the importance of combining rest and task measures to study aging cognition.<br />Competing Interests: Competing Interests: The authors have declared that no competing interests exist.<br /> (© 2024 Massachusetts Institute of Technology.)

Details

Language :
English
ISSN :
2472-1751
Volume :
8
Issue :
2
Database :
MEDLINE
Journal :
Network neuroscience (Cambridge, Mass.)
Publication Type :
Academic Journal
Accession number :
38952809
Full Text :
https://doi.org/10.1162/netn_a_00358