Functional maintenance in the multiple demand network characterizes superior fluid intelligence in aging.


Journal

Neurobiology of aging
ISSN: 1558-1497
Titre abrégé: Neurobiol Aging
Pays: United States
ID NLM: 8100437

Informations de publication

Date de publication:
01 2020
Historique:
received: 14 05 2019
revised: 20 08 2019
accepted: 14 09 2019
pubmed: 14 11 2019
medline: 12 9 2020
entrez: 14 11 2019
Statut: ppublish

Résumé

The multiple demand network (MDN) is conceptualized as the core processing system for multitasking. Increasing evidence also provides strong support for the involvement of the MDN in fluid intelligence (gF), that is, the ability to solve new problems. However, the underlying neural mechanisms of declining intelligence in old age are poorly explored, particularly whether maintenance of the functional architecture of the MDN can characterize superior intelligence in successful aging. Here, we used eigenvector centrality (EC) to explore the resting-state functional architecture of the MDN in terms of its communication across the entire brain. We found gF to be negatively associated with age and that the MDN EC competitively mediated age-related decline in gF over the aging lifespan, suggesting that excessive cross-talk from the MDN is deleterious for intelligence. Critically, older individuals with comparable MDN EC as younger individuals exhibited superior gF compared with their age-matched counterparts. Taken together, these data provide support for the maintenance of youth-like functional architecture of the MDN and its implication for superior intelligence in successful aging.

Identifiants

pubmed: 31718925
pii: S0197-4580(19)30327-6
doi: 10.1016/j.neurobiolaging.2019.09.006
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

145-153

Informations de copyright

Copyright © 2019 Elsevier Inc. All rights reserved.

Auteurs

Nianming Zuo (N)

Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Chinese Institute for Brain Research, Beijing (CIBR, Beijing), China. Electronic address: nmzuo@nlpr.ia.ac.cn.

Alireza Salami (A)

Aging Research Center, Karolinska Institute and Stockholm University, S-11330 Stockholm, Sweden; Umeå Center for Functional Brain Imaging, Umeå University, 901 87 Umeå, Sweden; Department of Integrative Medical Biology, Umeå University, 901 87 Umeå, Sweden; Wallenberg Center for Molecular Medicine, Umeå University, 901 87 Umeå, Sweden.

Hao Liu (H)

Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.

Zhengyi Yang (Z)

Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.

Tianzi Jiang (T)

Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Chinese Institute for Brain Research, Beijing (CIBR, Beijing), China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia.

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