Evidence map›Paper›PMID 40110011›Full record

ArticleWorld journal of psychiatry2025

Identification of key brain networks and functional connectivities of successful aging: A surface-based resting-state functional magnetic resonance study.

Jiao-Jiao Sun, Li Zhang, Ru-Hong Sun, Xue-Zheng Gao, Chun-Xia Fang, Zhen-He Zhou

Abstract read
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Article in World journal of psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jiao-Jiao SunDepartment of Psychiatry, The Affiliated Mental Health Center of Jiangnan University, Wuxi 214151, Jiangsu Province, China.
Li ZhangDepartment of Psychiatry, Huai'an Third People's Hospital, Huai'an 223300, Jiangsu Province, China.
Ru-Hong SunDepartment of Psychiatry, Yangzhou Wutaishan Hospital of Jiangsu Province, Teaching Hospital of Yangzhou University, Yangzhou 225000, Jiangsu Province, China.
Xue-Zheng GaoDepartment of Psychiatry, The Affiliated Mental Health Center of Jiangnan University, Wuxi 214151, Jiangsu Province, China.
Chun-Xia FangDepartment of Psychiatry, The Affiliated Mental Health Center of Jiangnan University, Wuxi 214151, Jiangsu Province, China.
Zhen-He ZhouDepartment of Psychiatry, The Affiliated Mental Health Center of Jiangnan University, Wuxi 214151, Jiangsu Province, China. zhouzh@njmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSuccessful aging (SA) refers to the ability to maintain high levels of physical, cognitive, psychological, and social engagement in old age, with high cognitive function being the key to achieving SA.

aimTo explore the potential characteristics of the brain network and functional connectivity (FC) of SA.

methodsTwenty-six SA individuals and 47 usual aging individuals were recruited from community-dwelling elderly, which were taken the magnetic resonance imaging scan and the global cognitive function assessment by Mini Mental State Examination (MMSE). The resting state-functional magnetic resonance imaging data were preprocessed by DPABISurf, and the brain functional network was conducted by DPABINet. The support vector machine model was constructed with altered functional connectivities to evaluate the identification value of SA.

resultsThe results found that the 6 inter-network FCs of 5 brain networks were significantly altered and related to MMSE performance. The FC of the right orbital part of the middle frontal gyrus and right angular gyrus was mostly increased and positively related to MMSE score, and the FC of the right supramarginal gyrus and right temporal pole: Middle temporal gyrus was the only one decreased and negatively related to MMSE score. All 17 significantly altered FCs of SA were taken into the support vector machine model, and the area under the curve was 0.895.

conclusionThe identification of key brain networks and FC of SA could help us better understand the brain mechanism and further explore neuroimaging biomarkers of SA.

Indexed as

Functional connectivityResting-state functional magnetic resonance imagingSuccessful agingSupport vector machine algorithmSurface-based brain network analysis

Identifiers

PMID40110011
PMCPMC11886311

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