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. 2025 Mar 19;15(3):100456.
doi: 10.5498/wjp.v15.i3.100456.

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

Jiao-Jiao Sun  1   2 , Li Zhang  3 , Ru-Hong Sun  2 , Xue-Zheng Gao  1 , Chun-Xia Fang  1 , Zhen-He Zhou  4
Affiliations

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

Jiao-Jiao Sun et al. World J Psychiatry. .

Abstract

Background: Successful 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.

Aim: To explore the potential characteristics of the brain network and functional connectivity (FC) of SA.

Methods: Twenty-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.

Results: The 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.

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

Keywords: Functional connectivity; Resting-state functional magnetic resonance imaging; Successful aging; Support vector machine algorithm; Surface-based brain network analysis.

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Conflict of interest statement

Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.

Figures

Figure 1
Figure 1
Alteration of functional connectivities of successful aging in BrainNet analysis. VN: Visual network; SMN: Somatomotor network; DAN: Dorsal attention network; VAN: Ventral Attention network; LN: Limbic network; FPN: Frontoparietal network; DMN: Default mode network; SC: Subcutaneous nucleus.
Figure 2
Figure 2
Correlations between functional connectivities and Mini Mental State Examination scores of successful aging. DAN: Dorsal attention network; VAN: Ventral Attention network; LN: Limbic network; FPN: Frontoparietal network; DMN: Default mode network.
Figure 3
Figure 3
Support vector machine model of successful aging with functional connectivities. SVM: Support vector machine; ROC: Receiver operating characteristic.
See this image and copyright information in PMC

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