Observational Study
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Psychiatry. Mar 19, 2025; 15(3): 100456
Published online Mar 19, 2025. 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, Li Zhang, Ru-Hong Sun, Xue-Zheng Gao, Chun-Xia Fang, Zhen-He Zhou
Jiao-Jiao Sun, Xue-Zheng Gao, Chun-Xia Fang, Zhen-He Zhou, Department of Psychiatry, The Affiliated Mental Health Center of Jiangnan University, Wuxi 214151, Jiangsu Province, China
Jiao-Jiao Sun, Ru-Hong Sun, Department of Psychiatry, Yangzhou Wutaishan Hospital of Jiangsu Province, Teaching Hospital of Yangzhou University, Yangzhou 225000, Jiangsu Province, China
Li Zhang, Department of Psychiatry, Huai’an Third People’s Hospital, Huai’an 223300, Jiangsu Province, China
Co-first authors: Jiao-Jiao Sun and Li Zhang.
Co-corresponding authors: Chun-Xia Fang and Zhen-He Zhou.
Author contributions: Sun JJ and Zhang L contributed to the software of the manuscript, they contributed equally to this article, they are the co-first authors of this manuscript; Sun RH and Gao XZ investigated and resourced the manuscript; Sun JJ, Zhang L, Sun RH, and Gao XZ wrote the original manuscript; Fang CX contributed to the conceptualization, methodology, data curation, writing, visualization, project management, and acquisition of funds; Sun JJ, Zhang L, and Fang CX formally analyzed the manuscript; Zhou ZH contributed to the manuscript with resources, data organization, and supervision; Fang CX and Zhou ZH they contributed equally to this article, they are the co-corresponding authors of this manuscript; and all authors contributed to the article and approved the submitted version.
Supported by the Wuxi Municipal Health Commission Major Project, No. Z202107.
Institutional review board statement: This study was approved by the Medical Ethics Committee of the Wuxi Mental Health Center, approval No. WXMHCIRB2017LL07; and all procedures performed in this study involving human participants were in accordance with the Declaration of Helsinki.
Informed consent statement: All participants enrolled into this study provided informed written consent prior to study enrollment.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: Data used in this study can be available from the corresponding author at zhouzh@njmu.edu.cn.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Zhen-He Zhou, MD, Professor, Department of Psychiatry, The Affiliated Mental Health Center of Jiangnan University, No. 156 Qianrong Road, Wuxi 214151, Jiangsu Province, China. zhouzh@njmu.edu.cn
Received: August 17, 2024
Revised: January 11, 2025
Accepted: January 22, 2025
Published online: March 19, 2025
Processing time: 192 Days and 15.2 Hours
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: Successful aging; Resting-state functional magnetic resonance imaging; Surface-based brain network analysis; Functional connectivity; Support vector machine algorithm

Core Tip: This study investigates the brain network characteristics and functional connectivity (FC) associated with successful aging (SA) using resting-state functional magnetic resonance imaging. The results found that the 6 inter-network FC of 5 brain networks were significantly altered and related to Mini Mental State Examination performance, of which the default mode network, attention network, and language network were the most concentrated networks. 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.