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

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

机构地区：[1]Department of Psychiatry,The Affiliated Mental Health Center of Jiangnan University,Wuxi 214151,Jiangsu Province,China [2]Department of Psychiatry,Yangzhou Wutaishan Hospital of Jiangsu Province,Teaching Hospital of Yangzhou University,Yangzhou 225000,Jiangsu Province,China [3]Department of Psychiatry,Huai’an Third People’s Hospital,Huai’an 223300,Jiangsu Province,China

出　　处：《World Journal of Psychiatry》2025年第3期216-226,共11页世界精神病学杂志(英文)

基　　金：Supported by the Wuxi Municipal Health Commission Major Project,No.Z202107。

摘　　要：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.

关 键 词：Successful aging Resting-state functional magnetic resonance imaging Surface-based brain network analysis Functional connectivity Support vector machine algorithm 

分 类 号：R749.3[医药卫生—神经病学与精神病学]