Sun Jiao-Jiao, Zhang Li, Sun Ru-Hong, Gao Xue-Zheng, Fang Chun-Xia, Zhou Zhen-He
Department of Psychiatry, The Affiliated Mental Health Center of Jiangnan University, Wuxi 214151, Jiangsu Province, China.
Department of Psychiatry, Yangzhou Wutaishan Hospital of Jiangsu Province, Teaching Hospital of Yangzhou University, Yangzhou 225000, Jiangsu Province, China.
World J Psychiatry. 2025 Mar 19;15(3):100456. doi: 10.5498/wjp.v15.i3.100456.
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.
To explore the potential characteristics of the brain network and functional connectivity (FC) of SA.
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.
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.
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.
成功老龄化(SA)是指在老年期保持高水平身体、认知、心理和社会参与的能力,其中高认知功能是实现成功老龄化的关键。
探讨成功老龄化的脑网络和功能连接(FC)的潜在特征。
从社区居住老年人中招募了26名成功老龄化个体和47名正常老龄化个体,对其进行磁共振成像扫描,并通过简易精神状态检查表(MMSE)进行整体认知功能评估。静息态功能磁共振成像数据由DPABISurf进行预处理,脑功能网络由DPABINet构建。构建支持向量机模型,利用改变的功能连接来评估成功老龄化的识别价值。
结果发现,5个脑网络的6个网络间功能连接显著改变,且与MMSE表现相关。额中回右侧眶部和角回右侧的功能连接大多增加,且与MMSE评分呈正相关,而上缘回右侧和颞极:颞中回右侧的功能连接是唯一降低的,且与MMSE评分呈负相关。将成功老龄化的所有17个显著改变的功能连接纳入支持向量机模型,曲线下面积为0.895。
识别成功老龄化的关键脑网络和功能连接有助于我们更好地理解脑机制,并进一步探索成功老龄化的神经影像学生物标志物。