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基于连接组的预测模型可预测老年人的衰弱程度。

Connectome-based predictive modelling predicts frailty levels in older adults.

作者信息

Ghaffari Amin, Abouzaki Majd, Romero Yasmine, Sun Andrew, Seitz Aaron, Langley Jason, Bennett Ilana J, Hu Xiaoping

机构信息

Department of Bioengineering, University of California Riverside, CA.

Department of cell, molecular, and developmental biology, University of California Riverside, CA.

出版信息

bioRxiv. 2025 Aug 30:2025.08.26.672253. doi: 10.1101/2025.08.26.672253.

Abstract

Frailty is characterized by a persistent and progressive decline in physiological reserves, leading to increased vulnerability to stressors and a heightened risk of adverse health outcomes, both physically and mentally. Despite frailty's prevalence in older adults, there is limited research on its neural substrates, especially using task-based brain functional connectivity. In this study, we used connectome-based predictive modelling (CPM) to find a linear relationship between task-based connectomes - taken from tasks that involved similar handgrip manipulations - and a separate measure of frailty: the maximum grip strength in older adults. We observed that the task-based connectomes were able to explain individual differences in grip strength, with the Subcortical and Cerebellum network, particularly the caudate nucleus, functional connectivity being the strongest predictor. These findings demonstrate that task-based functional connectomes can serve as personalized markers that can predict individual behavioral measures, including handgrip strength, and point to involvement of the caudate nucleus in frailty.

摘要

衰弱的特征是生理储备持续且渐进性下降,导致对压力源的易感性增加,以及身体和心理方面不良健康结局的风险升高。尽管衰弱在老年人中普遍存在,但对其神经基础的研究有限,尤其是基于任务的脑功能连接方面。在本研究中,我们使用基于连接组的预测模型(CPM)来寻找基于任务的连接组(取自涉及类似握力操作的任务)与衰弱的另一测量指标——老年人的最大握力之间的线性关系。我们观察到,基于任务的连接组能够解释握力的个体差异,其中皮质下和小脑网络,特别是尾状核的功能连接是最强的预测指标。这些发现表明,基于任务的功能连接组可作为个性化标志物,能够预测个体行为指标,包括握力,并指出尾状核与衰弱有关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6420/12407959/573dad6fd2db/nihpp-2025.08.26.672253v1-f0001.jpg

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