グリップ力のコネクトームベース予測モデリング：身体的フレイルのマーカー
Amin Ghaffari1, Majd Abouzaki2, Yasmine Romero3
1Department of Bioengineering, University of California, Riverside, Riverside, CA, United States.
Introduction:
Frailty is characterized by a persistent and progressive decline in functional capacity, 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.
Methods:
In this study, we used connectome-based predictive modeling (CPM) to find a linear relationship between task-based connectomes taken from tasks that involved similar handgrip manipulations and a separate measure of physical frailty: the maximum grip strength in older adults.
Results:
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.
Discussion:
These findings demonstrate that task-based functional connectomes can serve as personalized markers for predicting individual behavioral measures, such as handgrip strength, and highlight the role of the caudate nucleus in physical frailty.
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