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ORIGINAL RESEARCH article

Front. Neurosci.

Sec. Brain Imaging Methods

Connectome-Based Predictive Modeling of Grip Strength in Older Adults: A Frailty-Related Physical Marker

Provisionally accepted
Amin GhaffariAmin Ghaffari1Majd AbouzakiMajd Abouzaki2Yasmine RomeroYasmine Romero3Andrew SunAndrew Sun4Aaron SeitzAaron Seitz3,4Jason LangleyJason Langley3Ilana BennettIlana Bennett5Xiaoping HuXiaoping Hu1*
  • 1University of California Riverside Department of Bioengineering, Riverside, United States
  • 2University of California Riverside Department of Molecular Cell and Systems Biology, Riverside, United States
  • 3University of California Riverside, Riverside, United States
  • 4Northeastern University, Boston, United States
  • 5University of California Riverside Department of Psychology, Riverside, United States

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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. 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 physical 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 for predicting individual behavioral measures, such as handgrip strength, and highlight the role of the caudate nucleus in physical frailty.

Keywords: connectome, Frailty, Neuroimaging Biomarkers, connectome-based predictivemodelling, older adults, Caudate Nucleus

Received: 02 Sep 2025; Accepted: 07 Nov 2025.

Copyright: © 2025 Ghaffari, Abouzaki, Romero, Sun, Seitz, Langley, Bennett and Hu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Xiaoping Hu, xiaophu@ucr.edu

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Supplementary Material