Connectome-based predictive modelling predicts frailty levels in older adults

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 frailtys 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. Key pointsO_LIWe used connectome-based predictive modeling on task-based fMRI to predict grip strength in older adults, a key marker of physical frailty. C_LIO_LIThe model significantly explained inter-individual differences in contraction strength using functional connectivity patterns. C_LIO_LISubcortical regions, especially the caudate nucleus, played a major role in prediction, highlighting their relevance in frailty assessment and potential interventions. C_LI