A Network-Information Cluster Framework for Targeted Identification of Motor Function Biomarkers

Abstract

To advance motor function assessments, there is a growing need for mechanistic approaches that offer deeper physiological insight. Here, we present a fully end-to-end framework that integrates network science, information theory, and machine-learning to generate targeted biomarkers from large-scale motion data. Showcasing our approach, we perform a comprehensive spatio-spectral decomposition of muscle activations into functionally diverse muscle networks. Then, by incorporating rigorous feature selection and our newly developed clustering algorithm, we identify motor features optimally associated with a chosen clinical measure and cluster participants in a targeted, clinically meaningful way across scales. Framework applications illustrate the mechanistic insights provided into the underlying physiological constructs of any clinical measure, uncovering data-driven milestones of ageing and post-stroke impairment chronicity and neurocomputational motor characteristics. This adaptable framework bridges the underutilised large-scale motion data of clinical labs to the assessment tools they currently rely upon, offering in-depth characterisations of individual motor (dis)abilities, representing a powerful new assessment methodology.