Work

A network-information cluster framework for targeted identification of motor function biomarkers

Year: 2026

Type: article

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 population clusters of ageing and poststroke motor impairment chronicity and recovery. This adaptable framework bridges the underutilized large-scale motion data of clinical labs to the assessment tools they currently rely upon, offering in-depth characterizations of individual motor (dis)abilities, representing a powerful new assessment methodology. Future work should aim to establish concrete links between these biomarkers and underlying neurophysiological processes and test data capture protocols to optimize clinical relevance.

Source: Network Neuroscience

DOI: 10.1162/netn.a.597

Authors: David O’Reilly, Ioannis Delis

Institutions: University of Leeds, Fundacja NeuroLandscape, National Technical University of Athens

Language: English

Topic: Muscle activation and electromyography studies

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,750