Data-driven discovery of movement-linked heterogeneity in neurodegenerative diseases

Abstract

Neurodegenerative diseases manifest different motor and cognitive signs and symptoms that are highly heterogeneous. Parsing these heterogeneities may lead to an improved understanding of underlying disease mechanisms; however, current methods are dependent on clinical assessments and an arbitrary choice of behavioural tests. Here we present a data-driven subtyping approach using video-captured human motion and brain functional connectivity from resting-state functional magnetic resonance imaging. We applied our framework to a cohort of individuals at different stages of Parkinson’s disease. The process mapped the data to low-dimensional measures by projecting them onto a canonical correlation space that identified three Parkinson’s disease subtypes: subtype I was characterized by motor difficulties and poor visuospatial abilities; subtype II exhibited difficulties in non-motor components of activities of daily living and motor complications (dyskinesias and motor fluctuations) and subtype III was characterized by predominant tremor symptoms. We conducted a convergent validity analysis by comparing our approach to existing and widely used approaches. The compared approaches yielded subtypes that were adequately well-clustered in the motion-brain representation space we created to delineate subtypes. Our data-driven approach, contrary to other forms of subtyping, derived biomarkers predictive of motion impairment and subtype memberships that were captured objectively by digital videos. Diagnostic strategies for neurodegenerative diseases involve various data types, related to motor and cognitive signals. Endo et al. describe a data-driven subtyping approach for Parkinson’s disease, combining motion data (from videos) and brain functional connectivity data. The method reveals clinically relevant subtypes and digital biomarkers, uncovering movement-linked heterogeneities of Parkinson’s disease.