Authors: Mark Endo, Favour Nerrise, Qingyu Zhao, Edith V Sullivan, Li Fei-Fei, Victor W Henderson, Kilian M Pohl, Kathleen L Poston, Ehsan Adeli
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 somewhat arbitrary choice of behavioral tests. Herein, we present a data-driven subtyping approach using video-captured human motion and brain functional connectivity (FC) from resting-state (rs)-fMRI. We applied our framework to a cohort of individuals at different stages of Parkinson’s disease (PD). The process mapped the data to low-dimensional measures by projecting them onto a canonical correlation space that identified three PD 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.
Access on Pubmed: https://pubmed.ncbi.nlm.nih.gov/40357335/