Pathway Anchored Multimodal Clustering Reveals Circuit Level Signatures in Parkinsons Disease.

ABSTRACT: Parkinson's disease is increasingly understood as a disorder of distributed brain circuits, yet most imaging analyses do not explicitly respect pathway structure. We introduce a pathway-anchored, multimodal clustering framework based on Scalable Robust Variational Compositional Co-clustering (SRVCC) that integrates structural MRI, free-water-corrected diffusion MRI, and DAT-SPECT in anatomically defined circuits. For each pathway, we derive a simple Multimodal Pathway Integrity Score (MPIS) that aggregates z -normalised volume, microstructural, and dopaminergic measures into an interpretable summary of imaging integrity. In the PPMI cohort, SRVCC identifies stable imaging-derived patient clusters and feature modules under explicit model selection and bootstrap/stability checks, with covari

SUBMITTER: Vinod A 

PROVIDER: S-EPMC12724497 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature