Pathway Anchored Multimodal Clustering Reveals Circuit Level Signatures in Parkinsons Disease

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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>z</mml:mi></mml:math> -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 covariate-adjusted analyzes controlling for age, sex, education, and medication. MPIS shows coherent but modest structure-function associations: lower nigrostriatal and frontostriatal integrity relates to higher motor burden (UPDRS-III), while reduced sensory/visuospatial and limbic integrity is linked to lower global cognition (MoCA); microvascular markers robustly stratify imaging profiles but display minimal cross-sectional coupling to these global scales. Feature-level reports highlight dominant region-by-modality contributors (e.g., striatal DAT-SBR, thalamic and cerebellar morphology, white-matter hyperintensity metrics), providing a transparent bridge from multimodal data to circuit-level signatures. This pathway-aware representation offers a principled, reproducible way to summarise multimodal imaging in PD and may support future work on circuit-informed stratification, prognosis, and targeted outcome measures.