Abstract
Parkinson’s disease (PD) exhibits substantial clinical and neuroanatomical heterogeneity, limiting robust patient stratification and clinically meaningful progression modeling from MRI. We propose a unified multimodal 3D generative representation-learning framework that learns an interpretable latent space from co-registered baseline T1/T2 MRI with an edge-aware channel. Confound-corrected latent embeddings support unsupervised subtype discovery, while disease duration provides weak supervision to orient a continuous progression axis. On an independent external cohort (PPMI, N=171), the discovery-trained subtype structure shows significant partial replication (ARI=0.35; permutation test p=0.001) and enables longitudinal clinical stratification: mixed-effects modeling reveals subtype-dependent MDS-UPDRS III progression, with the strongest effect in subtype 2 (p< 0.001) persisting after adjustment for levodopa equivalent daily dose (LEDD), alongside differences in cognitive trajectories. Progression-axis scores correlate with disease duration (ρ=0.248), and decoder-based latent traversal yields voxel-wise visualizations consistent with known PD patterns, supporting generative multimodal MRI embeddings as interpretable imaging biomarkers for translational PD research.
Committee Chair
Dr. Shinjini Kundu
Committee Members
Dr. Aimilia Gastounioti, Dr. Joseph O’Sullivan
Degree
Master of Science (MS)
Author's Department
Electrical & Systems Engineering
Document Type
Thesis
Date of Award
Spring 5-2026
Language
English (en)
Recommended Citation
zhou, zihan, "Data-Driven Multimodal MRI Representation Learning for Subtype Discovery and Disease Progression Modeling in Parkinson’s Disease" (2026). McKelvey School of Engineering Theses & Dissertations. 1358.
https://openscholarship.wustl.edu/eng_etds/1358