Disease progression in multiple system atrophy (MSA) and Parkinson’s disease (PD) shows marked patient-to-patient heterogeneity. We hypothesize that machine learning methods applied to multimodal MRI data would aid in optimally identifying critical brain regions impacted in each patient, improve disease differentiation and longitudinal tracking. Using structural and diffusion MRI of MSA (cerebellar and parkinsonian subtypes), PD, and normal participants, we trained binary classifiers and utilized Shapley Additive exPlanations (SHAP) to quantify feature contributions to derive heterogeneity scores (HET). HET outperformed commonly available imaging tools when differentiating between MSA and PD, strongly correlated with clinical markers, and sensitively tracked longitudinal disease progression. HET correctly identified olivopontocerebellar atrophy and striatonigral degeneration as important for disease identification, shed light on the spatio-temporal disease progression, and identified widespread white matter involvement in MSA. Our machine learning approach quantifies MSA and PD heterogeneity and provides a patient-specific measure for precise disease quantification and longitudinal tracking.
Gebre et al. (Mon,) studied this question.