Parkinson’s disease (PD) is a prevalent neurodegenerative disorder characterized by tremor, rigidity, bradykinesia, and postural instability. Atypical Parkinsonian syndromes such as progressive supranuclear palsy (PSP) and multiple system atrophy (MSA) share overlapping clinical features, complicating accurate diagnosis. While prior diffusion MRI studies have used large, opaque machine-learning models, this study explored the best two diffusion metrics to differentiate Parkinsonian syndromes. This strategy introduces a compact and interpretable approach tailored for clinically relevant cohort sizes. We retrospectively analyzed diffusion MRI data from 199 patients (PD: 140, PSP: 20, MSA: 39) and constructed age- and sex-matched subsets comprising 40 PD, 20 PSP, and 34 MSA subjects to ensure controlled and balanced group comparisons. Mean diffusivity (MD) and fractional anisotropy (FA) were extracted from twelve predefined brain regions. Deformation-based morphometry and deterministic tractography were also used to map macrostructural and pathway-specific changes. Statistical comparisons and logistic regression with cross-validation assessed discriminatory power. MSA patients exhibited significant atrophy, increased MD, and decreased FA in the cerebellum. PSP showed pronounced changes in the superior cerebellar peduncle and corpus callosum. Putamen, corpus callosum, and cerebellum emerged as key discriminators for PD, PSP, and MSA, respectively. A compact and strategically selected set of diffusion features significantly improved disease differentiation. This study demonstrates that interpretable, region-specific complementarily paired diffusion patterns can robustly distinguish PD, PSP, and MSA, offering a transparent and biologically meaningful framework for differential diagnosis and mechanistic understanding.
Tessema et al. (Tue,) studied this question.