There is a critical need to develop valid, non-invasive biomarkers for Parkinsonian
syndromes. The current 17-site, international study assesses whether non-invasive
diffusion MRI (dMRI) can distinguish between Parkinsonian syndromes. We used dMRI
from 1002 subjects, along with the Movement Disorders Society Unified Parkinson’s
Disease Rating Scale part III (MDS-UPDRS III), to develop and validate disease-specific
machine learning comparisons using 60 template regions and tracts of interest in Montreal
Neurological Institute (MNI) space between Parkinson’s disease (PD) and Atypical Parkinsonism
(multiple system atrophy – MSA, progressive supranuclear palsy – PSP), as well as
between MSA and PSP. For each comparison, models were developed on a training/validation
cohort and evaluated in a test cohort by quantifying the area under the curve (AUC)
of receiving operating characteristic (ROC) curves. In the test cohort for both disease-specific
comparisons, AUCs were high in the dMRI + MDS-UPDRS (PD vs. Atypical Parkinsonism:
0·962; MSA vs. PSP: 0·897) and dMRI Only (PD vs. Atypical Parkinsonism: 0·955; MSA
vs. PSP: 0·926) models, whereas the MDS-UPDRS III Only models had significantly lower
AUCs (PD vs. Atypical Parkinsonism: 0·775; MSA vs. PSP: 0·582). This study provides
an objective, validated, and generalizable imaging approach to distinguish different
forms of Parkinsonian syndromes using multi-site dMRI cohorts. The dMRI method does
not involve radioactive tracers, is completely automated, and can be collected in
less than 12 minutes across 3T scanners worldwide. The use of this test could thus
positively impact the clinical care of patients with Parkinson’s disease and Parkinsonism
as well as reduce the number of misdiagnosed cases in clinical trials.