Comparison of NODDI and Freewater Diffusion in Parkinson’s Disease and atypical Parkinsonism

Neurite orientation dispersion and density imaging (NODDI) uses a three-compartment model to probe brain tissue microstructure, whereas free-water (FW) imaging models two-compartments. We acquired multi- and single-shell diffusion imaging at 3 Tesla across two sites. NODDI (using multi-shell; isotropic volume [Viso]; intracellular volume [Vic]; orientation dispersion [ODI]) and FW imaging (using single-shell; FW; free-water corrected fractional anisotropy [FAt]) were compared with 44 PD, 21 multiple system atrophy Parkinsonian variant (MSAp), 26 progressive supranuclear palsy (PSP), and 24 healthy control subjects in the basal ganglia, midbrain/thalamus, cerebellum, and corpus callosum. A key question addressed was if these techniques discriminate PD and atypical Parkinsonism. Both NODDI (AUC: 0.945) and FW imaging (AUC: 0.969) had high accuracy, with no significant difference between models. This study provides new evidence that NODDI and FW imaging offer similar discriminability between PD and atypical Parkinsonism, and FW had higher effect sizes for detecting Parkinsonism within regions across the basal ganglia and cerebellum.

Machine Learning model for differential diagnosis of PD and atypical Parkinsonism

Development of valid, non-invasive biomarkers for parkinsonian syndromes is crucially needed. We performed an international study at 17 MRI centres in Austria, Germany, and the USA using diffusion-weighted MRI from 1002 patients and 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 space between Parkinson's disease and atypical parkinsonism (multiple system atrophy and progressive supranuclear palsy) and between multiple system atrophy and progressive supranuclear palsy. For each comparison, models were developed on a training and validation cohort and evaluated in an independent test cohort by quantifying the area under the curve (AUC) of receiving operating characteristic curves. The primary outcomes were free water and free-water-corrected fractional anisotropy across 60 different template regions. In the test cohort for disease-specific comparisons, the diffusion-weighted MRI plus MDS-UPDRS III model (Parkinson's disease vs atypical parkinsonism had an AUC 0·962; multiple system atrophy vs progressive supranuclear palsy AUC 0·897) and diffusion-weighted MRI only model had high AUCs (Parkinson's disease vsatypical parkinsonism AUC 0·955; multiple system atrophy vs progressive supranuclear palsy AUC 0·926), whereas the MDS-UPDRS III only models had significantly lower AUCs (Parkinson's disease vs atypical parkinsonism 0·775; multiple system atrophy vs progressive supranuclear palsy 0·582). These results indicate that a non-invasive imaging approach is capable of differentiating forms of parkinsonism comparable to current gold standard methods.