MRI supervised and unsupervised classification of Parkinson's disease and multiple system atrophy

Abstract

Background: Multimodal MRI approach is based on a combination of MRI parameters sensitive to different tissue characteristics (eg, volume atrophy, iron deposition, and microstructural damage). The main objective of the present study was to use a multimodal MRI approach to identify brain differences that could discriminate between matched groups of patients with multiple system atrophy, Parkinson's disease, and healthy controls. We assessed the 2 different MSA variants, namely, MSA-P, with predominant parkinsonism, and MSA-C, with more prominent cerebellar symptoms.

Methods: Twenty-six PD patients, 29 MSA patients (16 MSA-P, 13 MSA-C), and 26 controls underwent 3-T MRI comprising T2*-weighted, T1-weighted, and diffusion tensor imaging scans. Using whole-brain voxel-based MRI, we combined gray-matter density, T2* relaxation rates, and diffusion tensor imaging scalars to compare and discriminate PD, MSA-P, MSA-C, and healthy controls.

Results: Our main results showed that this approach reveals multiparametric modifications within the cerebellum and putamen in both MSA-C and MSA-P patients, compared with PD patients. Furthermore, our findings revealed that specific single multimodal MRI markers were sufficient to discriminate MSA-P and MSA-C patients from PD patients. Moreover, the unsupervised analysis based on multimodal MRI data could regroup individuals according to their clinical diagnosis, in most cases.

Conclusions: This study demonstrates that multimodal MRI is able to discriminate patients with PD from those with MSA with high accuracy. The combination of different MR biomarkers could be a great tool in early stage of disease to help diagnosis. © 2018 International Parkinson and Movement Disorder Society

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