Volume 33, Issue 4 p. 600-608
Research Article

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

Patrice Péran PhD

Corresponding Author

Patrice Péran PhD

ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France

Correspondence to: Patrice Péran, UMR 1214 - INSERM/UPS – ToNIC, Toulouse NeuroImaging Center, CHU PURPAN - Pavillon Baudo - Place du Dr Baylac, 31024 Toulouse - Cedex 3, France; E-mail: [email protected]Search for more papers by this author
Gaetano Barbagallo MD

Gaetano Barbagallo MD

Institute of Neurology, University Magna Græcia, Catanzaro, Italy

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Federico Nemmi PhD

Federico Nemmi PhD

ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France

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Maria Sierra MD

Maria Sierra MD

Neurology Service, University Hospital Marqués de Valdecilla and Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Santander, Spain

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Monique Galitzky MD

Monique Galitzky MD

Centre d'Investigation Clinique (CIC), CHU de Toulouse, Toulouse, France

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Anne Pavy-Le Traon MD, PhD

Anne Pavy-Le Traon MD, PhD

UMR Institut National de la Santé et de la Recherche Médicale 1048, Institut des Maladies Métaboliques et Cardiovasculaires, Toulouse, France

Department of Neurology and Institute for Neurosciences, University Hospital of Toulouse, Toulouse, France

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Pierre Payoux MD, PhD

Pierre Payoux MD, PhD

ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France

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Wassilios G. Meissner MD, PhD

Wassilios G. Meissner MD, PhD

Service de Neurologie, CHU Bordeaux, Bordeaux, France

Univ. de Bordeaux, Institut des Maladies Neurodégénératives, UMR 5293, Bordeaux, France

CNRS, Institut des Maladies Neurodégénératives, UMR 5293, Bordeaux, France

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Olivier Rascol MD, PhD

Olivier Rascol MD, PhD

ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France

Université de Toulouse 3, CHU de Toulouse, INSERM, Centre de Reference AMS, Service de Neurologie et de Pharmacologie Clinique, Centre d'Investigation Clinique CIC1436, Réseau NS-Park/FCRIN et Centre of excellence for neurodegenerative disorders (COEN) de Toulouse, Toulouse, France

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First published: 23 February 2018
Citations: 57

Relevant conflicts of interest/financial disclosusres: Nothing to report.

Funding agencies: The study was sponsored by Inserm and funded by a “Recherche clinique translationnelle” grant from INSERM-DGOS (2013-2014).

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