Patrice Péran1, Gaetano Barbagallo2, Federico Nemmi1, Maria Sierra3, Monique Galitzky4, Anne Pavy-Le Traon5,6, Pierre Payoux1, Wassilios G Meissner7,8,9, Olivier Rascol1,10. 1. ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France. 2. Institute of Neurology, University Magna Graecia, Catanzaro, Italy. 3. Neurology Service, University Hospital Marqués de Valdecilla and Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Santander, Spain. 4. Centre d'Investigation Clinique (CIC), CHU de Toulouse, Toulouse, France. 5. UMR Institut National de la Santé et de la Recherche Médicale 1048, Institut des Maladies Métaboliques et Cardiovasculaires, Toulouse, France. 6. Department of Neurology and Institute for Neurosciences, University Hospital of Toulouse, Toulouse, France. 7. Service de Neurologie, CHU Bordeaux, Bordeaux, France. 8. Univ. de Bordeaux, Institut des Maladies Neurodégénératives, UMR 5293, Bordeaux, France. 9. CNRS, Institut des Maladies Neurodégénératives, UMR 5293, Bordeaux, France. 10. 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.
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.
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 PDpatients, 29 MSApatients (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 PDpatients. Furthermore, our findings revealed that specific single multimodal MRI markers were sufficient to discriminate MSA-P and MSA-C patients from PDpatients. 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.
Authors: Xueling Suo; Du Lei; Wenbin Li; Lei Li; Jing Dai; Song Wang; Nannan Li; Lan Cheng; Rong Peng; Graham J Kemp; Qiyong Gong Journal: Front Med Date: 2020-05-26 Impact factor: 4.592
Authors: Maria Teresa Pellecchia; Iva Stankovic; Alessandra Fanciulli; Florian Krismer; Wassilios G Meissner; Jose-Alberto Palma; Jalesh N Panicker; Klaus Seppi; Gregor K Wenning Journal: Mov Disord Clin Pract Date: 2020-09-03
Authors: Alexandra Abos; Barbara Segura; Hugo C Baggio; Anna Campabadal; Carme Uribe; Alicia Garrido; Ana Camara; Esteban Muñoz; Francesc Valldeoriola; Maria Jose Marti; Carme Junque; Yaroslau Compta Journal: Neuroimage Clin Date: 2019-06-15 Impact factor: 4.881