A totally data-driven whole-brain multimodal pipeline for the discrimination of Parkinson’s disease, multiple system atrophy and healthy control.
NeuroImage: Clinical. 2019-01-01; 23: 101858
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Nemmi F(1), Pavy-Le Traon A(2), Phillips OR(3), Galitzky M(4), Meissner WG(5), Rascol O(6), Péran P(7).
(1)ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France. Electronic address: .
(2)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.
(3)Brain Key, Palo Alto, California, USA; NeuroToul COEN Center, INSERM, CHU de Toulouse, Université de Toulouse 3, Toulouse, France.
(4)Centre d’Investigation Clinique (CIC), CHU de Toulouse, Toulouse, France.
(5)French Reference Center for MSA, Department of Neurology, University Hospital Bordeaux, Bordeaux and Institute of Neurodegenerative Disorders, University Bordeaux, CNRS UMR 5293, 33000 Bordeaux, France; Dept. Medicine, University of
Otago, Christchurch, and New Zealand Brain Research Institute, Christchurch, New Zealand.
(6)Departments of Clinical Pharmacology and Neurosciences, Clinical Investigation Center CIC 1436, NS-Park/FCRIN network and NeuroToul COEN Center, INSERM, CHU de Toulouse, Université de Toulouse 3, Toulouse, France.
(7)ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France.
Parkinson’s Disease (PD) and Multiple System Atrophy (MSA) are two parkinsonian syndromes that share many symptoms, albeit having very different prognosis. Although previous studies have proposed multimodal MRI protocols combined with multivariate analysis to discriminate between these two populations and healthy controls, studies combining all MRI indexes relevant for these disorders (i.e. grey matter volume, fractional anisotropy, mean diffusivity, iron deposition, brain activity at rest and brain connectivity) with a completely data-driven voxelwise analysis for discrimination are still lacking. In this study, we used such a complete MRI protocol and adapted a fully-data driven analysis pipeline to discriminate between these populations and a healthy controls (HC) group. The pipeline combined several feature selection and reduction steps to obtain interpretable models with a low number of discriminant features that can shed light onto the brain pathology of PD and MSA. Using this pipeline, we could discriminate between PD and HC (best accuracy = 0.78), MSA and HC (best accuracy = 0.94) and PD and MSA (best accuracy = 0.88). Moreover, we showed that indexes derived from resting-state fMRI alone could discriminate between PD and HC, while mean diffusivity in the cerebellum and the putamen alone could discriminate between MSA and HC. On the other hand, a more diverse set of indexes derived by multiple modalities was needed to discriminate between the two disorders. We showed that our pipeline was able to discriminate between distinct pathological populations while delivering sparse model that could be used to better understand the neural underpinning of the pathologies.