{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T16:31:29Z","timestamp":1790353889288,"version":"4.1.0"},"reference-count":45,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["Projet\u2010ANR\u201020\u2010CE17\u20100028"],"award-info":[{"award-number":["Projet\u2010ANR\u201020\u2010CE17\u20100028"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100027674","name":"Chugiak-Eagle River Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100027674","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["movementdisorders.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Movement Disorders"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The differentiation between multiple system atrophy (MSA) and Parkinson's disease (PD) based on clinical diagnostic criteria can be challenging, especially at an early stage. Leveraging deep learning methods and magnetic resonance imaging (MRI) data has shown great potential in aiding automatic diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The aim was to determine the feasibility of a three\u2010dimensional convolutional neural network (3D CNN)\u2013based approach using multimodal, multicentric MRI data for differentiating MSA and its variants from PD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>MRI data were retrospectively collected from three MSA French reference centers. We computed quantitative maps of gray matter density (GD) from a T1\u2010weighted sequence and mean diffusivity (MD) from diffusion tensor imaging. These maps were used as input to a 3D CNN, either individually (\u201cmonomodal,\u201d \u201cGD\u201d or \u201cMD\u201d) or in combination (\u201cbimodal,\u201d \u201cGD\u2010MD\u201d). Classification tasks included the differentiation of PD and MSA patients. Model interpretability was investigated by analyzing misclassified patients and providing a visual interpretation of the most activated regions in CNN predictions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The study population included 92 patients with MSA (50 with MSA\u2010P, parkinsonian variant; 33 with MSA\u2010C, cerebellar variant; 9 with MSA\u2010PC, mixed variant) and 64 with PD. The best accuracies were obtained for the PD\/MSA (0.88\u2009\u00b1\u20090.03 with GD\u2010MD), PD\/MSA\u2010C&amp;PC (0.84\u2009\u00b1\u20090.08 with MD), and PD\/MSA\u2010P (0.78\u2009\u00b1\u20090.09 with GD) tasks. Patients misclassified by the CNN exhibited fewer and milder image alterations, as found using an image\u2010based\n                      <jats:italic>z<\/jats:italic>\n                      score analysis. Activation maps highlighted regions involved in MSA pathophysiology, namely the putamen and cerebellum.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      Our findings hold promise for developing an efficient, MRI\u2010based, and user\u2010independent diagnostic tool suitable for differentiating parkinsonian syndromes in clinical practice. \u00a9 2025 The Author(s).\n                      <jats:italic>Movement Disorders<\/jats:italic>\n                      published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mds.30300","type":"journal-article","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T09:15:11Z","timestamp":1753348511000},"page":"2139-2148","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Deep Learning to Differentiate Parkinsonian Syndromes Using Multimodal\n                    <scp>Magnetic Resonance Imaging<\/scp>\n                    : A Proof\u2010of\u2010Concept Study"],"prefix":"10.1002","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6752-367X","authenticated-orcid":false,"given":"Giulia Maria","family":"Mattia","sequence":"first","affiliation":[{"name":"Univ Toulouse, Inserm, ToNIC  Toulouse France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9306-5687","authenticated-orcid":false,"given":"Lydia","family":"Chougar","sequence":"additional","affiliation":[{"name":"Sorbonne Universit\u00e9, Institut du Cerveau\u2010Paris Brain Institute\u2010ICM, AP\u2010HP, CNRS 7225, Inserm 1127, H\u00f4pital de la Piti\u00e9 Salp\u00eatri\u00e8re, Department of Neuroradiolog  Paris France"},{"name":"The Neuro\u2010Montreal Neurological Institute, McGill University  Montreal Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandra","family":"Foubert\u2010Samier","sequence":"additional","affiliation":[{"name":"Univ. de Bordeaux, CNRS, IMN, UMR 5293  Bordeaux France"},{"name":"CHU Bordeaux, Service de Neurologie des Maladies Neurod\u00e9g\u00e9n\u00e9ratives, IMNc, CRMR AMS, NS\u2010Park\/FCRIN Network  Bordeaux France"},{"name":"University of Bordeaux, INSERM, BPH, U1219, IPSED  Bordeaux France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2172-7527","authenticated-orcid":false,"given":"Wassilios G.","family":"Meissner","sequence":"additional","affiliation":[{"name":"Univ. de Bordeaux, CNRS, IMN, UMR 5293  Bordeaux France"},{"name":"CHU Bordeaux, Service de Neurologie des Maladies Neurod\u00e9g\u00e9n\u00e9ratives, IMNc, CRMR AMS, NS\u2010Park\/FCRIN Network  Bordeaux France"},{"name":"Department of Medicine University of Otago, Christchurch, and New Zealand Brain Research Institute  Christchurch New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7250-592X","authenticated-orcid":false,"given":"Margherita","family":"Fabbri","sequence":"additional","affiliation":[{"name":"Univ Toulouse, Inserm, ToNIC  Toulouse France"},{"name":"MSA French Reference Center, Univ. 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