{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:53:10Z","timestamp":1775562790783,"version":"3.50.1"},"reference-count":246,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T00:00:00Z","timestamp":1607990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-19-P3IA-0001"],"award-info":[{"award-number":["ANR-19-P3IA-0001"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-10-IAIHU-06"],"award-info":[{"award-number":["ANR-10-IAIHU-06"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Abeona Foundation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,3,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In order to reach precision medicine and improve patients\u2019 quality of life, machine learning is increasingly used in medicine. Brain disorders are often complex and heterogeneous, and several modalities such as demographic, clinical, imaging, genetics and environmental data have been studied to improve their understanding. Deep learning, a subpart of machine learning, provides complex algorithms that can learn from such various data. It has become state of the art in numerous fields, including computer vision and natural language processing, and is also growingly applied in medicine. In this article, we review the use of deep learning for brain disorders. More specifically, we identify the main applications, the concerned disorders and the types of architectures and data used. Finally, we provide guidelines to bridge the gap between research studies and clinical routine.<\/jats:p>","DOI":"10.1093\/bib\/bbaa310","type":"journal-article","created":{"date-parts":[[2020,10,13]],"date-time":"2020-10-13T11:38:50Z","timestamp":1602589130000},"page":"1560-1576","source":"Crossref","is-referenced-by-count":25,"title":["Deep learning for brain disorders: from data processing to disease treatment"],"prefix":"10.1093","volume":"22","author":[{"given":"Ninon","family":"Burgos","sequence":"first","affiliation":[{"name":"Paris Brain Institute, in the ARAMIS Lab"}]},{"given":"Simona","family":"Bottani","sequence":"additional","affiliation":[{"name":"Paris Brain Institute, in the ARAMIS Lab"}]},{"given":"Johann","family":"Faouzi","sequence":"additional","affiliation":[{"name":"Paris Brain Institute, in the ARAMIS Lab"}]},{"given":"Elina","family":"Thibeau-Sutre","sequence":"additional","affiliation":[{"name":"Paris Brain Institute, in the ARAMIS 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