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Currently, there are no clinically useful diagnostic biomarkers that are able to confirm a diagnosis of MDD from bipolar disorder (BD) in the early depressive episode. Therefore, exploring translational biomarkers of mood disorders based on machine learning is in pressing need, though it is challenging, but with great potential to improve our understanding of these disorders.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussions<\/jats:title><jats:p>In this study, we review popular machine\u2010learning methods used for brain imaging classification and predictions, and provide an overview of studies, specifically for MDD, that have used magnetic resonance imaging data to either (a) classify MDDs from controls or other mood disorders or (b) investigate treatment outcome predictors for individual patients. Finally, challenges, future directions, and potential limitations related to MDD biomarker identification are also discussed, with a goal of offering a comprehensive overview that may help readers to better understand the applications of neuroimaging data mining in depression.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We hope such efforts may highlight the need for an urgently needed paradigm shift in treatment, to guide personalized optimal clinical care.<\/jats:p><\/jats:sec>","DOI":"10.1111\/cns.13048","type":"journal-article","created":{"date-parts":[[2018,8,23]],"date-time":"2018-08-23T07:30:25Z","timestamp":1535009425000},"page":"1037-1052","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":337,"title":["Machine learning in major depression: From classification to treatment outcome prediction"],"prefix":"10.1111","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8747-8781","authenticated-orcid":false,"given":"Shuang","family":"Gao","sequence":"first","affiliation":[{"name":"Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences Beijing China"},{"name":"University of Chinese Academy of Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9058-0747","authenticated-orcid":false,"given":"Vince D.","family":"Calhoun","sequence":"additional","affiliation":[{"name":"The Mind Research Network Albuquerque New Mexico"},{"name":"Department of Electrical and Computer Engineering The University of New Mexico Albuquerque New Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6837-5966","authenticated-orcid":false,"given":"Jing","family":"Sui","sequence":"additional","affiliation":[{"name":"Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences Beijing China"},{"name":"University of Chinese Academy of Sciences  Beijing China"},{"name":"CAS Centre for Excellence in Brain Science and Intelligence Technology, Institute of Automation Chinese Academy of Sciences Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,8,23]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(12)61689-4"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.4088\/JCP.8133su1c.03"},{"key":"e_1_2_8_4_1","doi-asserted-by":"crossref","unstructured":"GaoS OsuchEA WammesM et al.Discriminating bipolar disorder from major depression based on kernel SVM using functional independent components. 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP); 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