Machine learning in major depression: From classification to treatment outcome prediction

Shuang Gao, Vince Daniel Calhoun, Jing Sui

Research output: Contribution to journalArticle

Abstract

Aims: Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. 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. Discussions: In this study, we review popular machine-learning 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. Conclusions: We hope such efforts may highlight the need for an urgently needed paradigm shift in treatment, to guide personalized optimal clinical care.

Original languageEnglish (US)
JournalCNS Neuroscience and Therapeutics
DOIs
StateAccepted/In press - Jan 1 2018
Externally publishedYes

Keywords

  • classification
  • machine learning
  • magnetic resonance imaging
  • major depressive disorder
  • review

ASJC Scopus subject areas

  • Pharmacology
  • Psychiatry and Mental health
  • Physiology (medical)
  • Pharmacology (medical)

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