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

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 depres...

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Bibliographic Details
Published in:CNS neuroscience & therapeutics 2018-11, Vol.24 (11), p.1037-1052
Main Authors: Gao, Shuang, Calhoun, Vince D., Sui, Jing
Format: Article
Language:English
Subjects:
Animals
Artificial intelligence
Biomarkers
Bipolar disorder
Brain - diagnostic imaging
Brain - pathology
Classification
Data processing
Depressive Disorder, Major - classification
Depressive Disorder, Major - diagnosis
Depressive Disorder, Major - therapy
Emotional disorders
Humans
Learning algorithms
Machine Learning
Magnetic resonance imaging
major depressive disorder
Mental depression
Mood
Morbidity
Neural Pathways - pathology
Neuroimaging
Psychiatric Status Rating Scales
Review
Treatment Outcome
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Summary: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.
ISSN:1755-5930
1755-5949
DOI:10.1111/cns.13048