Machine learning in major depression: from classification to treatment outcome prediction
Open accessAbstract
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.
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The classifiers used in this study have previously been shown to successfully predict clinical features in an MDD sample.
“The classifiers used in this study have previously been shown to successfully being able to predict clinical features in an MDD sample (Gao et al., 2018; Guo et al., 2012; Nakano et al., 2020; Sacchet et al., 2015; Schmaal et al., 2015; Yan et al., 2020).”
From Understanding and predicting future relapse in depression from resting state functional connectivity and self-referential processing (2023) · cites this paper as Gao et al., 2018
The review’s section on treatment-response prediction describes MRI-based machine learning in MDD patients, including a study that predicted a change in depression-rating scores. Its research overview also discusses classifiers used across MDD studies. But the cited paper does not establish that the particular classifiers used in the citing study previously predicted clinical features; it supports only the broader point that some machine-learning methods have done so. (LLM, conf=0.78)
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Self-citationIntrinsic Brain Network Biomarkers of Antidepressant Response: a Review
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- Authors
- Gao, S., Calhoun, V. D., & Sui, J.
- Affiliations
- Chinese Academy of Sciences (CN)
- Mind Research Network (US)
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences Beijing China
- Venue
- CNS Neuroscience & Therapeutics
- Publisher
- Wiley
- Published
- 2018-08-23
- Volume
- 24
- Issue
- 11
- Pages
- 1037-1052
- ISSN
- 1755-5930 (ISSN-L)
- 1755-5949
- PMC
- PMC6324186
- OpenAlex
- W2888505821
- Cited by
- 370
- References
- 148
- Citations by year
- 2026: 27 · 2025: 48 · 2024: 91 · 2023: 52 · 2022: 60 · 2021: 41 · 2020: 30 · 2019: 19 · 2018: 2
- FWCI
- 2.77
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CNS Neuroscience & Therapeutics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (1R01MH094524, P20GM103472, R01EB005846)
- Chinese Academy of Sciences (XDBS01000000, 61773380, 81471367)
- Topics
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Bipolar Disorder and Treatment
- Keywords
- major depressive disorder, machine learning, treatment outcome prediction, diagnostic biomarkers, bipolar disorder, mood disorders, brain imaging classification, magnetic resonance imaging, personalized treatment
- MeSH terms
- Machine Learning, Animals, Brain, Major Depressive Disorder, Humans, Neural Pathways, Psychiatric Status Rating Scales, Treatment Outcome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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