ReviewCNS neuroscience & therapeutics2018
Machine learning in major depression: From classification to treatment outcome prediction.
Review in CNS neuroscience & therapeutics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 151 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
151 citing papers in PubMed, 2 syntheses or guidelines pooled it, 373 citations in OpenAlex.
- Use of Social Media Data to Diagnose and Monitor Psychotic Disorders: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Individualized fMRI connectivity defines signatures of antidepressant and placebo responses in major depression.Molecular psychiatry · 2023Trial
- Heterogeneous structural brain signatures of methamphetamine and ketamine use disorders revealed by normative modeling.Molecular psychiatry · 2026Article
- Universum Inspired Graph Contrast Learning for Major Depressive Disorder Identification: In Functional Brain Network View.CNS neuroscience & therapeutics · 2026Article
- Resting-state fMRI-based machine learning for predicting SSRI treatment response in major depressive disorder.BMC psychiatry · 2026Article
- Dyscoordination of thalamic reticular spindles is associated with social memory deficits in mice and humans with autism spectrum disorder.Nature communications · 2026Article
- Discrimination of generalized anxiety and major depressive disorders by machine learning of resting-state functional MRI.BMC psychiatry · 2026Article
- A multiscale spatio-temporal convolutional attention network for depressive disorder diagnosis via coarse-grained signal reconstruction based on EEG.Scientific reports · 2026Article
- Predicting Depression Risk in Physically Inactive Older Adults Using Dietary Antioxidants and Machine Learning: A SHAP-Interpretable Analysis of NHANES.CNS neuroscience & therapeutics · 2026Article
- Identification of Major Depressive Disorder Using Multiple Functional Connection Patterns.CNS neuroscience & therapeutics · 2026Article
- Bridging molecules and connectome: network biomarkers guided by neurotransmitter architecture in major depressive disorder.Translational psychiatry · 2026Article
- Anticipatory pleasure as a key hedonic component in classifying major depressive disorder: multidimensional behavioral evidence from majority vote algorithm.BMC psychiatry · 2026Article
- circFKBP8(5S,6)-encoded protein promotes stress susceptibility in mice by down-regulating dopamine D3 receptor expression and its downstream AMPK/mTOR/ULK1 autophagy signaling.Genes & diseases · 2026Article
- Classification of major depressive disorder using vertex-wise brain sulcal depth, curvature, and thickness with a deep and a shallow learning model.Molecular psychiatry · 2026Article
- Using valence and arousal scoring in speech to detect depressive symptoms: A study on family caregivers.PloS one · 2026Article
- Identifying neuroimaging biomarkers for anxiety in emerging adults using machine learning and functional near-infrared spectroscopy during resting-state.Frontiers in psychiatry · 2026Article
- Intelligent diagnosis of major depressive disorder with edge convolution and contrastive learning.PloS one · 2026Article
- What machine learning teaches us about depression prediction across the life course: An exploratory comparison of predictive models.SSM - population health · 2025Article
- The delta/alpha ratio in sleep EEG increases with the severity of depression in patients over 50 years old.BMC psychiatry · 2025Article
91 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
aimsMajor 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.
conclusionsWe hope such efforts may highlight the need for an urgently needed paradigm shift in treatment, to guide personalized optimal clinical care.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.