SynthesisTranslational psychiatry2021
Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis.
Synthesis in Translational psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 7 of them syntheses that pooled it.
What it found
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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.
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Who cites it
36 citing papers in PubMed, 7 syntheses or guidelines pooled it, 75 citations in OpenAlex.
- Integrating Neuroimaging and Machine Learning to Predict Mental Disorder Outcomes: A Systematic Review.Advances in experimental medicine and biology · 2026Pooled it
- Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis.Molecular psychiatry · 2025Pooled it
- Prediction models for treatment response in migraine: a systematic review and meta-analysis.The journal of headache and pain · 2025Pooled it
- Convergent functional effects of antidepressants in major depressive disorder: a neuroimaging meta-analysis.Molecular psychiatry · 2025Pooled it
- The diagnosis of ASD with MRI: a systematic review and meta-analysis.Translational psychiatry · 2024Pooled it
- Cingulate prediction of response to antidepressant and cognitive behavioral therapies for depression: Meta-analysis and empirical application.Brain imaging and behavior · 2023Pooled it
- Predicting treatment response using EEG in major depressive disorder: A machine-learning meta-analysis.Translational psychiatry · 2022Pooled it
- Towards causal inference-based antidepressant selection with brain and blood biomarkers.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Trial
- Structural imaging predictors of ketamine response in treatment-resistant depression: a machine learning approach.Translational psychiatry · 2026Article
- Article
- Moderators of treatment response in late-life depression.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- Magnetic Resonance Imaging Textural Changes Are More Sensitive Than Volumetric Changes in the Amygdala of Cocaine Use Disorder Patients.NMR in biomedicine · 2026Article
- Federated foundation models for psychiatry: a new paradigm for diagnosis, prognosis, and treatment of mood disorders.Frontiers in psychiatry · 2026Article
- Early treatment-related changes in dorsolateral prefrontal cortex activity and functional connectivity as potential biomarkers for antidepressant response in major depressive disorder.Translational psychiatry · 2025Article
- A decision-space model explains context-specific decision-making.Nature communications · 2025Article
- A stratified treatment algorithm in psychiatry: a program on stratified pharmacogenomics in severe mental illness (Psych-STRATA): concept, objectives and methodologies of a multidisciplinary project funded by Horizon Europe.European archives of psychiatry and clinical neuroscience · 2025Article
- Generalizability of clinical prediction models in mental health.Molecular psychiatry · 2025Observational
- Decoding Depression from Different Brain Regions Using Hybrid Machine Learning Methods.Bioengineering (Basel, Switzerland) · 2025Article
- Towards closed-loop precision psychiatry: Integrating MRI biomarkers for individualized care of major depressive disorder.Psychoradiology · 2025Review
- Predicting Antidepressant Treatment Response From Cortical Structure on MRI: A Mega-Analysis From the ENIGMA-MDD Working Group.Human brain mapping · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
No tools are currently available to predict whether a patient suffering from major depressive disorder (MDD) will respond to a certain treatment. Machine learning analysis of magnetic resonance imaging (MRI) data has shown potential in predicting response for individual patients, which may enable personalized treatment decisions and increase treatment efficacy. Here, we evaluated the accuracy of MRI-guided response prediction in MDD. We conducted a systematic review and meta-analysis of all studies using MRI to predict single-subject response to antidepressant treatment in patients with MDD. Classification performance was calculated using a bivariate model and expressed as area under the curve, sensitivity, and specificity. In addition, we analyzed differences in classification performance between different interventions and MRI modalities. Meta-analysis of 22 samples including 957 patients showed an overall area under the bivariate summary receiver operating curve of 0.84 (95% CI 0.81-0.87), sensitivity of 77% (95% CI 71-82), and specificity of 79% (95% CI 73-84). Although classification performance was higher for electroconvulsive therapy outcome prediction (n = 285, 80% sensitivity, 83% specificity) than medication outcome prediction (n = 283, 75% sensitivity, 72% specificity), there was no significant difference in classification performance between treatments or MRI modalities. Prediction of treatment response using machine learning analysis of MRI data is promising but should not yet be implemented into clinical practice. Future studies with more generalizable samples and external validation are needed to establish the potential of MRI to realize individualized patient care in MDD.
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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 Socratic graph.