SynthesisBiological psychiatry2017
Detecting Neuroimaging Biomarkers for Depression: A Meta-analysis of Multivariate Pattern Recognition Studies.
Synthesis in Biological psychiatry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 5 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.
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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
72 citing papers in PubMed, 5 syntheses or guidelines pooled it, 234 citations in OpenAlex.
- Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.Psychiatry and clinical neurosciences · 2024Pooled it
- Efficacy of bio- and neurofeedback for depression: a meta-analysis.Psychological medicine · 2022Pooled it
- Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis.Translational psychiatry · 2021Pooled it
- Altered Resting-State Functional Activity in Medication-Naive Patients With First-Episode Major Depression Disorder vs. Healthy Control: A Quantitative Meta-Analysis.Frontiers in behavioral neuroscience · 2019Pooled it
- Electroencephalographic Biomarkers for Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis.The American journal of psychiatry · 2019Pooled it
- From Traditional Inspection to Quantitative Imaging: Tongue and Facial Color Features for Automated Machine Learning-Driven Depression and Schizophrenia Classification.Behavioral sciences (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Psychiatry: Five Decades of Progress and Persistent Translational Challenges.Psychiatric research and clinical practice · 2026Article
- Disrupted hierarchical functional brain organization in affective and psychotic disorders: insights from functional brain gradients.Translational psychiatry · 2026Article
- Urinary copper is linked to regional cortical volume reductions in adolescents with major depressive disorder.Translational psychiatry · 2026Article
- Multi-Site Transfer Classification of Major Depressive Disorder: An fMRI Study in 3335 Subjects.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 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
- Multimodal AI-based systems in major depressive disorder: a review of clinical and translational applications.Frontiers in digital health · 2026Review
- Generalizable structure-function covariation predictive of antidepressant response revealed by target-oriented multimodal fusion.Nature. Mental health · 2026Article
- Concurrent and Prospective Prediction of Suicidal Ideation in Adolescents Using Multimethod Data and Machine Learning: A Pilot Study.JAACAP open · 2025Article
- Article
- A Novel Depression Risk Prediction Model Using NHANES Data With Mendelian Randomization Validation.Brain and behavior · 2025Article
- Alterations in gray matter volume and associated transcriptomics after electroconvulsive therapy in major depressive disorder.Psychological medicine · 2025Article
- Eye Movement Indicator Difference Based on Binocular Color Fusion and Rivalry.Journal of eye movement research · 2025Article
- Resting-state brain functional connectivity in patients with chronic intractable pain who respond to spinal cord stimulation therapy.British journal of anaesthesia · 2025Article
- Optimizing Antidepressant Efficacy: Generalizable Multimodal Neuroimaging Biomarkers for Prediction of Treatment Response.medRxiv : the preprint server for health sciences · 2024Article
12 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 5 institutions in 4 countries.
Funding
Abstract
backgroundMultiple studies have examined functional and structural brain alteration in patients diagnosed with major depressive disorder (MDD). The introduction of multivariate statistical methods allows investigators to utilize data concerning these brain alterations to generate diagnostic models that accurately differentiate patients with MDD from healthy control subjects (HCs). However, there is substantial heterogeneity in the reported results, the methodological approaches, and the clinical characteristics of participants in these studies.
methodsWe conducted a meta-analysis of all studies using neuroimaging (volumetric measures derived from T1-weighted images, task-based functional magnetic resonance imaging [MRI], resting-state MRI, or diffusion tensor imaging) in combination with multivariate statistical methods to differentiate patients diagnosed with MDD from HCs.
resultsThirty-three (k = 33) samples including 912 patients with MDD and 894 HCs were included in the meta-analysis. Across all studies, patients with MDD were separated from HCs with 77% sensitivity and 78% specificity. Classification based on resting-state MRI (85% sensitivity, 83% specificity) and on diffusion tensor imaging data (88% sensitivity, 92% specificity) outperformed classifications based on structural MRI (70% sensitivity, 71% specificity) and task-based functional MRI (74% sensitivity, 77% specificity).
conclusionsOur results demonstrate the high representational capacity of multivariate statistical methods to identify neuroimaging-based biomarkers of depression. Future studies are needed to elucidate whether multivariate neuroimaging analysis has the potential to generate clinically useful tools for the differential diagnosis of affective disorders and the prediction of both treatment response and functional outcome.
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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.