ArticleHuman brain mapping2018
Development and evaluation of a multimodal marker of major depressive disorder.
Article in Human brain mapping, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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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
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Does the change in glutamate to GABA ratio correlate with change in depression severity? A randomized, double-blind clinical trial.Molecular psychiatry · 2022Trial
- Identifying transdiagnostic neurobiomarkers for precision major psychiatric disordersJournal of translational internal medicine · 2026Article
- Multimodal AI-based systems in major depressive disorder: a review of clinical and translational applications.Frontiers in digital health · 2026Review
- Implementation of support vector machines to classify abnormal neuronal response during emotion regulation at an individual level in patients with newly diagnosed bipolar disorder - and its association with subsequent functional changes and mood episodes.Psychological medicine · 2025Article
- Association of Hemispheric Asymmetry of Dopamine-Transporter Binding with Risk of Parkinsonian Depression.Movement disorders clinical practice · 2025Article
- Advances and Challenges in Depression Marker Research.Neuropsychiatric disease and treatment · 2025Review
- Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo.Nature. Mental health · 2024Article
- Article
- Functional and structural MRI based obsessive-compulsive disorder diagnosis using machine learning methods.BMC psychiatry · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale.BMC psychiatry · 2023Article
- Evaluation of brain structure and metabolism in currently depressed adults with a history of childhood trauma.Translational psychiatry · 2022Article
- Abnormal degree centrality in first-episode medication-free adolescent depression at rest: A functional magnetic resonance imaging study and support vector machine analysis.Frontiers in psychiatry · 2022Article
- Subthreshold depressive symptoms relate to cuneus structure: Thickness asymmetry and sex differences.Journal of psychiatric research · 2022Article
- Identifying subtypes of bipolar disorder based on clinical and neurobiological characteristics.Scientific reports · 2021Article
- Translational application of neuroimaging in major depressive disorder: a review of psychoradiological studies.Frontiers of medicine · 2021Review
- Multimodal MRI assessment for first episode psychosis: A major change in the thalamus and an efficient stratification of a subgroup.Human brain mapping · 2021Article
- Altered brain network centrality in middle-aged patients with retinitis pigmentosa: A resting-state functional magnetic resonance imaging study.Brain and behavior · 2021Article
Corrections and comments
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Authors and funding
27 authors.
Funding
Abstract
This study aimed to identify biomarkers of major depressive disorder (MDD), by relating neuroimage-derived measures to binary (MDD/control), ordinal (severe MDD/mild MDD/control), or continuous (depression severity) outcomes. To address MDD heterogeneity, factors (severity of psychic depression, motivation, anxiety, psychosis, and sleep disturbance) were also used as outcomes. A multisite, multimodal imaging (diffusion MRI [dMRI] and structural MRI [sMRI]) cohort (52 controls and 147 MDD patients) and several modeling techniques-penalized logistic regression, random forest, and support vector machine (SVM)-were used. An additional cohort (25 controls and 83 MDD patients) was used for validation. The optimally performing classifier (SVM) had a 26.0% misclassification rate (binary), 52.2 ± 1.69% accuracy (ordinal) and r = .36 correlation coefficient (p < .001, continuous). Using SVM, R
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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 OpenQuestion graph.