ArticlePsychiatry research. Neuroimaging2017
Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder.
Article in Psychiatry research. Neuroimaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it, 71 citations in OpenAlex.
- 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
- Identifying transdiagnostic neurobiomarkers for precision major psychiatric disordersJournal of translational internal medicine · 2026Article
- Applications of Artificial Intelligence in Psychiatry and Psychology Education: Scoping Review.JMIR medical education · 2025Article
- Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies.Brain and behavior · 2025Article
- The Clinical Utility of Biomarkers in Diagnosing Major Depressive Disorder in Adults: A Systematic Review of Literature From 2013 to 2023.Psychiatry investigation · 2025Article
- Integrative diagnosis of psychiatric conditions using ChatGPT and fMRI data.BMC psychiatry · 2025Article
- Resting-state functional magnetic resonance imaging and support vector machines for the diagnosis of major depressive disorder in adolescents.World journal of psychiatry · 2024Review
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Machine learning approach for obstructive sleep apnea screening using brain diffusion tensor imaging.Journal of sleep research · 2023Article
- Multivariate prediction of long COVID headache in adolescents using gray matter structural MRI features.Frontiers in human neuroscience · 2023Article
- An AI-Powered Clinical Decision Support System to Predict Flares in Rheumatoid Arthritis: A Pilot Study.Diagnostics (Basel, Switzerland) · 2023Article
- Revisiting Hemispheric Asymmetry in Mood Regulation: Implications for rTMS for Major Depressive Disorder.Brain sciences · 2022Review
- Machine-learning-based diagnosis of drug-naive adult patients with attention-deficit hyperactivity disorder using mismatch negativity.Translational psychiatry · 2021Article
- Classifying depression using blood biomarkers: A large population study.Journal of psychiatric research · 2021Article
- Computer Vision for Brain Disorders Based Primarily on Ocular Responses.Frontiers in neurology · 2021Article
- More stringent criteria are needed for diagnosing internet gaming disorder: Evidence from regional brain features and whole-brain functional connectivity multivariate pattern analyses.Journal of behavioral addictions · 2020Article
- Automated classification of depression from structural brain measures across two independent community-based cohorts.Human brain mapping · 2020Article
- Retraining an open-source pneumothorax detecting machine learning algorithm for improved performance to medical images.Clinical imaging · 2020Article
- Decreased effective connection from the parahippocampal gyrus to the prefrontal cortex in Internet gaming disorder: A MVPA and spDCM study.Journal of behavioral addictions · 2020Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Using MRI to diagnose mental disorders has been a long-term goal. Despite this, the vast majority of prior neuroimaging work has been descriptive rather than predictive. The current study applies support vector machine (SVM) learning to MRI measures of brain white matter to classify adults with Major Depressive Disorder (MDD) and healthy controls. In a precisely matched group of individuals with MDD (n =25) and healthy controls (n =25), SVM learning accurately (74%) classified patients and controls across a brain map of white matter fractional anisotropy values (FA). The study revealed three main findings: 1) SVM applied to DTI derived FA maps can accurately classify MDD vs. healthy controls; 2) prediction is strongest when only right hemisphere white matter is examined; and 3) removing FA values from a region identified by univariate contrast as significantly different between MDD and healthy controls does not change the SVM accuracy. These results indicate that SVM learning applied to neuroimaging data can classify the presence versus absence of MDD and that predictive information is distributed across brain networks rather than being highly localized. Finally, MDD group differences revealed through typical univariate contrasts do not necessarily reveal patterns that provide accurate predictive information.
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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.