ReviewFrontiers in neuroscience2023
The diagnostic performance of machine learning based on resting-state functional magnetic resonance imaging data for major depressive disorders: a systematic review and meta-analysis.
Review in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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
14 citing papers in PubMed.
- Discrimination of generalized anxiety and major depressive disorders by machine learning of resting-state functional MRI.BMC psychiatry · 2026Article
- Language-based detection of depression with machine learning: systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Novel TMS-derived metrics enable machine learning classification of major depressive disorder.NPP - digital psychiatry and neuroscience · 2026Article
- Brain entropy as a biomarker of major depression in adolescents and young adults: insights from multimodal resting-state functional magentic resonance imaging.Psychoradiology · 2026Article
- Molecular, Structural, and Functional Neuroimaging in Major Depression.Advances in experimental medicine and biology · 2026Review
- Attenuated cognitive control network connectivity as a mechanism in mother-daughter intergenerational transmission of depression: a preliminary study.Scientific reports · 2025Article
- Language-Based Detection of Depression with Machine Learning: Systematic Review and Meta- Analysis.Research square · 2025Article
- Diagnosis of adolescent depression with sleep disorder based on network topological attributes and functional connectivity.BMC psychiatry · 2025Article
- Differentiation between multiple sclerosis and neuromyelitis optic spectrum disorders with multilevel fMRI features: A machine learning analysis.Scientific reports · 2025Article
- Article
- A virtual reality-based multimodal framework for adolescent depression screening using machine learning.Frontiers in psychiatry · 2025Article
- Predicting rTMS treatment response in schizophrenia using interpretable machine learning: a SHAP-based analysis.Therapeutic advances in psychopharmacology · 2025Article
- Structural brain alterations in patients with anxious depression: evidence from the REST-meta-MDD project.Frontiers in psychiatry · 2025Article
- Innovative Neuroimaging Biomarker Distinction of Major Depressive Disorder and Bipolar Disorder through Structural Connectome Analysis and Machine Learning Models.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Machine learning (ML) has been widely used to detect and evaluate major depressive disorder (MDD) using neuroimaging data, i.e., resting-state functional magnetic resonance imaging (rs-fMRI). However, the diagnostic efficiency is unknown. The aim of the study is to conduct an updated meta-analysis to evaluate the diagnostic performance of ML based on rs-fMRI data for MDD. Methods: English databases were searched for relevant studies. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used to assess the methodological quality of the included studies. A random-effects meta-analytic model was implemented to investigate the diagnostic efficiency, including sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Regression meta-analysis and subgroup analysis were performed to investigate the cause of heterogeneity. Results: Thirty-one studies were included in this meta-analysis. The pooled sensitivity, specificity, DOR, and AUC with 95% confidence intervals were 0.80 (0.75, 0.83), 0.83 (0.74, 0.82), 14.00 (9, 22.00), and 0.86 (0.83, 0.89), respectively. Substantial heterogeneity was observed among the studies included. The meta-regression showed that the leave-one-out cross-validation (loocv) (sensitivity: Conclusion: ML showed high accuracy for the automatic diagnosis of MDD. Future studies are warranted to promote the potential use of these classification algorithms in clinical settings.
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.