ArticleScientific reports2024
Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Breaking the norm: population-scale deviations of brain structure in depression and anxiety.Molecular psychiatry · 2026Article
- Depression in Autism Spectrum Disorder: Neurobiological Convergence and Emerging Therapeutic Strategies.Biology · 2026Review
- Brain morphology in Anorexia Nervosa and its subtypes: A multi-cohort study of individual participant data.PLoS medicine · 2026Article
- Deep learning-based differential diagnosis of major depression and bipolar disorder using microglia-cellular sensors and patient-derived small extracellular vesicles.Scientific reports · 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
- Applying machine-learning and deep-learning to predict depression from brain MRI and identify depression-related brain biology.Translational psychiatry · 2026Article
- Can we predict sleep health based on brain features? A large-scale machine learning study using the UK Biobank.Brain communications · 2026Article
- The neuroimaging correlates of depression established across six large-scale population datasets.Nature. Mental health · 2026Article
- Extracting interpretable signatures of whole-brain dynamics through systematic comparison.PLoS computational biology · 2024Article
- Amygdala connectivity is associated with withdrawn/depressed behavior in a large sample of children from the Adolescent Brain Cognitive Development (ABCD) Study®.Psychiatry research. Neuroimaging · 2024Article
- Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo.Nature. Mental health · 2024Article
- Systematic Review: White Matter Microstructural Organization in Adolescents With Depression.JAACAP open · 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
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Authors and funding
66 authors.
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Abstract
Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.
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