ArticleHuman brain mapping2020
Automated classification of depression from structural brain measures across two independent community-based cohorts.
Article in Human brain mapping, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- 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
- Molecular, Structural, and Functional Neuroimaging in Major Depression.Advances in experimental medicine and biology · 2026Review
- Neuroanatomical dimensions in major depression linked to cognition, adverse life events, self-harm, metabolomics and genetics.Communications medicine · 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
- A Systematic Evaluation of Machine Learning-Based Biomarkers for Major Depressive Disorder.JAMA psychiatry · 2024Article
- Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures.Scientific reports · 2024Article
- Aberrant resting-state co-activation network dynamics in major depressive disorder.Translational psychiatry · 2024Article
- Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo.Nature. Mental health · 2024Article
- Classification accuracy of structural and functional connectomes across different depressive phenotypes.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Brain multi-contrast, multi-atlas segmentation of diffusion tensor imaging and ensemble learning automatically diagnose late-life depression.Scientific reports · 2023Article
- Systematic Review: White Matter Microstructural Organization in Adolescents With Depression.JAACAP open · 2023Article
- Structural brain correlates of childhood trauma with replication across two large, independent community-based samples.European psychiatry : the journal of the Association of European Psychiatrists · 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
- Identification of suicidality in patients with major depressive disorder via dynamic functional network connectivity signatures and machine learning.Translational psychiatry · 2022Article
- Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health.Nature communications · 2022Article
- Comparing personalized brain-based and genetic risk scores for major depressive disorder in large population samples of adults and adolescents.European psychiatry : the journal of the Association of European Psychiatrists · 2022Article
- Structural brain correlates of serum and epigenetic markers of inflammation in major depressive disorder.Brain, behavior, and immunity · 2021Article
- Automated classification of depression from structural brain measures across two independent community-based cohorts.Human brain mapping · 2020Article
Corrections and comments
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Authors and funding
13 authors.
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Abstract
Major depressive disorder (MDD) has been the subject of many neuroimaging case-control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically-ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well-phenotyped community-based group of current MDD cases with clinical interview-based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, 'STRADL'). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types-SVM, penalised logistic regression or decision tree-either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population-based sample with self-reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses-remitted MDD in STRADL, and lifetime-experienced MDD in UK Biobank. The highest cross-validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self-reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime-experienced MDD (52.68-60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.
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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.