ArticleMolecular psychiatry2023
Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies.
Article in Molecular psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled it.
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Who cites it
56 citing papers in PubMed, 1 synthesis or guideline pooled it, 106 citations in OpenAlex.
- Predicting the treatment outcomes of major depressive disorder interventions with baseline resting-state functional connectivity: a meta-analysis.BMC psychiatry · 2025Pooled it
- Altered resting-state functional connectivity in lung cancer patients with comorbid depressive symptoms.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Artificial Intelligence in Multimodal Neuroimaging Research on Depression: A Bibliometric Analysis.Psychiatry investigation · 2026Article
- Characterization of depressive symptoms in individuals with spinal cord injury through the Beck Depression Inventory, and their role in recovery.Scientific reports · 2026Observational
- HDGNN-Mamba2: Mamba-Based Spatiotemporal Heterogeneous Dynamic Graph Neural Network for Major Depressive Disorder Classification.Brain sciences · 2026Article
- Functional and structural connectivity of thalamic subnuclei in major depressive disorder at 7 Tesla.Psychiatry and clinical neurosciences · 2026Article
- Neural Responses to Affective Sentences Reveal Signatures of Depression.Translational psychiatry · 2026Article
- Multimodal fusion of brain imaging and proteomics reveals a brain-body pathway linking depression and metabolic dysfunction.Psychological medicine · 2026Article
- Time-resolved EEG decoding reveals altered neural dynamics of affective semantic evaluation in depression and suicidality.Communications biology · 2026Article
- Thalamocortical functional connectivity alterations and peripheral immune transcriptomic dysregulation in major depressive disorder patients with suicidal ideation.Translational psychiatry · 2026Article
- Multi-Site Transfer Classification of Major Depressive Disorder: An fMRI Study in 3335 Subjects.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Increased Aperiodic Exponents Track Depression Symptom Severity.bioRxiv : the preprint server for biology · 2026Article
- Enhancing depression diagnosis with augmented brain signal driven decorrelated graph neural networks.Communications medicine · 2026Article
- Functional connectivity-based classification and subtyping of major depression for precision mental health: An ensemble graph neural network approach.PLOS digital health · 2026Article
- Ecologically-Valid Emotion Signatures Enhance Mood Disorder Diagnostics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- [Application of support vector machine in identifying neuromodulation states in patients with zoster-associated pain].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Article
- Failure to integrate: Connector hub dysfunction in major depressive disorder.NeuroImage. Clinical · 2026Article
- Multidimensional Comparisons Between Constrained ICA/IVA Algorithms for Multi-Subject fMRI Data Analysis.IEEE access : practical innovations, open solutions · 2026Article
- A novel framework to quantify dynamic convergence and divergence of overlapping brain states characterizing four psychiatric disorders.Network neuroscience (Cambridge, Mass.) · 2026Article
- Optimal stochastic tracking control for brain network dynamics.Communications biology · 2025Article
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Authors and funding
31 authors at 15 institutions in 8 countries.
Funding
Abstract
The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.
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