ArticleEBioMedicine2022
Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites.
Article in EBioMedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Brain structural connectomic topology predicts medication response in youth with bipolar disorder: A randomized clinical trial.Journal of affective disorders · 2025Trial
- Graph Neural Networks in Neuroimaging: Current Status and Biostatistical Considerations for Clinical Deployment.Annals of biomedical engineering · 2026Review
- Benchmarking resting state fMRI connectivity pipelines for classification: robust accuracy despite processing variability in cross-site eye state prediction.Brain informatics · 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
- Altered subgenual anterior cingulate cortex connectivity in psoriasis patients with depression: a resting-state fMRI case-control study.Annals of general psychiatry · 2026Article
- Nonsuicidal self-injury prediction with pain-processing neural circuits using interpretable graph neural network.Annals of medicine · 2025Article
- Systematic Review and Meta-Analysis of Explainable Machine Learning Models for Clinical Depression Detection.Behavioral sciences (Basel, Switzerland) · 2025Review
- MAMVCL: Multi-Atlas Guided Multi-View Contrast Learning for Autism Spectrum Disorder Classification.Brain sciences · 2025Article
- Random Walk-Based Node Feature Learning for Major Depressive Disorder Identification Through Multi-Site rs-fMRI Data.Human brain mapping · 2025Article
- Default mode network static-dynamic functional signatures in first-episode drug-naive major depressive disorder.Comprehensive psychoneuroendocrinology · 2025Article
- Damage Resistance of an fMRI-Spiking Neural Network Based on Speech Recognition Against Stochastic Attack.Biomimetics (Basel, Switzerland) · 2025Article
- Classification of Major Depressive Disorder Using Graph Attention Mechanism with Multi-Site rs-fMRI Data.Neuroinformatics · 2025Article
- A graph neural network approach to investigate brain critical states over neurodevelopment.Network neuroscience (Cambridge, Mass.) · 2025Article
- Characterizing Psychiatric Disorders Through Graph Neural Networks: A Functional Connectivity Analysis of Depression and Schizophrenia.Depression and anxiety · 2025Article
- Multi-atlas ensemble graph neural network model for major depressive disorder detection using functional MRI data.Frontiers in computational neuroscience · 2025Article
- Towards closed-loop precision psychiatry: Integrating MRI biomarkers for individualized care of major depressive disorder.Psychoradiology · 2025Review
- An objective quantitative diagnosis of depression using a local-to-global multimodal fusion graph neural network.Patterns (New York, N.Y.) · 2024Article
- Aberrant brain network topology in youth with a familial risk for bipolar disorder: a task-based fMRI connectome study.Journal of child psychology and psychiatry, and allied disciplines · 2024Article
- Altered functional-structural coupling may predict Parkinson's patient's depression.Brain structure & function · 2024Article
- PARKINSON'S DISEASE CLASSIFICATION USING CONTRASTIVE GRAPH CROSS-VIEW LEARNING WITH MULTIMODAL FUSION OF SPECT IMAGES AND CLINICAL FEATURES.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
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9 authors.
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Abstract
backgroundEstablishing objective and quantitative neuroimaging biomarkers at individual level can assist in early and accurate diagnosis of major depressive disorder (MDD). However, most previous studies using machine learning to identify MDD were based on small sample size and did not account for the brain connectome that is associated with the pathophysiology of MDD. Here, we addressed these limitations by applying graph convolutional network (GCN) in a large multi-site MDD dataset.
methodsResting-state functional MRI scans of 1586 participants (821 MDD vs. 765 controls) across 16 sites of Rest-meta-MDD consortium were collected. GCN model was trained with individual whole-brain functional network to identify MDD patients from controls, characterize the most salient regions contributing to classification, and explore the relationship between topological characteristics of salient regions and clinical measures.
findingsGCN achieved an accuracy of 81·5% (95%CI: 80·5-82·5%, AUC: 0·865), which was higher than other common machine learning classifiers. The most salient regions contributing to classification were primarily identified within the default mode, fronto-parietal, and cingulo-opercular networks. Nodal topologies of the left inferior parietal lobule and left dorsolateral prefrontal cortex were associated with depressive severity and illness duration, respectively.
interpretationThese findings based on a large, multi-site dataset support the feasibility and effectiveness of GCN in characterizing MDD, and also illustrate the potential utility of GCN for enhancing understanding of the neurobiology of MDD by detecting clinically-relevant disruption in functional network topology.
fundingThis study was supported by the National Natural Science Foundation of China (Grant Nos. 81621003, 82027808, 81820108018).
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