ArticleSchizophrenia bulletin2022
Graph Convolutional Networks Reveal Network-Level Functional Dysconnectivity in Schizophrenia.
Article in Schizophrenia bulletin, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses that pooled it.
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Who cites it
37 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.Psychiatry and clinical neurosciences · 2024Pooled it
- A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis.Molecular psychiatry · 2023Pooled it
- Graph theory reveals functional connectome disruptions in adolescent major depressive disorder with childhood trauma.Communications medicine · 2026Article
- An EEG-based digital biomarker for personalizing transcranial magnetic stimulation in major depressive disorder.NPJ digital medicine · 2026Article
- Integrating multi-atlas neuroimaging data for robust biomarker identification in neuropsychiatric disorders.Frontiers in psychiatry · 2026Article
- From microstates to macroscales: A Critical Review of Maximum Entropy Modeling and Energy Landscape Analysis in functional MRI.Research square · 2025Article
- Nonsuicidal self-injury prediction with pain-processing neural circuits using interpretable graph neural network.Annals of medicine · 2025Article
- Prefronto-thalamic hypoconnectivity in schizophrenia: Monkey to human translation of critical pathways for symptom-related functions.PCN reports : psychiatry and clinical neurosciences · 2025Article
- Predicting axillary lymph node metastasis in breast cancer patients using CNN-GCN on DCE-MRI: a multicenter study.BMC medical imaging · 2025Article
- Morphometric similarity network-based graph convolutional networks for schizophrenia classification.Scientific reports · 2025Article
- Disentangling shared and unique brain functional changes associated with clinical severity and cognitive phenotypes in schizophrenia via deep learning.Communications biology · 2025Article
- Predicting antidepressant response via local-global graph neural network and neuroimaging biomarkers.NPJ digital medicine · 2025Article
- Independence-based causal discovery analysis reveals statistically non-significant regions to be functionally significant.bioRxiv : the preprint server for biology · 2025Article
- Graph Neural Networks and Multimodal DTI Features for Schizophrenia Classification: Insights from Brain Network Analysis and Gene Expression.Neuroscience bulletin · 2025Article
- Determining the atlas correspondence of Desikan-Killiany-Tourville and Glasser MMP1 atlases across magnetic field strengths.Journal of neuroscience methods · 2025Article
- Disorganized Striatal Functional Connectivity as a Partially Shared Pathophysiological Mechanism in Both Schizophrenia and Major Depressive Disorder: A Transdiagnostic fMRI Study.Brain topography · 2025Article
- Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade.Schizophrenia bulletin · 2025Review
- Schizophrenia and Neurodevelopment: Insights From Connectome Perspective.Schizophrenia bulletin · 2025Review
- Spatial Craving Patterns in Marijuana Users: Insights From fMRI Brain Connectivity Analysis With High-Order Graph Attention Neural Networks.IEEE journal of biomedical and health informatics · 2025Article
- Classification of psychosis spectrum disorders using graph convolutional networks with structurally constrained functional connectomes.Neural networks : the official journal of the International Neural Network Society · 2025Article
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
background and hypothesisSchizophrenia is increasingly understood as a disorder of brain dysconnectivity. Recently, graph-based approaches such as graph convolutional network (GCN) have been leveraged to explore complex pairwise similarities in imaging features among brain regions, which can reveal abstract and complex relationships within brain networks. STUDY
designWe used GCN to investigate topological abnormalities of functional brain networks in schizophrenia. Resting-state functional magnetic resonance imaging data were acquired from 505 individuals with schizophrenia and 907 controls across 6 sites. Whole-brain functional connectivity matrix was extracted for each individual. We examined the performance of GCN relative to support vector machine (SVM), extracted the most salient regions contributing to both classification models, investigated the topological profiles of identified salient regions, and explored correlation between nodal topological properties of each salient region and severity of symptom. STUDY
resultsGCN enabled nominally higher classification accuracy (85.8%) compared with SVM (80.9%). Based on the saliency map, the most discriminative brain regions were located in a distributed network including striatal areas (ie, putamen, pallidum, and caudate) and the amygdala. Significant differences in the nodal efficiency of bilateral putamen and pallidum between patients and controls and its correlations with negative symptoms were detected in post hoc analysis.
conclusionsThe present study demonstrates that GCN allows classification of schizophrenia at the individual level with high accuracy, indicating a promising direction for detection of individual patients with schizophrenia. Functional topological deficits of striatal areas may represent a focal neural deficit of negative symptomatology in schizophrenia.
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