ArticleBMC neuroscience2022
Diagnosis of schizophrenia with functional connectome data: a graph-based convolutional neural network approach.
Article in BMC neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 18 citations in OpenAlex.
- 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
- Independence-based causal discovery analysis reveals statistically non-significant regions to be functionally significant.bioRxiv : the preprint server for biology · 2025Article
- Characterizing Psychiatric Disorders Through Graph Neural Networks: A Functional Connectivity Analysis of Depression and Schizophrenia.Depression and anxiety · 2025Article
- Deep Learning-based Brain Age Prediction in Patients With Schizophrenia Spectrum Disorders.Schizophrenia bulletin · 2024Article
- Graph neural network and machine learning analysis of functional neuroimaging for understanding schizophrenia.BMC neuroscience · 2024Article
- Multi-modal deep learning from imaging genomic data for schizophrenia classification.Frontiers in psychiatry · 2024Article
- Elucidating salient site-specific functional connectivity features and site-invariant biomarkers in schizophrenia via deep neural networks.Scientific reports · 2023Article
- Hippocampal Network Dysfunction in Early Psychosis: A 2-Year Longitudinal Study.Biological psychiatry global open science · 2023Article
- The future of diagnosis in clinical neurosciences: Comparing multiple sclerosis and schizophrenia.European psychiatry : the journal of the Association of European Psychiatrists · 2023Review
- Discriminative analysis of schizophrenia patients using graph convolutional networks: A combined multimodal MRI and connectomics analysis.Frontiers in neuroscience · 2023Article
- Graph Convolutional Networks Reveal Network-Level Functional Dysconnectivity in Schizophrenia.Schizophrenia bulletin · 2022Article
- Correction to: Diagnosis of schizophrenia with functional connectome data: a graph-based convolutional neural network approach.BMC neuroscience · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors at 5 institutions in 2 countries.
Funding
Abstract
Previous deep learning methods have not captured graph or network representations of brain structural or functional connectome data. To address this, we developed the BrainNet-Global Covariance Pooling-Attention Convolutional Neural Network (BrainNet-GA CNN) by incorporating BrainNetCNN and global covariance pooling into the self-attention mechanism. Resting-state functional magnetic resonance imaging data were obtained from 171 patients with schizophrenia spectrum disorders (SSDs) and 161 healthy controls (HCs). We conducted an ablation analysis of the proposed BrainNet-GA CNN and quantitative performance comparisons with competing methods using the nested tenfold cross validation strategy. The performance of our model was compared with competing methods. Discriminative connections were visualized using the gradient-based explanation method and compared with the results obtained using functional connectivity analysis. The BrainNet-GA CNN showed an accuracy of 83.13%, outperforming other competing methods. Among the top 10 discriminative connections, some were associated with the default mode network and auditory network. Interestingly, these regions were also significant in the functional connectivity analysis. Our findings suggest that the proposed BrainNet-GA CNN can classify patients with SSDs and HCs with higher accuracy than other models. Visualization of salient regions provides important clinical information. These results highlight the potential use of the BrainNet-GA CNN in the diagnosis of schizophrenia.
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Registered trials
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.