ArticleMedical image analysis2021
BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 174 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The trial behind it
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.
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Who cites it
174 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Learning robust and task-invariant functional representation from fMRI through Siamese self-supervised learning.Medical image analysis · 2026Article
- ADGPT: A Prior-Guided and GPT-Empowered Framework for MRI-Based Alzheimer's Disease Diagnosis.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence and Digital Biomarkers for Early Detection and Monitoring of Neurological Disorders: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Benchmarking Synolitic Graphs for Autism Classification from Multisite Resting-State fMRI.Diagnostics (Basel, Switzerland) · 2026Article
- Responsible and innovative AI for mental health care: five priority themes.NPP - digital psychiatry and neuroscience · 2026Review
- HDGNN-Mamba2: Mamba-Based Spatiotemporal Heterogeneous Dynamic Graph Neural Network for Major Depressive Disorder Classification.Brain sciences · 2026Article
- A Multiscale Spatiotemporal Causal Mapping Algorithm for Revealing Neural Network Mechanisms of Transcutaneous Auricular Vagus Nerve Stimulation.Human brain mapping · 2026Article
- Decoding neuronal gene expression: integrative insights from omics and AI.Brain informatics · 2026Review
- Brain Graph Sparsification for fMRI-based Connectome Analysis: A Methodological Review.Neuroinformatics · 2026Review
- QG-WRN: A Quantum-Enhanced Graph Convolutional Wide Residual Network for ASD Diagnosis via Neuroimaging Sensing Technology.Sensors (Basel, Switzerland) · 2026Article
- Hierarchical sparse spatiotemporal graph neural network for brain graph classification.iScience · 2026Article
- Graph Neural Networks and sequential architectures for autism detection from eye-tracking biomarkers: a multi-site study.BMC psychiatry · 2026Article
- BrainUMA: A Unified multi-atlas learning framework for brain disorders diagnosis.Medical & biological engineering & computing · 2026Article
- Graph Neural Networks for Realistic Bleeding Prediction in Surgical Simulators.Journal of imaging informatics in medicine · 2026Article
- Integrating Time and Frequency Domain Features of fMRI Time Series for Alzheimer's Disease Classification Using Graph Neural Networks.Interdisciplinary sciences, computational life sciences · 2026Article
- Using deep learning to identify brain networks mediating cognitive and motor impairments in alcohol use disorder.Translational psychiatry · 2026Article
- Robust Multi-Site ADHD Classification via GraphSAGE-Based Functional Connectivity Modeling from rs-fMRI.Bioengineering (Basel, Switzerland) · 2026Article
- Estimating the fraction of variance of crystallized intelligence explained by cortical surface area in early adolescence.bioRxiv : the preprint server for biology · 2026Article
- [Research on weighted hypergraph attention neural network for the diagnosis of psychiatric disorders using brain functional connectivity networks].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
114 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Understanding which brain regions are related to a specific neurological disorder or cognitive stimuli has been an important area of neuroimaging research. We propose BrainGNN, a graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI) and discover neurological biomarkers. Considering the special property of brain graphs, we design novel ROI-aware graph convolutional (Ra-GConv) layers that leverage the topological and functional information of fMRI. Motivated by the need for transparency in medical image analysis, our BrainGNN contains ROI-selection pooling layers (R-pool) that highlight salient ROIs (nodes in the graph), so that we can infer which ROIs are important for prediction. Furthermore, we propose regularization terms-unit loss, topK pooling (TPK) loss and group-level consistency (GLC) loss-on pooling results to encourage reasonable ROI-selection and provide flexibility to encourage either fully individual- or patterns that agree with group-level data. We apply the BrainGNN framework on two independent fMRI datasets: an Autism Spectrum Disorder (ASD) fMRI dataset and data from the Human Connectome Project (HCP) 900 Subject Release. We investigate different choices of the hyper-parameters and show that BrainGNN outperforms the alternative fMRI image analysis methods in terms of four different evaluation metrics. The obtained community clustering and salient ROI detection results show a high correspondence with the previous neuroimaging-derived evidence of biomarkers for ASD and specific task states decoded for HCP. Our code is available at https://github.com/xxlya/BrainGNN_Pytorch.
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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.