ArticleIEEE transactions on medical imaging2023
BrainGB: A Benchmark for Brain Network Analysis With Graph Neural Networks.
Article in IEEE transactions on medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
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- QG-WRN: A Quantum-Enhanced Graph Convolutional Wide Residual Network for ASD Diagnosis via Neuroimaging Sensing Technology.Sensors (Basel, Switzerland) · 2026Article
- PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction.Science advances · 2026Article
- Machine Learning on Dynamic Functional Connectivity: Promise, Pitfalls, and Interpretations.Information sciences · 2026Article
- Multimodal Graphical Network Analysis of Small-for-Gestational-Age in Preterm Infants: Integrating Neonatal Brain Volume, Structural Connectivity, and Early Neurodevelopmental Outcome.Annals of biomedical engineering · 2026Article
- 3DViT-GAT: a unified atlas-based 3D vision transformer and graph learning framework for major depressive disorder detection using structural MRI data.Scientific reports · 2026Article
- dFCExpert: Learning Dynamic Functional Connectivity Patterns With Modularity and State Experts.IEEE transactions on medical imaging · 2026Article
- A dual attention transformer modelling for explainable mental health analysis in academic environments using TaBERT.Scientific reports · 2026Article
- Graph Learning in Bioinformatics: A Survey of Graph Neural Network Architectures, Biological Graph Construction and Bioinformatics Applications.Biomolecules · 2026Review
- Interpretable Network-Level Biomarker Discovery for Alzheimer's Stage Assessment Using Resting-State fNIRS Complexity Graphs.Brain sciences · 2026Article
- Algebraic Connectivity Reveals Modulated High-Order Functional Networks in Alzheimer's Disease.ArXiv · 2026Article
- Impact of tractogram filtering and graph creation for structural connectomics in subjects with Parkinson's disease.Frontiers in human neuroscience · 2026Article
- Interpretable deep learning for functional MRI-based auxiliary diagnosis of major depressive disorder with suicidal ideation.Frontiers in computational neuroscience · 2026Article
- Mapping longitudinally consistent intrinsic connectivity networks in macaque brain via longitudinal sparse dictionary learning.IBRO neuroscience reports · 2025Article
- Disentangling shared and unique brain functional changes associated with clinical severity and cognitive phenotypes in schizophrenia via deep learning.Communications biology · 2025Article
- SAGEFusionNet: An Auxiliary Supervised Graph Neural Network for Brain Age Prediction as a Neurodegenerative Biomarker.Brain sciences · 2025Article
- dFCExpert: Learning Dynamic Functional Connectivity Patterns with Modularity and State Experts.bioRxiv : the preprint server for biology · 2025Article
- DSAM: A deep learning framework for analyzing temporal and spatial dynamics in brain networks.Medical image analysis · 2025Article
- Multi-modal cross-domain self-supervised pre-training for fMRI and EEG fusion.Neural networks : the official journal of the International Neural Network Society · 2025Article
- Exploring the predictive value of structural covariance networks for the diagnosis of schizophrenia.Frontiers in psychiatry · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep learning have attracted broad interest due to their established power for modeling complex networked data. Despite their superior performance in many fields, there has not yet been a systematic study of how to design effective GNNs for brain network analysis. To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs. BrainGB standardizes the process by (1) summarizing brain network construction pipelines for both functional and structural neuroimaging modalities and (2) modularizing the implementation of GNN designs. We conduct extensive experiments on datasets across cohorts and modalities and recommend a set of general recipes for effective GNN designs on brain networks. To support open and reproducible research on GNN-based brain network analysis, we host the BrainGB website at https://braingb.us with models, tutorials, examples, as well as an out-of-box Python package. We hope that this work will provide useful empirical evidence and offer insights for future research in this novel and promising direction.
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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.