ReviewBrain sciences2024
Graph Neural Networks in Brain Connectivity Studies: Methods, Challenges, and Future Directions.
Review in Brain sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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
26 citing papers in PubMed.
- [Classification of systemic lupus erythematosus resting-state functional magnetic resonance imaging data based on the end-to-end model].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- AI-driven computational drug design: tools, workflow and challenges.RSC advances · 2026Review
- Causal graph neural networks for healthcare.Nature biomedical engineering · 2026Review
- In silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives.NPJ systems biology and applications · 2026Review
- Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging.Annals of biomedical engineering · 2026Review
- Demographic-aware temporal graph attention for fair and accurate cardiac abnormality detection in 12-lead ECG.Scientific reports · 2026Article
- Multimodal Representation Learning for Parsing Biological Heterogeneity in Psychiatric Neuroimaging.Biological psychiatry · 2026Review
- 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
- Graph theory reveals functional connectome disruptions in adolescent major depressive disorder with childhood trauma.Communications medicine · 2026Article
- From imaging to omics: deep learning is bridging MRI and liquid biopsy in bone tumor diagnosis.Journal of bone oncology · 2026Review
- Conditioned Graph Reconstruction of Brain Functional Network Connectivity Reveals Interpretable Latent Axes of Sex and Fluid Intelligence.bioRxiv : the preprint server for biology · 2026Article
- Interpretable Network-Level Biomarker Discovery for Alzheimer's Stage Assessment Using Resting-State fNIRS Complexity Graphs.Brain sciences · 2026Article
- Deep learning approaches to map individual differences in macroscopic neural structure with variations in spatial navigation behavior.Neuropsychologia · 2026Article
- Theoretical, Technical, and Analytical Foundations of Task-Based and Resting-State Functional Magnetic Resonance Imaging (fMRI)-A Narrative Review.Biomedicines · 2026Review
- Bio-RegNet: A Meta-Homeostatic Bayesian Neural Network Framework Integrating Treg-Inspired Immunoregulation and Autophagic Optimization for Adaptive Community Detection and Stable Intelligence.Biomimetics (Basel, Switzerland) · 2026Article
- Editorial: Complex network dynamics of cognitive processing in health and disease: current knowledge and future research.Frontiers in computational neuroscience · 2026Article
- Structurally restricted message-passing within shallow architectures for explainable network-level brain decoding on small cohorts.Frontiers in behavioral neuroscience · 2026Article
- Brain Age as a Biomarker in Alzheimer's Disease: Narrative Perspectives on Imaging, Biomarkers, Machine Learning, and Intervention Potential.Brain sciences · 2025Review
- A Multidimensional Benchmark of Public EEG Datasets for Driver State Monitoring in Brain-Computer Interfaces.Sensors (Basel, Switzerland) · 2025Review
- LPGGNet: Learning from Local-Partition-Global Graph Representations for Motor Imagery EEG Recognition.Brain sciences · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain connectivity analysis plays a crucial role in unraveling the complex network dynamics of the human brain, providing insights into cognitive functions, behaviors, and neurological disorders. Traditional graph-theoretical methods, while foundational, often fall short in capturing the high-dimensional and dynamic nature of brain connectivity. Graph Neural Networks (GNNs) have recently emerged as a powerful approach for this purpose, with the potential to improve diagnostics, prognostics, and personalized interventions. This review examines recent studies leveraging GNNs in brain connectivity analysis, focusing on key methodological advancements in multimodal data integration, dynamic connectivity, and interpretability across various imaging modalities, including fMRI, MRI, DTI, PET, and EEG. Findings reveal that GNNs excel in modeling complex, non-linear connectivity patterns and enable the integration of multiple neuroimaging modalities to provide richer insights into both healthy and pathological brain networks. However, challenges remain, particularly in interpretability, data scarcity, and multimodal integration, limiting the full clinical utility of GNNs. Addressing these limitations through enhanced interpretability, optimized multimodal techniques, and expanded labeled datasets is crucial to fully harness the potential of GNNs for neuroscience research and clinical applications.
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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.