ArticleMedical image analysis2025
Brain networks and intelligence: A graph neural network based approach to resting state fMRI data.
Article in Medical image analysis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Optimal exercise modalities and dose for enhancing intelligence in children and adolescents: a Bayesian network meta-analysis.Frontiers in physiology · 2025Pooled it
- Advances in artificial intelligence for neuroimaging.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Review
- Accurately modeling resting-brain functional connectivity using hypergraph neural field-Fourier deep neural network.Scientific reports · 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
- Task and Resting-State Functional Magnetic Resonance Imaging Modeling of Brain-Behavior Relationships in Developmental Cohorts.Biological psychiatry · 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
- Multimodal Fusion of Structural and Diffusion MRI for Intelligence Prediction.Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation · 2026Article
- HKD-MGIN: a physics informed graph neural network using heat kernel diffusion for mapping adolescent functional brain connectivity.Machine learning for computational science and engineering · 2025Article
- Timescale-normalized fMRI reveals disrupted dynamic signal-energy balance in schizophrenia.bioRxiv : the preprint server for biology · 2025Article
- Predicting antidepressant response via local-global graph neural network and neuroimaging biomarkers.NPJ digital medicine · 2025Article
- Longitudinal structural MRI-based deep learning and radiomics features for predicting Alzheimer's disease progression.Alzheimer's research & therapy · 2025Article
- Prediction of postoperative visual cognitive impairment using graph theory and machine learning based on resting-state brain networks.BMC medical imaging · 2025Article
- Neural correlates differ between crystallized and fluid intelligence in adolescents.Translational psychiatry · 2025Article
- Classification of Major Depressive Disorder Using Graph Attention Mechanism with Multi-Site rs-fMRI Data.Neuroinformatics · 2025Article
- SELF-CLUSTERING GRAPH TRANSFORMER APPROACH TO MODEL RESTING STATE FUNCTIONAL BRAIN ACTIVITY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- DSAM: A deep learning framework for analyzing temporal and spatial dynamics in brain networks.Medical image analysis · 2025Article
- Multimodal Brain Growth Patterns: Insights from Canonical Correlation Analysis and Deep Canonical Correlation Analysis with Auto-Encoder.Information (Basel) · 2025Article
- Both k-core percolation and directed graph analysis revealed succession and transition of voxels' spatiotemporal progress on dynamic correlation resting-state fMRI.Frontiers in human neuroscience · 2025Article
- Genetic contribution to intrinsic functional connectivity underlying general intelligence: evidence from adult twin study.Brain communications · 2025Article
- Graph Neural Networks in Brain Connectivity Studies: Methods, Challenges, and Future Directions.Brain sciences · 2024Review
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8 authors.
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Abstract
Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this paper, we present a novel modeling architecture called BrainRGIN for predicting intelligence (fluid, crystallized and total intelligence) using graph neural networks on rsfMRI derived static functional network connectivity matrices. Extending from the existing graph convolution networks, our approach incorporates a clustering-based embedding and graph isomorphism network in the graph convolutional layer to reflect the nature of the brain sub-network organization and efficient network expression, in combination with TopK pooling and attention-based readout functions. We evaluated our proposed architecture on a large dataset, specifically the Adolescent Brain Cognitive Development Dataset, and demonstrated its effectiveness in predicting individual differences in intelligence. Our model achieved lower mean squared errors and higher correlation scores than existing relevant graph architectures and other traditional machine learning models for all of the intelligence prediction tasks. The middle frontal gyrus exhibited a significant contribution to both fluid and crystallized intelligence, suggesting their pivotal role in these cognitive processes. Total composite scores identified a diverse set of brain regions to be relevant which underscores the complex nature of total intelligence. Our GitHub implementation is publicly available on https://github.com/bishalth01/BrainRGIN/.
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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.