ArticleFrontiers in neuroscience2020
Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- A view-engage-predict framework for enhancing brain-behavior mapping with naturalistic movie-watching fMRI.Communications biology · 2026Article
- A functional system-informed graph neural network framework to quantify interpretable brain dysfunction in ASD.Neural networks : the official journal of the International Neural Network Society · 2026Article
- dFCExpert: Learning Dynamic Functional Connectivity Patterns With Modularity and State Experts.IEEE transactions on medical imaging · 2026Article
- Conditioned Graph Reconstruction of Brain Functional Network Connectivity Reveals Interpretable Latent Axes of Sex and Fluid Intelligence.bioRxiv : the preprint server for biology · 2026Article
- Classifying irritable bowel syndrome using spatio-temporal graph convolution networks on brain functional MRI data.Brain communications · 2026Article
- Article
- 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
- Two-Dimensional Latent Space Manifold of Brain Connectomes Across the Spectrum of Clinical Cognitive Decline.Bioengineering (Basel, Switzerland) · 2025Article
- dFCExpert: Learning Dynamic Functional Connectivity Patterns with Modularity and State Experts.bioRxiv : the preprint server for biology · 2025Article
- Innovative biomarker exploration in ASD: Combining Graph Neural Networks and permutation testing on fMRI data.Neuroimage. Reports · 2025Article
- MASKED MOMENTUM CONTRASTIVE DYNAMIC TRANSFORMER FOR SELF-SUPERVISED FUNCTIONAL CONNECTIVITY REPRESENTATION LEARNING.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- Brain networks and intelligence: A graph neural network based approach to resting state fMRI data.Medical image analysis · 2025Article
- Quantifying the non-isomorphism of global urban road networks using GNNs and graph kernels.Scientific reports · 2025Article
- Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer's Disease Classification.Bioengineering (Basel, Switzerland) · 2025Article
- Knowledge concept recognition in the learning brain via fMRI classification.Frontiers in neuroscience · 2025Article
- ACTION: Augmentation and computation toolbox for brain network analysis with functional MRI.NeuroImage · 2025Article
- Multi-Modal Diagnosis of Alzheimer's Disease Using Interpretable Graph Convolutional Networks.IEEE transactions on medical imaging · 2025Article
- Article
- Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data.ArXiv · 2024Article
- A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging.Journal of imaging · 2024Article
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2 authors.
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Abstract
Graph neural networks (GNN) rely on graph operations that include neural network training for various graph related tasks. Recently, several attempts have been made to apply the GNNs to functional magnetic resonance image (fMRI) data. Despite recent progresses, a common limitation is its difficulty to explain the classification results in a neuroscientifically explainable way. Here, we develop a framework for analyzing the fMRI data using the Graph Isomorphism Network (GIN), which was recently proposed as a powerful GNN for graph classification. One of the important contributions of this paper is the observation that the GIN is a dual representation of convolutional neural network (CNN) in the graph space where the shift operation is defined using the adjacency matrix. This understanding enables us to exploit CNN-based saliency map techniques for the GNN, which we tailor to the proposed GIN with one-hot encoding, to visualize the important regions of the brain. We validate our proposed framework using large-scale resting-state fMRI (rs-fMRI) data for classifying the sex of the subject based on the graph structure of the brain. The experiment was consistent with our expectation such that the obtained saliency map show high correspondence with previous neuroimaging evidences related to sex differences.
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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.