# Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis

arXiv: https://arxiv.org/abs/2001.03690 · HTML: https://mlsanity.com/paper/2001.03690/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2020.

## Built on by (most cited first, 30 of 41)

- [Explainable artificial intelligence (XAI) in deep learning-based medical image analysis](https://mlsanity.com/paper/2107.10912/) (2021)
- [Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention](https://mlsanity.com/paper/2105.13495/) (2021)
- [Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future](https://mlsanity.com/paper/2105.13137/) (2021)
- [A Survey on Graph-Based Deep Learning for Computational Histopathology](https://mlsanity.com/paper/2107.00272/) (2021)
- [NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics](https://mlsanity.com/paper/2306.06202/) (2023)
- [EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals](https://mlsanity.com/paper/2106.09135/) (2021)
- [BolT: Fused Window Transformers for fMRI Time Series Analysis](https://mlsanity.com/paper/2205.11578/) (2022)
- [DreaMR: Diffusion-driven Counterfactual Explanation for Functional MRI](https://mlsanity.com/paper/2307.09547/) (2023)
- [Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data](https://mlsanity.com/paper/2311.03520/) (2023)
- [IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction](https://mlsanity.com/paper/2103.15587/) (2021)
- [BrainNPT: Pre-training of Transformer networks for brain network classification](https://mlsanity.com/paper/2305.01666/) (2023)
- [Heterogeneous Graph-Based Multimodal Brain Network Learning](https://mlsanity.com/paper/2110.08465/) (2021)
- [Learning to Model the Relationship Between Brain Structural and Functional Connectomes](https://mlsanity.com/paper/2112.09906/) (2021)
- [DynDepNet: Learning Time-Varying Dependency Structures from fMRI Data via Dynamic Graph Structure Learning](https://mlsanity.com/paper/2209.13513/) (2022)
- [Forecasting Brain Activity Based on Models of Spatio-Temporal Brain Dynamics: A Comparison of Graph Neural Network Architectures](https://mlsanity.com/paper/2112.04266/) (2021)
- [Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications](https://mlsanity.com/paper/2204.00323/) (2022)
- [Benchmarking Graph Neural Networks for FMRI analysis](https://mlsanity.com/paper/2211.08927/) (2022)
- [Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI](https://mlsanity.com/paper/2306.14080/) (2023)
- [Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identification](https://mlsanity.com/paper/2308.10302/) (2023)
- [Core-Periphery Principle Guided State Space Model for Functional Connectome Classification](https://mlsanity.com/paper/2503.14655/) (2025)
- [HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Data](https://mlsanity.com/paper/2507.22524/) (2025)
- [Deep Representations for Time-varying Brain Datasets](https://mlsanity.com/paper/2205.11648/) (2022)
- [Investigating Brain Connectivity with Graph Neural Networks and GNNExplainer](https://mlsanity.com/paper/2206.01930/) (2022)
- [Improving the Diagnosis of Psychiatric Disorders with Self-Supervised Graph State Space Models](https://mlsanity.com/paper/2206.03331/) (2022)
- [A Class-Aware Representation Refinement Framework for Graph Classification](https://mlsanity.com/paper/2209.00936/) (2022)
- [Graph Isomorphic Networks for Assessing Reliability of the Medium-Voltage Grid](https://mlsanity.com/paper/2310.01181/) (2023)
- [Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain](https://mlsanity.com/paper/2403.06432/) (2024)
- [Contextualized Messages Boost Graph Representations](https://mlsanity.com/paper/2403.12529/) (2024)
- [Applications of interpretable deep learning in neuroimaging: a comprehensive review](https://mlsanity.com/paper/2406.17792/) (2024)
- [Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series](https://mlsanity.com/paper/2202.02393/) (2022)

## Abstract (arXiv)

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.