# Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis

arXiv: https://arxiv.org/abs/2007.14589 · HTML: https://mlsanity.com/paper/2007.14589/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2020.

## Built on by (most cited first, 25 of 25)

- [Graph Neural Networks in Network Neuroscience](https://mlsanity.com/paper/2106.03535/) (2021)
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- [Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification](https://mlsanity.com/paper/2409.10944/) (2024)
- [Deep reinforcement learning guided graph neural networks for brain network analysis](https://mlsanity.com/paper/2203.10093/) (2022)
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- [Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications](https://mlsanity.com/paper/2202.08916/) (2022)
- [BrainOOD: Out-of-distribution Generalizable Brain Network Analysis](https://mlsanity.com/paper/2502.01688/) (2025)
- [BrainMAE: A Region-aware Self-supervised Learning Framework for Brain Signals](https://mlsanity.com/paper/2406.17086/) (2024)
- [Contrastive Graph Pooling for Explainable Classification of Brain Networks](https://mlsanity.com/paper/2307.11133/) (2023)
- [Identifying Autism Spectrum Disorder Based on Individual-Aware Down-Sampling and Multi-Modal Learning](https://mlsanity.com/paper/2109.09129/) (2021)
- [Deep Representations for Time-varying Brain Datasets](https://mlsanity.com/paper/2205.11648/) (2022)
- [Applications of interpretable deep learning in neuroimaging: a comprehensive review](https://mlsanity.com/paper/2406.17792/) (2024)
- [Ada-FCN: Adaptive Frequency-Coupled Network for fMRI-Based Brain Disorder Classification](https://mlsanity.com/paper/2511.04718/) (2025)
- [SELECTOR: Heterogeneous graph network with convolutional masked autoencoder for multimodal robust prediction of cancer survival](https://mlsanity.com/paper/2403.09290/) (2024)
- [Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder](https://mlsanity.com/paper/2206.11224/) (2022)
- [Analyzing the Traffic of MANETs using Graph Neural Networks](https://mlsanity.com/paper/2212.08923/) (2022)
- [Discovering robust biomarkers of psychiatric disorders from resting-state functional MRI via graph neural networks: A systematic review](https://mlsanity.com/paper/2405.00577/) (2024)
- [NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorders](https://mlsanity.com/paper/2502.18786/) (2025)
- [Efficient 4D fMRI ASD Classification using Spatial-Temporal-Omics-based Learning Framework](https://mlsanity.com/paper/2502.19386/) (2025)
- [gSuite: A Flexible and Framework Independent Benchmark Suite for Graph Neural Network Inference on GPUs](https://mlsanity.com/paper/2210.11601/) (2022)
- [Deep neuroevolution to predict primary brain tumor grade from functional MRI adjacency matrices](https://mlsanity.com/paper/2211.14500/) (2022)
- [Toward a Multi-View Brain Network Foundation Model: Cross-View Consistency Learning Across Arbitrary Atlases](https://mlsanity.com/paper/2603.20348/) (2026)

## Abstract (arXiv)

Understanding how certain brain regions relate to a specific neurological disorder has been an important area of neuroimaging research. A promising approach to identify the salient regions is using Graph Neural Networks (GNNs), which can be used to analyze graph structured data, e.g. brain networks constructed by functional magnetic resonance imaging (fMRI). We propose an interpretable GNN framework with a novel salient region selection mechanism to determine neurological brain biomarkers associated with disorders. Specifically, we design novel regularized pooling layers that highlight salient regions of interests (ROIs) so that we can infer which ROIs are important to identify a certain disease based on the node pooling scores calculated by the pooling layers. Our proposed framework, Pooling Regularized-GNN (PR-GNN), encourages reasonable ROI-selection and provides flexibility to preserve either individual- or group-level patterns. We apply the PR-GNN framework on a Biopoint Autism Spectral Disorder (ASD) fMRI dataset. We investigate different choices of the hyperparameters and show that PR-GNN outperforms baseline methods in terms of classification accuracy. The salient ROI detection results show high correspondence with the previous neuroimaging-derived biomarkers for ASD.