# Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI

arXiv: https://arxiv.org/abs/2306.14080 · HTML: https://mlsanity.com/paper/2306.14080/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 68 references that are in the map.
- First posted on arXiv 24 Jun 2023.

## Builds on (most cited first, 16 of 16)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [Contrastive Learning with Stronger Augmentations](https://mlsanity.com/paper/2104.07713/) (2021)
- [Graph Neural Networks in Network Neuroscience](https://mlsanity.com/paper/2106.03535/) (2021)
- [Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention](https://mlsanity.com/paper/2105.13495/) (2021)
- [BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks](https://mlsanity.com/paper/2204.07054/) (2022)
- [Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis](https://mlsanity.com/paper/2003.10613/) (2020)
- [Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis](https://mlsanity.com/paper/2001.03690/) (2020)
- [Explainable Medical Imaging AI Needs Human-Centered Design: Guidelines and Evidence from a Systematic Review](https://mlsanity.com/paper/2112.12596/) (2021)
- [Machine learning in resting-state fMRI analysis](https://mlsanity.com/paper/1812.11477/) (2018)
- [Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification](https://mlsanity.com/paper/2107.12838/) (2021)

## Built on by (most cited first, 4 of 4)

- [ACTION: Augmentation and Computation Toolbox for Brain Network Analysis with Functional MRI](https://mlsanity.com/paper/2405.06178/) (2024)
- [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)
- [Hyperbolic Kernel Graph Neural Networks for Neurocognitive Decline Analysis from Multimodal Brain Imaging](https://mlsanity.com/paper/2507.02908/) (2025)
- [Distance-Aware Joint Spatio-Temporal Graph Contrastive Learning for Major Depressive Disorder Diagnosis](https://mlsanity.com/paper/2605.24066/) (2026)

## Abstract (arXiv)

Resting-state functional magnetic resonance imaging (rs-fMRI) can reflect spontaneous neural activities in brain and is widely used for brain disorder analysis.Previous studies propose to extract fMRI representations through diverse machine/deep learning methods for subsequent analysis. But the learned features typically lack biological interpretability, which limits their clinical utility. From the view of graph theory, the brain exhibits a remarkable modular structure in spontaneous brain functional networks, with each module comprised of functionally interconnected brain regions-of-interest (ROIs). However, most existing learning-based methods for fMRI analysis fail to adequately utilize such brain modularity prior. In this paper, we propose a Brain Modularity-constrained dynamic Representation learning (BMR) framework for interpretable fMRI analysis, consisting of three major components: (1) dynamic graph construction, (2) dynamic graph learning via a novel modularity-constrained graph neural network(MGNN), (3) prediction and biomarker detection for interpretable fMRI analysis. Especially, three core neurocognitive modules (i.e., salience network, central executive network, and default mode network) are explicitly incorporated into the MGNN, encouraging the nodes/ROIs within the same module to share similar representations. To further enhance discriminative ability of learned features, we also encourage the MGNN to preserve the network topology of input graphs via a graph topology reconstruction constraint. Experimental results on 534 subjects with rs-fMRI scans from two datasets validate the effectiveness of the proposed method. The identified discriminative brain ROIs and functional connectivities can be regarded as potential fMRI biomarkers to aid in clinical diagnosis.