# Aiding Medical Diagnosis Through the Application of Graph Neural Networks to Functional MRI Scans

arXiv: https://arxiv.org/abs/2112.00738 · HTML: https://mlsanity.com/paper/2112.00738/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Structured Sequence Modeling with Graph Convolutional Recurrent Networks](https://mlsanity.com/paper/1612.07659/) (2016)
- [PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models](https://mlsanity.com/paper/2104.07788/) (2021)
- [Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis](https://mlsanity.com/paper/2001.03690/) (2020)

## Abstract (arXiv)

Graph Neural Networks (GNNs) have been shown to be a powerful tool for generating predictions from biological data. Their application to neuroimaging data such as functional magnetic resonance imaging (fMRI) scans has been limited. However, applying GNNs to fMRI scans may substantially improve predictive accuracy and could be used to inform clinical diagnosis in the future. In this paper, we present a novel approach to representing resting-state fMRI data as a graph containing nodes and edges without omitting any of the voxels and thus reducing information loss. We compare multiple GNN architectures and show that they can successfully predict the disease and sex of a person. We hope to provide a basis for future work to exploit the power of GNNs when applied to brain imaging data.