# Using Graph Convolutional Networks to Address fMRI Small Data Problems

arXiv: https://arxiv.org/abs/2502.17489 · HTML: https://mlsanity.com/paper/2502.17489/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 19 Feb 2025.

## Builds on (most cited first, 7 of 7)

- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Multiple Instance Learning: A Survey of Problem Characteristics and Applications](https://mlsanity.com/paper/1612.03365/) (2016)
- [Representation Learning for Attributed Multiplex Heterogeneous Network](https://mlsanity.com/paper/1905.01669/) (2019)
- [Inducing brain-relevant bias in natural language processing models](https://mlsanity.com/paper/1911.03268/) (2019)
- [Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence](https://mlsanity.com/paper/2006.07882/) (2020)
- [Classification of Alzheimer's Disease using fMRI Data and Deep Learning Convolutional Neural Networks](https://mlsanity.com/paper/1603.08631/) (2016)
- [Multi-View Multi-Instance Multi-Label Learning based on Collaborative Matrix Factorization](https://mlsanity.com/paper/1905.05061/) (2019)

## Abstract (arXiv)

Although great advances in the analysis of neuroimaging data have been made, a major challenge is a lack of training data. This is less problematic in tasks such as diagnosis, where much data exists, but particularly prevalent in harder problems such as predicting treatment responses (prognosis), where data is focused and hence limited. Here, we address the learning from small data problems for medical imaging using graph neural networks. This is particularly challenging as the information about the patients is themselves graphs (regions of interest connectivity graphs). We show how a spectral representation of the connectivity data allows for efficient propagation that can yield approximately 12% improvement over traditional deep learning methods using the exact same data. We show that our method's superior performance is due to a data smoothing result that can be measured by closing the number of triangle inequalities and thereby satisfying transitivity.