# Pruning Graph Convolutional Networks to select meaningful graph frequencies for fMRI decoding

arXiv: https://arxiv.org/abs/2203.04455 · HTML: https://mlsanity.com/paper/2203.04455/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Graph Signal Processing: Overview, Challenges and Applications](https://mlsanity.com/paper/1712.00468/) (2017)
- [Comparing Rewinding and Fine-tuning in Neural Network Pruning](https://mlsanity.com/paper/2003.02389/) (2020)
- [Decoupling of brain function from structure reveals regional behavioral specialization in humans](https://mlsanity.com/paper/1905.07813/) (2019)
- [Rethinking Weight Decay For Efficient Neural Network Pruning](https://mlsanity.com/paper/2011.10520/) (2020)
- [Extracting representations of cognition across neuroimaging studies improves brain decoding](https://mlsanity.com/paper/1809.06035/) (2018)
- [Few-shot Decoding of Brain Activation Maps](https://mlsanity.com/paper/2010.12500/) (2020)

## Abstract (arXiv)

Graph Signal Processing is a promising framework to manipulate brain signals as it allows to encompass the spatial dependencies between the activity in regions of interest in the brain. In this work, we are interested in better understanding what are the graph frequencies that are the most useful to decode fMRI signals. To this end, we introduce a deep learning architecture and adapt a pruning methodology to automatically identify such frequencies. We experiment with various datasets, architectures and graphs, and show that low graph frequencies are consistently identified as the most important for fMRI decoding, with a stronger contribution for the functional graph over the structural one. We believe that this work provides novel insights on how graph-based methods can be deployed to increase fMRI decoding accuracy and interpretability.