# Classifying irritable bowel syndrome using spatio-temporal graph convolution networks on brain functional MRI data

## TL;DR

This study uses brain MRI data and a new deep learning model to classify irritable bowel syndrome with high accuracy and identify key brain regions involved in the condition.

## Contribution

A spatio-temporal graph convolution network with an interpretability module for IBS classification and biomarker identification.

## Key findings

- The model achieved 83.51% accuracy in classifying IBS patients versus healthy controls.
- Five brain regions were identified as important for IBS classification, consistent with prior literature.
- External experiments validated the effectiveness of the interpretability module in identifying relevant brain regions.

## Abstract

Irritable bowel syndrome (IBS) is a functional gastrointestinal disorder marked by abdominal pain and changes in stool consistency or frequency. Recent studies have explored the link between IBS and alterations in brain networks using functional MRI. Despite these efforts, an effective diagnostic or predictive model for IBS remains elusive. This shortfall is twofold: firstly, the sample sizes in these studies are typically small, and secondly, the machine learning or deep learning models currently in use fail to adequately detect the subtle and dynamic pathological changes present in functional MRI data for IBS. In this study, we extracted rs-functional MRI of 79 subjects with IBS and 79 healthy controls, then put them into spatio-temporal graph convolution network (ST-GCN) for classification. We also incorporated a novel interpretability module into this model to identify potential regions of interest (ROI) associated with IBS. Our model outperformed other state-of-the-art machine learning and deep learning methods with the highest average accuracy of 83.51% on our dataset. Furthermore, based on the results of our interpretability module, the Inferior Parietal Lobule (IPL.R), Inferior Frontal Orbital part (ORBinf.R), Postcentral Gyrus (PCG.R), Middle Frontal Orbital part (ORBmid.R), and Superior Medial Frontal Orbital part (ORBsupmed.L) were identified as top 5 important brain regions for distinguishing IBS patients from the control group, which are consistent with the brain regions identified in previous literature reviews. We also conducted three external data-driven experiments to further validate the effectiveness of the interpretability module: (1) Experiment only on important brain regions; (2) Comparison with the Perturbation-Based Methods; (3) Correlation analysis. The results indicate that the selected regions significantly impact IBS.

Wu et al. developed a spatio-temporal graph convolutional network using resting-state functional MRI data to classify irritable bowel syndrome. Their model achieved 83.5% accuracy and identified key brain regions linked to irritable bowel syndrome, offering both high diagnostic performance and biological interpretability.

Graphical AbstractFor image description, please refer to the figure legend and surrounding text.