Self-supervised Graph Transformer with Contrastive Learning for Brain Connectivity Analysis towards Improving Autism Detection

TL;DR

This paper presents a novel self-supervised graph transformer framework utilizing contrastive learning and graph alterations to improve autism detection from fMRI brain connectivity data, achieving state-of-the-art performance.

Contribution

It introduces a contrastive self-supervised graph transformer model with brain network alterations, enhancing autism detection accuracy over existing methods.

Findings

Achieved an AUROC of 82.6 and accuracy of 74% on ABIDE data.

Outperformed current state-of-the-art autism detection methods.

Demonstrated the effectiveness of contrastive learning in brain connectivity analysis.

Abstract

Functional Magnetic Resonance Imaging (fMRI) provides useful insights into the brain function both during task or rest. Representing fMRI data using correlation matrices is found to be a reliable method of analyzing the inherent connectivity of the brain in the resting and active states. Graph Neural Networks (GNNs) have been widely used for brain network analysis due to their inherent explainability capability. In this work, we introduce a novel framework using contrastive self-supervised learning graph transformers, incorporating a brain network transformer encoder with random graph alterations. The proposed network leverages both contrastive learning and graph alterations to effectively train the graph transformer for autism detection. Our approach, tested on Autism Brain Imaging Data Exchange (ABIDE) data, demonstrates superior autism detection, achieving an AUROC of 82.6 and an accuracy of 74%, surpassing current state-of-the-art methods.