Self-supervised Graph Transformer with Contrastive Learning for Brain Connectivity Analysis towards Improving Autism Detection
Journal:
arXiv
Published Date:
Jan 18, 2025
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.
