Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification

TL;DR

Contrasformer is a novel contrastive Transformer model designed for brain network analysis, effectively addressing distribution shifts and node identity issues to improve neurological disorder classification from fMRI data.

Contribution

It introduces a contrastive Transformer with a prior-knowledge-enhanced graph and cross attention for node identity, outperforming existing methods in brain network disease identification.

Findings

Achieves up to 10.8% accuracy improvement over state-of-the-art methods.

Demonstrates robustness across 4 datasets and 4 diseases.

Provides interpretable insights into neurological disorders.

Abstract

Understanding neurological disorder is a fundamental problem in neuroscience, which often requires the analysis of brain networks derived from functional magnetic resonance imaging (fMRI) data. Despite the prevalence of Graph Neural Networks (GNNs) and Graph Transformers in various domains, applying them to brain networks faces challenges. Specifically, the datasets are severely impacted by the noises caused by distribution shifts across sub-populations and the neglect of node identities, both obstruct the identification of disease-specific patterns. To tackle these challenges, we propose Contrasformer, a novel contrastive brain network Transformer. It generates a prior-knowledge-enhanced contrast graph to address the distribution shifts across sub-populations by a two-stream attention mechanism. A cross attention with identity embedding highlights the identity of nodes, and three auxiliary losses ensure group consistency. Evaluated on 4 functional brain network datasets over 4 different diseases, Contrasformer outperforms the state-of-the-art methods for brain networks by achieving up to 10.8\% improvement in accuracy, which demonstrates its efficacy in neurological disorder identification. Case studies illustrate its interpretability, especially in the context of neuroscience. This paper provides a solution for analyzing brain networks, offering valuable insights into neurological disorders. Our code is available at \url{https://github.com/AngusMonroe/Contrasformer}.