Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion

TL;DR

This paper introduces AIDFusion, a novel neural network that integrates multiple brain atlases to improve the classification of brain disorders using fMRI data, ensuring consistency and interpretability.

Contribution

AIDFusion employs a disentangle Transformer and message-passing to effectively fuse multi-atlas brain network information, addressing previous limitations in consistency and ROI-level exchange.

Findings

Outperforms state-of-the-art methods on four disease datasets.

Enhances cross-atlas consistency and interpretability.

Demonstrates efficiency and robustness in brain network classification.

Abstract

In the realm of neuroscience, identifying distinctive patterns associated with neurological disorders via brain networks is crucial. Resting-state functional magnetic resonance imaging (fMRI) serves as a primary tool for mapping these networks by correlating blood-oxygen-level-dependent (BOLD) signals across different brain regions, defined as regions of interest (ROIs). Constructing these brain networks involves using atlases to parcellate the brain into ROIs based on various hypotheses of brain division. However, there is no standard atlas for brain network classification, leading to limitations in detecting abnormalities in disorders. Some recent methods have proposed utilizing multiple atlases, but they neglect consistency across atlases and lack ROI-level information exchange. To tackle these limitations, we propose an Atlas-Integrated Distillation and Fusion network (AIDFusion) to improve brain network classification using fMRI data. AIDFusion addresses the challenge of utilizing multiple atlases by employing a disentangle Transformer to filter out inconsistent atlas-specific information and distill distinguishable connections across atlases. It also incorporates subject- and population-level consistency constraints to enhance cross-atlas consistency. Additionally, AIDFusion employs an inter-atlas message-passing mechanism to fuse complementary information across brain regions. Experimental results on four datasets of different diseases demonstrate the effectiveness and efficiency of AIDFusion compared to state-of-the-art methods. A case study illustrates AIDFusion extract patterns that are both interpretable and consistent with established neuroscience findings.