D-CoRP: Differentiable Connectivity Refinement for Functional Brain Networks

TL;DR

This paper introduces D-CoRP, a differentiable connectivity refinement module for brain networks that improves model performance by filtering noise and redundancy in MRI-derived connectivities, adaptable to various graph neural networks.

Contribution

We propose a novel differentiable module based on information bottleneck theory to refine brain connectivity, enhancing existing graph neural network models for brain network analysis.

Findings

Significantly improves baseline model performance

Outperforms state-of-the-art connectivity refinement methods

Demonstrates effectiveness and generalizability across models

Abstract

Brain network is an important tool for understanding the brain, offering insights for scientific research and clinical diagnosis. Existing models for brain networks typically primarily focus on brain regions or overlook the complexity of brain connectivities. MRI-derived brain network data is commonly susceptible to connectivity noise, underscoring the necessity of incorporating connectivities into the modeling of brain networks. To address this gap, we introduce a differentiable module for refining brain connectivity. We develop the multivariate optimization based on information bottleneck theory to address the complexity of the brain network and filter noisy or redundant connections. Also, our method functions as a flexible plugin that is adaptable to most graph neural networks. Our extensive experimental results show that the proposed method can significantly improve the performance of various baseline models and outperform other state-of-the-art methods, indicating the effectiveness and generalizability of the proposed method in refining brain network connectivity. The code will be released for public availability.