Uncovering Latent Communication Patterns in Brain Networks via Adaptive Flow Routing

TL;DR

This paper introduces AFR-Net, a physics-informed neural network that models neural communication dynamics to uncover interpretable latent pathways linking brain structure and function, outperforming existing methods.

Contribution

The paper presents AFR-Net, a novel framework that models structural constraints and functional communication in the brain, providing new insights into neural interactions.

Findings

AFR-Net significantly outperforms state-of-the-art baselines.

The framework enables interpretable discovery of neural pathways.

It models how structural connectivity influences functional communication.

Abstract

Unraveling how macroscopic cognitive phenotypes emerge from microscopic neuronal connectivity remains one of the core pursuits of neuroscience. To this end, researchers typically leverage multi-modal information from structural connectivity (SC) and functional connectivity (FC) to complete downstream tasks. Recent methodologies explore the intricate coupling mechanisms between SC and FC, attempting to fuse their representations at the regional level. However, lacking fundamental neuroscientific insight, these approaches fail to uncover the latent interactions between neural regions underlying these connectomes, and thus cannot explain why SC and FC exhibit dynamic states of both coupling and heterogeneity. In this paper, we formulate multi-modal fusion through the lens of neural communication dynamics and propose the Adaptive Flow Routing Network (AFR-Net), a physics-informed framework that models how structural constraints (SC) give rise to functional communication patterns (FC), enabling interpretable discovery of critical neural pathways. Extensive experiments demonstrate that AFR-Net significantly outperforms state-of-the-art baselines. The code is available at https://anonymous.4open.science/r/DIAL-F0D1.