Uncovering Latent Communication Patterns in Brain Networks via Adaptive Flow Routing

2026-01-31

AI Summary

This study addresses the fundamental question of how macroscopic cognitive phenotypes emerge from microscopic neural connectivity, with a focus on the dynamic coupling and heterogeneity between structural connectivity (SC) and functional connectivity (FC). To this end, the authors propose a physics-informed Adaptive Flow Routing Network (AFR-Net), which, for the first time, integrates neural communication dynamics into SC–FC modeling. By simulating the dynamic propagation of neural signals under structural constraints, AFR-Net generates interpretable patterns of functional communication. This approach transcends the limitations of conventional region-level fusion methods, substantially outperforming existing techniques and effectively identifying critical neural pathways. The work thus establishes a novel paradigm for understanding the SC–FC relationship in brain networks.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Multiagent Systems: Agent Communication
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Social Networks and Social Media: Computational social science
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion

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.

Problem

• structural connectivity
• functional connectivity
• neural communication
• brain networks
• connectome

Innovation

• Adaptive Flow Routing
• Neural Communication Dynamics
• Structure-Function Coupling
• Physics-Informed Neural Network
• Brain Connectome