用动态路由模拟脑区间通信，揭示结构连接如何生成功能连接
Uncovering Latent Communication Patterns in Brain Networks via Adaptive Flow Routing
- 基于神经通信动力学建模，通过自适应路由发现关键路径
- 在多模态融合任务中显著优于现有方法，提升模型性能
- 适合关注脑网络机制与可解释性研究的神经科学与AI交叉学者
解析宏观认知表型如何从微观神经连接中产生，仍是神经科学的核心挑战。传统方法依赖结构连接（SC）与功能连接（FC）的多模态信息完成下游任务，虽尝试在区域层面融合二者表示，但缺乏基础神经科学洞察，无法揭示连接组之间潜在的神经区域交互机制，因而难以解释为何SC与FC同时表现出耦合与异质性的动态状态。本文从神经通信动力学视角出发，提出物理信息引导的自适应流路由网络（AFR-Net），建模结构约束（SC）如何生成功能通信模式（FC），实现对关键神经通路的可解释发现。大量实验表明，AFR-Net显著优于当前最优基线方法。代码已公开于 https://anonymous.4open.science/r/DIAL-F0D1。
原文摘要 · Abstract (English)
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.
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