# Uncovering Latent Communication Patterns in Brain Networks via Adaptive Flow Routing

arXiv: https://arxiv.org/abs/2602.00561 · HTML: https://mlsanity.com/paper/2602.00561/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 62 references that are in the map.
- First posted on arXiv 31 Jan 2026.

## Builds on (most cited first, 11 of 11)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Brain Network Transformer](https://mlsanity.com/paper/2210.06681/) (2022)
- [Integrated Brain Connectivity Analysis with fMRI, DTI, and sMRI Powered by Interpretable Graph Neural Networks](https://mlsanity.com/paper/2408.14254/) (2024)
- [Long-range Brain Graph Transformer](https://mlsanity.com/paper/2501.01100/) (2025)
- [NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes](https://mlsanity.com/paper/2409.17510/) (2024)
- [Navigation of brain networks](https://mlsanity.com/paper/1801.07938/) (2018)
- [Alzheimer's Disease Prediction via Brain Structural-Functional Deep Fusing Network](https://mlsanity.com/paper/2309.16206/) (2023)
- [A spectrum of routing strategies for brain networks](https://mlsanity.com/paper/1803.08541/) (2018)
- [Evolvable Graph Diffusion Optimal Transport with Pattern-Specific Alignment for Brain Connectome Modeling](https://mlsanity.com/paper/2509.16238/) (2025)

## Abstract (arXiv)

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.