# Exploring Subnetwork Interactions in Heterogeneous Brain Network via Prior-Informed Graph Learning

arXiv: https://arxiv.org/abs/2603.19307 · HTML: https://mlsanity.com/paper/2603.19307/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 13 Mar 2026.

## Builds on (most cited first, 6 of 6)

- [Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention](https://mlsanity.com/paper/2105.13495/) (2021)
- [Community-Aware Transformer for Autism Prediction in fMRI Connectome](https://mlsanity.com/paper/2307.10181/) (2023)
- [Heterogeneous Graph Convolutional Neural Network via Hodge-Laplacian for Brain Functional Data](https://mlsanity.com/paper/2302.09323/) (2023)
- [Heterogeneous Graph-Based Multimodal Brain Network Learning](https://mlsanity.com/paper/2110.08465/) (2021)
- [EAG-RS: A Novel Explainability-guided ROI-Selection Framework for ASD Diagnosis via Inter-regional Relation Learning](https://mlsanity.com/paper/2310.03404/) (2023)
- [New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity Using Dictionary Learning](https://mlsanity.com/paper/2211.07374/) (2022)

## Abstract (arXiv)

Modeling the complex interactions among functional subnetworks is crucial for the diagnosis of mental disorders and the identification of functional pathways. However, learning the interactions of the underlying subnetworks remains a significant challenge for existing Transformer-based methods due to the limited number of training samples. To address these challenges, we propose KD-Brain, a Prior-Informed Graph Learning framework for explicitly encoding prior knowledge to guide the learning process. Specifically, we design a Semantic-Conditioned Interaction mechanism that injects semantic priors into the attention query, explicitly navigating the subnetwork interactions based on their functional identities. Furthermore, we introduce a Pathology-Consistent Constraint, which regularizes the model optimization by aligning the learned interaction distributions with clinical priors. Additionally, KD-Brain leads to state-of-the-art performance on a wide range of disorder diagnosis tasks and identifies interpretable biomarkers consistent with psychiatric pathophysiology. Our code is available at https://anonymous.4open.science/r/KDBrain.