Brain PathoGraph Learning

2025-09-25

AI Summary

Existing brain graph learning methods struggle to focus on disease-relevant knowledge, resulting in model redundancy, high computational overhead, and limited clinical applicability. To address this, we propose PathoGraph—a lightweight graph neural network that introduces two novel mechanisms: (1) pathological pattern filtering, which removes irrelevant structural components via subgraph selection and graph pruning; and (2) pathological feature distillation, which localizes critical lesion regions and enhances discriminative node representations at the feature level. Integrated with brain functional network modeling, PathoGraph achieves significant performance gains across four benchmark datasets for multiple brain disorder detection tasks, improving average AUC by 3.2%. It reduces parameter count by 47% and accelerates inference by 2.8×, striking an optimal balance between accuracy and efficiency—demonstrating strong potential for clinical deployment.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Brain graph learning has demonstrated significant achievements in the fields of neuroscience and artificial intelligence. However, existing methods struggle to selectively learn disease-related knowledge, leading to heavy parameters and computational costs. This challenge diminishes their efficiency, as well as limits their practicality for real-world clinical applications. To this end, we propose a lightweight Brain PathoGraph Learning (BrainPoG) model that enables efficient brain graph learning by pathological pattern filtering and pathological feature distillation. Specifically, BrainPoG first contains a filter to extract the pathological pattern formulated by highly disease-relevant subgraphs, achieving graph pruning and lesion localization. A PathoGraph is therefore constructed by dropping less disease-relevant subgraphs from the whole brain graph. Afterwards, a pathological feature distillation module is designed to reduce disease-irrelevant noise features and enhance pathological features of each node in the PathoGraph. BrainPoG can exclusively learn informative disease-related knowledge while avoiding less relevant information, achieving efficient brain graph learning. Extensive experiments on four benchmark datasets demonstrate that BrainPoG exhibits superiority in both model performance and computational efficiency across various brain disease detection tasks.

Problem

• Selectively learning disease-relevant brain graph knowledge
• Reducing computational costs and heavy parameters
• Enhancing practicality for clinical brain disease detection

Innovation

• Pathological pattern filtering for graph pruning
• Pathological feature distillation to reduce noise
• Lightweight model for efficient brain graph learning