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Related Experiment Video

Updated: Jan 15, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

BrainGraphDiff: A framework for enhanced brain network analysis via adaptive subgraph generation.

Jingye Tang¹, Tianqing Zhu², Wanlei Zhou²

¹Faculty of Data Science, City University of Macau, Macau, 999078, China; Shenzhen University of Advanced Technology, Shenzhen, 518106, China.

Neural Networks : the Official Journal of the International Neural Network Society

|January 13, 2026

Summary

This summary is machine-generated.

This study introduces BrainGraphDiff, a novel framework using generative models to improve mental disorder diagnosis from brain scans. It addresses data scarcity and heterogeneity for more reliable brain network analysis.

Keywords:

Brain graph Diffusion model Graph classification Graph neural network Subgraph information bottleneck

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Area of Science:

Neuroscience
Artificial Intelligence
Medical Imaging

Background:

Mental disorder diagnosis is a global challenge.
Graph Neural Networks (GNNs) advance brain network analysis but face data limitations.
Heterogeneous and scarce medical imaging data hinder model generalization and clinical application.

Purpose of the Study:

To enhance the diversity of training data using generative models.
To address prediction bias and poor generalization caused by data scarcity.
To optimize brain network analysis model prediction results efficiently.

Main Methods:

Proposed the BrainGraphDiff framework with a partial graph generation module.
Introduced the GL-PGIB strategy for adaptive subgraph extraction.
Utilized key graph structures as anchors for label-related subgraph scope adjustment.

Main Results:

Demonstrated customized subgraph extraction for diverse samples.
Showcased effective balancing of model efficiency and performance through the generative module.
Validated the framework's ability to improve brain network analysis predictions.

Conclusions:

The BrainGraphDiff framework and GL-PGIB strategy effectively address data heterogeneity and scarcity in brain network analysis.
This approach enhances the reliability and scalability of diagnostic models for mental disorders.
Customized subgraph extraction and generative modeling offer a promising solution for clinical applications.