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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

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MD-DGNN: A Metadata-Driven Dual Graph Neural Network based on Brain Functional Connectivity for ASD Classification.

Jing Li, Jinbei Zhang, Tao Leng

IEEE Journal of Biomedical and Health Informatics

|June 10, 2026

Summary

This study introduces a novel metadata-driven dual graph neural network (MD-DGNN) for improved autism spectrum disorder (ASD) diagnosis using fMRI data. The method enhances diagnostic accuracy by adaptively learning brain graph structures and integrating metadata effectively.

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Published on: March 21, 2019

Area of Science:

Neuroscience
Computer Science
Medical Imaging

Background:

Autism spectrum disorder (ASD) diagnosis requires efficient methods.
Functional magnetic resonance imaging (fMRI) reveals brain functional connectivity.
Existing dual graph methods for ASD classification have limitations in graph topology optimization and metadata integration.

Purpose of the Study:

To propose a metadata-driven dual graph neural network (MD-DGNN) for enhanced ASD diagnosis.
To address limitations in adaptive graph topology learning and metadata-specific feature integration.
To improve the accuracy and clarity of inter-subject relationship modeling in ASD classification.

Main Methods:

Developed an edge-adaptive Graph Neural Network (GNN) with a dynamic edge sampling module for adaptive graph topology learning.
Created a metadata-aware GNN that constructs heterogeneous population graphs and employs hierarchical cross-fusion for metadata integration.
Utilized fMRI data from publicly available ABIDE I and TJAD-ASD MRI datasets.

Main Results:

The proposed MD-DGNN achieved state-of-the-art performance in ASD classification.
Demonstrated the superiority of the method in adaptively optimizing graph topologies.
Showcased effective integration of metadata-specific features through hierarchical cross-fusion.

Conclusions:

The MD-DGNN method significantly improves ASD diagnosis accuracy using fMRI data.
Adaptive graph topology learning and metadata integration are crucial for effective ASD classification.
The proposed approach offers a more robust and accurate tool for neurodevelopmental disorder research.