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Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos

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Exploring Implicit Biological Heterogeneity in ASD Diagnosis Using a Multi-Head Attention Graph Neural Network.

Hyung-Jun Moon¹, Sung-Bae Cho²

¹Department of Artificial Intelligence, Yonsei University, 03722 Seoul, Republic of Korea.

Journal of Integrative Neuroscience

|July 31, 2024

Summary

This study introduces a new deep learning method using multi-head attention to analyze brain functional connectivity (FC) in autism spectrum disorder (ASD). The approach improves diagnostic accuracy by capturing detailed connectivity patterns, outperforming existing methods.

Keywords:

autism spectrum disorder dynamic functional connectivity graph neural network multi-head attention

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Published on: October 24, 2012

Area of Science:

Neuroscience
Artificial Intelligence
Medical Imaging

Background:

Autism spectrum disorder (ASD) presents heterogeneous characteristics, influenced by sex and age, with current deep learning models for functional connectivity (FC) graphs lacking specialized regional analysis.
Existing methods focusing on generalized patterns fail to capture intricate, variable brain connectivity crucial for accurate ASD diagnosis.

Purpose of the Study:

To develop a novel deep learning method for modeling FC with multi-head attention to overcome limitations in analyzing ASD-related brain connectivity.
To accurately assess disease indications by extracting abnormal patterns in brain connectivity, considering region-specific correlations and transient time points.

Main Methods:

Proposed a deep learning method that models functional connectivity (FC) using multi-head attention to capture intricate and variable patterns.
Transformed FC data into a graph with weighted edge labels, processed by a graph neural network capable of handling edge labels.
Utilized the Autism Brain Imaging Data Exchange (ABIDE) I and II datasets for model validation.

Main Results:

The novel method demonstrated superior performance over state-of-the-art techniques on the ABIDE datasets, improving diagnostic accuracy by up to 3.7%p.
Multi-head attention significantly enhanced the differentiation between typical and ASD brains by analyzing FC.
Ablation studies confirmed the method's ability to validate diverse brain characteristics across different ages and sexes in ASD patients.

Conclusions:

The proposed deep learning method effectively enhances diagnostic accuracy for ASD.
This approach holds significant potential for advancing neurological research and improving ASD diagnosis.