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NF-GAT: A Node Feature-Based Graph Attention Network for ASD Classification
Shuaiqi Liu1,2, Beibei Liang3, Siqi Wang3
1College of Electronic and Information Engineering, Machine Vision Engineering Research Center of Hebei ProvinceHebei University Baoding 071002 China.
IEEE Open Journal of Engineering in Medicine and Biology
|June 20, 2024
Summary
This study introduces a novel graph attention network (NF-GAT) for diagnosing autism spectrum disorder (ASD). The NF-GAT model effectively utilizes functional connectivity features from fMRI data for accurate ASD classification.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Medical Imaging
Background:
- Autism spectrum disorder (ASD) presents diagnostic challenges.
- Functional connectivity (FC) analysis using fMRI shows promise in understanding brain differences in ASD.
Purpose of the Study:
- To develop and evaluate a graph attention network for recognizing autism spectrum disorders (ASD).
- To leverage functional connectivity (FC) features derived from fMRI data for improved ASD diagnosis.
Main Methods:
- A novel Node Features Graph Attention Network (NF-GAT) was proposed.
- Subject data were modeled as graphs with node features derived from fMRI.
- Graph attention layers were employed to learn discriminative node information for classification.
Main Results:
- The NF-GAT model demonstrated significant advantages over existing methods in ASD classification.
- The proposed NF-GAT achieved superior classification performance.
Conclusions:
- The NF-GAT model is effective for autism spectrum disorder classification.
- This approach offers a promising tool for objective ASD diagnosis using neuroimaging data.
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