NF-GAT: A Node Feature-Based Graph Attention Network for ASD Classification

NF-GAT: A Node Feature-Based Graph Attention Network for ASD Classification

<italic>Goal:</italic> The purpose of this paper is to recognize autism spectrum disorders (ASD) using graph attention network. <italic>Methods:</italic> we propose a node features graph attention network (NF-GAT) for learning functional connectivity (FC) features to achieve...

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Bibliographic Details
Main Authors: Shuaiqi Liu, Beibei Liang, Siqi Wang, Bing Li, Lidong Pan, Shui-Hua Wang
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Open Journal of Engineering in Medicine and Biology
Subjects:
Autism spectrum disorder
classification
functional connectivity
graphical attention network
Online Access:https://ieeexplore.ieee.org/document/10109139/
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Summary:<italic>Goal:</italic> The purpose of this paper is to recognize autism spectrum disorders (ASD) using graph attention network. <italic>Methods:</italic> we propose a node features graph attention network (NF-GAT) for learning functional connectivity (FC) features to achieve ASD diagnosis. Firstly, node features are modelled based on functional magnetic resonance imaging (fMRI) data, with each subject modelled as a graph. Next, we use the graph attention layer to learn the node features and gets the node information of different nodes for ASD classification. <italic>Results:</italic> Compared with other models, the NF-GAT has significant advantages in terms of classification results. <italic>Conclusions:</italic> NF-GAT can be effectively used for ASD classification.
ISSN:2644-1276

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