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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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
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| Online Access: | https://ieeexplore.ieee.org/document/10109139/ |
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| author | Shuaiqi Liu Beibei Liang Siqi Wang Bing Li Lidong Pan Shui-Hua Wang |
| author_facet | Shuaiqi Liu Beibei Liang Siqi Wang Bing Li Lidong Pan Shui-Hua Wang |
| author_sort | Shuaiqi Liu |
| collection | DOAJ |
| description | <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. |
| format | Article |
| id | doaj-art-42110df3d4ad4889b45d401d4b37aa43 |
| institution | Kabale University |
| issn | 2644-1276 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Engineering in Medicine and Biology |
| spelling | doaj-art-42110df3d4ad4889b45d401d4b37aa432025-01-29T00:01:30ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762024-01-01542843310.1109/OJEMB.2023.326761210109139NF-GAT: A Node Feature-Based Graph Attention Network for ASD ClassificationShuaiqi Liu0https://orcid.org/0000-0001-7520-8226Beibei Liang1Siqi Wang2https://orcid.org/0000-0002-5496-111XBing Li3https://orcid.org/0000-0002-5888-6735Lidong Pan4https://orcid.org/0009-0009-0609-4603Shui-Hua Wang5https://orcid.org/0000-0003-2238-6808College of Electronic and Information Engineering, Machine Vision Engineering Research Center of Hebei Province, Hebei University, Baoding, ChinaKey Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, ChinaKey Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, ChinaNational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, ChinaSchool of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China<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.https://ieeexplore.ieee.org/document/10109139/Autism spectrum disorderclassificationfunctional connectivitygraphical attention network |
| spellingShingle | Shuaiqi Liu Beibei Liang Siqi Wang Bing Li Lidong Pan Shui-Hua Wang NF-GAT: A Node Feature-Based Graph Attention Network for ASD Classification IEEE Open Journal of Engineering in Medicine and Biology Autism spectrum disorder classification functional connectivity graphical attention network |
| title | NF-GAT: A Node Feature-Based Graph Attention Network for ASD Classification |
| title_full | NF-GAT: A Node Feature-Based Graph Attention Network for ASD Classification |
| title_fullStr | NF-GAT: A Node Feature-Based Graph Attention Network for ASD Classification |
| title_full_unstemmed | NF-GAT: A Node Feature-Based Graph Attention Network for ASD Classification |
| title_short | NF-GAT: A Node Feature-Based Graph Attention Network for ASD Classification |
| title_sort | nf gat a node feature based graph attention network for asd classification |
| topic | Autism spectrum disorder classification functional connectivity graphical attention network |
| url | https://ieeexplore.ieee.org/document/10109139/ |
| work_keys_str_mv | AT shuaiqiliu nfgatanodefeaturebasedgraphattentionnetworkforasdclassification AT beibeiliang nfgatanodefeaturebasedgraphattentionnetworkforasdclassification AT siqiwang nfgatanodefeaturebasedgraphattentionnetworkforasdclassification AT bingli nfgatanodefeaturebasedgraphattentionnetworkforasdclassification AT lidongpan nfgatanodefeaturebasedgraphattentionnetworkforasdclassification AT shuihuawang nfgatanodefeaturebasedgraphattentionnetworkforasdclassification |