A heterogeneous graph convolutional attention network method for classification of autism spectrum disorder

BMC Bioinformatics

Research

Lizhen Shao¹  Xunying Chen²  Cong Fu² 

[1] Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, 100083, Beijing, China;Lancaster University, LA1 4YX, Lancaster, UK;Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, 100083, Beijing, China;Shunde Graduate School, University of Science and Technology Beijing, 528399, Foshan, China;

关键词: FMRI;    Heterogeneous graph convolution network;    ASD;    Attention mechanism;   

DOI  :  10.1186/s12859-023-05495-7

 received in 2023-04-18, accepted in 2023-09-21,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundAutism spectrum disorder (ASD) is a serious developmental disorder of the brain. Recently, various deep learning methods based on functional magnetic resonance imaging (fMRI) data have been developed for the classification of ASD. Among them, graph neural networks, which generalize deep neural network models to graph structured data, have shown great advantages. However, in graph neural methods, because the graphs constructed are homogeneous, the phenotype information of the subjects cannot be fully utilized. This affects the improvement of the classification performance.MethodsTo fully utilize the phenotype information, this paper proposes a heterogeneous graph convolutional attention network (HCAN) model to classify ASD. By combining an attention mechanism and a heterogeneous graph convolutional network, important aggregated features can be extracted in the HCAN. The model consists of a multilayer HCAN feature extractor and a multilayer perceptron (MLP) classifier. First, a heterogeneous population graph was constructed based on the fMRI and phenotypic data. Then, a multilayer HCAN is used to mine graph-based features from the heterogeneous graph. Finally, the extracted features are fed into an MLP for the final classification.ResultsThe proposed method is assessed on the autism brain imaging data exchange (ABIDE) repository. In total, 871 subjects in the ABIDE I dataset are used for the classification task. The best classification accuracy of 82.9% is achieved. Compared to the other methods using exactly the same subjects in the literature, the proposed method achieves superior performance to the best reported result.ConclusionsThe proposed method can effectively integrate heterogeneous graph convolutional networks with a semantic attention mechanism so that the phenotype features of the subjects can be fully utilized. Moreover, it shows great potential in the diagnosis of brain functional disorders with fMRI data.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

【 图 表 】

13063_2023_7648_Article_IEq5.gif

13690_2023_1170_Article_IEq25.gif

Fig. 3

13690_2023_1170_Article_IEq24.gif

Fig. 4

Fig. 2

Fig. 3

Fig. 1

13690_2023_1170_Article_IEq140.gif

Fig. 3

Fig. 1

Fig. 3

Fig. 1

Chart 2

Fig. 1

12936_2023_4724_Article_IEq82.gif

13690_2023_1170_Article_IEq23.gif

13690_2023_1170_Article_IEq138.gif

Fig. 4

Fig. 2

Fig. 5

12936_2023_4724_Article_IEq79.gif

Fig. 2

13690_2023_1170_Article_IEq21.gif

Fig. 2

Fig. 1

Fig. 3

Fig. 1

Fig. 2

Fig. 9

13690_2023_1170_Article_IEq137.gif

Fig. 7

Fig. 1

13063_2023_7612_Figbt_HTML.png

Fig. 3

Fig. 1

Fig. 4

Fig. 2

Fig. 1

13690_2023_1170_Article_IEq19.gif

12888_2023_5172_Article_IEq50.gif

12888_2023_5172_Article_IEq53.gif

Fig. 1

12888_2023_5172_Article_IEq48.gif

12888_2023_5172_Article_IEq47.gif

12888_2023_5172_Article_IEq46.gif

12888_2023_5172_Article_IEq45.gif

12888_2023_5172_Article_IEq44.gif

Fig. 2

12888_2023_5172_Article_IEq42.gif

12888_2023_5172_Article_IEq41.gif

12888_2023_5172_Article_IEq52.gif

Fig. 1

Fig. 1

13690_2023_1177_Fige_HTML.png

13690_2023_1170_Article_IEq18.gif

Fig. 2

Fig. 1

41408_2023_919_Article_IEq16.gif

41408_2023_919_Article_IEq15.gif

41408_2023_919_Article_IEq14.gif

41408_2023_919_Article_IEq13.gif

41408_2023_919_Article_IEq12.gif

41408_2023_919_Article_IEq11.gif

Fig. 2

Fig. 3

12936_2023_4724_Article_IEq75.gif

Fig. 7

Fig. 5

Fig. 2