A Hierarchical Graph Convolutional Network With Infomax-Guided Graph Embedding for Population-Based ASD Detection

• Published in: IEEE journal of biomedical and health informatics Vol. 29; no. 6; pp. 4349 - 4361
• Main Authors: Hao, Xiaoke, Ma, Mingming, Tao, Jiaqing, Cao, Jiahui, Qin, Jing, Liu, Feng, Zhang, Daoqiang, Ming, Dong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2025
• Subjects: Accuracy; Algorithms; Autism; Autism spectrum disorder; Autism Spectrum Disorder - diagnostic imaging; Brain - diagnostic imaging; Brain modeling; Child; Correlation; Feature extraction; Female; Functional magnetic resonance imaging; Graph classi-fication; Graph convolutional networks; Graph pooling; Humans; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Male; Neural Networks, Computer; Non-imaging information; Training; Variable speed drives; Vectors
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2025.3544302

Recently, functional magnetic resonance imaging (fMRI)-based brain networks have been shown to be an effective diagnostic tool with great potential for accurately detecting autism spectrum disorders (ASD). Meanwhile, the successful use of graph convolution networks (GCNs) methods based on fMRI information has improved the classification accuracy of ASD. However, many graph convolution-based methods do not fully utilize the topological information of the brain functional connectivity network (BFCN) or ignore the effect of non-imaging information. Therefore, we propose a hierarchical graph embedding model that leverage both the topological information of the BFCN and the non-imaging information of the subjects to improve the classification accuracy. Specifically, our model first use the Infomax Module to automatically identify embedded features in regions of interests (ROIs) in the brain. Then, these features, along with non-imaging information, is used to construct a population graph model. Finally, we design a graph convolution framework to propagate and aggregate the node features and obtain the results for ASD detection. Our model takes into account both the significance of the BFCN to individual subjects and relationships between subjects in the population graph. The model performed autism detection using the Autism Brain Imaging Data Exchange (ABIDE) dataset and obtained an average accuracy of 77.2% and an AUC of 87.2%. These results exceed those of the baseline approach. Through extensive experiments, we demonstrate the competitiveness, robustness and effectiveness of our model in aiding ASD diagnosis.