A novel autism spectrum disorder identification method: spectral graph network with brain-population graph structure joint learning

• Published in: International journal of machine learning and cybernetics Vol. 15; no. 4; pp. 1517 - 1532
• Main Authors: Li, Sihui, Li, Duo, Zhang, Rui, Cao, Feilong
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Autism; Biomarkers; Brain; Classification; Complex Systems; Computational Intelligence; Control; Diagnosis; Engineering; Graph neural networks; Graph representations; Graph theory; Identification; Identification methods; Machine learning; Mechatronics; Medical imaging; Methods; Modules; Neural networks; Original Article; Pattern Recognition; Representations; Robotics; Systems Biology; Graph structure learning; Feature extraction; Graph neural network; Autism spectrum disorder
• ISSN: 1868-8071; 1868-808X
• DOI: 10.1007/s13042-023-01980-w

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental condition. Its early and accurate diagnosis is critical in enhancing the quality of life for affected individuals. Graph neural networks supply promising approaches for ASD diagnosis. However, existing works typically focus on brain-level or population-level classification methods, where the former usually disregards subjects’ non-imaging information and inter-subject relationships, and the latter generally fails to adequately evaluate and detect disease-associated brain regions and biomarkers. Furthermore, relatively static graph structures and shallow network architectures hinder the abundant extraction of information, affecting the performance of ASD identification. Accordingly, this paper proposes a new spectral graph network with brain-population graph structure joint learning (BPGLNet) for ASD diagnosis. This new framework involves two main components. Firstly, a brain-level graph learning module is designed to acquire valuable features of brain regions and identify effective biomarkers for each subject. In particular, it constructs a brain-aware representation learning network by fusing an improved graph pooling strategy and spectral graph convolution to learn subgraph structures and features of brain regions. Subsequently, based on these generated features, a population-level graph learning module is developed to capture relationships between different subjects. It builds an adaptive edge generator network by integrating non-imaging and imaging data, forming a learnable population graph. Further, this module also devises a deep cascade spectral graph network to enrich high-level feature representation of data and complete ASD identification. Experiments on the benchmark dataset reveal the state-of-the-art performance of BPGLNet.