Prototype Learning of Inter-network Connectivity for ASD Diagnosis and Personalized Analysis

In recent studies, deep learning has shown great potential to explore topological properties of functional connectivity (FC), e.g., graph neural networks (GNNs), for brain disease diagnosis, e.g., Autism spectrum disorder (ASD). However, many of the existing methods integrate the information locally, e.g., among neighboring nodes in a graph, which hinders from learning complex patterns of FC globally. In addition, their analysis for discovering imaging biomarkers is confined to providing the most discriminating regions without considering individual variations over the average FC patterns of groups, i.e., patients and normal controls. To address these issues, we propose a unified framework that globally captures properties of inter-network connectivity for classification and provides individual-specific group characteristics for interpretation via prototype learning. In our experiments using the ABIDE dataset, we validated the effectiveness of the proposed framework by comparing with competing topological deep learning methods in the literature. Furthermore, we individually analyzed functional mechanisms of ASD for neurological interpretation.