Multi-View and Multimodal Graph Convolutional Neural Network for Autism Spectrum Disorder Diagnosis

Abstract

Autism Spectrum Disorder (ASD) presents significant diagnostic challenges due to its complex, heterogeneous nature. This study explores a novel approach to enhance the accuracy and reliability of ASD diagnosis by integrating resting-state functional magnetic resonance imaging with demographic data (age, gender, and IQ). This study is based on improving the spectral graph convolutional neural network (GCN). It introduces a multi-view attention fusion module to extract useful information from different views. The graph’s edges are informed by demographic data, wherein an edge-building network computes weights grounded in demographic information, thereby bolstering inter-subject correlation. To tackle the challenges of oversmoothing and neighborhood explosion inherent in deep GCNs, this study introduces DropEdge regularization and residual connections, thus augmenting feature diversity and model generalization. The proposed method is trained and evaluated on the ABIDE-I and ABIDE-II datasets. The experimental results underscore the potential of integrating multi-view and multimodal data to advance the diagnostic capabilities of GCNs for ASD.