DML-GNN: ASD Diagnosis Based on Dual-Atlas Multi-Feature Learning Graph Neural Network

Abstract

To better automate the diagnosis of autism spectrum disorder (ASD) and improve diagnostic accuracy, a graph neural network via dual-atlas multi-feature learning (DML-GNN) model for ASD diagnosis is constructed based on the local feature information of brain atlas and the global feature information from the multi-modal data. First, DML-GNN constructs a dual-atlas feature extraction module to capture the initial features of each subject. Second, it combines K-nearest-neighbor graphs, graph pooling, graph convolution (GCN) and graph channel attention (GCA) to construct a local feature learning module. This module extracts deep features for each subject and eliminate redundant features, and further fuses multi-atlases features efficiently. Third, DML-GNN constructs a global feature learning module by combining the non-imaging information of fMRI data and graph isomorphism network (GINConv), which combines the information of multi-modal data to construct comprehensive multi-graph features and learns node embeddings using GINConv. Finally, multi-layer perceptron (MLP) is used to obtain the final ASD diagnosis results. Compared with recent algorithms for ASD diagnosis on the public data set-Autism Brain Imaging Data Exchange I (ABIDE I), our method demonstrated superior performance, underscoring its potential as an effective tool.