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.