ABSTRACT As modern medical imaging technology advances, resting‐state functional magnetic resonance imaging (rs‐fMRI) is becoming a preferred method for studying brain activity and identifying autism spectrum disorder (ASD) because of its cost‐effectiveness and noninvasive approach. To better utilize the temporal and spatial dimensions of the rs‐fMRI signals, this paper proposes a spatiotemporal feature fusion adaptive learning graph neural network (SF 2 AL‐GNN) for ASD diagnosis. SF 2 AL‐GNN first creates a functional connectivity (FC) matrix for every individual. Then, combining gated recurrent units (GRU), transformer, and graph convolution, it develops a spatiotemporal local feature learning module to extract temporal features from 1D time series and the FC matrix–based spatial features. Subsequently, these features are fused to construct a global subject graph using multimodal information. A self‐adjusting global feature learning (SGFL) module adds adaptive weights during GCN updates to better obtain ASD‐related feature embeddings. Finally, an MLP is used for classification. Training and evaluation of the model were conducted using the ABIDE I dataset, outperforming the latest advanced approaches.
Liu et al. (Sun,) studied this question.