Multi-atlas functional and effective connectivity attention fusion method for autism spectrum disorder diagnosis

The brain connectivity patterns, such as functional connectivity (FC) and effective connectivity (EC), describe the complex interaction patterns in brain networks. By combining FC and EC information, essential biomarkers for characterizing brain abnormalities can be further extracted. However, existing FC–EC fusion methods often process FC and EC networks independently, neglecting the intrinsic interrelationships between them. In addition, current FC–EC fusion methods rely on single segmentation atlas data, whereas multi-atlas data provides a more comprehensive display of brain information. To address these issues, we propose a novel multi-atlas dual branch graph convolution functional effective connectivity attention fusion (MaFECAF) method. It comprises three modules: multi-atlas data fusion, functional and effective connectivity construction, and functional and effective connectivity attention fusion. First, two atlases are fused using the cosine similarity matrix as weights. Next, we use the fused data to construct the FC and EC networks via spatio-temporal graph convolutional network and variational autoencoder parameterized by graph neural network architecture, respectively. Subsequently, the FC and EC networks are merged via a dynamic graph convolutional network and an adaptive self-attention feature extraction unit, followed by the application of the obtained features to autism spectrum disorder (ASD) classification. The experimental results have demonstrated the effectiveness of the proposed method and the benefits of integrating FC and EC. The proposed method achieved a 74.7% classification accuracy for ASD and outperformed state-of-the-art methods. • Multi-atlas framework builds and merges functional effective connectivity networks. • Dual-branch brain network construction via pattern-specific feature extraction. • Dynamic fusion method learns and integrates brain network connection patterns.