FE-STGNN: Spatio-Temporal Graph Neural Network with Functional and Effective Connectivity Fusion for MCI Diagnosis

Abstract

Brain connectivity patterns such as functional connectivity (FC) and effective connectivity (EC), describing complex spatio-temporal dynamic interactions in the brain network, are highly desirable for mild cognitive impairment (MCI) diagnosis. Major FC methods are based on statistical dependence, usually evaluated in terms of correlations, while EC generally focuses on directional causal influences between brain regions. Therefore, comprehensive integration of FC and EC with complementary information can further extract essential biomarkers for characterizing brain abnormality. This paper proposes Spatio-Temporal Graph Neural Network with Dynamic Functional and Effective Connectivity Fusion (FE-STGNN) for MCI diagnosis using resting-state fMRI (rs-fMRI). First, dynamic FC and EC networks are constructed to encode the functional brain networks into multiple graphs. Then, spatial graph convolution is employed to process spatial structural features and temporal dynamic characteristics. Finally, we design the position encoding-based cross-attention mechanism, which utilizes the causal linkage of EC during time evolution to guide the fusion of FC networks for MCI classification. Qualitative and quantitative experimental results demonstrate the significance of the proposed FE-STGNN method and the benefit of fusing FC and EC, which achieves $82\%$ of MCI classification accuracy and outperforms state-of-the-art methods. Our code is available at https://github.com/haijunkenan/FE-STGNN.