Long-Interval Spatio-Temporal Graph Convolution for Brain Disease Diagnosis
Abstract: Functional magnetic resonance imaging (fMRI) is a noninvasive neuroimaging technique capable of recording dynamic brain activity, which can be used to construct a dynamic functional brain network (DFBN). DFBN adeptly captures intricate spatio-temporal characteristics of brain connectivities, and this proficiency is increasingly recognized in the field of brain disease diagnosis. However, the existing DFBN analysis methods predominantly focus on capturing the topological information between brain regions within individual time windows, often neglecting the critical long-interval temporal interactions that extend across these windows. Moreover, most of them extract temporal and spatial features independently, which hinders the effective mining of the complex spatio-temporal topological coupling in DFBNs. To tackle these problems, we propose a long-interval interactive graph convolutions method that collaboratively captures dynamic spatio-temporal topological features from DFBNs. Specifically, we first capture the brain networks’ long-interval communication patterns through the Hawkes process, which comprehensively exploits the impact of brain connectivity topological associations across different time windows. Then, we develop an interactive graph convolution framework to collaboratively extract spatio-temporal features in DFBNs, which overcomes the shortcomings of existing spatio-temporal models that struggle to characterize complex dynamic topological coupling. Finally, we design a hypergraph-based spatial information augmentation (SIA) module to further extract the high-order dependencies among brain regions. Numerous experimental results demonstrate that the proposed method is superior to several state-of-the-art methods, and can provide effective biomarkers for brain disease diagnosis.
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