THE MULTIVARIATE TRANSFORMER NETWORK FOR MILD COGNITIVE IMPAIRMENT IDENTIFICATION

The functional brain network (FBN) has emerged as a promising biomarker for classifying brain diseases. However, most existing studies ignored the extensive interactions between gray matter (GM) and white matter (WM) which would limit the performance of the process. In this paper, we presented a GM and WM FBNs based mild cognitive impairment (MCI) identification method by utilizing spatial independent component analysis (ICA) and transformer network with rs-fMRI data. Specifically, spatial ICA was performed on the GM and WM of the preprocessed fMRI data. Then the time courses of the GM and WM independent components were inputted into the transformer network with attention mechanism, which led to the optimized and extensive inter- and intra-matter functional connectivity. Finally, the fused GM and WM FBNs were constructed for MCI identification. Experimental results on ADNI dataset demonstrated the superior performance of the proposed method over the state-of-the-art methods.