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Cross-modal fusion of different types of neuroimaging data has shown greatpromise for predicting the progression of Alzheimer's Disease(AD). However,most existing methods applied in neuroimaging can not efficiently fuse thefunctional and structural information from multi-modal neuroimages. In thiswork, a novel cross-modal transformer generative adversarial network(CT-GAN) isproposed to fuse functional information contained in resting-state functionalmagnetic resonance imaging (rs-fMRI) and structural information contained inDiffusion Tensor Imaging (DTI). The developed bi-attention mechanism can matchfunctional information to structural information efficiently and maximize thecapability of extracting complementary information from rs-fMRI and DTI. Bycapturing the deep complementary information between structural features andfunctional features, the proposed CT-GAN can detect the AD-related brainconnectivity, which could be used as a bio-marker of AD. Experimental resultsshow that the proposed model can not only improve classification performancebut also detect the AD-related brain connectivity effectively.

Cross-Modal Transformer GAN: A Brain Structure-Function Deep Fusing Framework for Alzheimer's Disease