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Using multimodal neuroimaging data to characterize brain network is currentlyan advanced technique for Alzheimer's disease(AD) Analysis. Over recent yearsthe neuroimaging community has made tremendous progress in the study ofresting-state functional magnetic resonance imaging (rs-fMRI) derived fromblood-oxygen-level-dependent (BOLD) signals and Diffusion Tensor Imaging (DTI)derived from white matter fiber tractography. However, Due to the heterogeneityand complexity between BOLD signals and fiber tractography, Most existingmultimodal data fusion algorithms can not sufficiently take advantage of thecomplementary information between rs-fMRI and DTI. To overcome this problem, anovel Hypergraph Generative Adversarial Networks(HGGAN) is proposed in thispaper, which utilizes Interactive Hyperedge Neurons module (IHEN) and OptimalHypergraph Homomorphism algorithm(OHGH) to generate multimodal connectivity ofBrain Network from rs-fMRI combination with DTI. To evaluate the performance ofthis model, We use publicly available data from the ADNI database todemonstrate that the proposed model not only can identify discriminative brainregions of AD but also can effectively improve classification performance.

Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis