  Using multimodal neuroimaging data to characterize brain network is currently
an advanced technique for Alzheimer's disease(AD) Analysis. Over recent years
the neuroimaging community has made tremendous progress in the study of
resting-state functional magnetic resonance imaging (rs-fMRI) derived from
blood-oxygen-level-dependent (BOLD) signals and Diffusion Tensor Imaging (DTI)
derived from white matter fiber tractography. However, Due to the heterogeneity
and complexity between BOLD signals and fiber tractography, Most existing
multimodal data fusion algorithms can not sufficiently take advantage of the
complementary information between rs-fMRI and DTI. To overcome this problem, a
novel Hypergraph Generative Adversarial Networks(HGGAN) is proposed in this
paper, which utilizes Interactive Hyperedge Neurons module (IHEN) and Optimal
Hypergraph Homomorphism algorithm(OHGH) to generate multimodal connectivity of
Brain Network from rs-fMRI combination with DTI. To evaluate the performance of
this model, We use publicly available data from the ADNI database to
demonstrate that the proposed model not only can identify discriminative brain
regions of AD but also can effectively improve classification performance.
