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

TL;DR

This paper introduces a novel Hypergraph GAN model that fuses rs-fMRI and DTI data to better characterize brain connectivity for Alzheimer's disease analysis, improving identification and classification accuracy.

Contribution

The paper proposes HGGAN, a new hypergraph-based GAN framework with IHEN and OHGH modules for multimodal brain connectivity analysis in AD.

Findings

Effective identification of discriminative brain regions for AD

Improved classification performance on ADNI data

Demonstrated advantages over existing multimodal fusion methods

Abstract

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.