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

arXiv: https://arxiv.org/abs/2107.09953 · HTML: https://mlsanity.com/paper/2107.09953/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 46 references that are in the map.
- First posted on arXiv 21 Jul 2021.

## Builds on (most cited first, 3 of 3)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [HNHN: Hypergraph Networks with Hyperedge Neurons](https://mlsanity.com/paper/2006.12278/) (2020)
- [Tensorizing GAN with High-Order Pooling for Alzheimer's Disease Assessment](https://mlsanity.com/paper/2008.00748/) (2020)

## Built on by (most cited first, 18 of 18)

- [3D Brain Reconstruction by Hierarchical Shape-Perception Network from a Single Incomplete Image](https://mlsanity.com/paper/2107.11010/) (2021)
- [Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification](https://mlsanity.com/paper/2204.02399/) (2022)
- [Adversarial Learning Based Structural Brain-network Generative Model for Analyzing Mild Cognitive Impairment](https://mlsanity.com/paper/2208.08896/) (2022)
- [Recent Advances in Hypergraph Neural Networks](https://mlsanity.com/paper/2503.07959/) (2025)
- [CAT-Walk: Inductive Hypergraph Learning via Set Walks](https://mlsanity.com/paper/2306.11147/) (2023)
- [Brain Structure-Function Fusing Representation Learning using Adversarial Decomposed-VAE for Analyzing MCI](https://mlsanity.com/paper/2305.14404/) (2023)
- [Alzheimer's Disease Prediction via Brain Structural-Functional Deep Fusing Network](https://mlsanity.com/paper/2309.16206/) (2023)
- [Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition](https://mlsanity.com/paper/2405.19373/) (2024)
- [Cross-Modal Transformer GAN: A Brain Structure-Function Deep Fusing Framework for Alzheimer's Disease](https://mlsanity.com/paper/2206.13393/) (2022)
- [BDHT: Generative AI Enables Causality Analysis for Mild Cognitive Impairment](https://mlsanity.com/paper/2312.09022/) (2023)
- [Ricci flow-based brain surface covariance descriptors for diagnosing Alzheimer's disease](https://mlsanity.com/paper/2403.06645/) (2024)
- [EEG-DIF: Early Warning of Epileptic Seizures through Generative Diffusion Model-based Multi-channel EEG Signals Forecasting](https://mlsanity.com/paper/2410.17343/) (2024)
- [Feature-selected Graph Spatial Attention Network for Addictive Brain-Networks Identification](https://mlsanity.com/paper/2207.00583/) (2022)
- [Dynamic Community Detection via Adversarial Temporal Graph Representation Learning](https://mlsanity.com/paper/2207.03580/) (2022)
- [BG-GAN: Generative AI Enable Representing Brain Structure-Function Connections for Alzheimer's Disease](https://mlsanity.com/paper/2309.08916/) (2023)
- [Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis](https://mlsanity.com/paper/2403.12719/) (2024)
- [GAMMA-PD: Graph-based Analysis of Multi-Modal Motor Impairment Assessments in Parkinson's Disease](https://mlsanity.com/paper/2410.00944/) (2024)
- [UltraDfeGAN: Detail-Enhancing Generative Adversarial Networks for High-Fidelity Functional Ultrasound Synthesis](https://mlsanity.com/paper/2507.03341/) (2025)

## Abstract (arXiv)

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.