# Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction

arXiv: https://arxiv.org/abs/2107.09928 · HTML: https://mlsanity.com/paper/2107.09928/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 46 references that are in the map.
- First posted on arXiv 21 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [Adversarial Autoencoders](https://mlsanity.com/paper/1511.05644/) (2015)
- [Diffusion-Convolutional Neural Networks](https://mlsanity.com/paper/1511.02136/) (2015)
- [Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation](https://mlsanity.com/paper/1904.07773/) (2019)
- [Adversarially Regularized Graph Autoencoder for Graph Embedding](https://mlsanity.com/paper/1802.04407/) (2018)
- [Adversarial Network Embedding](https://mlsanity.com/paper/1711.07838/) (2017)
- [Tensorizing GAN with High-Order Pooling for Alzheimer's Disease Assessment](https://mlsanity.com/paper/2008.00748/) (2020)

## Built on by (most cited first, 20 of 20)

- [Generative AI Enables EEG Super-Resolution via Spatio-Temporal Adaptive Diffusion Learning](https://mlsanity.com/paper/2407.03089/) (2024)
- [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)
- [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)
- [Ricci flow-based brain surface covariance descriptors for diagnosing Alzheimer's disease](https://mlsanity.com/paper/2403.06645/) (2024)
- [Generative AI Enables Structural Brain Network Construction from fMRI via Symmetric Diffusion Learning](https://mlsanity.com/paper/2309.16205/) (2023)
- [PHGNN: A Novel Prompted Hypergraph Neural Network to Diagnose Alzheimer's Disease](https://mlsanity.com/paper/2503.14577/) (2025)
- [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)
- [Fusing Structural and Functional Connectivities using Disentangled VAE for Detecting MCI](https://mlsanity.com/paper/2306.09629/) (2023)
- [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)
- [HyperMM : Robust Multimodal Learning with Varying-sized Inputs](https://mlsanity.com/paper/2407.20768/) (2024)
- [PGAD: Prototype-Guided Adaptive Distillation for Multi-Modal Learning in AD Diagnosis](https://mlsanity.com/paper/2503.04836/) (2025)
- [UltraDfeGAN: Detail-Enhancing Generative Adversarial Networks for High-Fidelity Functional Ultrasound Synthesis](https://mlsanity.com/paper/2507.03341/) (2025)
- [Sparse Multi-Modal Transformer with Masking for Alzheimer's Disease Classification](https://mlsanity.com/paper/2512.14491/) (2025)
- [HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings](https://mlsanity.com/paper/2605.16836/) (2026)

## Abstract (arXiv)

Multimodal neuroimage can provide complementary information about the dementia, but small size of complete multimodal data limits the ability in representation learning. Moreover, the data distribution inconsistency from different modalities may lead to ineffective fusion, which fails to sufficiently explore the intra-modal and inter-modal interactions and compromises the disease diagnosis performance. To solve these problems, we proposed a novel multimodal representation learning and adversarial hypergraph fusion (MRL-AHF) framework for Alzheimer's disease diagnosis using complete trimodal images. First, adversarial strategy and pre-trained model are incorporated into the MRL to extract latent representations from multimodal data. Then two hypergraphs are constructed from the latent representations and the adversarial network based on graph convolution is employed to narrow the distribution difference of hyperedge features. Finally, the hyperedge-invariant features are fused for disease prediction by hyperedge convolution. Experiments on the public Alzheimer's Disease Neuroimaging Initiative(ADNI) database demonstrate that our model achieves superior performance on Alzheimer's disease detection compared with other related models and provides a possible way to understand the underlying mechanisms of disorder's progression by analyzing the abnormal brain connections.