# Alzheimer's Disease Prediction via Brain Structural-Functional Deep Fusing Network

arXiv: https://arxiv.org/abs/2309.16206 · HTML: https://mlsanity.com/paper/2309.16206/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 66 references that are in the map.
- First posted on arXiv 28 Sep 2023.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [General Multi-label Image Classification with Transformers](https://mlsanity.com/paper/2011.14027/) (2020)
- [Generative Adversarial Transformers](https://mlsanity.com/paper/2103.01209/) (2021)
- [Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis](https://mlsanity.com/paper/2008.03483/) (2020)
- [Tensorizing GAN with High-Order Pooling for Alzheimer's Disease Assessment](https://mlsanity.com/paper/2008.00748/) (2020)
- [Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction](https://mlsanity.com/paper/2107.09928/) (2021)
- [Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis](https://mlsanity.com/paper/2107.09953/) (2021)
- [Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN](https://mlsanity.com/paper/2111.12886/) (2021)
- [Brain Stroke Lesion Segmentation Using Consistent Perception Generative Adversarial Network](https://mlsanity.com/paper/2008.13109/) (2020)
- [Multiscale Autoencoder with Structural-Functional Attention Network for Alzheimer's Disease Prediction](https://mlsanity.com/paper/2208.04945/) (2022)

## Built on by (most cited first, 5 of 5)

- [A review of deep learning-based information fusion techniques for multimodal medical image classification](https://mlsanity.com/paper/2404.15022/) (2024)
- [BDHT: Generative AI Enables Causality Analysis for Mild Cognitive Impairment](https://mlsanity.com/paper/2312.09022/) (2023)
- [SFC-GAN: A Generative Adversarial Network for Brain Functional and Structural Connectome Translation](https://mlsanity.com/paper/2501.07055/) (2025)
- [UltraDfeGAN: Detail-Enhancing Generative Adversarial Networks for High-Fidelity Functional Ultrasound Synthesis](https://mlsanity.com/paper/2507.03341/) (2025)
- [Uncovering Latent Communication Patterns in Brain Networks via Adaptive Flow Routing](https://mlsanity.com/paper/2602.00561/) (2026)

## Abstract (arXiv)

Fusing structural-functional images of the brain has shown great potential to analyze the deterioration of Alzheimer's disease (AD). However, it is a big challenge to effectively fuse the correlated and complementary information from multimodal neuroimages. In this paper, a novel model termed cross-modal transformer generative adversarial network (CT-GAN) is proposed to effectively fuse the functional and structural information contained in functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). The CT-GAN can learn topological features and generate multimodal connectivity from multimodal imaging data in an efficient end-to-end manner. Moreover, the swapping bi-attention mechanism is designed to gradually align common features and effectively enhance the complementary features between modalities. By analyzing the generated connectivity features, the proposed model can identify AD-related brain connections. Evaluations on the public ADNI dataset show that the proposed CT-GAN can dramatically improve prediction performance and detect AD-related brain regions effectively. The proposed model also provides new insights for detecting AD-related abnormal neural circuits.