# BDHT: Generative AI Enables Causality Analysis for Mild Cognitive Impairment

arXiv: https://arxiv.org/abs/2312.09022 · HTML: https://mlsanity.com/paper/2312.09022/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 10 of its 60 references that are in the map.
- First posted on arXiv 14 Dec 2023.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 10 of 10)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal](https://mlsanity.com/paper/2212.04711/) (2022)
- [Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis](https://mlsanity.com/paper/2107.09953/) (2021)
- [3D Brain Reconstruction by Hierarchical Shape-Perception Network from a Single Incomplete Image](https://mlsanity.com/paper/2107.11010/) (2021)
- [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)
- [Brain Stroke Lesion Segmentation Using Consistent Perception Generative Adversarial Network](https://mlsanity.com/paper/2008.13109/) (2020)
- [A Structure-guided Effective and Temporal-lag Connectivity Network for Revealing Brain Disorder Mechanisms](https://mlsanity.com/paper/2212.00555/) (2022)

## Built on by (most cited first, 3 of 3)

- [High-Fidelity Functional Ultrasound Reconstruction via A Visual Auto-Regressive Framework](https://mlsanity.com/paper/2505.21530/) (2025)
- [ConnectomeDiffuser: Generative AI Enables Brain Network Construction from Diffusion Tensor Imaging](https://mlsanity.com/paper/2505.22683/) (2025)
- [Brain Network Analysis Based on Fine-tuned Self-supervised Model for Brain Disease Diagnosis](https://mlsanity.com/paper/2506.11671/) (2025)

## Abstract (arXiv)

Effective connectivity estimation plays a crucial role in understanding the interactions and information flow between different brain regions. However, the functional time series used for estimating effective connectivity is derived from certain software, which may lead to large computing errors because of different parameter settings and degrade the ability to model complex causal relationships between brain regions. In this paper, a brain diffuser with hierarchical transformer (BDHT) is proposed to estimate effective connectivity for mild cognitive impairment (MCI) analysis. To our best knowledge, the proposed brain diffuser is the first generative model to apply diffusion models to the application of generating and analyzing multimodal brain networks. Specifically, the BDHT leverages structural connectivity to guide the reverse processes in an efficient way. It makes the denoising process more reliable and guarantees effective connectivity estimation accuracy. To improve denoising quality, the hierarchical denoising transformer is designed to learn multi-scale features in topological space. By stacking the multi-head attention and graph convolutional network, the graph convolutional transformer (GraphConformer) module is devised to enhance structure-function complementarity and improve the ability in noise estimation. Experimental evaluations of the denoising diffusion model demonstrate its effectiveness in estimating effective connectivity. The proposed model achieves superior performance in terms of accuracy and robustness compared to existing approaches. Moreover, the proposed model can identify altered directional connections and provide a comprehensive understanding of parthenogenesis for MCI treatment.