BDHT: Generative AI Enables Causality Analysis for Mild Cognitive Impairment

作者：Qiankun Zuo, Ling Chen, Yanyan Shen, Michael K. Ng, Baiying Lei, Shuqiang Wang · 发表于：IEEE Transactions on Automation Science and Engineering · 年份：2024 · DOI：10.1109/tase.2024.3425949 · 被引用次数：16 · 研究领域：Explainable Artificial Intelligence (XAI)、Health, Environment, Cognitive Aging、Artificial Intelligence in Healthcare and Education

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 sup...