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 connentivity 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 diffuer is the first generative model to apply diffusion models
in the application of generating and analyzing multimodal brain networks.
Specifically, the BDHT leverages the 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. Furthermore, the GraphConFormer
block can concentrate on both global and adjacent connectivity information. By
stacking the multi-head attention and graph convolutional network, the proposed
model enhances structure-function complementarity and improves the ability in
noise estimation. Experimental evaluations of the denoising diffusion model
demonstrate its effectiveness in estimating effective connectivity. The method
achieves superior performance in terms of accuracy and robustness compared to
existing approaches. It can captures both unidirectal and bidirectional
interactions between brain regions, providing a comprehensive understanding of
the brain's information processing mechanisms.

提出了一种脑扩散器，利用层次化变换器来估计轻度认知障碍（MCI）分析的有效连接；该模型通过堆叠多头注意力和图卷积网络来增强结构 - 功能互补性并提高噪声估计能力，并有效地捕捉大脑区域之间的单向和双向相互作用。