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.

翻译：有效连接估计在理解不同脑区之间的交互与信息流中起着关键作用。然而，用于估计有效连接的功能时间序列通常源自特定软件，因参数设置差异可能导致较大计算误差，进而削弱对脑区间复杂因果关系的建模能力。本文提出一种基于分层Transformer的脑扩散模型（BDHT），用于轻度认知障碍（MCI）分析中的有效连接估计。据我们所知，所提脑扩散模型是首个将扩散模型应用于多模态脑网络生成与分析任务的生成式模型。具体而言，BDHT利用结构连接高效指导逆向过程，使去噪过程更为可靠，并确保有效连接估计的准确性。为提升去噪质量，我们设计了分层去噪Transformer以学习拓扑空间中的多尺度特征。此外，GraphConFormer模块可同时关注全局与邻接连接信息。通过堆叠多头注意力机制与图卷积网络，该模型增强了结构-功能互补性并提升了噪声估计能力。对去噪扩散模型的实验评估证明了其在有效连接估计中的有效性。与现有方法相比，该方法在准确性与鲁棒性方面均表现优异，能够同时捕获脑区间的单向与双向交互，为理解大脑信息处理机制提供全面视角。