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.

翻译：有效连接估计在理解不同脑区之间的相互作用和信息流动方面起着至关重要的作用。然而，用于估计有效连接的功能时间序列源自特定软件，由于参数设置不同可能导致较大的计算误差，并降低对脑区间复杂因果关系建模的能力。本文提出了一种采用分层Transformer的大脑扩散模型（BDHT），用于估计轻度认知障碍（MCI）分析中的有效连接。据我们所知，所提出的大脑扩散模型是首个将扩散模型应用于生成和分析多模态脑网络的生成模型。具体而言，BDHT利用结构连接以高效方式引导反向过程，这使得去噪过程更为可靠，并保证了有效连接估计的准确性。为提高去噪质量，设计了分层去噪Transformer以学习拓扑空间中的多尺度特征。通过堆叠多头注意力和图卷积网络，设计了图卷积Transformer（GraphConformer）模块，以增强结构-功能互补性并提升噪声估计能力。对去噪扩散模型的实验评估证明了其在估计有效连接方面的有效性。与现有方法相比，所提模型在准确性和鲁棒性方面均表现出优越性能。此外，该模型能够识别改变的方向性连接，并为MCI治疗的病理机制提供全面的理解。