Effective connectivity can describe the causal patterns among brain regions. These patterns have the potential to reveal the pathological mechanism and promote early diagnosis and effective drug development for cognitive disease. However, the current studies mainly focus on using empirical functional time series to calculate effective connections, which may not comprehensively capture the complex causal relationships between brain regions. In this paper, a novel Multi-resolution Spatiotemporal Enhanced Transformer Denoising (MSETD) network with an adversarially functional diffusion model is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment (MCI) analysis. To be specific, the denoising framework leverages a conditional diffusion process that progressively translates the noise and conditioning fMRI to effective connectivity in an end-to-end manner. To ensure reverse diffusion quality and diversity, the multi-resolution enhanced transformer generator is designed to extract local and global spatiotemporal features. Furthermore, a multi-scale diffusive transformer discriminator is devised to capture the temporal patterns at different scales for generation stability. Evaluations of the ADNI datasets demonstrate the feasibility and efficacy of the proposed model. The proposed model not only achieves superior prediction performance compared with other competing methods but also identifies MCI-related causal connections that are consistent with clinical studies.

翻译：有效连接能够描述大脑区域间的因果模式，这些模式有潜力揭示认知疾病的病理机制，促进早期诊断及有效药物研发。然而，当前研究主要依赖经验性功能时间序列计算有效连接，可能无法全面捕捉脑区之间复杂的因果关联。本文提出一种结合对抗性功能扩散模型的新型多分辨率时空增强Transformer去噪（MSETD）网络，将功能磁共振成像（fMRI）映射为轻度认知障碍（MCI）分析中的有效连接。具体而言，该去噪框架利用条件扩散过程，以端到端方式逐步将噪声与条件fMRI转化为有效连接。为确保反向扩散质量与多样性，设计了多分辨率增强Transformer生成器以提取局部与全局时空特征；同时，构建多尺度扩散Transformer判别器，捕捉不同尺度的时间模式以保障生成稳定性。在ADNI数据集上的评估验证了所提模型的可行性与有效性。该模型不仅实现了优于对比方法的预测性能，还识别出与临床研究一致的MCI相关因果连接。