Alzheimer's disease (AD) progression is often described through the amyloid-tau-neurodegeneration, or AT(N), cascade. However, most longitudinal models represent this cascade either as a fixed sequence of biomarkers or as a black-box forecasting task. This makes it difficult to determine when biologically guided biomarker relationships influence future regional pathology. In this study, we introduce Bayesian Networks with Latent Time Embedding (BN-LTE), a Bayesian structural framework for stage-aware modeling of AD progression. BN-LTE estimates disease pseudotime from baseline biomarker profiles and constrains directed dependencies according to biologically plausible AT(N) ordering. Posterior spline-varying structural equations are then used to link initial multimodal measurements with future annualized regional tau-PET change. Across repeated subject-disjoint evaluations using ADNI data, BN-LTE shows strong spatial reconstruction of tau progression compared with the included forecasting baselines. Beyond spatial reconstruction, BN-LTE recovers posterior stage-varying AT(N)-constrained effects and identifies a mid-pseudotime window of amyloid sensitivity. This window is supported by model-implied g-formula contrasts, root-adjusted AIPW, mechanism-sensitive ablations, and robustness analyses across spline and prior specifications. Overall, these findings position BN-LTE as a Bayesian structural framework for forecasting tau progression while examining stage-dependent AT(N)-cascade mechanisms in observational longitudinal neuroimaging data. Our code is available at https://github.com/danleneurocom/BN-LTE.

翻译：阿尔茨海默病通常通过淀粉样蛋白-τ-神经退行性变（AT(N)）级联来描述其进展过程。然而，大多数纵向模型要么将该级联表示为固定的生物标志物序列，要么视为一个黑箱预测任务，这使得难以确定生物标志物关系在何时会以生物学引导的方式影响未来的区域病理学。在本研究中，我们提出带有潜在时间嵌入的贝叶斯网络（BN-LTE），这是一种用于阿尔茨海默病阶段感知建模的贝叶斯结构框架。BN-LTE从基线生物标志物分布中估计疾病伪时间，并根据生物学合理的AT(N)顺序约束有向依赖关系。随后，利用后验样条变结构方程将初始多模态测量与未来年度区域tau-PET变化联系起来。在基于ADNI数据的多次非重叠受试者评估中，与纳入的预测基线模型相比，BN-LTE在tau蛋白扩散的空间重建方面表现出色。除空间重建外，BN-LTE恢复了后验阶段变化的AT(N)约束效应，并识别出一个淀粉样蛋白敏感性中伪时间窗口。该窗口得到模型隐含的g-formula对比、根调整AIPW、机制敏感性消融实验以及跨样条和先验设定的鲁棒性分析的支撑。总体而言，这些发现将BN-LTE定位为一个贝叶斯结构框架，可在观测性纵向神经影像数据中预测tau扩散的同时，检验阶段依赖的AT(N)级联机制。我们的代码已在https://github.com/danleneurocom/BN-LTE公开。