用潜时间嵌入构建阿尔茨海默病进展的阶段感知因果模型
Bayesian Networks with Latent Time Embedding for Stage-Aware Causal Modeling of Alzheimer's Disease Progression
- 基于生物可解释的AT(N)顺序约束，用贝叶斯网络建模疾病伪时间
- 在ADNI数据上实现比基线模型更优的τ-PET区域进展重建
- 发现中段伪时间存在淀粉样蛋白敏感窗口，适合神经影像研究者
阿尔茨海默病（AD）进展常被描述为淀粉样蛋白-τ蛋白-神经退行性变（AT(N)）级联。然而，多数纵向模型将此级联视为固定序列或黑箱预测任务，难以判断生物引导的生物标志物关系何时影响未来区域病理。本研究提出贝叶斯网络与潜时间嵌入（BN-LTE），一种阶段感知的AD进展结构建模框架。BN-LTE从基线多模态生物标志物估计疾病伪时间，并依据生物学合理的AT(N)排序约束有向依赖关系。随后，通过后验样条可变结构方程，将初始多模态测量与未来年度区域τ-PET变化关联。在使用ADNI数据进行多次无重叠受试者评估中，BN-LTE在τ进展的空间重构上优于所包含的预测基线。除空间重构外，BN-LTE还恢复了后验阶段可变的AT(N)约束效应，并识别出一个中段伪时间淀粉样蛋白敏感窗口。该窗口得到模型隐含g公式对比、根调整AIPW、机制敏感消融实验及样条和先验设定下的稳健性分析支持。总体而言，这些发现使BN-LTE成为一种可用于预测τ进展并检验观察性纵向神经影像数据中阶段依赖的AT(N)级联机制的贝叶斯结构框架。代码已开源：https://github.com/danleneurocom/BN-LTE。
原文摘要 · Abstract (English)
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.
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