用伪时间建模阿尔茨海默病演化，发现动态因果关系。
Dynamic causal discovery in Alzheimer's disease through latent pseudotime modelling
- 引入隐变量模型构建疾病伪时间，按病理进程排序患者
- 伪时间预测诊断准确率AUC达0.82，远超年龄的0.59
- 可识别新旧标志物间动态关联，适合研究进展性疾病
阿尔茨海默病（AD）的因果发现受限于多数方法的静态图假设，无法捕捉随疾病进展而变化的病理机制。本文将现有隐变量模型应用于真实AD数据，推断出一个独立于实际年龄、由数据驱动的疾病伪时间，进而学习因果关系的演变过程。伪时间在预测诊断上的表现优于年龄（AUC 0.82 vs 0.59）。引入少量无疾病特异性的先验知识显著提升了图结构准确性和方向判断。该框架揭示了新型标志物（NfL, GFAP）与经典标志物间的动态交互，实现了在违反静态假设条件下的实用因果发现。
原文摘要 · Abstract (English)
The application of causal discovery to diseases like Alzheimer's (AD) is limited by the static graph assumptions of most methods; such models cannot account for an evolving pathophysiology, modulated by a latent disease pseudotime. We propose to apply an existing latent variable model to real-world AD data, inferring a pseudotime that orders patients along a data-driven disease trajectory independent of chronological age, then learning how causal relationships evolve. Pseudotime outperformed age in predicting diagnosis (AUC 0.82 vs 0.59). Incorporating minimal, disease-agnostic background knowledge substantially improved graph accuracy and orientation. Our framework reveals dynamic interactions between novel (NfL, GFAP) and established AD markers, enabling practical causal discovery despite violated assumptions.
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