Dynamic causal discovery in Alzheimer's disease through latent pseudotime modelling
This work addresses the challenge of understanding evolving pathophysiology in Alzheimer's disease for medical researchers, though it is incremental as it applies an existing method to new data.
The researchers tackled the problem of causal discovery in Alzheimer's disease by modeling dynamic causal relationships using a latent pseudotime, which outperformed chronological age in predicting diagnosis with an AUC of 0.82 versus 0.59.
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.