Dynamic causal discovery in Alzheimer's disease through latent pseudotime modelling

• URL: http://arxiv.org/abs/2511.04619v1
• Date: Thu, 06 Nov 2025 18:12:09 GMT
• Title: Dynamic causal discovery in Alzheimer's disease through latent pseudotime modelling
• Abstract summary: We propose to apply an existing latent variable model to real-world Alzheimer's data, inferring a pseudotime that orders patients along a data-driven disease trajectory independent of chronological age.<n>Our framework reveals dynamic interactions between novel markers and established AD markers, enabling practical causal discovery despite violated assumptions.
• Score: 1.1222071637855076
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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