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arXiv · 2511.04619

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

Natalia Glazman·Jyoti Mangal·Pedro Borges·Sebastien Ourselin·M. Jorge Cardoso
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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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Natalia Glazman, Jyoti Mangal, Pedro Borges, Sebastien Ourselin, M. Jorge Cardoso. 2025-11-06. Dynamic causal discovery in Alzheimer's disease through latent pseudotime modelling. https://arxiv.org/abs/2511.04619

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