{"ID":2845460,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.04619","arxiv_id":"2511.04619","title":"Dynamic causal discovery in Alzheimer's disease through latent pseudotime modelling","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.","short_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...","url_abs":"https://arxiv.org/abs/2511.04619","url_pdf":"https://arxiv.org/pdf/2511.04619v1","authors":"[\"Natalia Glazman\",\"Jyoti Mangal\",\"Pedro Borges\",\"Sebastien Ourselin\",\"M. Jorge Cardoso\"]","published":"2025-11-06T18:12:09Z","proceeding":"stat.AP","tasks":"[\"stat.AP\",\"cs.CE\",\"cs.LG\"]","methods":"[]","has_code":false}