Dynamic causal discovery in Alzheimer's disease through latent pseudotime modelling
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915602743951360 |
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| author | Glazman, Natalia Mangal, Jyoti Borges, Pedro Ourselin, Sebastien Cardoso, M. Jorge |
| author_facet | Glazman, Natalia Mangal, Jyoti Borges, Pedro Ourselin, Sebastien Cardoso, M. Jorge |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04619 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic causal discovery in Alzheimer's disease through latent pseudotime modelling Glazman, Natalia Mangal, Jyoti Borges, Pedro Ourselin, Sebastien Cardoso, M. Jorge Applications Computational Engineering, Finance, and Science Machine Learning 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. |
| title | Dynamic causal discovery in Alzheimer's disease through latent pseudotime modelling |
| topic | Applications Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2511.04619 |