2025/11/06 by Natalia Glazman, Glazman, Natalia, Jyoti Mangal +7
Computer Science · Neuroscience · #Bayesian Modeling and Causal Inference #Machine Learning in Healthcare #Functional Brain Connectivity Studies
paper · pdf · doi:10.48550/arxiv.2511.04619
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.