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Abstract #0573

Integrating Event-Based and Biophysical Models to Predict Individual Tau Progression in Alzheimer's Disease

Robin Sandell1, Justin Torok1, Daren Ma1, and Ashish Raj1
1Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States

Synopsis

Keywords: Alzheimer's Disease, Diffusion Modeling, Biophysical modeling, event-based modeling

Motivation: Current modeling approaches fail to capture individual longitudinal variation in tau protein neurofibrillary tangle spread in Alzheimer's Disease (AD).

Goal(s): Our goal was to develop a modeling approach capable of predicting tau’s origin as well as its future spread for individual subjects.

Approach: We paired event-based and biophysical modeling techniques: first statistically extracting longitudinal tau trajectories from cross-sectional data, then optimizing network diffusion models to fit each subject’s regional tau distributions.

Results: Our combined modeling approach achieved strong correlations with empirical baseline and longitudinal tau data across subjects, revealing previously unobserved patterns of inter-subject convergence over time as well as distinct seeding archetypes.

Impact: Our method can be applied to explore inter-subject heterogeneity of protein spread patterns across a range of neurodegenerative conditions, enabling the development of precision therapeutic treatments that target individuals’ unique pathology.

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