A Novel Events-Based Model for Mapping Disease Progression & Its Application to Familial Alzheimer's Disease

Hubert Martinus Fonteijn¹, Matt J. Clarkson², Marc Modat¹, Josephine Barnes², Manja Lehmann², Sebastien Ourselin¹, Nick C. Fox², Daniel C. Alexander¹

This abstract introduces a novel method for studying disease progression using cross-sectional data. The model describes disease progression as a series of events and treats each data point as a snapshot of this series. We calculate the probability that an event has happened and use a MCMC algorithm to construct plausible series of events from this probability. We demonstrate our model on serial T1 MRI data from a familial Alzheimers disease cohort. We calculate regional atrophy using non-linear registration methods and show progression of atrophy on a much finer level than previous studies, confirming progression patterns from pathological studies.