An Unsupervised Data‐Driven Approach to Profiling Distinct Tau Progression Patterns

Given the central role that accumulation of tau pathology plays in the clinical progression of Alzheimer’s disease, the ability to quantify and predict the spread of tau is of great interest. Due to observed heterogeneity in patterns of tau deposition, though, stratification of individuals into homogenous phenotypes is critical to better understanding the natural course of this pathology progression, and ultimately the clinical disease progression. We propose an unsupervised data‐driven whole‐brain pattern analysis approach leveraging the spatially rich information provided by longitudinal tau‐PET, first to identify distinct tau accumulation profiles from longitudinal observation data and then to build baseline models predictive of tau accumulation type.