Unsupervised estimation of spatiotemporal atrophy progression patterns in autopsy-confirmed 4-repeat tauopathies

Motivation: To improve understanding of disease progression in four-repeat tauopathies and determine the value of MRI to predict specific pathologies. Goal(s): To estimate spatiotemporal atrophy progression patterns from 3D structural MRI and to examine the relationship between the atrophy patterns and pathological diagnosis in four repeat tauopathies. Approach: We applied an unsupervised machine learning algorithm called Subtype and Stage Inference (SuStaIn) to 3D structural MRI images in autopsy-confirmed four-repeat tauopathies. Results: The estimated subtype correlated well with the pathological diagnosis, and the estimated stage was negatively correlated with time from MRI to death. Impact: We identified two MRI atrophy subtypes with different patterns of progression that correlated to pathology in autopsy-confirmed four-repeat tauopathies. This improves understanding of how these pathologies spread through the brain and suggests that MRI could help predict pathology during life.