Profiling and predicting distinct tau progression patterns: An unsupervised data‐driven approach to flortaucipir positron emission tomography

Abstract

AbstractINTRODUCTIONHow to detect patterns of greater tau burden and accumulation is still an open question.METHODSAn unsupervised data‐driven whole‐brain pattern analysis of longitudinal tau positron emission tomography (PET) was used first to identify distinct tau accumulation profiles and then to build baseline models predictive of tau‐accumulation type.RESULTSThe data‐driven analysis of longitudinal flortaucipir PET from studies done by the Alzheimer's Disease Neuroimaging Initiative, Avid Pharmaceuticals, and Harvard Aging Brain Study (N = 348 cognitively unimpaired, N = 188 mild cognitive impairment, N = 77 dementia), yielded three distinct flortaucipir‐progression profiles: stable, moderate accumulator, and fast accumulator. Baseline flortaucipir levels, amyloid beta (Aβ) positivity, and clinical variables, identified moderate and fast accumulators with 81% and 95% positive predictive values, respectively. Screening for fast tau accumulation and Aβ positivity in early Alzheimer's disease, compared to Aβ positivity with variable tau progression profiles, required 46% to 77% lower sample size to achieve 80% power for 30% slowing of clinical decline.DISCUSSIONPredicting tau progression with baseline imaging and clinical markers could allow screening of high‐risk individuals most likely to benefit from a specific treatment regimen.