One stop shop: Flortaucipir PET differentiates amyloid positive and negative forms of neurodegenerative diseases.

Tau protein aggregations are a hallmark of pathology in the amyloid-associated Alzheimer's disease and some forms of non-amyloid-associated fronto-temporal lobar degeneration (FTLD). In recent years, several tracers for in-vivo tau imaging are under evaluation. This study investigates the ability of Flortaucipir PET to not only assess tau-positivity but in addition also to differentiate between amyloid-positive and -negative forms of neurodegeneration based on different Flortaucipir PET signatures. Methods: Flortaucipir PET data of 35 patients with amyloid-positive, 19 patients with amyloid-negative forms of neurodegeneration and 17 healthy controls were included in a data-driven scaled subprofile modelling/principal component analysis (SSM/PCA) identifying spatial covariance patterns. SSM/PCA component pattern expression strengths (PES) were tested for their ability to predict amyloid status in a receiver operating characteristic analysis and validated with a leave-one-out approach. Results: PES predicted amyloid status with a sensitivity of 0.94 and a specificity of 0.83. A support vector machine classification based on PES in two different SSM/PCA components yielded a prediction accuracy of 98%. Anatomically, prediction performance was driven by parietooccipital grey matter in amyloid-positive patients vs. predominant white matter binding in amyloid-negative neurodegeneration. Conclusion: SSM/PCA derived binding patterns of Flortaucipir differentiate between amyloid positive and negative neurodegenerative diseases with high accuracy. Flortaucipir PET alone may convey additional information equivalent to an amyloid PET. Together with a perfusion-weighted early-phase acquisition (FDG-PET equivalent), a single scan potentially contains comprehensive information on amyloid (A), tau (T) and neurodegeneration (N) status as required by recent biomarker classification algorithms (A/T/N).