TauIQ - A canonical image based algorithm to quantify tau PET scans

Abstract

Recently, AmyloidIQ was introduced as a new canonical image based algorithm to quantify amyloid PET scans and demonstrated increased power over traditional SUVR approaches when assessed in cross-sectional and longitudinal analyses. We build further on this mathematical framework to develop a TauIQ algorithm for the quantitative analysis of the more complex spatial distribution displayed by Tau PET radiotracers. Methods: Cross-sectional (N = 615) and Longitudinal (N = 149) [¹⁸F]Flortaucipir data were obtained from ADNI along with necessary adjunct amyloid PET and T1 structural MRI data. A subset of these data were used to derive a chronological tau data set, using AmyloidIQ analysis of associated amyloid PET data to calculate the subjects temporal position in the canonical AD disease process, from which canonical images for the non-specific and specific binding components of [¹⁸F]Flortaucipir in AD were calculated. These two canonical images were incorporated into the TauIQ algorithm that enables the quantification of both global and local tau outcome measures using an imaged based regression and statistical parametric analysis of the initial residual image. Performance of the TauIQ algorithm was compared with SUVR approaches for cross-sectional analyses, longitudinal analyses and correlation with clinical measures (ADAS-Cog, CDR-SB, MMSE). Results: TauIQ successfully calculated global tau load (TauL) in all 791 scans analysed (range: [-3.5%,185.2%], m±sd: 23%±20.5%) with a non-zero additional local tau component being required in 31% of all scans (CN = 22%, MCI=35%, Dementia=72%). TauIQ was compared to the best SUVR approach in the cross-sectional analysis (TauL increase in effect size: CN-vsCN+ [+45%], CN-vsMCI+ [-5.6%], CN-vsDementia+ [+2.3%]) and correlation with clinical scores (TauL increase in r2: CDR-SB +7%, MMSE +38%, ADAS-Cog + 0%). TauIQ substantially outperformed SUVR approaches in the longitudinal analysis (TauIQ increase in power: CN+ > 3.2-fold, MCI+ > 2.2-fold, Dementia+ > 2.9-fold). Conclusion: TauL as calculated by TauIQ provides a superior approach for the quantification of tau PET data. In particular, it provides a substantial improvement in power for longitudinal analyses and the early detection of tau deposition and thus it should have significant value for clinical imaging trials in AD that are investigating the attenuation of tau deposition with novel therapies.