Effect of Off-Target Binding on 18F-Flortaucipir Variability in Healthy Controls Across the Life Span

Abstract

Measuring early tau accumulation is important in studying aging and Alzheimer disease and is only as accurate as the signal-to-noise ratio of the tracer. Along with aggregated tau in the form of neurofibrillary tangles, ¹⁸F-flortaucipir has been reported to bind to neuromelanin, monoamine oxidase, calcifications, iron, leptomeningeal melanocytes, and microhemorrages. Although ¹⁸F-flortaucipir successfully differentiates healthy controls (HCs) from subjects with Alzheimer disease, variability exists in the cortical signal in amyloid-negative HCs. We aimed to explore the relationship between off-target binding signal and variability in the cortical signal in HCs. Methods: Subjects (n = 139) received ¹¹C-Pittsburgh compound B (PIB) and ¹⁸F-flortaucipir PET scans and a magnetization-prepared rapid gradient echo MRI scan. PET frames were realigned and coregistered to the MR images, which were segmented using FreeSurfer. In amyloid-negative HCs (n = 90; age range, 21-94 y), 7 nonspecific or off-target binding regions were considered: caudate, pallidum, putamen, thalamus, cerebellar white matter, hemispheric white matter, and choroid plexus. These regions of interest were assigned to 3 similarly behaving groups using principle components analysis, exploratory factor analysis, and Pearson correlations for caudate, putamen, and pallidum (also correlated with age); thalamus and white matter; and choroid plexus. In amyloid-negative HCs with ¹¹C-PIB and ¹⁸F-flortaucipir scans, correlations were calculated between white and gray matter before and after partial-volume correction. Results: The correlation between white and gray matter disappeared after partial-volume correction in ¹¹C-PIB (r² = 0) but persisted for ¹⁸F-flortaucipir (r² = 0.27), demonstrating that the correlation between white and gray matter signal in ¹⁸F-flortaucipir is not solely due to partial-volume effects. A linear regression showed that off-target signal from putamen and thalamus together explained 64% of the variability in the cortical signal in amyloid-negative HCs (not seen in amyloid-positive HCs). Variability in amyloid-negative HCs but not amyloid-positive HCs correlated with white matter signal (unrelated to partial-volume effects) and age-related off-target signal (possibly related to iron load). Conclusion: The noise in the ¹⁸F-flortaucipir measurement could pose challenges when studying early tau accumulation.

Keywords: 18F-flortaucipir PET; off-target binding; tau.

FIGURE 1.

High variability in HC_Aβ−. (A) ¹⁸F-flortaucipir SUVR images in Montreal Neurological Institute space in order of age. Start of each age decade is marked. (B) Age vs. non-PVC whole-cortex SUVR (r² = 0.06, P < 0.05). (C) Age vs. PVC whole-cortex SUVR (r² = 0.18, P < 0.001).

FIGURE 2.

Comparison between HC_Aβ− and HC_Aβ+ in Braak and OFF ROIs with and without PVC. Central mark indicates median. Top and bottom of box indicate 75th and 25th percentiles, respectively. Whisker extent indicates median ± 1.57(75th percentile – 25th percentile)/square root(number subjects). C represents subjects beyond whisker extent. Significance of P < 0.005 (*) and P < 0.001 (**) denoted for η_p².

FIGURE 3.

Pearson r² between ¹⁸F-flortaucipir SUVR in cortical regions, age, and OFF ROIs. P values were Bonferroni-corrected.

FIGURE 4.

Correlations between HemiW and whole-cortex SUVRs: non-PVC ¹⁸F-flortaucipir (A), non-PVC ¹¹C-PIB (B), PVC ¹⁸F-flortaucipir (C), and PVC ¹¹C-PIB (D). Gray line = linear model fit; dashed gray lines = 95% confidence interval. **P < 0.001.