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, 18 F-flortaucipir has been reported to bind to neuromelanin, monoamine oxidase, calcifications, iron, leptomeningeal melanocytes, and microhemorrages. Although 18 F-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 5 139) received 11 C-Pittsburgh compound B (PIB) and 18 F-flortaucipir PET scans and a magnetizationprepared 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 5 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 11 C-PIB and 18 F-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 11 C-PIB (r 2 5 0) but persisted for 18 F-flortaucipir (r 2 5 0.27), demonstrating that the correlation between white and gray matter signal in 18 F-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 18 Fflortaucipir measurement could pose challenges when studying early tau accumulation. Across the Life SpanF-Flortaucipir Variability in Healthy Controls 18 Effect of Off-Target Binding on http://jnm.snmjournals.org/content/60/10/1444 This article and updated information are available at: http://jnm.snmjournals.org/site/subscriptions/online.xhtml Information about subscriptions to JNM can be found at: http://jnm.snmjournals.org/site/misc/permission.xhtml

Functional Brain Network Connectivity Patterns Associated With Normal Cognition at Old-Age, Local β-amyloid, Tau, and APOE4

“…Overlap was present in the superior frontal gyrus, the cerebellum and the inferolateral remainder of the parietal lobe. While our data needs to be interpreted with caution due to the low level of PET tracer retention in an Aβ− population, possible off-target tau signal (Baker et al, 2019) and the possibility of slightly different attenuation correction due to the use of different PET scanners, our data nevertheless may accord with recent fMRI studies that suggest distinct PCP functional connectivity networks associated with β-amyloid and tau (Sepulcre et al, 2017;Franzmeier et al, 2019).…”

Spatial patterns of tau deposition are associated with amyloid, ApoE, sex, and cognitive decline in older adults

“…In addition, not all individuals had the same number of longitudinal cognitive visits, something we dealt with using linear mixed effects models. Moreover, although we did our best to reduce partial volume effects by excluding regions known to have off-target binding in FTP data such as the basal ganglia, the choroid plexus, and the meninges [28][29][30], it is still possible that off-target binding could have added some noise to our analyses. Our data-driven approach based on independent component analyses has been previously described as a useful technique to separate signal from noise [54].…”

“…In addition, in order to produce reliable components, we used the ICASSO algorithm implemented in GIFT, which ran ICA 10 times before selecting stable components that corresponded to tight clusters. A brain mask that excluded the basal ganglia, brainstem, cerebellum, subcortical white matter, and cerebrospinal fluid (including the choroid plexus) was included in the analyses to minimize contamination from regions considered to represent off-target binding in FTP data [28][29][30]. After identifying the spatial patterns, we thresholded them with a Z score > 2.0, which corresponds to a two-tailed significance value of p < 0.05.…”

Hide-and-seek for radiotracers and neurodegenerative pathology

“…As astrocytic activation and associated MAO-B expression are more pronounced at early than at late stage, this does not completely exclude off-target in vivo binding to higher MAO-B expression at an earlier disease stage. Off-target binding of 18F-flortaucipir has previously been noted for iron and neuromelanin (8). Thus, accumulation in svPPA could possibly also be related increased uptake of iron due to blood-brain barrier (BBB) alterations in atrophic brain areas (9).…”