Oh, Hwamee

Abstract Background The overlap index (OI), previously introduced by Lee et al. (2022), has proven to be a reliable method for detecting tau accumulation via tau‐PET imaging with flortaucipir ([ 18 F]AV‐1451). This technique identifies voxel‐wise increases in standardized uptake value ratio (SUVr) across serial scans. However, the relationship between tau‐PET measurements obtained with [ 18 F]MK‐6240 and [ 18 F]AV‐1451 using the OI remains unclear. This study aims to investigate the relationship between [ 18 F]MK‐6240 and [ 18 F]AV‐1451 tau‐PET using the tau‐PET OI. Method The study included 27 participants from the HEAD project, all of whom underwent two serial tau‐PET scans (each [ 18 F]MK‐6240 and [ 18 F]AV‐1451) along with 3T T1‐weighted MRI, acquired within an average interval of 18 months. SUVr maps for each tau‐PET tracer were normalized to the cerebellar crus grey matter, and MR images were co‐registered to the MCALT T1 template. Tau‐PET images were spatially resampled to the template space, and OI was computed within a predefined META‐ROI using an intensity threshold of 1.3 and a cutoff of 0.5. Clusters containing fewer than 20 contiguous voxels were excluded from analysis. The META‐ROI SUVr was calculated as the average SUVr across the selected region's SUVr. Additionally, a visual comparison was performed to assess the spatial overlap of OI‐identified voxels between the two tau‐PET tracers. Result OI values within the META‐ROI exhibited a broad distribution between [ 18 F]MK‐6240 and [ 18 F]AV‐1451 tau‐PET, particularly in cognitively unimpaired (CU) and younger individuals (Figure 1A). Unlike the OI distribution, the relationship between META‐ROI SUVr values of both tracers followed an exponential trend (Figure 1B). Visual inspection revealed that OI‐detected voxels in [ 18 F]MK‐6240 tau‐PET were frequently localized to the meninges, an established site of off‐target binding, which was not observed in [ 18 F]AV‐1451 tau‐PET (Figure 2). Conclusion The association between [ 18 F]MK‐6240 and [ 18 F]AV‐1451 tau‐PET, as assessed using the OI, exhibited considerable variability in CU and younger participants. Additionally, the presence of overlapping voxels in the meninges in [ 18 F]MK‐6240 tau‐PET highlights the need to account for and potentially exclude off‐target binding effects in these populations. The influence of meningeal signal and other off‐target binding on the OI in the temporal META‐ROI needs to be further evaluated.

Abstract Background Tau‐PET tracers are essential for visualizing pathology in Alzheimer's (AD). High detectability to tau is crucial for early detection and monitoring of tau deposition. This study compares the noise to dynamic range ratio (NRR) of [ 18 F]FTP, [ 18 F]MK6240, [ 18 F]PI2620, and [ 18 F]RO948, cross‐sectionally and longitudinally. Methods 460 individuals from the HEAD study (23 cognitively unimpaired (CU) young, 249 CU old and 188 cognitively impaired) underwent [ 18 F]FTP and [ 18 F]MK6240 tau‐PET scans; 94 additionally received [ 18 F]PI2620 and [ 18 F]RO948. A subset of 28 individuals (15 CU and 13 CI) underwent [ 18 F]FTP and [ 18 F]MK6240 follow‐up scans (1.5 ± 0.1 years later). Annual change was measured as [(followup‐baseline)/time between scans]. Noise was calculated as the standard deviation (SD) of CU Aβ‐ participants aged ≤65 (SD CUAβ‐≤65 ). The dynamic range was calculated as the SD across all subjects (SD range ). A lower the NRR (=SD CUAβ‐≤65 /SD range ) indicates more detectability. Analyses were performed at both region‐of‐interest and voxel‐wise levels. Results Across the whole cohort, [ 18 F]MK6240 exhibited lower NRR than [ 18 F]FTP in all regions. Voxel‐wise, differences were most pronounced in the frontal medial temporal regions (Figure 1). Within the four‐tracers subset, [ 18 F]PI2620 and [ 18 F]FTP showed the highest NRR in Braak II, followed by [ 18 F]RO948 and [ 18 F]MK6240. In Braak IV‐VI, [ 18 F]MK6240 consistently demonstrated the lowest values, followed by [ 18 F]PI2620, [ 18 F]FTP and [ 18 F]RO948. Similarly, in metatemporal‐ROI and Braak III, [ 18 F]MK6240 remained the lowest, however followed by [ 18 F]FTP, [ 18 F]PI2620 and [ 18 F]RO948 (Figure 2). Lower [ 18 F]MK6240 NRR was due to larger SD range , while [ 18 F]FTP's lower values stemmed from smaller SD CUAβ‐≤65 . Conversely, high [ 18 F]PI2620 and [ 18 F]RO948 values are driven by larger SD CUAβ‐≤65 . Longitudinal analyses further confirmed that [ 18 F]MK6240 exhibited the lowest NRR across the entire brain, including AD‐related regions, with voxel‐wise differences mainly in the temporal and parietal lobes. Conclusion Tau‐PET tracers exhibit significant variability in detectability. [ 18 F]MK6240 consistently demonstrated lower NRR, implying better detectability across all regions, cross‐sectionally and longitudinally. Despite differences in NRR, [ 18 F]FTP, [ 18 F]RO948 and [ 18 F]PI2620 followed similar pattern. These results provide insights into the differential tracer detectability, helping guide their optimal use in detecting and tracking tau pathology. Ongoing follow‐up scans will further clarify longitudinal tracer detectability and its implications for tau progression in AD.

Abstract Background Precise cross‐tracer harmonization of tau PET imaging is essential for comparing tau burden across different tracers and studies. Here, we evaluated the performance of the universal tau PET scale (Uniτ) in harmonizing Flortaucipir, MK6240, PI2620, and RO948 tau PET images into a universal scale. Method We assessed 485 individuals across aging and AD spectrum scanned head‐to‐head with Flortaucipir and MK6240, and a subset of 90 individuals with additional PI2620 and RO948. We generated tau PET SUVRs using the inferior cerebellar gray matter as reference with a common 8mm FWHM. We estimated Uniτ parameters on a training set ( n = 200) by fitting a smoothed hyperbolic tangent equation to the Meta‐Temporal ROI anchored in the mean SUVR of young participants and the 90th percentile from cognitively impaired individuals. We compared tau positivity on the Uniτ scale with classifications from SUVRs (mean + 3 SD from Youngs). We applied the same equation to all brain voxels to generate Uniτ 3D images. Then, we extracted mean Uniτ values for key ROIs and correlated them with ROI‐based Uniτ values to evaluate voxel‐wise estimates. Result Using tracer‐specific parameters, Uniτ harmonized Flortaucipir, MK6240, PI2620, and RO948 to the same scale, aligning values near the identity line and confirming its applicability across tau PET tracers (Figure 1). For Flortaucipir, Uniτ tau positivity matched original SUVR classifications (ground truth), with only one mismatch for MK6240 (Figure 1). Applying the Uniτ transformation to all brain voxels effectively harmonized 3D images to a common scale, reducing visual variabilities (Figure 2). Furthermore, ROI‐based estimates and those extracted from the 3D Uniτ images were identical (Figure 3). Conclusion Our results indicate that tau PET tracers can be harmonized to a common scale using a large head‐to‐head dataset. The Uniτ scale can harmonize entire 3D tau PET images, suggesting that there is no need to use pre‐established ROIs, which would constrain the analysis to a few brain regions. Uniτ is freely accessible across platforms ( www.unitau.app ) for ROI and 3D tau PET harmonization.

Abstract Background Tau PET measures are inherently continuous and applying dichotomized thresholds introduces conceptual and analytical idiosyncrasies. Understanding the limitations in the transition from tau‐negative to tau‐positive classifications is crucial for the effective use of these thresholds. This study aims to determine the confidence levels of tau PET thresholds of abnormality for different tau PET tracers by characterizing their “gray zone” using the universal tau PET scale (Uniτ, www.unitau.app ). Method We evaluated 485 individuals across the aging and AD spectrum from the HEAD study, with head‐to‐head scans for MK6240 and Flortaucipir. Uniτ estimates were derived from the Meta‐Temporal ROI. Tau positivity (T+) was defined as Uniτ values exceeding the mean plus 3 SD of individuals younger than 28 years ( n = 24). Two physicians independently performed a visual assessment of tau positivity for each tracer, with agreement indicating clear tau pathology (TVR+). Logistic regression was used to estimate the probability of TVR+ across Uniτ values for each tracer ( n = 189, CU elderly Aβ‐ and CI Aβ+). Individuals were classified as negative, positive, or within a “gray zone” between the most liberal T+ threshold and varying TVR+ probability thresholds (50%, 75%, 90%, 95%, and 99%). Result The Uniτ gray zone, defined as a function of TVR+ probabilities, demonstrated consistency between the two tracers, with differences observed only in the decimal range. The most liberal Uniτ threshold for T+ was 11.0 for both MK6240 and Flortaucipir, closely matching the 50% TVR+ probability threshold (11.1 for MK6240 and 11.7 for Flortaucipir). Higher TVR+ probabilities corresponded to increased Uniτ values with MK6240 and Flortaucipir showing near‐identical values between the tracers: 14.2 and 14.8 for 75%, 17.3 and 17.9 for 90%, 19.4 and 19.9 for 95%, and 24.1 and 24.6 for 99%, respectively (Figure 1). In total, 26 participants fell within the gray zone for MK6240, compared to 47 participants for Flortaucipir. Conclusion These findings highlight the potential of Uniτ to provide a standardized approach for assessing tau positivity across tracers. By combining quantitative Uniτ measures with visual assessments, we enhance the understanding of tau positivity certainty, particularly in the transition between negative and positive classifications.

Abstract Background Studies suggest that sex influences the deposition of tau in the human brain and impacts on the relationship of tau with AD‐related outcomes. The aim of this study is to evaluate the extent to which sex influences PET‐Tau related outcomes and whether this effect is related to specific tau PET tracers. Method We assessed 456 participants from the HEAD study (mean age = 66.07 ± 13.05). All individuals had available Tau‐PET with Flortaucipir and MK6240, Aβ‐PET and a subset had plasma p ‐Tau 217 ( n = 352). We conducted a linear regression analysis between sex and PET Tau to evaluate the direct influence of sex on PET Tau SUVR. Next, we assessed whether sex affected the relationship between Tau PET (in BRAAK I‐II, III‐IV, V‐VI regions) and Aβ‐PET or plasma p ‐Tau 217 by adding an interaction term in the associations. All analyses were corrected by age, clinical classification, and amyloid burden. Result No significant differences were observed between male and female SUVR levels in either Flortaucipir and MK6240 tracers (Figure 1). However, when assessing the relationship between Aβ‐PET and Tau, a statistically significant interaction with sex was seen between MK6240 in BRAAK III‐IV (β = 0.160, p = 0.020 ; Figure 2) and BRAAK V‐VI regions (β = 0.196, p = 0.008; Figure 2), with women having a stronger association. Furthermore, we found an interaction between sex and p ‐tau217 on Tau PET SUVR in BRAAK regions I‐II using MK6240 or Flortaucipir (β = ‐0.184, p = 0.014; β = ‐0.266, p = 0.002; Figure 3) indicating that women present a weaker relationship between p ‐tau217 and Tau PET. Conclusion Our results did not show any differences between men and women in Tau‐PET SUVR uptake across tau tracers. However, we found that sex affected how Aβ‐PET and plasma p ‐tau217 were associated with Tau‐PET uptake. Further studies are needed to elucidate the underpinnings of the link between sex and the association between these biomarkers.

Abstract Background The 2024 NIA‐AA criteria propose an Alzheimer's disease (AD) staging system based on medial temporal (MT) and neocortical tau burden. However, the optimal strategy to assess tau burden in these regions, which represent distinct phases of AD pathophysiology, remains unclear. Here, we compared tau load and spatial extent of tauopathy (SEOT) as markers of tau burden in participants scanned with [ 18 F]MK6240 and [ 18 F]FTP. We investigated the relationship between these metrics in MT and neocortical regions‐of‐interest (ROIs) and determined which better correlates with AD severity. Method A total of 257 cognitively unimpaired (CU) and 124 Aβ+ cognitively impaired participants (mean age 68.2 years; 58% female) from the HEAD study underwent tau‐PET imaging with both [ 18 F]MK6240 and [ 18 F]FTP. Tau load was quantified using regional SUVR, and SEOT was calculated as the proportion of abnormal voxels relative to young controls. Both metrics were derived from the MT and neocortical ROIs. Spearman's correlations were computed between SUVR and SEOT within the same tracer and across tracers in the same ROIs. We compared rho coefficients to assess whether one metric showed stronger concordance. Additionally, we compared the correlations of SEOT and SUVR with global cognition, cortical thickness, Aβ‐PET load, and plasma pTau‐217. Statistical comparisons were performed using the R package cocor . Result SUVR and SEOT exhibited non‐linear correlations in both MT and neocortical ROIs for both tracers, with SEOT enhancing the correlation between [ 18 F]MK6240 and [ 18 F]FTP in the neocortex ( p <.0001; Figure 1). Both metrics increased with AD severity, with MT SUVR outperforming SEOT in distinguishing Aβ‐ CU from Aβ+ CU individuals, and neocortical SEOT outperforming SUVR in distinguishing Aβ+ MCI from CU (Figure 2). Neocortical SEOT showed stronger correlations with AD severity markers than SUVR for both tracers. MT SEOT was comparable to SUVR, outperforming SUVR only in correlations with MMSE and Aβ‐PET for [ 18 F]FTP (Figure 3). Conclusion Tau extent improves the association of tau‐PET tracers with AD severity, particularly in the neocortex. These findings suggest that tau extent might be a more suitable surrogate marker of tau burden in neocortical regions, offering valuable insights for in vivo staging of tau pathology and clinical trials targeting late‐stage tau pathology.