Bellaver, Bruna

Abstract Background Tau‐PET tracers have been used to monitor the progression of Alzheimer's disease (AD). However, different tracers present distinct patterns of binding throughout the brain, challenging the harmonization of their findings. Leveraging the HEAD Study, the largest head‐to‐head study of tau‐PET tracers, we recently developed the Uniτ scale, which cross‐sectionally harmonizes Flortaucipir and MK6240 onto a universal tau‐PET measurement. Here, we provide a preliminary evaluation of the Uniτ scale's longitudinal performance in HEAD and two independent cohorts. Methods We assessed 422 individuals across the AD spectrum with longitudinal tau‐PET from three cohorts: HEAD [13 cognitively unimpaired (CU), 9 cognitively impaired (CI) individuals, scanned head‐to‐head with Flortaucipir and MK6240], ADNI [74 CU, 208 CI, tracer: Flortaucipir], and TRIAD [72 CU, 46 CI, tracer: MK6240]. Standardized uptake ratios (SUVRs) were harmonized to Uniτ using the Uniτ Ecosystem ( unitau.app ). Braak I‐II and the Meta‐Temporal regions were used as regions of interest. Annual tau‐PET uptake change was calculated, and the effect size was defined as the mean annual change divided by its standard deviation. Results In HEAD, annual change in tau‐PET uptake in Braak I‐II across CU and CI was only detectable in MK6240 (Figure 1A). However, both tracers detected significant annual changes in the Meta‐Temporal ROI for CI (Figure 1B). In both ADNI (Flortaucipir) and TRIAD (MK6240), changes in tau‐PET uptake in Braak I‐II regions were not significant (Figures 2‐3A). However, across all cohorts, the Meta‐Temporal ROI consistently showed detectable annual tau‐PET increases—most pronounced among CI—leading to larger effect sizes than in Braak I‐II (Figures 1‐3B). The Uniτ harmonization did not fundamentally alter the pattern of the findings. However, in ADNI (Flortaucipir), Uniτ yielded a slightly higher effect size than SUVR in CU for both Braak I‐II and Meta‐Temporal regions. Conclusion In this large longitudinal sample, our findings confirm that Flortaucipir and MK6240 can detect tau PET changes over time likely associated with the progression of tau tangle pathology. While MK6240 appears to show greater progression in CU, both tracers progress similarly in CI. Our data also suggested that Uniτ harmonized tau PET measurements maintain the longitudinal characteristics of each tau PET tracer for use in clinical trials.

Abstract Background We showed that astrocyte reactivity, as measured by plasma GFAP levels, influences Aβ mediated tau PET pathology in cognitively unimpaired (CU) Aβ‐positive individuals. However, the link between GFAP and tau PET in individuals without detectable Aβ pathology remains elusive. The aim of the current study is to investigate the association between plasma GFAP and tau PET in CU Aβ PET‐negative individuals. Method We studied 147 CU Aβ PET‐negative participants from the HEAD cohort with plasma GFAP and p ‐tau217, as well as tau PET Flortaucipir and MK6240 data. Aβ positivity was determined by Aβ PET visual reading and Centiloid 12. Voxel‐wise linear regression models adjusted for age and sex tested the association of plasma GFAP and p ‐tau217 with tau PET. Further, the associations of plasma GFAP with peak tau PET SUVR values extracted from voxel‐wise association were fitted with a linear regression model adjusted for age and sex. Result Voxel‐wise analysis showed that plasma GFAP levels, but not plasma p ‐tau217 levels, were associated with tau PET in the medial temporal lobe (e.g., amygdala, entorhinal cortex, hippocampus) predominantly for Flortaucipir tau PET [Figure 1A, B, C, D]. The association between plasma p ‐tau217 and tau PET was weak in Aβ‐negative individuals. These results were similar when Aβ positivity was defined based on Centiloid 12. Furthermore, plasma GFAP and peak tau PET SUVR of Flortaucipir showed stronger association than that of MK6240 [Flortaucipir: β=0.4017, p <0.0001; MK6240: β=0.194, p = 0.0424; Figure 2A, B]. Conclusion We found an association between GFAP levels and tau PET uptake in individuals not expected to exhibit high levels of tau tangle‐related tracer uptake. Further analysis will be designed to elucidate the underpinning of this association, which could represent low levels of tau pathology, astrogliosis, or other factors.

Abstract Background Quantifying tau aggregates in the human brain can be achieved using Positron Emission Tomography (PET) techniques, which can potentially be affected by binding competition due to medication use. Patients with dementia often have high rates of comorbidities and polypharmacy. Therefore, this study aims to investigate the potential influence of multiple medications on the uptake of the tau tracers MK6240 (MK) and Flortaucipir (FTP). Method Five classes of medications were evaluated: Anti‐Hypertensives, Statins, Anti‐Diabetics, Psychoactive drugs, and NSAIDs (Table 1). We included 292 individuals [170 cognitively unimpaired (CU) Aβ‐negative and 122 cognitively impaired (CI) Aβ‐positive] from the HEAD study (Table 2). We compared MK and FTP SUVR in the Medial Temporal Lobe (MTL) and Neotemporal Cortex (NTC) in individuals on and off medications. The linear regressions that tested associations were corrected for confounding factors, including age, sex, education, and MoCA score. Correction for multiple comparisons was applied using the Bonferroni method (adjusted p ‐value at 0.00125). Result Among CI Aβ‐positive individuals, Anti‐Diabetics were associated with lower SUVR in the NTC for both FTP and MK. However, these associations did not remain significant after correction for multiple comparisons. (Table 3). Conclusion Our findings indicate that there are no significant associations between the use of the medications studied and MK or FTP uptake when accounting for covariates and applying multiple comparison corrections.

Abstract Background Many risk factors can contribute to the occurrence of Alzheimer`s Disease (AD). However, little is known about the impact of dementia risk factors to the uptake of tau‐PET tracers. Therefore, in this work we aim to investigate the influence of dementia risk factors and comorbidities on 18 F‐Flortaucipir (FTP) and 18 F‐MK6240 (MK) tau‐PET tracers’ uptake. Additionally, we will assess how these factors impact the association of tau‐PET and cognition. Method We accessed 436 individuals across the aging and AD spectrum (251 amyloid negative and 185 amyloid positive) from the HEAD study, with available Aβ‐PET, FTP, MK, and clinical assessments. Linear regression models corrected for age, sex, clinical diagnosis, and study site tested the association of factors with tau‐PET tracers in the medial temporal lobe (MTL). A tau‐PET × risk factor term was added to test the influence of risk factors to the association of tau with cognition. Result In amyloid‐β negative individuals, high BMI were positively associated with the uptake of both FTP and MK, whereas hearing loss were positively associated only with MK in the MTL (Figure 1A). In amyloid‐β positive individuals, high body mass index (BMI), hearing loss and sleep disorders were negatively associated with the uptake of both tau‐PET tracers in the MTL. On the other hand, hypertension showed negative association only with MK uptake (Figure 1B). Using Mini‐Mental State Examination (MMSE) scores as outcome, we observed that amyloid‐β negative individuals with high BMI showed worse cognitive performance as a function of both MK and FTP in the MTL, whereas individuals with vision impairment and hearing loss showed worse cognitive performance as a function of MK only (Figure 2A). Amyloid‐β positive individuals with hypercholesterolemia and hypertension presented worse cognitive performance as a function of both MK and FTP in the MTL (Figure 2B). Conclusion In this preliminary analysis, sleep disorders, hypertension, and high BMI were independently associated with tau‐PET tracer uptake, with the effects varying according to amyloid‐β pathology. These prevalent factors in the elderly also changed the association between tau‐PET and cognition, underscoring the need for further studies to better understand their role in modulating this relationship.

Abstract Background The Alzheimer's Association workgroup criteria (AA‐2024) suggest four biological stages based on PET imaging. However, the application of this framework may vary depending on the tau PET tracer used, and the distribution of clinical stages by biological stage may differ. Here, we aim to compare the diagnostic performance of the biological staging of Alzheimer Disease using two different tau PET tracers. Method We analyzed 446 individuals and further stratified 167 Aβ‐positive participants from the HEAD study (Longitudinal multicenter head‐to‐head harmonization of tau PET tracers) that underwent both MK‐6240 and Flortaucipir (FTP) tau PET. Four clinical stages (normal, transitional, MCI, and dementia) were defined based on the AA‐2024 criteria. Within stage‐2 we assessed subgroups: subjective cognitive decline (SCD), subtle objective cognitive decline (SOCD), and mild behavioral impairment (MBI). Logistic and linear models corrected by age and sex tested association of groups with amyloid and tau pathology. The biological staging was performed using tracer‐specific thresholds for regional SUVR and testing two regions of interest schemes: (i) proposed by AA‐2024 and (ii) Braak stages (Figure 1). Cohen's weighted‐kappa (K) statistic measured clinical‐biological agreement. Positive predictive value (PPV) of an individual being clinically impaired (MCI or dementia) was calculated for each biological stage. Result Clinical stages showed a progressively increased prevalence of Aβ‐positivity and higher entorhinal tau PET pathology from cognitively normal (CN) to SOCD, MCI, and dementia in both tracers. MBI and SCD transitional groups showed no increased Aβ or tau pathology compared to CN (Figure 2). For this reason, only SOCD was considered part of stage 2 for following analyses. All tested biological staging strategies showed moderate agreement (0.34‐0.59) with clinical stages using either tracers. The distribution of biological across clinical stages were similar for FTP and MK, with MK‐6240 showing lower PPV values in earlier stages than FTP (Table 1). Conclusion Our results support that AA‐2024 biological and clinical stages show similar clinical‐biological concordance using either Flortaucipir or MK‐6240, although this can vary based on methods tested in the study. The MBI and SCD clinical subgroups showed no difference of pathological burden to clinical stage 1, while SOCD transitional stage showed higher burden compared to stage 1.

Abstract Background HEAD is a longitudinal, multi‐site, non‐randomized study aiming to harmonize different tau PET tracers onto a common scale for enrichment and monitoring of anti‐tau clinical trials. To achieve this, the Uniτ scale is being developed using data from participants scanned head‐to‐head with Flortaucipir and MK6240, with a subset also scanned with PI2620 and RO948. However, access to the Uniτ scale has been limited to the study community and collaborators. To address this limitation, we have developed the Uniτ Ecosystem – a set of tools for tau PET harmonization designed to support research and clinical trials ( www.unitau.app ). Method The Uniτ Ecosystem was developed for multiple operating systems (Web/macOS/Windows/Ubuntu) (Figure 1) and currently include four tools. The Uniτ harmonization is an online ROI harmonization module that allows the user to harmonize SUVRs from spreadsheets. The Uniτ packages (R, Python and MATLAB) facilitates the transformation of SUVR values to the Uniτ scale using the most used programming languages in research settings. The Uniτ calculator app allows rapid and convenient harmonization. The Uniτ desktop app is a 3D harmonization module for voxel‐wise harmonization. The Uniτ parameters were calculated using a training set of 200 participants with head‐to‐head MK6240 and Flortaucipir, and 90 individuals with additional PI2620 and RO948. Researchers can use their own datasets and convert tau PET data to the Uniτ scale. Result The online ROI harmonization module allows users to harmonize tau PET SUVRs uploaded from spreadsheets and offers interactive visualizations such as scatter plots and histograms for tau positivity thresholds (Figure 2). The calculator app enables users to harmonize single SUVR values effortlessly. The desktop application for 3D tau PET harmonization leverages the functionalities of the previous modules to harmonize entire 3D images, eliminating the need for predefined ROIs. Additionally, this module provides visualization tools for the parametric Uniτ images in 2D and 3D (Figure 3). Uniτ tools never store any data on any external servers. Conclusion The Uniτ Ecosystem represents a significant advance in tau PET harmonization, providing a user‐friendly platform to harmonize and visualize tau PET data for research and clinical trials.

Abstract Background Tau PET tracers present distinct binding characteristics that might influence their trajectories and relationship with other biomarkers along the AD continuum. In a head‐to‐head study, we investigated the relationship between the emergence of PET tracers MK6240 and Flortaucipir, and plasma p ‐tau217 abnormalities as a function of Aβ PET deposition. We further assessed the concordance between tau PET and plasma p ‐tau217 positivity and its relationship with cognitive scores. Method We evaluated 352 individuals from the HEAD study (205 cognitively unimpaired and 147 cognitively impaired) with Aβ PET, MK6240 and Flortaucipir tau PET, and plasma p ‐tau217 (ALZpath) and GFAP (Quanterix) measures. Tau PET Braak regions, plasma p ‐tau217 trajectories were modeled as functions of Aβ burden (Centiloid scale) using the Lowess method. Biomarkers were z‐scored anchored on young individuals ( n = 19, < 28 years old) as anchors. Tau PET (Braak 1 region) and plasma p ‐tau217 were considered positive/abnormal when surpassing 2.5 z‐score. Result Among the tested markers, MK6240 was the earliest to show abnormality as a function of Centiloid, occurring at 22 Centiloids, followed by plasma p ‐tau217 at 38 Centiloid and Flortaucipir at Centiloid 56 (Figure 1). Tau PET and plasma p ‐tau217 positivity showed an overall high concordance (∼80% for both tracers; Figure 2). In MK6240 discordant cases, most individuals were MK6240+/p‐tau217‐ (13.8%), while 6.6% were MK6240‐/p‐tau217+ (Figure 2A). For Flortaucipir, 15% of discordant cases were Flortaucipir‐/p‐tau217+, while 4.8% were Flortaucipir+/p‐tau217‐ (Figure 2B). The discordant groups present higher Centiloid than the ptau217‐/Tau‐ group and overall decreased scores in cognitive tests, except for p ‐tau217+/MK6240‐ group (Figure 3). Conclusion MK6240 becomes abnormal at lower levels of Aβ burden compared to plasma p ‐tau217 and Flortaucipir. The relatively high prevalence of discordant tau PET positive or plasma p ‐tau217 positive and suggests that some individuals may show tau PET positivity first, while others may exhibit plasma p ‐tau217 positivity first. The distinct cognitive profiles of this groups suggest potential clinical relevance, warranting further investigation.

Abstract Background Tau‐PET provides critical topographic information on tau deposition in the living brain. Integrating research‐based practices into clinical settings through qualitative methods, such as visual reads, is becoming increasingly important. This study aims to compare the performance of existing visual reading methods for [ 18 F]Flortaucipir (FTP) and [ 18 F]MK‐6240 (MK) and provide preliminary data toward a unified visual reading approach for all tau‐PET tracers using a head‐to‐head dataset. Methods The study design and development plans are illustrated in Figure 1. To evaluate previously published visual reading methods, two blinded raters conducted FTP and MK visual reads on 340 participants from the HEAD study. A unified visual reading method was developed and tested on 101 participants, using a composite of adjusted Braak regions to determine 3 stages of severity while harmonizing between tracers. For external validation, the method will be tested on four additional cohorts, each using one of the following tracers: FTP, MK, PI, or RO. Results Inter‐rater agreement using previous methods (Figure 2A) showed high Cohen's kappa values (0.71‐0.77) for the low and high tau burden categories in both FTP and MK visual reads. However, agreement was substantially lower in the non‐AD‐like category , leading to overall agreement rates of 0.55 for FTP and 0.69 for MK . In contrast, the method developed here improved inter‐rater agreement across all categories (0.87‐0.96) and introduced a moderate tau (0.79‐0.83) burden group (Figure 2B), leading to higher overall agreement rates of 0.81 for FTP and 0.87 for MK (Figure 2C). For inter‐tracer agreement (Figure 3), previous methods resulted in discordant visual reads for FTP and MK in 34.4% of cases for rater 1 and 24.7% for rater 2. The developed method reduced this disagreement to 11.9% (rater 1) and 15.8% (rater 2), demonstrating improved consistency across different tracers. Conclusion This study demonstrates that previously published visual reading methods produced varying classifications depending on the tracer used. The method proposed here improved inter‐rater and inter‐tracer visual read agreements by developing a unified, tracer‐agnostic approach easy to be used. Importantly, this clinician‐friendly method has the potential for widespread adoption, offering a single harmonized visual reading technique for all tau‐PET tracers.

Abstract Background One of the hallmarks of Alzheimer's disease (AD) is the progressive spread of tau pathology. However, heterogeneity in the spatial patterns of tau deposition at the individual level may contribute to distinct clinical presentations. Here, we investigated the different patterns of tau deposition across disease progression and the different cognitive profiles linked to these patterns. Method We investigated 106 tau PET positive individuals (16% cognitively unimpaired, 84% amnestic cognitively impaired) from the HEAD study using head‐to‐head Flortaucipir and MK6240 tau PET. For each tracer, we extracted the SUVR values from seven major brain networks: Limbic, Visual (Vis), Default Mode (Default), Dorsal Attention (DorsAttn), Frontoparietal, Somatomotor (SomMot) and Salience Ventral Attention (SalVentAttn) as well as the Entorhinal cortex. Pathways were identified by ranking regions from highest to lowest SUVR, with the region displaying the highest SUVR value being defined as the peak region. Individuals in late stages of tau deposition displaying distinct pathways were compared. Image averages and voxel‐wise group comparisons using tau negative individuals as the reference were generated for each pathway and tau PET tracer. Cognitive differences were assessed using ANOVA for MoCA score and domain‐specific composite scores for Memory, Attention and Visuospatial skills. Result In all individuals and tracers, tau in the Entorhinal cortex progressed to the Limbic network, supporting a common starting point for tau propagation. Beyond these initial stages, tau propagation followed three main distinct pathways following either the Vis, Default or DorsAttn networks (Figure 1). Voxel‐wise comparison between individuals across these three pathways captured clear and similar differences in tau PET uptake as measured with Flortaucipir and MK6240. (Figure 2). While MoCA and Memory scores did not differ between groups, individuals following the Vis network exhibited significantly lower Attention scores compared to those in the Default and DorsAttn networks. Additionally, individuals following the Vis network exhibited lower Visuospatial scores compared to the Default network group (Figure 3). Conclusion Our results indicate a model in which, although tau progresses hierarchically, its deposition peaks mainly follow specific networks, which drive tau deposition and, consequently, the clinical manifestations of AD.

Abstract Background Standardizing tau pathology quantification in vivo is challenged by inherent characteristics of tau‐PET tracers. The HEAD study aims to generate a leading, longitudinal head‐to‐head dataset of MK‐6240, Flortaucipir, RO948, and PI‐2620 tau‐PET to harmonize tracers' outcomes and develop tools to generalize findings across studies and trials. Here, we provide an update on the progression of the HEAD study. Method HEAD is a multicentric study comprising nine performance sites. Recruitment aimed for 620 individuals between 18‐28 or 50‐90 years, classified as Young/CU/MCI/Dementia. The HEAD protocol involves clinical assessment utilizing the NACC Uniform Data Set, blood collection for banking of plasma/serum/buffy coat/whole blood, and MRI acquisition based on ADNI4. All participants undergo amyloid‐PET with either PiB/NAV4694/Florbetaben/Flutemetamol. All undergo head‐to‐head tau‐PET with at least two tracers, including MK‐6240 (90‐110), Flortaucipir (80‐100), PI‐2620 (45‐75), and RO948 (70‐90). PET data is reconstructed and processed uniformly similarly to ADNI4. The Laboratory of Neuroimaging (LONI) provides centralized databasing, and the National Centralized Repository for ADRD (NCRAD) provides the blood biorepository for all samples. All study procedures are repeated at 18 months. Result Over 26 months, N = 679 participants were enrolled into HEAD, exceeding our proposed enrollment by 9.5%. Mean age of older adults is 72.1 years, female distribution is 54%, and 24% of individuals are from underrepresented groups (race/ethnicity/rurality). Progression in data collection has led to N = 551 (81%) of enrolled participants having a completed initial timepoint, and 1,489 total acquired head‐to‐head tau‐PET scans (mean acquisition window=34.9 days). Clinical characteristics including group distribution, APOEε4 carriership, plasma biomarker distribution (Aβ42/40 ratio/NfL/GFAP/PTau217), consensus visual rating of amyloid‐PET, and Braak stage classification are summarized in Figure 1. Two representative cases (CU/AD) of head‐to‐head tau‐PET with four tau tracers are shown in Figure 2. Longitudinal data collection has been initiated in N = 95 participants. Figure 3. demonstrates two cases (CU/MCI) of 18‐month longitudinal head‐to‐head tau‐PET with MK‐6240 and Flortaucipir. Conclusion The HEAD study cohort represents a continued effort in the optimization of AD imaging biomarkers. Cross‐sectional and longitudinal data collection in HEAD are ongoing, in addition to comprehensive plasma biomarker measurements. Generation of findings from HEAD cohort data will provide novel and crucial guidance on the use of tau‐PET tracers.