Pascual, Belén

Abstract Background Tau‐PET imaging enables in vivo detection of tau pathology in Alzheimer’s disease (AD), with early detection being critical for tracking disease progression. Here, we compare [ 18 F]FTP and [ 18 F]MK6240 in individuals with low tau burden. Method 450 individuals from the HEAD study (22 cognitively unimpaired (CU) young (CUY), 242 CU old and 186 cognitively impaired (CI)) underwent [ 18 F]FTP and [ 18 F]MK6240 tau‐PET and amyloid‐PET scans. Scans were processed using identical pipelines with inferior cerebellar gray matter as the reference region. Individuals were categorized as low tau if both [ 18 F]FTP and [ 18 F]MK6240 meta‐temporal SUVR were lower than 1.5. For comparison, [ 18 F]MK6240 and [ 18 F]FTP were transformed into one another using linear and non‐linear models; additionally, images were also z‐scored relative to the CUYs. Result Despite cognitive status and amyloid burden, several individuals showed elevated [ 18 F]FTP retention in temporal regions relative to [ 18 F]MK6240. As an example, Figure 1 shows [ 18 F]FTP and [ 18 F]MK6240 in 16 participants (4 per cognitive and amyloid status). Across most subjects with SUVRs<1.5, [ 18 F]FTP presented higher retention in meta‐temporal region as compared to [ 18 F]MK6240, though [ 18 F]MK6240 increased sharply above this threshold (Figure 2). In Figure 3, group‐average images and their transformed counterparts are shown for each group by amyloid and cognitive status, the subset of low‐tau participants in Aβ+ groups are also shown. In low tau groups, [ 18 F]FTP meta‐temporal retention is high in comparison to [ 18 F]MK6240. Additionally, z‐scored [ 18 F]FTP images show broader spread into temporal regions than [ 18 F]MK6240, reinforcing differences in retention. Conclusion [ 18 F]FTP shows meta‐temporal retention not seen with [ 18 F]MK6240 in low tau individuals. This discrepancy persists across transformed and z‐scored images, suggesting that there is a difference in early tracer retention between [ 18 F]FTP and [ 18 F]MK6240. It is unclear if the early [ 18 F]FTP retention is related to early maturity levels of tau or off‐target signal. These findings have important implications for basic understanding of early tau retention, early diagnosis, and biomarker harmonization efforts.

Abstract Background In vivo Braak staging stratifies patients across the AD spectrum and has the potential to harmonize tau PET tracer staging. This study aims to compare and test harmonization procedures for Braak staging individuals using MK6240 and Flortaucipir tau PET tracers. Methods We assessed 437 participants across the AD spectrum (245 cognitively unimpaired (CU) and 192 cognitively impaired; mean age 68.5 ± 8.6) using head‐to‐head MK6240 and Flortaucipir scans. We computed SUVRs in Braak regions of interest (ROIs) and assessed four cut‐off methods for Braak positivity: (a) mean + 2.5 SD of young controls (age <28 years), (b) mean + 2.5 SD of elderly CU Aβ−, (c) Gaussian mixture modeling (GMM), and (d) the Youden index. Braak stages were assigned using seven (0 to VI) or four (0, I–II, III–IV, V–VI) categories. We evaluated inter‐ and intra‐tracer concordance (intra‐tracer, i.e., whether it follows the sequential Braak pattern). Results The intra‐tracer seven‐class Braak staging concordance ranged from 63% to 94%. With the highest intra‐tracer Braak concordance being achieved when using GMM cutoffs: 94% (MK6240) and 89% (Flortaucipir; Figure 1). Inter‐tracer agreement concordance ranged from 56% to 76%. The highest concordance emerged from the CU Elderly Aβ– cutoff optimizing the Braak II region for spill‐off (Figure 2). Using the Braak staging simplified version improved intra‐tracer concordance in both tracers (MK6240as well as inter‐tracer agreement (86.5%). Most inter‐tracer discrepancies were observed at Braak stages II–IV. Despite showing staging discordances, the distribution of cognitive status across the Braak stages is similar for both tracers (Figure 3). Conclusion These preliminary findings reveal some discrepancies in Braak staging when comparing MK6240 and Flortaucipir. Our results also suggest that adjustments in cutoffs and regions of interest can partially mitigate both inter‐ and intra‐tracer divergences. Finally, our analysis suggests robust concordance after adjustment and using 4 classes (0, I‐II, III‐IV, V‐VI).

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 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 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 Previous studies demonstrated an association between tau pathology and the development of neuropsychiatric symptoms (NPS) in individuals with Alzheimer’s disease (AD). However, the extent to which tau influences each specific NPS domain remains unclear. Here, we aim to investigate the association of tau and each NPS domain in the AD continuum. We hypothesized that tau plays a comparatively greater effect on the emergence of hyperactive and psychotic symptoms compared to other NPS domains. Method We assessed 385 individuals (216 cognitively unimpaired (CU), 128 MCI, and 41 AD dementia) from the HEAD study who underwent clinical assessments with the Neuropsychiatry Inventory Questionnaire (NPI‐Q) and had positron emission tomography (PET) for amyloid‐β (Aβ) ([ 18 F]AZD4694 or [ 11 C]PiB), and tau tangles ([ 18 F]MK6240) at the same visit. Tau SUVR values were tailored with a mask from Braak stages I‐VI, using the inferior cerebellar gray matter as a reference region. All individuals with dementia had a positive Aß‐PET. Voxel‐wise and Tobit censored regression tested the association between NPS domains and biomarkers accounting for age, sex, cognitive status, and study site. We used censored regression models to account for skewed data alongside a leave‐one‐out (loo) approach to identify which NPS domains most contributed to results. Result CI individuals had significantly higher NPI‐Q scores and Tau PET SUVR than CU (Figure 1A, 1B). NPI‐Q score was significantly associated with tau‐PET predominantly in fronto‐parietal regions. Removing irritability from the models strengthened this association (Figures 2A‐C). Regression loo models revealed that motor disturbances contributed most to the association between NPS and tau‐PET across all Braak stages. Notably, irritability had a negative effect on the association in all Braak stages, suggesting that tau does not play a role in the development of irritability and is highly associated with motor disturbances (Figure 3). Conclusion Our study suggests that tau contributes to the development of motor disturbances but has no effect on the development of irritability across the AD continuum. These findings provide additional rationale for the development of new therapeutics aiming to mitigate motor disturbances and irritability in AD patients.

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.

Abstract Background Variability in meningeal signal has been observed with various tau‐PET tracers. Differences have been observed in sex and age. Furthermore, the potential role of glymphatic clearance in this variability remains unclear. Here we compare meningeal signal of [ 18 F]MK6240 and [ 18 F]FTP tau‐PET tracers in cognitively unimpaired (CU) amyloid‐β‐ (Aβ‐) individuals, in relation to age, sex and glymphatic clearance. Methods 92 CU Aβ‐ (7 CU young [18‐35yo] and 85 CU old) underwent [ 18 F]MK6240 and [ 18 F]FTP tau‐PET scans, structural MRI, and resting‐state functional MRI (rsfMRI). In native space, we defined nine distinct levels representing meningeal regions, organized from those closest to the cortex (1) to those furthest away (9). Glymphatic clearance, measured as gBOLD/CSF coupling, was calculated for each participant; a lower value means a more efficient glymphatic clearance. Associations between meningeal tracer signal and factors such as sex, age, and glymphatic clearance were analyzed using t‐tests and linear regression models. Results All 9 meningeal regions were tested and rendered similar results; we will be showing those for meninges level 2. [ 18 F]MK6240 and [ 18 F]FTP meningeal signal significantly correlated with each other in both sexes (Figure 1). We observed higher [ 18 F]MK6240 meningeal signal in females compared to males while no difference was found for [ 18 F]FTP signal (Figure 2). In females, [ 18 F]MK6240 meningeal signal did not show a significant association with age, whereas in males, a significant relationship was observed. Both males and females exhibited lower [ 18 F]FTP meningeal signal with increasing age (Figure 3A). Additionally, glymphatic clearance was significantly associated with age for females with [ 18 F]MK6240. No significant associations were found in males with [ 18 F]MK6240 or in either sex for [ 18 F]FTP (Figure 3B). Conclusion Our findings demonstrate that meningeal signal of [ 18 F]MK6240 is significantly associated with glymphatic clearance in females, while in males, it is associated with age. In contrast, [ 18 F]FTP signal is primarily influenced by age in both sexes. These results highlight the distinct relationship between [ 18 F]MK6240 signal and glymphatic function in females, while age appears to play a significant role in both males and females for other tracer signal patterns.

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.