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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 Individuals with preclinical AD are difficult to detect using traditional clinical tools. Yet, many individuals classified as cognitively unimpaired (CU) can exhibit a heterogeneous pattern of subtle clinical abnormalities, potentially linked to early Alzheimer's disease (AD). Primary care providers often have access only to clinical tests to study at‐risk community‐dwelling older adults who visit them without cognitive complaints. Since early intervention is crucial in AD, identifying clinical tools to detect preclinical AD is important in areas with limited access to blood or imaging biomarkers. In this study, we evaluate the link between Montreal Cognitive Assessment (MoCA) total scores and AD pathology in CU individuals. Methods We studied 204 cognitively unimpaired (CU) older adults who underwent both 18 F‐FTP and 18 F‐MK6240 scans. These individuals were stratified based on their amyloid PET status, determined by visual reading, into CU A+ ( n = 35) and CU A‐ ( n = 137), as part of the ongoing HEAD study. CU adults had Clinical Dementia Rating (CDR) of 0 and were clinically identified as non‐MCI and non‐demented. Voxel‐wise linear regression models tested the association between MoCA total scores and tau pathology. Results CU A+T+ had significantly lower total MoCA scores than A‐T‐ individuals (Figure 1). Lesser MoCA total score was significantly associated with greater mediobasal temporal tau deposition, using both 18 F‐MK6240 and 18 F‐FTP [Figure 2]. When we separated the population by Aβ status, we found that these results were driven by the CU A+ group (Figure 3A) and were not present in CU A‐ individuals (Figure 3B). Discussion These findings indicate that the MoCA, a simple routine in‐office clinical test, can detect subtle cognitive dysfunction associated with preclinical AD. This suggests that simple cognitive testing can play a role in the early detection of AD and therefore an option for prescreening older adults in non‐specialized clinical settings, the initial interface for most older adults without cognitive complaints. Conclusion MoCA total, the common dementia screening test also plays a valuable role in detecting preclinical AD.

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 Extensive evidence links cognition, tau, and neurodegeneration in Alzheimer's disease (AD). However, in U.S. POINTER imaging (a community‐recruited clinical trial cohort selected for high cardiovascular risk, ethnoracial diversity, and increased risk of cognitive decline and dementia) tau PET is more closely linked to cognition than in ADNI. Here, we further explore how ADNI and POINTER differ with respect to relationships between cross‐sectional AD biomarkers, cortical thickness, and different cognitive domains. Method U.S. POINTER imaging (Baseline; N = 775; 68.9±5.2 years, 62.8% female) and ADNI ( N = 405; 69.9±5.4 years, 58% female) were matched by age, sex, clinical status, and APOE4 genotype (Table 1). We examined associations between baseline AD biomarkers (Amyloid‐beta (Aβ) PET in centiloids, harmonized tau PET measures of MK6240 in POINTER and FTP in ADNI), regional cortical thickness, and verbal memory (Delayed Story Recall), executive function (Trail‐Making B), and global cognition (Preclinical Alzheimer's Cognitive Composite scores) across each cohort, controlling for age, sex, and education. Result Aβ burden in ADNI was associated with worse executive function ( p < .001) and global cognition ( p < .001) but not memory, whereas in POINTER Aβ burden was not associated with any cognitive domain (Figure 1A). Among Aβ+ individuals, tau burden was associated with poorer performance across domains in ADNI, but only with poorer verbal memory in POINTER (Figures 1B&1C). Additionally, there were stronger positive associations between cortical thickness and executive function and global cognition compared to verbal memory, particularly in ADNI Aβ+ individuals even after adjusting for entorhinal tau ( p < .01; Figure 2). Conclusion Cognitive performance was linked to all biomarkers (Aβ, tau, cortical thickness) but these associations were consistently stronger in ADNI compared to POINTER, particularly in Aβ+ individuals. The stronger thickness‐cognition relationships in ADNI were not driven by tau, indicating that there is a tau‐independent role of thickness in explaining cognition. In ADNI, there were subtle differences between cognitive domains such that executive function was more closely linked than verbal memory to Aβ and cortical thickness, whereas verbal memory was linked to tau in both cohorts. Our study highlights that frequently‐reported AD biomarker associations may be attenuated in heterogeneous, community‐recruited cohorts.

Abstract Background Our objective is to examine the generalizability of the relationships between four MRI markers of CVD: DTI‐derived free water (FW), fractional anisotropy (FA), perivascular space (PVS) and white matter hyperintensities (WMH) volume, with vascular risk factors (VRF) and cognition in the baseline POINTER Imaging and Framingham Heart Study (FHS) cohorts. Method We studied 907 U.S. POINTER and 797 FHS Offspring participants with available baseline demographic, clinical and imaging data (Table 1). Continuous FW, FA, WMH (log‐transformed) and PVS measurements were regressed against total cranial volume (TCV). Cognitive outcomes included a Global Cognitive Composite and executive function composite score for POINTER and FHS. For each MRI marker, we used linear and logistic regressions to assess the effect of age, sex, hypertension, diabetes and smoking status, adjusting for ethnicity. We then assessed, for each cognitive outcome, the individual effect of MRI markers using separate linear regressions adjusted for demographic and clinical factors, as well as TCV‐adjusted gray matter volume to control for atrophy. Result POINTER and FHS differed across most demographic and biomarker characteristics (Table 1). Despite these differences, there were similar associations between demographic and VRF and the 4 MRI vascular markers in POINTER and FHS (Figure 1): increased age was associated with worse outcomes for the 4 markers, hypertension was associated with increased FW, PVS and lower FA, diabetes was associated with increased FW only, smoking status did not impact any of the four markers. Only exception was that female had lower FW and increased WMH in POINTER and increased PVS in FHS. Associations between CVD measures and cognition were also similar across cohorts: reduced cognitive performance was significantly associated with both decreased FA and increased FW (Figure 2), but not with WMH nor PVS. Conclusion Our study explores four imaging markers representing diverse mechanisms by which CVD could impact brain structure and function in the POINTER and FHS studies. Despite differences in cohort characteristics, we found mostly consistent CVD relationships with demographic, VRF, and cognitive measures between cohorts which supports the generalizability of the relation of these markers with VRF and cognition identified in the POINTER cohort.

Abstract Background Given low representation of diverse populations in Alzheimer's disease (AD) research, it remains unclear if AD biomarker relationships observed primarily in non‐Hispanic White (NHW) cohorts generalize to other groups. Here we evaluate associations between key AD biomarkers and cognitive performance among cognitively unimpaired non‐Hispanic Blck (NHB) and NHW participants across 2 cohorts with unique sample characteristics. Method Participants included cognitively unimpaired NHB and NHW participants with Aβ and tau PET in ADNI (69 NHB, 402 NHW) and POINTER Imaging (135 NHB, 637 NHW). We examined harmonized measures of global Aβ (in centiloids), entorhinal cortex (EC) tau (quantified with MK6240 in POINTER and FTP in ADNI) and Preclinical Alzheimer's Cognitive Composite (PACC) scores. Regression models within each ethnoracial group evaluated relationships between 1) Aβ and EC tau, and 2) EC tau and PACC performance. Interaction models within each cohort were also conducted to assess differences in these associations by ethnoracial group. All models were adjusted for age and gender. Models with PACC additionally adjusted for education. Result In US POINTER, NHBs had lower Aβ and EC tau burden compared to NHWs (Table 1). In ADNI, there was no difference in EC tau between groups, and no differenence in Aβ after adjusting for age and gender. In US POINTER, the positive Aβ‐EC tau relationship was observed in both ethnoracial groups whereas in ADNI, Aβ was associated with higher EC tau in the NHW but not the NHB group. In both cohorts, EC tau was associated with worse PACC performance in both NHB and NHW and there were no differences in this association by ethnoracial group in interaction models (Figure 1). Conclusion NHBs had lower Aβ and EC tau than NHWs in POINTER but not ADNI, and there were inconsistent associations between Aβ and EC tau across cohorts. Despite these differences, AD biomarkers appear similarly related to cognitive outcomes across ethnoracial groups in both cohorts. Future analyses will attempt to further determine the generalizability of AD biomarker relationships in diverse samples by considering matching on relevant health and demographic characteristics, and inclusion of additional cohorts with harmonized biomarker and cognitive measurements.

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 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.