Rocha, Andreia

Abstract Background The Alzheimer's disease (AD) Braak staging is a key framework for classifying tau pathology progression in AD based on histopathological post‐mortem brain examinations. However, adapting it to PET imaging can be challenging due to differences in tracer uptake patterns and binding properties, which affect sensitivity, specificity, and regional staging. This study compares Braak staging across four tau PET tracers: Flortaucipir, MK6240, PI2620, and RO948. Methods We assessed 90 participants across the AD spectrum (46 CU, 31 MCI, 13 dementia; mean age 66.1 ± 7.8) using Aβ PET and four tau PET tracers: (Flortaucipir, MK6240, PI2620, and RO948). Braak positivity was defined based on Aβ− CU individuals (mean +2.5 SD, SUVR). To evaluate systematic bias and agreement between tracers, we computed pairwise differences at corresponding Braak stage estimates and applied the Bland‐Altman method to assess mean bias and limits of agreement. Additionally, Tau PET Braak region trajectories were modeled as functions of Aβ burden (Centiloid scale) using the Lowess method. Results Braak stage trajectories as a function of Aβ differ depending on the tracer and the sequential order of abnormality is highly variable. For instance, while for MK6240, RO948 and PI2620, Braak I is the first region to became abnormal, for Flortaucipir the earliest region to became abnormal is Braak IV (Figure 1). This variable pattern of abnormality impacts in the concordance of Braak staging between tracers with the highest Braak staging agreement resulting in concordance levels of approximately 70%. The highest levels of agreement between tracers usually happen at Braak 0 or Braak IV‐V, with intermediate stages showing very low concordance (Figure 2). The Bland‐Altman analysis identified wide limits of agreement, suggesting high variability and high tracer‐specific differences (Figure 3). On the other hand, it also identified that mean differences between tracers were small, indicating minimal systematic bias. Conclusion These preliminary findings reveal discrepancies in Braak staging when comparing Flortaucipir, MK6240, PI2620 and RO948. These findings suggest that while the tracers provide comparable stages on average, they may not be fully interchangeable in individual cases.

Abstract Background Older adults with brain tau pathology but no evident amyloid‐beta (A) pathology can be referred to as primary age‐related tauopathy (PART). However, in vivo characterization of PART using biomarkers remains unclear, varying based on the method used to define tau pathology (e.g. different tau‐PET tracers). Here, we conducted a head‐to‐head characterization of PART using two different tau‐PET tracers. Method We studied 433 individuals from the HEAD cohort (244 CU, 138 MCI, and 51 with dementia). Participants underwent clinical assessments, MRI, A‐PET, both MK‐6240 and Flortaucipir scans, and a subset with plasma biomarkers ( n = 332). A‐PET positivity was defined as Centiloid>24. Tau‐positivity(T) was determined by MK‐6240 and Flortaucipir SUVR values exceeding the mean+2SD in at least one Braak region anchored in CU A‐ individuals followed by visual confirmation in A‐/T+ cases. Concordance across biomarkers was assessed among PART (A‐/T+), A‐/T‐ and A+/T+ groups. Plasma GFAP, p ‐tau217, NFL levels, and Centiloids were compared across A/T groups using linear regression. Result We found a total of 25 (5.8 %) cases classified as A‐/T+ defined by at least one tau tracer (74±6.3 years, 52% females, 17 CU, 3 MCI, 5 with dementia). Detection of A‐/T+ cases varied by tracer: MK‐6240( n = 20), Flortaucipir( n = 16) (Figure 1A). Among A‐/T+ individuals, MK‐6240 identified 13 CU, 4 MCI, and 3 dementia cases, while Flortaucipir identified 11 CU, 3 MCI, and 2 dementia cases(Figure 1B). Concordance between MK‐6240 and Flortaucipir in A‐/T+ cases was low ( n = 11, 44%)(Figure 1C). A‐/T+ individuals showed no significant biomarker differences from A‐/T‐ but had lower biomarker levels than A+/T+: Centiloids (MK‐6240: T=15.08, p <0.001; Flortaucipir: T=14.36, p <0.001), GFAP (MK‐6240: T=5.04, p < 0.01; Flortaucipir: T=2.92, p <0.001), p ‐tau217 (MK‐6240: T=7.77, p <0.001; Flortaucipir: T=6.34, p <0.001), except for NFL (MK‐6240: T=1.35, p = 0.18; Flortaucipir: T=1.14, p = 0.25)(Figure 2). Representative A and tau biomarker cases (Figure 3A), along with discordant cases classified as T+ by only one tracer (Figure 3B), are illustrated. Conclusion Our preliminary analysis revealed a higher prevalence of A‐/T+ cases with MK‐6240 than Flortaucipir, low concordance between tracers and consistently lower biomarker levels in A‐/T+ compared to A+/T+. Larger studies and longitudinal analyses are necessary to ascertain the pathological trajectories of A‐/T+ individuals with varying tau biomarker profiles.

Abstract Background Tau phosphorylation and aggregation are hallmark features of Alzheimer's disease pathology(AD). Previous studies have shown that plasma phosphorylated tau( p ‐tau) at threonine 217 can detect AD‐related tau tangle pathology. However, the head‐to‐head association between p ‐tau217 and different tau PET tracers remains unexplored. Here, we conducted a head‐to‐head comparison of the association between plasma p ‐tau217 and four distinct tau PET tracers[MK6240, Flortaucipir(FTP), PI2620, RO948]. Method We studied 338 individuals across the AD continuum from the HEAD study[190 cognitively unimpaired elderly(CU), 109 with mild cognitive impairment(MCI), and 39 with dementia] with available FTP, MK6240, and plasma p ‐tau217 measured by ALZpath assay. A subset of 64 individuals(28 CU, 25 MCI, and 11 dementia) also had PI2620 and RO948, and p ‐tau217 measured by Lumipulse. The strength of the association was determined using t‐value maps from regression models testing the association between each tau PET tracer and plasma p ‐tau217, and the extent was measured by the percentage of significant voxels(t‐value>3.2). R‐square metric was used to determine how much of the variance in tau PET tracer estimates was explained by plasma p ‐tau217 concentrations. Associations between p ‐tau217 with tau PET were evaluated using the Spearman test. Result We found that in CU, both the extent and magnitude of the association with plasma p ‐tau217 were higher for MK6240 compared to FTP (Figure 1A‐D). Plasma p ‐tau217 explained a greater proportion of the variance in MK6240 than in FTP (Figure 1E‐F). In CI, the extent and magnitude of the association were similar for MK6240 and FTP. Additionally, plasma p ‐tau217 explained a similar proportion of the variance in MK6240 and FTP. Using the subset of individuals who had all tau tracers, MK6240 showed a slightly higher extent of association with p ‐tau217, followed by FTP, PI2620, and RO948 (Figure 2A‐B). The magnitude of association was stronger for MK6240, followed by PI2620, RO948, and FTP (Figure 2C‐D). Plasma p ‐tau217 explained a greater proportion of the variance in MK6240, followed by FTP, RO948, and PI2620 (Figure 2E‐F). We compared the associations between different p ‐tau217 assays, and both assays presented similar associations with all tau PET tracers (Figure 3). Conclusion In summary, our results indicate that the four tau PET tracers exhibit robust associations with plasma p ‐tau217 in similar topographical regions.

Abstract Background Tau accumulation in the brain is postulated to follow a hierarchical pattern, known as Braak staging, originally defined in postmortem brains. PET‐based Braak stages have since been proposed, however, tau PET tracers exhibit distinct binding characteristics that might influence the detectable trajectories of tau accumulation and its relationship with other biomarkers across the AD continuum. In a head‐to‐head study, we investigated the emergence of tau positivity in each Braak region, along with plasma p ‐tau217 and GFAP, as a function of Aβ PET deposition. Method We evaluated 352 individuals from the HEAD study (205 cognitively unimpaired and 147 cognitively impaired) with head‐to‐head Aβ PET, MK6240 and Flortaucipir tau PET, and plasma p ‐tau217 (ALZpath) and GFAP (Quanterix) measures. Tau PET Braak regions, plasma p ‐tau217 and GFAP 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). Positivity for plasma biomarkers and for each Braak region was determined when values surpassed 2.5 z‐score from the mean. Result Among the tested markers, plasma GFAP was the earliest to show abnormality as a function of Centiloid, occurring at 11 Centiloids. For MK6240, Braak I was the first regions to became positive (at 22 Centiloids), followed by Braak II (at 32 Centiloids), Braak IV (at 42 Centiloids), Braak III (at 52 Centiloids), Braak V (at 70 Centiloids) and Braak VI (at 123 Centiloid; Figure 1A). For Flortaucipir the first region to became positive was Braak IV (at 50 Centiloids), followed by Braak I and II (both at 56 Centiloids), Braak III (at 85 Centiloids) and Braak V (at 92 Centiloids). Braak VI region did not reach the threshold for positivity in our population (Figure 1B). Plasma p ‐tau217 reached the positivity threshold at 38 Centiloids. Conclusion MK6240 follows a Braak stage‐like pattern of abnormality as a function of Aβ burden, whereas for Flortaucipir, Braak IV is the first region to became abnormal. MK6240 becomes abnormal at lower Aβ burden levels compared to plasma p ‐tau217 and Flortaucipir but after plasma GFAP. Our findings suggest that MK6240 might detect tau pathology in earlier disease stages than Flortaucipir.

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 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 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 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.