Jagust, William J.

AbstractBackgroundIn vivo studies using the tau PET tracers have shown high performance for the diagnosis of Alzheimer’s disease dementia and patterns of tracer uptake that resemble those observed in post‐mortem studies. However, tau tracers present distinct patterns of binding that might influence their performance in detecting AD pathology. In a head‐to‐head study, we investigated the performance of [18F]MK6240 and [18F]Flortaucipir for the diagnosis of AD.MethodWe assessed 132 individuals from the HEAD study (58 CU Aβ‐, 15 CU Aβ+, 14 MCI Aβ‐, 32 MCI Aβ+ and 13 AD dementia) with Aβ‐PET, [18F]MK6240 and [18F]Flortaucipir. Voxel‐wise receiver operating characteristic curves (ROC) of the two tau tracers were used to contrast groups provided the area under the curve (AUC) for disease diagnosis or biomarkers positivity.ResultThe brain maps showed numerically higher and more spread AUC for [18F]Flortaucipir to discriminate CU Aβ‐ from CU Aβ+ (Figure 1A). The difference between tracers’ AUC was greater in Braaks IV and V (Figure 2A), both regions that are not expected to have tau accumulation in CU Aβ+ individuals, reflecting a potential off‐target binding for [18F]Flortaucipir. To differentiate CU Aβ‐ from MCI Aβ+ individuals, [18F]MK6240 presented a numerically higher AUC than [18F]Flortaucipir in Braak I and II and similar AUC in other Braak regions (Figure 1B, 2B). We observed a high performance of [18F]MK6240 and [18F]Flortaucipir in differentiating AD dementia from CU Aβ‐ individuals. However, [18F]MK6240 exhibits a higher AUC than [18F]Flortaucipir in all Braak regions, especially Braak V‐VI (Figure 1C, 2C). Finally, [18F]MK6240 presented higher AUC in all Braak regions to discriminate MCI Aβ‐ from MCI Aβ+ individuals (Figure 1D, 2D).ConclusionOur results indicate that [18F]MK6240 and [18F]Flortaucipir present high accuracy to discriminate AD from CU Aβ‐ individuals. However, [18F]MK6240 presents higher AUC to discriminate AD and MCI Aβ+ from CU Aβ‐ individuals than [18F]Flortaucipir. Together, our head‐to‐head study sheds light on the distinct patterns of binding for Tau‐PET tracers.

AbstractBackgroundThe HEAD study aims to collect a large dataset of multiple tau‐PET tracers to provide robust anchor values for tau‐PET harmonization. Here, we tested the hypothesis that anchoring two tau tracer uptake values using head‐to‐head measurements has the potential to generate an accurate universal tau‐PET scale, named Uniτ(tau).MethodsWe assessed 200 individuals across the aging and AD spectrum (Training:HEAD data freeze 2.0, n=185; Testing:UPitt dataset (Gogola et al.), n=15) with [18F]Flortaucipir and [18F]MK‐6240 tau‐PET. SUVRs were processed to a common 8mm FWHM, with inferior cerebellar gray matter as the reference region (Pascoal et al.). Uniτ explored two anchoring/harmonization methods. First, we examined within‐tracer anchoring by creating anchor values based on the mean SUVR of Youngs (<25 years) and 95th percentile voxels from cognitively impaired individuals. Second, we explored within‐ plus between‐tracer anchoring, employing linear (e.g., piecewise) and non‐linear regressions. To address the inherent problem of discontinuity of piecewise transformations, we implemented two smoothing methods at the inflection point between equations, transforming them into a continuous function.ResultsUniτ scale anchoring within‐tracer resulted in similar estimates for high values, but less accurate in the lower range (Figure 1,2). Anchoring within‐ plus between‐tracer improved estimate consistency, with the piecewise transformation generating the best results. The piecewise smoothing equation yielded estimates comparable to those obtained from the piecewise method without smoothing. This allowed for the use of a single formula. In addition, this leads to more robust results when the goal is to study longitudinal changes in the scale (data not shown). UPitt testing dataset showed similar results to the training set (Figure 3).ConclusionOur preliminary findings suggest that anchoring tau‐PET values both within and between tracers has the potential to harmonize tau‐PET tracers, while preserving their characteristics. Currently, piecewise smoothing is the preferred method for Uniτ, but we are continuously fine‐tuning scale parameters as we acquire more data. The final scale parameters will be determined based on extensive training and testing data from multiple tracers. This cautious methodology holds the promise of delivering reliable, robust, and reproducible results, ensuring safe usage of the scale in clinical trials, and potentially paving the way for future use in clinical practice.

AbstractBackgroundThe HEAD study focuses on collecting an extensive dataset from various tau‐PET tracers, aiming to establish robust anchor values, which are essential for harmonizing tau‐PET measurements. Here, we aim to showcase the capability of converting 3D tau‐PET images into a common scale using the Universal Tau‐PET Scale, Uniτ (tau), and to use these 3D images to subsequently obtain ROIs as needed.MethodsWe assessed 185 individuals across the aging and AD spectrum from the HEAD study, with [18F]Flortaucipir and [18F]MK‐6240 tau‐PET tracers. Tau‐PET SUVR images were standardized to a common 8mm FWHM, using the inferior cerebellar gray matter as the reference region. We generated Uniτ Tau‐PET 3D images using a single formula based on Meta‐temporal parameters in two steps: first, within‐tracer anchoring based on young (<25 years) and cognitively impaired individuals; second, between‐tracer anchoring using a piecewise transformation from [18F]Flortaucipir to [18F]MK‐6240, with smoothing at the piecewise inflection point. Subsequently, we extracted mean Uniτ values from these 3D images for key ROIs, including meta‐temporal, mesial, temporo‐parietal, frontal, and Braak stages III‐VI. Finally, we correlated Uniτ values across ROIs between the two tracers to evaluate the accuracy of estimates from the voxel‐wise transformation.ResultsThe original SUVR images present large differences between the two tau‐PET tracers (Figure 1). However, upon applying the Uniτ piecewise transformation with smoothing to all brain voxels, we were able to reasonably harmonize these images to the Uniτ scale, substantially reducing visual variability. Notably, the mean Uniτ values for the ROIs extracted from these harmonized 3D tau‐PET brain images demonstrated a high level of association between the two tracers (Figure 2). Furthermore, estimates generated from ROIs or extracted from our 3D parametric Uniτ model yielded identical estimates (Figure 3).ConclusionThe strong associations between tracers after directly harmonizing 3D images to Uniτ scale using the piecewise transformation with smoothing, underscore the effectiveness of the proposed method. This approach provides a reliable and standardized way to compare tau‐PET data across different tracers. Our results indicate the feasibility of harmonizing 3D tau‐PET images without relying on pre‐established ROIs, overcoming the limitation of restricting the analysis to only few brain regions.

AbstractBackgroundDefault mode network (DMN) resting state connectivity has been correlated with heightened amyloid and tau – hallmarks of Alzheimer’s Disease (AD). Tau is postulated to impact a meta‐temporal area including DMN‐associated regions like amygdala, entorhinal cortex, fusiform gyrus, parahippocampus, inferior temporal, and middle temporal gyrus. We recruited individuals with varying cognitive status to undergo resting state connectivity and imaging with two tau tracers (Flortaucipir and MK6240). We investigated the association between DMN connectivity and both global and regional (meta‐temporal) tau.MethodsWe studied 149 adults who were young cognitively healthy (n = 10, 21‐25 years, 5F), older cognitively healthy (n = 85, 50‐83 years, 51F), had MCI (n = 43, 53‐84 years, 23F), or had AD (n = 11, 62‐84, 6F). All individuals underwent resting state imaging and PET imaging with both Flortaucipir and MK6240 tau tracers. Resting data was preprocessed and DMN connectivity was estimated using bilateral posterior cingulate gyrus as region of interest. Tau tracer uptake was preprocessed (Pascoal. et al), and voxel‐wise standardized uptake value ratio (SUVR) was computed using inferior cerebellar gray matter as reference. We computed universal and meta‐temporal uptake for both tracers. We conducted four voxel‐wise associations between DMN connectivity and uptake in each tracer. We corrected for multiple comparisons using SnPM13 to estimate non‐parametric p‐values (10,000 permutations), we used a cluster forming threshold of p = 0.001 and corrected for multiple comparisons by controlling the family‐wise error rate (FWE) at p = 0.05.ResultsGreater universal Flortaucipir uptake was associated with lower within‐network DMN connectivity (bilateral posterior cingulate, precuneus, angular/supramarginal gyrus, and inferior parietal) but greater between‐network DMN connectivity (superior parietal, postcentral gyrus; Figure 1A). Greater universal MK6240 uptake was associated only with greater DMN‐cerebellum connectivity (Figure 1B). Greater meta‐temporal Flortaucipir was associated with lower within‐network DMN connectivity similar to universal uptake with addition of medial prefrontal cortex and middle temporal gyrus (Figure 2A). Greater meta‐temporal MK6240 was only associated with greater DMN‐superior parietal connectivity (Figure 2B).ConclusionsThese preliminary findings show that uptake using different tau tracers may lead to varying conclusions on association with connectivity. This may have implications for understanding the role of tau in altering resting state connectivity findings.

AbstractBackgroundIn vivo studies using the tau PET tracers have shown high performance for the diagnosis of Alzheimer’s disease dementia and patterns of tracer uptake that resemble those observed in post‐mortem studies. However, tau tracers present distinct patterns of binding that might influence their performance in detecting AD pathology. In a head‐to‐head study, we investigated the performance of [18F]MK6240 and [18F]Flortaucipir for the diagnosis of AD.MethodWe assessed 132 individuals from the HEAD study (58 CU Aß‐, 15 CU Aß+, 14 MCI Aß‐, 32 MCI Aß+ and 13 AD dementia) with Aß‐PET, [18F]MK6240 and [18F]Flortaucipir. Voxel‐wise receiver operating characteristic curves (ROC) of the two tau tracers were used to contrast groups provided the area under the curve (AUC) for disease diagnosis or biomarkers positivity.ResultThe brain maps showed numerically higher and more spread AUC for [18F]Flortaucipir to discriminate CU Aß‐ from CU Aß+ (Fig.1A). The difference between tracers’ AUC was greater in Braaks IV and V (Fig.2A), both regions that are not expected to have tau accumulation in CU Aß+ individuals, reflecting a potential off‐target binding for [18F]Flortaucipir. To differentiate CU Aß‐ from MCI Aß+ individuals, [18F]MK6240 presented a numerically higher AUC than [18F]Flortaucipir in Braak I and II and similar AUC in other Braak regions (Fig.1B, Fig. 2B). We observed a high performance of [18F]MK6240 and [18F]Flortaucipir in differentiating AD dementia from CU Aß‐ individuals. However, [18F]MK6240 exhibits a higher AUC than [18F]Flortaucipir in all Braak regions, especially Braak V‐VI (Fig.1C, Fig.2C). Finally, [18F]MK6240 presented higher AUC in all Braak regions to discriminate MCI Aß‐ from MCI Aß+ individuals (Fig.1D, Fig.2D).ConclusionOur results indicate that [18F]MK6240 and [18F]Flortaucipir present high accuracy to discriminate AD from CU Aß‐ individuals. However, [18F]MK6240 presents higher AUC to discriminate AD and MCI Aß+ from CU Aß‐ individuals than [18F]Flortaucipir. Together, our head‐to‐head study sheds light on the distinct patterns of binding for Tau‐PET tracers.

AbstractBackgroundTau PET provides continuous measurements of tau tangle pathology in the human brain. However, establishing cutoffs is crucial for selecting individuals for treatment in clinical trials or practice. In the absence of postmortem data, PET cutoffs must be established using statistical methods based on what is considered normal tracer uptake. In this study, we tested the impact of various methods to determine tau positivity using two different tau PET tracers in individuals scanned head‐to‐head.MethodsWe studied 147 individuals from Head‐to‐Head Harmonization of Tau Tracers in Alzheimer's Disease (HEAD) study with tau tangle PET scans with [18F]Flortaucipir and [18F]MK‐6240, and amyloid‐ß (Aß) PET. Tau deposition was measured with the standardized uptake value ratio (SUVR) of each agent in the Medial Temporal Lobe (MTL) and the Entorhinal Cortex (EC). To determine Tau positivity three different methods were used: >2.5 standard deviations (SD) than the mean of the young, >1.5 SD mean of the cognitively unimpaired (CU) and >1.5 SD mean of CUAß‐.ResultsDemographic characteristics of the study population are reported in Table 1. Using the cutoff >2.5 SD mean of young, [18F]Flortaucipir was positive in 35 (23.8%) and 69 (46.9%) individuals in the EC and MTL, respectively. [18F]MK‐6240 was positive in 49 (33.3%) and 58 (39.5%) individuals in EC and in MTL. Using >1.5 SD mean of CUAß‐, [18F]Flortaucipir was positive in 38 (25.9%) and 45 (30.6%) individuals in EC and MTL, while [18F]MK‐6240 was positive in 51 (34.7%) and 50 (34.0%) in EC and in MTL (Figure 1).ConclusionsOur findings indicate variations in tau positivity when employing different methods based on either the young or CUAß‐. [18F]Flortaucipir exhibited a higher rate of positive results when the method based on young individuals was applied in the MTL. Conversely, [18F]MK‐6240 showed more consistent and generally higher positivity when other methods were used for cutoff determination and/or in the EC region. Further research with a larger sample size is required to gain a better understanding of the optimal cutoff determination methods for these tracers.

AbstractBackgroundTau‐PET tracers have been used to diagnose and stage Alzheimer’s disease. However, different tau tracers present distinct patterns of binding throughout the brain, challenging the harmonization of their results. We hypothesize that the choice of a reference region can impact the harmonization of the tau‐PET standardized uptake value ratio (SUVR). In this context, we aimed to explore how different cerebellar reference regions impact the association between [18F]Flortaucipir and [18F]MK‐6240 SUVR values.MethodWe studied 185 individuals across the aging and AD spectrum with head‐to‐head Flortaucipir and MK‐6240 tau PET (HEAD Study). SUVRs were processed to a common 8mm FWHM using 15 different reference regions defined in the spatially unbiased atlas template of the cerebellum (SUIT) (Diedrichsen, 2009). Regression models investigated the association between Flortaucipir and MK‐6240 using multiple combinations of reference regions. R2 statistic was used to estimate goodness‐of‐fitting.Result225 combinations of associations Flortaucipir and MK‐6240 SUVR were tested, where the SUVRs are not necessarily quantified using the same reference region. Figure 1 presents the top 25 strongest and 25 weakest associations, ordered based on the coefficient of determination (R²). Flortaucipir and MK‐6240 exhibited the most robust associations when the inferior cerebellar gray matter or Crus I were employed for SUVR determination (R2 = 0.89, Figure 1‐2). Conversely, combinations incorporating the fastigial region consistently yielded weaker associations between the two tracers (R2<0.70, Figure 1‐2).ConclusionInterestingly, we showed that the inferior cerebellum, when used for both tracers, serves as a robust reference region for determining the most similar SUVRs for Flortaucipir and MK‐6240. Notably, these two regions have been commonly utilized in previous studies involving these tau tracers. Our results imply that the inferior cerebellum could be the optimal reference region for research aimed at harmonizing tau PET tracers using statistical scales.

AbstractBackgroundStandardizing tau pathology quantification in vivo is challenged by differences in binding characteristics between 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 these tracers' outcomes and develop tools allowing for the generalization of findings across large studies and trials. Here, we present current advancements in building the HEAD study cohort and dataset.MethodsThe HEAD study is managed at the University of Pittsburgh. HEAD comprises several sites across the US and Canada in which 620 subjects (young, cognitively unimpaired, mild cognitive impairment, and Alzheimer’s disease (AD)) will undergo tau‐PET with at least two tracers, amyloid‐PET with PiB or NAV4694, MRI, blood collection, and standardized neuropsychological testing at baseline and at 18‐month follow‐up. PET and MRI acquisition parameters are based on ADNI4 protocols and neuropsychological testing employs the NACC Uniform Data Set. The National Centralized Repository for AD serves as the biorepository for blood samples and the Laboratory of Neuroimaging provides a centralized database for imaging and neuropsychological archiving. PET data is reconstructed to maximize cross‐scanner harmonization and is processed uniformly similarly to ADNI4 PET.ResultsIn one year of active enrollment (Jan 2023‐Jan 2024), 362 participants were enrolled at six active sites; over 80% of participants completed neuropsychological testing and MRI, and over 70% completed Flortaucipir, MK‐6240, and amyloid‐PET, with a mean tau‐PET acquisition window of 29.1 days. RO948 and PI‐2620 tau‐PET are additionally acquired in a subset currently including 41 participants (Fig.1). Current HEAD cohort demographics including age, sex, underrepresented populations, and group distributions, APOEe4 carrier status, and amyloid positivity are shown in Fig.2. A case of a subject with AD with all 4 tau‐PET tracers acquired head‐to‐head within 49 days is shown in Fig.3.ConclusionThe HEAD study represents a continued effort in the optimization of AD imaging biomarkers. Baseline data collection is projected to be completed in 2024; results being generated by multiple groups from this dataset will provide novel and crucial guidance on the use of tau‐PET tracers in research, clinical trials, and prospectively in clinical practice.

AbstractBackgroundUtilizing PET amyloid‐beta (Aß) and tau for staging Alzheimer’s Disease (AD) has demonstrated potential in identifying individuals with varying degrees of disease severity, applicable to both clinical trials and practice. However, the diverse binding characteristics of tau tracers pose challenges to the application of this staging across different ligands. In this study, we evaluate a novel staging framework proposed by the AA working group, employing Aß PET and either [18F]MK6240 or [18F]Flortaucipir in individuals participating in a head‐to‐head study of tau PET tracers. This will provide insights into the robustness of this framework when applied across different tau ligands.MethodWe evaluated 154 people (89 CU and 65 CI), with MK6240, Flortaucipir, Aß‐PET, MRI. SUVRs used inferior cerebellar gray as reference. Participants were categorized in Aß positivity(A), using visual read, and tau positivity (T) in medial temporal lobe(MTL) and neocortex(NEO). Tau MTL(TMTL) and NEO positivity were defined as average +3 SD of youngs. NEO moderate(TMOD) and high(THIGH) was defined as below and above median SUVR of NEO‐positive individuals. Individuals were grouped as: A‐T‐, A+T‐, A+TMTL+(early), A+TMOD+(intermediary), and A+THIGH+(advanced).ResultsWe found that the distribution of the number of individuals across the five AD stages was comparable when using both MK6240 and Flortaucipir tracers (Figure 1). However, upon examining the concordance within each group, we found a low concordance of 30% (3/10) between tracers in the early tau stage (A+TMTL+). Concordance increased in the intermediary stage A+TMOD+, with a rate of 65% (9/14), and further increased to 79% (11/14) in the advanced stage (A+THIGH+). The A+T‐ group exhibited the highest concordance between tracer results, at 95% (21/22).ConclusionOur findings indicate that the application of this framework across different populations using different tau tracers can lead to a compatible distribution among groups. However, in a direct comparison, we noted that this did not necessarily result in high concordance between groups, as group shifts were observed that depended on the cutoffs used (data not shown). These results emphasize the necessity for harmonization of tau PET tracers prior to their utilization for stage determination.

AbstractBackgroundThe newly proposed criteria by the AA working group incorporates both biological and clinical stages to characterize the progression of AD. In this study, we aim to evaluate the agreement between these two complementary systems.MethodsUsing 188 participants from McGill TRIAD and 139 from the HEAD cohorts, we categorized participants into biological (0‐4) and clinical (0‐4) stages using amyloid PET, tau PET(MK‐6240), and clinical measures as described by the working group. Participants were then stratified into three categories: congruent (clinical = biological), higher in the biological stage (clinical < biological), and higher in the clinical stage (clinical > biological). In the TRIAD cohort, we further compared these groups for age, sex, years of education, vascular burden (WMH), microglial activation (PBR scan), Astrocyte reactivity (GFAP), amyloid load, tau load, and NPI‐Q scores using Welch two‐sided t‐test with FDR multiple comparison correction.ResultsIn the TRIAD cohort, 34% of participants were congruent, 24.5% had a higher clinical stage, and 41.5% had a higher biological stage. Meanwhile in the HEAD cohort 68.2% were congruent, 20.7% had a higher clinical stage, and 17% had a higher biological stage. TRIAD participants with a higher clinical stage had lower education (P = 0.02), more neuropsychiatric symptoms (P = 0.03), and a higher vascular burden (P = 0.03) (Table 1). As expected, people with higher biological stage had more amyloid(P<0.001), tau(P<0.001), and astrocyte reactivity(P<0.001) (Table 1, Figure 2).ConclusionsOur findings highlight important discordance between clinical and biological stages, which could be partially explained by cognitive reserve. This was supported by the protective effects of educational attainment in participants with a higher biological stage. Vascular burden played a major role in the cognitive impairment of individuals with higher clinical stages. Future studies should replicate these findings in larger more representative population‐based cohorts.