Citations

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.

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 (Figure 1). Current HEAD cohort demographics including age, sex, underrepresented populations, and group distributions, APOEε4 carrier status, and amyloid positivity are shown in Figure 2. A case of a subject with AD with all 4 tau‐PET tracers acquired head‐to‐head within 49 days is shown in Figure 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.

AbstractBackgroundDifferences between on‐ and off‐target retention characteristics between [18F]MK6240 and [18F]Flortaucipir (FTP) complicate the harmonization across tracers. Our objective here was to separate the impact of the reference region by evaluating correlations between [18F]MK6240 (MK) and [18F]FTP standard uptake values (SUVs).MethodParticipants (Figure 1, n=90) received an amyloid‐β (Aβ) PET scan ([11C]PIB or [18F]NAV4694) and two tau‐PET scans: [18F]MK (90‐110 minutes post‐injection) and [18F]FTP (80‐100 minutes post‐injection). For all PET scans, the individual frames were realigned, averaged, and co‐registered to a Freesurfer 7.1‐segmented T1‐MPRAGE. SUVs were calculated in the reference region (inferior cerebellar gray), on‐target regions (entorhinal cortex, hippocampus, Braak III, Braak IV, Braak V, Braak VI) and off‐target regions (caudate, pallidum, putamen, thalamus, cerebellar white matter, choroid plexus, meninges, eroded white matter). We examined correlations between [18F]MK and [18F]FTP SUV values as a function of Aβ status, as well as their associations with age.ResultAll correlations (Figure 2) were significant at p<0.001. The interaction with Aβ status was significant for hippocampus, BraakIII‐V and the metatemporal region. In Aβ+ participants, [18F]FTP and [18F]MK6240 have similar SUVs in on‐target regions with a slope close to 1 except for in hippocampus and Braak VI. However, [18F]FTP SUVs, on average, were greater than [18F]MK SUVs in Aβ+ and Aβ‐ subjects in the reference region and all off‐target regions except for meninges, and in Aβ‐ subjects in the on‐target regions. Age correlated with [18F]FTP SUVs in Aβ‐ subjects in all regions (except the reference region, Braak VI and meninges) and [18F]MK SUVs in putamen and pallidum, but this correlation was only significant (p<0.01) when young and old subjects were included.ConclusionDespite [18F]MK’s higher affinity for tau and SUVR dynamic range being twice that of [18F]FTP, the SUVs in on‐target regions are similar. The higher [18F]FTP signal in the reference region appears to be the contributing factor to the lower dynamic range. The higher [18F]FTP signal in on‐target regions for Aβ‐ subjects may reflect the presence of off‐target signals in these subjects. Taking off‐target signals into account should improve correlations between the two tracers, especially when measuring the earliest deposition of tau.

AbstractBackgroundThe association between [18F]Flortaucipir (FTP) and [18F]MK6240, two commonly used tau‐PET tracers in Alzheimer’s disease (AD), varies due to distinct binding properties and off‐target signal regions. Our study aims to elucidate the biological factors influencing this association and evaluate the applicability of a common equation across different on‐target regions.Method113 individuals from the HEAD dataset (11 young, 58 cognitively unimpaired elderly, and 44 cognitively impaired) underwent [18F]MK6240, [18F]FTP and Aβ‐PET scans. Images were processed using the inferior cerebellar grey (CG) as the reference region, with subjects categorized by Aβ status. Stepwise linear fitting identified off‐target (OFF) regions impacting tracer associations within the whole cortex, Braak and temporal‐meta regions of interest (ROIs). OFF regions included were a composite mask (pallidum, putamen, caudate ‐ PPC) to address collinearity in subcortical nuclei, as well as the region with the greatest partial volume effects (PVE) on the ROI (meninges, choroid plexus, and the hotspot medial to entorhinal cortices (EC)). Akaike information criteria (AIC) analyses tested linear models with various off‐target regions.ResultPPC and PVE regions influenced tracer association in on‐target regions. PPC binding is associated with age. PPC and meninges retention, along with Aβ status interaction, demonstrated the best model fit for the whole cortex (Figure 1). Weighting factors derived from the whole cortex model were applied to Braak regions and temporal meta‐ROI. Braak III‐VI and temporal‐meta ROI showed optimal models including OFF regions and Aβ status interaction (Figure 2). Forcing the whole equation gave optimal results. For Braak I (EC), adding the OFF covariates only was the best model. For Braak II (hippocampus), the best model included OFF regions and the interaction with Aβ status (Figure 2). Forcing the whole cortex equation onto Braak I and Braak II yielded suboptimal fits.ConclusionHarmonizing tau‐PET tracers involves considering significant covariates. Integrating meningeal and age‐related off‐target regions enhances [18F]MK6240 and [18F]FTP association in on‐target regions, further improved by Aβ status inclusion. While the whole cortex model may not suit early ROI, it proves effective for Braak III and beyond. Creating an equation for tracer translation necessitates incorporating meningeal and subnuclei off‐target retention.

AbstractBackgroundTau‐PET tracers allow for in vivo Braak staging of individuals in the Alzheimer’s disease (AD) continuum. The impact of tracers’ characteristics for Braak staging using tau‐PET remains unclear. Therefore, we performed a head‐to‐head comparison of Braak staging using first‐ and second‐generation tau‐PET tracers.MethodWe assessed 51 cognitively unimpaired (CU) and 49 cognitively impaired participants (mean [SD] age 69.4 [7.5] years) with at least two tau‐PET ligands ([18F]MK6240, [18F]AV1451, and/or [18F]RO948) at McGill University, as part of the HEAD study. We calculated standardized uptake value ratios (SUVR) in Braak‐like regions of interest (ROI) for each ligand and investigated their association using Spearman’s correlation. Thresholds defined the presence of tauopathy in each Braak ROI, which was used to assign a Braak stage to each participant. Finally, we assessed the agreement between the Braak staging provided by each tracer, both for the traditional (using separate Braak stages – 0, I, II, III, IV, V, and VI) and simplified (using joint Braak stages – 0, I‐II, III‐IV, and V‐VI) frameworks.ResultIn all Braak ROIs, we found positive correlations between the SUVR values of the three tracers (Figure 1). The strongest correlation was between [18F]MK6240 and [18F]RO948 in Braak II (r=0.94), and the weakest between [18F]AV1451 and [18F]RO948 in VI (r=0.51). Inter‐tracer agreement regarding the latest stage of abnormality was substantial or nearly perfect between pairs of tracers but moderate between the three tracers (Figure 2A). The simplified framework presented greater agreement compared to traditional Braak staging, except between [18F]MK6240 and [18F]RO948. At the ROI level, the lowest agreements were observed for Braak II and I‐II, when [18F]MK6240 and [18F]RO948 were compared to [18F]AV1451 (Figure 2B‐C). [18F]AV1451 had the highest probability of overestimating the Braak stage assigned by [18F]MK6240 and by [18F]RO948, especially at earlier stages (Figure 3).ConclusionThe ligands presented moderate to nearly perfect agreement for in vivo staging of AD severity. Off‐target binding of [18F]AV1451 to the choroid plexus might explain disagreements at early stages. Overall, slight improvements in agreements were achieved with the simplified framework, as observed in histopathological staging.

AbstractBackgroundMany proposed clinical decision support systems (CDSS) require multiple disparate data elements as input, which makes implementation difficult, and furthermore have a black‐box nature leading to low interpretability. Fluorodeoxyglucose Positron Emission Tomography (FDG‐PET) is an established modality for the diagnosis of dementia, and a CDSS that uses only an FDG‐PET image to produce a reliable and understandable result would ease both of these challenges to clinical application.MethodA deep variational autoencoder (VAE) was used to extract a latent representation of each image through prior training from FDG‐PET brain images (n=2000). This unsupervised VAE has a novel graph convolutional architecture that makes it applicable to masked template space images. A logistic regression model was used to classify each image. A parametric study of the latent space revealed the imaging features that were used in the logistic regression model to differentiate each class. A separate dataset of participants labeled with their clinical diagnosis (n=1239) was used to assess the logistic regression model’s diagnostic accuracy of cognitively unimpaired (CU) (n=679) and between common dementia subtypes: Alzheimer’s disease (AD) (n=310), Lewy body dementia (DLB) (n=151), and behavioral variant frontotemporal dementia (bvFTD) (n=99). The logistic regression classifier was evaluated using receiver‐operator characteristic (ROC‐AUC) curves.ResultROC‐AUC curves for CU and each dementia subtype are illustrated in Figure 1a. A k nearest neighbors model was used to develop a graphical representation of the latent space where nodes are images and edges are drawn between nearest neighbors, illustrated in Figure 1b. The graph of the latent space demonstrates a comparison between dementia subtypes and CU images.ConclusionIn this study, we developed a proof‐of‐concept VAE that can achieve differential diagnosis with 84.8% balanced accuracy. The model architecture provides visual interpretability, and shows that the hippocampi, parietal lobes, and cerebellum, were useful for distinguishing AD, the frontal lobe, posterior cingulate cortex, and hippocampi were useful for distinguishing DLB, and prefrontal pole, subcortical structures, and occipital lobe were useful for distinguishing bvFTD. These regions of metabolism align with clinical pathology and metabolic patterns.

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.

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‐β (Aβ) 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 CUAβ‐.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 CUAβ‐, [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 CUAβ‐. [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.

AbstractBackgroundUtilizing PET amyloid‐beta (Aβ) 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 Aβ 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, Aβ‐PET, MRI. SUVRs used inferior cerebellar gray as reference. Participants were categorized in Aβ 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.