Explore the vast range of titles available.

We investigated the association between olfactory identification and Alzheimer's disease biomarkers, including amyloid, tau, and neurodegeneration. Thirty-four older adults, including 19 cognitively normal (CN), 10 subjective cognitive decline (SCD), and 5 mild cognitive impairment, underwent amyloid positron emission tomography, magnetic resonance imaging, and the University of Pennsylvania Smell Identification Test (UPSIT). Twenty-six also underwent tau positron emission tomography. Associations between the UPSIT and regionally sampled amyloid, tau, and temporal atrophy were evaluated. Voxel-wise regression models were also utilized. Analyses were conducted with the full sample and only CN/SCD. Lower UPSIT scores were associated with increased temporal and parietal tau burden in regional and voxel-wise analyses in the full sample and in CN and SCD only. Temporal lobe atrophy was associated with lower UPSIT score. Amyloid was not associated with the UPSIT. Impairment on the UPSIT may be a good marker for tau and neurodegeneration in preclinical or prodromal Alzheimer's disease.

Tauvid has been approved by the U.S. Food and Drug Administration (FDA) in 2020 for positron emission tomography (PET) imaging of adult patients with cognitive impairments undergoing evaluation for Alzheimer’s disease (AD) based on tau pathology. Abnormal aggregation of tau proteins is one of the main pathologies present in AD and is receiving increasing attention as a diagnostic and therapeutic target. In this review, we summarised the production and quality control of Tauvid, its clinical application, pharmacology and pharmacokinetics, as well as its limitation due to off-target binding. Moreover, a brief overview on the second-generation of Tau PET tracers is provided. The approval of Tauvid marks a step forward in the field of AD research and opens up opportunities for second-generation tau tracers to advance tau PET imaging in the clinic.

INTRODUCTION Tau‐positron emission tomography (PET) outcome data of patients with Alzheimer's disease (AD) cannot currently be meaningfully compared or combined when different tracers are used due to differences in tracer properties, instrumentation, and methods of analysis. METHODS Using head‐to‐head data from five cohorts with tau PET radiotracers designed to target tau deposition in AD, we tested a joint propagation model (JPM) to harmonize quantification (units termed “CenTauR” [CTR]). JPM is a statistical model that simultaneously models the relationships between head‐to‐head and anchor point data. JPM was compared to a linear regression approach analogous to the one used in the amyloid PET Centiloid scale. RESULTS A strong linear relationship was observed between CTR values across brain regions. Using the JPM approach, CTR estimates were similar to, but more accurate than, those derived using the linear regression approach. DISCUSSION Preliminary findings using the JPM support the development and adoption of a universal scale for tau‐PET quantification. Highlights Tested a novel joint propagation model (JPM) to harmonize quantification of tau PET. Units of common scale are termed “CenTauRs”. Tested a Centiloid‐like linear regression approach. Using five cohorts with head‐to‐head tau PET, JPM outperformed linearregressionbased approach. Strong linear relationship was observed between CenTauRs values across brain regions.

•This study introduces a deep learning (DL)-based single-session triple-tracer brain PET imaging protocol.•The technique was validated using clinical studies aiming at reducing radiation exposure and improving diagnostic accuracy.•This study demonstrated that the suggested DL model, can separate the signals belonging to three different radiotracers from simultaneous triple-tracer PET scans.•This method may make multiplex scanning feasible in the clinic, hence reducing the scanning time, radiation hazard and improving patient comfort.•This work provides a more efficient, accurate, and comfortable solution for triple-tracer PET imaging. Multiplexed Positron Emission Tomography (PET) imaging allows simultaneous acquisition of multiple radiotracer signals, thus enhancing diagnostic capabilities, reducing scan times, and improving patient comfort. Traditional methods often require significant delays between tracer injections, leading to physiological changes and noise interference. Recent advancements, including multi-tracer compartment modeling and machine learning, provide promising solutions. This study explores the deep learning (DL)-based single-session triple-tracer brain PET imaging protocol, aiming at simplifying multi-tracer PET imaging, while reducing radiation exposure. The study uses the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, which includes cognitively normal (CN) patients, as well as patients with mild cognitive impairment (MCI) and dementia. The dataset also includes PET scans acquired with amyloid (18F-florbetaben [FBB] or 18F-florbetapir [FBP]), 18F-Fluorodeoxyglucose (FDG), and tau 18F-flortaucipir (FTP). To mimic the effect of simultaneous acquisition of multiple PET tracers, we generated synthetic dual- and triple-tracer images by summing FBP/FBB, FTP, and FDG scans. A DL model based on Swin Transformer architecture was developed to separate these signals, using five-fold cross-validation and mean squared error (MSE) loss. The synthetic PET images were evaluated using established image quality metrics, including MSE, structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). In addition, clinical evaluation was conducted by two nuclear medicine specialists to assess the amyloid and tau status in the synthetic and reference images. The proposed DL model effectively synthesized realistic FBB/FBP and FDG images from dual- and triple-tracer PET images. Although the proposed DL model’s performance in generating FTP images was less successful, it remains promising. The clinical evaluation revealed that the amyloid status estimated from the synthetic images led to a sensitivity of 92% and specificity of 86% for FBB, while it showed a sensitivity of 93% and specificity of 67% for tau status using FBP extracted from the triple-tracer images. The calculated quantitative metrics showed that the mean error for synthetic amyloid images (FBB: 0.03 SUV, FBP: 0.00 SUV) was higher than FDG for FBB (0.02 SUV) but lower than FDG for FBP (-0.01 SUV), and comparable to FTP (FBB: 0.03 SUV, FBP: 0.00 SUV). Voxel-wise correlation analysis demonstrated strong correlation between synthetic and reference images, particularly for amyloid images (FBB: y = 0.98x + 0.00, R² = 0.85; FBP: y = 1.11x + 0.04, R² = 0.73), while FTP (FBB: y = 0.87x + 0.14, R² = 0.51; FBP: y = 0.98x + 0.09, R² = 0.59) and FDG images (FBB: y = 1.01x + 0.18, R² = 0.85; FBP: y = 0.96x + 1.37, R² = 0.77) showed moderate correlations. Our study demonstrates that the suggested DL model can separate the signals belonging to three different radiotracers from simultaneous triple-tracer PET scans. This method may make multiplex scanning feasible in the clinic, hence reducing the scanning time, radiation hazard and improving patient comfort.

•Detection of individual brain tau deposition is important for the diagnosis of Alzheimer's disease.•The model based on the latent feature-enhanced generative adversarial network can be used to detect brain tau deposition in individuals.•This model has been demonstrated to have better performance in the diagnosis and prediction of Alzheimer's disease. The conventional methods for interpreting tau PET imaging in Alzheimer's disease (AD), including visual assessment and semi-quantitative analysis of fixed hallmark regions, are insensitive to detect individual small lesions because of the spatiotemporal neuropathology's heterogeneity. In this study, we proposed a latent feature-enhanced generative adversarial network model for the automatic extraction of individual brain tau deposition regions. The latent feature-enhanced generative adversarial network we propose can learn the distribution characteristics of tau PET images of cognitively normal individuals and output the abnormal distribution regions of patients. This model was trained and validated using 1131 tau PET images from multiple centres (with distinct races, i.e., Caucasian and Mongoloid) with different tau PET ligands. The overall quality of synthetic imaging was evaluated using structural similarity (SSIM), peak signal to noise ratio (PSNR), and mean square error (MSE). The model was compared to the fixed templates method for diagnosing and predicting AD. The reconstructed images archived good quality, with SSIM = 0.967 ± 0.008, PSNR = 31.377 ± 3.633, and MSE = 0.0011 ± 0.0007 in the independent test set. The model showed higher classification accuracy (AUC = 0.843, 95 % CI = 0.796−0.890) and stronger correlation with clinical scales (r = 0.508, P < 0.0001). The model also achieved superior predictive performance in the survival analysis of cognitive decline, with a higher hazard ratio: 3.662, P < 0.001. The LFGAN4Tau model presents a promising new approach for more accurate detection of individualized tau deposition. Its robustness across tracers and races makes it a potentially reliable diagnostic tool for AD in practice.

INTRODUCTION The tau positron emission tomography (PET) overlap index (OI) has shown promise in maximizing signal‐to‐noise for longitudinal tau PET imaging, particularly for early tau pathology, but requires validation against neuropathology. METHODS Fifty‐seven participants who underwent serial tau PET imaging (flortaucipir) and subsequent autopsy were included. Tau PET OI and standardized uptake value ratios (SUVRs) were compared across neuropathological diagnoses. RESULTS Tau PET OI showed greater concordance with neurofibrillary tangle (NFT) severity in the entorhinal cortex (a key region for Alzheimer's disease [AD] tauopathy) than SUVR, particularly in early Braak tangle stages (positivity: 52.2% for OI vs. 13.0% for SUVR). OI detected overlapping tau voxels that exhibited spatial correspondence with immunohistochemical and autoradiography measures of tau deposition across both AD and non‐AD tauopathies. DISCUSSION These findings demonstrate the enhanced capacity of OI in serial tau PET to robustly detect early and spatially localized tau pathology, supporting its application as a sensitive imaging metric in AD and select non‐AD tauopathies. Highlights Tau positron emission tomography (PET) overlap index (OI) quantifies the spatial consistency of elevated standardized uptake value ratio (SUVR) voxels across two serial tau PET scans and is applicable at this point to serial imaging only. Tau PET OI improves detection of tau signal compared to SUVR in the Alzheimer's disease spectrum as well as in various pathologic diagnosis groups (Lewy body dementia, argyrophilic grain disease, corticobasal degeneration, progressive supranuclear palsy, etc.). Tau PET OI can detect tau signal in gray matter and sometimes in white matter in the 4R tau pathological diagnosis group. Tau PET OI correlated with neuropathological immunohistochemistry (IHC) and autoradiography findings with various pathological diagnosis groups. Tau PET OI can capture tau overlapped signal at early Braak tangle stage that visually corresponds to neurofibrillary tangle patterns seen on IHC.

Background The aim of this longitudinal study was to assess with positron emission tomography (PET) the relationship between levels of inflammation and the loads of aggregated β-amyloid and tau at baseline and again after 2 years in prodromal Alzheimer's disease. Methods Forty-three subjects with mild cognitive impairment (MCI) had serial 11 C-PK11195 PET over 2 years to measure inflammation changes, and 11 C-PiB PET to determine β-amyloid fibril load; 22 also had serial 18 F-Flortaucipir PET to determine tau tangle load. Cortical surface statistical mapping was used to localise areas showing significant changes in tracer binding over time and to interrogate correlations between tracer binding of the tracers at baseline and after 2 years. Results Those MCI subjects with high 11 C-PiB uptake at baseline (classified as prodromal Alzheimer’s disease) had raised inflammation levels which significantly declined across cortical regions over 2 years although their β-amyloid levels continued to rise. Those MCI cases who had low/normal 11 C-PiB uptake at baseline but their levels then rose over 2 years were classified as prodromal AD with low Thal phase 1-2 amyloid deposition at baseline. They showed levels of cortical inflammation which correlated with their rising β-amyloid load. Those MCI cases with baseline low 11 C-PiB uptake that remained stable were classified as non-AD, and they showed no correlated inflammation levels. Finally, MCI cases which showed both high 11 C-PiB and 18 F-Flortaucipir uptake at baseline (MCI due to AD) showed a further rise in their tau tangle load over 2 years with a correlated rise in levels of inflammation. Conclusions Our baseline and 2-year imaging findings are compatible with a biphasic trajectory of inflammation in Alzheimer’s disease: MCI cases with low baseline but subsequently rising β-amyloid load show correlated levels of microglial activation which then later decline when the β-amyloid load approaches AD levels. Later, as tau tangles form in β-amyloid positive MCI cases with prodromal AD, the rising tau load is associated with higher levels of inflammation.

INTRODUCTION A subset of amyloid beta (Aβ)‐positive Alzheimer's disease (AD) patients is tau‐positron emission tomography (PET) negative. We aimed to characterize this subgroup using [18F]flortaucipir PET visual read (VR), as this is important for prognosis and selection for therapies. METHODS Aβ‐positive VR tau‐PET‐negative AD dementia patients (AD A+T−) were compared to tau‐PET‐positive AD patients (AD A+T+) and control groups (CU A−T−; CU A+T−) included from the Amsterdam‐based cohort and Alzheimer's Disease Neuroimaging Initiative (ADNI). We compared [18F]flortaucipir binding in an early‐ and late‐stage tau ROI, atrophy, cognition, and co‐pathologies. RESULTS AD A+T− were older, showed less hippocampal atrophy and slower cognitive decline compared to AD A+T+. In ADNI, AD A+T− showed higher early‐stage tau binding compared to both control groups and more late‐stage tau compared to CU A−T−, but no tau accumulation over time. DISCUSSION VR tau‐PET‐negative AD patients show neurodegenerative and cognitive processes consistent with the AD trajectory, but milder progression compared to tau‐PET‐positive AD patients. Highlights We used the novel Food and Drug Administration (FDA)‐approved VR method for defining tau‐PET positivity. AD A+T− patients were older and showed less atrophy and cognitive decline than AD A+T+. We did not find convincing evidence of tau accumulation in AD A+T− or copathologies. The group of AD A+T− patients is likely very heterogeneous.

The pattern of flortaucipir tau PET uptake is topographically similar to the pattern of magnetic susceptibility in progressive supranuclear palsy (PSP); both with increased signal in subcortical structures such as the basal ganglia and midbrain, suggesting that they may be closely related. However, their relationship remains unknown since no studies have directly compared these two modalities in the same PSP cohort. We hypothesized that some flortaucipir uptake in PSP is associated with magnetic susceptibility, and hence iron deposition. The aim of this study was to evaluate the regional relationship between flortaucipir uptake and magnetic susceptibility and to examine the effects of susceptibility on flortaucipir uptake in PSP. Fifty PSP patients and 67 cognitively normal controls were prospectively recruited and underwent three Tesla MRI and flortaucipir tau PET scans. Quantitative susceptibility maps were reconstructed from multi-echo gradient-echo MRI images. Region of interest (ROI) analysis was performed to obtain flortaucipir and susceptibility values in the subcortical regions. Relationships between flortaucipir and susceptibility signals were evaluated using partial correlation analysis in the subcortical ROIs and voxel-based analysis in the whole brain. The effects of susceptibility on flortaucipir uptake were examined by using the framework of mediation analysis. Both flortaucipir and susceptibility were greater in PSP compared to controls in the putamen, pallidum, subthalamic nucleus, red nucleus, and cerebellar dentate (p<0.05). The ROI-based and voxel-based analyses showed that these two signals were positively correlated in these five regions (r = 0.36–0.59, p<0.05). Mediation analysis showed that greater flortaucipir uptake was partially explained by susceptibility in the putamen, pallidum, subthalamic nucleus, and red nucleus, and fully explained in the cerebellar dentate. These results suggest that some of the flortaucipir uptake in subcortical regions in PSP is related to iron deposition. These findings will contribute to our understanding of the mechanisms underlying flortaucipir tau PET findings in PSP and other neurodegenerative diseases.

PurposeThis study aims to determine whether comparable target regions of interest (ROIs) and cut-offs can be used across [18F]flortaucipir, [18F]RO948, and [18F]MK6240 tau positron emission tomography (PET) tracers for differential diagnosis of Alzheimer’s disease (AD) dementia vs either cognitively unimpaired (CU) individuals or non-AD neurodegenerative diseases.MethodsA total of 1755 participants underwent tau PET using either [18F]flortaucipir (n = 975), [18F]RO948 (n = 493), or [18F]MK6240 (n = 287). SUVR values were calculated across four theory-driven ROIs and several tracer-specific data-driven (hierarchical clustering) regions of interest (ROIs). Diagnostic performance and cut-offs for ROIs were determined using receiver operating characteristic analyses and the Youden index, respectively.ResultsComparable diagnostic performance (area under the receiver operating characteristic curve [AUC]) was observed between theory- and data-driven ROIs. The theory-defined temporal meta-ROI generally performed very well for all three tracers (AUCs: 0.926–0.996). An SUVR value of approximately 1.35 was a common threshold when using this ROI.ConclusionThe temporal meta-ROI can be used for differential diagnosis of dementia patients with [18F]flortaucipir, [18F]RO948, and [18F]MK6240 tau PET with high accuracy, and that using very similar cut-offs of around 1.35 SUVR. This ROI/SUVR cut-off can also be applied across tracers to define tau positivity.