Feasibility of 18F-florzolotau quantification in patients with Alzheimer's disease based on an MRI-free tau PET template

Pilot implementation of the revised criteria for staging of Alzheimer's disease by the Alzheimer's Association Workgroup in a tertiary memory clinic

INTRODUCTION

We aimed to evaluate the feasibility of the 2024 Alzheimer's Association Workgroup's integrated clinical-biological staging scheme in outpatient settings within a tertiary memory clinic.

METHODS

The 2018 syndromal cognitive staging system, coupled with a binary biomarker classification, was implemented for 236 outpatients with cognitive concerns. The 2024 numeric clinical staging framework, incorporating biomarker staging, was specifically applied to 154 individuals within the Alzheimer's disease (AD) continuum.

RESULTS

The 2024 staging scheme accurately classified 95.5% AD. Among these, 56.5% exhibited concordant clinical and biological stages (canonical), 34.7% demonstrated more advanced clinical stages than biologically expected (susceptible), and 8.8% displayed the inverse pattern (resilient). The susceptible group was characterized by a higher burden of neurodegeneration and inflammation than anticipated from tau, whereas the resilient group showed the opposite.

DISCUSSION

The 2024 staging scheme is generally feasible. A discrepancy between clinical and biological stages is relatively frequent among symptomatic patients with AD.

HIGHLIGHTS

The 2024 AA staging scheme is generally feasible in a tertiary memory clinic. A discrepancy between clinical and biological stages is relatively frequent in AD. The mismatch may be influenced by a non-specific pathological process involved in AD. Individual profiles like aging and lifestyles may contribute to such a mismatch. Matched and mismatched cases converge toward similar clinical outcomes.

A Peculiar Tau Accumulation Pattern Identified Via 18 F-Florzolotau PET Imaging in a Patient With Frontotemporal Dementia Caused by a Mutation in the MAPT Gene

ABSTRACT

We reported imaging findings with a peculiar tau accumulation pattern in a 56 year-old woman with frontotemporal dementia caused by Q351R mutation in the microtubule-associated protein tau ( MAPT ) gene. She had a 10-year history of gradually worsening memory loss. 18 F-FDG PET demonstrated hypometabolism in medial temporal lobes, but 18 F-florbetapir PET manifested no abnormal amyloid beta deposition, which ruled out Alzheimer disease. 18 F-florzolotau PET showed tau proteins accumulated in medial temporal lobes, basal ganglia brainstem, and cerebellum. The pattern is different from other known MAPT gene mutation. Multitracer imaging can help differentiate between Alzheimer disease and frontotemporal dementia caused by MAPT mutation.

Detection of individual brain tau deposition in Alzheimer's disease based on latent feature-enhanced generative adversarial network

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

INTERPRETATION

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.

Characterization of tau propagation pattern and cascading hypometabolism from functional connectivity in Alzheimer's disease

Tau pathology and its spatial propagation in Alzheimer's disease (AD) play crucial roles in the neurodegenerative cascade leading to dementia. However, the underlying mechanisms linking tau spreading to glucose metabolism remain elusive. To address this, we aimed to examine the association between pathologic tau aggregation, functional connectivity, and cascading glucose metabolism and further explore the underlying interplay mechanisms. In this prospective cohort study, we enrolled 79 participants with 18F-Florzolotau positron emission tomography (PET), 18F-fluorodeoxyglucose PET, resting-state functional, and anatomical magnetic resonance imaging (MRI) images in the hospital-based Shanghai Memory Study. We employed generalized linear regression and correlation analyses to assess the associations between Florzolotau accumulation, functional connectivity, and glucose metabolism in whole-brain and network-specific manners. Causal mediation analysis was used to evaluate whether functional connectivity mediates the association between pathologic tau and cascading glucose metabolism. We examined 22 normal controls and 57 patients with AD. In the AD group, functional connectivity was associated with Florzolotau covariance (β = .837, r = 0.472, p < .001) and glucose covariance (β = 1.01, r = 0.499, p < .001). Brain regions with higher tau accumulation tend to be connected to other regions with high tau accumulation through functional connectivity or metabolic connectivity. Mediation analyses further suggest that functional connectivity partially modulates the influence of tau accumulation on downstream glucose metabolism (mediation proportion: 49.9%). Pathologic tau may affect functionally connected neurons directly, triggering downstream glucose metabolism changes. This study sheds light on the intricate relationship between tau pathology, functional connectivity, and downstream glucose metabolism, providing critical insights into AD pathophysiology and potential therapeutic targets.

Identifying Mixed Dementia With Lewy Bodies and Alzheimer Disease Using Multitracer PET Imaging: A Case Study

ABSTRACT

We reported imaging findings with complex signs that were corresponded with both dementia with Lewy bodies (DLB) and Alzheimer disease (AD) in the case of a 78-year-old woman. Initially suspected as DLB due to cognitive and movement issues, diagnostic support included the cingulate island sign on 18 F-FDG PET, positive 131 I-MIBG cardiac scintigraphy, and DAT PET. However, MRI indicated hippocampal atrophy, and 18 F-FDG PET showed hypometabolism in the medial temporal lobe, suggesting the possibility of concomitant AD. Subsequent detection of β-amyloid pathology and tau accumulation in the brain further supported the concurrent presence of AD pathology.

Improved interpretation of 18F-florzolotau PET in progressive supranuclear palsy using a normalization-free deep-learning classifier

While 18F-florzolotau tau PET is an emerging biomarker for progressive supranuclear palsy (PSP), its interpretation has been hindered by a lack of consensus on visual reading and potential biases in conventional semi-quantitative analysis. As clinical manifestations and regions of elevated 18F-florzolotau binding are highly overlapping in PSP and the Parkinsonian type of multiple system atrophy (MSA-P), developing a reliable discriminative classifier for 18F-florzolotau PET is urgently needed. Herein, we developed a normalization-free deep-learning (NFDL) model for 18F-florzolotau PET, which achieved significantly higher accuracy for both PSP and MSA-P compared to semi-quantitative classifiers. Regions driving the NFDL classifier's decision were consistent with disease-specific topographies. NFDL-guided radiomic features correlated with clinical severity of PSP. This suggests that the NFDL model has the potential for early and accurate differentiation of atypical parkinsonism and that it can be applied in various scenarios due to not requiring subjective interpretation, MR-dependent, and reference-based preprocessing.