Could tau-PET imaging contribute to a better understanding of the different patterns of clinical progression in Alzheimer's disease? A 2-year longitudinal study

Default mode network tau predicts future clinical decline in atypical early Alzheimer's disease

Identifying individuals with early-stage Alzheimer's disease (AD) at greater risk of steeper clinical decline would enable better-informed medical, support and life planning decisions. Despite accumulating evidence on the clinical prognostic value of tau PET in typical late-onset amnestic AD, its utility in predicting clinical decline in individuals with atypical forms of AD remains unclear. Across heterogeneous clinical phenotypes, patients with atypical AD consistently exhibit abnormal tau accumulation in the posterior nodes of the default mode network of the cerebral cortex. This evidence suggests that tau burden in this functional network could be a common imaging biomarker for prognostication across the syndromic spectrum of AD. Here, we examined the relationship between baseline tau PET signal and the rate of subsequent clinical decline in a sample of 48 A+/T+/N+ patients with mild cognitive impairment or mild dementia due to AD with atypical clinical phenotypes: Posterior Cortical Atrophy (n = 16); logopenic variant Primary Progressive Aphasia (n = 15); and amnestic syndrome with multi-domain impairment and young age of onset < 65 years (n = 17). All patients underwent MRI, tau PET and amyloid PET scans at baseline. Each patient's longitudinal clinical decline was assessed by calculating the annualized change in the Clinical Dementia Rating Sum-of-Boxes (CDR-SB) scores from baseline to follow-up (mean time interval = 14.55 ± 3.97 months). Atypical early AD patients showed an increase in CDR-SB by 1.18 ± 1.25 points per year: t(47) = 6.56, P < 0.001, Cohen's d = 0.95. Across clinical phenotypes, baseline tau in the default mode network was the strongest predictor of clinical decline (R2 = 0.30), outperforming a simpler model with baseline clinical impairment and demographic variables (R2 = 0.10), tau in other functional networks (R2 = 0.11-0.26) and the magnitude of cortical atrophy (R2 = 0.20) and amyloid burden (R2 = 0.09) in the default mode network. Overall, these findings point to the contribution of default mode network tau to predicting the magnitude of clinical decline in atypical early AD patients 1 year later. This simple measure could aid the development of a personalized prognostic, monitoring and treatment plan, which would help clinicians not only predict the natural evolution of the disease but also estimate the effect of disease-modifying therapies on slowing subsequent clinical decline given the patient's tau burden while still early in the disease course.

Explainable Artificial Intelligence in Neuroimaging of Alzheimer's Disease

Alzheimer's disease (AD) remains a significant global health challenge, affecting millions worldwide and imposing substantial burdens on healthcare systems. Advances in artificial intelligence (AI), particularly in deep learning and machine learning, have revolutionized neuroimaging-based AD diagnosis. However, the complexity and lack of interpretability of these models limit their clinical applicability. Explainable Artificial Intelligence (XAI) addresses this challenge by providing insights into model decision-making, enhancing transparency, and fostering trust in AI-driven diagnostics. This review explores the role of XAI in AD neuroimaging, highlighting key techniques such as SHAP, LIME, Grad-CAM, and Layer-wise Relevance Propagation (LRP). We examine their applications in identifying critical biomarkers, tracking disease progression, and distinguishing AD stages using various imaging modalities, including MRI and PET. Additionally, we discuss current challenges, including dataset limitations, regulatory concerns, and standardization issues, and propose future research directions to improve XAI's integration into clinical practice. By bridging the gap between AI and clinical interpretability, XAI holds the potential to refine AD diagnostics, personalize treatment strategies, and advance neuroimaging-based research.

Plasma pTau217 ratio predicts continuous regional brain tau accumulation in amyloid-positive early Alzheimer's disease

BACKGROUND

This study examines whether phosphorylated plasma Tau217 ratio (pTau217R) can predict tau accumulation in different brain regions, as measured by positron emission tomography (PET) standardized uptake value ratio (SUVR), for staging Alzheimer's disease (AD).

METHODS

Plasma pTau217R was measured using immunoprecipitation-mass spectrometry. Models for predicting tau PET SUVR, developed with 144 early AD individuals using [18F]MK6240, were validated in two validation sets, VS1 (98 early AD) and VS2 (47 preclinical/early AD with a different tracer, flortaucipir (Tauvid)), all amyloid-beta positive (Aβ+).

RESULTS

The pTau217R-based model predicted tau levels up to an SUVR of 2 in multiple brain regions, effectively assessing tau status at different tau levels with receiver operating characteristic (ROC) curve areas of 0.84-0.95 in VS1 and 0.71-0.88 in VS2 (using a different tracer). It reduced PET scan needs by 65% while maintaining 95% sensitivity.

DISCUSSION

PTau217R reliably predicts regional tau accumulation in early AD, reducing reliance on tau PET scans and broadening its clinical application.

CLINICAL TRIAL REGISTRATION NUMBER

NCT03887455 (ClarityAD) HIGHLIGHTS: Developed a model using plasma pTau217R to predict tau levels across brain regions. pTau217R model outperformed models based on clinical, MRI, and other blood biomarkers. The model reliably predicted tau levels exceeding tau positivity and higher thresholds. Screening with pTau217R could reduce tau PET scans by 65% at 95% sensitivity. pTau217R model aids in disease staging and monitoring in early AD.

Exploratory Tau PET/CT with [11C]PBB3 in Patients with Suspected Alzheimer's Disease and Frontotemporal Lobar Degeneration: A Pilot Study on Correlation with PET Imaging and Cerebrospinal Fluid Biomarkers

Accurately diagnosing Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) is challenging due to overlapping symptoms and limitations of current imaging methods. This study investigates the use of [11C]PBB3 PET/CT imaging to visualize tau pathology and improve diagnostic accuracy. Given diagnostic challenges with symptoms and conventional imaging, [11C]PBB3 PET/CT's potential to enhance accuracy was investigated by correlating tau pathology with cerebrospinal fluid (CSF) biomarkers, positron emission tomography (PET), computed tomography (CT), amyloid-beta, and Mini-Mental State Examination (MMSE). We conducted [11C]PBB3 PET/CT imaging on 24 patients with suspected AD or FTLD, alongside [11C]PiB PET/CT (13 patients) and [18F]FDG PET/CT (15 patients). Visual and quantitative assessments of [11C]PBB3 uptake using standardized uptake value ratios (SUV-Rs) and correlation analyses with clinical assessments were performed. The scans revealed distinct tau accumulation patterns; 13 patients had no or faint uptake (PBB3-negative) and 11 had moderate to pronounced uptake (PBB3-positive). Significant inverse correlations were found between [11C]PBB3 SUV-Rs and MMSE scores, but not with CSF-tau or CSF-amyloid-beta levels. Here, we show that [11C]PBB3 PET/CT imaging can reveal distinct tau accumulation patterns and correlate these with cognitive impairment in neurodegenerative diseases. Our study demonstrates the potential of [11C]PBB3-PET imaging for visualizing tau pathology and assessing disease severity, offering a promising tool for enhancing diagnostic accuracy in AD and FTLD. Further research is essential to validate these findings and refine the use of tau-specific PET imaging in clinical practice, ultimately improving patient care and treatment outcomes.

The path to next-generation disease-modifying immunomodulatory combination therapies in Alzheimer's disease

The cautious optimism following recent anti-amyloid therapeutic trials for Alzheimer's disease (AD) provides a glimmer of hope after years of disappointment. Although these encouraging results represent discernible progress, they also highlight the need to enhance further the still modest clinical efficacy of current disease-modifying immunotherapies. Here, we highlight crucial milestones essential for advancing precision medicine in AD. These include reevaluating the choice of therapeutic targets by considering the key role of both central neuroinflammation and peripheral immunity in disease pathogenesis, refining patient stratification by further defining the inflammatory component within the forthcoming ATN(I) (amyloid, tau and neurodegeneration (and inflammation)) classification of AD biomarkers and defining more accurate clinical outcomes and prognostic biomarkers that better reflect disease heterogeneity. Next-generation immunotherapies will need to go beyond the current antibody-only approach by simultaneously targeting pathological proteins together with innate neuroinflammation and/or peripheral-central immune crosstalk. Such innovative immunomodulatory combination therapy approaches should be evaluated in appropriately redesigned clinical therapeutic trials, which must carefully integrate the neuroimmune component.

Default mode network tau predicts future clinical decline in atypical early Alzheimer's disease

Identifying individuals with early stage Alzheimer's disease (AD) at greater risk of steeper clinical decline would allow professionals and loved ones to make better-informed medical, support, and life planning decisions. Despite accumulating evidence on the clinical prognostic value of tau PET in typical late-onset amnestic AD, its utility in predicting clinical decline in individuals with atypical forms of AD remains unclear. In this study, we examined the relationship between baseline tau PET signal and the rate of subsequent clinical decline in a sample of 48 A+/T+/N+ patients with mild cognitive impairment or mild dementia due to AD with atypical clinical phenotypes (Posterior Cortical Atrophy, logopenic variant Primary Progressive Aphasia, and amnestic syndrome with multi-domain impairment and age of onset < 65 years). All patients underwent structural magnetic resonance imaging (MRI), tau (18F-Flortaucipir) PET, and amyloid (either 18F-Florbetaben or 11C-Pittsburgh Compound B) PET scans at baseline. Each patient's longitudinal clinical decline was assessed by calculating the annualized change in the Clinical Dementia Rating Sum-of-Boxes (CDR-SB) scores from baseline to follow-up (mean time interval = 14.55 ± 3.97 months). Our sample of early atypical AD patients showed an increase in CDR-SB by 1.18 ± 1.25 points per year: t(47) = 6.56, p < .001, d = 0.95. These AD patients showed prominent baseline tau burden in posterior cortical regions including the major nodes of the default mode network, including the angular gyrus, posterior cingulate cortex/precuneus, and lateral temporal cortex. Greater baseline tau in the broader default mode network predicted faster clinical decline. Tau in the default mode network was the strongest predictor of clinical decline, outperforming baseline clinical impairment, tau in other functional networks, and the magnitude of cortical atrophy and amyloid burden in the default mode network. Overall, these findings point to the contribution of baseline tau burden within the default mode network of the cerebral cortex to predicting the magnitude of clinical decline in a sample of atypical early AD patients one year later. This simple measure based on a tau PET scan could aid the development of a personalized prognostic, monitoring, and treatment plan tailored to each individual patient, which would help clinicians not only predict the natural evolution of the disease but also estimate the effect of disease-modifying therapies on slowing subsequent clinical decline given the patient's tau burden while still early in the disease course.

Generating PET scan patterns in Alzheimer's by a mathematical model

Alzheimer disease (AD) is the most common form of dementia. The cause of the disease is unknown, and it has no cure. Symptoms include cognitive decline, memory loss, and impairment of daily functioning. The pathological hallmarks of the disease are aggregation of plaques of amyloid-β (Aβ) and neurofibrillary tangles of tau proteins (τ), which can be detected in PET scans of the brain. The disease can remain asymptomatic for decades, while the densities of Aβ and τ continue to grow. Inflammation is considered an early event that drives the disease. In this paper, we develop a mathematical model that can produce simulated patterns of (Aβ,τ) seen in PET scans of AD patients. The model is based on the assumption that early inflammations, R and [Formula: see text], drive the growth of Aβ and τ, respectively. Recently approved drugs can slow the progression of AD in patients, provided treatment begins early, before significant damage to the brain has occurred. In line with current longitudinal studies, we used the model to demonstrate how to assess the efficacy of such drugs when given years before the disease becomes symptomatic.