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Importance Amyloid positron emission tomography (PET) is increasingly used in research and clinical settings to determine the etiology of cognitive decline and eligibility for amyloid-targeting therapies. To assist with amyloid PET evaluation and to guide clinical decision-making, images can be quantified in a standardized unit called Centiloid, the interpretation of which can vary according to the method and threshold used. Objective To collect Centiloid values from available studies and determine robust positivity cutoffs using data-driven methods and correspondence with visual reads. Data Sources PubMed search (October 2024) identified studies with Centiloid values. Corresponding authors were invited to share individual participant data. Additional data were obtained through access-controlled repositories and conference outreach (July 2024-July 2025). Study Selection Studies were included if they provided Centiloids, radiotracer, age, and sex. Data Extraction and Synthesis Each study was analyzed using a unified statistical pipeline; study estimates were pooled using random-effects meta-analysis. Main Outcomes and Measures Gaussian mixture models (GMMs) were fitted to Centiloid values for each study. In studies with a bimodal distribution (per integrated completed likelihood), single cutoffs for positivity were set as mean plus 2 SDs of the lower gaussian component. Using GMMs, a double-cutoff approach defined a lower certainty range using a 90% posterior probability cutoff for assignment to the low (amyloid-negative) vs high (amyloid-positive) component. An alternative Centiloid cutoff was derived from maximizing the correspondence (Cohen κ) with the binary visual reads when available. Results This meta-analysis included cross-sectional amyloid PET scans acquired with 5 radiotracers from 49 227 participants across 53 studies from 15 countries (mean age, 71 years; 54% female, 62% cognitively impaired). The data-driven GMM approach identified a bimodal distribution in 51 studies (n = 48 786), resulting in a single cutoff for positivity of 18 Centiloids (95% CI,16-19; I 2 = 97%). The double-cutoff approach revealed high confidence for interpreting scans as negative when Centiloid values were lower than 11 (95% CI, 9-13; I 2 = 95%) and interpreting scans as positive if Centiloid values were higher than 26 (95% CI, 24-28; I 2 = 95%). In analyses of correspondence with binary (positive or negative) visual reads of amyloid PET scans (n = 35 045; 36 studies), Centiloids were highly predictive of visual positivity (Cohen κ, 0.86; 95% CI, 0.83-0.89; I 2 = 96%) with a cutoff of 27 Centiloids (95% CI, 24-30; I 2 = 80%). Conclusions and Relevance In this individual participant data meta-analysis, positivity cutoffs converged around 18 Centiloids (data-driven) and 27 Centiloids (visual reads). Findings from a double-cutoff analysis suggest that scans in the 11 to 26 Centiloid range should be interpreted with caution depending on the context of use.

Abstract INTRODUCTION The Centiloid scale is the standard for amyloid (Aβ) PET quantification in research and clinical settings. However, variability between tracers and scanners remains a challenge. METHODS This study introduces DeepSUVR, a deep learning method to correct Centiloid quantification, by penalizing implausible longitudinal trajectories during training. The model was trained using data from 2,129 participants (7,149 Aβ positron emission tomography [PET] scans) in the Australian Imaging, Biomarkers and Lifestyle Study of ageing (AIBL)/Alzheimer's Disease Neuroimaging Initiative (ADNI) and validated using 15,807 Aβ PET scans from 10,543 participants across 10 external datasets. RESULTS DeepSUVR increased correlation between tracers, and reduced variability in the Aß‐negatives. It showed significantly stronger association with cognition, visual reads, neuropathology, and increased longitudinal consistency between studies. DeepSUVR also increased the effect size for detecting small treatment related slowing of amyloid accumulation in the A4 study. DISCUSSION DeepSUVR substantially advances Aβ PET quantification, outperforming all standard approaches, which is particularly important for clinical decision making and to detect subtle or early changes in Aβ. Highlights Novel artificial intelligence (AI)‐method that penalizes biologically implausible longitudinal trajectories, enabling the model to learn standardized uptake value ratios (SUVR) correction factors without requiring longitudinal data at inference time. Improves Centiloid consistency across tracers and studies, significantly enhancing cross‐sectional and longitudinal amyloid positron emission tomography (PET) quantification. DeepSUVR‐derived Centiloids show stronger associations with cognition, visual reads, and neuropathology. Longitudinal variability is reduced three‐ to five‐fold, enabling more reliable tracking of amyloid accumulation and better detection of treatment effects. Novel reference and target masks derived from DeepSUVR replicate most of the model's performance, offering a practical alternative for integration into existing pipelines.

Abstract Background Ten years after the original publication, the Centiloid framework is now broadly used to harmonize amyloid‐PET quantification, facilitate data sharing and comparison across cohorts, and even assist visual interpretation in clinical settings. We evaluated the global implementation of Centiloids by comparing their distribution and corresponding positivity thresholds across cohorts. Methods We gathered data from publicly available cohorts and reached out to investigators across the world to collect cross‐sectional Centiloids, demographic and clinical information, and visual read data. Gaussian mixture models (GMM, k=2) were fitted to Centiloid values for each cohort and cutoffs were calculated as mean + 2SD of the lower Gaussian. When visual reads were available, we determined Centiloid cutoffs that maximized correspondence with visual reads (Cohen's kappa). Data was combined across cohorts using random effects meta‐analyses. Results As of January 2025, we included 37 cohorts ( n = 41,678 participants) with heterogeneous pipelines, radiotracers, and clinical and demographic characteristics (Table‐1). The low Gaussian peaks ranged from ‐9 to 10CL; the meta‐analysis identified a common peak at 1CL. The second peaks were more heterogeneous (range=38‐102CL; meta‐analysis outcome=64CL). Across cohorts, the proportion of cognitively unimpaired versus impaired participants impacted the position of both peaks, with better separation in cohorts enriched in impaired individuals (Figure 1C). The meta‐analysis indicated a GMM‐based cutoff of 19CL (95%CI: 16‐21CL, Figure 1B); subgroup analyses showed no evidence of significant effect between single versus multicenter settings (17 versus 20CL, p = 0.30), MRI‐based or PET‐only processing (18 versus 19CL, p = 0.88), and no evidence of difference across radiotracers (Flutemetamol: 16CL; PIB: 17CL, Flutafuranol: 18CL, Florbetaben: 19CL, Florbetapir: 20CL, p = 0.78). In a subset of 29,496 participants with visual reads available, binary visual reads corresponded well to Centiloids (common kappa=0.86, Figure 2A). The visual read‐based cutoff of 24CL (95%CI: 21‐27CL, Figure 2B) maximized correspondence between visual read and quantification and was slightly higher than the GMM‐based cutoff. All meta‐analysis models showed high non‐random heterogeneity (I 2 >80%) across studies, suggesting non‐random differences in peaks and cutoffs. Conclusions Meta‐analysis‐based cutoffs align well with thresholds from the existing literature. High heterogeneity among studies underscores the need to investigate contributing factors, raising concerns about applying common cutoffs.

Abstract Background Ten years after the original publication, the Centiloid framework is now broadly used to harmonize amyloid‐PET quantification, facilitate data sharing and comparison across cohorts, and even assist visual interpretation in clinical settings. We evaluated the global implementation of Centiloids by comparing their distribution and corresponding positivity thresholds across cohorts. Methods We gathered data from publicly available cohorts and reached out to investigators across the world to collect cross‐sectional Centiloids, demographic and clinical information, and visual read data. Gaussian mixture models (GMM, k=2) were fitted to Centiloid values for each cohort and cutoffs were calculated as mean + 2SD of the lower Gaussian. When visual reads were available, we determined Centiloid cutoffs that maximized correspondence with visual reads (Cohen’s kappa). Data was combined across cohorts using random effects meta‐analyses. Results As of January 2025, we included 37 cohorts ( n = 41,678 participants) with heterogeneous pipelines, radiotracers, and clinical and demographic characteristics (Table‐1). The low Gaussian peaks ranged from ‐9 to 10CL; the meta‐analysis identified a common peak at 1CL. The second peaks were more heterogeneous (range=38‐102CL; meta‐analysis outcome=64CL). Across cohorts, the proportion of cognitively unimpaired versus impaired participants impacted the position of both peaks, with better separation in cohorts enriched in impaired individuals (Figure‐1C). The meta‐analysis indicated a GMM‐based cutoff of 19CL (95%CI: 16‐21CL, Figure‐1B); subgroup analyses showed no evidence of significant effect between single versus multicenter settings (17 versus 20CL, p = 0.30), MRI‐based or PET‐only processing (18 versus 19CL, p = 0.88), and no evidence of difference across radiotracers (Flutemetamol: 16CL; PIB: 17CL, Flutafuranol: 18CL, Florbetaben: 19CL, Florbetapir: 20CL, p = 0.78). In a subset of 29,496 participants with visual reads available, binary visual reads corresponded well to Centiloids (common kappa=0.86, Figure‐2A). The visual read‐based cutoff of 24CL (95%CI: 21‐27CL, Figure‐2B) maximized correspondence between visual read and quantification and was slightly higher than the GMM‐based cutoff. All meta‐analysis models showed high non‐random heterogeneity (I 2 >80%) across studies, suggesting non‐random differences in peaks and cutoffs. Conclusions Meta‐analysis‐based cutoffs align well with thresholds from the existing literature. High heterogeneity among studies underscores the need to investigate contributing factors, raising concerns about applying common cutoffs.

Abstract The cortical asymmetry index evaluates the cortical thickness asymmetry between hemispheres. We investigated cortical asymmetry index in asymptomatic and symptomatic mutation carriers of autosomal dominant Alzheimer’s disease to explore the brain asymmetry within the Alzheimer’s disease continuum. Sixty baseline T1-weighted MRI scans were obtained from the Clinic Barcelona cohort. Baseline and longitudinal MRI data from 564 participants within the dominantly inherited Alzheimer network observational study were used as an independent, confirmatory cohort. Cerebrospinal fluid and plasma neurofilament light chain levels were included when available. Cortical thickness was calculated using Freesurfer and cortical asymmetry index was calculated via an open-source pipeline. Cross-sectional analyses examined cortical asymmetry index differences based on clinical classification and APOE ε4 status, adjusting for age, sex and estimated years from onset, while correlations were assessed with age, estimated years from onset, mini-mental state examination scores, and neurofilament light. Longitudinal cortical asymmetry index evolution was modelled using generalized additive models in the dominantly inherited Alzheimer network observational study cohort, incorporating age, sex, and the interaction between group and estimated years from onset. The cortical asymmetry index successfully distinguished asymptomatic mutation carrier and symptomatic mutation carriers from healthy controls in the Clinic Barcelona cohort and symptomatic mutation carriers from controls in dominantly inherited Alzheimer network observational study. Higher cortical asymmetry index in mutation carriers (asymptomatic mutation carrier and symptomatic mutation carriers combined) and in symptomatic mutation carriers were associated with higher plasma neurofilament light levels, a closer proximity to symptom onset, and lower mini-mental state examination in the Clinic Barcelona cohort. In the dominantly inherited Alzheimer network observational study cohort, mutation carriers exhibited increased cortical asymmetry index compared to controls and correlated with elevated neurofilament light (plasma and Cerebrospinal fluid), lower mini-mental state examination, and a closer proximity to symptom onset. APOE3/3 carriers showed greater asymmetry than other APOE genotypes and significant cortical asymmetry index differences between asymptomatic mutation carrier and symptomatic mutation carriers. Longitudinally, cortical asymmetry index increased over time significantly in symptomatic mutation carriers. These findings underscore brain asymmetry as a potential biomarker for early Alzheimer’s disease progression in autosomal dominant Alzheimer’s disease, with implications for detection and monitoring tracking disease-related neuroanatomical changes.

Abstract BACKGROUND Tau‐derived cerebrospinal fluid (CSF) biomarkers correlate with amyloid‐beta (Aβ) plaques or tau tangles in Alzheimer's disease (AD). This study assessed the effects of long‐term anti‐Aβ antibodies on amyloid plaques, tau tangles, and CSF tau species to determine the relationships between them. METHODS A post‐hoc analysis of the DIAN‐TU‐001 trial (NCT01760005) examined 142 participants at risk for dominantly inherited AD randomized to solanezumab ( n = 50), gantenerumab ( n = 52), or placebo ( n = 40). High‐resolution mass spectrometry quantified CSF tau species over four years. RESULTS Phosphorylated tau (p‐tau) species (153, 181, 217, 231) increased early in preclinical AD but were reduced with gantenerumab‐mediated Aβ plaque reduction. Nearly a decade later, MTBR‐tau243 and p‐tau205 increased, showing no association with Aβ reduction, aligning with tau tangle pathology progression. DISCUSSION Initially changing soluble p‐tau species track Aβ plaque reduction, while ptau205 and MTBR‐243 reflect tau tangle pathology, informing different pathways of therapeutic strategies. Highlights p‐tau217 and p‐tau231 correlate with Aβ‐PET and respond to Aβ‐plaque lowering therapies. Aβ immunotherapy trials support a direct link between p‐tau changes and Aβ plaques Gantenerumab reduces Aβ plaques but does not affect tau NFT‐related biomarkers. Blood‐based p‐tau217 assays may provide a non‐invasive tool to monitor Aβ therapies. MTBR‐tau243 strongly correlates with tau PET and tracks NFT pathology progression. Further studies are needed to validate tau biomarkers for tracking NFT‐targeting therapies.

Abstract INTRODUCTION Monoclonal anti‐amyloid therapies are now accessible, but how these treatments influence changes within the brain is still not clear. We investigated overall and regional change in amyloid removal, glucose metabolism, and atrophy in trial participants with dominantly inherited Alzheimer's disease (DIAD). METHODS In the DIAN‐TU‐001 trial, 92 carriers received gantenerumab or placebo and underwent serial neuroimaging assessments including [ 11 C]‐Pittsburgh compound‐B (PiB) positron emission tomography (PET), [ 18 F]‐fluoro‐2‐deoxyglucose (FDG) PET, and magnetic resonance imaging (MRI). RESULTS Gantenerumab significantly reduced PiB‐PET uptake overall and in most regions and showed no changes in FDG‐PET or MRI measures. Drug effects were associated with baseline PiB‐PET uptake, and the largest effects occurred in medial regions. DISCUSSION Treated DIAD participants, and especially those with higher amyloid burden, showed a decrease in PiB‐PET uptake, which was more pronounced in the basal ganglia and medial frontal structures. These results may inform patient response and future drug trial design. Highlights Gantenerumab unevenly decreased Aβ burden as measured by PiB‐PET across brain regions. The strongest decrease in PiB‐PET uptake was in basal ganglia and medial frontal structures. Variable drug effect on Aβ was partly due to the amount of burden present before treatment. There was no regional effect on FDG‐PET metabolism or MRI volumetrics after 4 years.

Abstract The Centiloid scale is the standard for Amyloid (Aβ) PET quantification, widely used in research, clinical settings, and trial stratification. However, variability between tracers and scanners remains a challenge. This study introduces DeepSUVR, a deep learning method to correct Centiloid quantification, by penalising implausible longitudinal trajectories during training. The model was trained using data from 2,098 participants (6,762 Aβ PET scans) in AIBL/ADNI and validated using 15,806 Aβ PET scans from 10,543 participants across 10 external datasets. DeepSUVR increased correlation between tracers, and reduced variability in the Aβ-negatives. It showed the strongest association with cognition, highest AUC against visual reads and best longitudinal consistency between studies. DeepSUVR also increased the effect size for detecting lower Centiloid increase per year in the A4 study. DeepSUVR advances Aβ PET quantification, outperforming standard approaches, which is particularly important for consistent decision making and to detect subtle and early changes in clinical interventions.

Background: Cognitive change is an important factor in understanding dementia. Estimating effects of exposures on cognitive change requires choosing an analytical timescale, typically time on study or current age. There is limited consensus regarding timescale choice in epidemiologic cognitive aging research. Methods: Using a coordinated analytic approach in 10 cohorts of older adults, we evaluated whether estimated effects of two exposures on memory change differed depending on timescale (time on study or current age). We modeled effects of apolipoprotein-E (APOE) ε4 genotype (a time-invariant exposure) and diabetes (a potentially time-varying/acquired exposure evaluated at baseline) using mixed-effects models with linear and nonlinear time specifications for both timescales. Results: Among APOE ε4 carriers, model-estimated memory scores at baseline (time on study timescale) or at each cohort’s median baseline age (current age timescale) were lower, and the average rate of decline was faster than among APOE ε4 noncarriers. Model-estimated memory scores at baseline or at median baseline age were generally similar across baseline diabetes status, with variability across cohorts in the diabetes-memory change association. In some cohorts, trends in diabetes-memory change associations differed in direction across timescales. Conclusions: Timescale was largely inconsequential for estimated effects of APOE genotype, but yielded differences in estimated diabetes effects, raising questions about the appropriate scale. The current age scale may be problematic because diabetes was measured heterogeneously in age across individuals, a common issue in aging cohorts. Our work demonstrates approaches to evaluate alternative timescales, including in multicohort analyses, and highlights potential implications for estimated exposure effects on cognitive change.

AbstractBackgroundThe residual approach has found wide application in researching cognitive resilience, a phenomenon conceptually understood as cognitive performance being better‐than‐typical for an individual, despite apparent AD pathology. The standard residual approach extracts information about an individual’s resilience from the residuals of a linear model predicting cognition. This approach is subject to several limiting assumptions. Here, we explore the implications of these assumptions and propose an alternative predictive modeling approach which circumvents them.MethodsOur alternative approach relies on training a machine learning model on a sample of participants who follow a “typical” clinical progression (Fig. 1A). Residuals are then extracted for a heterogeneous dataset that potentially includes resilient participants by comparing observed cognitive outcomes to those predicted by the “typical” model (Fig. 1B). Additionally, our estimate can be refined by effectively estimating and removing model error from the extracted residual by leveraging a second predictive model (Fig. 1C). Using simulated data, we explore the assumptions made by the standard approach and the consequences of their violation. We then contrast this against our approach.ResultsOur simulations demonstrated that the standard residual approach only extracted accurate resilience estimates if the predictors used in the model explained cognition sufficiently well, while being unrelated to resilience itself. That is, as the predictors explain more of the resilience, the residuals became increasingly determined by model error (Fig. 2). We found that residuals achieved with our approach are more precise and provide individualized estimates that are unaffected by the aforementioned trade‐off (Fig. 3).ConclusionsOur approach holds several advantages over the standard approach: The definition of resilience can be freely adjusted and enforced via different definitions of “typical”, it allows for incorporating complex, non‐linear models without removing relevant information from the residual, the error component of the residual can be estimated through cross‐validation, and all predictors that potentially explain cognition can be included into the model without losing resilience information from the residual. Our approach can be used to measure any phenomenon that can be operationalized as a deviation from a typical outcome and, thus, is also applicable for researching resistance to pathology.