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Background In recent years, longitudinal studies of Alzheimer's disease (AD) have been successively concluded. Our aim is to determine the efficacy of amyloid‐β (Aβ) PET in diagnosing AD and early prediction of mild cognitive impairment (MCI) converting to AD. By pooling studies from different centers to explore in‐depth whether diagnostic performance varies by population type, radiotracer type, and diagnostic approach, thus providing a more comprehensive theoretical basis for the subsequent widespread application of Aβ PET in the clinical setting. Methods Relevant studies were searched through PubMed. The pooled sensitivities, specificities, DOR, and the summary ROC curve were obtained based on a Bayesian random‐effects model. Results Forty‐eight studies, including 5967 patients, were included. Overall, the pooled sensitivity, specificity, DOR, and AUC of Aβ PET for diagnosing AD were 0.90, 0.80, 35.68, and 0.91, respectively. Subgroup analysis showed that Aβ PET had high sensitivity (0.91) and specificity (0.81) for differentiating AD from normal controls but very poor specificity (0.49) for determining AD from MCI. The pooled sensitivity and specificity were 0.84 and 0.62, respectively, for predicting the conversion of MCI to AD. The differences in diagnostic efficacy between visual assessment and quantitative analysis and between 11C‐PIB PET and 18F‐florbetapir PET were insignificant. Conclusions The overall performance of Aβ PET in diagnosing AD is favorable, but the differentiation between MCI and AD patients should consider that some MCI may be at risk of conversion to AD and may be misdiagnosed. A multimodal diagnostic approach and machine learning analysis may be effective in improving diagnostic accuracy.

Background Inconsistent positivity thresholds, image analysis pipelines, and quantitative outcomes are key challenges of multisite studies using more than one β-amyloid (Aβ) radiotracer in positron emission tomography (PET). Variability related to these factors contributes to disagreement and lack of replicability in research and clinical trials. To address these problems and promote Aβ PET harmonization, we used [ 18 F]florbetaben (FBB) and [ 18 F]florbetapir (FBP) data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to derive (1) standardized Centiloid (CL) transformations and (2) internally consistent positivity thresholds based on separate young control samples. Methods We analyzed Aβ PET data using a native-space, automated image processing pipeline that is used for PET quantification in many large, multisite AD studies and trials and made available to the research community. With this pipeline, we derived SUVR-to-CL transformations using the Global Alzheimer’s Association Interactive Network data; we used reference regions for cross-sectional (whole cerebellum) and longitudinal (subcortical white matter, brain stem, whole cerebellum) analyses. Finally, we developed a FBB positivity threshold using an independent young control sample ( N =62) with methods parallel to our existing FBP positivity threshold and validated the FBB threshold using a data-driven approach in ADNI participants ( N =295). Results The FBB threshold based on the young sample (1.08; 18 CL) was consistent with that of the data-driven approach (1.10; 21 CL), and the existing FBP threshold converted to CL with the derived transformation (1.11; 20 CL). The following equations can be used to convert whole cerebellum- (cross-sectional) and composite- (longitudinal) normalized FBB and FBP data quantified with the native-space pipeline to CL units: [ 18 F]FBB: CL whole cerebellum  = 157.15 × SUVR FBB  − 151.87; threshold=1.08, 18 CL [ 18 F]FBP: CL whole cerebellum  = 188.22 × SUVR FBP  − 189.16; threshold=1.11, 20 CL [ 18 F]FBB: CL composite  = 244.20 × SUVR FBB  − 170.80 [ 18 F]FBP: CL composite  = 300.66 × SUVR FBP  − 208.84 Conclusions FBB and FBP positivity thresholds derived from independent young control samples and quantified using an automated, native-space approach result in similar CL values. These findings are applicable to thousands of available and anticipated outcomes analyzed using this pipeline and shared with the scientific community. This work demonstrates the feasibility of harmonized PET acquisition and analysis in multisite PET studies and internal consistency of positivity thresholds in standardized units.

Subjective cognitive decline (SCD) is regarded as the first clinical manifestation in the Alzheimer’s disease (AD) continuum. Investigating populations with SCD is important for understanding the early pathological mechanisms of AD and identifying SCD-related biomarkers, which are critical for the early detection of AD. With the advent of advanced neuroimaging techniques, such as positron emission tomography (PET) and magnetic resonance imaging (MRI), accumulating evidence has revealed structural and functional brain alterations related to the symptoms of SCD. In this review, we summarize the main imaging features and key findings regarding SCD related to AD, from local and regional data to connectivity-based imaging measures, with the aim of delineating a multimodal imaging signature of SCD due to AD. Additionally, the interaction of SCD with other risk factors for dementia due to AD, such as age and the Apolipoprotein E ( ApoE ) ɛ4 status, has also been described. Finally, the possible explanations for the inconsistent and heterogeneous neuroimaging findings observed in individuals with SCD are discussed, along with future directions. Overall, the literature reveals a preferential vulnerability of AD signature regions in SCD in the context of AD, supporting the notion that individuals with SCD share a similar pattern of brain alterations with patients with mild cognitive impairment (MCI) and dementia due to AD. We conclude that these neuroimaging techniques, particularly multimodal neuroimaging techniques, have great potential for identifying the underlying pathological alterations associated with SCD. More longitudinal studies with larger sample sizes combined with more advanced imaging modeling approaches such as artificial intelligence are still warranted to establish their clinical utility.

Background and purpose This study was undertaken to compare the performance of plasma p‐tau181 with that of [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) in the identification of early biological Alzheimer disease (AD). Methods We included 533 cognitively impaired participants from the Alzheimer's Disease Neuroimaging Initiative. Participants underwent PET scans, biofluid collection, and cognitive tests. Receiver operating characteristic analyses were used to determine the diagnostic accuracy of plasma p‐tau181 and [18F]FDG‐PET using clinical diagnosis and core AD biomarkers ([18F]florbetapir‐PET and cerebrospinal fluid [CSF] p‐tau181) as reference standards. Differences in the diagnostic accuracy between plasma p‐tau181 and [18F]FDG‐PET were determined by bootstrap‐based tests. Correlations of [18F]FDG‐PET and plasma p‐tau181 with CSF p‐tau181, amyloid β (Aβ) PET, and cognitive performance were evaluated to compare associations between measurements. Results We observed that both plasma p‐tau181 and [18F]FDG‐PET identified individuals with positive AD biomarkers in CSF or on Aβ‐PET. In the MCI group, plasma p‐tau181 outperformed [18F]FDG‐PET in identifying AD measured by CSF (p = 0.0007) and by Aβ‐PET (p = 0.001). We also observed that both plasma p‐tau181 and [18F]FDG‐PET metabolism were associated with core AD biomarkers. However, [18F]FDG‐PET uptake was more closely associated with cognitive outcomes (Montreal Cognitive Assessment, Mini‐Mental State Examination, Clinical Dementia Rating Sum of Boxes, and logical memory delayed recall, p < 0.001) than plasma p‐tau181. Conclusions Overall, although both plasma p‐tau181 and [18F]FDG‐PET were associated with core AD biomarkers, plasma p‐tau181 outperformed [18F]FDG‐PET in identifying individuals with early AD pathophysiology. Taken together, our study suggests that plasma p‐tau181 may aid in detecting individuals with underlying early AD.

INTRODUCTION While amyloid cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers are considered interchangeable indicators of Alzheimer's disease (AD) pathology, biomarker discrepancies can occur but remain poorly characterized. METHODS We evaluated 18F‐florbetapir amyloid PET, 18F‐Florzolotau PET (tau pathology), magnetic resonance imaging (MRI) findings, and CSF biomarkers in a 59‐year‐old man carrying the pathogenic APP p.K687Q mutation, who presented with possible corticobasal syndrome. RESULTS CSF analysis revealed reduced amyloid beta (Aβ)1‐42 (503.44 pg/mL) and Aβ1‐42/Aβ1‐40 ratio (0.044), indicating amyloid pathology. Conversely, 18F‐florbetapir PET was visually negative (standardized uptake value ratio [SUVR] 0.97; −11.8 Centiloids). 18F‐Florzolotau PET demonstrated AD‐typical tau deposition, whereas MRI revealed extensive white matter hyperintensities, enlarged perivascular spaces, and a temporal microbleed. DISCUSSION The observed discordance suggests that CSF and PET amyloid biomarkers can diverge in certain patients. Potential mechanisms include polymorphic Aβ fibrils lacking 18F‐florbetapir binding sites, excess non‐fibrillar aggregates, low fibril density, or contributions from cerebral amyloid angiopathy. Highlights CSF Aβ and 18F‐florbetapir PET findings showed a mismatch in a patient with an APP mutation. Amyloid pathology should not be excluded despite negative 18F‐florbetapir PET findings. Mismatch may reflect altered ligand binding or fibril structural variants. Comorbid cerebral amyloid angiopathy may contribute to biomarker discrepancies.

Currently, over five million Americans suffer with Alzheimer’s disease (AD). In the absence of a cure, this number could increase to 13.8 million by 2050. A critical goal of biomedical research is to establish indicators of AD during the preclinical stage (i.e. biomarkers) allowing for early diagnosis and intervention. Numerous advances have been made in developing biomarkers for AD using neuroimaging approaches. These approaches offer tremendous versatility in terms of targeting distinct age-related and pathophysiological mechanisms such as structural decline (e.g. volumetry, cortical thinning), functional decline (e.g. fMRI activity, network correlations), connectivity decline (e.g. diffusion anisotropy), and pathological aggregates (e.g. amyloid and tau PET). In this review, we survey the state of the literature on neuroimaging approaches to developing novel biomarkers for the amnestic form of AD, with an emphasis on combining approaches into multimodal biomarkers. We also discuss emerging methods including imaging epigenetics, neuroinflammation, and synaptic integrity using PET tracers. Finally, we review the complementary information that neuroimaging biomarkers provide, which highlights the potential utility of composite biomarkers as suitable outcome measures for proof-of-concept clinical trials with experimental therapeutics.

Imaging or tissue biomarker evidence has been introduced into the core diagnostic pathway for Alzheimer's disease (AD). PET using (18)F-labelled beta-amyloid PET tracers has shown promise for the early diagnosis of AD. However, most studies included only small numbers of participants and no consensus has been reached as to which radiotracer has the highest diagnostic accuracy. First, we performed a systematic review of the literature published between 1990 and 2014 for studies exploring the diagnostic accuracy of florbetaben, florbetapir and flutemetamol in AD. The included studies were analysed using the QUADAS assessment of methodological quality. A meta-analysis of the sensitivity and specificity reported within each study was performed. Pooled values were calculated for each radiotracer and for visual or quantitative analysis by population included. The systematic review identified nine studies eligible for inclusion. There were limited variations in the methods between studies reporting the same radiotracer. The meta-analysis results showed that pooled sensitivity and specificity values were in general high for all tracers. This was confirmed by calculating likelihood ratios. A patient with a positive ratio is much more likely to have AD than a patient with a negative ratio, and vice versa. However, specificity was higher when only patients with AD were compared with healthy controls. This systematic review and meta-analysis found no marked differences in the diagnostic accuracy of the three beta-amyloid radiotracers. All tracers perform better when used to discriminate between patients with AD and healthy controls. The sensitivity and specificity for quantitative and visual analysis are comparable to those of other imaging or biomarker techniques used to diagnose AD. Further research is required to identify the combination of tests that provides the highest sensitivity and specificity, and to identify the most suitable position for the tracer in the clinical pathway.

To determine the effect of self-reported clinical diagnosis of obstructive sleep apnea (OSA) on longitudinal changes in brain amyloid PET and CSF biomarkers (Aβ42, T-tau, and P-tau) in cognitively normal (NL), mild cognitive impairment (MCI), and Alzheimer's disease (AD) elderly. Longitudinal study with mean follow-up time of 2.52 ± 0.51 years. Data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Participants included 516 NL, 798 MCI, and 325 AD elderly. Main outcomes were annual rate of change in brain amyloid burden (i.e. longitudinal increases in florbetapir PET uptake or decreases in CSF Aβ42 levels); and tau protein aggregation (i.e. longitudinal increases in CSF total tau [T-tau] and phosphorylated tau [P-tau]). Adjusted multilevel mixed effects linear regression models with randomly varying intercepts and slopes was used to test whether the rate of biomarker change differed between participants with and without OSA. In NL and MCI groups, OSA+ subjects experienced faster annual increase in florbetapir uptake (B = .06, 95% CI = .02, .11 and B = .08, 95% CI = .05, .12, respectively) and decrease in CSF Aβ42 levels (B = -2.71, 95% CI = -3.11, -2.35 and B = -2.62, 95% CI = -3.23, -2.03, respectively); as well as increases in CSF T-tau (B = 3.68, 95% CI = 3.31, 4.07 and B = 2.21, 95% CI = 1.58, 2.86, respectively) and P-tau (B = 1.221, 95% CI = 1.02, 1.42 and B = 1.74, 95% CI = 1.22, 2.27, respectively); compared with OSA- participants. No significant variations in the biomarker changes over time were seen in the AD group. In both NL and MCI, elderly, clinical interventions aimed to treat OSA are needed to test if OSA treatment may affect the progression of cognitive impairment due to AD.

Quantitative in vivo measurement of brain amyloid burden is important for both research and clinical purposes. However, the existence of multiple imaging tracers presents challenges to the interpretation of such measurements. This study presents a direct comparison of Pittsburgh compound B–based and florbetapir-based amyloid imaging in the same participants from two independent cohorts using a crossover design. Pittsburgh compound B and florbetapir amyloid PET imaging data from three different cohorts were analyzed using previously established pipelines to obtain global amyloid burden measurements. These measurements were converted to the Centiloid scale to allow fair comparison between the two tracers. The mean and inter-individual variability of the two tracers were compared using multivariate linear models both cross-sectionally and longitudinally. Global amyloid burden measured using the two tracers were strongly correlated in both cohorts. However, higher variability was observed when florbetapir was used as the imaging tracer. The variability may be partially caused by white matter signal as partial volume correction reduces the variability and improves the correlations between the two tracers. Amyloid burden measured using both tracers was found to be in association with clinical and psychometric measurements. Longitudinal comparison of the two tracers was also performed in similar but separate cohorts whose baseline amyloid load was considered elevated (i.e., amyloid positive). No significant difference was detected in the average annualized rate of change measurements made with these two tracers. Although the amyloid burden measurements were quite similar using these two tracers as expected, difference was observable even after conversion into the Centiloid scale. Further investigation is warranted to identify optimal strategies to harmonize amyloid imaging data acquired using different tracers.

There is an increasing role for biological markers (biomarkers) in the understanding and diagnosis of neurodegenerative disorders. The application of imaging biomarkers specifically for the in vivo investigation of neurodegenerative disorders has increased substantially over the past decades and continues to provide further benefits both to the diagnosis and understanding of these diseases. This review forms part of a series of articles which stem from the University College London/University of Gothenburg course “Biomarkers in neurodegenerative diseases”. In this review, we focus on neuroimaging, specifically positron emission tomography (PET) and magnetic resonance imaging (MRI), giving an overview of the current established practices clinically and in research as well as new techniques being developed. We will also discuss the use of machine learning (ML) techniques within these fields to provide additional insights to early diagnosis and multimodal analysis.