The accurate measurement of β-amyloid (Aβ) change using amyloid PET imaging is important for Alzheimer disease research and clinical trials but poses several unique challenges. In particular, reference region measurement instability may lead to spurious changes in cortical regions of interest. To optimize our ability to measure 18F-florbetapir longitudinal change, we evaluated several candidate regions of interest and their influence on cortical florbetapir change over a 2-y period in participants from the Alzheimer Disease Neuroimaging Initiative (ADNI). Methods We examined the agreement in cortical florbetapir change detected using 6 candidate reference regions (cerebellar gray matter, whole cerebellum, brain stem/pons, eroded subcortical white matter [WM], and 2 additional combinations of these regions) in 520 ADNI subjects. We used concurrent cerebrospinal fluid Aβ1–42 measurements to identify subgroups of ADNI subjects expected to remain stable over follow-up (stable Aβ group; n = 14) and subjects expected to increase (increasing Aβ group; n = 91). We then evaluated reference regions according to whether cortical change was minimal in the stable Aβ group and cortical retention increased in the increasing Aβ group. Results There was poor agreement across reference regions in the amount of cortical change observed across all 520 ADNI subjects. Within the stable Aβ group, however, cortical florbetapir change was 1%–2% across all reference regions, indicating high consistency. In the increasing Aβ group, cortical increases were significant with all reference regions. Reference regions containing WM (as opposed to cerebellum or pons) enabled detection of cortical change that was more physiologically plausible and more likely to increase over time. Conclusion Reference region selection has an important influence on the detection of florbetapir change. Compared with cerebellum or pons alone, reference regions that included subcortical WM resulted in change measurements that are more accurate. In addition, because use of WM-containing reference regions involves dividing out cortical signal contained in the reference region (via partial-volume effects), use of these WM-containing regions may result in more conservative estimates of actual change. Future analyses using different tracers, tracer–kinetic models, pipelines, and comparisons with other biomarkers will further optimize our ability to accurately measure Aβ changes over time.

Objective: To examine the clinical and biomarker characteristics of patients with amyloid-negative Alzheimer disease (AD) and mild cognitive impairment (MCI) from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a prospective cohort study. Methods:We first investigated the reliability of florbetapir2 PET in patients with AD and patients with MCI using CSF-Ab 1-42 as a comparison amyloid measurement. We then compared florbetapir2 vs florbetapir1 patients with respect to several AD-specific biomarkers, baseline and longitudinal cognitive measurements, and demographic and clinician report data.Results: Florbetapir and CSF-Ab 1-42 1/2 status agreed for 98% of ADs (89% of MCIs), indicating that most florbetapir2 scans were a reliable representation of amyloid status. Florbetapir2 AD (n 5 27/177; 15%) and MCI (n 5 74/217, 34%) were more likely to be APOE4-negative (MCI 83%, AD 96%) than their florbetapir1 counterparts (MCI 30%, AD 24%). Florbetapir2 patients also had less AD-specific hypometabolism, lower CSF p-tau and t-tau, and better longitudinal cognitive performance, and were more likely to be taking medication for depression. In MCI only, florbetapir2 participants had less hippocampal atrophy and hypometabolism and lower functional activity questionnaire scores compared to florbetapir1 participants. Conclusions:Overall, image analysis problems do not appear to be a primary explanation of amyloid negativity. Florbetapir2 ADNI patients have a variety of clinical and biomarker features that differ from their florbetapir1 counterparts, suggesting that one or more non-AD etiologies (which may include vascular disease and depression) account for their AD-like phenotype. Alzheimer's Disease Neuroimaging Initiative; AGD 5 argyrophilic grain disease; MCI 5 mild cognitive impairment; metaROI 5 previously validated region of interest; MMSE 5 Mini-Mental State Examination; MPRAGE 5 magnetization-prepared rapid gradient echo; RAVLT 5 Rey Auditory Verbal Learning Test; SUVR 5 standardized uptake value ratio; TBM-SyN 5 tensorbased morphometry-symmetric diffeomorphic image normalization method; VBM 5 voxel-based morphometry; WM 5 white matter.The rate of b-amyloid (Ab) negativity in clinically diagnosed Alzheimer disease (AD) varies across a variety of study populations and as a function of APOE genotype status.1-6 Previous studies of patients with clinically diagnosed AD have shown that 12% were negative on amyloid PET in a recent meta-analysis, 7 and 10%-25% of APOE4-negative patients with AD did not meet the neuropathologic criteria for AD at autopsy. 8,9 Older adults with an amnestic profile that is suggestive of AD comprise a diverse group with heterogeneous pathology. Hippocampal sclerosis, argyrophilic grain disease, vascular dementia, Lewy body disease, and frontotemporal dementia have been observed at autopsy in addition to AD pathology 10 and in Ab2 cases with an antemortem AD diagnosis.

The applicability of β-amyloid peptide (Aβ) positron emission tomography (PET) as a biomarker in clinical settings to aid in selection of individuals at preclinical and prodromal Alzheimer disease (AD) will depend on the practicality of PET image analysis. In this context, visual-based Aβ PET assessment seems to be the most feasible approach.OBJECTIVES To determine the agreement between visual and quantitative Aβ PET analysis and to assess the ability of both techniques to predict conversion from mild cognitive impairment (MCI) to AD. DESIGN, SETTING, AND PARTICIPANTS A longitudinal study was conducted among the Alzheimer's Disease Neuroimaging Initiative (ADNI) sites in the United States and Canada during a 1.6-year mean follow-up period. The study was performed from September 21, 2010, to August 11, 2014; data analysis was conducted from September 21, 2014, to May 26, 2015. Participants included 401 individuals with MCI receiving care at a specialty clinic (219 [54.6%] men; mean [SD] age, 71.6 [7.5] years; 16.2 [2.7] years of education). All participants were studied with florbetapir F 18 [ 18 F] PET. The standardized uptake value ratio (SUVR) positivity threshold was 1.11, and one reader rated all images, with a subset of 125 scans rated by a second reader. MAIN OUTCOMES AND MEASURESSensitivity and specificity of positive and negative [ 18 F] florbetapir PET categorization, which was estimated with cerebrospinal fluid Aβ1-42 as the reference standard. Risk for conversion to AD was assessed using Cox proportional hazards regression models. RESULTSThe frequency of Aβ positivity was 48.9% (196 patients; visual analysis), 55.1% (221 patients; SUVR), and 64.8% (166 patients; cerebrospinal fluid), yielding substantial agreement between visual and SUVR data (κ = 0.74) and between all methods (Fleiss κ = 0.71). For approximately 10% of the 401 participants in whom visual and SUVR data disagreed, interrater reliability was moderate (κ = 0.44), but it was very high if visual and quantitative results agreed (κ = 0.92). Visual analysis had a lower sensitivity (79% vs 85%) but higher specificity (96% vs 90%), respectively, compared with SUVR. The conversion rate was 15.2% within a mean of 1.6 years, and a positive [ 18 F] florbetapir baseline scan was associated with a 6.91-fold (SUVR) or 11.38-fold (visual) greater hazard for AD conversion, which changed only modestly after covariate adjustment for apolipoprotein ε4, concurrent fludeoxyglucose F 18 PET scan, and baseline cognitive status. CONCLUSIONS AND RELEVANCEVisual and SUVR Aβ PET analysis may be equivalently used to determine Aβ status for individuals with MCI participating in clinical trials, and both approaches add significant value for clinical course prognostication.