Over the past six months, we have been in discussion with you and the authors of a recent publication in NeuroImage: Clinical (Amyloid pathology fingerprint differentiates posttraumatic stress disorder and traumatic brain injury. Mohamed AZ, et al. Neuroimaging Clinical 2018 Jun 5;19:716-726). We raised a variety of methodological concerns with Mohamed and colleagues and discussion about these concerns led to their preparation of a corrigendum to be published in NeuroImage: Clinical. We appreciate that the authors were responsive to our concerns and made a number of changes as noted in the corrigendum. Ultimately, however, the corrigendum does not address the primary conclusions of the study, which we were unable to replicate. We are troubled by the publication of results that cannot be replicated using identical methods and participant data. This is a letter to the authors to address these concerns.

Objective

To understand the time course of β-amyloid (Aβ) deposition in the brain, which is crucial for planning therapeutic trials of Aβ-lowering therapies in Alzheimer disease (AD).

Methods

Two samples of participants from the Alzheimer's Disease Neuroimaging Initiative were studied with [¹⁸F]Florbetapir (FBP) Aβ PET and followed for up to 9 years. Sample A included 475 cognitively normal (CN) older people and those with mild cognitive impairment (MCI) and AD and sample B included 220 CN Aβ- individuals. We examined the trajectory of FBP over time in sample A and the incidence rate of conversion from negative to positive Aβ PET scans in sample B.

Results

The relationship between time and brain Aβ was sigmoidal, taking 6.4 years to transition from amyloid negative to positive and another 13.9 years to the onset of MCI. Aβ deposition rates began to slow only 3.8 years after reaching the positivity threshold. The incidence rate for scan positivity was 38/1,000 person-years, and factors associated with conversion were age, baseline FBP, and being a female APOE ε4 carrier. Among CN Aβ- individuals, FBP slopes were associated with rates of memory decline and brain tau measured with [¹⁸F]Flortaucipir PET 5 years after baseline.

Conclusions

Lowering brain Aβ must be accomplished early in the evolution of AD. Transitions of PET scans from Aβ- to Aβ+ should be predictable, and it is reasonable to expect that lowering rates of Aβ even in early stages could produce clinically significant benefits.

Objective

Extensive cortical β-amyloid (Aβ positivity) has been linked to cognitive decline, but the clinical significance of elevations in Aβ within the negative range is unknown.

Methods

We examined amyloid and cognitive trajectories (memory, executive function) in 142 cognitively normal older individuals enrolled in the Alzheimer's Disease Neuroimaging Initiative who were Aβ-negative at baseline and who had at least 2 [¹⁸F]-florbetapir PET scans over 3.9 ± 1.4 years. We determined whether Aβ accumulation was associated with longitudinal changes in memory or executive function.

Results

Among baseline-negative individuals, florbetapir slope (mean annual increase 0.002 ± 0.008 standardized uptake value ratio units/y) was not related to age, sex, education, APOE4 status, baseline memory or executive function, temporoparietal glucose metabolism, baseline hippocampal volume, or hippocampal volume change; but it was related to higher baseline cortical florbetapir, indicating that Aβ accumulation was ongoing at baseline in those who accumulated during the study. Over the course of follow-up, 13 individuals converted to florbetapir+ and 14 nearly nonoverlapping individuals converted to mild cognitive impairment or Alzheimer disease. Amyloid accumulation among baseline-negative individuals was associated with poorer longitudinal memory performance (p = 0.019), but it was not associated with changes in executive function. Reducing the sample to individuals with at least 3 timepoints to estimate the florbetapir slope strengthened the relationship further between florbetapir accumulation and memory decline (p = 0.007).

Conclusions

Memory decline accompanies Aβ accumulation in otherwise healthy, Aβ-negative older adults. Amyloid increases within the negative range may represent the earliest detectable indication of pathology with domain-specific cognitive consequences.

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 florbetapir- PET in patients with AD and patients with MCI using CSF-Aβ1-42 as a comparison amyloid measurement. We then compared florbetapir- vs florbetapir+ patients with respect to several AD-specific biomarkers, baseline and longitudinal cognitive measurements, and demographic and clinician report data.

Results

Florbetapir and CSF-Aβ1-42 +/- status agreed for 98% of ADs (89% of MCIs), indicating that most florbetapir- scans were a reliable representation of amyloid status. Florbetapir- AD (n = 27/177; 15%) and MCI (n = 74/217, 34%) were more likely to be APOE4-negative (MCI 83%, AD 96%) than their florbetapir+ counterparts (MCI 30%, AD 24%). Florbetapir- 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, florbetapir- participants had less hippocampal atrophy and hypometabolism and lower functional activity questionnaire scores compared to florbetapir+ participants.

Conclusions

Overall, image analysis problems do not appear to be a primary explanation of amyloid negativity. Florbetapir- ADNI patients have a variety of clinical and biomarker features that differ from their florbetapir+ counterparts, suggesting that one or more non-AD etiologies (which may include vascular disease and depression) account for their AD-like phenotype.

Importance

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 [18F] 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 measures

Sensitivity and specificity of positive and negative [18F] 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.

Results

The 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 [18F] 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 relevance

Visual 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.

Tau PET has allowed for critical insights into in vivo patterns of tau accumulation and change in individuals early in the Alzheimer's disease (AD) continuum. A key methodological step in tau PET analyses is the selection of a reference region, but there is not yet consensus on the optimal region especially for longitudinal tau PET analyses. This study examines how reference region selection influences results related to disease stage at baseline and over time. Longitudinal flortaucipir ([18F]-AV1451) PET scans were examined using several common reference regions (e.g., eroded subcortical white matter, inferior cerebellar gray matter) in 62 clinically unimpaired amyloid negative (CU A-) individuals, 73 CU amyloid positive (CU A+) individuals, and 64 amyloid positive individuals with mild cognitive impairment (MCI A+) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Cross-sectionally, both reference regions resulted in robust group differences between CU A-, CU A+, and MCI A+ groups, along with significant associations with CSF phosphorylated tau (pTau-181). However, these results were more focally specific and akin to Braak Staging when using eroded white matter, whereas effects with inferior cerebellum were globally distributed across most cortical regions. Longitudinally, utilization of eroded white matter revealed significant accumulation greater than zero across more regions whereas change over time was diminished using inferior cerebellum. Interestingly, the inferior temporal target region seemed most robust to reference region selection with expected cross-sectional and longitudinal signal across both reference regions. With few exceptions, baseline tau did not significantly predict longitudinal change in tau in the same region regardless of reference region. In summary, reference region selection deserves further evaluation as this methodological step may lead to disparate findings. Inferior cerebellar gray matter may be more sensitive to cross-sectional flortaucipir differences, whereas eroded subcortical white matter may be more sensitive for longitudinal analyses examining regional patterns of change.

Background

Previous work examining normal controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI) identified substantial biological heterogeneity. We hypothesized that ADNI mild cognitive impairment (MCI) subjects would also exhibit heterogeneity with possible clinical implications.

Methods

ADNI subjects diagnosed with amnestic MCI (n=138) were clustered based on baseline magnetic resonance imaging, cerebrospinal fluid, and serum biomarkers. The clusters were compared with respect to longitudinal atrophy, cognitive trajectory, and time to conversion.

Results

Four clusters emerged with distinct biomarker patterns: The first cluster was biologically similar to normal controls and rarely converted to Alzheimer's disease (AD) during follow-up. The second cluster had characteristics of early Alzheimer's pathology. The third cluster showed the most severe atrophy but barely abnormal tau levels and a substantial proportion converted to clinical AD. The fourth cluster appeared to be pre-AD and nearly all converted to AD.

Conclusions

Subjects with MCI who were clinically similar showed substantial heterogeneity in biomarkers.

The amyloid hypothesis suggests that beta-amyloid (Aβ) deposition leads to alterations in neural function and ultimately to cognitive decline in Alzheimer's disease. However, factors that underlie Aβ deposition are incompletely understood. One proposed model suggests that synaptic activity leads to increased Aβ deposition. More specifically, hyperactivity in the hippocampus may be detrimental and could be one factor that drives Aβ deposition. To test this model, we examined the relationship between hippocampal activity during a memory task using fMRI and subsequent longitudinal change in Aβ using PIB-PET imaging in cognitively normal older adults. We found that greater hippocampal activation at baseline was associated with increased Aβ accumulation. Furthermore, increasing Aβ accumulation mediated the influence of hippocampal activation on declining memory performance, demonstrating a crucial role of Aβ in linking hippocampal activation and memory. These findings support a model linking increased hippocampal activation to subsequent Aβ deposition and cognitive decline.

Amyloid plaque aggregation is a pathologic hallmark of Alzheimer's disease (AD) that occurs early in the disease. However, little is known about its progression throughout the brain. Using Pittsburgh Compound B (PIB)-PET imaging, we investigated the progression of regional amyloid accumulation in cognitively normal older adults. We found that all examined regions reached their peak accumulation rates 24-28 years after an estimated initiation corresponding to the mean baseline PIB-PET signal in amyloid-negative older adults. We also investigated the effect of increased genetic risk conferred by the apolipoprotein-E ɛ4 allele on rates of amyloid accumulation, as well as the relationship between regional amyloid accumulation and regional tau pathology, another hallmark of AD, measured with Flortaucipir-PET. Carriers of the ɛ4 allele had faster amyloid accumulation in all brain regions. Furthermore, in all regions excluding the temporal lobe, faster amyloid accumulation was associated with greater tau burden. These results indicate that amyloid accumulates near-simultaneously throughout the brain and is associated with higher AD pathology, and that genetic risk of AD is associated with faster amyloid accumulation.