Validation of amyloid PET positivity thresholds in centiloids: a multisite PET study approach.

BACKGROUND: Inconsistent positivity thresholds, image analysis pipelines, and quantitative outcomes are key challenges of multisite studies using more than one ß-amyloid (Aß) 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 Aß PET harmonization, we used [18F]florbetaben (FBB) and [18F]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 Aß 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: [18F]FBB: CLwhole cerebellum = 157.15 × SUVRFBB - 151.87; threshold=1.08, 18 CL [18F]FBP: CLwhole cerebellum = 188.22 × SUVRFBP - 189.16; threshold=1.11, 20 CL [18F]FBB: CLcomposite = 244.20 × SUVRFBB - 170.80 [18F]FBP: CLcomposite = 300.66 × SUVRFBP - 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.

Multiple comorbid neuropathologies in the setting of Alzheimer's disease neuropathology and implications for drug development.

Dementia is often characterized as being caused by one of several major diseases, such as Alzheimer's disease (AD), cerebrovascular disease, Lewy body disease, or a frontotemporal degeneration. Failure to acknowledge that more than one entity may be present precludes attempts to understand interactive relationships. The clinicopathological studies of dementia demonstrate that multiple pathologic processes often coexist. How overlapping pathologic findings affect the diagnosis and treatment of clinical AD and other dementia phenotypes was the topic taken up by the Alzheimer's Association's Research Roundtable in October 2014. This review will cover the neuropathologic basis of dementia, provide clinical perspectives on multiple pathologies, and discuss therapeutics and biomarkers targeting overlapping pathologies and how these issues impact clinical trials.High prevalence of multiple pathologic findings among individuals with clinical diagnosis of AD suggests that new treatment strategies may be needed to effectively treat AD and other dementing illnesses.

Assessing cognition and function in Alzheimer's disease clinical trials: do we have the right tools?

Several lines of evidence from Alzheimer's disease (AD) research continue to support the notion that the biological changes associated with AD are occurring possibly several decades before an individual will experience the cognitive and functional changes associated with the disease. The National Institute on Aging-Alzheimer's Association revised criteria for AD provided a framework for this new thinking. As a result of this growing understanding, several research efforts have launched or will be launching large secondary prevention trials in AD. These and other efforts have clearly demonstrated a need for better measures of cognitive and functional change in people with the earliest changes associated with AD. Recent draft guidance from the US Food and Drug Administration further elevated the importance of cognitive and functional assessments in early stage clinical trials by proposing that even in the pre-symptomatic stages of the disease, approval will be contingent on demonstrating clinical meaningfulness. The Alzheimer's Association's Research Roundtable addressed these issues at its fall meeting October 28-29, 2013, in Washington, D.C. The focus of the discussion included the need for improved cognitive and functional outcome measures for clinical of participants with preclinical AD and those diagnosed with Mild Cognitive Impairment due to AD.

Beyond amyloid: getting real about nonamyloid targets in Alzheimer's disease.

For decades, researchers have focused primarily on a pathway initiated by amyloid beta aggregation, amyloid deposition, and accumulation in the brain as the key mechanism underlying the disease and the most important treatment target. However, evidence increasingly suggests that amyloid is deposited early during the course of disease, even prior to the onset of clinical symptoms. Thus, targeting amyloid in patients with mild to moderate Alzheimer's disease (AD), as past failed clinical trials have done, may be insufficient to halt further disease progression. Scientists are investigating other molecular and cellular pathways and processes that contribute to AD pathogenesis. Thus, the Alzheimer's Association's Research Roundtable convened a meeting in April 2012 to move beyond amyloid and explore AD as a complex multifactorial disease, with the goal of using a more inclusive perspective to identify novel treatment strategies.

A preliminary, randomized, double-blind, placebo-controlled trial of L-carnosine to improve cognition in schizophrenia.

BACKGROUND: Targeting glutamatergic dysfunction provides an exciting opportunity to improve cognitive impairment in schizophrenia. One treatment approach has targeted inadequate antioxidant defenses at glutamatergic synapses. Animal and human data suggest NMDA antagonists worsen executive cognitive controls--e.g. increase perseverative responses and impair set-shifting. We conducted a preliminary study to test the hypothesis that L-carnosine, an antioxidant and anti-glycation agent which is co-localized and released with glutamate would improve executive dysfunction, a cognitive domain associated with glutamate. METHODS: Seventy-five symptomatically stable adults with chronic schizophrenia were randomly assigned to L-carnosine as adjunctive treatment (2 g/day) or a matched placebo in a double-blind manner for 3 months. Cognitive domains (executive dysfunction, memory, attention and motor speed) were assessed using a computerized battery at baseline, 4 and 12 weeks, along with psychopathology ratings and safety parameters. RESULTS: The L-carnosine group performed significantly faster on non-reversal condition trials of the set-shifting test compared with placebo but reversal reaction times and errors were not significantly different between treatments. On the strategic target detection test, the L-carnosine group displayed significantly improved strategic efficiency and made fewer perseverative errors compared with placebo. Other cognitive tests showed no significant differences between treatments. Psychopathology scores remained stable. The carnosine group reported more adverse events (30%) compared with the placebo group (14%). Laboratory indices remained within acceptable ranges. CONCLUSIONS: These preliminary findings suggest that L-carnosine merits further consideration as adjunctive treatment to improve executive dysfunction in persons with schizophrenia.

Regional analysis of FDG and PIB-PET images in normal aging, mild cognitive impairment, and Alzheimer's disease.

OBJECTIVE: The objective of the study is to compare the diagnostic value of regional sampling of the cerebral metabolic rate of glucose metabolism (MRglc) using [18F]-fluoro-2-deoxyglucose ([18F]FDG)-positron emission tomography (PET) and amyloid-beta pathology using Pittsburgh Compound-B ([11C]PIB)-PET in the evaluation of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) compared to normal elderly (NL). MATERIALS AND METHODS: AD patients, 7 NL, 13 MCI, and 17, received clinical, neuropsychological, magnetic resonance imaging (MRI), FDG, and PIB-PET exams. Parametric images of PIB uptake and MRglc were sampled using automated regions-of-interest (ROI). RESULTS: AD showed global MRglc reductions, and MCI showed reduced hippocampus (HIP) and inferior parietal lobe (IP) MRglc compared to NL. On PIB, AD patients showed significantly increased uptake in the middle frontal gyrus (MFG), posterior cingulate cortex (PCC), and IP (ps < 0.05). PIB uptake in MCI subjects was either AD or NL-like. HIP MRglc and MFG PIB uptake were the best discriminators of NL from MCI and NL from AD. These two best measures showed high diagnostic agreement for AD (94%) and poor agreement for MCI (54%). For the NL vs. MCI discrimination, combining the two best measures increased the accuracy for PIB (75%) and for FDG (85%) to 90%. CONCLUSION: For AD, the pattern of regional involvement for FDG and PIB differ, but both techniques show high diagnostic accuracy and 94% case by case agreement. In the classification of NL and MCI, FDG is superior to PIB, but there is only 54% agreement at a case level. Combining the two modalities improves the diagnostic accuracy for MCI.

Bayesian applications to longitudinal analysis on medical data with discrete outcomes.

Many prediction studies of medical research lead to discrete longitudinal data with repeated measurement and categorical outcomes. Therefore the traditional likelihood-based methods for continuous outcome measures are no longer suitable. With the development of modern computing technologies and improved scope for estimation via iterative sampling methods, Bayesian analysis is becoming increasingly popular among biostatisticians. Markov Chain Monte Carlo (MCMC), for the implementation of Bayesian methods has rendered the implementation of complex Bayesian models a reality. In addition, the availability of software like WinBUGS has made the utilization of MCMC straightforward. In this study, we developed a full Bayesian version of generalized linear models for binary longitudinal data and applied it to a longitudinal prediction study of Alzheimer's disease conducted at New York University School of Medicine.