Evidence before this study Using PubMed and Google Scholar the authors reviewed prior work on longitudinal neuroimaging markers of Alzheimer pathology with a focus on autosomal dominant Alzheimer disease (ADAD). We searched for all articles prior to October 31 st , 2017 with no language restrictions for the keywords Alzheimer's, Alzheimer, longitudinal, positron emission tomography, PET, MRI, atrophy, FDG, hypometabolism, familial, and autosomal. Theories proposed initially in 2010 by Jack and colleagues and revised in 2013 posited temporal trajectories of Alzheimer biomarkers relative to each other and clinical decline. Work by Bateman and colleagues in 2012, Benzinger and colleagues in 2013, and Fleisher and colleagues in 2015 depict such temporal ordering of biomarkers in ADAD populations derived from cross-sectional analyses. There was also a small subset of longitudinal ADAD studies, but these had one or more limitation such as small populations (n<50), examination of only one biomarker, not accounting for regional differences or correlations in the brain, or had a short duration of longitudinal followup. Added value of this studyOur study presents the first known work examining both the longitudinal temporal trajectories and spatial patterns of Alzheimer pathology in ADAD cohorts using neuroimaging. This work also presents the largest known cohort to date of ADAD individuals studied longitudinally with multiple neuroimaging biomarkers. Longitudinal analyses can provide a more accurate and powerful way to model the temporal emergence of pathology in ADAD. We find that mutation carriers first display Aβ accumulation, followed by hypometabolism, and finally structural atrophy; this is consistent with theoretical models and cross-sectional estimates from ADAD. Most importantly we consider such temporal relationships not in one singular summary measure, but characterize these trajectories throughout the brain. We found that the accrual of pathology varied throughout the brain and by modality in terms of the time of initial emergence and the rates of longitudinal change. These findings suggest region specific vulnerabilities to β-amyloidosis, metabolic decline, and atrophy that change over the course of the disease. Implications of all the available evidenceOur results build upon existing evidence characterizing biomarkers in clinical and preclinical Alzheimer disease. Our findings suggest that imaging biomarkers follow a sequential pattern, with β-amyloidosis, hypometabolism, and structural atrophy emerging more than twenty, fifteen, and ten years respectively before the expected onset of dementia. Although there is a general hierarchical pattern, there was considerable regional heterogeneity. Most commonly, regions demonstrated an increase in β-amyloidosis and structural atrophy, but there was not evidence of metabolic declines. Further, rather than being homogenous, the same biomarker often demonstrates different longitudinal trajectories across brain regions. Characterizing the temporal and regional dynamics...

, and structural atrophy (imaged by MRI). Recently we published the initial subset of imaging findings for specific regions in a cohort of individuals with autosomal dominant Alzheimer's disease. We now extend this work to include a larger cohort, wholebrain analyses integrating all three imaging modalities, and longitudinal data to examine regional differences in imaging biomarker dynamics. The anatomical distribution of imaging biomarkers is described in relation to estimated years from symptom onset. Autosomal dominant Alzheimer's disease mutation carrier individuals have elevated PiB levels in nearly every cortical region 15 y before the estimated age of onset. Reduced cortical glucose metabolism and cortical thinning in the medial and lateral parietal lobe appeared 10 and 5 y, respectively, before estimated age of onset. Importantly, however, a divergent pattern was observed subcortically. All subcortical gray-matter regions exhibited elevated PiB uptake, but despite this, only the hippocampus showed reduced glucose metabolism. Similarly, atrophy was not observed in the caudate and pallidum despite marked amyloid accumulation. Finally, before hypometabolism, a hypermetabolic phase was identified for some cortical regions, including the precuneus and posterior cingulate. Additional analyses of individuals in which longitudinal data were available suggested that an accelerated appearance of volumetric declines approximately coincides with the onset of the symptomatic phase of the disease.neuroimaging | aging | dementia | neurodegeneration | DIAN T he pathological mechanisms underlying nondominantly inherited late onset Alzheimer's disease (LOAD) remain an active area of investigation (1). According to the amyloid cascade hypothesis, the precipitating event in LOAD is an alteration of the balance between production and clearance of the metabolites of amyloid precursor protein (APP) (2). Abnormalities in APP metabolism then lead to β-amyloid (Aβ) deposition in the cerebral cortex, the formation of neurofibrillary tangles (NFTs) containing hyperphosphorylated tau protein, neuronal dysfunction, cell loss, and, ultimately, dementia. In vivo biomarkers of LOAD include cerebrospinal fluid (CSF) Aβ 42 , CSF tau, amyloid deposition imaged with Pittsburgh compound B PET (PiB PET) and other amyloid tracers, altered glucose metabolism imaged with fluro-deoxyglucose PET (FDG PET), and structural atrophy assessed by volumetric MRI. A theoretical model of biomarker changes has been proposed by Jack et al. (3) that links these Significance Beta-amyloid plaque accumulation, glucose hypometabolism, and neuronal atrophy are hallmarks of Alzheimer's disease. However, the regional ordering of these biomarkers prior to dementia remains untested. In a cohort with Alzheimer's disease mutations, we performed an integrated whole-brain analysis of three major imaging techniques: amyloid PET, [18 F] fluro-deoxyglucose PET, and structural MRI. We found that most gray-matter structures with amyloid plaques later have hypometabolism follo...

OASIS-3 is a compilation of MRI and PET imaging and related clinical data for 1098 participants who were collected across several ongoing studies in the Washington University Knight Alzheimer Disease Research Center over the course of 15 years. Participants include 605 cognitively normal adults and 493 individuals at various stages of cognitive decline ranging in age from 42 to 95 years. The OASIS-3 dataset contains over 2000 MR sessions, including multiple structural and functional sequences. PET metabolic and amyloid imaging includes over 1500 raw imaging scans and the accompanying post-processed files from the PET Unified Pipeline (PUP) are also available in OASIS-3. OASIS-3 also contains post-processed imaging data such as volumetric segmentations and PET analyses. Imaging data is accompanied by dementia and APOE status and longitudinal clinical and cognitive outcomes. OASIS-3 is available as an open access data set to the scientific community to answer questions related to healthy aging and dementia.

Amyloid imaging is a valuable tool for research and diagnosis in dementing disorders. As positron emission tomography (PET) scanners have limited spatial resolution, measured signals are distorted by partial volume effects. Various techniques have been proposed for correcting partial volume effects, but there is no consensus as to whether these techniques are necessary in amyloid imaging, and, if so, how they should be implemented. We evaluated a two-component partial volume correction technique and a regional spread function technique using both simulated and human Pittsburgh compound B (PiB) PET imaging data. Both correction techniques compensated for partial volume effects and yielded improved detection of subtle changes in PiB retention. However, the regional spread function technique was more accurate in application to simulated data. Because PiB retention estimates depend on the correction technique, standardization is necessary to compare results across groups. Partial volume correction has sometimes been avoided because it increases the sensitivity to inaccuracy in image registration and segmentation. However, our results indicate that appropriate PVC may enhance our ability to detect changes in amyloid deposition.

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Utilizing [18F]-AV-1451 tau positron emission tomography (PET) as an Alzheimer disease (AD) biomarker will require identification of brain regions that are most important in detecting elevated tau pathology in preclinical AD. Here, we utilized an unsupervised learning, data-driven approach to identify brain regions whose tau PET is most informative in discriminating low and high levels of [18F]-AV-1451 binding. 84 cognitively normal participants who had undergone AV-1451 PET imaging were used in a sparse k-means clustering with resampling analysis to identify the regions most informative in dividing a cognitively normal population into high tau and low tau groups. The highest-weighted FreeSurfer regions of interest (ROIs) separating these groups were the entorhinal cortex, amygdala, lateral occipital cortex, and inferior temporal cortex, and an average SUVR in these four ROIs was used as a summary metric for AV-1451 uptake. We propose an AV-1451 SUVR cut-off of 1.25 to define high tau as described by imaging. This spatial distribution of tau PET is a more widespread pattern than that predicted by pathological staging schemes. Our data-derived metric was validated first in this cognitively normal cohort by correlating with early measures of cognitive dysfunction, and with disease progression as measured by β-amyloid PET imaging. We additionally validated this summary metric in a cohort of 13 Alzheimer disease patients, and showed that this measure correlates with cognitive dysfunction and β-amyloid PET imaging in a diseased population.

The Dominantly Inherited Alzheimer’s Network Trials Unit (DIAN-TU) was formed to direct the design and management of interventional therapeutic trials of international DIAN and autosomal dominant Alzheimer’s disease (ADAD) participants. The goal of the DIAN-TU is to implement safe trials that have the highest likelihood of success while advancing scientific understanding of these diseases and clinical effects of proposed therapies. The DIAN-TU has launched a trial design that leverages the existing infrastructure of the ongoing DIAN observational study, takes advantage of a variety of drug targets, incorporates the latest results of biomarker and cognitive data collected during the observational study, and implements biomarkers measuring Alzheimer’s disease (AD) biological processes to improve the efficiency of trial design. The DIAN-TU trial design is unique due to the sophisticated design of multiple drugs, multiple pharmaceutical partners, academics servings as sponsor, geographic distribution of a rare population and intensive safety and biomarker assessments. The implementation of the operational aspects such as home health research delivery, safety magnetic resonance imagings (MRIs) at remote locations, monitoring clinical and cognitive measures, and regulatory management involving multiple pharmaceutical sponsors of the complex DIAN-TU trial are described.

Introduction 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. Methods 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. Results 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. Discussion 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.