Petersen, Ronald C.

Objective There is a need for inexpensive noninvasive tests to identify older healthy persons at risk for Alzheimer disease (AD) for enrollment in AD prevention trials. Our objective was to examine whether abnormalities in neuroimaging measures of amyloid and neurodegeneration are correlated with odor identification (OI) in the population‐based Mayo Clinic Study of Aging. Methods Cognitively normal (CN) participants had olfactory function assessed using the Brief Smell Identification Test (B‐SIT), underwent magnetic resonance imaging (n = 829) to assess a composite AD signature cortical thickness and hippocampal volume (HVa), and underwent 11 C‐Pittsburgh compound B (n = 306) and 18 fluorodeoxyglucose (n = 305) positron emission tomography scanning to assess amyloid accumulation and brain hypometabolism, respectively. The association of neuroimaging biomarkers with OI was examined using multinomial logistic regression and simple linear regression models adjusted for potential confounders. Results Among 829 CN participants (mean age = 79.2 years; 51.5% men), 248 (29.9%) were normosmic and 78 (9.4%) had anosmia (B‐SIT score < 6). Abnormal AD signature cortical thickness and reduced HVa were associated with decreased OI as a continuous measure (slope = −0.43, 95% confidence interval [CI] = −0.76 to −0.09, p = 0.01 and slope = −0.72, 95% CI = −1.15 to −0.28, p < 0.01, respectively). Reduced HVa, decreased AD signature cortical thickness, and increased amyloid accumulation were significantly associated with increased odds of anosmia. Interpretation Our findings suggest that OI may be a noninvasive, inexpensive marker for risk stratification, for identifying participants at the preclinical stage of AD who may be at risk for cognitive impairment and eligible for inclusion in AD prevention clinical trials. These cross‐sectional findings remain to be validated prospectively. Ann Neurol 2017;81:871–882

Abstract The launch of the NIA‐AA research criteria for Alzheimer's disease (AD) diagnosis illustrates the large advances that have been made in the field of AD diagnosis. These new possibilities however also introduce new dilemmas into the consulting room, and this provides room for shared decision making (SDM). SDM refers to clinicians and patients (and/or their caregivers) working together to decide which care plan best fits individual patients and their lives, when there is more than one reasonable option. Here, we describe how SDM in the diagnosis of AD promotes patient‐centered care, as it helps to adapt the diagnostic process to the patients' values and preferences. We provide an outline for a research agenda, as SDM in the diagnosis of dementia should be studied intensively incorporating the views of both patients and caregivers.

Background: There is growing interest in white matter (WM) imaging with positron emission tomography (PET). Objectives: We studied the association of cognitive function in late multiple sclerosis (MS) with cortical and WM Pittsburgh compound-B PET (PiB-PET) binding. Methods: In the population-based Mayo Clinic Study of Aging, 24 of 4869 participants had MS (12 underwent PiB-PET). Controls were age and sex matched (5:1). We used automated or semi-automated processing for quantitative image analyses and conditional logistic regression for group differences. Results: MS patients had lower memory ( p = 0.03) and language ( p = 0.02) performance; smaller thalamic volumes ( p = 0.003); and thinner temporal ( p = 0.001) and frontal ( p = 0.045) cortices on magnetic resonance imaging (MRI) than controls. There was no difference in global cortical PiB standardized uptake value ratios between MS and controls ( p = 0.35). PiB uptake was lower in areas of WM hyperintensities compared to normal-appearing white matter (NAWM) in MS ( p = 0.0002). Reduced PiB uptake in both the areas of WM hyperintensities ( r = 0.65; p = 0.02) and NAWM ( r = 0.69; p = 0.01) was associated with decreased visuospatial performance in MS. Conclusion: PiB uptake in the cortex in late MS is not different from normal age-matched controls. PiB uptake in the WM in late MS may be a marker of the large network structures’ integrity such as those involved in visuospatial performance.

AbstractQuantitative measurement of β‐amyloid from amyloid PET scans typically relies on localizing target and reference regions by image registration to MRI. In this work, we present a series of simulations where 50 small random perturbations of starting location and orientation were applied to each subject's PET scan, and rigid registration usingspm_coregwas performed between each perturbed PET scan and its corresponding MRI. We then measured variation in the output PET‐MRI registrations and how this variation affected the resulting SUVR measurements. We performed these experiments using scans of 1196 participants, half using 18F florbetapir and half using 11C PiB. From these experiments, we measured the magnitude of the imprecision in the rigid registration steps used to localize measurement regions, and how this contributes to the overall imprecision in SUVR measurements. Unexpectedly, we found for both tracers that the imprecision in these measurements depends on the degree of amyloid tracer uptake, and thus also indirectly on Alzheimer's disease clinical status. We then examined common choices of reference regions, and we show that SUVR measurements using supratentorial white matter references are relatively resistant to this source of error. We also show that the use of partial volume correction further magnifies the effects of registration imprecision on SUVR measurements. Together, these results suggest that this rigid registration step is an attractive target for future work in improving measurement techniques.Hum Brain Mapp 38:3323–3336, 2017. ©2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Introduction The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. Methods We used standard searches to find publications using ADNI data. Results (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β‐amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white‐matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion‐like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by “classic” AD pathology but also by normal biomarkers, accelerated decline, and suspected non‐Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. Discussion Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.