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Supplemental Material for Field strength difference in extent of artifacts induced by CERTAS Plus valves in patients with idiopathic normal pressure hydrocephalus by Emanuele Camerucci, Benjamin D Elder, Yunhong Shu, Steven A Messina, Jeffrey L Gunter, Jonathan Graff-Radford, David T Jones, Hugo Botha, Jeremy K Cutsforth-Gregory, Clifford R Jack, John Huston, and Petrice M Cogswell in The Neuroradiology Journal.

Quantitative 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 using spm_coreg was 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.

ObjectiveTo determine the frontal lobe proton magnetic resonance spectroscopy (1H MRS) abnormalities in asymptomatic and symptomatic carriers of microtubule-associated protein tau (MAPT) mutations.MethodsWe recruited patients with MAPT mutations from 5 individual families, who underwent single voxel 1H MRS from the medial frontal lobe at 3T (n = 19) from the Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects (LEFFTDS) Study at the Mayo Clinic site. Asymptomatic MAPT mutation carriers (n = 9) had Frontotemporal Lobar Degeneration Clinical Dementia Rating Sum of Boxes (FTLD-CDR SOB) score of zero, and symptomatic MAPT mutation carriers (n = 10) had a median FTLD-CDR SOB score of 5. Noncarriers from healthy first-degree relatives of the patients were recruited as controls (n = 25). The demographic aspects and 1H MRS metabolite ratios were compared by use of the Fisher exact test for sex and linear mixed models to account for within-family correlations. We used Tukey contrasts for pair-wise comparisons.ResultsAsymptomatic MAPT mutation carriers had lower neuronal marker N-acetylaspartate (NAA)/creatine (Cr) (p = 0.001) and lower NAA/myo-inositol (mI) (p = 0.026) than noncarriers after adjustment for age. Symptomatic MAPT mutation carriers had lower NAA/Cr (p = 0.01) and NAA/mI (p = 0.01) and higher mI/Cr (p = 0.02) compared to noncarriers after adjustment for age. Furthermore, NAA/Cr (p = 0.006) and NAA/mI (p < 0.001) ratios decreased, accompanied by an increase in mI/Cr ratio (p = 0.001), as the ages of carriers approached and passed the age at symptom onset.ConclusionFrontal lobe neurochemical alterations measured with 1H MRS precede the symptom onset in MAPT mutation carriers. Frontal lobe 1H MRS is a potential biomarker for early neurodegenerative processes in MAPT mutation carriers.

Structural MRI is widely used for investigating brain atrophy in many neurodegenerative disorders, with several research groups developing and publishing techniques to provide quantitative assessments of this longitudinal change. Often techniques are compared through computation of required sample size estimates for future clinical trials. However interpretation of such comparisons is rendered complex because, despite using the same publicly available cohorts, the various techniques have been assessed with different data exclusions and different statistical analysis models. We created the MIRIAD atrophy challenge in order to test various capabilities of atrophy measurement techniques. The data consisted of 69 subjects (46 Alzheimer's disease, 23 control) who were scanned multiple (up to twelve) times at nine visits over a follow-up period of one to two years, resulting in 708 total image sets. Nine participating groups from 6 countries completed the challenge by providing volumetric measurements of key structures (whole brain, lateral ventricle, left and right hippocampi) for each dataset and atrophy measurements of these structures for each time point pair (both forward and backward) of a given subject. From these results, we formally compared techniques using exactly the same dataset. First, we assessed the repeatability of each technique using rates obtained from short intervals where no measurable atrophy is expected. For those measures that provided direct measures of atrophy between pairs of images, we also assessed symmetry and transitivity. Then, we performed a statistical analysis in a consistent manner using linear mixed effect models. The models, one for repeated measures of volume made at multiple time-points and a second for repeated “direct” measures of change in brain volume, appropriately allowed for the correlation between measures made on the same subject and were shown to fit the data well. From these models, we obtained estimates of the distribution of atrophy rates in the Alzheimer's disease (AD) and control groups and of required sample sizes to detect a 25% treatment effect, in relation to healthy ageing, with 95% significance and 80% power over follow-up periods of 6, 12, and 24 months. Uncertainty in these estimates, and head-to-head comparisons between techniques, were carried out using the bootstrap. The lateral ventricles provided the most stable measurements, followed by the brain. The hippocampi had much more variability across participants, likely because of differences in segmentation protocol and less distinct boundaries. Most methods showed no indication of bias based on the short-term interval results, and direct measures provided good consistency in terms of symmetry and transitivity. The resulting annualized rates of change derived from the model ranged from, for whole brain: − 1.4% to − 2.2% (AD) and − 0.35% to − 0.67% (control), for ventricles: 4.6% to 10.2% (AD) and 1.2% to 3.4% (control), and for hippocampi: − 1.5% to − 7.0% (AD) and − 0.4% to − 1.4% (control). There were large and statistically significant differences in the sample size requirements between many of the techniques. The lowest sample sizes for each of these structures, for a trial with a 12 month follow-up period, were 242 (95% CI: 154 to 422) for whole brain, 168 (95% CI: 112 to 282) for ventricles, 190 (95% CI: 146 to 268) for left hippocampi, and 158 (95% CI: 116 to 228) for right hippocampi. This analysis represents one of the most extensive statistical comparisons of a large number of different atrophy measurement techniques from around the globe. The challenge data will remain online and publicly available so that other groups can assess their methods., Highlights • We compared numerous brain atrophy measurement techniques using multiple metrics. • Each participant produced measures on the exact same dataset, blinded to disease. • A central statistical analysis using linear mixed effect models was performed. • Head to head comparisons for each region were performed using sample size estimates. • Brain and ventricle measures were more consistent across groups than for hippocampi.

Data diversity is critical to success when training deep learning models. Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces significant challenges when training deep learning models. In this work, we propose a method to generate synthetic abnormal MRI images with brain tumors by training a generative adversarial network using two publicly available data sets of brain MRI. We demonstrate two unique benefits that the synthetic images provide. First, we illustrate improved performance on tumor segmentation by leveraging the synthetic images as a form of data augmentation. Second, we demonstrate the value of generative models as an anonymization tool, achieving comparable tumor segmentation results when trained on the synthetic data versus when trained on real subject data. Together, these results offer a potential solution to two of the largest challenges facing machine learning in medical imaging, namely the small incidence of pathological findings, and the restrictions around sharing of patient data., Comment: Accepted for 2018 Workshop on Simulation and Synthesis in Medical Imaging - SASHIMI2018

The clinical course of dementia with Lewy bodies patients is heterogeneous. The ability to more accurately prognosticate survival is important.The objective of this study was to investigate hippocampal volume as a predictor of survival in dementia with Lewy bodies patients.Survival analysis for time from onset of cognitive symptoms to death was carried out using Cox proportional hazards models. Given their age and total intracranial volume, patients were dichotomized into low/medium (0%-66.7%) and high (66. 7%-100%) hippocampal volume categories. The models using these categories to predict survival were adjusted for field strength, APOE ε4 status, and estimated onset age of cognitive problems.We investigated 167 consecutive patients with dementia with Lewy bodies. The median age at MRI was 72 years (interquartile range 67-76), and 80% were male. The median time from estimated first cognitive symptom to death was 7.4 years (interquartile range:5.7-10.2). Lower hippocampal volumes were significantly associated with higher risk of death (hazard ratio 1.28; 95% confidence interval 1.04-1.58; P = .024). The predicted median survival for participants with onset of cognitive symptoms at age 68 was 10.63 years (95% confidence interval 8.66-14.54) for APOE ε4 negative, high hippocampal volume participants; 8.89 years (95% confidence interval 7.56-12.36) for APOE ε4 positive, high hippocampal volume participants; 8.10 years (95% confidence interval 7.34-11.08) for APOE ε4 negative, low/medium hippocampal volume participants; and 7.38 (95% confidence interval 6.74-9.29) years for APOE ε4 positive, low/medium hippocampal volume participants.Among patients with clinically diagnosed dementia with Lewy bodies, those with neuroimaging evidence of hippocampal atrophy have shorter survival times. © 2016 International Parkinson and Movement Disorder Society.

Complex biological systems are organized across various spatiotemporal scales with particular scientific disciplines dedicated to the study of each scale (e.g. genetics, molecular biology and cognitive neuroscience). When considering disease pathophysiology, one must contemplate the scale at which the disease process is being observed and how these processes impact other levels of organization. Historically Alzheimer's disease has been viewed as a disease of abnormally aggregated proteins by pathologists and molecular biologists and a disease of clinical symptoms by neurologists and psychologists. Bridging the divide between these scales has been elusive, but the study of brain networks appears to be a pivotal inroad to accomplish this task. In this study, we were guided by an emerging systems-based conceptualization of Alzheimer's disease and investigated changes in brain networks across the disease spectrum. The default mode network has distinct subsystems with unique functional-anatomic connectivity, cognitive associations, and responses to Alzheimer's pathophysiology. These distinctions provide a window into the systems-level pathophysiology of Alzheimer's disease. Using clinical phenotyping, metadata, and multimodal neuroimaging data from the Alzheimer's Disease Neuroimaging Initiative, we characterized the pattern of default mode network subsystem connectivity changes across the entire disease spectrum (n = 128). The two main findings of this paper are (i) the posterior default mode network fails before measurable amyloid plaques and appears to initiate a connectivity cascade that continues throughout the disease spectrum; and (ii) high connectivity between the posterior default mode network and hubs of high connectivity (many located in the frontal lobe) is associated with amyloid accumulation. These findings support a system model best characterized by a cascading network failure--analogous to cascading failures seen in power grids triggered by local overloads proliferating to downstream nodes eventually leading to widespread power outages, or systems failures. The failure begins in the posterior default mode network, which then shifts processing burden to other systems containing prominent connectivity hubs. This model predicts a connectivity 'overload' that precedes structural and functional declines and recasts the interpretation of high connectivity from that of a positive compensatory phenomenon to that of a load-shifting process transiently serving a compensatory role. It is unknown whether this systems-level pathophysiology is the inciting event driving downstream molecular events related to synaptic activity embedded in these systems. Possible interpretations include that the molecular-level events drive the network failure, a pathological interaction between the network-level and the molecular-level, or other upstream factors are driving both.

Alzheimer's disease (AD) researchers commonly use MRI as a quantitative measure of disease severity. Historically, hippocampal volume has been favored. Recently, "AD signature" measurements of gray matter (GM) volumes or cortical thicknesses have gained attention. Here, we systematically evaluate multiple thickness- and volume-based candidate-methods side-by-side, built using the popular FreeSurfer, SPM, and ANTs packages, according to the following criteria: (a) ability to separate clinically normal individuals from those with AD; (b) (extent of) correlation with head size, a nuisance covariatel (c) reliability on repeated scans; and (d) correlation with Braak neurofibrillary tangle stage in a group with autopsy. We show that volume- and thickness-based measures generally perform similarly for separating clinically normal from AD populations, and in correlation with Braak neurofibrillary tangle stage at autopsy. Volume-based measures are generally more reliable than thickness measures. As expected, volume measures are highly correlated with head size, while thickness measures are generally not. Because approaches to statistically correcting volumes for head size vary and may be inadequate to deal with this underlying confound, and because our goal is to determine a measure which can be used to examine age and sex effects in a cohort across a large age range, we thus recommend thickness-based measures. Ultimately, based on these criteria and additional practical considerations of run-time and failure rates, we recommend an AD signature measure formed from a composite of thickness measurements in the entorhinal, fusiform, parahippocampal, mid-temporal, inferior-temporal, and angular gyrus ROIs using ANTs with input segmentations from SPM12.

ObjectiveOur primary objective was to compare the performance of unaccelerated vs. accelerated structural MRI for measuring disease progression using serial scans in Alzheimer's disease (AD).MethodsWe identified cognitively normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI) and AD subjects from all available Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects with usable pairs of accelerated and unaccelerated scans. There were a total of 696 subjects with baseline and 3 month scans, 628 subjects with baseline and 6 month scans and 464 subjects with baseline and 12 month scans available. We employed the Symmetric Diffeomorphic Image Normalization method (SyN) for normalization of the serial scans to obtain tensor based morphometry (TBM) maps which indicate the structural changes between pairs of scans. We computed a TBM-SyN summary score of annualized structural changes over 31 regions of interest (ROIs) that are characteristically affected in AD. TBM-SyN scores were computed using accelerated and unaccelerated scan pairs and compared in terms of agreement, group-wise discrimination, and sample size estimates for a hypothetical therapeutic trial.ResultsWe observed a number of systematic differences between TBM-SyN scores computed from accelerated and unaccelerated pairs of scans. TBM-SyN scores computed from accelerated scans tended to have overall higher estimated values than those from unaccelerated scans. However, the performance of accelerated scans was comparable to unaccelerated scans in terms of discrimination between clinical groups and sample sizes required in each clinical group for a therapeutic trial. We also found that the quality of both accelerated vs. unaccelerated scans were similar.ConclusionsAccelerated scanning protocols reduce scan time considerably. Their group-wise discrimination and sample size estimates were comparable to those obtained with unaccelerated scans. The two protocols did not produce interchangeable TBM-SyN estimates, so it is arguably important to use either accelerated pairs of scans or unaccelerated pairs of scans throughout the study duration.

The aim of our study was to determine the utility of longitudinal magnetic resonance imaging (MRI) measurements as potential biomarkers in the main genetic variants of frontotemporal dementia (FTD), including microtubule-associated protein tau (MAPT) and progranulin (GRN) mutations and C9ORF72 repeat expansions, as well as sporadic FTD.In this longitudinal study, 58 subjects were identified who had at least two MRI and MAPT mutations (n = 21), GRN mutations (n = 11), C9ORF72 repeat expansions (n = 11) or sporadic FTD (n = 15). A total of 198 serial MRI measurements were analyzed. Rates of whole brain atrophy were calculated using the boundary shift integral. Regional rates of atrophy were calculated using tensor-based morphometry. Sample size estimates were calculated.Progressive brain atrophy was observed in all groups, with fastest rates of whole brain atrophy in GRN, followed by sporadic FTD, C9ORF72 and MAPT. All variants showed greatest rates in the frontal and temporal lobes, with parietal lobes also strikingly affected in GRN. Regional rates of atrophy across all lobes were greater in GRN compared to the other groups. C9ORF72 showed greater rates of atrophy in the left cerebellum and right occipital lobe than MAPT, and sporadic FTD showed greater rates in the anterior cingulate than C9ORF72 and MAPT. Sample size estimates were lowest using temporal lobe rates in GRN, ventricular rates in MAPT and C9ORF72, and whole brain rates in sporadic FTD.These data support the utility of using rates of atrophy as outcome measures in future drug trials in FTD and show that different imaging biomarkers may offer advantages in the different variants of FTD.

Structural MRI is widely used for investigating brain atrophy in many neurodegenerative disorders, with several research groups developing and publishing techniques to provide quantitative assessments of this longitudinal change. Often techniques are compared through computation of required sample size estimates for future clinical trials. However interpretation of such comparisons is rendered complex because, despite using the same publicly available cohorts, the various techniques have been assessed with different data exclusions and different statistical analysis models. We created the MIRIAD atrophy challenge in order to test various capabilities of atrophy measurement techniques. The data consisted of 69 subjects (46 Alzheimer's disease, 23 control) who were scanned multiple (up to twelve) times at nine visits over a follow-up period of one to two years, resulting in 708 total image sets. Nine participating groups from 6 countries completed the challenge by providing volumetric measurements of key structures (whole brain, lateral ventricle, left and right hippocampi) for each dataset and atrophy measurements of these structures for each time point pair (both forward and backward) of a given subject. From these results, we formally compared techniques using exactly the same dataset. First, we assessed the repeatability of each technique using rates obtained from short intervals where no measurable atrophy is expected. For those measures that provided direct measures of atrophy between pairs of images, we also assessed symmetry and transitivity. Then, we performed a statistical analysis in a consistent manner using linear mixed effect models. The models, one for repeated measures of volume made at multiple time-points and a second for repeated "direct" measures of change in brain volume, appropriately allowed for the correlation between measures made on the same subject and were shown to fit the data well. From these models, we obtained estimates of the distribution of atrophy rates in the Alzheimer's disease (AD) and control groups and of required sample sizes to detect a 25% treatment effect, in relation to healthy ageing, with 95% significance and 80% power over follow-up periods of 6, 12, and 24 months. Uncertainty in these estimates, and head-to-head comparisons between techniques, were carried out using the bootstrap. The lateral ventricles provided the most stable measurements, followed by the brain. The hippocampi had much more variability across participants, likely because of differences in segmentation protocol and less distinct boundaries. Most methods showed no indication of bias based on the short-term interval results, and direct measures provided good consistency in terms of symmetry and transitivity. The resulting annualized rates of change derived from the model ranged from, for whole brain: -1.4% to -2.2% (AD) and -0.35% to -0.67% (control), for ventricles: 4.6% to 10.2% (AD) and 1.2% to 3.4% (control), and for hippocampi: -1.5% to -7.0% (AD) and -0.4% to -1.4% (control). There were large and statistically significant differences in the sample size requirements between many of the techniques. The lowest sample sizes for each of these structures, for a trial with a 12 month follow-up period, were 242 (95% CI: 154 to 422) for whole brain, 168 (95% CI: 112 to 282) for ventricles, 190 (95% CI: 146 to 268) for left hippocampi, and 158 (95% CI: 116 to 228) for right hippocampi.

Pathophysiologic mechanisms leading to loss of white matter integrity and the temporal positioning of biomarkers of white matter integrity relative to the biomarkers of gray matter neurodegeneration and amyloid load in the course of Alzheimer disease (AD) are poorly understood.To investigate the effects of AD-related gray matter neurodegeneration and high β-amyloid on white matter microstructure in older adults without dementia.A population-based, longitudinal cohort study was conducted. Participants included in the Mayo Clinic Study of Aging (N = 701) who underwent magnetic resonance imaging, diffusion tensor imaging (DTI), and positron emission tomography studies with diagnoses of cognitively normal ([CN] n = 570) or mild cognitive impairment ([MCI] n = 131) were included. Both groups were divided into biomarker-negative, amyloid-positive-only, neurodegeneration-positive-only, and amyloid plus neurodegeneration-positive groups based on their amyloid load shown on carbon 11-labeled Pittsburgh Compound B positron emission tomography, AD hypometabolic pattern shown on fludeoxyglucose F 18 positron emission tomography, and/or hippocampal atrophy shown on magnetic resonance imaging.Fractional anisotropy (FA) determined using DTI.No FA alterations were observed in biomarker-negative MCI and amyloid-positive-only CN and MCI groups compared with biomarker-negative CN participants on voxel-based analysis (P.05; familywise error corrected). Conversely, the neurodegeneration-positive-only and amyloid plus neurodegeneration-positive CN and MCI groups consistently had decreased FA in the fornix, which correlated with cognitive performance (ρ = 0.38; P.001). Patients with MCI had more extensive white matter involvement than did those with CN, and the greatest FA decreases were observed in the amyloid plus neurodegeneration-positive MCI group (P.05; familywise error corrected).A high amyloid load does not influence diffusion tensor imaging-based measures of white matter integrity in the absence of coexistent gray matter neurodegeneration in older adults without dementia.

BackgroundPrevalence and risk factors for focal hemosiderin deposits are important considerations when planning amyloid-modifying trials for treatment and prevention of Alzheimer's disease (AD).MethodsSubjects were cognitively normal (n = 171), early-mild cognitive impairment (MCI)(n=240), late-MCI (n = 111), and AD (n = 40) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Microhemorrhages and superficial siderosis were assessed at baseline and on all available MRIs at 3, 6, and 12 months. β-amyloid load was assessed with (18)F-florbetapir positron emission tomography.ResultsPrevalence of superficial siderosis was 1% and prevalence of microhemorrhages was 25% increasing with age (P < .001) and β-amyloid load (P < .001). Topographic densities of microhemorrhages were highest in the occipital lobes and lowest in the deep/infratentorial regions. A greater number of microhemorrhages at baseline was associated with a greater annualized rate of additional microhemorrhages by last follow-up (rank correlation=0.49; P < .001).ConclusionsFocal hemosiderin deposits are relatively common in the ADNI cohort and are associated with β-amyloid load.

Midbrain atrophy is a characteristic feature of progressive supranuclear palsy (PSP), although it is unclear whether it is associated with the PSP syndrome (PSPS) or PSP pathology. The aim of the present study was to determine whether midbrain atrophy is a useful biomarker of PSP pathology, or whether it is only associated with typical PSPS.All autopsy-confirmed subjects were identified with the PSP clinical phenotype (i.e. PSPS) or PSP pathology and a volumetric MRI. Of 24 subjects with PSP pathology, 11 had a clinical diagnosis of PSPS (PSP-PSPS), and 13 had a non-PSPS clinical diagnosis (PSP-other). Three subjects had PSPS and corticobasal degeneration pathology (CBD-PSPS). Healthy control and disease control groups (i.e. a group without PSPS or PSP pathology) and a group with CBD pathology and corticobasal syndrome (CBD-CBS) were selected. The midbrain area was measured in all subjects. [Correction added on 21 June 2013, after first online publication: the abbreviation of corticobasal degeneration pathology was changed from CBD-PSP to CBD-PSPS.]The midbrain area was reduced in each group with clinical PSPS (with and without PSP pathology). The group with PSP pathology and non-PSPS clinical syndromes did not show reduced midbrain area. Midbrain area was smaller in the subjects with PSPS than in those without PSPS (P 0.0001), with an area under the receiver operator curve of 0.99 (0.88, 0.99). A midbrain area cut-point of 92 mm(2) provided optimum sensitivity (93%) and specificity (89%) for differentiation.Midbrain atrophy is associated with the clinical presentation of PSPS, but not with the pathological diagnosis of PSP in the absence of clinical PSPS. This finding has important implications for the utility of midbrain measurements as diagnostic biomarkers for PSP pathology.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) three-dimensional T1-weighted magnetic resonance imaging (MRI) acquisitions provide a rich data set for developing and testing analysis techniques for extracting structural endpoints. To promote greater rigor in analysis and meaningful comparison of different algorithms, the ADNI MRI Core has created standardized analysis sets of data comprising scans that met minimum quality control requirements. We encourage researchers to test and report their techniques against these data. Standard analysis sets of volumetric scans from ADNI-1 have been created, comprising screening visits, 1-year completers (subjects who all have screening, 6- and 12-month scans), 2-year annual completers (screening, 1-year and 2-year scans), 2-year completers (screening, 6-months, 1-year, 18-months [mild cognitive impaired (MCI) only], and 2-year scans), and complete visits (screening, 6-month, 1-year, 18-month [MCI only], 2-year, and 3-year [normal and MCI only] scans). As the ADNI-GO/ADNI-2 data become available, updated standard analysis sets will be posted regularly.

The new criteria for preclinical Alzheimer disease (AD) proposed 3 stages: abnormal levels of β-amyloid (stage 1), stage 1 plus evidence of brain injury (stage 2), and stage 2 plus subtle cognitive changes (stage 3). However, a large group of subjects with normal β-amyloid biomarkers have evidence of brain injury; we labeled them as the "suspected non-Alzheimer pathophysiology" (sNAP) group. The characteristics of the sNAP group are poorly understood.Using the preclinical AD classification, 430 cognitively normal subjects from the Mayo Clinic Study of Aging who underwent brain magnetic resonance (MR), (18)fluorodeoxyglucose (FDG), and Pittsburgh compound B positron emission tomography (PET) were evaluated for FDG PET regional volumetrics, MR regional brain volumetrics, white matter hyperintensity volume, and number of infarcts. We examined cross-sectional associations across AD preclinical stages, those with all biomarkers normal, and the sNAP group.The sNAP group had a lower proportion (14%) with apolipoprotein E ε4 genotype than the preclinical AD stages 2 + 3. The sNAP group did not show any group differences compared to stages 2 + 3 of the preclinical AD group on measures of FDG PET regional hypometabolism, MR regional brain volume loss, cerebrovascular imaging lesions, vascular risk factors, imaging changes associated with α-synucleinopathy, or physical findings of parkinsonism.Cognitively normal persons with brain injury biomarker abnormalities, with or without abnormal levels of β-amyloid, were indistinguishable on a variety of imaging markers, clinical features, and risk factors. The initial appearance of brain injury biomarkers that occurs in cognitively normal persons with preclinical AD may not depend on β-amyloidosis.

A study was undertaken to investigate the association of intellectual and physical activity with biomarkers of Alzheimer disease (AD) pathophysiology and cognition in a nondemented elderly population. The biomarkers evaluated were brain Aβ load via Pittsburgh compound B (PiB)-positron emission tomography (PET), neuronal dysfunction via (18) F-fluorodeoxyglucose (FDG)-PET, and neurodegeneration via structural magnetic resonance imaging (MRI).We studied 515 nondemented (428 cognitively normal and 87 mild cognitive impairment) participants in the population-based Mayo Clinic Study of Aging who completed a 3T MRI, PET scans, and APOE genotype, and had lifestyle activity measures and cognition data available. The imaging measures computed were global PiB-PET uptake, and global FDG-PET and MRI based hippocampal volume. We consolidated activity variables into lifetime intellectual, current intellectual, and current physical activities. We used a global cognitive z score as a measure of cognition. We applied 2 independent methods-partial correlation analysis adjusted for age and gender and path analysis using structural equations-to evaluate the associations between lifestyle activities, imaging biomarkers, and global cognition.None of the lifestyle variables were correlated with the biomarkers, and the path associations between lifestyle variables and biomarkers were not significant (p0.05). Conversely, all the biomarkers were correlated with global cognitive z score (p0.05), and the path associations between (lifetime and current) intellectual activities and global z score were significant (p0.01).Intellectual and physical activity lifestyle factors were not associated with AD biomarkers, but intellectual lifestyle factors explained variability in the cognitive performance in this nondemented population. This study provides evidence that lifestyle activities may delay the onset of dementia but do not significantly influence the expression of AD pathophysiology.

A workgroup commissioned by the Alzheimer's Association (AA) and the National Institute on Aging (NIA) recently published research criteria for preclinical Alzheimer disease (AD). We performed a preliminary assessment of these guidelines.We employed Pittsburgh compound B positron emission tomography (PET) imaging as our biomarker of cerebral amyloidosis, and (18) fluorodeoxyglucose PET imaging and hippocampal volume as biomarkers of neurodegeneration. A group of 42 clinically diagnosed AD subjects was used to create imaging biomarker cutpoints. A group of 450 cognitively normal (CN) subjects from a population-based sample was used to develop cognitive cutpoints and to assess population frequencies of the different preclinical AD stages using different cutpoint criteria.The new criteria subdivide the preclinical phase of AD into stages 1 to 3. To classify our CN subjects, 2 additional categories were needed. Stage 0 denotes subjects with normal AD biomarkers and no evidence of subtle cognitive impairment. Suspected non-AD pathophysiology (SNAP) denotes subjects with normal amyloid PET imaging, but abnormal neurodegeneration biomarker studies. At fixed cutpoints corresponding to 90% sensitivity for diagnosing AD and the 10th percentile of CN cognitive scores, 43% of our sample was classified as stage 0, 16% stage 1, 12 % stage 2, 3% stage 3, and 23% SNAP.This cross-sectional evaluation of the NIA-AA criteria for preclinical AD indicates that the 1-3 staging criteria coupled with stage 0 and SNAP categories classify 97% of CN subjects from a population-based sample, leaving only 3% unclassified. Future longitudinal validation of the criteria will be important.

The objectives of this study are as follows: to describe practical implementation challenges of multisite, multivendor quantitative studies; to describe the MRI phantom and analysis software used in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, illustrate the utility of the system for measuring scanner performance, the ability to assess gradient field nonlinearity corrections: and to recover human brain images without geometric scaling errors in multisite studies. ADNI is a large multicenter study with each center having its own copy of the phantom. The design of the phantom and analysis software are presented as results from predistribution systematics studies and results from field experience with the phantom at 58 enrolling ADNI sites over a 3 year period. The estimated coefficients of variation intrinsic to measurements of geometry in a single phantom are in the range of 3-5 parts in 10(4). Phantom measurements accurately detect linear and nonlinear scaling in images. Gradient unwarping methods are readily assessed by phantom nonlinearity measurements. Phantom-based scaling correction reduces observed geometric drift in human images by one-third or more. Repair or replacement of phantoms between scans, however, is a confounding factor. The ADNI phantom can be used to assess both scanner performance and the validity of postprocessing image corrections in order to reduce systematic errors in human images. Reduced measurement errors should decrease measurement bias and increase statistical power for measurements of rates of change in the brain structure in AD treatment trials. Perhaps the greatest practical value of incorporating ADNI phantom measurements in a multisite study is to identify scanner errors through central monitoring. This approach has resulted in identification of system errors including sites misidentification of their own gradient hardware and the disabling of autoshim, and a miscalibrated laser alignment light. If undetected, these errors would have contributed to imprecision in quantitative metrics at over 25% of all enrolling ADNI sites.

Quality assessment of MRI is of great importance to derive reliable diagnostic information. As automated quantitative image analysis is being increasingly used in routine, automated measures of quality are needed. Based on a single magnitude image, we propose a procedure that automates the classification of data quality and allows detecting patient-/scanner-related artifacts. Validated on 750 datasets, the approach proofs to be a very promising candidate to perform quality assurance analysis for clinical practice and research. It could greatly improve clinical workflow through its ability to rule-out the need for a repeat-scan while the patient is still in the magnet bore.