Task-free functional magnetic resonance imaging (TF-fMRI) has great potential for advancing the understanding and treatment of neurologic illness. However, as with all measures of neural activity, ...variability is a hallmark of intrinsic connectivity networks (ICNs) identified by TF-fMRI. This variability has hampered efforts to define a robust metric of connectivity suitable as a biomarker for neurologic illness. We hypothesized that some of this variability rather than representing noise in the measurement process, is related to a fundamental feature of connectivity within ICNs, which is their non-stationary nature. To test this hypothesis, we used a large (n = 892) population-based sample of older subjects to construct a well characterized atlas of 68 functional regions, which were categorized based on independent component analysis network of origin, anatomical locations, and a functional meta-analysis. These regions were then used to construct dynamic graphical representations of brain connectivity within a sliding time window for each subject. This allowed us to demonstrate the non-stationary nature of the brain's modular organization and assign each region to a "meta-modular" group. Using this grouping, we then compared dwell time in strong sub-network configurations of the default mode network (DMN) between 28 subjects with Alzheimer's dementia and 56 cognitively normal elderly subjects matched 1:2 on age, gender, and education. We found that differences in connectivity we and others have previously observed in Alzheimer's disease can be explained by differences in dwell time in DMN sub-network configurations, rather than steady state connectivity magnitude. DMN dwell time in specific modular configurations may also underlie the TF-fMRI findings that have been described in mild cognitive impairment and cognitively normal subjects who are at risk for Alzheimer's dementia.
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.
Abstract Introduction Our goal was to develop cut points for amyloid positron emission tomography (PET), tau PET, flouro-deoxyglucose (FDG) PET, and MRI cortical thickness. Methods We examined five ...methods for determining cut points. Results The reliable worsening method produced a cut point only for amyloid PET. The specificity, sensitivity, and accuracy of cognitively impaired versus young clinically normal (CN) methods labeled the most people abnormal and all gave similar cut points for tau PET, FDG PET, and cortical thickness. Cut points defined using the accuracy of cognitively impaired versus age-matched CN method labeled fewer people abnormal. Discussion In the future, we will use a single cut point for amyloid PET (standardized uptake value ratio, 1.42; centiloid, 19) based on the reliable worsening cut point method. We will base lenient cut points for tau PET, FDG PET, and cortical thickness on the accuracy of cognitively impaired versus young CN method and base conservative cut points on the accuracy of cognitively impaired versus age-matched CN method.
Summary Background A new classification for biomarkers in Alzheimer's disease and cognitive ageing research is based on grouping the markers into three categories: amyloid deposition (A), tauopathy ...(T), and neurodegeneration or neuronal injury (N). Dichotomising these biomarkers as normal or abnormal results in eight possible profiles. We determined the clinical characteristics and prevalence of each ATN profile in cognitively unimpaired individuals aged 50 years and older. Methods All participants were in the Mayo Clinic Study of Aging, a population-based study that uses a medical records linkage system to enumerate all individuals aged 50–89 years in Olmsted County, MN, USA. Potential participants are randomly selected, stratified by age and sex, and invited to participate in cognitive assessments; individuals without medical contraindications are invited to participate in brain imaging studies. Participants who were judged clinically as having no cognitive impairment and underwent multimodality imaging between Oct 11, 2006, and Oct 5, 2016, were included in the current study. Participants were classified as having normal (A−) or abnormal (A+) amyloid using amyloid PET, normal (T−) or abnormal (T+) tau using tau PET, and normal (N−) or abnormal (N+) neurodegeneration or neuronal injury using cortical thickness assessed by MRI. We used the cutoff points of standard uptake value ratio (SUVR) 1·42 (centiloid 19) for amyloid PET, 1·23 SUVR for tau PET, and 2·67 mm for MRI cortical thickness. Age-specific and sex-specific prevalences of the eight groups were determined using multinomial models combining data from 435 individuals with amyloid PET, tau PET, and MRI assessments, and 1113 individuals who underwent amyloid PET and MRI, but not tau PET imaging. Findings The numbers of participants in each profile group were 165 A−T−N−, 35 A−T+N−, 63 A−T−N+, 19 A−T+N+, 44 A+T−N−, 25 A+T+N−, 35 A+T−N+, and 49 A+T+N+. Age differed by ATN group (p<0·0001), ranging from a median 58 years (IQR 55–64) in A–T–N– and 57 years (54–64) in A–T+N– to a median 80 years (75–84) in A+T–N+ and 79 years (73–87) in A+T+N+. The number of APOE ε4 carriers differed by ATN group (p=0·04), with carriers roughly twice as frequent in each A+ group versus the corresponding A– group. White matter hyperintensity volume (p<0·0001) and cognitive performance (p<0·0001) also differed by ATN group. Tau PET and neurodegeneration biomarkers were discordant in most individuals who would be categorised as stage 2 or 3 preclinical Alzheimer's disease (A+T+N−, A+T−N+, and A+T+N+; 86% at age 65 years and 51% at age 80 years) or with suspected non-Alzheimer's pathophysiology (A−T+N−, A−T−N+, and A−T+N+; 92% at age 65 years and 78% at age 80 years). From age 50 years, A−T−N− prevalence declined and A+T+N+ and A−T+N+ prevalence increased. In both men and women, A−T−N− was the most prevalent until age late 70s. After about age 80 years, A+T+N+ was most prevalent. By age 85 years, more than 90% of men and women had one or more biomarker abnormalities. Interpretation Biomarkers of fibrillar tau deposition can be included with those of β-amyloidosis and neurodegeneration or neuronal injury to more fully characterise the heterogeneous pathological profiles in the population. Both amyloid- dependent and amyloid-independent pathological profiles can be identified in the cognitively unimpaired population. The prevalence of each ATN group changed substantially with age, with progression towards more biomarker abnormalities among individuals who remained cognitively unimpaired. Funding National Institute on Aging (part of the US National Institutes of Health), the Alexander Family Professorship of Alzheimer's Disease Research, the Mayo Clinic, and the GHR Foundation.
Objective:
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.
Methods:
We employed Pittsburgh compound B positron emission tomography (PET) imaging as our biomarker of cerebral amyloidosis, and 18fluorodeoxyglucose 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.
Results:
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.
Interpretation:
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 ANN NEUROL 2012;
The logopenic variant of primary progressive aphasia is an atypical clinical variant of Alzheimer's disease which is typically characterized by left temporoparietal atrophy on magnetic resonance ...imaging and hypometabolism on F-18 fluorodeoxyglucose positron emission tomography. We aimed to characterize and compare patterns of atrophy and hypometabolism in logopenic primary progressive aphasia, and determine which brain regions and imaging modality best differentiates logopenic primary progressive aphasia from typical dementia of the Alzheimer's type.
A total of 27 logopenic primary progressive aphasia subjects underwent fluorodeoxyglucose positron emission tomography and volumetric magnetic resonance imaging. These subjects were matched to 27 controls and 27 subjects with dementia of the Alzheimer's type. Patterns of atrophy and hypometabolism were assessed at the voxel and region-level using Statistical Parametric Mapping. Penalized logistic regression analysis was used to determine what combinations of regions best discriminate between groups.
Atrophy and hypometabolism was observed in lateral temporoparietal and medial parietal lobes, left greater than right, and left frontal lobe in the logopenic group. The logopenic group showed greater left inferior, middle and superior lateral temporal atrophy (inferior p = 0.02; middle p = 0.007, superior p = 0.002) and hypometabolism (inferior p = 0.006, middle p = 0.002, superior p = 0.001), and less right medial temporal atrophy (p = 0.02) and hypometabolism (p<0.001), and right posterior cingulate hypometabolism (p<0.001) than dementia of the Alzheimer's type. An age-adjusted penalized logistic model incorporating atrophy and hypometabolism achieved excellent discrimination (area under the receiver operator characteristic curve = 0.89) between logopenic and dementia of the Alzheimer's type subjects, with optimal discrimination achieved using right medial temporal and posterior cingulate hypometabolism, left inferior, middle and superior temporal hypometabolism, and left superior temporal volume.
Patterns of atrophy and hypometabolism both differ between logopenic primary progressive aphasia and dementia of the Alzheimer's type and both modalities provide excellent discrimination between groups.
Summary Background As preclinical Alzheimer's disease becomes a target for therapeutic intervention, the overlap between imaging abnormalities associated with typical ageing and those associated with ...Alzheimer's disease needs to be recognised. We aimed to characterise how typical ageing and preclinical Alzheimer's disease overlap in terms of β-amyloidosis and neurodegeneration. Methods We measured age-specific frequencies of amyloidosis and neurodegeneration in individuals with normal cognitive function aged 50–89 years. Potential participants were randomly selected from the Olmsted County (MN, USA) population-based study of cognitive ageing and invited to participate in cognitive and imaging assessments. To be eligible for inclusion, individuals must have been judged clinically to have no cognitive impairment and have undergone amyloid PET,18 F-fluorodeoxyglucose (18 F-FDG) PET, and MRI. Imaging results were obtained from March 28, 2006, to Dec 3, 2013. Amyloid status (positive A+ or negative A– ) was determined by amyloid PET with11 C Pittsburgh compound B. Neurodegeneration status (positive N+ or negative N– ) was determined by an Alzheimer's disease signature18 F-FDG PET or hippocampal volume on MRI. We determined age-specific frequencies of the four groups (amyloid negative and neurodegeneration negative A– N– , amyloid positive and neurodegeneration negative A+ N– , amyloid negative and neurodegeneration positive A– N+ , or amyloid positive and neurodegeneration positive A+ N+ ) cross-sectionally using multinomial regression models. We also investigated associations of group frequencies with APOE ɛ4 status (assessed with DNA extracted from blood) and sex by including these covariates in the multinomial models. Findings The study population consisted of 985 eligible participants. The population frequency of A– N– was 100% (n=985) at age 50 years and fell to 17% (95% CI 11–24) by age 89 years. The frequency of A+ N– increased to 28% (24–32) at age 74 years, then decreased to 17% (11–25) by age 89 years. The frequency of A– N+ increased from age 60 years, reaching 24% (16–34) by age 89 years. The frequency of A+ N+ increased from age 65 years, reaching 42% (31–52) by age 89 years. The results from our multinomial models suggest that A+ N– and A+ N+ were more frequent in APOE ɛ4 carriers than in non-carriers and that A+ N+ was more, and A+ N– less frequent in men than in women. Interpretation Accumulation of amyloid and neurodegeneration are nearly inevitable by old age, but many people are able to maintain normal cognitive function despite these imaging abnormalities. Changes in the frequency of amyloidosis and neurodegeneration with age, which seem to be modified by APOE ɛ4 and sex, suggest that pathophysiological sequences might differ between individuals. Funding US National Institute on Aging and Alexander Family Professorship of Alzheimer's Disease Research.
Purpose
A standard MRI system phantom has been designed and fabricated to assess scanner performance, stability, comparability and assess the accuracy of quantitative relaxation time imaging. The ...phantom is unique in having traceability to the International System of Units, a high level of precision, and monitoring by a national metrology institute. Here, we describe the phantom design, construction, imaging protocols, and measurement of geometric distortion, resolution, slice profile, signal‐to‐noise ratio (SNR), proton‐spin relaxation times, image uniformity and proton density.
Methods
The system phantom, designed by the International Society of Magnetic Resonance in Medicine ad hoc committee on Standards for Quantitative MR, is a 200 mm spherical structure that contains a 57‐element fiducial array; two relaxation time arrays; a proton density/SNR array; resolution and slice‐profile insets. Standard imaging protocols are presented, which provide rapid assessment of geometric distortion, image uniformity, T1 and T2 mapping, image resolution, slice profile, and SNR.
Results
Fiducial array analysis gives assessment of intrinsic geometric distortions, which can vary considerably between scanners and correction techniques. This analysis also measures scanner/coil image uniformity, spatial calibration accuracy, and local volume distortion. An advanced resolution analysis gives both scanner and protocol contributions. SNR analysis gives both temporal and spatial contributions.
Conclusions
A standard system phantom is useful for characterization of scanner performance, monitoring a scanner over time, and to compare different scanners. This type of calibration structure is useful for quality assurance, benchmarking quantitative MRI protocols, and to transition MRI from a qualitative imaging technique to a precise metrology with documented accuracy and uncertainty.
Summary Background In a 2014 cross-sectional analysis, we showed that amyloid and neurodegeneration biomarker states in participants with no clinical impairment varied greatly with age, suggesting ...dynamic within-person processes. In this longitudinal study, we aimed to estimate rates of transition from a less to a more abnormal biomarker state by age in individuals without dementia, as well as to assess rates of transition to dementia from an abnormal state. Methods Participants from the Mayo Clinic Study of Aging (Olmsted County, MN, USA) without dementia at baseline were included in this study, a subset of whom agreed to multimodality imaging. Amyloid PET (with11 C-Pittsburgh compound B) was used to classify individuals as amyloid positive (A+ ) or negative (A− ).18 F-fluorodeoxyglucose (18 F-FDG)-PET and MRI were used to classify individuals as neurodegeneration positive (N+ ) or negative (N− ). We used all observations, including those from participants who did not have imaging results, to construct a multistate Markov model to estimate four different age-specific biomarker state transition rates: A− N− to A+ N− ; A− N− to A− N+ (suspected non-Alzheimer's pathology); A+ N− to A+ N+ ; and A− N+ to A+ N+ . We also estimated two age-specific rates to dementia: A+ N+ to dementia and A− N+ to dementia. Using these state-to-state transition rates, we estimated biomarker state frequencies by age. Findings At baseline (between Nov 29, 2004, to March 7, 2015), 4049 participants did not have dementia (3512 87% were clinically normal and 537 13% had mild cognitive impairment). 1541 individuals underwent imaging between March 28, 2006, to April 30, 2015. Transition rates were low at age 50 years and, with one exception, exponentially increased with age. At age 85 years compared with age 65 years, the rate was nearly 11-times higher (17·2 vs 1·6 per 100 person-years) for the transition from A− N− to A− N+ , three-times higher (20·8 vs 6·1) for A+ N− to A+ N+ , and five-times higher (13·2 vs 2·6) for A− N+ to A+ N+ . The rate of transition was also increased at age 85 years compared with age 65 years for A+ N+ to dementia (7·0 vs 0·8) and for A− N+ to dementia (1·7 vs 0·6). The one exception to an exponential increase with age was the transition rate from A− N− to A+ N− , which increased from 4·0 transitions per 100 person-years at age 65 years to 6·9 transitions per 100 person-years at age 75 and then plateaued beyond that age. Estimated biomarker frequencies by age from the multistate model were similar to cross-sectional biomarker frequencies. Interpretation Our transition rates suggest that brain ageing is a nearly inevitable acceleration toward worse biomarker and clinical states. The one exception is the transition to amyloidosis without neurodegeneration, which is most dynamic from age 60 years to 70 years and then plateaus beyond that age. We found that simple transition rates can explain complex, highly interdependent biomarker state frequencies in our population. Funding National Institute on Aging, Alexander Family Professorship of Alzheimer's Disease Research, the GHR Foundation.
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