To develop a reliable magnetic resonance elastography (MRE)-based method for measuring regional brain stiffness.
First, simulation studies were used to demonstrate how stiffness measurements can be biased by changes in brain morphometry, such as those due to atrophy. Adaptive postprocessing methods were created that significantly reduce the spatial extent of edge artifacts and eliminate atrophy-related bias. Second, a pipeline for regional brain stiffness measurement was developed and evaluated for test-retest reliability in 10 healthy control subjects.
This technique indicates high test-retest repeatability with a typical coefficient of variation of less than 1% for global brain stiffness and less than 2% for the lobes of the brain and the cerebellum. Furthermore, this study reveals that the brain possesses a characteristic topography of mechanical properties, and also that lobar stiffness measurements tend to correlate with one another within an individual.
The methods presented in this work are resistant to noise- and edge-related biases that are common in the field of brain MRE, demonstrate high test-retest reliability, and provide independent regional stiffness measurements. This pipeline will allow future investigations to measure changes to the brain’s mechanical properties and how they relate to the characteristic topographies that are typical of many neurologic diseases.
Atypical variants of Alzheimer’s disease (AD) have been pathologically defined based on the distribution of neurofibrillary tangles; hippocampal sparing (HpSp) AD shows minimal involvement of the hippocampus and limbic predominant (LP) AD shows neurofibrillary tangles restricted to the medial temporal lobe. We aimed to determine whether MRI patterns of atrophy differ across HpSp AD, LP AD and typical AD, and whether imaging could be a useful predictor of pathological subtype during life.
In this case-control study, we identified 177 patients who had been prospectively followed in the Mayo Clinic Alzheimer’s Disease Research Center, were demented during life, had AD pathology at autopsy (Braak stage ≥ IV, intermediate-high probability AD) and an antemortem MRI. Cases were assigned to one of three pathological subtypes (HpSp n=19, typical n=125, or LP AD n=33) based on neurofibrillary tangle counts and their ratio in association cortices to hippocampus, without reference to neuronal loss. Voxel-based morphometry and atlas-based parcellation were used to compare patterns of grey matter loss across groups, and to controls.
The severity of medial temporal and cortical grey matter atrophy differed across subtypes. The most severe medial temporal atrophy was observed in LP AD, followed by typical AD, and then HpSp AD. Conversely, the most severe cortical atrophy was observed in HpSp AD, followed by typical AD, and then LP AD. A ratio of hippocampal-to-cortical volume provided the best discrimination across all three AD subtypes. The majority of typical AD (98/125;78%) and LP AD (31/33;94%) subjects, but only 8/19 (42%) of the HpSp AD subjects, presented with a dominant amnestic syndrome.
Patterns of atrophy on MRI differ across the pathological subtypes of AD, suggesting that MR regional volumetrics reliably track the distribution of neurofibrillary tangle pathology and can predict pathological subtype during life.
US National Institutes of Health (National Institute on Aging)
Dementia with Lewy bodies (DLB) is the second most common cause of neurodegenerative dementia after Alzheimer's disease (AD). Our objective was to determine whether the 11C–Pittsburgh Compound-B (PiB) retention and regional hypometabolism on PET and regional cortical atrophy on MRI are complementary in characterizing patients with DLB and differentiating them from AD. We studied age, gender and education matched patients with a clinical diagnosis of DLB (n=21), AD (n=21), and cognitively normal subjects (n=42). Hippocampal atrophy, global cortical PiB retention and occipital lobe metabolism in combination distinguished DLB from AD better than any of the measurements alone (area under the receiver operating characteristic=0.98).Five of the DLB and AD patients who underwent autopsy were distinguished through multimodality imaging. These data demonstrate that MRI and PiB PET contribute to characterizing the distinct pathological mechanisms in patients with AD compared to DLB. Occipital and posterior parietotemporal lobe hypometabolism is a distinguishing feature of DLB and this regional hypometabolic pattern is independent of the amyloid pathology.
Dementia with Lewy bodies; MRI; PET; FDG; PiB; Alzheimer's disease
Acetylcholinesterase inhibitors are commonly used to treat patients with dementia with Lewy bodies. Hippocampal atrophy on magnetic resonance imaging and amyloid-β load on positron emission tomography are associated with the Alzheimer’s disease-related pathology in patients with dementia with Lewy bodies. To date, few studies have investigated imaging markers that predict treatment response in patients with dementia with Lewy bodies. Our objective was to determine whether imaging markers of Alzheimer’s disease-related pathology such as hippocampal volume, brain amyloid-β load on 11C Pittsburgh compound B positron emission tomography predict treatment response to acetylcholinesterase inhibitors in patients with dementia with Lewy bodies. We performed a retrospective analysis on consecutive treatment-naive patients with dementia with Lewy bodies (n = 54) from the Mayo Clinic Alzheimer’s Disease Research Centre who subsequently received acetylcholinesterase inhibitors and underwent magnetic resonance imaging with hippocampal volumetry. Baseline and follow-up assessments were obtained with the Mattis Dementia Rating Scale. Subjects were divided into three groups (reliable improvement, stable or reliable decline) using Dementia Rating Scale reliable change indices determined previously. Associations between hippocampal volumes and treatment response were tested with analysis of covariance adjusting for baseline Dementia Rating Scale, age, gender, magnetic resonance field strength and Dementia Rating Scale interval. Seven subjects underwent 11C Pittsburgh compound B imaging within 12 weeks of magnetic resonance imaging. Global cortical 11C Pittsburgh compound B retention (scaled to cerebellar retention) was calculated in these patients. Using a conservative psychometric method of assessing treatment response, there were 12 patients with reliable decline, 29 stable cases and 13 patients with reliable improvement. The improvers had significantly larger hippocampi than those that declined (P = 0.02) and the stable (P = 0.04) group. An exploratory analysis demonstrated larger grey matter volumes in the temporal and parietal lobes in improvers compared with those who declined (P < 0.05). The two patients who had a positive 11C Pittsburgh compound B positron emission tomography scan declined and those who had a negative 11C Pittsburgh compound B positron emission tomography scan improved or were stable after treatment. Patients with dementia with Lewy bodies who do not have the imaging features of coexistent Alzheimer’s disease-related pathology are more likely to cognitively improve with acetylcholinesterase inhibitor treatment.
dementia with Lewy bodies; acetylcholinesterase inhibitors; MRI; PiB; PET; amyloid
The association between antemortem [11C]-Pittsburgh Compound B (PiB) retention and β-amyloid (Aβ) load, Lewy body (LB) and neurofibrillary tangle (NFT) densities were investigated in a pathologically confirmed case of dementia with LB (DLB). 76-year-old man presenting with a clinical diagnosis of DLB had undergone PiB–positron emission tomography (PET), 18F FDG-PET and MRI 18 months before death. The pathologic diagnosis was DLB neocortical-type with low-likelihood of Alzheimer's disease by NIA-Reagan criteria. Sections from regions of interest (ROI) on post-mortem examination were studied. A significant correlation was found between cortical Aβ density and PiB retention in the 17 corresponding ROIs (r=0.899; p<0.0001). Bielschowsky silver stain revealed mostly sparse neocortical neuritic plaques; whereas diffuse plaques were frequent. There was no correlation between LB density and PiB retention (r=0.13; p=0.66); nor between NFT density and PiB retention (r=−0.36; p=0.17). The ROI-based analysis of imaging and histopathological data confirms that PiB uptake on PET is a specific marker for Aβ density, but cannot differentiate neuritic from diffuse amyloid plaques in this case with DLB.
Dementia with Lewy bodies; amyloid imaging; PET; pathology; amyloid
To characterize the shape of the trajectories of Alzheimer’s Disease (AD) biomarkers as a function of MMSE.
Longitudinal registries from the Mayo Clinic and the Alzheimer’s Disease Neuroimaging Initiative (ADNI).
Two different samples (n=343 and n=598) were created that spanned the cognitive spectrum from normal to AD dementia. Subgroup analyses were performed in members of both cohorts (n=243 and n=328) who were amyloid positive at baseline.
Main Outcome Measures
The shape of biomarker trajectories as a function of MMSE, adjusted for age, was modeled and described as baseline (cross-sectional) and within-subject longitudinal effects. Biomarkers evaluated were cerebro spinal fluid (CSF) Aβ42 and tau; amyloid and fluoro deoxyglucose position emission tomography (PET) imaging, and structural magnetic resonance imaging (MRI).
Baseline biomarker values generally worsened (i.e., non-zero slope) with lower baseline MMSE. Baseline hippocampal volume, amyloid PET and FDG PET values plateaued (i.e., non-linear slope) with lower MMSE in one or more analyses. Longitudinally, within-subject rates of biomarker change were associated with worsening MMSE. Non-constant within-subject rates (deceleration) of biomarker change were found in only one model.
Biomarker trajectory shapes by MMSE were complex and were affected by interactions with age and APOE status. Non-linearity was found in several baseline effects models. Non-constant within-subject rates of biomarker change were found in only one model, likely due to limited within-subject longitudinal follow up. Creating reliable models that describe the full trajectories of AD biomarkers will require significant additional longitudinal data in individual participants.
Alzheimer’s disease biomarkers; Magnetic Resonance Imaging; cerebro spinal fluid; amyloid PET imaging; FDG PET imaging
A workgroup commissioned by the Alzheimer’s Association (AA) and the National Institute on Aging (NIA) recently published research criteria for preclinical Alzheimer’s 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 18fluorodeoxyglucose PET imaging and hippocampal volume as biomarkers of neurodegeneration. A group of 42 clinically diagnosed AD subjects was used to create imaging biomarker cut-points. A group of 450 cognitively normal (CN) subjects from a population based sample was used to develop cognitive cut-points and to assess population frequencies of the different preclinical AD stages using different cut-point criteria.
The new criteria subdivide the preclinical phase of AD into stages 1–3. To classify our CN subjects, two 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 cut-points 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 just 3% unclassified. Future longitudinal validation of the criteria will be important.
The objective of our study was to evaluate whether cognitively normal (CN) elderly participants showing elevated cortical beta-amyloid (Aβ) deposition have a consistent neuroanatomical signature of brain atrophy that may characterize preclinical Alzheimer's disease (AD). 115 CN participants who were Aβ-positive (CN +) by amyloid PET imaging; 115 CN participants who were Aβ-negative (CN −); and 88 Aβ-positive mild cognitive impairment or AD participants (MCI/AD +) were identified. Cortical thickness (FreeSurfer) and gray matter volume (SPM5) were measured for 28 regions-of-interest (ROIs) across the brain and compared across groups. ROIs that best discriminated CN − from CN + differed for FreeSurfer cortical thickness and SPM5 gray matter volume. Group-wise discrimination was poor with a high degree of uncertainty in terms of the rank ordering of ROIs. In contrast, both techniques showed strong and consistent findings comparing MCI/AD + to both CN − and CN + groups, with entorhinal cortex, middle and inferior temporal lobe, inferior parietal lobe, and hippocampus providing the best discrimination for both techniques. Concordance across techniques was higher for the CN − and CN + versus MCI/AD + comparisons, compared to the CN − versus CN + comparison. The weak and inconsistent nature of the findings across technique in this study cast doubt on the existence of a reliable neuroanatomical signature of preclinical AD in elderly PiB-positive CN participants.
► We measured atrophy in cognitively normal subjects with amyloid deposition (CN +). ► Findings in CN + subjects were weak and disconcordant across Freesurfer and SPM5. ► Concordance across techniques was higher when assessing Alzheimer disease subjects. ► Evidence for a neuroanatomical signature of preclinical AD in CN + subjects is weak.
Amyloid; Preclinical; Alzheimer's disease; Freesurfer; Voxel-based morphometry; Cognitively normal
Alzheimer's disease (AD) can present with non-amnestic clinical syndromes. We investigated whether there is an imaging signature of AD pathology in these atypical subjects. We identified 14 subjects that had pathological AD, a non-amnestic presentation (i.e. atypical AD), and MRI. These subjects were matched to 14 with clinical and pathological AD (i.e. typical AD), 14 with the same non-amnestic presentations with frontotemporal lobar degeneration (FTLD) pathology, and 20 controls. Voxel-based morphometry and region-of-interest (ROI) analysis were used to assess patterns of grey matter loss. Loss was observed in the temporoparietal cortex in both typical and atypical AD, and showed significantly greater loss than FTLD. However, the medial temporal lobes were more severely affected in typical AD and FTLD compared to atypical AD. A ratio of hippocampal and temporoparietal volumes provided excellent discrimination of atypical AD from FTLD subjects. Temporoparietal atrophy may therefore provide a useful marker of the presence of AD pathology even in subjects with atypical clinical presentations, especially in the context of relative sparing of the hippocampus.
Alzheimer's disease; pathology; voxel-based morphometry; atypical presentation; frontotemporal lobar degeneration; temporoparietal cortex; hippocampus
Progressive supranuclear palsy (PSP) is associated with pathological changes along the dentatorubrothalamic tract and in premotor cortex. We aimed to assess whether functional neural connectivity is disrupted along this pathway in PSP, and to determine how functional changes relate to changes in structure and diffusion. Eighteen probable PSP subjects and 18 controls had resting-state (task-free) fMRI, diffusion tensor imaging and structural MRI. Functional connectivity was assessed between thalamus and the rest of the brain, and within the basal ganglia, salience and default mode networks (DMN). Patterns of atrophy were assessed using voxel-based morphometry, and patterns of white matter tract degeneration were assessed using tract-based spatial statistics. Reduced in-phase functional connectivity was observed between the thalamus and premotor cortex including supplemental motor area (SMA), striatum, thalamus and cerebellum in PSP. Reduced connectivity in premotor cortex, striatum and thalamus were observed in the basal ganglia network and DMN, with subcortical salience network reductions. Tract degeneration was observed between cerebellum and thalamus and in superior longitudinal fasciculus, with grey matter loss in frontal lobe, premotor cortex, SMA and caudate. SMA functional connectivity correlated with SMA volume and measures of cognitive and motor dysfunction, while thalamic connectivity correlated with degeneration of superior cerebellar peduncles. PSP is therefore associated with disrupted thalamocortical connectivity that is associated with degeneration of the dentatorubrothalamic tract and the presence of cortical atrophy.
Resting state fMRI; functional connectivity; white matter tracts; atrophy; dentatorubrothalamic tract
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.
The common neurodegenerative pathologies underlying dementia are Alzheimer’s disease (AD), Lewy body disease (LBD) and Frontotemporal lobar degeneration (FTLD). Our aim was to identify patterns of atrophy unique to each of these diseases using antemortem structural-MRI scans of pathologically-confirmed dementia cases and build an MRI-based differential diagnosis system. Our approach of creating atrophy maps using structural-MRI and applying them for classification of new incoming patients is labeled Differential-STAND (Differential-diagnosis based on STructural Abnormality in NeuroDegeneration). Pathologically-confirmed subjects with a single dementing pathologic diagnosis who had an MRI at the time of clinical diagnosis of dementia were identified: 48 AD, 20 LBD, 47 FTLD-TDP (pathology-confirmed FTLD with TDP-43). Gray matter density in 91 regions-of-interest was measured in each subject and adjusted for head-size and age using a database of 120 cognitively normal elderly. The atrophy patterns in each dementia type when compared to pathologically-confirmed controls mirrored known disease-specific anatomic patterns: AD-temporoparietal association cortices and medial temporal lobe; FTLD-TDP-frontal and temporal lobes and LBD-bilateral amygdalae, dorsal midbrain and inferior temporal lobes. Differential-STAND based classification of each case was done based on a mixture model generated using bisecting k-means clustering of the information from the MRI scans. Leave-one-out classification showed reasonable performance compared to the autopsy gold-standard and clinical diagnosis: AD (sensitivity:90.7%; specificity:84 %), LBD (sensitivity:78.6%; specificity:98.8%) and FTLD-TDP (sensitivity:84.4%; specificity:93.8%). The proposed approach establishes a direct a priori relationship between specific topographic patterns on MRI and “gold standard” of pathology which can then be used to predict underlying dementia pathology in new incoming patients.
MRI; Alzheimer’s disease; Lewy body disease; Frontotemporal lobar degeneration
PIB PET and CSF Aβ42 demonstrate a highly significant inverse correlation. Both are presumed to measure brain Aβ amyloid load. Our objectives were to develop a method to transform CSF Aβ42 measures into calculated PIB measures (PIBcalc) of Aβ amyloid load, and to partially validate the method in an independent sample of subjects.
Forty-one ADNI subjects underwent PIB PET imaging and lumbar puncture (LP) at the same time. This sample, referred to as the “training” sample (9 cognitively normal (CN), 22 MCI, and 10 AD), was used to develop a regression model by which CSF Aβ42 (with APOE ε4 genotype as a covariate) was transformed into units of PIB PET (PIBcalc). An independent “supporting” sample of 362 (105 CN, 164 MCI, 93AD) ADNI subjects who underwent LP but not PIB PET imaging had their CSF Aβ42 values converted to PIBcalc. These values were compared to the overall PIB PET distribution found in the ADNI subjects (n = 102).
A linear regression model demonstrates good prediction of actual PIB PET from CSF Aβ42 measures obtained in the training sample (R2 = 0.77, P<0.001). PIBcalc data (derived from CSF Aβ42) in the supporting sample of 362 ADNI subjects who underwent LP but not PIB PET imaging demonstrates group-wise distributions that are highly consistent with the larger ADNI PIB PET distribution and with published PIB PET imaging studies.
Although the precise parameters of this model are specific for the ADNI sample, we conclude that CSF Aβ42 can be transformed into calculated PIB (PIBcalc) measures of Aβ amyloid load. Brain Aβ amyloid load can be ascertained at baseline in therapeutic or observational studies by either CSF or amyloid PET imaging and the data can be pooled using well-established multiple imputation techniques that account for the uncertainty in a CSF-based calculated PIB value.
Alzheimer's disease; Pittsburgh Compound B; amyloid imaging; Aβ amyloid; cerebrospinal fluid; Alzheimer's disease biomarkers
Apraxia of speech is a disorder of speech motor planning and/or programming that is distinguishable from aphasia and dysarthria. It most commonly results from vascular insults but can occur in degenerative diseases where it has typically been subsumed under aphasia, or it occurs in the context of more widespread neurodegeneration. The aim of this study was to determine whether apraxia of speech can present as an isolated sign of neurodegenerative disease. Between July 2010 and July 2011, 37 subjects with a neurodegenerative speech and language disorder were prospectively recruited and underwent detailed speech and language, neurological, neuropsychological and neuroimaging testing. The neuroimaging battery included 3.0 tesla volumetric head magnetic resonance imaging, [18F]-fluorodeoxyglucose and [11C] Pittsburg compound B positron emission tomography scanning. Twelve subjects were identified as having apraxia of speech without any signs of aphasia based on a comprehensive battery of language tests; hence, none met criteria for primary progressive aphasia. These subjects with primary progressive apraxia of speech included eight females and four males, with a mean age of onset of 73 years (range: 49–82). There were no specific additional shared patterns of neurological or neuropsychological impairment in the subjects with primary progressive apraxia of speech, but there was individual variability. Some subjects, for example, had mild features of behavioural change, executive dysfunction, limb apraxia or Parkinsonism. Voxel-based morphometry of grey matter revealed focal atrophy of superior lateral premotor cortex and supplementary motor area. Voxel-based morphometry of white matter showed volume loss in these same regions but with extension of loss involving the inferior premotor cortex and body of the corpus callosum. These same areas of white matter loss were observed with diffusion tensor imaging analysis, which also demonstrated reduced fractional anisotropy and increased mean diffusivity of the superior longitudinal fasciculus, particularly the premotor components. Statistical parametric mapping of the [18F]-fluorodeoxyglucose positron emission tomography scans revealed focal hypometabolism of superior lateral premotor cortex and supplementary motor area, although there was some variability across subjects noted with CortexID analysis. [11C]-Pittsburg compound B positron emission tomography binding was increased in only one of the 12 subjects, although it was unclear whether the increase was actually related to the primary progressive apraxia of speech. A syndrome characterized by progressive pure apraxia of speech clearly exists, with a neuroanatomic correlate of superior lateral premotor and supplementary motor atrophy, making this syndrome distinct from primary progressive aphasia.
primary progressive apraxia of speech; apraxia of speech; primary progressive aphasia; voxel-based morphometry; diffusion tensor imaging; fluorodeoxyglucose; Pittsburg compound B; supplementary motor area
A major recent discovery was the identification of an expansion of a non-coding GGGGCC hexanucleotide repeat in the C9ORF72 gene in patients with frontotemporal dementia and amyotrophic lateral sclerosis. Mutations in two other genes are known to account for familial frontotemporal dementia: microtubule-associated protein tau and progranulin. Although imaging features have been previously reported in subjects with mutations in tau and progranulin, no imaging features have been published in C9ORF72. Furthermore, it remains unknown whether there are differences in atrophy patterns across these mutations, and whether regional differences could help differentiate C9ORF72 from the other two mutations at the single-subject level. We aimed to determine the regional pattern of brain atrophy associated with the C9ORF72 gene mutation, and to determine which regions best differentiate C9ORF72 from subjects with mutations in tau and progranulin, and from sporadic frontotemporal dementia. A total of 76 subjects, including 56 with a clinical diagnosis of behavioural variant frontotemporal dementia and a mutation in one of these genes (19 with C9ORF72 mutations, 25 with tau mutations and 12 with progranulin mutations) and 20 sporadic subjects with behavioural variant frontotemporal dementia (including 50% with amyotrophic lateral sclerosis), with magnetic resonance imaging were included in this study. Voxel-based morphometry was used to assess and compare patterns of grey matter atrophy. Atlas-based parcellation was performed utilizing the automated anatomical labelling atlas and Statistical Parametric Mapping software to compute volumes of 37 regions of interest. Hemispheric asymmetry was calculated. Penalized multinomial logistic regression was utilized to create a prediction model to discriminate among groups using regional volumes and asymmetry score. Principal component analysis assessed for variance within groups. C9ORF72 was associated with symmetric atrophy predominantly involving dorsolateral, medial and orbitofrontal lobes, with additional loss in anterior temporal lobes, parietal lobes, occipital lobes and cerebellum. In contrast, striking anteromedial temporal atrophy was associated with tau mutations and temporoparietal atrophy was associated with progranulin mutations. The sporadic group was associated with frontal and anterior temporal atrophy. A conservative penalized multinomial logistic regression model identified 14 variables that could accurately classify subjects, including frontal, temporal, parietal, occipital and cerebellum volume. The principal component analysis revealed similar degrees of heterogeneity within all disease groups. Patterns of atrophy therefore differed across subjects with C9ORF72, tau and progranulin mutations and sporadic frontotemporal dementia. Our analysis suggested that imaging has the potential to be useful to help differentiate C9ORF72 from these other groups at the single-subject level.
frontotemporal dementia; magnetic resonance imaging; C9ORF72; tau; progranulin
Behavioural variant frontotemporal dementia is characterized by a change in comportment. It is associated with considerable functional decline over the course of the illness albeit with sometimes dramatic variability among patients. It is unknown whether any baseline features, or combination of features, could predict rate of functional decline in behavioural variant frontotemporal dementia. The aim of this study was to investigate the effects of different baseline clinical, neuropsychological, neuropsychiatric, genetic and anatomic predictors on the rate of functional decline as measured by the Clinical Dementia Rating Sum of Boxes scale. We identified 86 subjects with behavioural variant frontotemporal dementia that had multiple serial Clinical Dementia Rating Sum of Boxes assessments (mean 4, range 2–18). Atlas-based parcellation was used to generate volumes for specific regions of interest at baseline. Volumes were utilized to classify subjects into different anatomical subtypes using the advanced statistical technique of cluster analysis and were assessed as predictor variables. Composite scores were generated for the neuropsychological domains of executive, language, memory and visuospatial function. Behaviours from the brief questionnaire form of the Neuropsychiatric Inventory were assessed. Linear mixed-effects regression modelling was used to determine which baseline features predict rate of future functional decline. Rates of functional decline differed across the anatomical subtypes of behavioural variant frontotemporal dementia, with faster rates observed in the frontal dominant and frontotemporal subtypes. In addition, subjects with poorer performance on neuropsychological tests of executive, language and visuospatial function, less disinhibition, agitation/aggression and night-time behaviours at presentation, and smaller medial, lateral and orbital frontal lobe volumes showed faster rates of decline. In many instances, the effect of the predictor variables observed across all subjects was also preserved within anatomical subtypes. Furthermore, some of the predictor variables improved our prediction of rate of functional decline after anatomical subtype was taken into account. In particular, age at onset was a highly significant predictor but only after adjusting for subtype. We also found that although some predictor variables, for example gender, Mini-Mental State Examination score, and apathy/indifference, did not affect the rate of functional decline; these variables were associated with the actual Clinical Dementia Rating Sum of Boxes score estimated for any given time-point. These findings suggest that in behavioural variant frontotemporal dementia, rate of functional decline is driven by the combination of anatomical pattern of atrophy, age at onset, and neuropsychiatric characteristics of the subject at baseline.
frontotemporal dementia; behaviour; functional decline; brain volumes; mixed effects models
To investigate the impact white matter hyperintensities (WMH) detected on MRI have on motor dysfunction and cognitive impairment in non-demented elderly subjects.
Population-based study on the incidence and prevalence of cognitive impairment in Olmsted County, MN.
A total of 148 non-demented elderly (65 males) ranging in age from 73 to 91 years.
Main Outcome Measures
We measured the percentage of the total white matter volume classified as WMH (WMHp) in a priori defined brain regions (i.e. frontal, temporal, parietal, occipital, periventricular or subcortical). Motor impairment was evaluated qualitatively using the Unified Parkinson’s Disease Rating Scale (UPDRS) summary measures of motor skills and quantitatively using a digitized portable walkway system. Four cognitive domains were evaluated using z-scores of memory, language, executive function, and visuospatial reasoning.
A higher WMHp in all regions except occipital was associated with lower executive function z-score (p-value<0.01). A higher WMHp in all regions, but most strongly for parietal lobe, correlated with higher gait/postural-stability/posture UPDRS sum (p-value<0.01). A higher WMHp whether periventricular, subcortical or lobar correlated with reduced velocity (p-value<0.001).
We conclude that executive function is the primary cognitive domain affected by WMH burden. The data suggests that WMH in the parietal lobe are chiefly responsible for reduced balance and postural support compared to the other three lobes and may alter integration of sensory information via parietal lobe dysfunction in the aging brain. It is of interest that parietal WM changes were not the predominant correlate with motor speed, lending evidence to a global involvement of neural networks in gait velocity.
The behavioural variant of frontotemporal dementia is a progressive neurodegenerative syndrome characterized by changes in personality and behaviour. It is typically associated with frontal lobe atrophy, although patterns of atrophy are heterogeneous. The objective of this study was to examine case-by-case variability in patterns of grey matter atrophy in subjects with the behavioural variant of frontotemporal dementia and to investigate whether behavioural variant of frontotemporal dementia can be divided into distinct anatomical subtypes. Sixty-six subjects that fulfilled clinical criteria for a diagnosis of the behavioural variant of frontotemporal dementia with a volumetric magnetic resonance imaging scan were identified. Grey matter volumes were obtained for 26 regions of interest, covering frontal, temporal and parietal lobes, striatum, insula and supplemental motor area, using the automated anatomical labelling atlas. Regional volumes were divided by total grey matter volume. A hierarchical agglomerative cluster analysis using Ward's clustering linkage method was performed to cluster the behavioural variant of frontotemporal dementia subjects into different anatomical clusters. Voxel-based morphometry was used to assess patterns of grey matter loss in each identified cluster of subjects compared to an age and gender-matched control group at P < 0.05 (family-wise error corrected). We identified four potentially useful clusters with distinct patterns of grey matter loss, which we posit represent anatomical subtypes of the behavioural variant of frontotemporal dementia. Two of these subtypes were associated with temporal lobe volume loss, with one subtype showing loss restricted to temporal lobe regions (temporal-dominant subtype) and the other showing grey matter loss in the temporal lobes as well as frontal and parietal lobes (temporofrontoparietal subtype). Another two subtypes were characterized by a large amount of frontal lobe volume loss, with one subtype showing grey matter loss in the frontal lobes as well as loss of the temporal lobes (frontotemporal subtype) and the other subtype showing loss relatively restricted to the frontal lobes (frontal-dominant subtype). These four subtypes differed on clinical measures of executive function, episodic memory and confrontation naming. There were also associations between the four subtypes and genetic or pathological diagnoses which were obtained in 48% of the cohort. The clusters did not differ in behavioural severity as measured by the Neuropsychiatric Inventory; supporting the original classification of the behavioural variant of frontotemporal dementia in these subjects. Our findings suggest behavioural variant of frontotemporal dementia can therefore be subdivided into four different anatomical subtypes.
behavioural variant frontotemporal dementia; atrophy; cluster analysis; voxel-based morphometry
Biomarkers of brain Aβ amyloid deposition can be measured either by cerebrospinal fluid Aβ42 or Pittsburgh compound B positron emission tomography imaging. Our objective was to evaluate the ability of Aβ load and neurodegenerative atrophy on magnetic resonance imaging to predict shorter time-to-progression from mild cognitive impairment to Alzheimer’s dementia and to characterize the effect of these biomarkers on the risk of progression as they become increasingly abnormal. A total of 218 subjects with mild cognitive impairment were identified from the Alzheimer’s Disease Neuroimaging Initiative. The primary outcome was time-to-progression to Alzheimer’s dementia. Hippocampal volumes were measured and adjusted for intracranial volume. We used a new method of pooling cerebrospinal fluid Aβ42 and Pittsburgh compound B positron emission tomography measures to produce equivalent measures of brain Aβ load from either source and analysed the results using multiple imputation methods. We performed our analyses in two phases. First, we grouped our subjects into those who were ‘amyloid positive’ (n = 165, with the assumption that Alzheimer's pathology is dominant in this group) and those who were ‘amyloid negative’ (n = 53). In the second phase, we included all 218 subjects with mild cognitive impairment to evaluate the biomarkers in a sample that we assumed to contain a full spectrum of expected pathologies. In a Kaplan–Meier analysis, amyloid positive subjects with mild cognitive impairment were much more likely to progress to dementia within 2 years than amyloid negative subjects with mild cognitive impairment (50 versus 19%). Among amyloid positive subjects with mild cognitive impairment only, hippocampal atrophy predicted shorter time-to-progression (P < 0.001) while Aβ load did not (P = 0.44). In contrast, when all 218 subjects with mild cognitive impairment were combined (amyloid positive and negative), hippocampal atrophy and Aβ load predicted shorter time-to-progression with comparable power (hazard ratio for an inter-quartile difference of 2.6 for both); however, the risk profile was linear throughout the range of hippocampal atrophy values but reached a ceiling at higher values of brain Aβ load. Our results are consistent with a model of Alzheimer’s disease in which Aβ deposition initiates the pathological cascade but is not the direct cause of cognitive impairment as evidenced by the fact that Aβ load severity is decoupled from risk of progression at high levels. In contrast, hippocampal atrophy indicates how far along the neurodegenerative path one is, and hence how close to progressing to dementia. Possible explanations for our finding that many subjects with mild cognitive impairment have intermediate levels of Aβ load include: (i) individual subjects may reach an Aβ load plateau at varying absolute levels; (ii) some subjects may be more biologically susceptible to Aβ than others; and (iii) subjects with mild cognitive impairment with intermediate levels of Aβ may represent individuals with Alzheimer’s disease co-existent with other pathologies.
mild cognitive impairment; amyloid imaging; magnetic resonance imaging; cerebrospinal fluid; Alzheimer’s disease biomarkers
Background and Purpose
Decreased glucose metabolism in the temporal and parietal lobes on [18F]fluorodeoxyglucose (FDG) PET is recognized as an early imaging marker for the Alzheimer’s disease (AD) pathology. Our objective was to investigate the effects of age on FDG PET findings in aMCI.
25 patients with aMCI at 55–86 years of age (median = 73), and 25 age and gender matched cognitively normal (CN) subjects underwent FDG PET. SPM5 was used to compare the FDG uptake in aMCI-old (>73 years) and aMCI-young (>73 years) patients to CN subjects. The findings in the aMCI-old patients were independently validated in a separate cohort of 10 aMCI and 13 CN subjects older than 73 years of age.
The pattern of decreased glucose metabolism and gray matter atrophy in the medial temporal, posterior cingulate, precuneus, lateral parietal and temporal lobes in aMCI-young subjects was consistent with the typical pattern observed in AD. The pattern of glucose metabolic changes in aMCI-old subjects was different, predominantly involving the frontal lobes and the left parietal lobe. Gray matter atrophy in aMCI-old subjects was less pronounced than the aMCI-young subjects involving the hippocampus and the basal forebrain in both hemispheres
Pathological heterogeneity may be underlying the absence of AD-like glucose metabolic changes in older compared to younger aMCI patients. This may be an important consideration for the clinical use of temporoparietal hypometabolism on FDG PET as a marker for early diagnosis of AD in aMCI.
Functional MRI (fMRI) shows changes in multiple regions in amnestic MCI (aMCI). The concept of MCI recently evolved to include non-amnestic syndromes so little is known about fMRI changes in these individuals. This study investigated activation during visual complex scene encoding and recognition in 29 cognitively normal (CN) elderly, 19 individuals with aMCI and 12 individuals with non-amnestic MCI (naMCI). During encoding CN activated an extensive network that included bilateral occipital-parietal-temporal cortex, precuneus, posterior cingulate, thalamus, insula, and medial, anterior, and lateral frontal regions. Amnestic MCI activated an anatomic subset of these regions. Non-amnestic MCI activated an even smaller anatomic subset. During recognition, CN activated the same regions observed during encoding except the precuneus. Both MCI groups again activated a subset of the regions activated by CN. During encoding, CN had greater activation than aMCI and naMCI in bilateral temporo-parietal and frontal regions. During recognition, CN had greater activation than aMCI in predominantly temporo-parietal regions bilaterally while CN had greater activation than naMCI in larger areas involving bilateral temporo-parietal and frontal regions. The diminished parietal and frontal activation in naMCI may reflect compromised ability to perform non-memory (i.e., attention/executive, visuospatial function) components of the task.
Magnetic resonance imaging; Neuropsychology; Frontal Lobe; Parietal Lobe; Temporal Lobe; Dementia
We used voxel-based morphometry to compare the patterns of brain atrophy in two variants of pathologically confirmed frontotemporal lobar degeneration (FTLD), FTLD with motor neuron disease (FTLD-MND) and FTLD with ubiquitin-only-immunoreactive neuronal changes (FTLD-U). Patterns of atrophy were distinct and different from each other. A localized pattern of frontal lobe atrophy was found in FTLD-MND with a more widespread pattern of atrophy affecting the frontal and temporal lobes in FTLD-U.
dementia; motor neuron disease; MRI; ubiquitin; voxel-based morphometry
Frontotemporal lobar degeneration (FTLD) can be classified based on the presence of the microtubule associated protein tau and the TAR DNA binding protein-43 (TDP-43). Future treatments will likely target these proteins; therefore it is important to identify biomarkers to help predict protein biochemistry.
To determine whether there is an MRI signature pattern of tau or TDP-43 using a large cohort of FTLD subjects and to investigate how patterns of atrophy change according to disease severity using a large autopsy-confirmed cohort of FTLD subjects.
Patterns of grey matter loss were assessed using voxel-based morphometry in 37 tau-positive and 44 TDP-43 positive subjects compared to 35 age and gender-matched controls, and compared to each other. Comparisons were also repeated in behavioral variant frontotemporal dementia (bvFTD) subjects (n=15 tau-positive and n=30 TDP-43 positive). Patterns of atrophy were also assessed according to performance on the clinical dementia rating (CDR) scale and mini-mental state examination (MMSE).
The tau-positive and TDP-43 positive groups showed patterns of frontotemporal grey matter loss compared to controls with no differences observed between the groups, for all subjects and for bvFTD subjects. Patterns of grey matter loss increased in a graded manner by CDR and MMSE with loss in the frontal lobes, insula and hippocampus in mild subjects, spreading to the temporal and parietal cortices and striatum in more advanced disease.
There is no signature pattern of atrophy for tau or TDP-43; however patterns of atrophy in FTLD progress with measures of clinical disease severity.
frontotemporal lobar degeneration; autopsy; tau; TAR DNA binding protein-43; voxel-based morphometry; Clinical Dementia Rating Scale; Mini-Mental State Examination
Voxel-based morphometry (VBM) is a popular method for probing inter-group differences in brain morphology. Variation in the detailed implementation of the algorithm, however, will affect the apparent results of VBM analyses and in turn the inferences drawn about the anatomic expression of specific disease states. We qualitatively assessed group comparisons of 43 normal elderly control subjects and 51 patients with probable Alzheimer's disease, using five different VBM variations. Based on the known pathologic expression of the disease, we evaluated the biological plausibility of each. The use of a custom template and custom tissue class prior probability images (priors) produced inter-group comparison maps with greater biological plausibility than the use of the Montreal Neurological Institute (MNI) template and priors. We present a method for initializing the normalization to a custom template, and conclude that, when incorporated into the VBM processing chain, it yields the most biologically plausible inter-group differences of the five methods presented.
Twenty cognitively normal (CN), 17 amnestic mild cognitive impairment (aMCI), and 8 subjects with probable Alzheimer's disease (AD) were imaged with both magnetic resonance imaging (MRI) and the amyloid labeling ligand 11C Pittsburgh Compound B (PiB). PiB retention was quantified as the ratio of uptake in cortical regions of interest (ROIs) to the uptake in the cerebellar ROI in images acquired 40-60 minute post injection. A global cortical PiB retention summary measure was derived from six cortical ROIs. Statistical parametric mapping (SPM) and voxel-based morphometry (VBM) were used to evaluate PiB retention and grey matter loss on a 3D voxel-wise basis.
AD subjects had high global cortical PiB retention and low hippocampal volume; most CN subjects had low PiB retention and high hippocampal volume; and on average aMCI subjects were intermediate on both PiB and hippocampal volume. A target-to-cerebellar ratio of 1.5 was used to designate subjects as high vs. low PiB cortical retention. All AD subjects fell above this ratio as did 6/20 CN subjects and 9/17 MCI subjects, indicating bi-modal PiB retention in CN and aMCI. Interestingly, we found no consistent differences in learning and memory performance between high vs. low PiB CN subjects or high vs. low aMCI subjects.
The SPM/VBM voxel-wise comparisons of AD vs. CN subjects provided complementary information in that clear and meaningful similarities and differences in topographic distribution of amyloid deposition and grey matter loss were shown. The frontal lobes had high PiB retention with little grey matter loss. Anteromedial temporal areas had low PiB retention with significant grey matter loss. Lateral temporoparietal association cortex displayed both significant PiB retention and grey matter loss.
A voxel-wise SPM conjunction analysis of PiB uptake revealed that subjects with high PiB retention (high CN, high aMCI, and AD) shared a common PiB retention topographic pattern regardless of clinical category, and this PiB topographic pattern matched that of amyloid plaque distribution that has been established in autopsy studies of AD.
Both global cortical PiB retention and hippocampal volumes demonstrated significant correlation in the expected direction with cognitive testing performance; however, correlations were stronger with MRI than PiB. Pair-wise inter-group diagnostic separation was significant for all group-wise pairs for both PiB and hippocampal volume with the exception of CN vs. aMCI which was not significant for PiB. PiB and MRI provided complementary information such that clinical diagnostic classification with both, in combination, was superior to either alone.
Alzheimer's disease; Mild Cognitive Impairment; Pittsburgh Compound B; amyloid imaging; Magnetic Resonance Imaging; hippocampus