Citations

ObjectiveRecent availability of amyloid and tau positron emission tomography (PET) has provided us with a unique opportunity to measure the association of systemic vascular health with brain health after accounting for the impact of Alzheimer disease (AD) pathologies. We wanted to quantify early cerebrovascular health–related magnetic resonance imaging brain measures (structure, perfusion, microstructural integrity) and evaluate their utility as a biomarker for cerebrovascular health.MethodsWe used 2 independent samples (discovery, n = 390; validation, n = 1,035) of individuals, aged ≥ 60 years, along the cognitive continuum with imaging from the population‐based sample of Mayo Clinic Study of Aging. We ascertained vascular health by summing up recently existing cardiovascular and metabolic conditions (CMC) from health care records (hypertension, hyperlipidemia, cardiac arrhythmias, coronary artery disease, congestive heart failure, diabetes mellitus, and stroke). Using multiple regression models, we quantified associations between CMC and brain health after accounting for age, sex, education/occupation, and AD burden (from amyloid and tau PET).ResultsSystemic vascular health was associated with medial temporal lobe thinning, widespread cerebral hypoperfusion, and loss of microstructural integrity in several white matter tracts including the corpus callosum and fornix. Further investigations suggested that microstructural integrity of the genu of the corpus callosum was suitable for assessing prodromal cerebrovascular health, had similar distributions in the discovery and independent validation datasets, and predicted cognitive performance above and beyond amyloid deposition.InterpretationSystemic vascular health has significant impact on brain structure and function. Quantifying prodromal cerebrovascular health–related brain measures that are independent of AD pathology–related changes has great utility for cognitive aging. Ann Neurol 2018;84:713–724

AbstractIntroduction18F‐fluorodeoxyglucose (FDG) positron emission tomography (PET) is commonly used to estimate neuronal injury in Alzheimer's disease (AD). Here, we evaluate the utility of dynamic PET measures of perfusion using 11C‐Pittsburgh compound B (PiB) to estimate neuronal injury in comparison to FDG PET.MethodsFDG, early frames of PiB images, and relative PiB delivery rate constants (PiB‐R1) were obtained from 110 participants from the Dominantly Inherited Alzheimer Network. Voxelwise, regional cross‐sectional, and longitudinal analyses were done to evaluate the correlation between images and estimate the relationship of the imaging biomarkers with estimated time to disease progression based on family history.ResultsMetabolism and perfusion images were spatially correlated. Regional PiB‐R1 values and FDG, but not early frames of PiB images, significantly decreased in the mutation carriers with estimated year to onset and with increasing dementia severity.DiscussionHypometabolism estimated by PiB‐R1 may provide a measure of brain perfusion without increasing radiation exposure.

2067 Background: An estimated 2% of the general population has a meningioma (Vernooij et al. 2007), which accounts for about 36% of all primary intracranial tumors (Ostrom et al. 2015). The most established risk factors are older age and female gender. One small study identified gender but no other risk factors with meningioma (Krampla et al 2004). A larger study using the Iowa Women’s Health study data found lower levels of physical activity, greater body mass index (BMI), greater height and uterine fibroids were associated with meningioma (Johnson et al. 2011). We sought to replicate these findings and to identify additional risk factors related to meningioma in a large population-based sample. Methods: Study participants were enrolled in the Mayo Clinic Study of Aging (MCSA), a population-based sample of Olmsted County, Minnesota residents used to study prevalence, incidence, and risk-factors for Mild Cognitive Impairment and dementia and includes a variety of medical factors. Using a text search of radiologists’ notes of 2,402 MCSA individuals, mean age 77±8 years and scanned between 2004-2014.We identified 52 subjects who had at least one meningioma. We estimated the association of selected potential risk factors with presence of meningioma using odds ratios and 95% confidence intervals from logistic regression models adjusted for age and gender, which informed the multivariable models. Results: In the initial models, significant risk factors identified included BMI (as a continuous variable) (OR = 1.06 95%CI 1.01 to 1.12), taking NSAIDS (OR = 2.11, 95%CI 1.13 to 3.95), aspirin (OR = 1.90, 95%CI 1.04 to 3.46), and blood pressure lowering medication (OR = 2.06, 95%CI 1.07 to 3.99). Protective factors included male gender (OR = 0.51, 95%CI 0.29 to 0.90), coronary artery disease (CAD; OR = 0.46, 95%CI 0.22 to 0.97) and higher Beck Anxiety Inventory (BAI) total score (OR = 0.88, 95%CI 0.78 to 0.98). Simultaneous adjustment for these factors in a multivariable model did not attenuate these associations. Conclusions: Findings reveal gender and BMI as risk factors for meningioma. Additionally, certain medications such as NSAIDS and BP lowering medications warrant follow up as potential factors related to development of meningioma.

Background: Statins have been proposed to reduce the risk of Alzheimer’s disease (AD). Objective: Assess whether long-term statin use was associated with neuroimaging biomarkers of aging and dementia. Methods: Methods: We analyzed neuroimaging biomarkers in 1,160 individuals aged 65+ from the Mayo Clinic Study of Aging, a population-based prospective longitudinal study of cognitive aging. Results: Statin-treated (5+ years of therapy) individuals had greater burden of mid-and late-life cardiovascular disease ( p < 0.001) than statin-untreated (≤3 months) individuals. Lower fractional anisotropy in the genu of the corpus callosum, an early marker of cerebrovascular disease, was associated with long-term statin exposure ( p < 0.035). No significant associations were identified between long-term statin exposure and cerebral amyloid or tau burden, AD pattern neurodegeneration, or white matter hyperintensity burden. Conclusions: Long-term statin therapy was not associated with differences in AD biomarkers. Individuals with long-term statin exposure had worse white matter integrity in the genu of the corpus callosum, consistent with the coexistence of higher cerebrovascular risk factor burden in this group.

Background and purposeThe non‐fluent/agrammatic variant of primary progressive aphasia (agPPA) is a heterogeneous diagnosis wherein some individuals have apraxia of speech (AOS). When agPPA includes AOS, a tauopathy is the likely underlying pathology. Recently, [18F]AV‐1451 was developed for the in‐vivo assessment of tau. In this study, we compared patterns of tau tracer uptake in patients with agPPA with and without AOS.MethodsNine patients with agPPA (four without AOS) underwent tau positron emission tomography imaging with [18F]AV‐1451. Uptake of [18F]AV‐1451 was assessed as cortical to cerebellar crus ratio (standard uptake value ratio) in cortical regions of interest measured using the MCALT atlas and compared voxel‐wise in SPM12. Each patient was age‐ and sex‐matched to three controls.ResultsThe agPPA without AOS showed uptake in the left frontal and temporal lobes, whereas agPPA with AOS showed uptake in the bilateral supplementary motor areas, frontal lobes, precuneus and precentral gyrus relative to controls. The left precentral gyrus had uptake in agPPA with AOS relative to those without AOS.ConclusionsThis cross‐sectional study suggests that [18F]AV‐1451 uptake in the precentral gyrus is implicated in AOS in agPPA.

Background: There is accumulating evidence suggesting that diet may play a role in preventing or delaying cognitive decline and dementia, but the underlying biological mechanisms are not well understood. Objectives: To examine the cross-sectional associations of the Mediterranean diet (MeDi) and its components with 11 C-PiB-PET scan measures of amyloid-β (Aβ) deposition. Methods: The study consisted of 278 Mayo Clinic Study of Aging participants 70+ years old, who were cognitively unimpaired (CU) at the time of completion of the Food Frequency Questionnaire (FFQ) and when they underwent PET imaging. Adherence to the MeDi was assessed by computing the MeDi score for each participant. All scans were performed after the FFQ completion; median [IQR] time between FFQ and Aβ PET was 3.5 (1.4) years. Z-scores were created for component, macro- and micronutrients measured. Linear and logistic regression models were adjusted for age, sex, education, apolipoprotein E (APOE) ɛ4 allele carrier status, time interval between the FFQ completion and PET scan, and total energy intake. Results: Participants’ median age at FFQ was 77.7 years (55.8% men; 26.6% with an APOE ɛ4 allele). Higher MeDi score (linear regression slope (beta):–0.035, p = 0.012; per standard deviation increase), vegetable intake (beta:–0.043, p = 0.002), intake of vitamin A (beta:–0.041, p = 0.003) or β-carotene (beta: –0.039, p = 0.005) from food sources and moderate alcohol consumption (beta: –0.074, p = 0.03) were associated with lower 11 C-PiB standardized uptake value ratio. Conclusion: Findings are consistent with previous studies suggesting that higher adherence to a MeDi pattern and higher vegetable consumption are associated with better neuroimaging biomarker profile. Prospective studies are needed to validate current findings.

Background: Microbleeds in the brain have been shown to occur in Alzheimer’s disease (AD), affecting approximately a third of subjects that present with typical dementia of the Alzheimer’s type (DAT). However, little is known about the frequency or distribution of microbleeds in subjects with AD that present with atypical clinical presentations. Objective: To determine whether the frequency and regional distribution of microbleeds in atypical AD differs from that observed in subjects with DAT, and to determine whether microbleeds in atypical AD are associated with age, demographics, or cognitive impairment. Methods: Fifty-five subjects with amyloid-β deposition on Pittsburgh compound B (PiB) PET who presented with predominant language (n = 37) or visuospatial/perceptual (n = 18) deficits underwent T2*weighted MRI. These subjects were compared to 41 PiB-positive subjects with DAT. Microbleeds were identified and assigned a lobar location. Results: The proportion of subjects with microbleeds did not differ between atypical AD (42%) and DAT (32%). However, atypical AD had larger numbers of microbleeds than DAT. In addition, the topographic distribution of microbleeds differed between atypical AD and DAT, with atypical AD showing the highest density of microbleeds in the frontal lobes. Among atypical AD, number of microbleeds was associated with age, but not gender or cognition. Microbleeds were more common in subjects with language (51%) versus visuospatial/perceptual deficits (22%). Conclusions: Microbleeds are relatively common in both DAT and atypical AD, although atypical AD subjects appear to be at particular risk for developing large numbers of microbleeds and for developing microbleeds in the frontal lobe.

T1-weighted MRI has been extensively used to extract imaging biomarkers and build classification models for differentiating Alzheimer’s disease (AD) patients from healthy controls, but only recently have brain connectome networks derived from diffusion-weighted MRI been used to model AD progression and various stages of disease such as mild cognitive impairment (MCI). MCI, as a possible prodromal stage of AD, has gained intense interest recently, since it may be used to assess risk factors for AD. Little work has been done to combine information from both white matter and gray matter, and it is unknown how much classification power the diffusion-weighted MRI-derived structural connectome could provide beyond information available from T1-weighted MRI. In this paper, we focused on investigating whether diffusion-weighted MRI-derived structural connectome can improve differentiating healthy controls subjects from those with MCI. Specifically, we proposed a novel feature-ranking method to build classification models using the most highly ranked feature variables to classify MCI with healthy controls. We verified our method on two independent cohorts including the second stage of Alzheimer’s Disease Neuroimaging Initiative (ADNI2) database and the National Alzheimer’s Coordinating Center (NACC) database. Our results indicated that 1) diffusion-weighted MRI-derived structural connectome can complement T1-weighted MRI in the classification task; 2) the feature-rank method is effective because of the identified consistent T1-weighted MRI and network feature variables on ADNI2 and NACC. Furthermore, by comparing the top-ranked feature variables from ADNI2, NACC, and combined dataset, we concluded that cross-validation using independent cohorts is necessary and highly recommended.