Citations

AbstractThe mechanisms through which tau and amyloid-beta (Aβ) accumulate in the brain of Alzheimer’s disease patients may differ but both are related to neuronal networks. We examined such mechanisms on neuroimaging in 58 participants with atypical Alzheimer’s disease (posterior cortical atrophy or logopenic progressive aphasia). Participants underwent Aβ-PET, longitudinal tau-PET, structural MRI and resting-state functional MRI, which was analyzed with graph theory. Regions with high levels of Aβ were more likely to be functional hubs, with a high number of functional connections important for resilience to cascading network failures. Regions with high levels of tau were more likely to have low clustering coefficients and degrees, suggesting a lack of trophic support or vulnerability to local network failures. Regions strongly functionally connected to the disease epicenters were more likely to have higher levels of tau and, less strongly, of Aβ. The regional rate of tau accumulation was associated with tau levels in functionally connected regions, in support of tau accumulation in a functional network. This study elucidates the relations of tau and Aβ to functional connectivity metrics in atypical Alzheimer’s disease, strengthening the hypothesis that the spread of the 2 proteins is driven by different biological mechanisms related to functional networks.

ObjectiveTo examine associations between tau and amyloid β (Aβ) molecular positron emission tomography (PET) and both Alzheimer‐related pathology and 4‐repeat tau pathology in autopsy‐confirmed frontotemporal lobar degeneration (FTLD).MethodsTwenty‐four patients had [18F]‐flortaucipir–PET and died with FTLD (progressive supranuclear palsy [PSP], n = 10; corticobasal degeneration [CBD], n = 10; FTLD‐TDP, n = 3; and Pick disease, n = 1). All but 1 had Pittsburgh compound B (PiB)‐PET. Braak staging, Aβ plaque and neurofibrillary tangle counts, and semiquantitative tau lesion scores were performed. Flortaucipir standard uptake value ratios (SUVRs) were calculated in a temporal meta region of interest (meta‐ROI), entorhinal cortex and cortical/subcortical regions selected to match the tau lesion analysis. Global PiB SUVR was calculated. Autoradiography was performed in 1 PSP patient, with digital pathology used to quantify tau burden.ResultsNine cases (37.5%) had Aβ plaques. Global PiB SUVR correlated with Aβ plaque count, with 100% specificity and 50% sensitivity for diffuse plaques. Twenty‐one (87.5%) had Braak stages I to IV. Flortaucipir correlated with neurofibrillary tangle counts in entorhinal cortex, but entorhinal and meta‐ROI SUVRs were not elevated in Braak IV or primary age‐related tauopathy. Flortaucipir uptake patterns differed across FTLD pathologies and could separate PSP and CBD. Flortaucipir correlated with tau lesion score in red nucleus and midbrain tegmentum across patients, but not in cortical or basal ganglia regions. Autoradiography demonstrated minimal uptake of flortaucipir, although flortaucipir correlated with quantitative tau burden across regions.InterpretationMolecular PET shows expected correlations with Alzheimer‐related pathology but lacks sensitivity to detect mild Alzheimer pathology in FTLD. Regional flortaucipir uptake was able to separate CBD and PSP. ANN NEUROL 2020;88:1009–1022

Abstract Clinical trials with anti-tau drugs will need to target individuals at risk of accumulating tau. Our objective was to identify variables available in a research setting that predict future rates of tau PET accumulation separately among individuals who were either cognitively unimpaired or cognitively impaired. All 337 participants had: a baseline study visit with MRI, amyloid PET, and tau PET exams, at least one follow-up tau PET exam; and met clinical criteria for membership in one of two clinical diagnostic groups: cognitively unimpaired (n = 203); or cognitively impaired (n = 134, a combined group of participants with either mild cognitive impairment or dementia with Alzheimer’s clinical syndrome). Our primary analyses were in these two clinical groups; however, we also evaluated subgroups dividing the unimpaired group by normal/abnormal amyloid PET and the impaired group by clinical phenotype (mild cognitive impairment, amnestic dementia, and non-amnestic dementia). Linear mixed effects models were used to estimate associations between age, sex, education, APOE genotype, amyloid and tau PET standardized uptake value ratio (SUVR), cognitive performance, cortical thickness, and white matter hyperintensity volume at baseline, and the rate of subsequent tau PET accumulation. Log-transformed tau PET SUVR was used as the response and rates were summarized as annual per cent change. A temporal lobe tau PET meta-region of interest was used. In the cognitively unimpaired group, only higher baseline amyloid PET was a significant independent predictor of higher tau accumulation rates (P < 0.001). Higher rates of tau accumulation were associated with faster rates of cognitive decline in the cognitively unimpaired subgroup with abnormal amyloid PET (P = 0.03), but among the subgroup with normal amyloid PET. In the cognitively impaired group, younger age (P = 0.02), higher baseline amyloid PET (P = 0.05), APOE ε4 (P = 0.05), and better cognitive performance (P = 0.05) were significant independent predictors of higher tau accumulation rates. Among impaired individuals, faster cognitive decline was associated with faster rates of tau accumulation (P = 0.01). While we examined many possible predictor variables, our results indicate that screening of unimpaired individuals for potential inclusion in anti-tau trials may be straightforward because the only independent predictor of high tau rates was amyloidosis. In cognitively impaired individuals, imaging and clinical variables consistent with early onset Alzheimer’s disease phenotype were associated with higher rates of tau PET accumulation suggesting this may be a highly advantageous group in which to conduct proof-of-concept clinical trials that target tau-related mechanisms. The nature of the dementia phenotype (amnestic versus non-amnestic) did not affect this conclusion.

Abstract Introduction Sleepiness has been associated with functional and cognitive decline, and may present with excessive daytime sleepiness (EDS) and/or increased sleep duration. We investigated whether sleepiness and changes in sleep patterns are associated with FDG-PET levels in wake-promoting regions. Methods From the Mayo Clinic Study of Aging cohort, we identified 373 cognitively-unimpaired middle-aged and older adults (mean +/- s.d. 66.1 +/- 13.2 yo) who underwent FDG-PET. EDS was defined as ESS score >=10. Changes in sleep patterns (sleeping more, less, or no change) were assessed using question #16 of the Beck Depression Inventory-2. We used probabilistic maps to create regions of interest (ROIs): the locus coeruleus (LC), posterior lateral hypothalamus (PLH), and the basal forebrain divided in 1) medial septum/diagonal band of Broca (MS/DB) and 2) nucleus basalis of Meynert (nbM). FDG-PET levels were referenced to the pons (SUVR). In this cross-sectional analysis, we fit linear models to assess the association between EDS and changes in sleeping patterns with FDG SUVR in in each ROI, while controlling for age, sex, education, BMI, witnessed apneas, and cardiovascular risk factors. Results 10.5% had EDS, 15% reported sleeping more and 21% reported sleeping less than usual. 30.7% of participants with EDS reported sleeping more, 25.6% less, and 43.5% the same. EDS was associated with an elevation in FDG-PET SUVR in the MS/DB region (.035 [95% CI .008; .063], p=.012), while sleeping more was associated with a decrease in FDG-PET SUVR in the same region (-.027 [95%CI -.052; -.002], p=.036). Sleeping less was associated with an increase in FDG-PET SUVR in the PLH (.021 [95% CI .005; .03], p=.019). No associations were found in other ROIs. Conclusion Our results suggest that sleepiness and changes in sleep patterns in cognitively-unimpaired middle-aged and older adults were associated with measurable metabolic changes in areas of the brain involved in sleep and wakefulness. Further research should clarify whether these findings could represent different phenotypes of sleepiness with potential diagnostic and prognostic implications. Support NIA/NIH

AbstractIntroductionWe examined differences in cortical thickness in empirically derived mild cognitive impairment (MCI) subtypes in the Mayo Clinic Study of Aging.MethodsWe compared cortical thickness of four incident MCI subtypes (n = 192) to 1257 cognitive unimpaired individuals.ResultsThe subtle cognitive impairment cluster had atrophy in the entorhinal and parahippocampal cortex. The amnestic, dysnomic, and dysexecutive clusters also demonstrated entorhinal cortex atrophy as well as thinning in temporal, parietal, and frontal isocortex in somewhat different patterns.DiscussionWe found patterns of atrophy in each of the incident MCI clusters that corresponded to their patterns of cognitive impairment. The identification of MCI subtypes based on cognitive and structural features may allow for more efficient trial and study designs. Given individuals in the subtle cognitive impairment cluster have less structural changes and cognitive decline and may represent the earliest group, this could be a unique group to target with early interventions.

Abstract Alzheimer’s disease can present clinically with either the typical amnestic phenotype or with atypical phenotypes, such as logopenic progressive aphasia and posterior cortical atrophy. We have recently described longitudinal patterns of flortaucipir PET uptake and grey matter atrophy in the atypical phenotypes, demonstrating a longitudinal regional disconnect between flortaucipir accumulation and brain atrophy. However, it is unclear how these longitudinal patterns differ from typical Alzheimer’s disease, to what degree flortaucipir and atrophy mirror clinical phenotype in Alzheimer’s disease, and whether optimal longitudinal neuroimaging biomarkers would also differ across phenotypes. We aimed to address these unknowns using a cohort of 57 participants diagnosed with Alzheimer’s disease (18 with typical amnestic Alzheimer’s disease, 17 with posterior cortical atrophy and 22 with logopenic progressive aphasia) that had undergone baseline and 1-year follow-up MRI and flortaucipir PET. Typical Alzheimer’s disease participants were selected to be over 65 years old at baseline scan, while no age criterion was used for atypical Alzheimer’s disease participants. Region and voxel-level rates of tau accumulation and atrophy were assessed relative to 49 cognitively unimpaired individuals and among phenotypes. Principal component analysis was implemented to describe variability in baseline tau uptake and rates of accumulation and baseline grey matter volumes and rates of atrophy across phenotypes. The capability of the principal components to discriminate between phenotypes was assessed with logistic regression. The topography of longitudinal tau accumulation and atrophy differed across phenotypes, with key regions of tau accumulation in the frontal and temporal lobes for all phenotypes and key regions of atrophy in the occipitotemporal regions for posterior cortical atrophy, left temporal lobe for logopenic progressive aphasia and medial and lateral temporal lobe for typical Alzheimer’s disease. Principal component analysis identified patterns of variation in baseline and longitudinal measures of tau uptake and volume that were significantly different across phenotypes. Baseline tau uptake mapped better onto clinical phenotype than longitudinal tau and MRI measures. Our study suggests that optimal longitudinal neuroimaging biomarkers for future clinical treatment trials in Alzheimer’s disease are different for MRI and tau-PET and may differ across phenotypes, particularly for MRI. Baseline tau tracer retention showed the highest fidelity to clinical phenotype, supporting the important causal role of tau as a driver of clinical dysfunction in Alzheimer’s disease.