Senjem, Matthew L

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 Alzheimer’s disease is characterized by the presence of amyloid-β and tau deposition in the brain, hippocampal atrophy and increased rates of hippocampal atrophy over time. Another protein, TAR DNA binding protein 43 (TDP-43) has been identified in up to 75% of cases of Alzheimer’s disease. TDP-43, tau and amyloid-β have all been linked to hippocampal atrophy. TDP-43 and tau have also been linked to hippocampal atrophy in cases of primary age-related tauopathy, a pathological entity with features that strongly overlap with those of Alzheimer’s disease. At present, it is unclear whether and how TDP-43 and tau are associated with early or late hippocampal atrophy in Alzheimer’s disease and primary age-related tauopathy, whether either protein is also associated with faster rates of atrophy of other brain regions and whether there is evidence for protein-associated acceleration/deceleration of atrophy rates. We therefore aimed to model how these proteins, particularly TDP-43, influence non-linear trajectories of hippocampal and neocortical atrophy in Alzheimer’s disease and primary age-related tauopathy. In this longitudinal retrospective study, 557 autopsied cases with Alzheimer’s disease neuropathological changes with 1638 ante-mortem volumetric head MRI scans spanning 1.0–16.8 years of disease duration prior to death were analysed. TDP-43 and Braak neurofibrillary tangle pathological staging schemes were constructed, and hippocampal and neocortical (inferior temporal and middle frontal) brain volumes determined using longitudinal FreeSurfer. Bayesian bivariate-outcome hierarchical models were utilized to estimate associations between proteins and volume, early rate of atrophy and acceleration in atrophy rates across brain regions. High TDP-43 stage was associated with smaller cross-sectional brain volumes, faster rates of brain atrophy and acceleration of atrophy rates, more than a decade prior to death, with deceleration occurring closer to death. Stronger associations were observed with hippocampus compared to temporal and frontal neocortex. Conversely, low TDP-43 stage was associated with slower early rates but later acceleration. This later acceleration was associated with high Braak neurofibrillary tangle stage. Somewhat similar, but less striking, findings were observed between TDP-43 and neocortical rates. Braak stage appeared to have stronger associations with neocortex compared to TDP-43. The association between TDP-43 and brain atrophy occurred slightly later in time (∼3 years) in cases of primary age-related tauopathy compared to Alzheimer’s disease. The results suggest that TDP-43 and tau have different contributions to acceleration and deceleration of brain atrophy rates over time in both Alzheimer’s disease and primary age-related tauopathy.

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 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.

AbstractObjectiveTo assess the relationships between MRI volumetry and [18F]flortaucipir PET of typical and atypical clinical phenotypes of Alzheimer’s disease, by genarian (age by decade).MethodsFive‐hundred and sixty‐four participants including those with typical (n = 86) or atypical (n = 80) Alzheimer’s dementia and normal controls (n = 398) underwent apolipoprotein E genotyping, MRI, flortaucipir, and 11C‐PiB; all 166 Alzheimer’s participants were beta‐amyloid positive and all controls were beta‐amyloid negative. Grey matter volume and flortaucipir standard uptake value ratios were calculated for hippocampus, entorhinal cortex, and neocortex. Ratios of hippocampal‐to‐neocortical and entorhinal‐to‐neocortical volume and flortaucipir uptake were also calculated. Linear regression models assessed relationships among regional volume, flortaucipir uptake, and ratios and phenotypes, within three genarians (50–59, 60–69, and 70+). Voxel‐level analyses were also performed.ResultsFor 50–59 greater medial temporal atrophy and flortaucipir uptake was observed in the typical compared with atypical phenotype. The typical phenotype also showed greater frontal neocortex uptake with the voxel‐level analysis. For 60–69 and 70+ there was greater hippocampal volume loss in the typical compared with atypical phenotype while only the 60–69, but not the 70+ group, showed a difference in hippocampal flortaucipir uptake. We also observed a pattern for higher neocortical flortaucipir uptake to correlate with younger age decade for both phenotypes.InterpretationMRI volumetry versus flortaucipir PET relationships differ across Alzheimer’s clinical phenotypes, and also within phenotype across age decades. This suggests that there is potential risk of masked effects by not accounting for genarian in participants with beta‐amyloid and tau‐positive biomarker defined Alzheimer’s disease.

ObjectivesThe aim of this study was to perform a whole-brain analysis of alterations in brain mechanical properties due to normal pressure hydrocephalus (NPH).Materials and MethodsMagnetic resonance elastography (MRE) examinations were performed on 85 participants, including 44 cognitively unimpaired controls, 33 with NPH, and 8 who were amyloid-positive with Alzheimer clinical syndrome. A custom neural network inversion was used to estimate stiffness and damping ratio from patches of displacement data, accounting for edges by training the network to estimate the mechanical properties in the presence of missing data. This learned inversion was first compared with a standard analytical approach in simulation experiments and then applied to the in vivo MRE measurements. The effect of NPH on the mechanical properties was then assessed by voxel-wise modeling of the stiffness and damping ratio maps. Finally, a pattern analysis was performed on each individual's mechanical property maps by computing the correlation between each person's maps with the expected NPH effect. These features were used to fit a classifier and assess diagnostic accuracy.ResultsThe voxel-wise analysis of the in vivo mechanical property maps revealed a unique pattern in participants with NPH, including a concentric pattern of stiffening near the dural surface and softening near the ventricles, as well as decreased damping ratio predominantly in superior regions of the white matter (family-wise error correctedP< 0.05 at cluster level). The pattern of viscoelastic changes in each participant predicted NPH status in this cohort, separating participants with NPH from the control and the amyloid-positive with Alzheimer clinical syndrome groups, with areas under the receiver operating characteristic curve of 0.999 and 1, respectively.ConclusionsThis study provides motivation for further development of the neural network inversion framework and demonstrates the potential of MRE as a novel tool to diagnose NPH and provide a window into its pathogenesis.