Citations

Abstract Introduction Neuroimaging modalities can measure different aspects of the disease process in Alzheimer's disease, although the relationship between these modalities is unclear. Methods We assessed subject‐level regional correlations between tau on [ 18 F]AV‐1451 positron emission tomography (PET), β amyloid on Pittsburgh compound B PET, hypometabolism on [ 18 F] fluorodeoxyglucose PET, and cortical thickness on magnetic resonance imaging in 96 participants with typical and atypical Alzheimer's disease presentations. We also assessed how correlations between modalities varied according to age, presenting syndrome, tau‐PET severity, and asymmetry. Results [ 18 F]AV‐1451 uptake showed the strongest regional correlation with hypometabolism. Correlations between [ 18 F]AV‐1451 uptake and both hypometabolism and cortical thickness were stronger in participants with greater cortical tau severity. In addition, age, tau asymmetry, and clinical diagnosis influenced the strength of the correlation between [ 18 F]AV‐1451 uptake and cortical thickness. Discussion These findings support a close relationship between tau and hypometabolism in Alzheimer's disease but show that correlations between neuroimaging modalities vary across participants.

ObjectiveTo use a cluster analysis of [18F]AV‐1451 tau‐PET data to determine how subjects with Alzheimer's disease (AD) vary in the relative involvement of the entorhinal cortex and neocortex, and determine whether relative involvement of these two regions can help explain variability in age and clinical phenotype in AD.MethodsWe calculated [18F]AV‐1451 uptake in entorhinal cortex and neocortex in 62 amyloid‐positive AD patients (39 typical and 23 atypical presentation). tau‐PET (positron emission tomography) values were normalized to the cerebellum to create standard uptake value ratios (SUVRs). tau‐PET SUVRs were log‐transformed and clustered blinded to clinical information into three groups using K‐median cluster analysis. Demographics, clinical phenotype, cognitive performance, and apolipoprotein e4 frequency were compared across clusters.ResultsThe cluster analysis identified a cluster with low entorhinal and cortical uptake (ELo/CLo), one with low entorhinal but high cortical uptake (ELo/CHi), and one with high cortical and entorhinal uptake (EHi/CHi). Clinical phenotype differed across clusters, with typical AD most commonly observed in the ELo/CLo and EHi/CHi clusters, and atypical AD most commonly observed in the ELo/CHi cluster. The ELo/CLo cluster had an older age at PET and onset than the other clusters. Apolipoprotein e4 frequency was lower in the ELo/CHi cluster. The EHi/CHi cluster had the worst memory impairment, whereas the ELo/CHi cluster had the worst impairment in nonmemory domains.InterpretationThis study demonstrates considerable variability in [18F]AV‐1451 tau‐PET uptake in AD, but shows that a straightforward clustering based on entorhinal and cortical uptake maps well onto age and clinical presentation in AD. Ann Neurol 2018 Ann Neurol 2018;83:248–257

Abstract Introduction Comparative transcriptome analyses in Alzheimer's disease (AD) and other neurodegenerative proteinopathies can uncover both shared and distinct disease pathways. Methods We analyzed 940 brain transcriptomes including patients with AD, progressive supranuclear palsy (PSP; a primary tauopathy), and control subjects. Results We identified transcriptional coexpression networks implicated in myelination, which were lower in PSP temporal cortex (TCX) compared with AD. Some of these associations were retained even after adjustments for brain cell population changes. These TCX myelination network structures were preserved in cerebellum but they were not differentially expressed in cerebellum between AD and PSP. Myelination networks were downregulated in both AD and PSP, when compared with control TCX samples. Discussion Downregulation of myelination networks may underlie both PSP and AD pathophysiology, but may be more pronounced in PSP. These data also highlight conservation of transcriptional networks across brain regions and the influence of cell type changes on these networks.