Distinct 18F-AV-1451 tau PET retention patterns in early- and late-onset Alzheimer’s disease

Schöll, Michael; Ossenkoppele, Rik; Strandberg, Olof; Palmqvist, Sebastian; Jögi, Jonas; Ohlsson, Tomas; Smith, Ruben; Hansson, Oskar

doi:10.1093/brain/awx171

Cited by 155 publications

(156 citation statements)

References 24 publications

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“…For voxel‐wise analyses, [ 18 F]flortaucipir images were warped into the Montreal Neurological Institute standard space using the nonlinear transformation calculated by normalizing the T1‐weighted MRI scan to the Montreal Neurological Institute 152 1 × 1 × 1 mm 3 template with Advanced Normalization ToolS. Before voxel‐wise analyses, images were smoothed with an 8‐mm full width at half maximum Gaussian kernel …”

Section: Methodsmentioning

confidence: 99%

Distinct tau PET patterns in atrophy‐defined subtypes of Alzheimer's disease

Ossenkoppele

Lyoo

Sudre

et al. 2020

Alzheimer's & Dementia

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Introduction: Differential patterns of brain atrophy on structural magnetic resonance imaging (MRI) revealed four reproducible subtypes of Alzheimer's disease (AD): (1) "typical", (2) "limbic-predominant", (3) "hippocampal-sparing", and (4) "mild atrophy". We examined the neurobiological characteristics and clinical progression of these atrophydefined subtypes. Methods:The four subtypes were replicated using a clustering method on MRI data in 260 amyloid--positive patients with mild cognitive impairment or AD dementia, and we subsequently tested whether the subtypes differed on [ 18 F]flortaucipir (tau) positron emission tomography, white matter hyperintensity burden, and rate of global cognitive decline. Results: Voxel-wise and region-of-interest analyses revealed the greatest neocortical tau load in hippocampal-sparing (frontoparietal-predominant) and typicalThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. c ○ 2019 The Authors. Alzheimer's & Dementia published by Wiley Periodicals, Inc. on behalf of Alzheimer's Association. Alzheimer's Dement. 2020;16:335-344. wileyonlinelibrary.com/journal/alz 335 336 OSSENKOPPELE ET AL.

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Section: Methodsmentioning

confidence: 99%

Distinct tau PET patterns in atrophy‐defined subtypes of Alzheimer's disease

Ossenkoppele

Lyoo

Sudre

et al. 2020

Alzheimer's & Dementia

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“…The ability to assess and monitor pathological brain changes in the preclinical and clinical stages of AD is limited, especially in the asymptomatic phase of the disease. A current focus of AD biomarker research is on measurement of brain amyloid-β and tau protein dynamics as assessed by radiolabeled tracers to aggregated forms of these proteins, in addition to established measurements of amyloid-β and tau protein levels in cerebrospinal fluid (CSF) 50,51 . Fluid biomarkers of neurodegeneration are also in development 52 .…”

Section: M4 Astrocyte/microglial Metabolism Module Proteins Can Be Mementioning

confidence: 99%

A Consensus Proteomic Analysis of Alzheimer’s Disease Brain and Cerebrospinal Fluid Reveals Early Changes in Energy Metabolism Associated with Microglia and Astrocyte Activation

Johnson

Dammer

Duong

et al. 2019

Preprint

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Our understanding of the biological changes in the brain associated with Alzheimer's disease (AD) pathology and cognitive impairment remains incomplete. To increase our understanding of these changes, we analyzed dorsolateral prefrontal cortex of control, asymptomatic AD, and AD brains from four different centers by label-free quantitative mass spectrometry and weighted protein co-expression analysis to obtain a consensus protein co-expression network of AD brain.This network consisted of 13 protein co-expression modules. Six of these modules correlated with amyloid-β plaque burden, tau neurofibrillary tangle burden, cognitive function, and clinical functional status, and were altered in asymptomatic AD, AD, or in both disease states. These modules reflected synaptic, mitochondrial, sugar metabolism, extracellular matrix, cytoskeletal, and RNA binding/splicing biological functions. The identified protein network modules were preserved in a community-based cohort analyzed by a different quantitative mass spectrometry approach. They were also preserved in temporal lobe and precuneus brain regions. Some of the modules were influenced by aging, and showed changes in other neurodegenerative diseases such as frontotemporal dementia and corticobasal degeneration. The module most strongly associated with AD pathology and cognitive impairment was the sugar metabolism module, which was enriched in AD genetic risk factors and correlated with APOE genetic risk. This module was also highly enriched in microglia and astrocyte protein markers associated with an antiinflammatory state, suggesting that the biological functions it represents serve a protective role in AD. Proteins from this module were increased in cerebrospinal fluid from asymptomatic AD and AD cases, highlighting their potential as biomarkers of the altered brain network. In this study of >2000 brains and nearly 400 cerebrospinal fluid samples by quantitative proteomics, we identify proteins and biological processes in AD brain that may serve as therapeutic targets and fluid biomarkers for the disease. IntroductionAlzheimer's disease (AD) is a leading cause of death worldwide, with increasing prevalence as global life expectancy increases 1 . Although AD is currently defined on the basis of amyloid-β plaque and tau neurofibrillary tangle deposition within the neocortex 2 , the biochemical and cellular changes in the brain that characterize the disease beyond amyloid-β and tau deposition remain incompletely understood. The genetic architecture of late-onset AD has been extensively studied, and the results of these studies implicate multiple biological pathways that contribute to development of the disease, including immune function, endocytic vesicle trafficking, and lipid homeostasis, among others 3-5 . In addition to genetic studies, transcriptomic studies on postmortem AD brain tissue have identified changes in mRNA co-expression that correlate with disease traits and cognitive decline 6,7 . However, given that mRNA levels correlate only modestly to protein le...

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“…Tau aggregations into neurofibrillary tangles are a hallmark pathological feature of Alzheimer's disease (Braak and Braak, 1991), and are consistently shown to correlate more strongly with cognitive functions than Amyloid-β plaques (Nelson et al, 2012;Rolstad et al, 2013). Excitingly, the advent of PET ligands that bind to Tau aggregations, such as 18 F-Flortaucipir (Chien et al, 2013), allows us to investigate the relationship between the spatial distribution of Tau in vivo throughout the course of Alzheimer's disease (James et al, 2015;LaPoint et al, 2017;Lowe et al, 2017;Schöll et al, 2017;Xia et al, 2017), and has been shown to relate to clinical symptoms associated with atypical AD such as Logopenic Aphasia and Posterior Cortical Atrophy (Ossenkoppele et al, 2016).…”

Section: Association With Tau Pathologymentioning

confidence: 99%

Multi-modal Latent Factor Exploration of Atrophy, Cognitive and Tau Heterogeneity in Alzheimer’s Disease

Sun

Mormino

Chen

et al. 2018

Preprint

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Individuals with Alzheimer's disease (AD) dementia exhibit significant heterogeneity across clinical symptoms, atrophy patterns, and spatial distribution of Tau deposition. Most previous studies of AD heterogeneity have focused on atypical clinical subtypes, defined subtypes with a single modality, or restricted their analyses to a priori brain regions and cognitive tests. Here, we considered a data-driven hierarchical Bayesian model to identify latent factors from atrophy patterns and cognitive deficits simultaneously, thus exploiting the rich dimensionality within each modality. Unlike most previous studies, our model allows each factor to be expressed to varying degrees within an individual, in order to reflect potential multiple co-existing pathologies. By applying our model to ADNI-GO/2 AD dementia participants, we found three atrophy-cognitive factors. The first factor was associated with medial temporal lobe atrophy, episodic memory deficits and disorientation to time/place ("MTL-Memory"). The second factor was associated with lateral temporal atrophy and language deficits ("Lateral Temporal-Language"). The third factor was associated with atrophy in posterior bilateral cortex, and visuospatial executive function deficits ("Posterior Cortical-Executive"). While the MTL-Memory and Posterior Cortical-Executive factors were discussed in previous literature, the Lateral Temporal-Language factor is novel and emerged only by considering atrophy and cognition jointly. Several analyses were performed to ensure generalizability, replicability and stability of the estimated factors. First, the factors generalized to new participants within a 10fold cross-validation of ADNI-GO/2 AD dementia participants. Second, the factors were replicated in an independent ADNI-1 AD dementia cohort. Third, factor loadings of ADNI-GO/2 AD dementia participants were longitudinally stable, suggesting that these factors capture heterogeneity across patients, rather than longitudinal disease progression. Fourth, the model outperformed canonical correlation analysis at capturing associations between atrophy patterns and cognitive deficits.To explore the influence of the factors early in the disease process, factor loadings were estimated in ADNI-GO/2 mild cognitively impaired (MCI) participants. Although the associations between the atrophy patterns and cognitive profiles were weak in MCI compared to AD, we found that factor loadings were associated with inter-individual regional variation in Tau uptake. Taken together, these results suggest that distinct atrophy-cognitive patterns exist in typical Alzheimer's disease, and are associated with distinct patterns of Tau depositions before clinical dementia emerges. Highlights:1. Bayesian model reveals 3 atrophy-cognitive factors in typical AD from ADNI-GO/2 2. Replicated in independent ADNI-1 cohort; longitudinally stable within individuals 3. Triple cognitive dissociations among atrophy patterns suggest subtypes, not stages 4. Outperforms canonical correlation analysis 5. Factor loadings as...

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Distinct 18F-AV-1451 tau PET retention patterns in early- and late-onset Alzheimer’s disease

Cited by 155 publications

References 24 publications

Distinct tau PET patterns in atrophy‐defined subtypes of Alzheimer's disease

Distinct tau PET patterns in atrophy‐defined subtypes of Alzheimer's disease

A Consensus Proteomic Analysis of Alzheimer’s Disease Brain and Cerebrospinal Fluid Reveals Early Changes in Energy Metabolism Associated with Microglia and Astrocyte Activation

Multi-modal Latent Factor Exploration of Atrophy, Cognitive and Tau Heterogeneity in Alzheimer’s Disease

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