Alzheimer's disease neuroimaging

Whitwell, Jennifer L.

doi:10.1097/wco.0000000000000570

Cited by 33 publications

(29 citation statements)

References 48 publications

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“…Patients in early stages of AD usually show a reduced glucose metabolism in 18 F-FDG-PET within the posterior cingulate cortex. During the progress of the disease lower 18 F-FDG uptake can also be measured in the posterior temporal and parietal cortex and eventually in the frontal lobe (35)(36)(37)(38)(39).…”

Section: Discussionmentioning

confidence: 99%

In vivo Imaging With 18F-FDG- and 18F-Florbetaben-PET/MRI Detects Pathological Changes in the Brain of the Commonly Used 5XFAD Mouse Model of Alzheimer's Disease

et al. 2020

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Imaging biomarkers of Alzheimer's disease (AD) that are able to detect molecular changes in vivo and transgenic animal models mimicking AD pathologies are essential for the evaluation of new therapeutic strategies. Positron-emission tomography (PET) using either 18 F-Fluorodeoxyglucose (18 F-FDG) or amyloid-tracers is a well-established, non-invasive tool in the clinical diagnostics of AD assessing two major pathological hallmarks. 18 F-FDG-PET is able to detect early changes in cerebral glucose metabolism and amyloid-PET shows cerebral amyloid load. However, the suitability of 18 F-FDGand amyloid-PET in the widely used 5XFAD mouse model of AD is unclear as only a few studies on the use of PET biomarkers are available showing some conflicting results. The aim of this study was the evaluation of 18 F-FDG-PET and amyloid-PET in 5XFAD mice in comparison to neurological deficits and neuropathological changes. Seven-and 12-month-old male 5XFAD mice showed a significant reduction in brain glucose metabolism in 18 F-FDG-PET and amyloid-PET with 18 F-Florbetaben demonstrated an increased cerebral amyloid deposition (n = 4-6 per group). Deficits in spatial reference memory were detected in 12-month-old 5XFAD mice in the Morris Water Maze (n = 10-12 per group). Furthermore, an increased plaque load and gliosis could be proven immunohistochemically in 5XFAD mice (n = 4-6 per group). PET biomarkers 18 F-FDG and 18 F-Florbetaben detected cerebral hypometabolism and increased plaque load even before the onset of severe memory deficits. Therefore, the 5XFAD mouse model of AD is well-suited for in vivo monitoring of AD pathologies and longitudinal testing of new therapeutic approaches.

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Section: Discussionmentioning

confidence: 99%

In vivo Imaging With 18F-FDG- and 18F-Florbetaben-PET/MRI Detects Pathological Changes in the Brain of the Commonly Used 5XFAD Mouse Model of Alzheimer's Disease

et al. 2020

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“…Imaging biomarkers of brain morphology are increasingly used in research and clinical routine [ 1 ]. Dementia research has utilized such markers for the investigation of disease-related patterns from different cohorts [ 2 , 3 ]. Structural neuroimaging markers are also used for selection of participants for clinical trials in Alzheimer’s disease (AD) [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Fully bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression

Poulakis

Ferreira

Pereira

et al. 2020

Aging

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Tau pathology and brain atrophy are the closest correlate of cognitive decline in Alzheimer’s disease (AD). Understanding heterogeneity and longitudinal progression of atrophy during the disease course will play a key role in understanding AD pathogenesis. We propose a framework for longitudinal clustering that simultaneously: 1) incorporates whole brain data, 2) leverages unequal visits per individual, 3) compares clusters with a control group, 4) allows for study confounding effects, 5) provides cluster visualization, 6) measures clustering uncertainty. We used amyloid-β positive AD and negative healthy subjects, three longitudinal structural magnetic resonance imaging scans (cortical thickness and subcortical volume) over two years. We found three distinct longitudinal AD brain atrophy patterns: one typical diffuse pattern (n=34, 47.2%), and two atypical patterns: minimal atrophy (n=23 31.9%) and hippocampal sparing (n=9, 12.5%). We also identified outliers (n=3, 4.2%) and observations with uncertain classification (n=3, 4.2%). The clusters differed not only in regional distributions of atrophy at baseline, but also longitudinal atrophy progression, age at AD onset, and cognitive decline. A framework for the longitudinal assessment of variability in cohorts with several neuroimaging measures was successfully developed. We believe this framework may aid in disentangling distinct subtypes of AD from disease staging.

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“…Imaging biomarkers of brain morphology have been increasingly used in research and clinical routine during the last decades (Dickerson & Sperling, 2005). More specifically, dementia research has utilized such markers for the investigation of disease-related patterns from populations around the world and many cohorts with complete neuroimaging data are now available to the research community (Frisoni et al, 2013;Whitwell, 2018). Structural neuroimaging markers are also used for selection of participants for clinical trials in Alzheimer's disease (AD) (Menéndez-González, de Celis Alonso, Salas-Pacheco, & Arias-Carrión, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Unsupervised classification (clustering) is widely applied to neuroimaging data when the aim is to unveil heterogeneous features within samples (Whitwell, 2018). When samples include only one diagnosis, a common use of clustering methods is to investigate whether the neuroimaging measures of interest show heterogeneous patterns within that same diagnostic label.…”

Section: Introductionmentioning

confidence: 99%

Fully Bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression

Initiative¹

2019

Preprint

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Brain atrophy, largely driven by tau deposition, is the most proximal correlate of cognitive decline in Alzheimer's disease (AD). Understanding the heterogeneity and longitudinal progression of brain atrophy during the disease course will play a key role in understanding the mechanisms of AD.The aim of this study is to propose a framework for longitudinal clustering that: 1) incorporates simultaneous clustering of longitudinal multivariate neuroimaging measures, 2) leverages information of individuals with irregularly sampled observations (different sampling times), 3) compares clusters with a control group, 4) allows the study and fixation of potential confounding effects, 5) can provide visualization of the resulting clusters for interpretation, 6) measures the uncertainty of clustering. We aimed to include amyloid-β positive AD patients and amyloid-β negative cognitively unimpaired (CU) subjects with longitudinal data, three sMRI scans over two years. Cortical thickness and subcortical volume measures from the longitudinal stream of FreeSurfer 6.0 pipeline were used as input for cluster analysis.Using the proposed methodology, we found 3 distinct longitudinal brain atrophy patterns in AD patients: a typical diffuse AD pattern (n=34, 47.2%), and 2 atypical AD patterns: Minimal atrophy (n=23 31.9%) and Hippocampal sparing (n=9, 12.5%). We also identified outlier observations (n=3, 4.2%) and observations with uncertain classification (n=3, 4.2%). The clusters of AD patients differed not only in regional distributions of atrophy at baseline, but also in atrophy progression over time, age at AD onset, cognitive deficits at baseline and cognitive decline over time.A framework for the longitudinal assessment of variability in cohorts with several neuroimaging measures was successfully developed and the results show that it can be used to understand heterogeneity in the context of AD.

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Alzheimer's disease neuroimaging

Abstract: The findings shed light on the role of tau and Aβ, as well as age and other comorbidities, in the neurodegenerative process in Alzheimer's disease. This knowledge will be crucial in the development of better disease biomarkers and targeted therapeutic approaches.

Cited by 33 publications

References 48 publications

In vivo Imaging With 18F-FDG- and 18F-Florbetaben-PET/MRI Detects Pathological Changes in the Brain of the Commonly Used 5XFAD Mouse Model of Alzheimer's Disease

In vivo Imaging With 18F-FDG- and 18F-Florbetaben-PET/MRI Detects Pathological Changes in the Brain of the Commonly Used 5XFAD Mouse Model of Alzheimer's Disease

Fully bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression

Fully Bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression

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