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  Generative FDG-PET and MRI model of aging and disease progression in Alzheimer's disease

Dukart, J., Kherif, F., Mueller, K., Adaszewski, S., Schroeter, M. L., Frackowiak, R. S. J., et al. (2013). Generative FDG-PET and MRI model of aging and disease progression in Alzheimer's disease. PLoS Computational Biology, 9(4): e1002987. doi:10.1371/journal.pcbi.1002987.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-000E-F0E9-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-AD12-6
Genre: Journal Article

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 Creators:
Dukart, Jürgen1, 2, Author              
Kherif, Ferath2, Author
Mueller, Karsten3, Author              
Adaszewski, Stanislaw2, Author
Schroeter, Matthias L.1, 4, 5, 6, Author              
Frackowiak, Richard S. J.2, Author
Draganski, Bogdan1, 2, Author              
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2Laboratoire de Recherche en Neuroimagerie (LREN), Centre hospitalier universitaire vaudois, Lausanne, Switzerland, ou_persistent22              
3Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634558              
4Clinic for Cognitive Neurology, University of Leipzig, Germany, ou_persistent22              
5Leipzig Research Center for Civilization Diseases (LIFE), University of Leipzig, Germany, ou_persistent22              
6Consortium for Frontotemporal Lobar Degeneration, Leipzig, Germany, ou_persistent22              

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 Abstract: The failure of current strategies to provide an explanation for controversial findings on the pattern of pathophysiological changes in Alzheimer's Disease (AD) motivates the necessity to develop new integrative approaches based on multi-modal neuroimaging data that captures various aspects of disease pathology. Previous studies using [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) and structural magnetic resonance imaging (sMRI) report controversial results about time-line, spatial extent and magnitude of glucose hypometabolism and atrophy in AD that depend on clinical and demographic characteristics of the studied populations. Here, we provide and validate at a group level a generative anatomical model of glucose hypo-metabolism and atrophy progression in AD based on FDG-PET and sMRI data of 80 patients and 79 healthy controls to describe expected age and symptom severity related changes in AD relative to a baseline provided by healthy aging. We demonstrate a high level of anatomical accuracy for both modalities yielding strongly age- and symptom-severity- dependant glucose hypometabolism in temporal, parietal and precuneal regions and a more extensive network of atrophy in hippocampal, temporal, parietal, occipital and posterior caudate regions. The model suggests greater and more consistent changes in FDG-PET compared to sMRI at earlier and the inversion of this pattern at more advanced AD stages. Our model describes, integrates and predicts characteristic patterns of AD related pathology, uncontaminated by normal age effects, derived from multi-modal data. It further provides an integrative explanation for findings suggesting a dissociation between early- and late-onset AD. The generative model offers a basis for further development of individualized biomarkers allowing accurate early diagnosis and treatment evaluation.

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Language(s): eng - English
 Dates: 2012-12-112013-01-272013-04-04
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1002987
PMID: 23592957
PMC: PMC3616972
Other: Epub 2013
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Title: PLoS Computational Biology
Source Genre: Journal
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Pages: - Volume / Issue: 9 (4) Sequence Number: e1002987 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1