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Multimodal Image Analysis in Alzheimer’s Disease via Statistical Modelling of Non-local Intensity Correlations

The joint analysis of brain atrophy measured with magnetic resonance imaging (MRI) and hypometabolism measured with positron emission tomography with fluorodeoxyglucose (FDG-PET) is of primary importance in developing models of pathological changes in Alzheimer’s disease (AD). Most of the current mu...

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Autores principales: Lorenzi, Marco, Simpson, Ivor J., Mendelson, Alex F., Vos, Sjoerd B., Cardoso, M. Jorge, Modat, Marc, Schott, Jonathan M., Ourselin, Sebastien
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4827392/
https://www.ncbi.nlm.nih.gov/pubmed/27064442
http://dx.doi.org/10.1038/srep22161
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author Lorenzi, Marco
Simpson, Ivor J.
Mendelson, Alex F.
Vos, Sjoerd B.
Cardoso, M. Jorge
Modat, Marc
Schott, Jonathan M.
Ourselin, Sebastien
author_facet Lorenzi, Marco
Simpson, Ivor J.
Mendelson, Alex F.
Vos, Sjoerd B.
Cardoso, M. Jorge
Modat, Marc
Schott, Jonathan M.
Ourselin, Sebastien
author_sort Lorenzi, Marco
collection PubMed
description The joint analysis of brain atrophy measured with magnetic resonance imaging (MRI) and hypometabolism measured with positron emission tomography with fluorodeoxyglucose (FDG-PET) is of primary importance in developing models of pathological changes in Alzheimer’s disease (AD). Most of the current multimodal analyses in AD assume a local (spatially overlapping) relationship between MR and FDG-PET intensities. However, it is well known that atrophy and hypometabolism are prominent in different anatomical areas. The aim of this work is to describe the relationship between atrophy and hypometabolism by means of a data-driven statistical model of non-overlapping intensity correlations. For this purpose, FDG-PET and MRI signals are jointly analyzed through a computationally tractable formulation of partial least squares regression (PLSR). The PLSR model is estimated and validated on a large clinical cohort of 1049 individuals from the ADNI dataset. Results show that the proposed non-local analysis outperforms classical local approaches in terms of predictive accuracy while providing a plausible description of disease dynamics: early AD is characterised by non-overlapping temporal atrophy and temporo-parietal hypometabolism, while the later disease stages show overlapping brain atrophy and hypometabolism spread in temporal, parietal and cortical areas.
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spelling pubmed-48273922016-04-19 Multimodal Image Analysis in Alzheimer’s Disease via Statistical Modelling of Non-local Intensity Correlations Lorenzi, Marco Simpson, Ivor J. Mendelson, Alex F. Vos, Sjoerd B. Cardoso, M. Jorge Modat, Marc Schott, Jonathan M. Ourselin, Sebastien Sci Rep Article The joint analysis of brain atrophy measured with magnetic resonance imaging (MRI) and hypometabolism measured with positron emission tomography with fluorodeoxyglucose (FDG-PET) is of primary importance in developing models of pathological changes in Alzheimer’s disease (AD). Most of the current multimodal analyses in AD assume a local (spatially overlapping) relationship between MR and FDG-PET intensities. However, it is well known that atrophy and hypometabolism are prominent in different anatomical areas. The aim of this work is to describe the relationship between atrophy and hypometabolism by means of a data-driven statistical model of non-overlapping intensity correlations. For this purpose, FDG-PET and MRI signals are jointly analyzed through a computationally tractable formulation of partial least squares regression (PLSR). The PLSR model is estimated and validated on a large clinical cohort of 1049 individuals from the ADNI dataset. Results show that the proposed non-local analysis outperforms classical local approaches in terms of predictive accuracy while providing a plausible description of disease dynamics: early AD is characterised by non-overlapping temporal atrophy and temporo-parietal hypometabolism, while the later disease stages show overlapping brain atrophy and hypometabolism spread in temporal, parietal and cortical areas. Nature Publishing Group 2016-04-11 /pmc/articles/PMC4827392/ /pubmed/27064442 http://dx.doi.org/10.1038/srep22161 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Lorenzi, Marco
Simpson, Ivor J.
Mendelson, Alex F.
Vos, Sjoerd B.
Cardoso, M. Jorge
Modat, Marc
Schott, Jonathan M.
Ourselin, Sebastien
Multimodal Image Analysis in Alzheimer’s Disease via Statistical Modelling of Non-local Intensity Correlations
title Multimodal Image Analysis in Alzheimer’s Disease via Statistical Modelling of Non-local Intensity Correlations
title_full Multimodal Image Analysis in Alzheimer’s Disease via Statistical Modelling of Non-local Intensity Correlations
title_fullStr Multimodal Image Analysis in Alzheimer’s Disease via Statistical Modelling of Non-local Intensity Correlations
title_full_unstemmed Multimodal Image Analysis in Alzheimer’s Disease via Statistical Modelling of Non-local Intensity Correlations
title_short Multimodal Image Analysis in Alzheimer’s Disease via Statistical Modelling of Non-local Intensity Correlations
title_sort multimodal image analysis in alzheimer’s disease via statistical modelling of non-local intensity correlations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4827392/
https://www.ncbi.nlm.nih.gov/pubmed/27064442
http://dx.doi.org/10.1038/srep22161
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