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Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease()

Detection of Alzheimer's disease (AD) at the first stages of the pathology is an important task to accelerate the development of new therapies and improve treatment. Compared to AD detection, the prediction of AD using structural MRI at the mild cognitive impairment (MCI) or pre-MCI stage is mo...

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Autores principales: Coupé, Pierrick, Eskildsen, Simon F., Manjón, José V., Fonov, Vladimir S., Pruessner, Jens C., Allard, Michèle, Collins, D. Louis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3757726/
https://www.ncbi.nlm.nih.gov/pubmed/24179747
http://dx.doi.org/10.1016/j.nicl.2012.10.002
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author Coupé, Pierrick
Eskildsen, Simon F.
Manjón, José V.
Fonov, Vladimir S.
Pruessner, Jens C.
Allard, Michèle
Collins, D. Louis
author_facet Coupé, Pierrick
Eskildsen, Simon F.
Manjón, José V.
Fonov, Vladimir S.
Pruessner, Jens C.
Allard, Michèle
Collins, D. Louis
author_sort Coupé, Pierrick
collection PubMed
description Detection of Alzheimer's disease (AD) at the first stages of the pathology is an important task to accelerate the development of new therapies and improve treatment. Compared to AD detection, the prediction of AD using structural MRI at the mild cognitive impairment (MCI) or pre-MCI stage is more complex because the associated anatomical changes are more subtle. In this study, we analyzed the capability of a recently proposed method, SNIPE (Scoring by Nonlocal Image Patch Estimator), to predict AD by analyzing entorhinal cortex (EC) and hippocampus (HC) scoring over the entire ADNI database (834 scans). Detection (AD vs. CN) and prediction (progressive — pMCI vs. stable — sMCI) efficiency of SNIPE were studied using volumetric and grading biomarkers. First, our results indicate that grading-based biomarkers are more relevant for prediction than volume-based biomarkers. Second, we show that HC-based biomarkers are more important than EC-based biomarkers for prediction. Third, we demonstrate that the results obtained by SNIPE are similar to or better than results obtained in an independent study using HC volume, cortical thickness, and tensor-based morphometry, individually and in combination. Fourth, a comparison of new patch-based methods shows that the nonlocal redundancy strategy involved in SNIPE obtained similar results to a new local sparse-based approach. Finally, we present the first results of patch-based morphometry to illustrate the progression of the pathology.
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spelling pubmed-37577262013-10-31 Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease() Coupé, Pierrick Eskildsen, Simon F. Manjón, José V. Fonov, Vladimir S. Pruessner, Jens C. Allard, Michèle Collins, D. Louis Neuroimage Clin Article Detection of Alzheimer's disease (AD) at the first stages of the pathology is an important task to accelerate the development of new therapies and improve treatment. Compared to AD detection, the prediction of AD using structural MRI at the mild cognitive impairment (MCI) or pre-MCI stage is more complex because the associated anatomical changes are more subtle. In this study, we analyzed the capability of a recently proposed method, SNIPE (Scoring by Nonlocal Image Patch Estimator), to predict AD by analyzing entorhinal cortex (EC) and hippocampus (HC) scoring over the entire ADNI database (834 scans). Detection (AD vs. CN) and prediction (progressive — pMCI vs. stable — sMCI) efficiency of SNIPE were studied using volumetric and grading biomarkers. First, our results indicate that grading-based biomarkers are more relevant for prediction than volume-based biomarkers. Second, we show that HC-based biomarkers are more important than EC-based biomarkers for prediction. Third, we demonstrate that the results obtained by SNIPE are similar to or better than results obtained in an independent study using HC volume, cortical thickness, and tensor-based morphometry, individually and in combination. Fourth, a comparison of new patch-based methods shows that the nonlocal redundancy strategy involved in SNIPE obtained similar results to a new local sparse-based approach. Finally, we present the first results of patch-based morphometry to illustrate the progression of the pathology. Elsevier 2012-10-17 /pmc/articles/PMC3757726/ /pubmed/24179747 http://dx.doi.org/10.1016/j.nicl.2012.10.002 Text en © 2012 The Authors http://creativecommons.org/licenses/by-nc-nd/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-No Derivative Works License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Article
Coupé, Pierrick
Eskildsen, Simon F.
Manjón, José V.
Fonov, Vladimir S.
Pruessner, Jens C.
Allard, Michèle
Collins, D. Louis
Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease()
title Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease()
title_full Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease()
title_fullStr Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease()
title_full_unstemmed Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease()
title_short Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease()
title_sort scoring by nonlocal image patch estimator for early detection of alzheimer's disease()
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3757726/
https://www.ncbi.nlm.nih.gov/pubmed/24179747
http://dx.doi.org/10.1016/j.nicl.2012.10.002
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