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Statistical image analysis of longitudinal RAVENS images

Regional analysis of volumes examined in normalized space (RAVENS) are transformation images used in the study of brain morphometry. In this paper, RAVENS images are analyzed using a longitudinal variant of voxel-based morphometry (VBM) and longitudinal functional principal component analysis (LFPCA...

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Autores principales: Lee, Seonjoo, Zipunnikov, Vadim, Reich, Daniel S., Pham, Dzung L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4611144/
https://www.ncbi.nlm.nih.gov/pubmed/26539071
http://dx.doi.org/10.3389/fnins.2015.00368
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author Lee, Seonjoo
Zipunnikov, Vadim
Reich, Daniel S.
Pham, Dzung L.
author_facet Lee, Seonjoo
Zipunnikov, Vadim
Reich, Daniel S.
Pham, Dzung L.
author_sort Lee, Seonjoo
collection PubMed
description Regional analysis of volumes examined in normalized space (RAVENS) are transformation images used in the study of brain morphometry. In this paper, RAVENS images are analyzed using a longitudinal variant of voxel-based morphometry (VBM) and longitudinal functional principal component analysis (LFPCA) for high-dimensional images. We demonstrate that the latter overcomes the limitations of standard longitudinal VBM analyses, which does not separate registration errors from other longitudinal changes and baseline patterns. This is especially important in contexts where longitudinal changes are only a small fraction of the overall observed variability, which is typical in normal aging and many chronic diseases. Our simulation study shows that LFPCA effectively separates registration error from baseline and longitudinal signals of interest by decomposing RAVENS images measured at multiple visits into three components: a subject-specific imaging random intercept that quantifies the cross-sectional variability, a subject-specific imaging slope that quantifies the irreversible changes over multiple visits, and a subject-visit specific imaging deviation. We describe strategies to identify baseline/longitudinal variation and registration errors combined with covariates of interest. Our analysis suggests that specific regional brain atrophy and ventricular enlargement are associated with multiple sclerosis (MS) disease progression.
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spelling pubmed-46111442015-11-04 Statistical image analysis of longitudinal RAVENS images Lee, Seonjoo Zipunnikov, Vadim Reich, Daniel S. Pham, Dzung L. Front Neurosci Neuroscience Regional analysis of volumes examined in normalized space (RAVENS) are transformation images used in the study of brain morphometry. In this paper, RAVENS images are analyzed using a longitudinal variant of voxel-based morphometry (VBM) and longitudinal functional principal component analysis (LFPCA) for high-dimensional images. We demonstrate that the latter overcomes the limitations of standard longitudinal VBM analyses, which does not separate registration errors from other longitudinal changes and baseline patterns. This is especially important in contexts where longitudinal changes are only a small fraction of the overall observed variability, which is typical in normal aging and many chronic diseases. Our simulation study shows that LFPCA effectively separates registration error from baseline and longitudinal signals of interest by decomposing RAVENS images measured at multiple visits into three components: a subject-specific imaging random intercept that quantifies the cross-sectional variability, a subject-specific imaging slope that quantifies the irreversible changes over multiple visits, and a subject-visit specific imaging deviation. We describe strategies to identify baseline/longitudinal variation and registration errors combined with covariates of interest. Our analysis suggests that specific regional brain atrophy and ventricular enlargement are associated with multiple sclerosis (MS) disease progression. Frontiers Media S.A. 2015-10-20 /pmc/articles/PMC4611144/ /pubmed/26539071 http://dx.doi.org/10.3389/fnins.2015.00368 Text en Copyright © 2015 Lee, Zipunnikov, Reich and Pham. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Lee, Seonjoo
Zipunnikov, Vadim
Reich, Daniel S.
Pham, Dzung L.
Statistical image analysis of longitudinal RAVENS images
title Statistical image analysis of longitudinal RAVENS images
title_full Statistical image analysis of longitudinal RAVENS images
title_fullStr Statistical image analysis of longitudinal RAVENS images
title_full_unstemmed Statistical image analysis of longitudinal RAVENS images
title_short Statistical image analysis of longitudinal RAVENS images
title_sort statistical image analysis of longitudinal ravens images
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4611144/
https://www.ncbi.nlm.nih.gov/pubmed/26539071
http://dx.doi.org/10.3389/fnins.2015.00368
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