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Assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures

Healthy ageing coincides with a progressive decline of brain gray matter (GM) ultimately affecting the entire brain. For a long time, manual delineation-based volumetry within predefined regions of interest (ROI) has been the gold standard for assessing such degeneration. Voxel-Based Morphometry (VB...

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Autores principales: Callaert, Dorothée V., Ribbens, Annemie, Maes, Frederik, Swinnen, Stephan P., Wenderoth, Nicole
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4066859/
https://www.ncbi.nlm.nih.gov/pubmed/25002845
http://dx.doi.org/10.3389/fnagi.2014.00124
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author Callaert, Dorothée V.
Ribbens, Annemie
Maes, Frederik
Swinnen, Stephan P.
Wenderoth, Nicole
author_facet Callaert, Dorothée V.
Ribbens, Annemie
Maes, Frederik
Swinnen, Stephan P.
Wenderoth, Nicole
author_sort Callaert, Dorothée V.
collection PubMed
description Healthy ageing coincides with a progressive decline of brain gray matter (GM) ultimately affecting the entire brain. For a long time, manual delineation-based volumetry within predefined regions of interest (ROI) has been the gold standard for assessing such degeneration. Voxel-Based Morphometry (VBM) offers an automated alternative approach that, however, relies critically on the segmentation and spatial normalization of a large collection of images from different subjects. This can be achieved via different algorithms, with SPM5/SPM8, DARTEL of SPM8 and FSL tools (FAST, FNIRT) being three of the most frequently used. We complemented these voxel based measurements with a ROI based approach, whereby the ROIs are defined by transforms of an atlas (containing different tissue probability maps as well as predefined anatomic labels) to the individual subject images in order to obtain volumetric information at the level of the whole brain or within separate ROIs. Comparing GM decline between 21 young subjects (mean age 23) and 18 elderly (mean age 66) revealed that volumetric measurements differed significantly between methods. The unified segmentation/normalization of SPM5/SPM8 revealed the largest age-related differences and DARTEL the smallest, with FSL being more similar to the DARTEL approach. Method specific differences were substantial after segmentation and most pronounced for the cortical structures in close vicinity to major sulci and fissures. Our findings suggest that algorithms that provide only limited degrees of freedom for local deformations (such as the unified segmentation and normalization of SPM5/SPM8) tend to overestimate between-group differences in VBM results when compared to methods providing more flexible warping. This difference seems to be most pronounced if the anatomy of one of the groups deviates from custom templates, a finding that is of particular importance when results are compared across studies using different VBM methods.
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spelling pubmed-40668592014-07-07 Assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures Callaert, Dorothée V. Ribbens, Annemie Maes, Frederik Swinnen, Stephan P. Wenderoth, Nicole Front Aging Neurosci Neuroscience Healthy ageing coincides with a progressive decline of brain gray matter (GM) ultimately affecting the entire brain. For a long time, manual delineation-based volumetry within predefined regions of interest (ROI) has been the gold standard for assessing such degeneration. Voxel-Based Morphometry (VBM) offers an automated alternative approach that, however, relies critically on the segmentation and spatial normalization of a large collection of images from different subjects. This can be achieved via different algorithms, with SPM5/SPM8, DARTEL of SPM8 and FSL tools (FAST, FNIRT) being three of the most frequently used. We complemented these voxel based measurements with a ROI based approach, whereby the ROIs are defined by transforms of an atlas (containing different tissue probability maps as well as predefined anatomic labels) to the individual subject images in order to obtain volumetric information at the level of the whole brain or within separate ROIs. Comparing GM decline between 21 young subjects (mean age 23) and 18 elderly (mean age 66) revealed that volumetric measurements differed significantly between methods. The unified segmentation/normalization of SPM5/SPM8 revealed the largest age-related differences and DARTEL the smallest, with FSL being more similar to the DARTEL approach. Method specific differences were substantial after segmentation and most pronounced for the cortical structures in close vicinity to major sulci and fissures. Our findings suggest that algorithms that provide only limited degrees of freedom for local deformations (such as the unified segmentation and normalization of SPM5/SPM8) tend to overestimate between-group differences in VBM results when compared to methods providing more flexible warping. This difference seems to be most pronounced if the anatomy of one of the groups deviates from custom templates, a finding that is of particular importance when results are compared across studies using different VBM methods. Frontiers Media S.A. 2014-06-23 /pmc/articles/PMC4066859/ /pubmed/25002845 http://dx.doi.org/10.3389/fnagi.2014.00124 Text en Copyright © 2014 Callaert, Ribbens, Maes, Swinnen and Wenderoth. http://creativecommons.org/licenses/by/3.0/ 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
Callaert, Dorothée V.
Ribbens, Annemie
Maes, Frederik
Swinnen, Stephan P.
Wenderoth, Nicole
Assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures
title Assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures
title_full Assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures
title_fullStr Assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures
title_full_unstemmed Assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures
title_short Assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures
title_sort assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4066859/
https://www.ncbi.nlm.nih.gov/pubmed/25002845
http://dx.doi.org/10.3389/fnagi.2014.00124
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