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Automated measurement of the human corpus callosum using MRI

The corpus callosum includes the majority of fibers that connect the two cortical hemispheres. Studies of cross-sectional callosal morphometry and area have revealed developmental, gender, and hemispheric differences in healthy populations and callosal deficits associated with neurodegenerative dise...

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Autores principales: Herron, Timothy J., Kang, Xiaojian, Woods, David L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3439830/
https://www.ncbi.nlm.nih.gov/pubmed/22988433
http://dx.doi.org/10.3389/fninf.2012.00025
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author Herron, Timothy J.
Kang, Xiaojian
Woods, David L.
author_facet Herron, Timothy J.
Kang, Xiaojian
Woods, David L.
author_sort Herron, Timothy J.
collection PubMed
description The corpus callosum includes the majority of fibers that connect the two cortical hemispheres. Studies of cross-sectional callosal morphometry and area have revealed developmental, gender, and hemispheric differences in healthy populations and callosal deficits associated with neurodegenerative disease and brain injury. However, accurate quantification of the callosum using magnetic resonance imaging is complicated by intersubject variability in callosal size, shape, and location and often requires manual outlining of the callosum in order to achieve adequate performance. Here we describe an objective, fully automated protocol that utilizes voxel-based images to quantify the area and thickness both of the entire callosum and of different callosal compartments. We verify the method's accuracy, reliability, robustness, and multisite consistency and make comparisons with manual measurements using public brain-image databases. An analysis of age-related changes in the callosum showed increases in length and reductions in thickness and area with age. A comparison of older subjects with and without mild dementia revealed that reductions in anterior callosal area independently predicted poorer cognitive performance after factoring out Mini-Mental Status Examination scores and normalized whole brain volume. Open-source software implementing the algorithm is available at www.nitrc.org/projects/c8c8.
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spelling pubmed-34398302012-09-17 Automated measurement of the human corpus callosum using MRI Herron, Timothy J. Kang, Xiaojian Woods, David L. Front Neuroinform Neuroscience The corpus callosum includes the majority of fibers that connect the two cortical hemispheres. Studies of cross-sectional callosal morphometry and area have revealed developmental, gender, and hemispheric differences in healthy populations and callosal deficits associated with neurodegenerative disease and brain injury. However, accurate quantification of the callosum using magnetic resonance imaging is complicated by intersubject variability in callosal size, shape, and location and often requires manual outlining of the callosum in order to achieve adequate performance. Here we describe an objective, fully automated protocol that utilizes voxel-based images to quantify the area and thickness both of the entire callosum and of different callosal compartments. We verify the method's accuracy, reliability, robustness, and multisite consistency and make comparisons with manual measurements using public brain-image databases. An analysis of age-related changes in the callosum showed increases in length and reductions in thickness and area with age. A comparison of older subjects with and without mild dementia revealed that reductions in anterior callosal area independently predicted poorer cognitive performance after factoring out Mini-Mental Status Examination scores and normalized whole brain volume. Open-source software implementing the algorithm is available at www.nitrc.org/projects/c8c8. Frontiers Media S.A. 2012-09-12 /pmc/articles/PMC3439830/ /pubmed/22988433 http://dx.doi.org/10.3389/fninf.2012.00025 Text en Copyright © 2012 Herron, Kang and Woods. http://www.frontiersin.org/licenseagreement This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
spellingShingle Neuroscience
Herron, Timothy J.
Kang, Xiaojian
Woods, David L.
Automated measurement of the human corpus callosum using MRI
title Automated measurement of the human corpus callosum using MRI
title_full Automated measurement of the human corpus callosum using MRI
title_fullStr Automated measurement of the human corpus callosum using MRI
title_full_unstemmed Automated measurement of the human corpus callosum using MRI
title_short Automated measurement of the human corpus callosum using MRI
title_sort automated measurement of the human corpus callosum using mri
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3439830/
https://www.ncbi.nlm.nih.gov/pubmed/22988433
http://dx.doi.org/10.3389/fninf.2012.00025
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