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Manually-parcellated gyral data accounting for all known anatomical variability

Morphometric brain changes occur throughout the lifetime and are often investigated to understand healthy ageing and disease, to identify novel biomarkers, and to classify patient groups. Yet, to accurately characterise such changes, an accurate parcellation of the brain must be achieved. Here, we p...

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Autores principales: Mikhael, Shadia S., Mair, Grant, Valdes-Hernandez, Maria, Hoogendoorn, Corné, Wardlaw, Joanna M., Bastin, Mark E., Pernet, Cyril
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350632/
https://www.ncbi.nlm.nih.gov/pubmed/30694228
http://dx.doi.org/10.1038/sdata.2019.1
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author Mikhael, Shadia S.
Mair, Grant
Valdes-Hernandez, Maria
Hoogendoorn, Corné
Wardlaw, Joanna M.
Bastin, Mark E.
Pernet, Cyril
author_facet Mikhael, Shadia S.
Mair, Grant
Valdes-Hernandez, Maria
Hoogendoorn, Corné
Wardlaw, Joanna M.
Bastin, Mark E.
Pernet, Cyril
author_sort Mikhael, Shadia S.
collection PubMed
description Morphometric brain changes occur throughout the lifetime and are often investigated to understand healthy ageing and disease, to identify novel biomarkers, and to classify patient groups. Yet, to accurately characterise such changes, an accurate parcellation of the brain must be achieved. Here, we present a manually-parcellated dataset of the superior frontal, the supramarginal, and the cingulate gyri of 10 healthy middle-aged subjects along with a fully detailed protocol based on two anatomical atlases. Gyral parcels were hand-drawn then reviewed by specialists blinded from the protocol to ensure consistency. Importantly, we follow a procedure that allows accounting for anatomical variability beyond what is usually achieved by standard analysis packages and avoids mutually referring to neighbouring gyri when defining gyral edges. We also provide grey matter thickness, grey matter volume, and white matter surface area information for each parcel. This dataset and corresponding measurements are useful in assessing the accuracy of equivalent parcels and metrics generated by image analysis tools and their impact on morphometric studies.
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spelling pubmed-63506322019-01-30 Manually-parcellated gyral data accounting for all known anatomical variability Mikhael, Shadia S. Mair, Grant Valdes-Hernandez, Maria Hoogendoorn, Corné Wardlaw, Joanna M. Bastin, Mark E. Pernet, Cyril Sci Data Data Descriptor Morphometric brain changes occur throughout the lifetime and are often investigated to understand healthy ageing and disease, to identify novel biomarkers, and to classify patient groups. Yet, to accurately characterise such changes, an accurate parcellation of the brain must be achieved. Here, we present a manually-parcellated dataset of the superior frontal, the supramarginal, and the cingulate gyri of 10 healthy middle-aged subjects along with a fully detailed protocol based on two anatomical atlases. Gyral parcels were hand-drawn then reviewed by specialists blinded from the protocol to ensure consistency. Importantly, we follow a procedure that allows accounting for anatomical variability beyond what is usually achieved by standard analysis packages and avoids mutually referring to neighbouring gyri when defining gyral edges. We also provide grey matter thickness, grey matter volume, and white matter surface area information for each parcel. This dataset and corresponding measurements are useful in assessing the accuracy of equivalent parcels and metrics generated by image analysis tools and their impact on morphometric studies. Nature Publishing Group 2019-01-29 /pmc/articles/PMC6350632/ /pubmed/30694228 http://dx.doi.org/10.1038/sdata.2019.1 Text en Copyright © 2019, The Author(s) http://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article.
spellingShingle Data Descriptor
Mikhael, Shadia S.
Mair, Grant
Valdes-Hernandez, Maria
Hoogendoorn, Corné
Wardlaw, Joanna M.
Bastin, Mark E.
Pernet, Cyril
Manually-parcellated gyral data accounting for all known anatomical variability
title Manually-parcellated gyral data accounting for all known anatomical variability
title_full Manually-parcellated gyral data accounting for all known anatomical variability
title_fullStr Manually-parcellated gyral data accounting for all known anatomical variability
title_full_unstemmed Manually-parcellated gyral data accounting for all known anatomical variability
title_short Manually-parcellated gyral data accounting for all known anatomical variability
title_sort manually-parcellated gyral data accounting for all known anatomical variability
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350632/
https://www.ncbi.nlm.nih.gov/pubmed/30694228
http://dx.doi.org/10.1038/sdata.2019.1
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