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An accurate registration of the BigBrain dataset with the MNI PD25 and ICBM152 atlases

Brain atlases that encompass detailed anatomical or physiological features are instrumental in the research and surgical planning of various neurological conditions. Magnetic resonance imaging (MRI) has played important roles in neuro-image analysis while histological data remain crucial as a gold s...

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Autores principales: Xiao, Yiming, Lau, Jonathan C., Anderson, Taylor, DeKraker, Jordan, Collins, D. Louis, Peters, Terry, Khan, Ali R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6797784/
https://www.ncbi.nlm.nih.gov/pubmed/31624250
http://dx.doi.org/10.1038/s41597-019-0217-0
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author Xiao, Yiming
Lau, Jonathan C.
Anderson, Taylor
DeKraker, Jordan
Collins, D. Louis
Peters, Terry
Khan, Ali R.
author_facet Xiao, Yiming
Lau, Jonathan C.
Anderson, Taylor
DeKraker, Jordan
Collins, D. Louis
Peters, Terry
Khan, Ali R.
author_sort Xiao, Yiming
collection PubMed
description Brain atlases that encompass detailed anatomical or physiological features are instrumental in the research and surgical planning of various neurological conditions. Magnetic resonance imaging (MRI) has played important roles in neuro-image analysis while histological data remain crucial as a gold standard to guide and validate such analyses. With cellular-scale resolution, the BigBrain atlas offers 3D histology of a complete human brain, and is highly valuable to the research and clinical community. To bridge the insights at macro- and micro-levels, accurate mapping of BigBrain and established MRI brain atlases is necessary, but the existing registration is unsatisfactory. The described dataset includes co-registration of the BigBrain atlas to the MNI PD25 atlas and the ICBM152 2009b atlases (symmetric and asymmetric versions) in addition to manual segmentation of the basal ganglia, red nucleus, amygdala, and hippocampus for all mentioned atlases. The dataset intends to provide a bridge between insights from histological data and MRI studies in research and neurosurgical planning. The registered atlases, anatomical segmentations, and deformation matrices are available at: https://osf.io/xkqb3/.
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spelling pubmed-67977842019-10-21 An accurate registration of the BigBrain dataset with the MNI PD25 and ICBM152 atlases Xiao, Yiming Lau, Jonathan C. Anderson, Taylor DeKraker, Jordan Collins, D. Louis Peters, Terry Khan, Ali R. Sci Data Data Descriptor Brain atlases that encompass detailed anatomical or physiological features are instrumental in the research and surgical planning of various neurological conditions. Magnetic resonance imaging (MRI) has played important roles in neuro-image analysis while histological data remain crucial as a gold standard to guide and validate such analyses. With cellular-scale resolution, the BigBrain atlas offers 3D histology of a complete human brain, and is highly valuable to the research and clinical community. To bridge the insights at macro- and micro-levels, accurate mapping of BigBrain and established MRI brain atlases is necessary, but the existing registration is unsatisfactory. The described dataset includes co-registration of the BigBrain atlas to the MNI PD25 atlas and the ICBM152 2009b atlases (symmetric and asymmetric versions) in addition to manual segmentation of the basal ganglia, red nucleus, amygdala, and hippocampus for all mentioned atlases. The dataset intends to provide a bridge between insights from histological data and MRI studies in research and neurosurgical planning. The registered atlases, anatomical segmentations, and deformation matrices are available at: https://osf.io/xkqb3/. Nature Publishing Group UK 2019-10-17 /pmc/articles/PMC6797784/ /pubmed/31624250 http://dx.doi.org/10.1038/s41597-019-0217-0 Text en © The Author(s) 2019 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 associated with this article.
spellingShingle Data Descriptor
Xiao, Yiming
Lau, Jonathan C.
Anderson, Taylor
DeKraker, Jordan
Collins, D. Louis
Peters, Terry
Khan, Ali R.
An accurate registration of the BigBrain dataset with the MNI PD25 and ICBM152 atlases
title An accurate registration of the BigBrain dataset with the MNI PD25 and ICBM152 atlases
title_full An accurate registration of the BigBrain dataset with the MNI PD25 and ICBM152 atlases
title_fullStr An accurate registration of the BigBrain dataset with the MNI PD25 and ICBM152 atlases
title_full_unstemmed An accurate registration of the BigBrain dataset with the MNI PD25 and ICBM152 atlases
title_short An accurate registration of the BigBrain dataset with the MNI PD25 and ICBM152 atlases
title_sort accurate registration of the bigbrain dataset with the mni pd25 and icbm152 atlases
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6797784/
https://www.ncbi.nlm.nih.gov/pubmed/31624250
http://dx.doi.org/10.1038/s41597-019-0217-0
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