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A three-dimensional thalamocortical dataset for characterizing brain heterogeneity

Neural microarchitecture is heterogeneous, varying both across and within brain regions. The consistent identification of regions of interest is one of the most critical aspects in examining neurocircuitry, as these structures serve as the vital landmarks with which to map brain pathways. Access to...

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Autores principales: Prasad, Judy A., Balwani, Aishwarya H., Johnson, Erik C., Miano, Joseph D., Sampathkumar, Vandana, De Andrade, Vincent, Fezzaa, Kamel, Du, Ming, Vescovi, Rafael, Jacobsen, Chris, Kording, Konrad P., Gürsoy, Doga, Gray Roncal, William, Kasthuri, Narayanan, Dyer, Eva L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7576781/
https://www.ncbi.nlm.nih.gov/pubmed/33082340
http://dx.doi.org/10.1038/s41597-020-00692-y
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author Prasad, Judy A.
Balwani, Aishwarya H.
Johnson, Erik C.
Miano, Joseph D.
Sampathkumar, Vandana
De Andrade, Vincent
Fezzaa, Kamel
Du, Ming
Vescovi, Rafael
Jacobsen, Chris
Kording, Konrad P.
Gürsoy, Doga
Gray Roncal, William
Kasthuri, Narayanan
Dyer, Eva L.
author_facet Prasad, Judy A.
Balwani, Aishwarya H.
Johnson, Erik C.
Miano, Joseph D.
Sampathkumar, Vandana
De Andrade, Vincent
Fezzaa, Kamel
Du, Ming
Vescovi, Rafael
Jacobsen, Chris
Kording, Konrad P.
Gürsoy, Doga
Gray Roncal, William
Kasthuri, Narayanan
Dyer, Eva L.
author_sort Prasad, Judy A.
collection PubMed
description Neural microarchitecture is heterogeneous, varying both across and within brain regions. The consistent identification of regions of interest is one of the most critical aspects in examining neurocircuitry, as these structures serve as the vital landmarks with which to map brain pathways. Access to continuous, three-dimensional volumes that span multiple brain areas not only provides richer context for identifying such landmarks, but also enables a deeper probing of the microstructures within. Here, we describe a three-dimensional X-ray microtomography imaging dataset of a well-known and validated thalamocortical sample, encompassing a range of cortical and subcortical structures from the mouse brain . In doing so, we provide the field with access to a micron-scale anatomical imaging dataset ideal for studying heterogeneity of neural structure.
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spelling pubmed-75767812020-10-23 A three-dimensional thalamocortical dataset for characterizing brain heterogeneity Prasad, Judy A. Balwani, Aishwarya H. Johnson, Erik C. Miano, Joseph D. Sampathkumar, Vandana De Andrade, Vincent Fezzaa, Kamel Du, Ming Vescovi, Rafael Jacobsen, Chris Kording, Konrad P. Gürsoy, Doga Gray Roncal, William Kasthuri, Narayanan Dyer, Eva L. Sci Data Data Descriptor Neural microarchitecture is heterogeneous, varying both across and within brain regions. The consistent identification of regions of interest is one of the most critical aspects in examining neurocircuitry, as these structures serve as the vital landmarks with which to map brain pathways. Access to continuous, three-dimensional volumes that span multiple brain areas not only provides richer context for identifying such landmarks, but also enables a deeper probing of the microstructures within. Here, we describe a three-dimensional X-ray microtomography imaging dataset of a well-known and validated thalamocortical sample, encompassing a range of cortical and subcortical structures from the mouse brain . In doing so, we provide the field with access to a micron-scale anatomical imaging dataset ideal for studying heterogeneity of neural structure. Nature Publishing Group UK 2020-10-20 /pmc/articles/PMC7576781/ /pubmed/33082340 http://dx.doi.org/10.1038/s41597-020-00692-y Text en © The Author(s) 2020 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
Prasad, Judy A.
Balwani, Aishwarya H.
Johnson, Erik C.
Miano, Joseph D.
Sampathkumar, Vandana
De Andrade, Vincent
Fezzaa, Kamel
Du, Ming
Vescovi, Rafael
Jacobsen, Chris
Kording, Konrad P.
Gürsoy, Doga
Gray Roncal, William
Kasthuri, Narayanan
Dyer, Eva L.
A three-dimensional thalamocortical dataset for characterizing brain heterogeneity
title A three-dimensional thalamocortical dataset for characterizing brain heterogeneity
title_full A three-dimensional thalamocortical dataset for characterizing brain heterogeneity
title_fullStr A three-dimensional thalamocortical dataset for characterizing brain heterogeneity
title_full_unstemmed A three-dimensional thalamocortical dataset for characterizing brain heterogeneity
title_short A three-dimensional thalamocortical dataset for characterizing brain heterogeneity
title_sort three-dimensional thalamocortical dataset for characterizing brain heterogeneity
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7576781/
https://www.ncbi.nlm.nih.gov/pubmed/33082340
http://dx.doi.org/10.1038/s41597-020-00692-y
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