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High-resolution cortical parcellation based on conserved brain landmarks for localization of multimodal data to the nearest centimeter
Precise cortical brain localization presents an important challenge in the literature. Brain atlases provide data-guided parcellation based on functional and structural brain metrics, and each atlas has its own unique benefits for localization. We offer a parcellation guided by intracranial electroe...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9637135/ https://www.ncbi.nlm.nih.gov/pubmed/36335146 http://dx.doi.org/10.1038/s41598-022-21543-3 |
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author | McGrath, Hari Zaveri, Hitten P. Collins, Evan Jafar, Tamara Chishti, Omar Obaid, Sami Ksendzovsky, Alexander Wu, Kun Papademetris, Xenophon Spencer, Dennis D. |
author_facet | McGrath, Hari Zaveri, Hitten P. Collins, Evan Jafar, Tamara Chishti, Omar Obaid, Sami Ksendzovsky, Alexander Wu, Kun Papademetris, Xenophon Spencer, Dennis D. |
author_sort | McGrath, Hari |
collection | PubMed |
description | Precise cortical brain localization presents an important challenge in the literature. Brain atlases provide data-guided parcellation based on functional and structural brain metrics, and each atlas has its own unique benefits for localization. We offer a parcellation guided by intracranial electroencephalography, a technique which has historically provided pioneering advances in our understanding of brain structure–function relationships. We used a consensus boundary mapping approach combining anatomical designations in Duvernoy’s Atlas of the Human Brain, a widely recognized textbook of human brain anatomy, with the anatomy of the MNI152 template and the magnetic resonance imaging scans of an epilepsy surgery cohort. The Yale Brain Atlas consists of 690 one-square centimeter parcels based around conserved anatomical features and each with a unique identifier to communicate anatomically unambiguous localization. We report on the methodology we used to create the Atlas along with the findings of a neuroimaging study assessing the accuracy and clinical usefulness of cortical localization using the Atlas. We also share our vision for the Atlas as a tool in the clinical and research neurosciences, where it may facilitate precise localization of data on the cortex, accurate description of anatomical locations, and modern data science approaches using standardized brain regions. |
format | Online Article Text |
id | pubmed-9637135 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96371352022-11-07 High-resolution cortical parcellation based on conserved brain landmarks for localization of multimodal data to the nearest centimeter McGrath, Hari Zaveri, Hitten P. Collins, Evan Jafar, Tamara Chishti, Omar Obaid, Sami Ksendzovsky, Alexander Wu, Kun Papademetris, Xenophon Spencer, Dennis D. Sci Rep Article Precise cortical brain localization presents an important challenge in the literature. Brain atlases provide data-guided parcellation based on functional and structural brain metrics, and each atlas has its own unique benefits for localization. We offer a parcellation guided by intracranial electroencephalography, a technique which has historically provided pioneering advances in our understanding of brain structure–function relationships. We used a consensus boundary mapping approach combining anatomical designations in Duvernoy’s Atlas of the Human Brain, a widely recognized textbook of human brain anatomy, with the anatomy of the MNI152 template and the magnetic resonance imaging scans of an epilepsy surgery cohort. The Yale Brain Atlas consists of 690 one-square centimeter parcels based around conserved anatomical features and each with a unique identifier to communicate anatomically unambiguous localization. We report on the methodology we used to create the Atlas along with the findings of a neuroimaging study assessing the accuracy and clinical usefulness of cortical localization using the Atlas. We also share our vision for the Atlas as a tool in the clinical and research neurosciences, where it may facilitate precise localization of data on the cortex, accurate description of anatomical locations, and modern data science approaches using standardized brain regions. Nature Publishing Group UK 2022-11-05 /pmc/articles/PMC9637135/ /pubmed/36335146 http://dx.doi.org/10.1038/s41598-022-21543-3 Text en © The Author(s) 2022 https://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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article McGrath, Hari Zaveri, Hitten P. Collins, Evan Jafar, Tamara Chishti, Omar Obaid, Sami Ksendzovsky, Alexander Wu, Kun Papademetris, Xenophon Spencer, Dennis D. High-resolution cortical parcellation based on conserved brain landmarks for localization of multimodal data to the nearest centimeter |
title | High-resolution cortical parcellation based on conserved brain landmarks for localization of multimodal data to the nearest centimeter |
title_full | High-resolution cortical parcellation based on conserved brain landmarks for localization of multimodal data to the nearest centimeter |
title_fullStr | High-resolution cortical parcellation based on conserved brain landmarks for localization of multimodal data to the nearest centimeter |
title_full_unstemmed | High-resolution cortical parcellation based on conserved brain landmarks for localization of multimodal data to the nearest centimeter |
title_short | High-resolution cortical parcellation based on conserved brain landmarks for localization of multimodal data to the nearest centimeter |
title_sort | high-resolution cortical parcellation based on conserved brain landmarks for localization of multimodal data to the nearest centimeter |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9637135/ https://www.ncbi.nlm.nih.gov/pubmed/36335146 http://dx.doi.org/10.1038/s41598-022-21543-3 |
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