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A multi-scale probabilistic atlas of the human connectome

The human brain is a complex system that can be efficiently represented as a network of structural connectivity. Many imaging studies would benefit from such network information, which is not always available. In this work, we present a whole-brain multi-scale structural connectome atlas. This tool...

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Autores principales: Alemán-Gómez, Yasser, Griffa, Alessandra, Houde, Jean-Christophe, Najdenovska, Elena, Magon, Stefano, Cuadra, Meritxell Bach, Descoteaux, Maxime, Hagmann, Patric
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399115/
https://www.ncbi.nlm.nih.gov/pubmed/35999243
http://dx.doi.org/10.1038/s41597-022-01624-8
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author Alemán-Gómez, Yasser
Griffa, Alessandra
Houde, Jean-Christophe
Najdenovska, Elena
Magon, Stefano
Cuadra, Meritxell Bach
Descoteaux, Maxime
Hagmann, Patric
author_facet Alemán-Gómez, Yasser
Griffa, Alessandra
Houde, Jean-Christophe
Najdenovska, Elena
Magon, Stefano
Cuadra, Meritxell Bach
Descoteaux, Maxime
Hagmann, Patric
author_sort Alemán-Gómez, Yasser
collection PubMed
description The human brain is a complex system that can be efficiently represented as a network of structural connectivity. Many imaging studies would benefit from such network information, which is not always available. In this work, we present a whole-brain multi-scale structural connectome atlas. This tool has been derived from a cohort of 66 healthy subjects imaged with optimal technology in the setting of the Human Connectome Project. From these data we created, using extensively validated diffusion-data processing, tractography and gray-matter parcellation tools, a multi-scale probabilistic atlas of the human connectome. In addition, we provide user-friendly and accessible code to match this atlas to individual brain imaging data to extract connection-specific quantitative information. This can be used to associate individual imaging findings, such as focal white-matter lesions or regional alterations, to specific connections and brain circuits. Accordingly, network-level consequences of regional changes can be analyzed even in absence of diffusion and tractography data. This method is expected to broaden the accessibility and lower the yield for connectome research.
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spelling pubmed-93991152022-08-25 A multi-scale probabilistic atlas of the human connectome Alemán-Gómez, Yasser Griffa, Alessandra Houde, Jean-Christophe Najdenovska, Elena Magon, Stefano Cuadra, Meritxell Bach Descoteaux, Maxime Hagmann, Patric Sci Data Data Descriptor The human brain is a complex system that can be efficiently represented as a network of structural connectivity. Many imaging studies would benefit from such network information, which is not always available. In this work, we present a whole-brain multi-scale structural connectome atlas. This tool has been derived from a cohort of 66 healthy subjects imaged with optimal technology in the setting of the Human Connectome Project. From these data we created, using extensively validated diffusion-data processing, tractography and gray-matter parcellation tools, a multi-scale probabilistic atlas of the human connectome. In addition, we provide user-friendly and accessible code to match this atlas to individual brain imaging data to extract connection-specific quantitative information. This can be used to associate individual imaging findings, such as focal white-matter lesions or regional alterations, to specific connections and brain circuits. Accordingly, network-level consequences of regional changes can be analyzed even in absence of diffusion and tractography data. This method is expected to broaden the accessibility and lower the yield for connectome research. Nature Publishing Group UK 2022-08-23 /pmc/articles/PMC9399115/ /pubmed/35999243 http://dx.doi.org/10.1038/s41597-022-01624-8 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 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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Alemán-Gómez, Yasser
Griffa, Alessandra
Houde, Jean-Christophe
Najdenovska, Elena
Magon, Stefano
Cuadra, Meritxell Bach
Descoteaux, Maxime
Hagmann, Patric
A multi-scale probabilistic atlas of the human connectome
title A multi-scale probabilistic atlas of the human connectome
title_full A multi-scale probabilistic atlas of the human connectome
title_fullStr A multi-scale probabilistic atlas of the human connectome
title_full_unstemmed A multi-scale probabilistic atlas of the human connectome
title_short A multi-scale probabilistic atlas of the human connectome
title_sort multi-scale probabilistic atlas of the human connectome
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399115/
https://www.ncbi.nlm.nih.gov/pubmed/35999243
http://dx.doi.org/10.1038/s41597-022-01624-8
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