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A macaque connectome for large-scale network simulations in TheVirtualBrain
Models of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of b...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6637142/ https://www.ncbi.nlm.nih.gov/pubmed/31316116 http://dx.doi.org/10.1038/s41597-019-0129-z |
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author | Shen, Kelly Bezgin, Gleb Schirner, Michael Ritter, Petra Everling, Stefan McIntosh, Anthony R. |
author_facet | Shen, Kelly Bezgin, Gleb Schirner, Michael Ritter, Petra Everling, Stefan McIntosh, Anthony R. |
author_sort | Shen, Kelly |
collection | PubMed |
description | Models of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque (Macaca mulatta and Macaca fascicularis) connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data. |
format | Online Article Text |
id | pubmed-6637142 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-66371422019-07-19 A macaque connectome for large-scale network simulations in TheVirtualBrain Shen, Kelly Bezgin, Gleb Schirner, Michael Ritter, Petra Everling, Stefan McIntosh, Anthony R. Sci Data Data Descriptor Models of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque (Macaca mulatta and Macaca fascicularis) connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data. Nature Publishing Group UK 2019-07-17 /pmc/articles/PMC6637142/ /pubmed/31316116 http://dx.doi.org/10.1038/s41597-019-0129-z 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 Shen, Kelly Bezgin, Gleb Schirner, Michael Ritter, Petra Everling, Stefan McIntosh, Anthony R. A macaque connectome for large-scale network simulations in TheVirtualBrain |
title | A macaque connectome for large-scale network simulations in TheVirtualBrain |
title_full | A macaque connectome for large-scale network simulations in TheVirtualBrain |
title_fullStr | A macaque connectome for large-scale network simulations in TheVirtualBrain |
title_full_unstemmed | A macaque connectome for large-scale network simulations in TheVirtualBrain |
title_short | A macaque connectome for large-scale network simulations in TheVirtualBrain |
title_sort | macaque connectome for large-scale network simulations in thevirtualbrain |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6637142/ https://www.ncbi.nlm.nih.gov/pubmed/31316116 http://dx.doi.org/10.1038/s41597-019-0129-z |
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