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Dissecting whole-brain conduction delays through MRI microstructural measures
Network models based on structural connectivity have been increasingly used as the blueprint for large-scale simulations of the human brain. As the nodes of this network are distributed through the cortex and interconnected by white matter pathways with different characteristics, modeling the associ...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8448685/ https://www.ncbi.nlm.nih.gov/pubmed/34390416 http://dx.doi.org/10.1007/s00429-021-02358-w |
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author | Mancini, Matteo Tian, Qiyuan Fan, Qiuyun Cercignani, Mara Huang, Susie Y. |
author_facet | Mancini, Matteo Tian, Qiyuan Fan, Qiuyun Cercignani, Mara Huang, Susie Y. |
author_sort | Mancini, Matteo |
collection | PubMed |
description | Network models based on structural connectivity have been increasingly used as the blueprint for large-scale simulations of the human brain. As the nodes of this network are distributed through the cortex and interconnected by white matter pathways with different characteristics, modeling the associated conduction delays becomes important. The goal of this study is to estimate and characterize these delays directly from the brain structure. To achieve this, we leveraged microstructural measures from a combination of advanced magnetic resonance imaging acquisitions and computed the main determinants of conduction velocity, namely axonal diameter and myelin content. Using the model proposed by Rushton, we used these measures to calculate the conduction velocity and estimated the associated delays using tractography. We observed that both the axonal diameter and conduction velocity distributions presented a rather constant trend across different connection lengths, with resulting delays that scale linearly with the connection length. Relying on insights from graph theory and Kuramoto simulations, our results support the approximation of constant conduction velocity but also show path- and region-specific differences. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00429-021-02358-w. |
format | Online Article Text |
id | pubmed-8448685 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-84486852021-10-01 Dissecting whole-brain conduction delays through MRI microstructural measures Mancini, Matteo Tian, Qiyuan Fan, Qiuyun Cercignani, Mara Huang, Susie Y. Brain Struct Funct Original Article Network models based on structural connectivity have been increasingly used as the blueprint for large-scale simulations of the human brain. As the nodes of this network are distributed through the cortex and interconnected by white matter pathways with different characteristics, modeling the associated conduction delays becomes important. The goal of this study is to estimate and characterize these delays directly from the brain structure. To achieve this, we leveraged microstructural measures from a combination of advanced magnetic resonance imaging acquisitions and computed the main determinants of conduction velocity, namely axonal diameter and myelin content. Using the model proposed by Rushton, we used these measures to calculate the conduction velocity and estimated the associated delays using tractography. We observed that both the axonal diameter and conduction velocity distributions presented a rather constant trend across different connection lengths, with resulting delays that scale linearly with the connection length. Relying on insights from graph theory and Kuramoto simulations, our results support the approximation of constant conduction velocity but also show path- and region-specific differences. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00429-021-02358-w. Springer Berlin Heidelberg 2021-08-14 2021 /pmc/articles/PMC8448685/ /pubmed/34390416 http://dx.doi.org/10.1007/s00429-021-02358-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 | Original Article Mancini, Matteo Tian, Qiyuan Fan, Qiuyun Cercignani, Mara Huang, Susie Y. Dissecting whole-brain conduction delays through MRI microstructural measures |
title | Dissecting whole-brain conduction delays through MRI microstructural measures |
title_full | Dissecting whole-brain conduction delays through MRI microstructural measures |
title_fullStr | Dissecting whole-brain conduction delays through MRI microstructural measures |
title_full_unstemmed | Dissecting whole-brain conduction delays through MRI microstructural measures |
title_short | Dissecting whole-brain conduction delays through MRI microstructural measures |
title_sort | dissecting whole-brain conduction delays through mri microstructural measures |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8448685/ https://www.ncbi.nlm.nih.gov/pubmed/34390416 http://dx.doi.org/10.1007/s00429-021-02358-w |
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