Cargando…
Dependence of connectivity on geometric distance in brain networks
In any network, the dependence of connectivity on physical distance between nodes is a direct consequence of trade-off mechanisms between costs of establishing and sustaining links, processing rates, propagation speed of signals between nodes. Despite its universality, there are still few studies ad...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6746748/ https://www.ncbi.nlm.nih.gov/pubmed/31527782 http://dx.doi.org/10.1038/s41598-019-50106-2 |
_version_ | 1783451743019859968 |
---|---|
author | Perinelli, Alessio Tabarelli, Davide Miniussi, Carlo Ricci, Leonardo |
author_facet | Perinelli, Alessio Tabarelli, Davide Miniussi, Carlo Ricci, Leonardo |
author_sort | Perinelli, Alessio |
collection | PubMed |
description | In any network, the dependence of connectivity on physical distance between nodes is a direct consequence of trade-off mechanisms between costs of establishing and sustaining links, processing rates, propagation speed of signals between nodes. Despite its universality, there are still few studies addressing this issue. Here we apply a recently–developed method to infer links between nodes, and possibly subnetwork structures, to determine connectivity strength as a function of physical distance between nodes. The model system we investigate is brain activity reconstructed on the cortex out of magnetoencephalography recordings sampled on a set of healthy subjects in resting state. We found that the dependence of the time scale of observability of a link on its geometric length follows a power–law characterized by an exponent whose extent is inversely proportional to connectivity. Our method provides a new tool to highlight and investigate networks in neuroscience. |
format | Online Article Text |
id | pubmed-6746748 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-67467482019-09-27 Dependence of connectivity on geometric distance in brain networks Perinelli, Alessio Tabarelli, Davide Miniussi, Carlo Ricci, Leonardo Sci Rep Article In any network, the dependence of connectivity on physical distance between nodes is a direct consequence of trade-off mechanisms between costs of establishing and sustaining links, processing rates, propagation speed of signals between nodes. Despite its universality, there are still few studies addressing this issue. Here we apply a recently–developed method to infer links between nodes, and possibly subnetwork structures, to determine connectivity strength as a function of physical distance between nodes. The model system we investigate is brain activity reconstructed on the cortex out of magnetoencephalography recordings sampled on a set of healthy subjects in resting state. We found that the dependence of the time scale of observability of a link on its geometric length follows a power–law characterized by an exponent whose extent is inversely proportional to connectivity. Our method provides a new tool to highlight and investigate networks in neuroscience. Nature Publishing Group UK 2019-09-16 /pmc/articles/PMC6746748/ /pubmed/31527782 http://dx.doi.org/10.1038/s41598-019-50106-2 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/. |
spellingShingle | Article Perinelli, Alessio Tabarelli, Davide Miniussi, Carlo Ricci, Leonardo Dependence of connectivity on geometric distance in brain networks |
title | Dependence of connectivity on geometric distance in brain networks |
title_full | Dependence of connectivity on geometric distance in brain networks |
title_fullStr | Dependence of connectivity on geometric distance in brain networks |
title_full_unstemmed | Dependence of connectivity on geometric distance in brain networks |
title_short | Dependence of connectivity on geometric distance in brain networks |
title_sort | dependence of connectivity on geometric distance in brain networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6746748/ https://www.ncbi.nlm.nih.gov/pubmed/31527782 http://dx.doi.org/10.1038/s41598-019-50106-2 |
work_keys_str_mv | AT perinellialessio dependenceofconnectivityongeometricdistanceinbrainnetworks AT tabarellidavide dependenceofconnectivityongeometricdistanceinbrainnetworks AT miniussicarlo dependenceofconnectivityongeometricdistanceinbrainnetworks AT riccileonardo dependenceofconnectivityongeometricdistanceinbrainnetworks |