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A predictive model of the cat cortical connectome based on cytoarchitecture and distance
Information processing in the brain is strongly constrained by anatomical connectivity. However, the principles governing the organization of corticocortical connections remain elusive. Here, we tested three models of relationships between the organization of cortical structure and features of conne...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4575693/ https://www.ncbi.nlm.nih.gov/pubmed/25062666 http://dx.doi.org/10.1007/s00429-014-0849-y |
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author | Beul, Sarah F. Grant, Simon Hilgetag, Claus C. |
author_facet | Beul, Sarah F. Grant, Simon Hilgetag, Claus C. |
author_sort | Beul, Sarah F. |
collection | PubMed |
description | Information processing in the brain is strongly constrained by anatomical connectivity. However, the principles governing the organization of corticocortical connections remain elusive. Here, we tested three models of relationships between the organization of cortical structure and features of connections linking 49 areas of the cat cerebral cortex. Factors taken into account were relative cytoarchitectonic differentiation (‘structural model’), relative spatial position (‘distance model’), or relative hierarchical position (‘hierarchical model’) of the areas. Cytoarchitectonic differentiation and spatial distance (themselves uncorrelated) correlated strongly with the existence of inter-areal connections, whereas no correlation was found with relative hierarchical position. Moreover, a strong correlation was observed between patterns of laminar projection origin or termination and cytoarchitectonic differentiation. Additionally, cytoarchitectonic differentiation correlated with the absolute number of corticocortical connections formed by areas, and varied characteristically between different cortical subnetworks, including a ‘rich-club’ module of hub areas. Thus, connections between areas of the cat cerebral cortex can, to a large part, be explained by the two independent factors of relative cytoarchitectonic differentiation and spatial distance of brain regions. As both the structural and distance model were originally formulated in the macaque monkey, their applicability in another mammalian species suggests a general principle of global cortical organization. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00429-014-0849-y) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4575693 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-45756932015-09-24 A predictive model of the cat cortical connectome based on cytoarchitecture and distance Beul, Sarah F. Grant, Simon Hilgetag, Claus C. Brain Struct Funct Original Article Information processing in the brain is strongly constrained by anatomical connectivity. However, the principles governing the organization of corticocortical connections remain elusive. Here, we tested three models of relationships between the organization of cortical structure and features of connections linking 49 areas of the cat cerebral cortex. Factors taken into account were relative cytoarchitectonic differentiation (‘structural model’), relative spatial position (‘distance model’), or relative hierarchical position (‘hierarchical model’) of the areas. Cytoarchitectonic differentiation and spatial distance (themselves uncorrelated) correlated strongly with the existence of inter-areal connections, whereas no correlation was found with relative hierarchical position. Moreover, a strong correlation was observed between patterns of laminar projection origin or termination and cytoarchitectonic differentiation. Additionally, cytoarchitectonic differentiation correlated with the absolute number of corticocortical connections formed by areas, and varied characteristically between different cortical subnetworks, including a ‘rich-club’ module of hub areas. Thus, connections between areas of the cat cerebral cortex can, to a large part, be explained by the two independent factors of relative cytoarchitectonic differentiation and spatial distance of brain regions. As both the structural and distance model were originally formulated in the macaque monkey, their applicability in another mammalian species suggests a general principle of global cortical organization. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00429-014-0849-y) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2014-07-26 2015 /pmc/articles/PMC4575693/ /pubmed/25062666 http://dx.doi.org/10.1007/s00429-014-0849-y Text en © The Author(s) 2014 https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited. |
spellingShingle | Original Article Beul, Sarah F. Grant, Simon Hilgetag, Claus C. A predictive model of the cat cortical connectome based on cytoarchitecture and distance |
title | A predictive model of the cat cortical connectome based on cytoarchitecture and distance |
title_full | A predictive model of the cat cortical connectome based on cytoarchitecture and distance |
title_fullStr | A predictive model of the cat cortical connectome based on cytoarchitecture and distance |
title_full_unstemmed | A predictive model of the cat cortical connectome based on cytoarchitecture and distance |
title_short | A predictive model of the cat cortical connectome based on cytoarchitecture and distance |
title_sort | predictive model of the cat cortical connectome based on cytoarchitecture and distance |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4575693/ https://www.ncbi.nlm.nih.gov/pubmed/25062666 http://dx.doi.org/10.1007/s00429-014-0849-y |
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