Scaling in topological properties of brain networks

The organization in brain networks shows highly modular features with weak inter-modular interaction. The topology of the networks involves emergence of modules and sub-modules at different levels of constitution governed by fractal laws that are signatures of self-organization in complex networks....

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Main Authors:	Singh, Soibam Shyamchand, Khundrakpam, Budhachandra, Reid, Andrew T., Lewis, John D., Evans, Alan C., Ishrat, Romana, Sharma, B. Indrajit, Singh, R. K. Brojen
Format:	Online
Language:	English
Published:	Nature Publishing Group 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4845066/

id	pubmed-4845066
recordtype	oai_dc
spelling	pubmed-48450662016-04-29 Scaling in topological properties of brain networks Singh, Soibam Shyamchand Khundrakpam, Budhachandra Reid, Andrew T. Lewis, John D. Evans, Alan C. Ishrat, Romana Sharma, B. Indrajit Singh, R. K. Brojen Article The organization in brain networks shows highly modular features with weak inter-modular interaction. The topology of the networks involves emergence of modules and sub-modules at different levels of constitution governed by fractal laws that are signatures of self-organization in complex networks. The modular organization, in terms of modular mass, inter-modular, and intra-modular interaction, also obeys fractal nature. The parameters which characterize topological properties of brain networks follow one parameter scaling theory in all levels of network structure, which reveals the self-similar rules governing the network structure. Further, the calculated fractal dimensions of brain networks of different species are found to decrease when one goes from lower to higher level species which implicates the more ordered and self-organized topography at higher level species. The sparsely distributed hubs in brain networks may be most influencing nodes but their absence may not cause network breakdown, and centrality parameters characterizing them also follow one parameter scaling law indicating self-similar roles of these hubs at different levels of organization in brain networks. The local-community-paradigm decomposition plot and calculated local-community-paradigm-correlation co-efficient of brain networks also shows the evidence for self-organization in these networks. Nature Publishing Group 2016-04-26 /pmc/articles/PMC4845066/ /pubmed/27112129 http://dx.doi.org/10.1038/srep24926 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Singh, Soibam Shyamchand Khundrakpam, Budhachandra Reid, Andrew T. Lewis, John D. Evans, Alan C. Ishrat, Romana Sharma, B. Indrajit Singh, R. K. Brojen
spellingShingle	Singh, Soibam Shyamchand Khundrakpam, Budhachandra Reid, Andrew T. Lewis, John D. Evans, Alan C. Ishrat, Romana Sharma, B. Indrajit Singh, R. K. Brojen Scaling in topological properties of brain networks
author_facet	Singh, Soibam Shyamchand Khundrakpam, Budhachandra Reid, Andrew T. Lewis, John D. Evans, Alan C. Ishrat, Romana Sharma, B. Indrajit Singh, R. K. Brojen
author_sort	Singh, Soibam Shyamchand
title	Scaling in topological properties of brain networks
title_short	Scaling in topological properties of brain networks
title_full	Scaling in topological properties of brain networks
title_fullStr	Scaling in topological properties of brain networks
title_full_unstemmed	Scaling in topological properties of brain networks
title_sort	scaling in topological properties of brain networks
description	The organization in brain networks shows highly modular features with weak inter-modular interaction. The topology of the networks involves emergence of modules and sub-modules at different levels of constitution governed by fractal laws that are signatures of self-organization in complex networks. The modular organization, in terms of modular mass, inter-modular, and intra-modular interaction, also obeys fractal nature. The parameters which characterize topological properties of brain networks follow one parameter scaling theory in all levels of network structure, which reveals the self-similar rules governing the network structure. Further, the calculated fractal dimensions of brain networks of different species are found to decrease when one goes from lower to higher level species which implicates the more ordered and self-organized topography at higher level species. The sparsely distributed hubs in brain networks may be most influencing nodes but their absence may not cause network breakdown, and centrality parameters characterizing them also follow one parameter scaling law indicating self-similar roles of these hubs at different levels of organization in brain networks. The local-community-paradigm decomposition plot and calculated local-community-paradigm-correlation co-efficient of brain networks also shows the evidence for self-organization in these networks.
publisher	Nature Publishing Group
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4845066/
_version_	1613570964373635072

Scaling in topological properties of brain networks

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