Functional brain networks: random, "small world" or deterministic?

Lately the problem of connectivity in brain networks is being approached frequently by graph theoretical analysis. In several publications based on bivariate estimators of relations between EEG channels authors reported random or "small world" structure of networks. The results of these wo...

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Autores principales:	Katarzyna J Blinowska, Maciej Kaminski
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/eae49ccc510143899ecdaf406d7d4859
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spelling	oai:doaj.org-article:eae49ccc510143899ecdaf406d7d48592021-11-18T08:48:52ZFunctional brain networks: random, "small world" or deterministic?1932-620310.1371/journal.pone.0078763https://doaj.org/article/eae49ccc510143899ecdaf406d7d48592013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24205313/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Lately the problem of connectivity in brain networks is being approached frequently by graph theoretical analysis. In several publications based on bivariate estimators of relations between EEG channels authors reported random or "small world" structure of networks. The results of these works often have no relation to other evidence based on imaging, inverse solutions methods, physiological and anatomical data. Herein we try to find reasons for this discrepancy. We point out that EEG signals are very much interdependent, thus bivariate measures applied to them may produce many spurious connections. In fact, they may outnumber the true connections. Giving all connections equal weights, as it is usual in the framework of graph theoretical analysis, further enhances these spurious links. In effect, close to random and disorganized patterns of connections emerge. On the other hand, multivariate connectivity estimators, which are free of the artificial links, show specific, well determined patterns, which are in a very good agreement with other evidence. The modular structure of brain networks may be identified by multivariate estimators based on Granger causality and formalism of assortative mixing. In this way, the strength of coupling may be evaluated quantitatively. During working memory task, by means of multivariate Directed Transfer Function, it was demonstrated that the modules characterized by strong internal bonds exchange the information by weaker connections.Katarzyna J BlinowskaMaciej KaminskiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 10, p e78763 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Katarzyna J Blinowska Maciej Kaminski Functional brain networks: random, "small world" or deterministic?
description	Lately the problem of connectivity in brain networks is being approached frequently by graph theoretical analysis. In several publications based on bivariate estimators of relations between EEG channels authors reported random or "small world" structure of networks. The results of these works often have no relation to other evidence based on imaging, inverse solutions methods, physiological and anatomical data. Herein we try to find reasons for this discrepancy. We point out that EEG signals are very much interdependent, thus bivariate measures applied to them may produce many spurious connections. In fact, they may outnumber the true connections. Giving all connections equal weights, as it is usual in the framework of graph theoretical analysis, further enhances these spurious links. In effect, close to random and disorganized patterns of connections emerge. On the other hand, multivariate connectivity estimators, which are free of the artificial links, show specific, well determined patterns, which are in a very good agreement with other evidence. The modular structure of brain networks may be identified by multivariate estimators based on Granger causality and formalism of assortative mixing. In this way, the strength of coupling may be evaluated quantitatively. During working memory task, by means of multivariate Directed Transfer Function, it was demonstrated that the modules characterized by strong internal bonds exchange the information by weaker connections.
format	article
author	Katarzyna J Blinowska Maciej Kaminski
author_facet	Katarzyna J Blinowska Maciej Kaminski
author_sort	Katarzyna J Blinowska
title	Functional brain networks: random, "small world" or deterministic?
title_short	Functional brain networks: random, "small world" or deterministic?
title_full	Functional brain networks: random, "small world" or deterministic?
title_fullStr	Functional brain networks: random, "small world" or deterministic?
title_full_unstemmed	Functional brain networks: random, "small world" or deterministic?
title_sort	functional brain networks: random, "small world" or deterministic?
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/eae49ccc510143899ecdaf406d7d4859
work_keys_str_mv	AT katarzynajblinowska functionalbrainnetworksrandomsmallworldordeterministic AT maciejkaminski functionalbrainnetworksrandomsmallworldordeterministic
_version_	1718421296199499776

Functional brain networks: random, "small world" or deterministic?

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