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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 works often...

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Autores principales: Blinowska, Katarzyna J., Kaminski, Maciej
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3813572/
https://www.ncbi.nlm.nih.gov/pubmed/24205313
http://dx.doi.org/10.1371/journal.pone.0078763
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author Blinowska, Katarzyna J.
Kaminski, Maciej
author_facet Blinowska, Katarzyna J.
Kaminski, Maciej
author_sort Blinowska, Katarzyna J.
collection PubMed
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.
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spelling pubmed-38135722013-11-07 Functional Brain Networks: Random, “Small World” or Deterministic? Blinowska, Katarzyna J. Kaminski, Maciej PLoS One Research Article 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. Public Library of Science 2013-10-30 /pmc/articles/PMC3813572/ /pubmed/24205313 http://dx.doi.org/10.1371/journal.pone.0078763 Text en © 2013 Blinowska, Kaminski http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Blinowska, Katarzyna J.
Kaminski, Maciej
Functional Brain Networks: Random, “Small World” or Deterministic?
title 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_short Functional Brain Networks: Random, “Small World” or Deterministic?
title_sort functional brain networks: random, “small world” or deterministic?
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3813572/
https://www.ncbi.nlm.nih.gov/pubmed/24205313
http://dx.doi.org/10.1371/journal.pone.0078763
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