Characterizing complex networks using entropy-degree diagrams: unveiling changes in functional brain connectivity induced by Ayahuasca

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dc.contributor.author Viol, Aline
dc.contributor.author Palhano-Fontes, Fernanda
dc.contributor.author Onias, Heloisa
dc.contributor.author de Araujo, Draulio B.
dc.contributor.author Hövel, Philipp
dc.contributor.author Viswanathan, Gandhi M.
dc.date.accessioned 2020-04-28T11:46:09Z
dc.date.available 2020-04-28T11:46:09Z
dc.date.issued 2019-01-30
dc.identifier.citation Viol, A., Palhano-Fontes, F., Onias, H., de Araujo, D. B., Hövell, P. and Viswanathan, G. M. (2019) 'Characterizing Complex Networks Using Entropy-Degree Diagrams: Unveiling Changes in Functional Brain Connectivity Induced by Ayahuasca', Entropy, 21, 128 (12 pp). doi: 10.3390/e21020128 en
dc.identifier.volume 21 en
dc.identifier.issued 2 en
dc.identifier.startpage 1 en
dc.identifier.endpage 12 en
dc.identifier.issn 1099-4300
dc.identifier.uri http://hdl.handle.net/10468/9882
dc.identifier.doi 10.3390/e21020128 en
dc.description.abstract With the aim of further advancing the understanding of the human brain’s functional connectivity, we propose a network metric which we term the geodesic entropy. This metric quantifies the Shannon entropy of the distance distribution to a specific node from all other nodes. It allows to characterize the influence exerted on a specific node considering statistics of the overall network structure. The measurement and characterization of this structural information has the potential to greatly improve our understanding of sustained activity and other emergent behaviors in networks. We apply this method to study how the psychedelic infusion Ayahuasca affects the functional connectivity of the human brain in resting state. We show that the geodesic entropy is able to differentiate functional networks of the human brain associated with two different states of consciousness in the awaking resting state: (i) the ordinary state and (ii) a state altered by ingestion of the Ayahuasca. The functional brain networks from subjects in the altered state have, on average, a larger geodesic entropy compared to the ordinary state. Finally, we discuss why the geodesic entropy may bring even further valuable insights into the study of the human brain and other empirical networks. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher MDPI en
dc.rights © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). en
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ en
dc.subject Entropy en
dc.subject Functional brain networks en
dc.subject Psychedelic state en
dc.subject Ayahuasca en
dc.subject Complex networks en
dc.title Characterizing complex networks using entropy-degree diagrams: unveiling changes in functional brain connectivity induced by Ayahuasca en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Philipp Hövel, School Of Mathematical Sciences, University College Cork, Cork, Ireland. +353-21-490-3000 Email: philipp.hoevel@ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2020-04-28T07:58:36Z
dc.description.version Accepted Version en
dc.internal.rssid 499774844
dc.contributor.funder Deutsche Forschungsgemeinschaft en
dc.contributor.funder Coordenação de Aperfeiçoamento de Pessoal de Nível Superior en
dc.contributor.funder Conselho Nacional de Desenvolvimento Científico e Tecnológico en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Entropy en
dc.internal.copyrightchecked No
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress philipp.hoevel@ucc.ie en
dc.identifier.articleid 128 en


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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Except where otherwise noted, this item's license is described as © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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