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

Detalhes bibliográficos
Autor(a) principal: Viol, Aline
Data de Publicação: 2019
Outros Autores: Palhano-Fontes, Fernanda, Onias, Heloisa, Araújo, Dráulio Barros de, Hövel, Philipp, Viswanathan, Gandhi M.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFRN
Texto Completo: https://repositorio.ufrn.br/jspui/handle/123456789/26758
Resumo: 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.
id UFRN_1a3c6f359b65604be8fcccc0f09f08d6
oai_identifier_str oai:https://repositorio.ufrn.br:123456789/26758
network_acronym_str UFRN
network_name_str Repositório Institucional da UFRN
repository_id_str
spelling Viol, AlinePalhano-Fontes, FernandaOnias, HeloisaAraújo, Dráulio Barros deHövel, PhilippViswanathan, Gandhi M.2019-03-13T17:42:33Z2019-03-13T17:42:33Z2019-01-30VIOL, A. et al. Characterizing complex networks using entropy-degree diagrams: unveiling changes in functional brain connectivity induced by ayahuasca. Entropy, v. 21, n. 2, p. 128, jan. 2019.https://repositorio.ufrn.br/jspui/handle/123456789/2675810.3390/e21020128entropyfunctional brain networkspsychedelic stateAyahuascacomplex networksCharacterizing complex networks using entropy-degree diagrams: unveiling changes in functional brain connectivity induced by ayahuascainfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleWith 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.engreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNinfo:eu-repo/semantics/openAccessTEXTDraulioAraujo_ICe_2019_Characterizing complex networks.pdf.txtDraulioAraujo_ICe_2019_Characterizing complex networks.pdf.txtExtracted texttext/plain37372https://repositorio.ufrn.br/bitstream/123456789/26758/3/DraulioAraujo_ICe_2019_Characterizing%20complex%20networks.pdf.txtdbcb7b878c0a894f704446cc0be3aa5bMD53THUMBNAILDraulioAraujo_ICe_2019_Characterizing complex networks.pdf.jpgDraulioAraujo_ICe_2019_Characterizing complex networks.pdf.jpgGenerated Thumbnailimage/jpeg1630https://repositorio.ufrn.br/bitstream/123456789/26758/4/DraulioAraujo_ICe_2019_Characterizing%20complex%20networks.pdf.jpg2a2ee0d8a66b2cf4572cf0d5e9109d42MD54ORIGINALDraulioAraujo_ICe_2019_Characterizing complex networks.pdfDraulioAraujo_ICe_2019_Characterizing complex networks.pdfDraulioAraujo_ICe_2019_Characterizing complex networksapplication/pdf991630https://repositorio.ufrn.br/bitstream/123456789/26758/1/DraulioAraujo_ICe_2019_Characterizing%20complex%20networks.pdf5971854b499dd1c09a78edf04515953fMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81484https://repositorio.ufrn.br/bitstream/123456789/26758/2/license.txte9597aa2854d128fd968be5edc8a28d9MD52123456789/267582021-07-08 15:32:28.331oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2021-07-08T18:32:28Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false
dc.title.pt_BR.fl_str_mv Characterizing complex networks using entropy-degree diagrams: unveiling changes in functional brain connectivity induced by ayahuasca
title Characterizing complex networks using entropy-degree diagrams: unveiling changes in functional brain connectivity induced by ayahuasca
spellingShingle Characterizing complex networks using entropy-degree diagrams: unveiling changes in functional brain connectivity induced by ayahuasca
Viol, Aline
entropy
functional brain networks
psychedelic state
Ayahuasca
complex networks
title_short Characterizing complex networks using entropy-degree diagrams: unveiling changes in functional brain connectivity induced by ayahuasca
title_full Characterizing complex networks using entropy-degree diagrams: unveiling changes in functional brain connectivity induced by ayahuasca
title_fullStr Characterizing complex networks using entropy-degree diagrams: unveiling changes in functional brain connectivity induced by ayahuasca
title_full_unstemmed Characterizing complex networks using entropy-degree diagrams: unveiling changes in functional brain connectivity induced by ayahuasca
title_sort Characterizing complex networks using entropy-degree diagrams: unveiling changes in functional brain connectivity induced by ayahuasca
author Viol, Aline
author_facet Viol, Aline
Palhano-Fontes, Fernanda
Onias, Heloisa
Araújo, Dráulio Barros de
Hövel, Philipp
Viswanathan, Gandhi M.
author_role author
author2 Palhano-Fontes, Fernanda
Onias, Heloisa
Araújo, Dráulio Barros de
Hövel, Philipp
Viswanathan, Gandhi M.
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Viol, Aline
Palhano-Fontes, Fernanda
Onias, Heloisa
Araújo, Dráulio Barros de
Hövel, Philipp
Viswanathan, Gandhi M.
dc.subject.por.fl_str_mv entropy
functional brain networks
psychedelic state
Ayahuasca
complex networks
topic entropy
functional brain networks
psychedelic state
Ayahuasca
complex networks
description 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.
publishDate 2019
dc.date.accessioned.fl_str_mv 2019-03-13T17:42:33Z
dc.date.available.fl_str_mv 2019-03-13T17:42:33Z
dc.date.issued.fl_str_mv 2019-01-30
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.citation.fl_str_mv VIOL, A. et al. Characterizing complex networks using entropy-degree diagrams: unveiling changes in functional brain connectivity induced by ayahuasca. Entropy, v. 21, n. 2, p. 128, jan. 2019.
dc.identifier.uri.fl_str_mv https://repositorio.ufrn.br/jspui/handle/123456789/26758
dc.identifier.doi.none.fl_str_mv 10.3390/e21020128
identifier_str_mv VIOL, A. et al. Characterizing complex networks using entropy-degree diagrams: unveiling changes in functional brain connectivity induced by ayahuasca. Entropy, v. 21, n. 2, p. 128, jan. 2019.
10.3390/e21020128
url https://repositorio.ufrn.br/jspui/handle/123456789/26758
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFRN
instname:Universidade Federal do Rio Grande do Norte (UFRN)
instacron:UFRN
instname_str Universidade Federal do Rio Grande do Norte (UFRN)
instacron_str UFRN
institution UFRN
reponame_str Repositório Institucional da UFRN
collection Repositório Institucional da UFRN
bitstream.url.fl_str_mv https://repositorio.ufrn.br/bitstream/123456789/26758/3/DraulioAraujo_ICe_2019_Characterizing%20complex%20networks.pdf.txt
https://repositorio.ufrn.br/bitstream/123456789/26758/4/DraulioAraujo_ICe_2019_Characterizing%20complex%20networks.pdf.jpg
https://repositorio.ufrn.br/bitstream/123456789/26758/1/DraulioAraujo_ICe_2019_Characterizing%20complex%20networks.pdf
https://repositorio.ufrn.br/bitstream/123456789/26758/2/license.txt
bitstream.checksum.fl_str_mv dbcb7b878c0a894f704446cc0be3aa5b
2a2ee0d8a66b2cf4572cf0d5e9109d42
5971854b499dd1c09a78edf04515953f
e9597aa2854d128fd968be5edc8a28d9
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)
repository.mail.fl_str_mv
_version_ 1802117691643789312