Characterizing complex networks using entropy-degree diagrams: unveiling changes in functional brain connectivity induced by ayahuasca
Autor(a) principal: | |
---|---|
Data de Publicação: | 2019 |
Outros Autores: | , , , , |
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_ |
1814832833563197440 |