Granger causality analysis of steady-state electroencephalographic signals during propofol-induced anaesthesia.

Changes in conscious level have been associated with changes in dynamical integration and segregation among distributed brain regions. Recent theoretical developments emphasize changes in directed functional (i.e., causal) connectivity as reflected in quantities such as 'integrated information&...

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Autores principales:	Adam B Barrett, Michael Murphy, Marie-Aurélie Bruno, Quentin Noirhomme, Mélanie Boly, Steven Laureys, Anil K Seth
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1ecebe5211e447348565762588e7f720
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spelling	oai:doaj.org-article:1ecebe5211e447348565762588e7f7202021-11-18T07:30:53ZGranger causality analysis of steady-state electroencephalographic signals during propofol-induced anaesthesia.1932-620310.1371/journal.pone.0029072https://doaj.org/article/1ecebe5211e447348565762588e7f7202012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22242156/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Changes in conscious level have been associated with changes in dynamical integration and segregation among distributed brain regions. Recent theoretical developments emphasize changes in directed functional (i.e., causal) connectivity as reflected in quantities such as 'integrated information' and 'causal density'. Here we develop and illustrate a rigorous methodology for assessing causal connectivity from electroencephalographic (EEG) signals using Granger causality (GC). Our method addresses the challenges of non-stationarity and bias by dividing data into short segments and applying permutation analysis. We apply the method to EEG data obtained from subjects undergoing propofol-induced anaesthesia, with signals source-localized to the anterior and posterior cingulate cortices. We found significant increases in bidirectional GC in most subjects during loss-of-consciousness, especially in the beta and gamma frequency ranges. Corroborating a previous analysis we also found increases in synchrony in these ranges; importantly, the Granger causality analysis showed higher inter-subject consistency than the synchrony analysis. Finally, we validate our method using simulated data generated from a model for which GC values can be analytically derived. In summary, our findings advance the methodology of Granger causality analysis of EEG data and carry implications for integrated information and causal density theories of consciousness.Adam B BarrettMichael MurphyMarie-Aurélie BrunoQuentin NoirhommeMélanie BolySteven LaureysAnil K SethPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 1, p e29072 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Adam B Barrett Michael Murphy Marie-Aurélie Bruno Quentin Noirhomme Mélanie Boly Steven Laureys Anil K Seth Granger causality analysis of steady-state electroencephalographic signals during propofol-induced anaesthesia.
description	Changes in conscious level have been associated with changes in dynamical integration and segregation among distributed brain regions. Recent theoretical developments emphasize changes in directed functional (i.e., causal) connectivity as reflected in quantities such as 'integrated information' and 'causal density'. Here we develop and illustrate a rigorous methodology for assessing causal connectivity from electroencephalographic (EEG) signals using Granger causality (GC). Our method addresses the challenges of non-stationarity and bias by dividing data into short segments and applying permutation analysis. We apply the method to EEG data obtained from subjects undergoing propofol-induced anaesthesia, with signals source-localized to the anterior and posterior cingulate cortices. We found significant increases in bidirectional GC in most subjects during loss-of-consciousness, especially in the beta and gamma frequency ranges. Corroborating a previous analysis we also found increases in synchrony in these ranges; importantly, the Granger causality analysis showed higher inter-subject consistency than the synchrony analysis. Finally, we validate our method using simulated data generated from a model for which GC values can be analytically derived. In summary, our findings advance the methodology of Granger causality analysis of EEG data and carry implications for integrated information and causal density theories of consciousness.
format	article
author	Adam B Barrett Michael Murphy Marie-Aurélie Bruno Quentin Noirhomme Mélanie Boly Steven Laureys Anil K Seth
author_facet	Adam B Barrett Michael Murphy Marie-Aurélie Bruno Quentin Noirhomme Mélanie Boly Steven Laureys Anil K Seth
author_sort	Adam B Barrett
title	Granger causality analysis of steady-state electroencephalographic signals during propofol-induced anaesthesia.
title_short	Granger causality analysis of steady-state electroencephalographic signals during propofol-induced anaesthesia.
title_full	Granger causality analysis of steady-state electroencephalographic signals during propofol-induced anaesthesia.
title_fullStr	Granger causality analysis of steady-state electroencephalographic signals during propofol-induced anaesthesia.
title_full_unstemmed	Granger causality analysis of steady-state electroencephalographic signals during propofol-induced anaesthesia.
title_sort	granger causality analysis of steady-state electroencephalographic signals during propofol-induced anaesthesia.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/1ecebe5211e447348565762588e7f720
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Granger causality analysis of steady-state electroencephalographic signals during propofol-induced anaesthesia.

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