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&...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22242156/pdf/?tool=EBI |
| _version_ | 1818722711705223168 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-17T20:58:58Z |
| format | Article |
| id | doaj.art-1ecebe5211e447348565762588e7f720 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-17T20:58:58Z |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-1ecebe5211e447348565762588e7f7202022-12-21T21:32:46ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-0171e2907210.1371/journal.pone.0029072Granger causality analysis of steady-state electroencephalographic signals during propofol-induced anaesthesia.Adam B BarrettMichael MurphyMarie-Aurélie BrunoQuentin NoirhommeMélanie BolySteven LaureysAnil K SethChanges 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.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22242156/pdf/?tool=EBI |
| spellingShingle | 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. PLoS ONE |
| title | 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_short | 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 |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22242156/pdf/?tool=EBI |
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