Reference : Granger causality analysis of steady-state electroencephalographic signals during propof...
Scientific journals : Article
Engineering, computing & technology : Multidisciplinary, general & others
Human health sciences : Anesthesia & intensive care
http://hdl.handle.net/2268/112254
Granger causality analysis of steady-state electroencephalographic signals during propofol-induced anaesthesia.
English
Barrett, Adam B. [University of Sussex (United Kingdom) > Department of Informatics > Sackler Centre for Consciousness Science >]
Murphy, Michael [University of Wisconsin-Madison > Department of Psychiatry >]
Bruno, Marie-Aurélie mailto [Université de Liège - ULg > > Centre de recherches du cyclotron >]
Noirhomme, Quentin mailto [Université de Liège - ULg > > Centre de recherches du cyclotron >]
Boly, Mélanie mailto [Université de Liège - ULg > Département des sciences cliniques > Neurologie >]
Laureys, Steven mailto [Université de Liège - ULg > > Centre de recherches du cyclotron >]
Seth, Anil K. [> >]
2012
PLoS ONE
Public Library of Science
7
1
e29072
Yes (verified by ORBi)
International
1932-6203
San Franscisco
CA
[en] Anesthesia ; Computer Simulation ; Consciousness/drug effects ; Cortical Synchronization/drug effects ; Electroencephalography/instrumentation/methods ; Gyrus Cinguli/drug effects/physiology ; Humans ; Propofol/administration & dosage/pharmacology ; Signal Processing, Computer-Assisted ; Statistics as Topic ; Wakefulness/drug effects
[en] 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.
AKS and ABB are supported by Engineering and Physical Sciences Research Council Leadership Fellowship EP/G007543/1. Support is also gratefully acknowledged from the Dr. Mortimer and Theresa Sackler Foundation. SL is a Belgian Funds for Scientific Research (FRS) Senior Research Associate and MAB, QN and MB are FRS Postdoctoral Researchers. This work was also supported by the European Commission (DECODER), Fondazione Europea di Ricerca Biomedica, McDonnell Foundation, Mind Science Foundation, Public Utility Foundation “Université Européenne du Travail” and the University of Liège. Possible inaccuracies of information are the responsibility of the project team. The text reflects solely the views of its authors. The European Commission is not liable for any use that may be made of the information contained herein. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
http://hdl.handle.net/2268/112254
also: http://hdl.handle.net/2268/138141
10.1371/journal.pone.0029072
http://dx.plos.org/10.1371/journal.pone.0029072.
Copyright: © 2012 Barrett et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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