[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.
Disciplines :
Anesthesia & intensive care Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
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 ; Université de Liège - ULiège > Centre de recherches du cyclotron
Noirhomme, Quentin ; Université de Liège - ULiège > Centre de recherches du cyclotron
Boly, Mélanie ; Université de Liège - ULiège > Département des sciences cliniques > Neurologie
Laureys, Steven ; Université de Liège - ULiège > Centre de recherches du cyclotron
Seth, Anil K.
Language :
English
Title :
Granger causality analysis of steady-state electroencephalographic signals during propofol-induced anaesthesia.
Publication date :
2012
Journal title :
PLoS ONE
eISSN :
1932-6203
Publisher :
Public Library of Science, San Franscisco, United States - California
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.
Myles PS, Leslie K, McNeil J, Forbes A, Chan MTV, (2004) Bispectral index monitoring to prevent awareness during anaesthesia: The B-Aware randomised controlled trial. Lancet 363: 1757-1763.
Tononi G, (2008) Consciousness as integrated information: A provisional manifesto. Biol Bull 215 (3): 216-42.
Seth AK, Barrett AB, Barnett L, (2011) Causal density and integrated information as measures of conscious level. Phil Trans R Soc A Math Phys Eng Sci 369: 3748-3767.
Wiener N, (1956) The theory of prediction. In: Beckenbach E, editors. Modern mathematics for engineers New York McGraw-Hill.
Granger C, (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37: 424-438.
Ding M, Chen Y, Bressler S, (2006) Granger causality: Basic theory and application to neuroscience. In: Schelter S, Winterhalder M, Timmer J, editors. Handbook of Time Series Analysis Wienheim Wiley pp. 438-460.
Bressler SL, Seth AK, (2011) Wiener-Granger causality: A well established methodology. Neuroimage 58: 323-329.
Seth AK, (2010) A MATLAB toolbox for Granger causal connectivity analysis. Neurosci Meth 186: 262-273.
Barnett L, Seth AK, (2011) Behaviour of Granger causality under filtering: Theoretical invariance and practical application. J Neurosci Meth 201: 404-419.
Murphy M, Bruno MA, Riedner B, Boveroux P, Noirhomme Q, et al. (2011) Propofol anesthesia and sleep: A high-density EEG study. Sleep 34 (3): 283-291.
Ramsay MA, Savege TM, Simpson BR, Goodwin R, (1974) Controlled sedation with alphaxalonealphadolone. Br Med J 2: 656-9.
Shafer A, (1995) Metaphor and anesthesia. Anesthesiology 83: 1331-42.
Brovelli A, Ding M, Ledberg A, Chen Y, Nakamura R, et al. (2004) Beta oscillations in a largescale sensorimotor cortical network: Directional influences revealed by Granger causality. Proc Natl Acad Sci U S A 101: 9849-9854.
Kay S, (1988) Modern Spectral Estimation: Theory and Application Englewood Cliffs, NJ Prentice-Hall.
Geweke J, (1982) Measurement of linear dependence and feedback between multiple time series. J Am Statist Assoc 77: 304-13.
Geweke J, (1984) Measures of conditional linear dependence and feedback between time series. J Am Statist Assoc 79: 907-915.
Palais R, (2001) Pi is wrong! The Mathematical Intelligencer 23 (3): 7-8.
Bernasconi C, Konig P, (1999) On the directionality of cortical interactions studied by structural analysis of electrophysiological recordings. Biol Cybern 81: 199-210.
Hesse W, Möller E, Arnold M, Schack B, (2003) The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies. J Neurosci Meth 124: 27-44.
Pardey J, Roberts S, Tarassenko L, Stradling J, (1996) A new approach to the analysis of the human sleep/wakefulness continuum. J Sleep Res 5: 201210.
Olbrich E, Achermann P, Meier P, (2003) Dynamics of human sleep EEG. Neurocomputing 52-54: 857-862.
Akaike H, (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19: 716-723.
Schwartz G, (1978) Estimating the dimension of a model. Ann Statist 5: 461-464.
Durbin J, Watson GS, (1950) Testing for serial correlation in least squares regression I. Biometrika 37: 409-428.
Ding M, Bressler S, Yang W, Liang H, (2000) Short-window spectral analysis of cortical eventrelated potentials by adaptive multivariate autoregressive modeling: data prepocessing, model validation, and variability assessment. Biol Cybern 83: 35-45.
Uhlhaas PJ, Pipa G, Lima B, Melloni L, Neuenschwander S, et al. (2009) Neural synchrony in cortical networks: History, concept and current status. Front Integr Neurosci 3: 17.
Pockett S, Bold GEJ, Freeman WJ, (2009) EEG synchrony during a perceptual-cognitive task: Widespread phase synchrony at all frequencies. Clinical Neurophysiology 120: 695-708.
Bressler SL, Menon V, (2010) Large-scale brain networks in cognition: Emerging methods and principles. Trends Cogn Sci 14: 277-290.
Friston KJ, (2011) Functional and effective connectivity: A review. Brain Connectivity 1: 13-36.
Barnett L, Barrett AB, Seth AK, (2009) Granger causality and transfer entropy are equivalent for Gaussian variables. Phys Rev Lett 103: 238701.
Barrett AB, Barnett L, Seth AK, (2010) Multivariate Granger causality and generalized variance. Phys Rev E 81: 041907.
Alkire MT, Haier RJ, Barker SJ, Shah NK, Wu JC, et al. (1995) Cerebral metabolism during propofol anesthesia in humans studied with positron emission tomography. Anesthesiology 82: 393-403.
Laureys S, Goldman S, Phillips C, Van Bogaert P, Aerts J, et al. (1999) Impaired effective cortical connectivity in vegetative state: Preliminary investigation using PET. Neuroimage 9: 377-382.
Mhuircheartaigh RN, Rosenorn-Lanng D, Wise R, Jbabdi S, Rogers R, et al. (2010) Cortical and subcortical connectivity changes during decreasing levels of consciousness in humans: a functional magnetic resonance imaging study using propofol. J Neurosci 30: 9095-9102.
Stamatakis EA, Adapa RM, Absalom AR, Menon DK, (2010) Changes in resting neural connectivity during propofol sedation. PLoS One 5: e14224.
Schrouff J, Perlbarg V, Boly M, Marrelec G, Boveroux P, et al. (2011) Brain functional integration decreases during propofol-induced loss of consciousness. Neuroimage 57: 198-205.
Boveroux P, Vanhaudenhuyse A, Bruno MA, Noirhomme Q, Lauwick S, et al. (2010) Breakdown of within- and between-network resting state functional magnetic resonance imaging connectivity during propofol-induced loss of consciousness. Anesthesiology 113: 1038-1053.
Lee U, Müller M, Noh GJ, Choi B, Mashour GA, (2011) Dissociable network properties of anesthetic state transitions. Anesthesiology 114: 872-881.
Lee U, Kim S, Noh GJ, Choi BM, Hwang E, et al. (2009) The directionality and functional organization of frontoparietal connectivity during consciousness and anesthesia in humans. Conscious Cogn 18: 1069-1078.
Breshears JD, Roland JL, Sharma M, Gaona CM, Freudenburg ZV, et al. (2010) Stable and dynamic cortical electrophysiology of induction and emergence with propofol anesthesia. Proc Natl Acad Sci U S A 107: 21170-21175.
White NS, Alkire MT, (2003) Impaired thalamocortical connectivity in humans during generalanesthetic- induced unconsciousness. Neuroimage 19: 402-411.
Ferrarelli F, Massimini M, Sarasso S, Casali A, Riedner BA, et al. (2010) Breakdown in cortical effective connectivity during midazolam-induced loss of consciousness. Proc Natl Acad Sci U S A 107: 2681-2686.
Mashour GA, (2005) Cognitive unbinding in sleep and anesthesia. Science 310: 1768-9.
John ER, Prichep LS, (2005) The anesthetic cascade: A theory of how anesthesia suppresses consciousness. Anesthesiology 102: 447-471.
Barrett AB, Seth AK, (2011) Practical measures of integrated information for time series data. PLoS Comput Biol 7 (1): e1001052.
Seth A, Izhikevich E, Reeke G, Edelman G, (2006) Theories and measures of consciousness: An extended framework. Proc Natl Acad Sci U S A 103: 10799-10804.
Balduzzi D, Tononi G, (2008) Integrated information in discrete dynamical systems: Motivation and theoretical framework. PLoS Comput Biol 4 (6): e1000091.
Massimini M, Ferrarelli F, Huber R, Esser S, Singh H, et al. (2005) Breakdown of corticaleffective connectivity during sleep. Science 309: 2228-2232.
Arthuis M, Valton L, Régis J, Chauvel P, Wendling F, et al. (2009) Impaired consciousness during temporal lobe seizures is related to increased long-distance cortical-subcortical synchronization. Brain 132: 2091-2101.
Valdes-Sosa PA, Roebroeck A, Daunizeau J, Friston K, (2011) Effective connectivity: Influence, causality and biophysical modeling. Neuroimage 58 (2): 339-361.
Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, et al. (2008) Mapping the structural core of human cerebral cortex. PLoS Biol 6: e159.
Seth AK, Dienes Z, Cleeremans A, Overgaard M, Pessoa L, (2008) Measuring consciousness: Relating behavioural and neurophysiological approaches. Trends Cogn Sci 12: 314-321.
Boly M, Seth AK, (in press) Modes and models in disorders of consciousness science. Arch Ital Biol xx: xx-xx.
