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 distributedbrain regions. Recent theoretical developments emphasize changes in directed functional (i.e., causal) connectivity asreflected in quantities such as ‘integrated information’ and ‘causal density’. Here we develop and illustrate a rigorousmethodology 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 applyingpermutation 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 bidirectionalGC in most subjects during loss-of-consciousness, especially in the beta and gamma frequency ranges. Corroborating aprevious analysis we also found increases in synchrony in these ranges; importantly, the Granger causality analysis showedhigher inter-subject consistency than the synchrony analysis. Finally, we validate our method using simulated datagenerated from a model for which GC values can be analytically derived. In summary, our findings advance themethodology of Granger causality analysis of EEG data and carry implications for integrated information and causal densitytheories of consciousness.
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