Abstract
The integrated information theory (IIT) proposes a quantitative measure, denoted as Φ, of the amount of integrated information in a physical system, which is postulated to have an identity relationship with consciousness. IIT predicts that the value of Φ estimated from brain activities represents the level of consciousness across phylogeny and functional states. Practical limitations, such as the explosive computational demands required to estimate Φ for real systems, have hindered its application to the brain and raised questions about the utility of IIT in general. To achieve practical relevance for studying the human brain, it will be beneficial to establish the reliable estimation of Φ from multichannel electroencephalogram (EEG) and define the relationship of Φ to EEG properties conventionally used to define states of consciousness. In this study, we introduce a practical method to estimate Φ from high-density (128-channel) EEG and determine the contribution of each channel to Φ. We examine the correlation of power, frequency, functional connectivity, and modularity of EEG with regional Φ in various states of consciousness as modulated by diverse anesthetics. We find that our approximation of Φ alone is insufficient to discriminate certain states of anesthesia. However, a multi-dimensional parameter space extended by four parameters related to Φ and EEG connectivity is able to differentiate all states of consciousness. The association of Φ with EEG connectivity during clinically defined anesthetic states represents a new practical approach to the application of IIT, which may be used to characterize various physiological (sleep), pharmacological (anesthesia), and pathological (coma) states of consciousness in the human brain.
Highlights
Integrated information theory (IIT) postulates that consciousness is identical to integrated information and that a system’s capacity for consciousness can be expressed by a quantitative measure referred to as (Tononi, 2004; Oizumi et al, 2014, 2016a,b; Tononi et al, 2016)
This study introduced a novel and practical method to estimate from high density EEG and applied it to various states of consciousness altered by general anesthesia induced by ketamine and propofol-isoflurane
The multidimensional parameter space consisting of various EEG-derived measures of connectivity and R was efficacious in differentiating various states of consciousness and sub-states during burst and suppression
Summary
Integrated information theory (IIT) postulates that consciousness is identical to integrated information and that a system’s capacity for consciousness can be expressed by a quantitative measure referred to as (Tononi, 2004; Oizumi et al, 2014, 2016a,b; Tononi et al, 2016). Estimating Phi from High-Density Electroencephalography and recovery of consciousness are associated with, respectively, the breakdown and restoration of integrated information in the brain (Alkire et al, 2008; Lee et al, 2009; Tononi, 2010; Tononi and Koch, 2015). This prediction should hold true for physiological (slow-wave sleep), pharmacological (anesthesia), and pathological (coma) states of unconsciousness. Far, only surrogates of integrated information have been amenable to quantitative analysis due to the explosive computational demand associated with calculating for real systems of interest including the brain. Several attempts have been made to overcome the computational limitations (Tegmark, 2016; Krohn and Ostwald, 2017; Toker and Sommer, 2017)
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