Dataset Information

Estimating the Integrated Information Measure Phi from High-Density Electroencephalography during States of Consciousness in Humans.

ABSTRACT: The integrated information theory (IIT) proposes a quantitative measure, denoted as ?, 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 ? 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 ? 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 ? from multichannel electroencephalogram (EEG) and define the relationship of ? to EEG properties conventionally used to define states of consciousness. In this study, we introduce a practical method to estimate ? from high-density (128-channel) EEG and determine the contribution of each channel to ?. We examine the correlation of power, frequency, functional connectivity, and modularity of EEG with regional ? in various states of consciousness as modulated by diverse anesthetics. We find that our approximation of ? alone is insufficient to discriminate certain states of anesthesia. However, a multi-dimensional parameter space extended by four parameters related to ? and EEG connectivity is able to differentiate all states of consciousness. The association of ? 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.

SUBMITTER: Kim H

PROVIDER: S-EPMC5821001 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Estimating the Integrated Information Measure Phi from High-Density Electroencephalography during States of Consciousness in Humans.

Kim Hyoungkyu H Hudetz Anthony G AG Lee Joseph J Mashour George A GA Lee UnCheol U

Frontiers in human neuroscience 20180216

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 applicati ...[more]

PMID: 29503611

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Project description:Background/Introduction: Concurrent electroencephalography and resting-state functional magnetic resonance imaging (rsfMRI) have been widely used for studying the (presumably) awake and alert human brain with high temporal/spatial resolution. Although rsfMRI scans are typically collected while individuals are instructed to focus their eyes on a fixated cross, objective and verified experimental measures to quantify degree of vigilance are not readily available. Electroencephalography (EEG) is the modality extensively used for estimating vigilance, especially during eyes-closed resting state. However, pupil size measured using an eye-tracker device could provide an indirect index of vigilance. Methods: Three 12-min resting scans (eyes open, fixating on the cross) were collected from 10 healthy control participants. We simultaneously collected EEG, fMRI, physiological, and eye-tracker data and investigated the correlation between EEG features, pupil size, and heart rate. Furthermore, we used pupil size and EEG features as regressors to find their correlations with blood-oxygen-level-dependent fMRI measures. Results: EEG frontal and occipital beta power (FOBP) correlates with pupil size changes, an indirect index for locus coeruleus activity implicated in vigilance regulation (r = 0.306, p < 0.001). Moreover, FOBP also correlated with heart rate (r = 0.255, p < 0.001), as well as several brain regions in the anticorrelated network, including the bilateral insula and inferior parietal lobule. Discussion: In this study, we investigated whether simultaneous EEG-fMRI combined with eye-tracker measurements can be used to determine EEG signal feature associated with vigilance measures during eyes-open rsfMRI. Our results support the conclusion that FOBP is an objective measure of vigilance in healthy human subjects. Impact statement We revealed an association between electroencephalography frontal and occipital beta power (FOBP) and pupil size changes during an eyes-open resting state, which supports the conclusion that FOBP could serve as an objective measure of vigilance in healthy human subjects. The results were validated by using simultaneously recorded heart rate and functional magnetic resonance imaging (fMRI). Interestingly, independently verified heart rate changes can also provide an easy-to-determine measure of vigilance during resting-state fMRI. These findings have important implications for an analysis and interpretation of dynamic resting-state fMRI connectivity studies in health and disease.

Dataset Information

Estimating the Integrated Information Measure Phi from High-Density Electroencephalography during States of Consciousness in Humans.

Publications

Estimating the Integrated Information Measure Phi from High-Density Electroencephalography during States of Consciousness in Humans.

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