Estimating the Integrated Information Measure Phi from High-Density Electroencephalography during States of Consciousness in Humans.
Estimating the Integrated Information Measure Phi from High-Density Electroencephalography during States of Consciousness in Humans.
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DOI：
10.3389/fnhum.2018.00042
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发表时间：
2018
影响因子：
2.9
通讯作者：
ReCCognition Study Group
中科院分区：
文献类型：
--
作者：
Kim H;Hudetz AG;Lee J;Mashour GA;Lee U;ReCCognition Study Group
综合信息论（integrated information theory， IIT）提出了物理系统中综合信息量的定量度量，表示为Φ，该系统假定与意识具有同一性关系。IIT预测，从大脑活动中估计的Φ值代表了跨越系统发育和功能状态的意识水平。实际的限制，例如估算真实系统Φ所需的爆炸性计算需求，阻碍了它在大脑中的应用，并对IIT的总体效用提出了质疑。为了实现对人类大脑研究的实际意义，建立多通道脑电图（EEG）对Φ的可靠估计，并定义Φ与通常用于定义意识状态的脑电图属性的关系将是有益的。在本研究中，我们引入了一种实用的方法，从高密度（128通道）EEG中估计Φ，并确定每个通道对Φ的贡献。我们研究了在不同麻醉剂调节的不同意识状态下，EEG的功率、频率、功能连接和模块性与区域Φ的相关性。我们发现，仅靠Φ的近似值不足以区分麻醉的某些状态。然而，由Φ和脑电图连接相关的四个参数扩展的多维参数空间能够区分所有的意识状态。Φ与临床定义的麻醉状态下脑电图连通性的关联代表了IIT应用的一种新的实用方法，可用于表征人类大脑中各种生理（睡眠），药理（麻醉）和病理（昏迷）意识状态。
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.
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影响因子：
4.3
作者：
Oizumi M;Amari S;Yanagawa T;Fujii N;Tsuchiya N
通讯作者：
Tsuchiya N
影响因子：
2.9
作者：
Blain-Moraes S;Tarnal V;Vanini G;Bel-Behar T;Janke E;Picton P;Golmirzaie G;Palanca BJA;Avidan MS;Kelz MB;Mashour GA
通讯作者：
Mashour GA
DOI：
10.1073/pnas.0601602103
发表时间：
2006-06-06
影响因子：
11.1
作者：
Newman, M. E. J.
通讯作者：
Newman, M. E. J.
影响因子：
2.3
作者：
Barbosa, Valmir C.
通讯作者：
Barbosa, Valmir C.
影响因子：
2.4
作者：
Tononi G
通讯作者：
Tononi G