Mechanisms of hysteresis in human brain networks during transitions of consciousness and unconsciousness: Theoretical principles and empirical evidence

Lee

2019

Entropy

Integrated information theory (IIT) describes consciousness as information integrated across highly differentiated but irreducible constituent parts in a system. However, in a complex dynamic system such as the brain, the optimal conditions for large integrated information systems have not been elucidated. In this study, we hypothesized that network criticality, a balanced state between a large variation in functional network configuration and a large constraint on structural network configuration, may be the basis of the emergence of a large Φ, a surrogate of integrated information. We also hypothesized that as consciousness diminishes, the brain loses network criticality and Φ decreases. We tested these hypotheses with a large-scale brain network model and high-density electroencephalography (EEG) acquired during various levels of human consciousness under general anesthesia. In the modeling study, maximal criticality coincided with maximal Φ. The EEG study demonstrated an explicit relationship between Φ, criticality, and level of consciousness. The conscious resting state showed the largest Φ and criticality, whereas the balance between variation and constraint in the brain network broke down as the response rate dwindled. The results suggest network criticality as a necessary condition of a large Φ in the human brain.

Section: Methodsmentioning

confidence: 67%

Criticality as a Determinant of Integrated Information Φ in Human Brain Networks

Lee

2019

Entropy

“…Criticality, a boundary state between order and disorder, has long been proposed to play an important role in neural dynamics and brain function. Empirical evidence supports the hypothesis that the brain operates at or near the critical point, not only at the neuronal network level [22,40,41], but also at the large-scale or global network level [9,12,42–44]. Until now, most studies have focused on scale-free behavior, showing power law distribution of empirically observed variables.…”

Section: Methodsmentioning

confidence: 72%

Criticality as a determinant of integrated information Φ in human brain networks

Hudetz

Mashour

et al. 2019

Preprint

Self Cite

21Integrated information theory (IIT) postulates that consciousness arises from the cause-effect 22 structure of a system but the optimal network conditions for this structure have not been 23 elucidated. In the study, we test the hypothesis that network criticality, a dynamically balanced 24 state between a large variation of functional network configurations and a large constraint of 25 structural network configurations, is a necessary condition for the emergence of a cause-effect 26 structure that results in a large Φ, a surrogate of integrated information. We also hypothesized 27 that if the brain deviates from criticality, the cause-effect structure is obscured and Φ diminishes. 28We tested these hypotheses with a large-scale brain network model and high-density 29 electroencephalography (EEG) acquired during various levels of human consciousness during 30 general anesthesia. In the modeling study, maximal criticality coincided with maximal Φ. The 31 constraint of the structural network on the functional network is maximized in the maximal 32criticality. The EEG study demonstrated an explicit relationship between Φ, criticality, and level 33 of consciousness. Functional brain network significantly correlated with structural brain network 34 only in conscious states. The results support the hypothesis that network criticality maximizes 35 Φ. 36 37 Introduction 38Integrated information theory (IIT) proposes that consciousness equates with integrated 39 information in a system and the integrated information is maximized when integration and 40 differentiation of the systems' components are balanced. IIT proposes algorithmic methods to 41 identify the differentiated parts of a system and quantify the integrated information across the 42 parts [1-6]. Φ is a measure of complexity of the cause-effect structure of the minimum 43 information partition among all possible partitions. However, in a dynamic system such as the 44 brain, the optimal conditions under which the cause-effect structure-that is, the basis of 45 integrated information-arises has not been elucidated. In this study, we hypothesized that 46 network criticality, a balanced state between a large variation of functional network 47 configurations and a large constraint of structural network configurations, may be the basis of 48 the high Φ in conscious brains. 49Criticality was originally introduced for studying phase transition in physics, which was simply 50 defined as a balanced state between order and disorder in the activities of the elements that 51 make up a system [7,8]. This property has been observed broadly in physical and non-physical 52 systems and has been suggested as an optimal state for information storage, transmission, and 53 integration with high susceptibility to external stimuli [9][10][11][12][13]. In particular, several computational 54 modeling and empirical studies suggest that the brain dynamics associated with consciousness 55 reside near a critical state [14][15][16][17][18][19]. Furthermore, recent studies have attempted n...

“…We constructed a large-scale functional brain network using coupled Stuart-Landau models on the human anatomical brain structure to generate spontaneous oscillations. The Stuart-Landau model with brain topology has been used to replicate the oscillatory dynamics of different types of electromagnetic brain signals such as electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) [17,18,20,24,49]. The coupled Stuart-Landau model is defined as following:…”

Section: Simulation Of Networked Alpha Oscillations In a Human Brainmentioning

confidence: 99%

“…The = 2 and is an initial angular natural frequency of each ℎ oscillator. We used a Gaussian distribution for natural frequency with a mean frequency of 10 Hz and standard deviation of 0.5 Hz to simulate the alpha bandwidth of human EEG activity [20][21][22]24,51]. We also used the homogeneous coupling term = between the ℎ and ℎ oscillators from 0 to 0.4 with = 0.002, which determines the global connection strength among brain regions.…”

Section: Simulation Of Networked Alpha Oscillations In a Human Brainmentioning

confidence: 99%

“…In this study, we hypothesized that perceptual binding associates with the brain's network response, and the network response largely depends on the ongoing coupled oscillation properties at stimulus onset. Recent studies have revealed that alpha oscillations transfer information through traveling waves in the cortex, and the global functional connectivity of alpha oscillations reflect conscious and unconscious states [17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Oscillations, criticality and responsiveness in complex brain networks

Lee

2019

Preprint

Self Cite

Brains in sleep, anesthesia, and traumatic injury are characterized by significantly limited responsiveness to stimuli. Even during conscious wakefulness, responsiveness is highly dependent on on-going brain activity, specifically, of alpha oscillations (~10Hz). However, despite many empirical studies, the ways in which specific alpha oscillations induce a large or small response to stimuli have not been elucidated. We hypothesized that the variety of responses to sensory stimuli result from the interaction between state-specific and transient alpha oscillations and stimuli. To justify this hypothesis, we simulated various alpha oscillations in the human brain network, modulating network criticality (a balanced state between order and disorder), and investigated specific alpha oscillation properties (instantaneous amplitude, phase, and global synchronization) that induce a large or small response. We found that near a critical state, a large, complex response is induced when a stimulus is given to globally desynchronized and lowamplitude alpha oscillations, and we also found specific phases of alpha oscillation that barely respond to stimuli. These results imply the presence of temporal windows in the alpha cycle for a large or small response to external stimuli, which is consistent with the periodic perceptual binding at alpha frequency found in empirical studies.