Structure Shapes Dynamics and Directionality in Diverse Brain Networks: Mathematical Principles and Empirical Confirmation in Three Species

Moon, Joon-Young; Kim, Junhyeok; Ko, Tae Wook; Kim, Minkyung; Iturria-Medina, Yasser; Choi, Jee Hyun; Lee, Joseph; Mashour, George A.; Lee, Un Cheol

doi:10.1038/srep46606

Cited by 30 publications

(48 citation statements)

References 54 publications

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“…All parameters for the models were set to simulate alpha oscillations in the brain. Alpha oscillation models have successfully explained empirically observed brain network behaviors such as functional connectivity, traveling waves, and network state transitions based on electroencephalography (EEG) and magnetoencephalography (MEG) [25,26,29–37]. Thus, alpha oscillations were analyzed to understand the behaviors of Φ and the criticality at the brain network level.…”

Section: Methodsmentioning

confidence: 99%

“…Until now, most studies have focused on scale-free behavior, showing power law distribution of empirically observed variables. It has also been recently proposed that high correlation between functional and structural brain networks [14,23,25,26,45] and a large pair correlation function (PCF) [24,46] is evidence of criticality. In both the brain network model and empirical EEG data, we estimated criticality with PCF, which is the variance of global phase synchronization and is equivalent to susceptibility in statistical physics where Re[ z(t) ] is the real part of the z(t) in Eq.…”

Section: Methodsmentioning

confidence: 99%

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

Kim

Hudetz

Mashour

et al. 2019

Preprint

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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...

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Kim

Hudetz

Mashour

et al. 2019

Preprint

Self Cite

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“…Like mammals, wakeful insect brains also face functional challenges in adjusting fast sensory inputs based on prior experience on a slower time scale (Card and Dickinson, 2008; Combes, 2015; Kim et al, 2015; Mischiati et al, 2015). In addition, recent work suggests that the topological organization of the mammalian brain, which shares a number of similarities with the topological organization of the fly brain (Shih et al, 2015), influences the direction of information flow (Moon et al, 2015; Mejias et al, 2016; Moon et al, 2017). This evidence suggests that similar dynamic characteristics of FF and FB processing will be present in the insect brain.…”

Section: Introductionmentioning

confidence: 99%

Isoflurane Impairs Low-Frequency Feedback but Leaves High-Frequency Feedforward Connectivity Intact in the Fly Brain

Cohen

Swinderen

Tsuchiya

2018

eNeuro

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Hierarchically organized brains communicate through feedforward (FF) and feedback (FB) pathways. In mammals, FF and FB are mediated by higher and lower frequencies during wakefulness. FB is preferentially impaired by general anesthetics in multiple mammalian species. This suggests FB serves critical functions in waking brains. The brain of Drosophila melanogaster (fruit fly) is also hierarchically organized, but the presence of FB in these brains is not established. Here, we studied FB in the fly brain, by simultaneously recording local field potentials (LFPs) from low-order peripheral structures and higher-order central structures. We analyzed the data using Granger causality (GC), the first application of this analysis technique to recordings from the insect brain. Our analysis revealed that low frequencies (0.1–5 Hz) mediated FB from the center to the periphery, while higher frequencies (10–45 Hz) mediated FF in the opposite direction. Further, isoflurane anesthesia preferentially reduced FB. Our results imply that the spectral characteristics of FF and FB may be a signature of hierarchically organized brains that is conserved from insects to mammals. We speculate that general anesthetics may induce unresponsiveness across species by targeting the mechanisms that support FB.

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“…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 Brain mentioning

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%

Oscillations, criticality and responsiveness in complex brain networks

Kim

Lee

2019

Preprint

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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.

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Structure Shapes Dynamics and Directionality in Diverse Brain Networks: Mathematical Principles and Empirical Confirmation in Three Species

Cited by 30 publications

References 54 publications

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

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

Isoflurane Impairs Low-Frequency Feedback but Leaves High-Frequency Feedforward Connectivity Intact in the Fly Brain

Oscillations, criticality and responsiveness in complex brain networks

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