Criticality in the Healthy Brain

Shi, Jifan; Kirihara, Kazuhiro; Tada, Mariko; Fujioka, Masahiro; Usui, Kaori; Koshiyama, Daisuke; Araki, Tsuyoshi; Chen, Luonan; Kasai, Kazuhiko; Aihara, Kazuyuki

doi:10.3389/fnetp.2021.755685

Cited by 14 publications

(14 citation statements)

References 44 publications

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“…Frontiers in Network Physiology frontiersin.org modeling of epileptic-seizure-related synchronization phenomena (Gerster et al, 2020), where a part of the brain synchronizes, it turned out that such a cross-coupling is important. The subtle interplay of excitatory and inhibitory interaction is typical of the critical state at the edge of different dynamical regimes in which the brain operates (Massobrio et al, 2015;Shi et al, 2022), and gives rise to partial synchronization patterns which are not found otherwise.…”

Section: Neural Network Modelmentioning

confidence: 99%

Modelling the perception of music in brain network dynamics

Sawicki

Hartmann

Bader

et al. 2022

Front. Netw. Physiol.

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We analyze the influence of music in a network of FitzHugh-Nagumo oscillators with empirical structural connectivity measured in healthy human subjects. We report an increase of coherence between the global dynamics in our network and the input signal induced by a specific music song. We show that the level of coherence depends crucially on the frequency band. We compare our results with experimental data, which also describe global neural synchronization between different brain regions in the gamma-band range in a time-dependent manner correlated with musical large-scale form, showing increased synchronization just before transitions between different parts in a musical piece (musical high-level events). The results also suggest a separation in musical form-related brain synchronization between high brain frequencies, associated with neocortical activity, and low frequencies in the range of dance movements, associated with interactivity between cortical and subcortical regions.

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Section: Neural Network Modelmentioning

confidence: 99%

Modelling the perception of music in brain network dynamics

Sawicki

Hartmann

Bader

et al. 2022

Front. Netw. Physiol.

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“…Besides, an alternation between synchronization and desynchronization reflects the variability of the system; this can be seen as a critical state between a fully synchronized and a desynchronized state. It is known that the brain is operating in a critical state at the edge of different dynamical regimes (Massobrio et al, 2015;Shi et al, 2022), exhibiting hysteresis and avalanche phenomena as seen in critical phenomena and phase transitions (Kim et al, 2018;Ribeiro et al, 2010;Steyn-Ross and Steyn-Ross, 2010).…”

Section: Discussionmentioning

confidence: 99%

“…Also in the modeling of epileptic-seizure-related synchronization phenomena (Gerster et al, 2020), where a part of the brain synchronizes, it turned out that such a cross-coupling is important. The subtle interplay of excitatory and inhibitory interaction is typical of the critical state at the edge of different dynamical regimes in which the brain operates (Massobrio et al, 2015;Shi et al, 2022), and gives rise to partial synchronization patterns which are not found otherwise.…”

Section: Neuronal Network Modelmentioning

confidence: 99%

Influence of music on empirical brain networks

Sawicki¹,

Hartmann²,

Bader³

et al. 2022

Preprint

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We analyze the influence of music in a network of FitzHugh-Nagumo oscillators with empirical structural connectivity measured in healthy human subjects. We report an increase of coherence between the global dynamics in our network and the input signal induced by a specific music song. We show that the level of coherence depends crucially on the frequency band. We compare our results with experimental data, which also describe global neural synchronization between different brain regions in the gamma-band range and its increase just before transitions between different parts of the musical form (musical high-level events). The results also suggest a split in musical form-related brain synchronization between high brain frequencies, associated with neocortical activity, and low frequencies in the range of dance movements, associated with interactivity between cortical and subcortical regions. Finally, we discuss the general modalities of the influence of music on the human brain.

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“…The data are provided in electronic supplementary material [44]. The codes for calculating embedding entropy/conditional embedding entropy are available at .…”

Section: Data Accessibilitymentioning

confidence: 99%

Embedding entropy: a nonlinear measure of dynamical causality

Shi

Chen

Aihara

2022

J. R. Soc. Interface.

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Research on concepts and computational methods of causality has a long history, and there are various concepts of causality as well as corresponding computing algorithms based on measured data. Here, by considering causes and effects from a dynamical perspective, we present a unified mathematical framework for the so-called dynamical causality (DC), which can detect causal interactions over time; notably, this framework covers Granger causality, transfer entropy, embedding causality and their conditional versions. Based on this framework, we further propose a causality criterion called embedding entropy (EE) to measure the DC between two variables. Moreover, its conditional version, conditional embedding entropy (cEE), is also derived for detecting conditional/direct causality. The significant advantages of EE and cEE are that they can be employed for solving not only nonlinear causal inference but also the non-separability problem, and they can reduce the scale bias in numerical calculation. The performance and robustness of EE and cEE were demonstrated through numerical simulations, and causal inference on various real-world datasets validated their effectiveness.

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Criticality in the Healthy Brain

Cited by 14 publications

References 44 publications

Modelling the perception of music in brain network dynamics

Modelling the perception of music in brain network dynamics

Influence of music on empirical brain networks

Embedding entropy: a nonlinear measure of dynamical causality

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