scholarly journals Turbulent-like dynamics in the human brain

2019 ◽  
Author(s):  
Gustavo Deco ◽  
Morten L. Kringelbach

SummaryTurbulence facilitates fast energy/information transfer across scales in physical systems. These qualities are important for brain function, but it is currently unknown if the dynamic intrinsic backbone of brain also exhibits turbulence. Using large-scale neuroimaging empirical data from 1003 healthy participants, we demonstrate Kuramoto’s amplitude turbulence in human brain dynamics. Furthermore, we build a whole-brain model with coupled oscillators to demonstrate that the best fit to the data corresponds to a region of maximally developed amplitude turbulence, which also corresponds to maximal sensitivity to the processing of external stimulations (information capability). The model shows the economy of anatomy by following the Exponential Distance Rule of anatomical connections as a cost-of-wiring principle. This establishes a firm link between turbulence and optimal brain function. Overall, our results reveal a way of analysing and modelling whole-brain dynamics that suggests turbulence as the dynamic intrinsic backbone facilitating large scale network communication.

Entropy ◽  
2017 ◽  
Vol 19 (9) ◽  
pp. 471 ◽  
Author(s):  
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2019 ◽  
Author(s):  
Takumi Sase ◽  
Keiichi Kitajo

AbstractRecent studies suggest that the resting brain utilizes metastability such that the large-scale network can spontaneously yield transition dynamics across a repertoire of oscillatory states. By analyzing resting-state electroencephalographic signals and the autism-spectrum quotient acquired from healthy humans, we show experimental evidence of how autistic-like traits may be associated with the metastable human brain. Observed macroscopic brain signals exhibited slow and fast oscillations forming phase-amplitude coupling (PAC) with dynamically changing modulation strengths, resulting in oscillatory states characterized by different PAC strengths. In individuals with the ability to maintain a strong focus of attention to detail and less attention switching, these transient PAC dynamics tended to stay in a state for a longer time, to visit a lower number of states, and to oscillate at a higher frequency than in individuals with a lower attention span. We further show that attractors underlying the transient PAC could be multiple tori and consistent across individuals, with evidence that the dynamic changes in PAC strength can be attributed to changes in the strength of phase-phase coupling, that is, to dynamic functional connectivity in an electrophysiological sense. Our findings suggest that the metastable human brain can organize spontaneous events dynamically and selectively in a hierarchy of macroscopic oscillations with multiple timescales, and that such dynamic organization might encode a spectrum of individual traits covering typical and atypical development.Significance StatementMetastability in the brain is thought to be a mechanism involving spontaneous transitions among oscillatory states of the large-scale network. We show experimental evidence of how autistic-like traits may be associated with the metastable human brain by analyzing resting-state electroencephalographic signals and scores for the autism-spectrum quotient acquired from healthy humans. We found that slow and fast neural oscillations can form phase-amplitude coupling with dynamically changing modulation strengths, and that these transient dynamics can depend on the ability to maintain attention to detail and to switch attention. These results suggest that the metastable human brain can encode a spectrum of individual traits by realizing the dynamic organization of spontaneous events in a hierarchy of macroscopic oscillations with multiple timescales.


2018 ◽  
Author(s):  
Kelly Shen ◽  
Gleb Bezgin ◽  
Michael Schirner ◽  
Petra Ritter ◽  
Stefan Everling ◽  
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AbstractModels of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data.


2021 ◽  
Author(s):  
Yontatan Sanz Perl ◽  
Anira Escrichs ◽  
Enzo Tagliazucchi ◽  
Morten L Kringelbach ◽  
Gustavo Deco

Going beyond previous research, we use strength-dependent perturbation to obtain a deeper understanding of the mechanisms underlying the emergence of large-scale brain activity. Despite decades of research, we still have a shallow understanding of the role and generating mechanisms of the ubiquitous fluctuations and oscillations found in recordings of brain dynamics. Here, we used global strength-dependent perturbation to give a causal mechanistic description of human brain function providing a delicate balance between fluctuation and oscillation on the edge of criticality. After application of precise local strength-dependent perturbations and measuring the well-known perturbative complexity index, we demonstrated that the overall balance is shifted towards a fluctuating regime which is superior in terms of enhancing different functional networks compared to the oscillatory regime. This framework can generate specific, testable empirical predictions to be tested in human stimulation studies with strength-dependent rather than constant perturbation. Overall, our novel strength-dependent perturbation framework demonstrates that the human brain is poised on the edge of criticality, between fluctuations to oscillations, allowing for maximal flexibility.


MIS Quarterly ◽  
2016 ◽  
Vol 40 (4) ◽  
pp. 849-868 ◽  
Author(s):  
Kunpeng Zhang ◽  
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Siddhartha Bhattacharyya ◽  
Sudha Ram ◽  
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