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      Scale-free dynamics in human neonatal cortex following perinatal hypoxia

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      1 , , 1 , 2 , 2 , 2 , 3 , 1
      BMC Neuroscience
      BioMed Central
      Twenty Second Annual Computational Neuroscience Meeting: CNS*2013
      13-18 July 2013

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          Abstract

          Complications at birth can interrupt blood supply to the baby, leading to hypoxia in the neonatal cortex. Once oxygen supply resumes, cortical activity follows a stereotypical recovery sequence that includes a period termed burst suppression, during which the EEG exhibits sudden, irregular fluctuations of highly variable size and shape. Clinical outcome depends critically on this phase, ranging from complete recovery to permanent cognitive or motor disability and even death. Despite its importance in the recovery process, burst suppression's mechanisms remain poorly understood, and objective diagnostics are needed to guide treatment [1]. Here, we analyze the statistical properties of burst suppression in neonatal EEG recordings and show that simple dynamical models capture key features of the data. We find that fluctuations in burst size exhibit long-tailed power law distributions spanning up to five orders of magnitude. Despite this immense variability, their average shape at all temporal scales can be rescaled to a near universal template (Figure 1). Deviations from universality include a flattening of fluctuation shapes at long time scales and the expression of leftward or rightward asymmetry. These features are consistent with the phenomenon of crackling noise that arises in disparate physical systems such as crumpling paper, magnetizing a ferromagnet, and earthquakes, all of which exhibit scale-free bursty events [2]. Similar behavior has recently been observed in neuronal avalanches recorded in cortical slices [3]. In our data, as in studies of crackling noise, the average shapes shed light on the underlying mechanisms [4]. Using simple phenomenological models, we show how changes to the average shapes can arise from different forms of state-dependent damping, representing resource depletion in cortical neurons. Statistical analysis of the variability and average shapes of bursts holds promise for new diagnostic opportunities in this critical clinical window and will inform future biologically-detailed models. Figure 1 Example of single-subject average burst shapes collapsing to a simple symmetric functional form over temporal scales of T = 40 ms - 4 s.

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          Crackling Noise

          Crackling noise arises when a system responds to changing external conditions through discrete, impulsive events spanning a broad range of sizes. A wide variety of physical systems exhibiting crackling noise have been studied, from earthquakes on faults to paper crumpling. Because these systems exhibit regular behavior over many decades of sizes, their behavior is likely independent of microscopic and macroscopic details, and progress can be made by the use of very simple models. The fact that simple models and real systems can share the same behavior on a wide range of scales is called universality. We illustrate these ideas using results for our model of crackling noise in magnets, explaining the use of the renormalization group and scaling collapses. This field is still developing: we describe a number of continuing challenges.
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            Universal critical dynamics in high resolution neuronal avalanche data.

            The tasks of neural computation are remarkably diverse. To function optimally, neuronal networks have been hypothesized to operate near a nonequilibrium critical point. However, experimental evidence for critical dynamics has been inconclusive. Here, we show that the dynamics of cultured cortical networks are critical. We analyze neuronal network data collected at the individual neuron level using the framework of nonequilibrium phase transitions. Among the most striking predictions confirmed is that the mean temporal profiles of avalanches of widely varying durations are quantitatively described by a single universal scaling function. We also show that the data have three additional features predicted by critical phenomena: approximate power law distributions of avalanche sizes and durations, samples in subcritical and supercritical phases, and scaling laws between anomalous exponents.
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              Signature of effective mass in crackling-noise asymmetry

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                Author and article information

                Contributors
                Conference
                BMC Neurosci
                BMC Neurosci
                BMC Neuroscience
                BioMed Central
                1471-2202
                2013
                8 July 2013
                : 14
                : Suppl 1
                : P36
                Affiliations
                [1 ]Systems Neuroscience Group, Queensland Institute of Medical Research, Herston, Brisbane, QLD 4006, Australia
                [2 ]Centre for Clinical Research and Perinatal Research Centre, University of Queensland, Brisbane, QLD 4006, Australia
                [3 ]Department of Children's Clinical Neurophysiology, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland
                Article
                1471-2202-14-S1-P36
                10.1186/1471-2202-14-S1-P36
                3704720
                918e8478-a4cd-4ac1-a22f-735bcbc2a54c
                Copyright ©2013 Roberts et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Twenty Second Annual Computational Neuroscience Meeting: CNS*2013
                Paris, France
                13-18 July 2013
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                Poster Presentation

                Neurosciences
                Neurosciences

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