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      Transition from Persistent to Anti-Persistent Correlations in Postural Sway Indicates Velocity-Based Control

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          Abstract

          The displacement of the center-of-pressure (COP) during quiet stance has often been accounted for by the control of COP position dynamics. In this paper, we discuss the conclusions drawn from previous analyses of COP dynamics using fractal-related methods. On the basis of some methodological clarification and the analysis of experimental data using stabilogram diffusion analysis, detrended fluctuation analysis, and an improved version of spectral analysis, we show that COP velocity is typically bounded between upper and lower limits. We argue that the hypothesis of an intermittent velocity-based control of posture is more relevant than position-based control. A simple model for COP velocity dynamics, based on a bounded correlated random walk, reproduces the main statistical signatures evidenced in the experimental series. The implications of these results are discussed.

          Author Summary

          Postural control during quiet standing is usually conceived of as the control of position: when position goes beyond a given threshold, corrective mechanisms are engaged to restore equilibrium. In this paper, we question this conception and show that postural control is based on an intermittent control of velocity, with a reversal in its dynamics when the absolute value of velocity reaches a given threshold. This hypothesis presents some counterintuitive implications. Notably, it means that the active control or correction processes do not intervene at the periphery of postural sways, as generally assumed. According to our findings, control occurs in the central region of the posturogram, where velocity reaches its maximal absolute values. The present study suggests new variables of interest in the study of postural control, especially the maximal absolute velocity of the center-of-pressure, which could describe and predict postural disorders.

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          Most cited references18

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          Random walking during quiet standing.

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            Physiological time series: distinguishing fractal noises from motions.

            Many physiological signals appear fractal, in having self-similarity over a large range of their power spectral densities. They are analogous to one of two classes of discretely sampled pure fractal time signals, fractional Gaussian noise (fGn) or fractional Brownian motion (fBm). The fGn series are the successive differences between elements of a fBm series; they are stationary and are completely characterized by two parameters, sigma2, the variance, and H, the Hurst coefficient. Such efficient characterization of physiological signals is valuable since H defines the autocorrelation and the fractal dimension of the time series. Estimation of H from Fourier analysis is inaccurate, so more robust methods are needed. Dispersional analysis (Disp) is good for noise signals while bridge detrended scaled windowed variance analysis (bdSWV) is good for motion signals. Signals whose slopes of their power spectral densities lie near the border between fGn and fBm are difficult to classify. A new method using signal summation conversion (SSC), wherein an fGn is converted to an fBm or an fBm to a summed fBm and bdSWV then applied, greatly improves the classification and the reliability of H, the estimates of H, for the times series. Applying these methods to laser-Doppler blood cell perfusion signals obtained from the brain cortex of anesthetized rats gave H of 0.24+/-0.02 (SD, n=8) and defined the signal as a fractional Brownian motion. The implication is that the flow signal is the summation (motion) of a set of local velocities from neighboring vessels that are negatively correlated, as if induced by local resistance fluctuations.
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              On the use of sample entropy to analyze human postural sway data.

              We analyze the irregularity of human postural sway data during quiet standing using the sample entropy (SampEn) algorithm. By considering recent methodological developments, we show that the SampEn parameter is able to characterize the irregularity of the center of pressure fluctuations through the analysis of the velocity variable. We present a practical method to select the input parameters of the SampEn algorithm. We show that the computed SampEn successfully discriminates two sensory conditions (eyes-open and eyes-closed) in a group of healthy young adults. We also perform surrogate data tests to investigate the nature of the underlying dynamics of our experimental data. Finally, the results of the proposed approach are compared to those obtained with the multiscale entropy algorithm.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                February 2011
                February 2011
                24 February 2011
                : 7
                : 2
                : e1001089
                Affiliations
                [1]EA 2991 Movement To Health, Montpellier-1 University, Euromov, Montpellier, France
                University College London, United Kingdom
                Author notes

                Conceived and designed the experiments: DD KT PLB. Performed the experiments: PLB. Analyzed the data: DD KT. Contributed reagents/materials/analysis tools: DD KT. Wrote the paper: DD KT.

                Article
                10-PLCB-RA-2551R3
                10.1371/journal.pcbi.1001089
                3044760
                21390333
                c1e3dc1d-cddd-4093-9571-99ce1c201142
                Delignières et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 15 July 2010
                : 20 January 2011
                Page count
                Pages: 10
                Categories
                Research Article
                Computational Biology/Computational Neuroscience
                Neuroscience/Motor Systems

                Quantitative & Systems biology
                Quantitative & Systems biology

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