Literature DB >> 29564503

Transitions in persistence of postural dynamics depend on the velocity and structure of postural perturbations.

Troy J Rand1, Mukul Mukherjee2.   

Abstract

The sensorimotor system prefers sway velocity information when maintaining upright posture. Sway velocity has a unique characteristic of being persistent on a short time-scale and anti-persistent on a longer time-scale. The time where the transition from persistence to anti-persistence occurs provides information about how sway velocity is controlled. It is, however, not clear what factors affect shifts in this transition point. This research investigated postural responses to support surface movements of different temporal correlations and movement velocities. Participants stood on a force platform that was translated according to three different levels of temporal correlation. White noise had no correlation, pink noise had moderate correlation, and sine wave movements had very strong correlation. Each correlation structure was analyzed at five different average movement velocities (0.5, 1.0, 2.0, 3.0, and 4.0 cm·s-1), as well as one trial of quiet stance. Center of pressure velocity was analyzed using fractal analysis to determine the transition from persistent to anti-persistent behavior, as well as the strength of persistence. As movement velocity increased, the time to transition became longer for the sine wave and shorter for the white and pink noise movements. Likewise, during the persistent time-scale, the sine wave resulted in the strongest correlation, while white and pink noise had weaker correlations. At the highest three movement velocities, the strength of persistence was lower for the white noise compared to pink noise movements. These results demonstrate that the predictability and velocity of support surface oscillations affect the time-scale threshold between persistent and anti-persistent postural responses. Consequently, whether a feedforward or feedback control is utilized for appropriate postural responses may also be determined by the predictability and velocity of environmental stimuli. The study provides new insight into flexibility and adaptability in postural control. This information has implications for the design of rehabilitative protocols in neuromuscular control.

Entities:  

Keywords:  Crossover; Detrended fluctuation analysis; Feedback; Feedforward; Fractal; Temporal correlation

Mesh:

Year:  2018        PMID: 29564503      PMCID: PMC5936470          DOI: 10.1007/s00221-018-5235-1

Source DB:  PubMed          Journal:  Exp Brain Res        ISSN: 0014-4819            Impact factor:   1.972


  27 in total

1.  Postural control model interpretation of stabilogram diffusion analysis.

Authors:  R J Peterka
Journal:  Biol Cybern       Date:  2000-04       Impact factor: 2.086

2.  Controlling human upright posture: velocity information is more accurate than position or acceleration.

Authors:  John Jeka; Tim Kiemel; Robert Creath; Fay Horak; Robert Peterka
Journal:  J Neurophysiol       Date:  2004-05-12       Impact factor: 2.714

3.  Postural responses evoked by platform pertubations are dominated by continuous feedback.

Authors:  Herman van der Kooij; Erwin de Vlugt
Journal:  J Neurophysiol       Date:  2007-04-25       Impact factor: 2.714

4.  An empirical examination of detrended fluctuation analysis for gait data.

Authors:  Sotirios Damouras; Matthew D Chang; Ervin Sejdić; Tom Chau
Journal:  Gait Posture       Date:  2010-01-13       Impact factor: 2.840

Review 5.  Human movement variability, nonlinear dynamics, and pathology: is there a connection?

Authors:  Nicholas Stergiou; Leslie M Decker
Journal:  Hum Mov Sci       Date:  2011-07-29       Impact factor: 2.161

6.  Open-loop and closed-loop control of posture: a random-walk analysis of center-of-pressure trajectories.

Authors:  J J Collins; C J De Luca
Journal:  Exp Brain Res       Date:  1993       Impact factor: 1.972

7.  Long-range temporal correlations in the spontaneous spiking of neurons in the hippocampal-amygdala complex of humans.

Authors:  J Bhattacharya; J Edwards; A N Mamelak; E M Schuman
Journal:  Neuroscience       Date:  2005       Impact factor: 3.590

8.  Postural coordination patterns as a function of rhythmical dynamics of the surface of support.

Authors:  Ji-Hyun Ko; John H Challis; Karl M Newell
Journal:  Exp Brain Res       Date:  2013-02-08       Impact factor: 1.972

9.  Postural coordination in elderly subjects standing on a periodically moving platform.

Authors:  A Nardone; M Grasso; J Tarantola; S Corna; M Schieppati
Journal:  Arch Phys Med Rehabil       Date:  2000-09       Impact factor: 3.966

10.  Transition from persistent to anti-persistent correlations in postural sway indicates velocity-based control.

Authors:  Didier Delignières; Kjerstin Torre; Pierre-Louis Bernard
Journal:  PLoS Comput Biol       Date:  2011-02-24       Impact factor: 4.475

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  4 in total

1.  Persistence in postural dynamics is dependent on constraints of vision, postural orientation, and the temporal structure of support surface translations.

Authors:  Troy J Rand; Venkata Naga Pradeep Ambati; Mukul Mukherjee
Journal:  Exp Brain Res       Date:  2018-12-01       Impact factor: 1.972

2.  Adaptive treadmill walking encourages persistent propulsion.

Authors:  Margo C Donlin; Kayla M Pariser; Kaitlyn E Downer; Jill S Higginson
Journal:  Gait Posture       Date:  2022-02-16       Impact factor: 2.840

3.  Comparison of a portable balance board for measures of persistence in postural sway.

Authors:  Zachary S Meade; Vivien Marmelat; Mukul Mukherjee; Takashi Sado; Kota Z Takahashi
Journal:  J Biomech       Date:  2020-01-03       Impact factor: 2.712

4.  Characterizing stroke-induced changes in the variability of lower limb kinematics using multifractal detrended fluctuation analysis.

Authors:  Pan Xu; Hairong Yu; Xiaoyun Wang; Rong Song
Journal:  Front Neurol       Date:  2022-08-05       Impact factor: 4.086

  4 in total

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