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      Asymmetric Sensory Reweighting in Human Upright Stance

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      1 , 2 , 1 , 1 , 2 , 3 , 4 , *
      PLoS ONE
      Public Library of Science

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          Abstract

          To investigate sensory reweighting as a fundamental property of sensor fusion during standing, we probed postural control with simultaneous rotations of the visual scene and surface of support. Nineteen subjects were presented with pseudo-random pitch rotations of visual scene and platform at the ankle to test for amplitude dependencies in the following conditions: low amplitude vision: high amplitude platform, low amplitude vision: low amplitude platform, and high amplitude vision: low amplitude platform. Gain and phase of frequency response functions (FRFs) to each stimulus were computed for two body sway angles and a single weighted EMG signal recorded from seven muscles. When platform stimulus amplitude was increased while visual stimulus amplitude remained constant, gain to vision increased, providing strong evidence for inter-modal reweighting between vision and somatosensation during standing. Intra-modal reweighting of vision was also observed as gains to vision decreased as visual stimulus amplitude increased. Such intra-modal and inter-modal amplitude dependent changes in gain were also observed in muscular activity. Gains of leg segment angle and muscular activity relative to the platform, on the other hand, showed only intra-modal reweighting. That is, changing platform motion amplitude altered the responses to both visual and support surface motion whereas changing visual scene motion amplitude did not significantly affect responses to support surface motion, indicating that the sensory integration scheme between somatosensation (at the support surface) and vision is asymmetric.

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          Most cited references22

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          Dynamic regulation of sensorimotor integration in human postural control.

          Upright stance in humans is inherently unstable, requiring corrective action based on spatial-orientation information from sensory systems. One might logically predict that environments providing access to accurate orientation information from multiple sensory systems would facilitate postural stability. However, we show that, after a period in which access to accurate sensory information was reduced, the restoration of accurate information disrupted postural stability. In eyes-closed trials, proprioceptive information was altered by rotating the support surface in proportion to body sway (support surface "sway-referencing"). When the support surface returned to a level orientation, most subjects developed a transient 1-Hz body sway oscillation that differed significantly from the low-amplitude body sway typically observed during quiet stance. Additional experiments showed further enhancement of the 1-Hz oscillation when the surface transitioned from a sway-referenced to a reverse sway-referenced motion. Oscillatory behavior declined with repetition of trials, suggesting a learning effect. A simple negative feedback-control model of the postural control system predicted the occurrence of this 1-Hz oscillation in conditions where too much corrective torque is generated in proportion to body sway. Model simulations were used to distinguish between two alternative explanations for the excessive corrective torque generation. Simulation results favor an explanation based on the dynamic reweighting of sensory contributions to postural control rather than a load-compensation mechanism that scales torque in proportion to a fixed combination of sensory-orientation information.
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            Adaptation to altered support and visual conditions during stance: patients with vestibular deficits.

            Patients whose deficits were limited to clinically well qualified vestibular disorders have been exposed to a number of altered support surface and visual environments while standing unsupported. A six-degrees-of-freedom platform employing movable support surfaces for each foot and a movable visual surround deprived patients of normal inputs derived from a fixed level support surface and from an immobile surround. Various tests employing EMG, force, and body movement recording identified quantitative changes in the patients' strategy for the relative weighting of proprioceptive, vestibular, and visual inputs. The most dramatic performance deficit of patients was their inability to suppress the influence of visual and proprioceptive inputs appropriately whenever motions of external surface disturbed the orientation information provided by these inputs. Thus, the more mildly afflicted patients experienced instability not so much because of the loss of vestibular inputs directly to posture but because of their inappropriate responses to proprioceptive inputs and vision. Discussion is centered on the role of vestibular input as an internal reference system for orientation about which adaptive changes in proprioceptive and visual inputs are made.
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              Multisensory control of human upright stance.

              The interaction of different orientation senses contributing to posture control is not well understood. We therefore performed experiments in which we measured the postural responses of normal subjects and vestibular loss patients during perturbation of their stance. Subjects stood on a motion platform with their eyes closed and auditory cues masked. The perturbing stimuli consisted of either platform tilts or external torque produced by force-controlled pull of the subjects' body on a stationary platform. Furthermore, we presented trials in which these two stimuli were applied when the platform was body-sway referenced (i.e., coupled 1:1 to body position, by which ankle joint proprioceptive feedback is essentially removed). We analyzed subjects' postural responses, i.e., the excursions of their center of mass (COM) and center of pressure (COP), using a systems analysis approach. We found gain and phase of the responses to vary as a function of stimulus frequency and in relation to the absence versus presence of vestibular and proprioceptive cues. In addition, gain depended on stimulus amplitude, reflecting a non-linearity in the control. The experimental results were compared to simulation results obtained from an 'inverted pendulum' model of posture control. In the model, sensor fusion mechanisms yield internal estimates of the external stimuli, i.e., of the external torque (pull), the platform tilt and gravity. These estimates are derived from three sensor systems: ankle proprioceptors, vestibular sensors and plantar pressure sensors (somatosensory graviceptors). They are fed as global set point signals into a local control loop of the ankle joints, which is based on proprioceptive negative feedback. This local loop stabilizes the body-on-foot support, while the set point signals upgrade the loop into a body-in-space control. Amplitude non-linearity was implemented in the model in the form of central threshold mechanisms. In model simulations that combined sensor fusion and thresholds, an automatic context-specific sensory re-weighting across stimulus conditions occurred. Model parameters were identified using an optimization procedure. Results suggested that in the sway-referenced condition normal subjects altered their postural strategy by strongly weighting feedback from plantar somatosensory force sensors. Taking this strategy change into account, the model's simulation results well paralleled all experimental results across all conditions tested.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                24 June 2014
                : 9
                : 6
                : e100418
                Affiliations
                [1 ]Department of Kinesiology, University of Maryland, College Park, Maryland, United States of America
                [2 ]Neuroscience and Cognitive Science Program, University of Maryland, College Park, Maryland, United States of America
                [3 ]Biomedical Engineering Graduate Program, University of Maryland, College Park, Maryland, United States of America
                [4 ]Department of Kinesiology, Temple University, Philadelphia, Pennsylvania, United States of America
                VU University Amsterdam, Netherlands
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: TK JJJ DL. Performed the experiments: DL. Analyzed the data: DL TK. Contributed reagents/materials/analysis tools: TK JJJ. Wrote the paper: DL TK JJJ.

                Article
                PONE-D-13-05626
                10.1371/journal.pone.0100418
                4069006
                24959665
                2c06cf85-4130-4d0a-87aa-afa44d9868e1
                Copyright @ 2014

                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
                : 7 February 2013
                : 27 May 2014
                Page count
                Pages: 10
                Funding
                Funding for this research was provided by National Institute of Neurological Disorders and Stroke grant R01 NS-35070 (PI: J. Jeka). NIH Reporter: http://projectreporter.nih.gov/project_info_description.cfm?aid=6911493&icde=15217597&ddparam=&ddvalue=&ddsub=&cr=3&csb=default&cs=ASC. Funding for Open Access provided by the UMD Libraries Open Access Publishing Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Anatomy
                Nervous System
                Motor System
                Computational Biology
                Computational Neuroscience
                Neuroscience
                Cognitive Neuroscience
                Motor Reactions
                Sensory Systems
                Visual System
                Sensory Perception

                Uncategorized
                Uncategorized

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