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Experimental Brain Research

, Volume 207, Issue 3–4, pp 233–247 | Cite as

Functional reorganization of upper-body movement after spinal cord injury

  • Maura CasadioEmail author
  • Assaf Pressman
  • Alon Fishbach
  • Zachary Danziger
  • Santiago Acosta
  • David Chen
  • Hsiang-Yi Tseng
  • Ferdinando A. Mussa-Ivaldi
Research Article

Abstract

Survivors of spinal cord injury need to reorganize their residual body movements for interacting with assistive devices and performing activities that used to be easy and natural. To investigate movement reorganization, we asked subjects with high-level spinal cord injury (SCI) and unimpaired subjects to control a cursor on a screen by performing upper-body motions. While this task would be normally accomplished by operating a computer mouse, here shoulder motions were mapped into the cursor position. Both the control and the SCI subjects were rapidly able to reorganize their movements and to successfully control the cursor. The majority of the subjects in both groups were successful in reducing the movements that were not effective at producing cursor motions. This is inconsistent with the hypothesis that the control system is merely concerned with the accurate acquisition of the targets and is unconcerned with motions that are not relevant to this goal. In contrast, our findings suggest that subjects can learn to reorganize coordination so as to increase the correspondence between the subspace of their upper-body motions with the plane in which the controlled cursor moves. This is effectively equivalent to constructing an inverse internal model of the map from body motions to cursor motions, established by the experiment. These results are relevant to the development of interfaces for assistive devices that optimize the use of residual voluntary control and enhance the learning process in disabled users, searching for an easily learnable map between their body motor space and control space of the device.

Keywords

Spinal cord injury Reorganization of movement Motor learning Human–machine interface 

Notes

Acknowledgments

This work was supported by NINDS grants 1R21HD053608 and 1R01NS053581-01A2, by the Neilsen Foundation and by the Brinson Foundation.

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Maura Casadio
    • 1
    • 2
    Email author
  • Assaf Pressman
    • 1
    • 3
  • Alon Fishbach
    • 1
    • 4
  • Zachary Danziger
    • 1
    • 4
  • Santiago Acosta
    • 1
  • David Chen
    • 5
  • Hsiang-Yi Tseng
    • 5
  • Ferdinando A. Mussa-Ivaldi
    • 1
    • 2
    • 4
  1. 1.Sensory Motor Performance ProgramRehabilitation Institute of ChicagoChicagoUSA
  2. 2.Department of PhysiologyNorthwestern UniversityChicagoUSA
  3. 3.Department of Biomedical EngineeringBen-Gurion University of the NegevBe’er-ShevaIsrael
  4. 4.Department of Physical Medicine and RehabilitationNorthwestern UniversityChicagoUSA
  5. 5.Rehabilitation Institute of ChicagoChicagoUSA

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