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      Preserved motor learning after stroke is related to the degree of proprioceptive deficit

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      1 , 2 ,
      Behavioral and Brain Functions : BBF
      BioMed Central

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          Abstract

          Background

          Most motor learning theories posit that proprioceptive sensation serves an important role in acquiring and performing movement patterns. However, we recently demonstrated that experimental disruption of proprioception peripherally altered motor performance but not motor learning in humans. Little work has considered humans with central nervous system damage. The purpose of the present study was to specifically consider the relationship between proprioception and motor learning at the level of the central nervous system in humans.

          Methods

          Individuals with chronic (> 6mo) stroke and similarly aged healthy participants performed a continuous tracking task with an embedded repeating segment over two days and returned on a third day for retention testing. A limb-position matching task was used to quantify proprioception.

          Results

          Individuals with chronic stroke demonstrated the ability to learn to track a repeating segment; however, the magnitude of behavioral change associated with repeated segment-specific learning was directly related to the integrity of central proprioceptive processing as indexed by our limb-position matching task.

          Conclusion

          These results support the importance of central sensory processing for motor learning. The confirmation of central sensory processing dependent motor learning in humans is discussed in the context of our prior report of preserved motor learning when sensation is disrupted peripherally.

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          Most cited references49

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          Attentional requirements of learning: Evidence from performance measures

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            Adaptive representation of dynamics during learning of a motor task.

            We investigated how the CNS learns to control movements in different dynamical conditions, and how this learned behavior is represented. In particular, we considered the task of making reaching movements in the presence of externally imposed forces from a mechanical environment. This environment was a force field produced by a robot manipulandum, and the subjects made reaching movements while holding the end-effector of this manipulandum. Since the force field significantly changed the dynamics of the task, subjects' initial movements in the force field were grossly distorted compared to their movements in free space. However, with practice, hand trajectories in the force field converged to a path very similar to that observed in free space. This indicated that for reaching movements, there was a kinematic plan independent of dynamical conditions. The recovery of performance within the changed mechanical environment is motor adaptation. In order to investigate the mechanism underlying this adaptation, we considered the response to the sudden removal of the field after a training phase. The resulting trajectories, named aftereffects, were approximately mirror images of those that were observed when the subjects were initially exposed to the field. This suggested that the motor controller was gradually composing a model of the force field, a model that the nervous system used to predict and compensate for the forces imposed by the environment. In order to explore the structure of the model, we investigated whether adaptation to a force field, as presented in a small region, led to aftereffects in other regions of the workspace. We found that indeed there were aftereffects in workspace regions where no exposure to the field had taken place; that is, there was transfer beyond the boundary of the training data. This observation rules out the hypothesis that the subject's model of the force field was constructed as a narrow association between visited states and experienced forces; that is, adaptation was not via composition of a look-up table. In contrast, subjects modeled the force field by a combination of computational elements whose output was broadly tuned across the motor state space. These elements formed a model that extrapolated to outside the training region in a coordinate system similar to that of the joints and muscles rather than end-point forces. This geometric property suggests that the elements of the adaptive process represent dynamics of a motor task in terms of the intrinsic coordinate system of the sensors and actuators.
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              Maintaining internal representations: the role of the human superior parietal lobe.

              In sensorimotor integration, sensory input and motor output signals are combined to provide an internal estimate of the state of both the world and one's own body. Although a single perceptual and motor snapshot can provide information about the current state, computational models show that the state can be optimally estimated by a recursive process in which an internal estimate is maintained and updated by the current sensory and motor signals. These models predict that an internal state estimate is maintained or stored in the brain. Here we report a patient with a lesion of the superior parietal lobe who shows both sensory and motor deficits consistent with an inability to maintain such an internal representation between updates. Our findings suggest that the superior parietal lobe is critical for sensorimotor integration, by maintaining an internal representation of the body's state.
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                Author and article information

                Journal
                Behav Brain Funct
                Behavioral and Brain Functions : BBF
                BioMed Central
                1744-9081
                2009
                28 August 2009
                : 5
                : 36
                Affiliations
                [1 ]Department of Neurology, University of Kansas Medical Center, Kansas City, USA
                [2 ]Department of Physical Therapy, University of British Columbia Vancouver, British Columbia, Canada
                Article
                1744-9081-5-36
                10.1186/1744-9081-5-36
                2740848
                19715593
                1f3610d9-adeb-4784-b687-bf6b167585c6
                Copyright © 2009 Vidoni and Boyd; 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.

                History
                : 10 February 2009
                : 28 August 2009
                Categories
                Research

                Neurology
                Neurology

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