Literature DB >> 17267755

Reach adaptation and final position control amid environmental uncertainty after stroke.

Robert A Scheidt1, Tina Stoeckmann.   

Abstract

We characterized how hemiparetic stroke survivors and neurologically intact individuals adapt reaching movements to compensate for unpredictable environmental perturbations. We tested the hypotheses that like unimpaired subjects, hemiparetic stroke survivors adapt using sensory information obtained during only the most recent movements and that the reliability of target acquisition decreases as the degree of sensorimotor impairment increases. Subjects held the handle of a two-joint robotic arm that applied forces to the hand while reaching between targets in a horizontal plane. The robot simulated a dynamic environment that varied randomly in strength from one trial to the next. The trial sequence of perturbations had a nonzero mean value corresponding to information about the environment that subjects might learn. Stroke subjects were less effective than control subjects at adapting reaches to the perturbations. From a family of potential adaptation models, we found that the compensatory strategy patients used was the same as that used by neurologically intact subjects. However, analysis of model coefficients found that the relative weighting of prior perturbations and prior movement errors on subsequent reach attempts was significantly depressed poststroke. Regulation of final hand position was also impaired in the paretic limbs. Measures of trajectory adaptation and final position regulation deficits were significantly dependent on the integrity of limb proprioception and the amount of time poststroke. However, whereas model coefficients varied systematically with impairment level poststroke, variability of final positioning in the contralesional limb did not. This difference suggests that these two aspects of limb control may be differentially impaired poststroke.

Entities:  

Mesh:

Year:  2007        PMID: 17267755     DOI: 10.1152/jn.00870.2006

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  37 in total

1.  Visual, motor and attentional influences on proprioceptive contributions to perception of hand path rectilinearity during reaching.

Authors:  Robert A Scheidt; Kyle P Lillis; Scott J Emerson
Journal:  Exp Brain Res       Date:  2010-06-08       Impact factor: 1.972

Review 2.  Are we ready for a natural history of motor learning?

Authors:  Lior Shmuelof; John W Krakauer
Journal:  Neuron       Date:  2011-11-03       Impact factor: 17.173

3.  Greater reliance on impedance control in the nondominant arm compared with the dominant arm when adapting to a novel dynamic environment.

Authors:  Christopher N Schabowsky; Joseph M Hidler; Peter S Lum
Journal:  Exp Brain Res       Date:  2007-07-05       Impact factor: 1.972

4.  Patterns of hypermetria and terminal cocontraction during point-to-point movements demonstrate independent action of trajectory and postural controllers.

Authors:  Robert A Scheidt; Claude Ghez; Supriya Asnani
Journal:  J Neurophysiol       Date:  2011-08-17       Impact factor: 2.714

5.  Supplemental vibrotactile feedback of real-time limb position enhances precision of goal-directed reaching.

Authors:  Nicoletta Risi; Valay Shah; Leigh A Mrotek; Maura Casadio; Robert A Scheidt
Journal:  J Neurophysiol       Date:  2019-04-17       Impact factor: 2.714

Review 6.  Understanding sensorimotor adaptation and learning for rehabilitation.

Authors:  Amy J Bastian
Journal:  Curr Opin Neurol       Date:  2008-12       Impact factor: 5.710

7.  Overcoming motor "forgetting" through reinforcement of learned actions.

Authors:  Lior Shmuelof; Vincent S Huang; Adrian M Haith; Raymond J Delnicki; Pietro Mazzoni; John W Krakauer
Journal:  J Neurosci       Date:  2012-10-17       Impact factor: 6.167

8.  Trans-radial upper extremity amputees are capable of adapting to a novel dynamic environment.

Authors:  Christopher N Schabowsky; Alexander W Dromerick; Rahsaan J Holley; Brian Monroe; Peter S Lum
Journal:  Exp Brain Res       Date:  2008-04-29       Impact factor: 1.972

Review 9.  Robotic neurorehabilitation: a computational motor learning perspective.

Authors:  Vincent S Huang; John W Krakauer
Journal:  J Neuroeng Rehabil       Date:  2009-02-25       Impact factor: 4.262

10.  A pilot study evaluating use of a computer-assisted neurorehabilitation platform for upper-extremity stroke assessment.

Authors:  Xin Feng; Jack M Winters
Journal:  J Neuroeng Rehabil       Date:  2009-05-28       Impact factor: 4.262

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