Literature DB >> 18640668

Submovement changes characterize generalization of motor recovery after stroke.

Laura Dipietro1, Hermano I Krebs, Susan E Fasoli, Bruce T Volpe, Neville Hogan.   

Abstract

Submovements are hypothesized to be discrete building blocks of human movement. Changes in their parameters appear to account for features observed in processes of motor learning and motor recovery from stroke. Our previous studies analyzed submovement changes in subjects recovering from stroke. Subjects were trained on point-to-point movements with the assistance of a rehabilitation robot as part of a stroke treatment protocol. Results suggested that recovery starts first by regaining the ability to generate submovements and then, over a longer time period, by reacquiring the means to combine submovements. Over recovery submovements became fewer, longer, and faster and such changes contributed to changes in movement smoothness. Taken together these results lent support to the theory that movement is produced via centrally generated submovements and that changes in submovements characterize recovery. More recently, we investigated generalization of training. We found that stroke subjects trained on point-to-point movements became progressively better able to draw circles, a task on which they had received no training. The goal of this paper was to further investigate the changes that occur in untrained movements during motor recovery from stroke. Specifically we wanted to test whether changes in smoothness and submovements also characterize untrained movements. We analyzed circle drawing movements performed by 47 chronic stroke subjects who underwent training on point-to-point movements over an 18-session robot-assisted therapy program. We found that during recovery the shapes drawn by subjects became not only closer to circles (a task not trained during therapy) but also smoother. Concurrently, submovements grew fewer, longer, and faster. These results are consistent with the theory that movement is produced via submovements and suggest that changes in smoothness and submovements might characterize and describe the process of motor recovery from stroke. Also, they are consistent with the idea that motor recovery after a stroke shares similar traits with motor learning.

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Year:  2008        PMID: 18640668     DOI: 10.1016/j.cortex.2008.02.008

Source DB:  PubMed          Journal:  Cortex        ISSN: 0010-9452            Impact factor:   4.027


  37 in total

1.  Bilateral assessment of functional tasks for robot-assisted therapy applications.

Authors:  Michelle J Johnson; Sarah Wang; Ping Bai; Elaine Strachota; Guennady Tchekanov; Jeff Melbye; John McGuire
Journal:  Med Biol Eng Comput       Date:  2011-09-01       Impact factor: 2.602

2.  Proximal-distal differences in movement smoothness reflect differences in biomechanics.

Authors:  Layne H Salmond; Andrew D Davidson; Steven K Charles
Journal:  J Neurophysiol       Date:  2016-12-21       Impact factor: 2.714

3.  Spatiotemporal dynamics of online motor correction processing revealed by high-density electroencephalography.

Authors:  Laura Dipietro; Howard Poizner; Hermano I Krebs
Journal:  J Cogn Neurosci       Date:  2014-02-24       Impact factor: 3.225

4.  Robotically facilitated virtual rehabilitation of arm transport integrated with finger movement in persons with hemiparesis.

Authors:  Alma S Merians; Gerard G Fluet; Qinyin Qiu; Soha Saleh; Ian Lafond; Amy Davidow; Sergei V Adamovich
Journal:  J Neuroeng Rehabil       Date:  2011-05-16       Impact factor: 4.262

5.  Learning, not adaptation, characterizes stroke motor recovery: evidence from kinematic changes induced by robot-assisted therapy in trained and untrained task in the same workspace.

Authors:  L Dipietro; H I Krebs; B T Volpe; J Stein; C Bever; S T Mernoff; S E Fasoli; N Hogan
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2011-12-16       Impact factor: 3.802

6.  Timing variability of reach trajectories in left versus right hemisphere stroke.

Authors:  Sandra Maria Sbeghen Ferreira Freitas; Geetanjali Gera; John Peter Scholz
Journal:  Brain Res       Date:  2011-08-22       Impact factor: 3.252

7.  A comparative analysis of speed profile models for wrist pointing movements.

Authors:  Lev Vaisman; Laura Dipietro; Hermano Igo Krebs
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2012-12-10       Impact factor: 3.802

8.  Robotics: A Rehabilitation Modality.

Authors:  Hermano Igo Krebs; Bruce T Volpe
Journal:  Curr Phys Med Rehabil Rep       Date:  2015-10-13

9.  Rhythmic arm movements are less affected than discrete ones after a stroke.

Authors:  Patricia Leconte; Jean-Jacques Orban de Xivry; Gaëtan Stoquart; Thierry Lejeune; Renaud Ronsse
Journal:  Exp Brain Res       Date:  2016-01-09       Impact factor: 1.972

Review 10.  Robotic devices as therapeutic and diagnostic tools for stroke recovery.

Authors:  Bruce T Volpe; Patricio T Huerta; Johanna L Zipse; Avrielle Rykman; Dylan Edwards; Laura Dipietro; Neville Hogan; Hermano I Krebs
Journal:  Arch Neurol       Date:  2009-09
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