Adaptation to visuomotor rotation in isometric reaching is similar to movement adaptation

Isometric reaching, in which the arm remains stationary and the user controls a virtual cursor via force input, is a motor task that has not been thoroughly compared to real reaching. In this study, we ask if isometric adaptation to a kinematic perturbation is similar to adaptation in movement, and if the type of isometric mapping (position or velocity control) influences learning. Healthy subjects made real and virtual reaches with the arm in plane. In some trials, the cursor was rotated counter clockwise by 45° to perturb the kinematic mapping. To assess adaptation, the angular error of cursor movement at 150 ms from movement onset was measured for each reach; error was averaged across subjects and a two-state learning mode was fit to error data. For movement and isometric groups, average angular error peaked at perturbation onset, reduced over 200 reaches, and reversed direction when the perturbation was removed. We show that subjects are able to adapt to a visuomotor rotation in both position- and velocity-based cursor control, and that the time course of adaptation resembles that of movement adaptation. Training of virtual reaching using force/torque input could be particularly applicable for stroke patients with significant movement deficits, who could benefit from intensive treatments using simple, cost-effective devices.

[1]  Stephan Riek,et al.  Visual target separation determines the extent of generalisation between opposing visuomotor rotations , 2011, Experimental Brain Research.

[2]  Julius P A Dewald,et al.  Modifiability of abnormal isometric elbow and shoulder joint torque coupling after stroke , 2005, Muscle & nerve.

[3]  Allison M. Okamura,et al.  Gradual anisometric-isometric transition for human-machine interfaces , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Daniel M. Wolpert,et al.  A modular planar robotic manipulandum with end-point torque control , 2009, Journal of Neuroscience Methods.

[5]  William S. Harwin,et al.  Challenges and Opportunities for Robot-Mediated Neurorehabilitation , 2006, Proceedings of the IEEE.

[6]  Hermano I Krebs,et al.  Rehabilitation robotics: pilot trial of a spatial extension for MIT-Manus , 2004, Journal of NeuroEngineering and Rehabilitation.

[7]  Antony J Hodgson,et al.  Time and magnitude of torque generation is impaired in both arms following stroke , 2003, Muscle & nerve.

[8]  S. Riek,et al.  Dual adaptation to two opposing visuomotor rotations when each is associated with different regions of workspace , 2007, Experimental Brain Research.

[9]  J P Dewald,et al.  Upper-Limb Discoordination in Hemiparetic Stroke: Implications for Neurorehabilitation , 2001, Topics in stroke rehabilitation.

[10]  R. Shadmehr,et al.  Interacting Adaptive Processes with Different Timescales Underlie Short-Term Motor Learning , 2006, PLoS biology.

[11]  C. Ghez,et al.  Discrete and continuous planning of hand movements and isometric force trajectories , 1997, Experimental Brain Research.

[12]  Paolo Bonato,et al.  JNER: a forum to discuss how neuroscience and biomedical engineering are reshaping physical medicine & rehabilitation , 2004, Journal of NeuroEngineering and Rehabilitation.

[13]  Ian M Franks,et al.  Updating of an internal model without proprioception: a deafferentation study , 2006, Neuroreport.

[14]  C Ghez,et al.  Learning of Visuomotor Transformations for Vectorial Planning of Reaching Trajectories , 2000, The Journal of Neuroscience.

[15]  Shumin Zhai,et al.  Human performance evaluation of manipulation schemes in virtual environments , 1993, Proceedings of IEEE Virtual Reality Annual International Symposium.

[16]  N. Schweighofer,et al.  Dual Adaptation Supports a Parallel Architecture of Motor Memory , 2009, The Journal of Neuroscience.

[17]  Aymar de Rugy,et al.  The synergistic organization of muscle recruitment constrains visuomotor adaptation. , 2009, Journal of neurophysiology.

[18]  Roberta Klatzky,et al.  Visual feedback distortion in a robotic environment for hand rehabilitation , 2008, Brain Research Bulletin.

[19]  John W. Krakauer,et al.  Independent learning of internal models for kinematic and dynamic control of reaching , 1999, Nature Neuroscience.

[20]  A Melendez-Calderon,et al.  Force Field Adaptation Can Be Learned Using Vision in the Absence of Proprioceptive Error , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.