Virtual reality to maximize function for hand and arm rehabilitation: exploration of neural mechanisms.

Stroke patients report hand function as the most disabling motor deficit. Current evidence shows that learning new motor skills is essential for inducing functional neuroplasticity and functional recovery. Adaptive training paradigms that continually and interactively move a motor outcome closer to the targeted skill are important to motor recovery. Computerized virtual reality simulations when interfaced with robots, movement tracking and sensing glove systems, are particularly adaptable, allowing for online and offline modifications of task based activities using the participant's current performance and success rate. We have developed a second generation system that can exercise the hand and the arm together or in isolation and provide for both unilateral and bilateral hand and arm activities in three-dimensional space. We demonstrate that by providing haptic assistance for the hand and arm and adaptive anti-gravity support, the system can accommodate patients with lower level impairments. We hypothesize that combining training in virtual environments (VE) with observation of motor actions can bring additional benefits. We present a proof of concept of a novel system that integrates interactive VE with functional neuroimaging to address this issue. Three components of this system are synchronized, the presentation of the visual display of the virtual hands, the collection of fMRI images and the collection of hand joint angles from the instrumented gloves. We show that interactive VEs can facilitate activation of brain areas during training by providing appropriately modified visual feedback. We predict that visual augmentation can become a tool to facilitate functional neuroplasticity.

[1]  C. Burgar,et al.  MIME robotic device for upper-limb neurorehabilitation in subacute stroke subjects: A follow-up study. , 2006, Journal of rehabilitation research and development.

[2]  N. Hogan,et al.  Movement Smoothness Changes during Stroke Recovery , 2002, The Journal of Neuroscience.

[3]  E. Tunik,et al.  Innovative approaches to the rehabilitation of upper extremity hemiparesis using virtual environments. , 2009, European journal of physical and rehabilitation medicine.

[4]  G. Rizzolatti,et al.  Motor facilitation during action observation: a magnetic stimulation study. , 1995, Journal of neurophysiology.

[5]  Mark Hallett,et al.  Cortical reorganization induced by virtual reality therapy in a child with hemiparetic cerebral palsy , 2005, Developmental medicine and child neurology.

[6]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[7]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[8]  N. Hogan,et al.  Robot-aided neurorehabilitation. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[9]  A. Merians,et al.  Strategies for Incorporating Bilateral Training into a Virtual Environment , 2007, 2007 IEEE/ICME International Conference on Complex Medical Engineering.

[10]  J. Sage,et al.  The interaction of visual and proprioceptive inputs in pointing to actual and remembered targets in Parkinson’s disease , 2001, Neuroscience.

[11]  K. Hiraoka Rehabilitation Effort to Improve Upper Extremity Function in Post-Stroke Patients: A Meta-Analysis , 2001 .

[12]  Ferdinando A. Mussa-Ivaldi,et al.  Robot-assisted adaptive training: custom force fields for teaching movement patterns , 2004, IEEE Transactions on Biomedical Engineering.

[13]  M. Hallett,et al.  Improving hand function in chronic stroke. , 2002, Archives of neurology.

[14]  J. Mazziotta,et al.  Grasping the Intentions of Others with One's Own Mirror Neuron System , 2005, PLoS biology.

[15]  Marc M. Sebrechts,et al.  HANDBOOK OF VIRTUAL ENVIRONMENTS , 2014 .

[16]  Qinyin Qiu,et al.  Incorporating Haptic Effects Into Three-Dimensional Virtual Environments to Train the Hemiparetic Upper Extremity , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[17]  Philippe Coiffet,et al.  Virtual Reality Technology , 1992 .

[18]  E. Taub,et al.  The EXCITE Trial: Attributes of the Wolf Motor Function Test in Patients with Subacute Stroke , 2005, Neurorehabilitation and neural repair.

[19]  Jill Whitall,et al.  Fine motor control in adults with and without chronic hemiparesis: baseline comparison to nondisabled adults and effects of bilateral arm training. , 2004, Archives of physical medicine and rehabilitation.

[20]  B. Brewer,et al.  Poststroke Upper Extremity Rehabilitation: A Review of Robotic Systems and Clinical Results , 2007, Topics in stroke rehabilitation.

[21]  A. G. Feldman,et al.  The timing of arm-trunk coordination is deficient and vision-dependent in Parkinson's patients during reaching movements , 2000, Experimental Brain Research.

[22]  T. Olsen,et al.  Arm and leg paresis as outcome predictors in stroke rehabilitation. , 1990, Stroke.

[23]  R. H. Jebsen,et al.  An objective and standardized test of hand function. , 1969, Archives of physical medicine and rehabilitation.

[24]  S. Adamovich,et al.  Sensorimotor Training in a Virtual Reality Environment: Does It Improve Functional Recovery Poststroke? , 2006, Neurorehabilitation and neural repair.

[25]  N. A. Borghese,et al.  Different Brain Correlates for Watching Real and Virtual Hand Actions , 2001, NeuroImage.

[26]  F. Binkofski,et al.  The mirror neuron system and action recognition , 2004, Brain and Language.

[27]  Emily S. Cross,et al.  Building a motor simulation de novo: Observation of dance by dancers , 2006, NeuroImage.

[28]  W. Rymer,et al.  Robot-assisted movement training for the stroke-impaired arm: Does it matter what the robot does? , 2006, Journal of rehabilitation research and development.

[29]  Soha Saleh,et al.  Journal of Neuroengineering and Rehabilitation Open Access the New Jersey Institute of Technology Robot-assisted Virtual Rehabilitation (njit-ravr) System for Children with Cerebral Palsy: a Feasibility Study , 2009 .

[30]  S. Adamovich,et al.  Virtual reality-augmented rehabilitation for patients following stroke. , 2002, Physical therapy.

[31]  M. Hallett,et al.  Virtual Reality–Induced Cortical Reorganization and Associated Locomotor Recovery in Chronic Stroke: An Experimenter-Blind Randomized Study , 2005, Stroke.

[32]  Scott T. Grafton,et al.  BOLD coherence reveals segregated functional neural interactions when adapting to distinct torque perturbations. , 2007, Journal of neurophysiology.

[33]  Á. Pascual-Leone,et al.  Phase-specific modulation of cortical motor output during movement observation , 2001, Neuroreport.

[34]  J. Mazziotta,et al.  Mirror neuron system: basic findings and clinical applications , 2007, Annals of neurology.

[35]  L. Stone,et al.  Rehabilitation of hemiparesis after stroke with a mirror , 1999, The Lancet.

[36]  Qinyin Qiu,et al.  Design of a Virtual Reality-Based System For Hand and Arm Rehabilitation , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

[37]  Nava Rubin,et al.  Brain areas selective for both observed and executed movements. , 2007, Journal of neurophysiology.

[38]  Howard Poizner,et al.  Development and application of virtual reality technology to improve hand use and gait of individuals post-stroke. , 2004, Restorative neurology and neuroscience.

[39]  G. Rizzolatti,et al.  The mirror-neuron system. , 2004, Annual review of neuroscience.

[40]  Scott T. Grafton,et al.  Goal Representation in Human Anterior Intraparietal Sulcus , 2006, The Journal of Neuroscience.

[41]  Maureen K. Holden,et al.  NEUROREHABILITATION USING ‘ LEARNING BY IMITATION ’ IN VIRTUAL ENVIRONMENTS , 2002 .