Optimizing Hand Rehabilitation Post-Stroke Using Interactive Virtual Environments

Overview

The complexity of sensorimotor control required for hand function as well as the wide range of recovery of manipulative abilities makes rehabilitation of the hand most challenging. The investigators past work has shown that training in a virtual environment (VE) using repetitive, adaptive algorithms has the potential to be an effective rehabilitation medium to facilitate motor recovery of hand function. These findings are in accordance with current neuroscience literature in animals and motor control literature in humans. The investigators are now in a position to refine and optimize elements of the training paradigms to enhance neuroplasticity. The investigators first aim tests if and how competition among body parts for neural representations stifles functional gains from different types of training regimens. The second aim tests the functional benefits of unilateral versus bilateral training regimens.The third aim tests whether functional improvements gained from training in a virtual environment transfer to other (untrained) skills in the real world.

Study Type

  • Study Type: Interventional
  • Study Design
    • Allocation: Randomized
    • Intervention Model: Parallel Assignment
    • Primary Purpose: Treatment
    • Masking: Double (Participant, Outcomes Assessor)
  • Study Primary Completion Date: March 2013

Detailed Description

The complexity of sensorimotor control required for hand function as well as the wide range of recovery of manipulative abilities makes rehabilitation of the hand most challenging. The investigators past work has shown that training in a virtual environment (VE) using repetitive, adaptive algorithms has the potential to be an effective rehabilitation medium to facilitate motor recovery of hand function. These findings are in accordance with current neuroscience literature in animals and motor control literature in humans. The investigators are now in a position to refine and optimize elements of the training paradigms to enhance neuroplasticity. The investigators first aim tests if and how competition among body parts for neural representations stifles functional gains from different types of training regimens. The second aim tests the functional benefits of unilateral versus bilateral training regimens.The third aim tests whether functional improvements gained from training in a virtual environment transfer to other (untrained) skills in the real world.

Interventions

  • Behavioral: HAS Training
    • Robotically measured and facilitated training of the hemiparetic hand and arm in isolation, in a three dimensional haptically rendered virtual environment.
  • Behavioral: HAT training
    • Robotically measured and facilitated training of the hemiparetic hand and arm as an integrated functional unit, in a three dimensional haptically rendered virtual environment
  • Behavioral: Bimanual Training
    • Robotically measured and facilitated training of the hemiparetic hand and non-hemiparetic hand together, in a three dimensional haptically rendered virtual environment

Arms, Groups and Cohorts

  • Active Comparator: Train Paretic Hand and Arm Separate
    • Eight three hour training sessions of robotically facilitated hand and arm training in complex virtual environments, using activities that train the fingers in isolation and other activities that train the arm in isolation.
  • Experimental: Train Paretic Hand and Arm Together
  • Experimental: Train Both Hands Together in VE

Clinical Trial Outcome Measures

Primary Measures

  • Change in Jebsen Test of Hand Function
    • Time Frame: Two Weeks Prior to Training, Immediately Prior to Training, Immediately After Training, 3 Months After Training

Secondary Measures

  • Change in Wolf Motor Function Test
    • Time Frame: Two Weeks Prior to Training, Immediately Prior to Training, Immediately After Training, 3 Months After Training
  • Change in 9 Hole Peg Test
    • Time Frame: Two Weeks Prior to Training, Immediately Prior to Training, Immediately After Training, 3 Months After Training
  • Change in Box and Blocks Test
    • Time Frame: Two Weeks Prior to Training, Immediately Prior to Training, Immediately After Training, 3 Months After Training
  • Change in Robotically Collected Kinematics
    • Time Frame: 1 day before training and 1 day after training
  • Change in Reach to Grasp Test
    • Time Frame: 1 day before training and 1 day after training

Participating in This Clinical Trial

Inclusion Criteria

  • Six months post cerebrovascular accident
  • Residual upper extremity impairment that affects participation
  • At least ten degrees of active finger extension
  • Tolerate passive shoulder flexion to chest level

Exclusion Criteria

  • Severe neglect
  • Severe aphasia

Gender Eligibility: All

Minimum Age: 18 Years

Maximum Age: 80 Years

Are Healthy Volunteers Accepted: No

Investigator Details

  • Lead Sponsor
    • New Jersey Institute of Technology
  • Collaborator
    • Rutgers, The State University of New Jersey
  • Provider of Information About this Clinical Study
    • Principal Investigator: Sergei V. Adamovich PhD, Associate Professor – New Jersey Institute of Technology
  • Overall Official(s)
    • Sergei V. Adamovich, PhD, Principal Investigator, New Jersey Institute of Technology

References

Adamovich SV, Fluet GG, Tunik E, Merians AS. Sensorimotor training in virtual reality: a review. NeuroRehabilitation. 2009;25(1):29-44. doi: 10.3233/NRE-2009-0497. Review.

Tunik E, Adamovich SV. Remapping in the ipsilesional motor cortex after VR-based training: a pilot fMRI study. Conf Proc IEEE Eng Med Biol Soc. 2009;2009:1139-42. doi: 10.1109/IEMBS.2009.5335392.

Fluet GG, Merians AS, Qiu Q, Lafond I, Saleh S, Ruano V, Delmonico AR, Adamovich SV. Robots integrated with virtual reality simulations for customized motor training in a person with upper extremity hemiparesis: a case study. J Neurol Phys Ther. 2012 Jun;36(2):79-86. doi: 10.1097/NPT.0b013e3182566f3f.

Tunik E, Saleh S, Adamovich SV. Visuomotor discordance during visually-guided hand movement in virtual reality modulates sensorimotor cortical activity in healthy and hemiparetic subjects. IEEE Trans Neural Syst Rehabil Eng. 2013 Mar;21(2):198-207. doi: 10.1109/TNSRE.2013.2238250. Epub 2013 Jan 9.

Bagce HF, Saleh S, Adamovich SV, Krakauer JW, Tunik E. Corticospinal excitability is enhanced after visuomotor adaptation and depends on learning rather than performance or error. J Neurophysiol. 2013 Feb;109(4):1097-106. doi: 10.1152/jn.00304.2012. Epub 2012 Nov 28.

Bagce HF, Saleh S, Adamovich SV, Tunik E. Visuomotor gain distortion alters online motor performance and enhances primary motor cortex excitability in patients with stroke. Neuromodulation. 2012 Jul;15(4):361-6. doi: 10.1111/j.1525-1403.2012.00467.x. Epub 2012 Jun 1.

Saleh S, Adamovich SV, Tunik E. Resting state functional connectivity and task-related effective connectivity changes after upper extremity rehabilitation: a pilot study. Conf Proc IEEE Eng Med Biol Soc. 2012;2012:4559-62. doi: 10.1109/EMBC.2012.6346981.

Saleh S, Adamovich SV, Tunik E. Mirrored feedback in chronic stroke: recruitment and effective connectivity of ipsilesional sensorimotor networks. Neurorehabil Neural Repair. 2014 May;28(4):344-54. doi: 10.1177/1545968313513074. Epub 2013 Dec 26.

Yarossi M, Adamovich S, Tunik E. Sensorimotor cortex reorganization in subacute and chronic stroke: A neuronavigated TMS study. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:5788-91. doi: 10.1109/EMBC.2014.6944943.

Schettino LF, Adamovich SV, Bagce H, Yarossi M, Tunik E. Disruption of activity in the ventral premotor but not the anterior intraparietal area interferes with on-line correction to a haptic perturbation during grasping. J Neurosci. 2015 Feb 4;35(5):2112-7. doi: 10.1523/JNEUROSCI.3000-14.2015.

Citations Reporting on Results

Qiu Q, Fluet GG, Lafond I, Merians AS, Adamovich SV. Coordination changes demonstrated by subjects with hemiparesis performing hand-arm training using the NJIT-RAVR robotically assisted virtual rehabilitation system. Conf Proc IEEE Eng Med Biol Soc. 2009;2009:1143-6. doi: 10.1109/IEMBS.2009.5335384.

Adamovich SV, Fluet GG, Merians AS, Mathai A, Qiu Q. Incorporating haptic effects into three-dimensional virtual environments to train the hemiparetic upper extremity. IEEE Trans Neural Syst Rehabil Eng. 2009 Oct;17(5):512-20. doi: 10.1109/TNSRE.2009.2028830. Epub 2009 Aug 7.

Adamovich SV, Fluet GG, Mathai A, Qiu Q, Lewis J, Merians AS. Design of a complex virtual reality simulation to train finger motion for persons with hemiparesis: a proof of concept study. J Neuroeng Rehabil. 2009 Jul 17;6:28. doi: 10.1186/1743-0003-6-28.

Merians AS, Fluet GG, Qiu Q, Saleh S, Lafond I, Davidow A, Adamovich SV. Robotically facilitated virtual rehabilitation of arm transport integrated with finger movement in persons with hemiparesis. J Neuroeng Rehabil. 2011 May 16;8:27. doi: 10.1186/1743-0003-8-27.

Fluet GG, Merians AS, Qiu Q, Davidow A, Adamovich SV. Comparing integrated training of the hand and arm with isolated training of the same effectors in persons with stroke using haptically rendered virtual environments, a randomized clinical trial. J Neuroeng Rehabil. 2014 Aug 23;11:126. doi: 10.1186/1743-0003-11-126.

Fluet GG, Merians AS, Qiu Q, Rohafaza M, VanWingerden AM, Adamovich SV. Does training with traditionally presented and virtually simulated tasks elicit differing changes in object interaction kinematics in persons with upper extremity hemiparesis? Top Stroke Rehabil. 2015 Jun;22(3):176-84. doi: 10.1179/1074935714Z.0000000008. Epub 2015 Jan 22.

Puthenveettil S, Fluet G, Qiu Q, Adamovich S. Classification of hand preshaping in persons with stroke using Linear Discriminant Analysis. Conf Proc IEEE Eng Med Biol Soc. 2012;2012:4563-6. doi: 10.1109/EMBC.2012.6346982.

Boos A, Qiu Q, Fluet GG, Adamovich SV. Haptically facilitated bimanual training combined with augmented visual feedback in moderate to severe hemiplegia. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:3111-4. doi: 10.1109/IEMBS.2011.6090849.

Qiu Q, Adamovich S, Saleh S, Lafond I, Merians AS, Fluet GG. A comparison of motor adaptations to robotically facilitated upper extremity task practice demonstrated by children with cerebral palsy and adults with stroke. IEEE Int Conf Rehabil Robot. 2011;2011:5975431. doi: 10.1109/ICORR.2011.5975431.

Rohafza M, Fluet GG, Qiu Q, Adamovich S. Correlations between statistical models of robotically collected kinematics and clinical measures of upper extremity function. Conf Proc IEEE Eng Med Biol Soc. 2012;2012:4120-3. doi: 10.1109/EMBC.2012.6346873.

Rohafza M, Fluet GG, Qiu Q, Adamovich S. Correlation of reaching and grasping kinematics and clinical measures of upper extremity function in persons with stroke related hemiplegia. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:3610-3. doi: 10.1109/EMBC.2014.6944404.

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