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. Our 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. We are now in a position to refine and optimize elements of the training paradigms to enhance neuroplasticity.
Our 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.

Public Health Relevance

The potential benefit of this study will be to provide more targeted therapeutic interventions so as to maximize recovery of function in the hemiplegic hand of patients who have had a stroke. The study will use interactive virtual reality-based gaming simulations to encourage the intensity of practice needed for motor recovery. Information gained will help to uncover the potential benefits of these interventions on facilitating recovery of hand function.

Agency
National Institute of Health (NIH)
Institute
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Type
Research Project (R01)
Project #
5R01HD058301-04
Application #
8238319
Study Section
Musculoskeletal Rehabilitation Sciences Study Section (MRS)
Program Officer
Shinowara, Nancy
Project Start
2009-03-05
Project End
2014-02-28
Budget Start
2012-03-01
Budget End
2014-02-28
Support Year
4
Fiscal Year
2012
Total Cost
$274,440
Indirect Cost
$48,032
Name
Rutgers University
Department
Biomedical Engineering
Type
Schools of Engineering
DUNS #
075162990
City
Newark
State
NJ
Country
United States
Zip Code
07102
Fluet, Gerard G; Merians, Alma S; Qiu, Qinyin et al. (2015) 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 22:176-84
Schettino, Luis F; Adamovich, Sergei V; Bagce, Hamid et al. (2015) 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 35:2112-7
Fluet, Gerard G; Merians, Alma S; Qiu, Qinyin et al. (2014) 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 11:126
Guo, Yi; Foulds, Richard A; Adamovich, Sergei V et al. (2014) Encoding of forelimb forces by corticospinal tract activity in the rat. Front Neurosci 8:62
Saleh, Soha; Adamovich, Sergei V; Tunik, Eugene (2014) Mirrored feedback in chronic stroke: recruitment and effective connectivity of ipsilesional sensorimotor networks. Neurorehabil Neural Repair 28:344-54
Rohafza, Maryam; Fluet, Gerard G; Qiu, Qinyin et al. (2014) 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 2014:3610-3
Yarossi, Mathew; Adamovich, Sergei; Tunik, Eugene (2014) Sensorimotor cortex reorganization in subacute and chronic stroke: A neuronavigated TMS study. Conf Proc IEEE Eng Med Biol Soc 2014:5788-91
Tunik, Eugene; Saleh, Soha; Adamovich, Sergei V (2013) 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 21:198-207
Bagce, Hamid F; Saleh, Soha; Adamovich, Sergei V et al. (2013) Corticospinal excitability is enhanced after visuomotor adaptation and depends on learning rather than performance or error. J Neurophysiol 109:1097-106
Guo, Yi; Mesut, Sahin; Foulds, Richard A et al. (2013) Corticospinal signals recorded with MEAs can predict the volitional forearm forces in rats. Conf Proc IEEE Eng Med Biol Soc 2013:1984-7

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