Motor Cortex Activation is the Same Between Virtual Reality and Physical Performance of the Box and Block Test in Individuals Post-Stroke
Presenter Type
UNO Graduate Student (Doctoral)
Major/Field of Study
Biomechanics
Author ORCID Identifier
https://orcid.org/0000-0002-7546-7524
Advisor Information
Department of Biomechanics
Location
MBSC304 - G (Doctoral)
Presentation Type
Oral Presentation
Start Date
24-3-2023 10:30 AM
End Date
4-3-2023 11:45 AM
Abstract
Conventional stroke rehabilitation currently focuses on improving total affected hand dexterity to increase the individual’s functional task performance typically using the Box and Block test (BBT). Previous research shows that access to post-stroke rehabilitation can improve functional outcomes [1-4]. However, there is a need for more accessible home-based rehabilitation programs, especially considering situations like the recent pandemic. Virtual Reality (VR) is a novel technology that allows for games and tasks to be performed remotely and in a virtual environment that uses less physical space. Therefore, we hypothesized in this study that there will be no difference in whole motor cortex activations while there will be an increase in peak upper extremity neuromuscular activity when performing the BBT in VR compared to the physical BBT. Participants completed the BBT manually and in VR under observation with a brain imaging device, functional near-infrared spectroscopy (fNIRS), and eight electromyography (EMG) sensors placed bilaterally on the wrist flexors, wrist extensors, biceps brachii, and, lateral triceps. Three BBT trials were performed with the less-affected and affected hands in both conditions for a total of 12 trials collected with the conditions randomly ordered. fNIRS was used to measure motor cortex activation with an 8x7 sensor-detector montage centered over the head vertex. A general linear model analysis was applied to the metric of hemoglobin concentration with an auto-regressive-iterative least-squares function to obtain levels of beta value activation. A mixed effects model was performed comparing VR to manual BBT conditions using beta values with significance correction at the level of q≤0.05 [5]. Neuromuscular activations were calculated using the amplitude probability distribution function (APDF). A Friedman test with follow-up Wilcoxon Sign Rank tests were performed for each hand-muscle at 10%, 50%, and, 90% APDF. A Bonferroni statistical correction was applied with significance set at p0.05). This indicates that similar levels of activation are occurring during both conditions. Interestingly, there was also no statistical difference between the manual and VR BBT conditions for the wrist flexors, wrist extensors, biceps brachii, and, lateral triceps of either less-affected or affected hands (all p>0.006). These results indicate that a VR-based rehabilitation simulation, like the BBT, may be suitable for clinical populations that would benefit from increased motor repetitions.
References
[1] Yin et al., 2014. Clin Rehabil. 28: 1107-1114.
[2] Cruz ET et al., 2005. Brain. 128(5):1112-1121.
[3] Duncan PW et al., 2003. Stroke. 34(9):2173-2180.
[4] Paolucci S et al., 2001. Arch Phys Med Rehabil. 82(1):2-8.
[5] Santosa et al., 2018. Algorithms. 11: 73.
Scheduling
10:45 a.m.-Noon, 1-2:15 p.m., 2:30 -3:45 p.m.
Motor Cortex Activation is the Same Between Virtual Reality and Physical Performance of the Box and Block Test in Individuals Post-Stroke
MBSC304 - G (Doctoral)
Conventional stroke rehabilitation currently focuses on improving total affected hand dexterity to increase the individual’s functional task performance typically using the Box and Block test (BBT). Previous research shows that access to post-stroke rehabilitation can improve functional outcomes [1-4]. However, there is a need for more accessible home-based rehabilitation programs, especially considering situations like the recent pandemic. Virtual Reality (VR) is a novel technology that allows for games and tasks to be performed remotely and in a virtual environment that uses less physical space. Therefore, we hypothesized in this study that there will be no difference in whole motor cortex activations while there will be an increase in peak upper extremity neuromuscular activity when performing the BBT in VR compared to the physical BBT. Participants completed the BBT manually and in VR under observation with a brain imaging device, functional near-infrared spectroscopy (fNIRS), and eight electromyography (EMG) sensors placed bilaterally on the wrist flexors, wrist extensors, biceps brachii, and, lateral triceps. Three BBT trials were performed with the less-affected and affected hands in both conditions for a total of 12 trials collected with the conditions randomly ordered. fNIRS was used to measure motor cortex activation with an 8x7 sensor-detector montage centered over the head vertex. A general linear model analysis was applied to the metric of hemoglobin concentration with an auto-regressive-iterative least-squares function to obtain levels of beta value activation. A mixed effects model was performed comparing VR to manual BBT conditions using beta values with significance correction at the level of q≤0.05 [5]. Neuromuscular activations were calculated using the amplitude probability distribution function (APDF). A Friedman test with follow-up Wilcoxon Sign Rank tests were performed for each hand-muscle at 10%, 50%, and, 90% APDF. A Bonferroni statistical correction was applied with significance set at p0.05). This indicates that similar levels of activation are occurring during both conditions. Interestingly, there was also no statistical difference between the manual and VR BBT conditions for the wrist flexors, wrist extensors, biceps brachii, and, lateral triceps of either less-affected or affected hands (all p>0.006). These results indicate that a VR-based rehabilitation simulation, like the BBT, may be suitable for clinical populations that would benefit from increased motor repetitions.
References
[1] Yin et al., 2014. Clin Rehabil. 28: 1107-1114.
[2] Cruz ET et al., 2005. Brain. 128(5):1112-1121.
[3] Duncan PW et al., 2003. Stroke. 34(9):2173-2180.
[4] Paolucci S et al., 2001. Arch Phys Med Rehabil. 82(1):2-8.
[5] Santosa et al., 2018. Algorithms. 11: 73.