Similar Motor Cortex Activations Exhibited with Virtual Reality Use in Healthy Adults
Author ORCID Identifier
https://orcid.org/0000-0002-7546-7524
Advisor Information
Dr. Brian Knarr
Location
MBSC Gallery Room 308 - G
Presentation Type
Oral Presentation
Start Date
4-3-2022 12:30 PM
End Date
4-3-2022 1:45 PM
Abstract
Conventional neurorehabilitation focuses on addressing functional deficits through improving hand function and increases in functional performance are reported with virtual reality (VR) use [1,2,3]. However, the neural activations between manual and VR tasks are not well understood. We hypothesize that there will be no difference in whole motor cortex activation between manual and VR box and block test (BBT) conditions for the left and right hands. Fourteen participants (7F/7M, 2 left-/12 right-hand dominant, 25±3.82 years old) were included in the analysis. Participants completed the BBT manually and in VR under observation with a brain imaging device, functional near-infrared spectroscopy (fNIRS). Three BBT trials were performed with the left and right hands in both conditions for a total of 12 trials collected with conditions randomized. fNIRS was used to measure motor cortex activation using an 8x8 sensor-detector montage centered over the head vertex. A general linear model analysis was applied to 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 [4]. Analyses were performed in MATLAB R2018a with a significance of q≤0.05. There is no statical difference between the VR and manual BBT conditions for whole motor cortex activation (all channels: q>0.05). This indicates that similar levels of cortical motor cortex activation are exhibited between the manual and VR conditions. Employing VR in neurorehabilitation may be beneficial for clinical populations that benefit from increased neuromotor training.
Scheduling Link
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Similar Motor Cortex Activations Exhibited with Virtual Reality Use in Healthy Adults
MBSC Gallery Room 308 - G
Conventional neurorehabilitation focuses on addressing functional deficits through improving hand function and increases in functional performance are reported with virtual reality (VR) use [1,2,3]. However, the neural activations between manual and VR tasks are not well understood. We hypothesize that there will be no difference in whole motor cortex activation between manual and VR box and block test (BBT) conditions for the left and right hands. Fourteen participants (7F/7M, 2 left-/12 right-hand dominant, 25±3.82 years old) were included in the analysis. Participants completed the BBT manually and in VR under observation with a brain imaging device, functional near-infrared spectroscopy (fNIRS). Three BBT trials were performed with the left and right hands in both conditions for a total of 12 trials collected with conditions randomized. fNIRS was used to measure motor cortex activation using an 8x8 sensor-detector montage centered over the head vertex. A general linear model analysis was applied to 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 [4]. Analyses were performed in MATLAB R2018a with a significance of q≤0.05. There is no statical difference between the VR and manual BBT conditions for whole motor cortex activation (all channels: q>0.05). This indicates that similar levels of cortical motor cortex activation are exhibited between the manual and VR conditions. Employing VR in neurorehabilitation may be beneficial for clinical populations that benefit from increased neuromotor training.
Additional Information (Optional)
References
[1] Saposnik et al., 2011. Stroke. 42: 1380-1386.
[2] Yin et al., 2014. Clin Rehabil. 28: 1107-1114.
[3] Cameirao et al., 2012. Stroke. 43: 2720-2728.
[4[ Santosa et al., 2018. Algorithms. 11: 73.