Brain Activation Pattern Caused by Soft Rehabilitation Glove and Virtual Reality Scenes: A Pilot fNIRS Study.

Journal: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
PMID:

Abstract

Clinical studies have proved significant improvements in hand motor function in stroke patients when assisted by robotic devices. However, there were few studies on neural activity changes in the brain during execution. This study aimed to investigate the brain activation pattern caused by soft rehabilitation glove and virtual reality scenes. Twenty healthy subjects and twenty stroke patients were recruited to complete three controlled trials: grasping passively with robotic glove assistance (RA), watching grasping movement video in virtual reality (VR), and the joint use of robotic glove and virtual reality (VRA). Neural activity in the prefrontal cortex, motor cortex and occipital lobe was synchronously collected by the functional near-infrared spectroscopy (fNIRS) device. Activation level and functional connectivity of these brain regions were subsequently calculated and statistically analyzed. For both groups, the VR and VRA tasks induced activation of larger cortical areas. Stroke group had higher average cortical activation in all three tasks compared to healthy group, especially in the prefrontal cortex ( [Formula: see text]). Functional connectivity was weaker in the stroke group than in the healthy group across most regions, but was significantly stronger across some regions of the right hemisphere. These findings suggest significant differences in activation patterns across three tasks. In addition, multi-sensory stimulation can promote functional communication between more brain regions in patients. It has potential for neuromodulation in rehabilitation training by setting up different sensory stimulation modalities.

Authors

  • Pengju Liu
  • Xinyi Yang
    Colorado School of Public Health-Biostatistics and Informatics, Aurora, CO, USA.
  • Fenglin Han
  • Guangshuai Peng
  • Qiao Li
    Department of Biomedical Informatics, Emory University, Atlanta, GA, United States of America.
  • Liping Huang
    Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China.
  • Lizhen Wang
    School of Biological Science and Medical Engineering, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing 100191, China.
  • Yubo Fan
    State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing, China. yubofan@buaa.edu.cn.