Differentiated effects of robot hand training with and without neural guidance on neuroplasticity patterns in chronic stroke

Xin Wang, Wan Wa Wong, Rui Sun, Winnie Chiu Wing Chu, Kai Yu Tong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

30 Citations (Scopus)

Abstract

Robot-assisted training combined with neural guided strategy has been increasingly applied to stroke rehabilitation. However, the induced neuroplasticity is seldom characterized. It is still uncertain whether this kind of guidance could enhance the long-term training effect for stroke motor recovery. This study was conducted to explore the clinical improvement and the neurological changes after 20-session guided or non-guided robot hand training using two measures: changes in brain discriminant ability between motor-imagery and resting states revealed from electroencephalography (EEG) signals and changes in brain network variability revealed from resting-state functional magnetic resonance imaging (fMRI) data in 24 chronic stroke subjects. The subjects were randomly assigned to receive either combined action observation (AO) with EEG-guided robot-hand training (RobotEEG-AO, n = 13) or robot-hand training without AO and EEG guidance (Robotnon-EEG-Text, n = 11). The robot hand in RobotEEG-AO group was activated only when significant mu suppression (8-12 Hz) was detected from subjects' EEG signals in ipsilesional hemisphere, while the robot hand in Robotnon-EEG-Text group was randomly activated regardless of their EEG signals. Paretic upper-limb motor functions were evaluated at three time-points: before, immediately after and 6 months after the interventions. Only RobotEEG-AO group showed a long-term significant improvement in their upper-limb motor functions while no significant and long-lasting training effect on the paretic motor functions was shown in Robotnon-EEG-Text group. Significant neuroplasticity changes were only observed in RobotEEG-AO group as well. The brain discriminant ability based on the ipsilesional EEG signals significantly improved after intervention. For brain network variability, the whole brain was first divided into six functional subnetworks, and significant increase in the temporal variability was found in four out of the six subnetworks, including sensory-motor areas, attention network, auditory network, and default mode network after intervention. Our results revealed the differences in the long-term training effect and the neuroplasticity changes following the two interventional strategies: with and without neural guidance. The findings might imply that sustainable motor function improvement could be achieved through proper neural guidance, which might provide insights into strategies for effective stroke rehabilitation. Furthermore, neuroplasticity could be promoted more profoundly by the intervention with proper neurofeedback, and might be shaped in relation to better motor skill acquisition.

Original languageEnglish
Article number810
JournalFrontiers in Neurology
Volume9
Issue numberOCT
DOIs
Publication statusPublished - 8 Oct 2018

Keywords

  • Action observation
  • Brain network
  • EEG discriminant rate
  • Long-term training effect
  • Motor imagery
  • Motor recovery
  • Resting state fMRI
  • Temporal variability

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology

Fingerprint

Dive into the research topics of 'Differentiated effects of robot hand training with and without neural guidance on neuroplasticity patterns in chronic stroke'. Together they form a unique fingerprint.

Cite this