Differentiated Effects of Robot Hand Training With and Without Neural Guidance on Neuroplasticity Patterns in Chronic Stroke

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.

Synchronization lag in post stroke: relation to motor function and structural connectivity

“…Other nuisances included motion parameters and motion parameter time derivatives. Volumes with excessive motion were censored if the Euclidean norm of the derivatives of the motion parameters exceeded 0.2 by using the function regress_censor_motion in AFNI (X. Wang, Wong, Sun, Chu, & Tong, 2018). Bandpass filtering (0.009–0.08 Hz) was also applied.…”

Brain-Computer Interface Robotics for Hand Rehabilitation After Stroke: A Systematic Review

“…The lack of control groups and random allocation in clinical studies (e.g., only 2 out of 7 studies are in the huge sample size RCT stage) made us unable to perform a meta-analysis of effects and continue the study by Cervera et al [36] with a focus on BCI-hand robot interventions. Results from the methodological quality assessment showed that only two studies [68,69] had a score of 7 in the PEDro scale. Although non-conclusive, these results support the notion that most of the studies are not aligned with the criteria of high-quality evidence-based interventions.…”

“…A final list with 30 studies was identified as eligible for qualitative review. Among the 30 studies, 11 [60][61][62][63][64][65][66][67][68][69][70] were involved in testing the BCI-hand robot system on chronic and subacute stroke patients ( [60,65] are RCTs) while the rest involved testing on healthy participants [71][72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89].…”

“…Five studies were uncontrolledwhere the aims were either to study classification accuracies during sessions [61], to monitor clinical outcomes improvement from Day 0 until the end of the programme [62,70] or both [64,67]. Two studies [68,69] For the studies that had a clinical testing component, a methodological quality assessment by the PEDro Scale was performed. Two studies which appeared on the PEDro search [60,65] had predetermined scores in the scale and were extracted for this part while the rest were manually evaluated by the authors.…”

“…Another study[63] compared the classification accuracies of healthy and hemiplegic stroke patients against two BCI classifiers while the remaining study[66] compared classification accuracies from stroke patients who receive congruent or incongruent visual and kinaesthetic feedback.Most of the studies adopted FMMA, ARAT and GS measurements to assess clinical outcomes. Six studies[60,62,64,65,68,70] reported patient improvement in these measures when subjected to BCI-hand robot interventions; in contrast with their respective controls or as recorded through time in the programme. For Ang et al[60], FMMA Distal scores were reported in weeks 3, 6, 12 and 24 and the BCI-device group (N=6) yielded the highest improvement in scores across all time points as compared to the device only (N=8) and SAT (N=7) groups.…”