Effects of somatosensory electrical stimulation on motor function and cortical oscillations

Abstract

Background: Few patients recover full hand dexterity after an acquired brain injury such as stroke. Repetitive somatosensory electrical stimulation (SES) is a promising method to promote recovery of hand function. However, studies using SES have largely focused on gross motor function; it remains unclear if it can modulate distal hand functions such as finger individuation.

Objective: The specific goal of this study was to monitor the effects of SES on individuation as well as on cortical oscillations measured using EEG, with the additional goal of identifying neurophysiological biomarkers.

Methods: Eight participants with a history of acquired brain injury and distal upper limb motor impairments received a single two-hour session of SES using transcutaneous electrical nerve stimulation. Pre- and post-intervention assessments consisted of the Action Research Arm Test (ARAT), finger fractionation, pinch force, and the modified Ashworth scale (MAS), along with resting-state EEG monitoring.

Results: SES was associated with significant improvements in ARAT, MAS and finger fractionation. Moreover, SES was associated with a decrease in low frequency (0.9-4 Hz delta) ipsilesional parietomotor EEG power. Interestingly, changes in ipsilesional motor theta (4.8-7.9 Hz) and alpha (8.8-11.7 Hz) power were significantly correlated with finger fractionation improvements when using a multivariate model.

Conclusions: We show the positive effects of SES on finger individuation and identify cortical oscillations that may be important electrophysiological biomarkers of individual responsiveness to SES. These biomarkers can be potential targets when customizing SES parameters to individuals with hand dexterity deficits.

Trial registration: NCT03176550; retrospectively registered.

Keywords: Brain injury; Electroencephalography; Rehabilitation; Stroke; Transcutaneous electric nerve stimulation; Upper extremity.

Fig. 1

a Schematic representation of the method used for calculating the FCI. The participant is instructed to flex only the index finger as much as possible without flexing the other digits. b FCI is defined mathematically as the angle traversed by the middle finger (digit A) divided by the angle tranversed by the index finger (digit B) relative to the horizontal starting position. c Statistically significant change in mean fractionation from baseline to immediately after peripheral nerve stimulation. Fractionation improvement is indicated by a decrease in finger coupling index (FCI)

Fig. 2

Distribution of percentage change in mean resting state EEG power across the eight subjects, pre to post intervention, within the (a) delta frequency band and (b) theta frequency band with head plots depicting 1/coefficient of variation (mean/standard deviation) of group level percentage changes. Star sign represents a significant change in group level resting state EEG power from zero. c Magnitude of the coefficients of the multivariate robust ridge model from regressing mean FCI changes to mean power changes, pre to post intervention, with the star sign depicting coefficients whose absolute magnitude were greater than 95% of those produced by random data permutation. M: electrodes over Motor cortex; P: electrodes over Parietal cortex