Enhancing touch sensibility with sensory electrical stimulation and sensory retraining.

A large proportion of stroke survivors suffer from sensory loss, negatively impacting their independence, quality of life, and neurorehabilitation prognosis. Despite the high prevalence of somatosensory impairments, our understanding of somatosensory interventions such as sensory electrical stimulation (SES) in neurorehabilitation is limited. We aimed to study the effectiveness of SES combined with a sensory discrimination task in a well-controlled virtual environment in healthy participants, setting a foundation for its potential application in stroke rehabilitation. We employed electroencephalography (EEG) to gain a better understanding of the underlying neural mechanisms and dynamics associated with sensory training and SES. We conducted a single-session experiment with 26 healthy participants who explored a set of three visually identical virtual textures-haptically rendered by a robotic device and that differed in their spatial period-while physically guided by the robot to identify the odd texture. The experiment consisted of three phases: pre-intervention, intervention, and post-intervention. Half the participants received subthreshold whole-hand SES during the intervention, while the other half received sham stimulation. We evaluated changes in task performance-assessed by the probability of correct responses-before and after intervention and between groups. We also evaluated differences in the exploration behavior, e.g., scanning speed. EEG was employed to examine the effects of the intervention on brain activity, particularly in the alpha frequency band (8-13 Hz) associated with sensory processing. We found that participants in the SES group improved their task performance after intervention and their scanning speed during and after intervention, while the sham group did not improve their task performance. However, the differences in task performance improvements between groups only approached significance. Furthermore, we found that alpha power was sensitive to the effects of SES; participants in the stimulation group exhibited enhanced brain signals associated with improved touch sensitivity likely due to the effects of SES on the central nervous system, while the increase in alpha power for the sham group was less pronounced. Our findings suggest that SES enhances texture discrimination after training and has a positive effect on sensory-related brain areas. Further research involving brain-injured patients is needed to confirm the potential benefit of our solution in neurorehabilitation.

Effects of 10-kHz Subthreshold Stimulation on Human Peripheral Nerve Activation.

OBJECTIVE: The mechanisms of action of high-frequency stimulation (HFS) are unknown. We investigated the possible mechanism of subthreshold superexcitability of HFS on the excitability of the peripheral nerve. MATERIALS AND METHODS: The ulnar nerve was stimulated at the wrist in six healthy participants with a single (control) stimulus, and the responses were compared with the responses to a continuous train of 5 seconds at frequencies of 500 Hz, 2.5 kHz, 5 kHz, and 10 kHz. Threshold intensity for compound muscle action potential (CMAP) was defined as intensity producing a 100-µV amplitude in ten sequential trials and "subthreshold" as 10% below the CMAP threshold. HFS threshold was defined as stimulation intensity eliciting visible tetanic contraction. RESULTS: Comparing the threshold of single pulse stimulation for eliciting CMAP vs threshold for HFS response and pooling data at different frequencies (500 Hz-10 kHz) revealed a significant difference (p = 0.00015). This difference was most obvious at 10 kHz, with a mean value for threshold reduction of 42.2%. CONCLUSIONS: HFS with a stimulation intensity below the threshold for a single pulse induces axonal superexcitability if applied in a train. It can activate the peripheral nerve and produce a tetanic muscle response. Subthreshold superexcitability may allow new insights into the mechanism of HFS.

Enhancing touch sensibility by sensory retraining in a sensory discrimination task via haptic rendering.

Stroke survivors are commonly affected by somatosensory impairment, hampering their ability to interpret somatosensory information. Somatosensory information has been shown to critically support movement execution in healthy individuals and stroke survivors. Despite the detrimental effect of somatosensory impairments on performing activities of daily living, somatosensory training-in stark contrast to motor training-does not represent standard care in neurorehabilitation. Reasons for the neglected somatosensory treatment are the lack of high-quality research demonstrating the benefits of somatosensory interventions on stroke recovery, the unavailability of reliable quantitative assessments of sensorimotor deficits, and the labor-intensive nature of somatosensory training that relies on therapists guiding the hands of patients with motor impairments. To address this clinical need, we developed a virtual reality-based robotic texture discrimination task to assess and train touch sensibility. Our system incorporates the possibility to robotically guide the participants' hands during texture exploration (i.e., passive touch) and no-guided free texture exploration (i.e., active touch). We ran a 3-day experiment with thirty-six healthy participants who were asked to discriminate the odd texture among three visually identical textures -haptically rendered with the robotic device- following the method of constant stimuli. All participants trained with the passive and active conditions in randomized order on different days. We investigated the reliability of our system using the Intraclass Correlation Coefficient (ICC). We also evaluated the enhancement of participants' touch sensibility via somatosensory retraining and compared whether this enhancement differed between training with active vs. passive conditions. Our results showed that participants significantly improved their task performance after training. Moreover, we found that training effects were not significantly different between active and passive conditions, yet, passive exploration seemed to increase participants' perceived competence. The reliability of our system ranged from poor (in active condition) to moderate and good (in passive condition), probably due to the dependence of the ICC on the between-subject variability, which in a healthy population is usually small. Together, our virtual reality-based robotic haptic system may be a key asset for evaluating and retraining sensory loss with minimal supervision, especially for brain-injured patients who require guidance to move their hands.