Corticospinal excitability is enhanced after visuomotor adaptation and depends on learning rather than performance or error.

We used adaptation to high and low gains in a virtual reality setup of the hand to test competing hypotheses about the excitability changes that accompany motor learning. Excitability was assayed through changes in amplitude of motor evoked potentials (MEPs) in relevant hand muscles elicited with single-pulse transcranial magnetic stimulation (TMS). One hypothesis is that MEPs will either increase or decrease, directly reflecting the effect of low or high gain on motor output. The alternative hypothesis is that MEP changes are not sign dependent but rather serve as a marker of visuomotor learning, independent of performance or visual-to-motor mismatch (i.e., error). Subjects were required to make flexion movements of a virtual forefinger to visual targets. A gain of 1 meant that the excursions of their real finger and virtual finger matched. A gain of 0.25 ("low gain") indicated a 75% reduction in visual versus real finger displacement, a gain of 1.75 ("high gain") the opposite. MEP increases (>40%) were noted in the tonically activated task-relevant agonist muscle for both high- and low-gain perturbations after adaptation reached asymptote with kinematics matched to veridical levels. Conversely, only small changes in excitability occurred in a control task of pseudorandom gains that required adjustments to large errors but in which learning could not accumulate. We conclude that changes in corticospinal excitability are related to learning rather than performance or error.

Visuomotor gain distortion alters online motor performance and enhances primary motor cortex excitability in patients with stroke.

OBJECTIVES: Determine if ipsilesional primary motor cortex (M1) in stroke patients processes online visuomotor discordance in gain between finger movement and observed feedback in virtual reality (VR). MATERIALS AND METHODS: Chronic stroke patients flexed (N= 7) or extended (N= 1) their finger with real-time feedback of a virtual hand presented in VR. Virtual finger excursion was scaled by applying a low-gain (G(0.25) ), high-gain (G(1.75) ), or veridical (G(1.00) ) scaling factor to real-time data streaming from a sensor glove. Effects of visuomotor discordance were assessed through analysis of movement kinematics (joint excursion, movement smoothness, and angular velocity) and amplitude of motor evoked potentials (MEPs) elicited with transcranial magnetic stimulation applied to ipsilesional M1. Data were analyzed with a repeated-measures analysis of variance (significance set at 0.05). RESULTS: G(0.25) discordance (relative to veridical) leads to significantly larger joint excursion, online visuomotor correction evidenced by decreased trajectory smoothness, and significantly facilitated agonist MEPs. This effect could not be explained by potential differences in motor drive (background electromyographic) or by possible differences related to joint angle or angular velocity, as these variables remained invariant across conditions at the time of MEP assessment. M1 was not significantly facilitated in the G(1.75) condition. MEPs recorded in an adjacent muscle that was not involved in the task were unaffected by visual feedback in either discordance condition. These data suggest that the neuromodulatory effects of visuomotor discordance on M1 were relatively selective. CONCLUSIONS: Visuomotor discordance may be used to alter movement performance and augment M1 excitability in patients following stroke. Our data illustrate that visual feedback may be a robust way to selectively modulate M1 activity. These data may have important clinical implications for the development of future VR training protocols.

Visuomotor discordance in virtual reality: effects on online motor control.

Virtual reality (VR) applications are rapidly permeating fields such as medicine, rehabilitation, research, and military training. However, VR-induced effects on human performance remain poorly understood, particularly in relation to fine-grained motor control of the hand and fingers. We designed a novel virtual reality environment suitable for hand-finger interactions and examined the ability to use visual feedback manipulations in VR to affect online motor performance. Ten healthy subjects performed a simple finger flexion movement toward a kinesthetically-defined 45° target angle while receiving one of three types of VR-based visual feedback in real-time: veridical (in which the virtual hand motion corresponded to subjects' actual motion), or scaled-down / scaled-up feedback (in which virtual finger motion was scaled by 25% / 175% relative to actual motion). Scaled down-and scaled-up feedback led to significant online modifications (increases and decreases, respectively) in angular excursion, despite explicit instructions for subjects to maintain constant movements across conditions. The latency of these modifications was similar across conditions. These findings demonstrate that a VR-based platform may be a robust medium for presenting visuomotor discordances to engender a sense of ownership and drive sensorimotor adaptation for (retraining motor skills. This may prove to be particularly important for retraining motor skills in patients with neurologically-based movement disorders.