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

Abstract

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.

Figure 1

Virtual reality setup (top) and raw kinematic and EMG data (bottom) acquired from a typical subject. Dashed line shows that TMS stimulation was automatically triggered when the subject's metacarpophalangeal (MCP) joint reached 40°, regardless of visual feedback.

Figure 2

Kinematic and electrophysiological data for a typical subject. Mean angle (°), jerk (rad/s³), and MEP (mV) traces for the G_1.00 (thin line), G_0.25 (thick line), and G_1.75 (dashed line) visual feedback conditions, aligned in time to movement onset. The figure demonstrates that maximum excursion, peak jerk, and M1 facilitation were all greater in the G_0.25 condition, but not the G_1.75 condition, relative to G_1.00 (control condition). Also shown is the TMS artifact (dashed grey line), set to trigger at 40° for every trial of every condition.

Figure 3

Kinematic and electrophysiological group data. Group mean (±1SEM) for the maximum excursion angle, maximum jerk, and MEP are shown for each condition. Asterisk denotes significant differences between the altered feedback and the veridical condition.