Neurophysiology of robot-mediated training and therapy: a perspective for future use in clinical populations

Abstract

The recovery of functional movements following injury to the central nervous system (CNS) is multifaceted and is accompanied by processes occurring in the injured and non-injured hemispheres of the brain or above/below a spinal cord lesion. The changes in the CNS are the consequence of functional and structural processes collectively termed neuroplasticity and these may occur spontaneously and/or be induced by movement practice. The neurophysiological mechanisms underlying such brain plasticity may take different forms in different types of injury, for example stroke vs. spinal cord injury (SCI). Recovery of movement can be enhanced by intensive, repetitive, variable, and rewarding motor practice. To this end, robots that enable or facilitate repetitive movements have been developed to assist recovery and rehabilitation. Here, we suggest that some elements of robot-mediated training such as assistance and perturbation may have the potential to enhance neuroplasticity. Together the elemental components for developing integrated robot-mediated training protocols may form part of a neurorehabilitation framework alongside those methods already employed by therapists. Robots could thus open up a wider choice of options for delivering movement rehabilitation grounded on the principles underpinning neuroplasticity in the human CNS.

Keywords: motor adaptation; motor cortex; motor learning; rehabilitation; spinal cord.

Figures

Figure 1

An upper limb end-effector robotic device can be used to monitor cortical and neuromuscular responses with TMS, EEG, and EMG (electrodes placed on multiple shoulder, arm, forearm muscles) during performance of reaching movements in different directions in the x-y axis (A,B). The motors can be switched off to measure “free” movements or switched on to induce force fields (perturbation or resistance). Other adjunct methods of brain stimulation can be used during robot-mediated reaching movements such as tDCS (C); different types of tDCS include: unilateral anodal motor cortex – black arrow, unilateral anodal premotor or visual cortex – red arrows, unilateral cathodal stimulation – blue arrows or directional stimulation – yellow arrow; Ref, reference electrode, Active, active electrode). The robotic device can be used to assist acute stroke patients in reaching motor practice in therapy or be programed to perturb motor performance to measure patient kinematic performance and muscle responses in different tasks such as position holding [(D); see also Figure 4].

Figure 2

Cortical excitability of contralateral motor cortex is significantly increased (*, vs. BASELINE condition) during robot-mediated clockwise force field perturbation adaptation in healthy subjects (ADAPTATION condition cf. BASELINE and DEADAPTATION conditions). TMS was used to measure cortical excitability during the movement preparation period before reaching (TMS time interval is time after visual signal to start reach and is set at x = 0 on x-axis) to two different directions [(A,B) = 135°, away from the body; (C,D) = 270°, toward the chest] and for two different upper limb muscles [(A,C) = biceps; (B,D) = triceps]. Note that cortical excitability is only increased for one muscle (biceps) in one direction of perturbed reaching (135°), so cortical neuroplasticity is thus muscle- and direction-specific. The increase in cortical excitability precedes reaching movement and suggests that there is a change in the “internal model” of the biceps muscle within the cortex [from Ref. (65) with permission].

Figure 3

Unilateral anodal tDCS (black arrow with the cathodal electrode applied supraorbitally; (A) was applied to contralateral motor cortex during force-field adaptation in order to augment ongoing neuroplastic changes in cortical neurophysiology (see Figure 2). Interestingly, online tDCS stimulation did not change the reduction of movement error or recovery of velocity during motor adaptation [red lines in (B,C)], but did result in a significant increase (*) in offline movement error once tDCS and the robot-induced force field were both switched off (blue lines in (C,D); black bars in (D). Reaching blocks N1–N4 and N5–N6 are without force field and reaching blocks F1–F4 are with force field perturbation. From (74) with permission).

Figure 4

Robot-mediated perturbations can be used to evaluate acute stroke patient motor performance in a “holding” task. The patient is instructed to hold the joystick in the middle of a computer screen and the robot exerts “pulling” forces to the joystick (see also Figure 1D). This acute stroke patient undertook 20, 1 h therapy sessions, each including ∼1000 robot-assisted reaches to peripheral targets on a computer screen in different directions. The ability to hold the joystick in a central position whilst the robot applied “pulling” forces in different directions, was measured before (red traces; RRA3 first) and after (green traces; RRA3 fifth) the robot-assisted therapy program. The overall x-y position error (top panel) was significantly reduced after robot-assisted therapy toward that measured in healthy subjects (gray traces; HS2 first and second). Note that position holding performance was direction-specific in this patient. The kinematic improvement in position holding was the result of increases in kinetic force production (bottom panel) and also the rate of force production (UP) and relaxation (DOWN; middle panel) toward that of healthy subjects. From Ref. (117) with permission.