Making Training Better: Error Augmentation Motor Learning in Stroke

Recovery of upper limb movement after stroke is incomplete. Stroke is a leading cause of long-term sensorimotor disability including persistent deficits in upper limb (UL) function. Understanding how to improve UL recovery is a major scientific, clinical and patient priority. Yet, despite numerous studies attempting to identify the most effective rehabilitation interventions based on established principles of motor learning and neural plasticity, post-stroke UL recovery remains incomplete. Indeed, even with therapy, UL sensorimotor deficits persist in a large proportion (up to 62%) of stroke survivors for >6 months leading to a high socio-economic burden.

MOTOR CONTROL DISORDERS: A consequence of the underlying control deficit after stroke is hemiparesis, characterized by a diminished capacity to recruit agonist muscles, unwanted/inappropriate muscle activation (i.e., spasticity, agonist and antagonist muscle co-contraction), abnormal muscle activation timing, weakness and muscle fiber property changes. This leads to deficits in the ability to isolate joint movement and appropriately combine different joints to accomplish task-related functions. We have accumulated substantial evidence suggesting that movement deficits and spasticity are associated with a common control deficit in the specification and regulation of spatia thresholds (ST) of the stretch reflex and other proprioceptive reflexes. STs are expressed in the spatial (angular) rather than the temporal (latency) domain. ST regulation is a well-established mechanism of control of stretch reflexes in animals and reflexes and movements in humans.

ST DEFINITION AND ACTION MECHANISMS: Spatial threshold (ST) is the joint angle at which muscles begin to be recruited and postural reflexes and other reflexes begin to act. By shifting ST, the brain resets posture-stabilizing mechanisms to a new limb or body position. These mechanisms combine to regulate STs in multi-muscle systems according to body configuration and task demands. Stroke results in deficits in ST regulation. Central nervous system (CNS) injuries affecting descending and spinal mechanisms and intrinsic lead to limitations in ST regulation. As a result, passive or active movements past the angular threshold, ST (spasticity range), elicit abnormal reflex muscle activation. The ST is velocity-dependent reducing the active control range in stroke patients and their ability to make faster movements.

INTERVENTION APPROACH: Our approach is designed to increase the reflex-free range of elbow motion in stroke. Adaptation of elbow movement to a new load (i.e., the ability to correct errors) in patients with chronic stroke was substantially improved when movement was made within the active control range (where spasticity did not affect muscle contraction) compared to when the reflex-free range was not identified. Accordingly, the potential for motor learning may be improved by considering the range of impaired elbow movement in properly designed trials. To avoid eliciting movements made with abnormal muscle activation patterns and other compensations (bad plasticity), training programs will be tailored to the movement capacity of the individual and incorporate approaches that quantify and enlarge the joint range made with typical muscle activation patterns. In this proposal, we will use a robot and a novel VR learning interface to manipulate the ability to produce controlled movement at the elbow, which is a common impairment in people with moderate to severe stroke. The proposed personalized training approach focuses on providing specific feedback to increase an individual's ST regulation range.

ERROR AUGMENTATION FEEDBACK (EA): Error Augmentation feedback will be used to increase the active control ST zone of the elbow. EA uses intrinsic error-driven learning to enhance the CNS's ability to take advantage of kinematic redundancy and find meaningful motor task solutions. Specifically, subjects are provided with feedback that enhances their motor errors. Manipulation of error signals has been shown to stimulate UL sensorimotor improvement in both healthy and stroke subjects with greater learning gains occurring when errors are larger. EA feedback will be used to dynamically remap the active elbow control range. Visual feedback about elbow angle will be modified, to make it seem as if the elbow moves less than in reality. Thus, when the actual elbow moves, the subject perceives the elbow as having moved less and attempts to correct the error by extending the elbow further. The active control range will be expanded by having subjects working near or just at the limit of their ST range. Remapping of the perception/action relationship will occur when the afferent feedback becomes associated with a greater elbow angle. Given the key role of errors in motor learning, artificially increasing the performance error via EA will increase each individual's active control range and cause learning to occur more quickly.