Construction of efficacious gait and upper limb functional interventions based on brain plasticity evidence and...

For neurorehabilitation to advance from art to science, it must become evidence-based. Historically, there has been a dearth of evidence from which to construct rehabilitation interventions that are properly framed, accurately targeted, and credibly measured. In many instances, evidence of treatment response has not been sufficiently robust to demonstrate a change in function that is clinically, statistically, and economically important. Research evidence of activity-dependent central nervous system (CNS) plasticity and the requisite motor learning principles can be used to construct an efficacious motor recovery intervention. Brain plasticity after stroke refers to the regeneration of brain neuronal structures and/or reorganization of the function of neurons. Not only can CNS structure and function change in response to injury, but also, the changes may be modified by "activity". For gait training or upper limb functional training for stroke survivors, the "activity" is motor behavior, including coordination and strengthening exercise and functional training that comprise motor learning. Critical principles of motor learning required for CNS activity-dependent plasticity include: close-to-normal movements, muscle activation driving practice of movement; focused attention, repetition of desired movements, and training specificity. The ultimate goal of rehabilitation is to restore function so that a satisfying quality of life can be experienced. Accurate measurement of dysfunction and its underlying impairments are critical to the development of accurately targeted interventions that are sufficiently robust to produce gains, not only in function, but also in quality of life. The Classification of Functioning, Disability, and Health Model (ICF) model of disablement, put forth by the World Health Organization, can provide not only some guidance in measurement level selection, but also can serve as a guide to incorporate function and quality of life enhancement as the ultimate goals of rehabilitation interventions. Based on the evidence and principles of activity-dependent plasticity and motor learning, we developed gait training and upper limb functional training protocols. Guided by the ICF model, we selected and developed measures with characteristics rendering them most likely to capture change in the targeted aspects of intervention, as well as measures having membership not only in the impairment, but also in the functional or life role participation levels contained in the ICF model. We measured response to innovative gait training using a knee flexion coordination measure, coefficient of coordination consistency (ACC) of relative hip/knee (H/K) movement across multiple steps (H/K ACC), and milestones of participation in life role activities. We measured response to upper limb functional training according to measures designed to quantify functional gains in response to treatment targeted at wrist/hand or shoulder elbow training (Arm Motor Ability Test for wrist/hand (AMAT W/H) or shoulder/elbow (AMAT S/E)). We found that there was a statistically significant advantage for adding FES-IM gait training to an otherwise comparable and comprehensive gait training, according to the following measures: H/K ACC, the measure of consistently executed hip/knee coordination during walking; a specific measure of isolated joint knee flexion coordination; and a measure of multiple coordinated gait components. Further, enhanced gains in gait component coordination were robust enough to result in achievement of milestones in participation in life role activities. In the upper limb functional training study, we found that robotics + motor learning (ROB ML; shoulder/elbow robotics practice plus motor learning) produced a statistically significant gain in AMAT S/E; whereas functional electrical stimulation + motor learning (FES ML) did not. We found that FES ML (wrist/hand FES plus motor learning) produced a statistically significant gain in AMAT W/H; whereas ROB ML did not. These results together, support the phenomenon of training specificity in that the most practiced joint movements improved in comparison to joint movements that were practiced at a lesser intensity and frequency. Both ROB ML and FES ML protocols addressed an array of impairments thought to underlie dysfunction. If we are willing to adhere to the ICF model, we accept the challenge that the goal of rehabilitation is life role participation, with functional improvement as in important intermediary step. The ICF model suggests that we intervene at multiple lower levels (e.g., pathology and impairment) in order to improve the higher levels of function and life role participation. The ICF model also suggests that we measure at each level. Not only can we then understand response to treatment at each level, but also, we can begin to understand relationships between levels (e.g., impairment and function). With the ICF model proffering the challenge of restoring life role participation, it then becomes important to design and test interventions that result in impairment gains sufficiently robust to be reflected in functional activities and further, in life role participation. Fortunately, CNS plasticity and associated motor learning principles can serve well as the basis for generating such interventions. These principles were useful in generating both efficacious gait training and efficacious upper limb functional training interventions. These principles led to the use of therapeutic agents (FES and robotics) so that close-to-normal movements could be practiced. These principles supported the use of specific therapeutic agents (BWSTT, FES, and robotics) so that sufficient movement repetition could be provided. These principles also supported incorporation of functional task practice and the demand of attention to task practice within the intervention. The ICF model provided the challenge to restore function and life role participation. The means to that end was provided by principles of CNS plasticity and motor learning.