Facilitating Implicit Learning to Improve Neurorehabilitation in Stroke

Study Overview
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Brief Summary
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Stroke is one of the leading causes of chronic disability in Veterans. Stroke is associated with significant loss of mobility, increased risk of falling, cardiovascular disease, depression and neuro-cognitive impairment. These deficits negatively impact the independent completion of the Activities of Daily Living (ADLs). Task-oriented training has emerged as the dominant therapeutic intervention in the rehabilitation of chronic stroke victims. The effectiveness of these interventions may be enhanced through facilitation of implicit knowledge rather than explicit knowledge. Specifically, implicit learning increases retention and improves transfer of the improved motor function outside of the lab environment. Moreover, implicit motor control reduces the burden imposed on cognitive resources as the skill is performed automatically (i.e. do not have to 'think' about it). The amount and type of feedback individuals receive while learning a new task (or relearning in the case of rehabilitation) has been shown to influence the type of learning (i.e. implicit or explicit). Thus the purpose of the current study is to determine the effect of different types of feedback during motor learning on the learning type and the resultant impact on functional outcomes (i.e. motor performance, retention, and cognitive workload) in chronic stroke patients.

Detailed Description
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Someone has a stroke in the US every 45 seconds, resulting in over 700,000 new strokes every year and stroke is the leading cause of disability in Veterans (American Heart Association Statistics Committee and Stroke Statistics Sub-Committee). The vast majority of these cases result in motor impairments, which frequently cause individuals to become dependent on others for daily functioning (modified Rankin Scale 3-5, see Lees et al., 2006). Specifically, upper extremity hemiparesis is the leading cause of functional disability after stroke and upper arm function explains about 50% of the variability in reported quality of life (Wyller et al, 1997). As such optimizing upper arm neurorehabilitation is a critical problem to address in the aging Veteran population. Rehabilitation, for patients, is fundamentally a process of relearning how to move to carry out their needs successfully (Carr & Shepherd, 1987). This statement posits that at its core neurorehabilitation is motor learning, but despite this principle, research in motor learning has had little impact on stroke rehabilitation (Krakauer, 2006). Recently there has been an interest in developing and testing new methods to optimize upper extremity rehabilitation. Investigators at the Baltimore VAMC have pioneered task oriented training paradigms to improve mobility (Macko et al., 2005) in those with chronic stroke. As part of this programmatic approach novel upper extremity robotics training programs have been developed to improve reaching, and limb coordination. However, the majority of these interventions rely on error-based learning strategies during rehabilitation, which foster task-related explicit knowledge. However, a corpus of motor learning research indicates that this may not be the best strategy to optimize motor learning, and thus neurorehabilitation. Error-based learning involves receiving continual feedback of movement with the intent that the learner will make corrections to the movement in real time. Thus learning occurs through a series of repetitions in which the learner continually reduces the discrepancy between the ideal behavior and the observation of their own behavior. In other words, error-based learning fosters an adaptation to achieve the desired behavior. In contrast, operant conditioning learning strategies consists of the learner only receiving feedback about the quality of their movement at the end of the behavior. Thus, learning occurs through a series of reinforcement of the desired behavior in its entirety, which is more model-free than the adaptation incurred during error-based learning. A primary distinction between these two learning strategies is that error-based learning fosters explicit knowledge of the task, whereas operant conditioning fosters implicit knowledge (Krakauer & Mazzoni, 2011). These two types of knowledge have drastic implications for functional outcomes (i.e. motor performance, cognitive workload, and retention). Prior to stroke, upper arm functions such as reaching and grasping were largely done without the use of explicit knowledge. In other words, healthy individuals devote little conscious effort about how they are controlling their limbs, they just 'do it'. Although, using explicit strategies during learning can facilitate the rate of learning, if given enough time, individuals who have limited explicit knowledge will perform equally well (Maxwell et al, 1999). Despite a slower rate of learning, the payoff of reducing explicit knowledge of the task can be very advantageous during motor performance. Notably, retention of the learned behavior is greater in individuals who learned under conditions that inhibit explicit knowledge. For example Malone and Bastian (2010) had individuals learn a novel walking task (split belt treadmill where the belts move at different rates) and in those in which explicit knowledge was limited exhibited learning that persisted longer than those who relied on explicit knowledge during learning. In addition, limiting explicit knowledge during motor learning may result in reduced cognitive workload and maintained performance under conditions of challenge (Zhu et al., 2011). In conclusion, promoting explicit knowledge during rehabilitation rather than unconscious control (limiting explicit knowledge) reduces sustainability of the newly acquired motor skill, and consumes cognitive resources, which need to be available for other demands. As such, automatic control of these behaviors is critical to perform daily activities, suggesting operant conditioning (which limits explicit knowledge) as superior to error-based learning. Those with stroke are able to learn tasks implicitly, although the rate of learning may be delayed as compared to healthy controls (Pohl et al., 2001) and delayed further as a function of stroke severity (Boyd et al., 2007). Further, simply providing explicit information about an implicit task has been shown to reduce the learning rate and retention in those with basal ganglia stroke (Boyd et al., 2004; Boyd et al., 2006) and damage to sensorimotor areas (Boyd et al., 2003; Boyd et al., 2006; Winstein et al., 2003). While these studies highlight the importance limiting explicit knowledge during learning they were done in the context of learning implicit sequences rather than the development of skill, which while related, rely on different aspects of motor learning (Krakauer & Mazonni, 2011, Yarrow et al., 2009). In the context of functional skill learning, the timing/ type of feedback have been robustly shown to affect the learning rate as well as retention and have been implicated to affect knowledge type (Levin et al., 2010). Specifically, providing feedback about task performance less frequently and after performance rather than during (i.e. delayed) have been shown to increase learning retention and likely facilitate implicit learning (Cirstea et al., 2006; Winstein et al., 1996). Additionally, feedback about the results (knowledge of results) rather than the performance (knowledge of performance) has shown to increase retention and limit explicit knowledge (Cirstea el al., 2006; Sidaway et al., 2008; Winstein, 1991). Accordingly, the current proposal will attempt to foster implicit knowledge during the development of motor skill by manipulating when feedback is given and type of feedback. The aim of the current study is to determine the effect of error-based learning versus operant conditioning learning on critical outcomes of neurorehabilitation (i.e. performance after learning, generalizability, cognitive workload imposed by the task, and retention).

Official Title
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Facilitating Implicit Learning to Improve Neurorehabilitation in Stroke

Conditions
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Cerebral Stroke

Intervention / Treatment
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* Behavioral: Reaching Task


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Ischemic stroke greater than 3 months prior. Between 45 and 80 years of age. Residual hemiparetic upper extremity deficits. Adequate language and neurocognitive function to participate in training (MMSE, CESD, aphasia screening). Right hand dominant. Upper Extremity Fugl-Meyer score of 25 or greater. Exclusion Criteria: History of cortical stroke. No mobility of less affected arm. Failure to meet the RRDC assessment clinic criteria for medical eligibility. MMSE score less than 27. CES-D score greater than 16. Unable to pass a hearing test (i.e. must be able to hear sounds of 45 dB or less).

Ages Eligible for Study
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Minimum Age: 45 Years
Maximum Age: 80 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Basic Science
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Implicit Group<br>Receives little feedback about task performance during learning | Behavioral: Reaching Task<br>* Learn a reaching task that requires coordination of the arm segments<br>|
| Active Comparator: Control<br>Receives detailed feedback about task performance during learning | Behavioral: Reaching Task<br>* Learn a reaching task that requires coordination of the arm segments<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Quality of Motor Performance | Quality of motor behavior was indexed by the percentage of samples in which the participants were within the trained (i.e. optimal) trajectory. The trained trajectory was a 2cm wide channel in the shape of a half circle between two targets which were 25cm apart from each other. Therefore, the scale measure is a percentage which can range between 0 and 100%. | 2 Years | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| EEG Derived High Alpha Power | Brain electrophysiology measure of attentional processes as indexed by high alpha power (10-13 Hz). The unit of measurement is a percentage as the amount of power (microvolts squared) in the high alpha band was divided by the total power in the spectrum (i.e. 1-50 Hz). This method is commonly employed to normalize the power of a particular frequency if the statistical design includes a between subjects factor. | 2 Years | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Stroke, Cerebral Infarction