Motor Learning in Stroke

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).

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).