| Summary: | Background:
Due to a high occurrence of motor impairment following stroke, motor learning is fundamentally important for stroke rehabilitation. Motor learning interventions can be difficult to deliver by clinicians and researchers due to individual differences in motor abilities that are compounded by neural changes associated with an ischemic insult. To enhance motor learning, interventions must be grounded in understanding patterns of performance change, both behaviourally and neurologically.
Methods:
In Chapters 2 and 3, performance data were fitted to exponential curves to measure performance change during skill acquisition. In Chapter 2, curve parameters were used to create an individualized learner-adapted algorithm to manipulate the level of difficulty of motor practice conditions. In Chapter 3, curve parameters were used to measure the rate of skill acquisition in healthy individuals and stroke populations. Chapter 4 explored the functional connectivity of motor-related brain networks before and after skill acquisition in healthy individuals and individuals with stroke. In Chapter 5, residual white matter in a motor-related brain network was used to examine individual responses following an intervention combining non-invasive brain stimulation and motor skill practice in individuals with stroke.
Findings:
In Chapter 2, skill acquisition under a learner-adapted algorithm, developed from curve parameters, showed that higher individualized levels of difficulty in practice were better for skill retention. In Chapter 3, individuals with stroke showed a slower rate of skill acquisition compared to healthy individuals, which was associated with worse motor performance-related change at a delayed retention test. In Chapter 4, individuals with stroke did not activate motor learning–related functional brain networks in the same manner as healthy individuals following motor skill practice. In Chapter 5, the integrity of white matter in the motor-related brain network was higher in individuals who positively responded to the intervention.
Conclusions:
The findings from this dissertation highlight the importance of modelling performance data to advance the evaluation of stroke rehabilitative interventions. This dissertation contributes new knowledge of a gray matter motor network associated with motor learning, and a white matter motor network biomarker that characterizes the response to non-invasive brain stimulation paired with motor practice in individuals with stroke. === Medicine, Faculty of === Graduate
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