Parkinson's disease (PD) is characterized by severe motor symptoms, including upper limb dysfunction, that is only partially alleviated by medication. PD is also a motor learning disease due to the degradation of the striatum, involved in the consolidation of motor memory. We showed earlier that motor practice improves writing deficits and that there is long term potential when it is applied in a focused manner. However, retention difficulties were also apparent. What is currently unclear, is which learning method leads to optimal retention in PD and how it is expressed in underlying neural network changes. In healthy controls, retention is improved by incorporating dual task (DT) conditions or by loading cognition during learning. Our own work showed that DT training also led to better retention than single task (ST) learning, at least in a subgroup of PD. Using a combination of behavioral assessment, functional magnetic resonance imaging and upper limb task training, this project aims to understand how to boost the robustness of practice in PD. Throughout, we will contrast ST with DT learning. As complex practice can now easily be delivered via novel technology, this study will set out future avenues for rehabilitation targeted at specific neural circuitry.
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Change in movement time (s) of trained pattern
Timeframe: 7 days
Change in dual task effect
Timeframe: 7 days
Change in brain activity during performance of trained pattern
Timeframe: 7 days
Change in brain connectivity during performance of trained pattern
Timeframe: 7 days
Diffusion weighted imaging as a predictor
Timeframe: 7 days