Are There Brain-Based Predictors of the Ability to Learn a New Skill in Healthy Ageing and Can They Help in the Design of Effective Therapy after Stroke?

This thesis aimed at looking for neural correlates of motor adaptation as a model of rehabilitation after brain injury. Healthy adults across the lifespan and stroke patients were tested in a force-field learning paradigm. This thesis focuses on EEG analysis and the complex relationship of brain-derived measures with observed behaviour.
To describe each domain in detail, the focus was first on finding group differences between older and younger healthy adults in a similar manner as it was later between stroke patients versus healthy controls. The analyses were finalised by looking for relationships between the EEG and motor performance data in a multiple linear regression approach.

As candidate EEG biomarkers of motor adaptation, error related event related potential around movement onset in the frontocentral electrodes was chosen in time domain. In the time-frequency domain, the focus was on movement related beta band spectral perturbation, looking at the electrodes over the primary motor cortex and the frontocentral ROI found significant in the time domain. Finally, functional connectivity was analysed focusing first on electrode over the primary motor cortex contralateral to the movement as a seed region, to narrow down the analysis to bilateral motor cortex connectivity and connectivity between primary motor cortex contralateral to the movement and the frontocentral region identified as important in the time domain analysis. The crucial part of the project was analysing the relationship between the neural and kinematic measures.

The most important predictor of summed error in motor adaptation was the connectivity between C3 and C4 electrode at the baseline prestimulus period in motor adaptation condition and pinch asymmetry. Higher prestimulus interhemispheric connectivity was associated with bigger deviation from the optimal trajectory. When looking at summed error dynamic derivative as a dependent variable - performance index - it was the ERP at the central error-related ROI that explained the most variance. It can be concluded that higher baseline interhemispheric connectivity can be a reflection of a maladaptive process, perhaps related to increased interhemispheric inhibition. It is important to also note that the same connectivity at different timepoints in the movement can be of different significance - differences between stroke patients and controls were present in the postmovement period.

In conclusion, brain information could be helpful for e.g. stratifying patients into different intensity programs based on their predicted potential to recover. Moreover, brain information could be utilised to apply closed-loop systems modulating the intensity of tasks to reach the optimal brain state that facilitates learning. I believe this work will help incorporating brain-derived measures in informing neurorehabilitation programmes in the future.