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

EEG and TMS-EEG Studies on the Cortical Excitability and Plasticity associated with Human Motor Control and Learning

More than half of the activities of daily living rely on upper limb functions (Ingram et al., 2008). Humans perform upper limb movements with great ease and flexibility but even simple tasks require complex computations in the brain and can be affected following stroke leaving survivors with debilitating movement impairments. Hemispheric asymmetries related to motor dominance, imbalances between contralateral and ipsilateral primary motor cortices (M1) activity and the ability to adapt movements to novel environments play a key role in upper limb motor control and can affect recovery. Motor learning and control are critical in neurorehabilitation, however to effectively integrate these concepts into upper limb recovery treatments, a deeper understanding of the basic mechanisms of unimanual control is needed. This thesis aimed to investigate hemispheric asymmetries related to motor dominance, to evaluate the relative contribution of the contralateral and ipsilateral M1 during unilateral reaching preparation and finally to identify the neural correlates underlying the formation of a predictive internal model enabling to adapt movements to new environments. To this end electroencephalography (EEG), transcranial magnetic stimulation (TMS), simultaneous TMS-EEG were employed during a simple motor and a highly standardised robot-mediated task. The first study used TMS-EEG to examine differences in cortical excitability related to motor dominance by applying TMS over the dominant and non-dominant M1 at rest and during contraction. No hemispheric asymmetries related to hand dominance were found. The second study assessed the temporal dynamics of bi-hemispheric motor cortical excitability during right arm reaching preparation. TMS was applied either to the ipsilateral or contralateral M1 during different times of movement preparation. Significant bilateral M1 activation during unilateral reaching preparation was observed, with no significant differences between the contralateral and ipsilateral M1. Unimanual reaching preparation was associated with significant interactions of excitatory and inhibitory processes in both motor cortices. The third study investigated the neural correlates of motor adaptation. EEG was recorded during a robot-mediated adaptation task involving right arm reaching movements and cortical excitability was assessed by applying TMS over the contralateral M1 and simultaneously recording TMS responses with EEG before and after motor adaptation. It was found that an error-related negativity (ERN) over fronto-central regions correlated with performance improvements during adaptation, suggesting that this neural activity reflects the formation of a predictive internal model. Motor adaptation underlay significant modulations in cortical excitability (i.e. neuroplasticity) in sensorimotor regions. Finally, it was shown that native cortical excitability was linked to motor learning improvements during motor adaptation and explained the variability in motor learning across individuals. These experiments demonstrated that even unimanual motor control relies on interactions between excitatory and inhibitory mechanisms not only in the contralateral M1 but in a wider range of brain regions, shown by a bi-hemispheric activity during movement preparation, the formation of a predictive model in fronto-central regions during motor adaptation and neuroplastic changes in sensorimotor regions underlying motor adaptation during unimanual reaching