Neural Signatures of Motor Adaptation Dynamics across the Lifespan

Brain structure and function can change as a direct response to what we see, think and do. These changes are referred to as Brain Plasticity. On the other hand, Brain plasticity enables us to learn and improve our performance on specific tasks. Motor adaptation is a prototypical example of such learning processes. In addition, these complex behaviors also need to be adapted to different situations. This is typically studied by investigating how people adjust their motor behavior when confronted to an external perturbation, which requires a new coordination pattern of the arm kinematics in order to achieve the task such as reaching a target on a screen. Adaptation to such perturbation requires a vast network of brain areas and this network might be different in function of the age of the participant. The main goal of this project will be to study how activity of brain networks, identified through high-density electroencephalography (EEG) recordings, are modulated depending on motor adaptation. To this end, we will set up a computational approach to study dynamics of EEG network activity in relation to the kinematics of (learned or innate) complex motor adaptation. We will design an experimental paradigm involving visuomotor rotation and/or force field adaptation. We will apply functional connectivity analysis to high-density EEG data to investigate the difference in motor networks supporting adaptation to a perturbation in young and elderly participants. To study the dynamic functional interactions in brain networks in relation to motor adaptation, we will apply functional connectivity analyses to high-density EEG data to investigate electrophysiological mechanisms underlying time-dependent long-range interactions in brain networks. We aim to develop a computational approach that allows studying the dynamics of long-range neuronal communication at the whole-brain level, and link those dynamics to motor adaptation at the kinematic and muscular levels. This will be an extremely important step to understand basic neuronal mechanisms through which complex behavior emerges in real-time.