Previous research suggests that so-called implicit and explicit processes of motor adaptation are implemented by distinct neural structures. Here we tested whether implicit sensorimotor adaptation and strategic re-aiming used to reduce movement error are reflected by spatially distinct EEG oscillatory components. We analyzed beta-band oscillations (ϳ13-30 Hz), which have long been linked to sensorimotor functions, at the time when these adaptive processes intervene for movement planning. We hypothesized that beta-band activity within sensorimotor regions relates to implicit adaptive processes, whereas beta-band activity within medial motor areas reflects deliberate re-aiming. In female and male human volunteers, we recorded EEG in a motor adaptation task in which a visual rotation was introduced in short series of trials separated by unperturbed trials. Participants were instructed in advance about the nature of the visual perturbation and trained to counter it by strategically re-aiming at a neighboring target. Consistent with our hypothesis, we found that preparatory beta-band activities within the two regions exhibited different patterns of modulation. Beta power in lateral central regions was attenuated when a change in the visual condition rendered internal-model predictions uncertain. In contrast, beta power in medial frontal regions was selectively decreased when participants strategically re-aimed their reaches. We propose that the reduction in lateral central beta power reflects an increased weighting of peripheral sensory information implicitly triggered when an adaptive change in the sensorimotor mapping is required, whereas the reduction in medial frontal beta-band activity relates to the inhibition of automatic motor responses in favor of cognitively controlled movements.
We show that sensorimotor behavior can be reliably predicted from single-trial EEG oscillations fluctuating in a coordinated manner across brain regions, frequency bands and movement time epochs. We define high-dimensionaloscillatory portraitsto capture the interdependence between basicoscillatory elements, quantifying oscillations occurring in single-trials at specific frequencies, locations and time epochs. We find that the general structure of the element-interdependence networks (effective connectivity) remains stable across task conditions, reflecting an intrinsic coordination architecture and responds to changes in task constraints by subtle but consistently distinct topological reorganizations. Trial categories are reliably and significantly better separated using oscillatory portraits, than from the information contained in individual oscillatory elements, suggesting an inter-element coordination-based encoding. Furthermore, single-trial oscillatory portrait fluctuations are predictive of fine trial-to-trial variations in movement kinematics. Remarkably, movement accuracy appears to be reflected in the capacity of the oscillatory coordination architecture to flexibly update as an effect of movement-error integration.
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