Brain oscillatory correlates of visuomotor adaptive learning

Abstract

Sensorimotor adaptation involves the recalibration of the mapping between motor command and sensory feedback in response to movement errors. Although adaptation operates within individual movements on a trial-to-trial basis, it can also undergo learning when adaptive responses improve over the course of many trials. Brain oscillatory activities related to these “adaptation” and “learning” processes remain unclear. The main reason for this is that previous studies principally focused on the beta band, which confined the outcome message to trial-to-trial adaptation. To provide a wider understanding of adaptive learning, we decoded visuomotor tasks with constant, random or no perturbation from EEG recordings in different bandwidths and brain regions using a multiple kernel learning approach. These different experimental tasks were intended to separate trial-to-trial adaptation from the formation of the new visuomotor mapping across trials. We found changes in EEG power in the post-movement period during the course of the visuomotor-constant rotation task, in particular an increased (i) theta power in prefrontal region, (ii) beta power in supplementary motor area, and (iii) gamma power in motor regions. Classifying the visuomotor task with constant rotation versus those with random or no rotation, we were able to relate power changes in beta band mainly to trial-to-trial adaptation to error while changes in theta band would relate rather to the learning of the new mapping. Altogether, this suggested that there is a tight relationship between modulation of the synchronization of low (theta) and higher (essentially beta) frequency oscillations in prefrontal and sensorimotor regions, respectively, and adaptive learning.

Keywords

• Sensorimotor adaptive learning
• Neural oscillations
• Electroencephalography
• Machine learning
• Sparse modeling