Decoding center-out hand velocity from MEG signals during visuomotor adaptation

During reaching or drawing, the primate cortex carries information about the current and upcoming position of the hand. Researchers have decoded hand position, velocity, and acceleration during center-out reaching or drawing tasks from neural recordings acquired invasively at the microscale and mesoscale levels. Here we report that we can continuously decode information about hand velocity at the macroscale level from magnetoencephalography (MEG) data acquired from the scalp during a center-out drawing task with an imposed hand-cursor rotation. The grand mean (n = 5) correlation coefficients (CCs) between measured and decoded velocity profiles were 0.48, 0.40, 0.38, and 0.28 for the horizontal dimension of movement and 0.32, 0.49, 0.56, and 0.23 for the vertical dimension of movement where the order of the CCs indicates pre-exposure, early-exposure, late-exposure, and post-exposure to the hand-cursor rotation. By projecting the sensor contributions to decoding onto whole-head scalp maps, we found that a macroscale sensorimotor network carries information about detailed hand velocity and that contributions from sensors over central and parietal scalp areas change due to adaptation to the rotated environment. Moreover, a 3-D linear estimation of distributed current sources using standardized low-resolution brain electromagnetic tomography (sLORETA) permitted a more detailed investigation into the cortical network that encodes for hand velocity in each of the adaptation phases. Beneficial implications of these findings include a non-invasive methodology to examine the neural correlates of behavior on a macroscale with high temporal resolution and the potential to provide continuous, complex control of a non-invasive neuromotor prosthesis for movement-impaired individuals.