Recent studies have revealed rapid (e.g., hours to days) training-induced cortical structural changes using magnetic resonance imaging (MRI). Currently, there is great interest in studying how such a rapid brain structural change affects behavioral improvement. Structural reorganization contributes to memory or enhanced information processing in the brain and may increase its capability of skill learning. If the gray matter (GM) is capable of such rapid structural reorganization upon training, the extent of volume increase may characterize the learning process. To shed light on this issue, we conducted a case series study of 5-day visuomotor learning using neuroanatomical imaging, and analyzed the effect of rapid brain structural change on motor performance improvement via regression analysis. Participants performed an upper-arm reaching task under left-right mirror-reversal for five consecutive days; T1-weighted MR imaging was performed before training, after the first and fifth days, and 1 week and 1 month after training. We detected increase in GM volume on the first day (i.e., a few hours after the first training session) in the primary motor cortex (M1), primary sensory cortex (S1), and in the hippocampal areas. Notably, regression analysis revealed that individual differences in such short-term increases were associated with the learning levels after 5 days of training. These results suggest that GM structural changes are not simply a footprint of previous motor learning but have some relationship with future motor learning. In conclusion, the present study provides new insight into the role of structural changes in causing functional changes during motor learning.

Neurofeedback training using electroencephalogram (EEG)-based brain–computer interfaces (BCIs) combined with mental rehearsals of motor behavior has demonstrated successful self-regulation of motor cortical excitability. However, it remains unclear whether the acquisition of skills to voluntarily control neural excitability is accompanied by structural plasticity boosted by neurofeedback. Here, we sought short-term changes in cortical structures induced by 30 min of BCI-based neurofeedback training, which aimed at the regulation of sensorimotor rhythm (SMR) in scalp EEG. When participants performed kinesthetic motor imagery of right finger movement with online feedback of either event-related desynchronisation (ERD) of SMR magnitude from the contralateral sensorimotor cortex (SM1) or those from other participants (i.e. placebo), the learning rate of SMR-ERD control was significantly different. Although overlapped structural changes in gray matter volumes were found in both groups, significant differences revealed by group-by-group comparison were spatially different; whereas the veritable neurofeedback group exhibited sensorimotor area-specific changes, the placebo exhibited spatially distributed changes. The white matter change indicated a significant decrease in the corpus callosum in the verum group. Furthermore, the learning rate of SMR regulation was correlated with the volume changes in the ipsilateral SM1, suggesting the involvement of interhemispheric motor control circuitries in BCI control tasks.

Real-time functional imaging of human neural activity and its closed-loop feedback enable voluntary control of targeted brain regions. In particular, a brain-computer interface (BCI), a direct bridge of neural activities and machine actuation is one promising clinical application of neurofeedback. Although a variety of studies reported successful self-regulation of motor cortical activities probed by scalp electroencephalogram (EEG), it remains unclear how neurophysiological, experimental conditions or BCI designs influence variability in BCI learning. Here, we provide the EEG data during using BCIs based on sensorimotor rhythm (SMR), consisting of 4 separate datasets. All EEG data were acquired with a high-density scalp EEG setup containing 128 channels covering the whole head. All participants were instructed to perform motor imagery of right-hand movement as the strategy to control BCIs based on the task-related power attenuation of SMR magnitude, that is event-related desynchronization. This dataset would allow researchers to explore the potential source of variability in BCI learning efficiency and facilitate follow-up studies to test the explicit hypotheses explored by the dataset.