Recent advances in second-person neuroscience have allowed the underlying neural mechanisms involved in teaching-learning interactions to be better understood. Teaching is not merely a one-way transfer of information from teacher to student; it is a complex interaction that requires metacognitive and mentalizing skills to understand others’ intentions and integrate information regarding oneself and others. Physiotherapy involving therapists instructing patients on how to improve their motor skills is a clinical field in which teaching-learning interactions play a central role. Accumulating evidence suggests (...) that non-invasive brain stimulation modulates cognitive functions; however, NIBS approaches to teaching-learning interactions are yet to be utilized in rehabilitation. In this review, I evaluate the present research into NIBS and its role in enhancing metacognitive and mentalizing abilities; I then review hyperscanning studies of teaching-learning interactions and explore the potential clinical applications of NIBS in rehabilitation. Dual-brain stimulation using NIBS has been developed based on findings of brain-to-brain synchrony in hyperscanning studies, and it is delivered simultaneously to two individuals to increase inter-brain synchronized oscillations at the stimulated frequency. Artificial induction of brain-to-brain synchrony has the potential to promote instruction-based learning. The brain-to-brain interface, which induces inter-brain synchronization by adjusting the patient’s brain activity, using NIBS, to the therapist’s brain activity, could have a positive effect on both therapist-patient interactions and rehabilitation outcomes. NIBS based on second-person neuroscience has the potential to serve as a useful addition to the current neuroscientific methods used in complementary interventions for rehabilitation. (shrink)

Hyperscanning studies using functional Near-Infrared Spectroscopy have been performed to understand the neural mechanisms underlying human-human interactions. In this study, we propose a novel methodological approach that is developed for fNIRS multi-brain analysis. Our method uses support vector regression to predict one brain activity time series using another as the predictor. We applied the proposed methodology to explore the teacher-student interaction, which plays a critical role in the formal learning process. In an illustrative application, we collected fNIRS data of the (...) teacher and preschoolers’ dyads performing an interaction task. The teacher explained to the child how to add two numbers in the context of a game. The Prefrontal cortex and temporal-parietal junction of both teacher and student were recorded. A multivariate regression model was built for each channel in each dyad, with the student’s signal as the response variable and the teacher’s ones as the predictors. We compared the predictions of SVR with the conventional ordinary least square predictor. The results predicted by the SVR model were statistically significantly correlated with the actual test data at least one channel-pair for all dyads. Overall, 29/90 channel-pairs across the five dyads presented significant signal predictions withthe SVR approach. The conventional OLS resulted in only 4 out of 90 valid predictions. These results demonstrated that the SVR could be used to perform channel-wise predictions across individuals, and the teachers’ cortical activity can be used to predict the student brain hemodynamic response. (shrink)