Hyperscanning studies using functional Near-Infrared Spectroscopy (fNIRS) 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 (SVR) 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 (OLS) 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 (18 channels 5 dyads = 90 channel-pairs) 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.

No abstract

Attention is a basic human function underlying every other cognitive process. It is demonstrated in the functional Magnetic Resonance Imaging literature that frontoparietal networks are involved with attentive performance while default mode networks are involved with inattentive performance. Yet, it is still not clear whether similar results would be found with functional Near-Infrared Spectroscopy. The goal of our study was to investigate differences in hemodynamic activity measured by functional Near-Infrared Spectroscopy between fast and slow responses on a simple sustained attention task both before and after stimulus onset. Thirty healthy adults took part in the study. Our results have shown differences between fast and slow responses only on channels over medial frontal cortex and inferior parietal cortex (p < 0,05). These differences were observed both before and after stimulus presentation. It is discussed that functional Near-Infrared Spectroscopy is a good tool to investigate the frontoparietal network and its relationship with performance on attention tasks; it could be used to further investigate other approaches on attention, such as the dual network model of cognitive control and brain states views based on complex systems analysis; and nally, it could be used to investigate attention on naturalistic settings. Consent to participateAll participants signed a freely given, informed consent to participate term. This term was previously approved by the aforementioned committee. Consent to publishThis work does not include materials from participants that require consent to publish. Author's contributionAuthor contributions included conception and study design (MGN, CEB, JRS, AFB), data collection or acquisition (MGN), statistical analysis (MGN, MS, CSFB), interpretation of results (MGN, CEB, JRS, RCM, AFB

Hyperscanning is a promising tool for investigating the neurobiological underpinning of social interactions and affective bonds. Recently, graph theory measures, such as modularity, have been proposed for estimating the global synchronization between brains. This paper proposes the bootstrap modularity test as a way of determining whether a pair of brains is coactivated. This test is illustrated as a screening tool in an application to fNIRS data collected from the prefrontal cortex and temporoparietal junction of five dyads composed of a teacher and a preschooler while performing an interaction task. In this application, graph hub centrality measures identify that the dyad's synchronization is critically explained by the relation between teacher's language and number processing and the child's phonological processing. The analysis of these metrics may provide further insights into the neurobiological underpinnings of interaction, such as in educational contexts.

Background: Educational research has been conducted mainly by using behavioural approaches. Whilst such methods provide invaluable insights into the field, several important questions such as ‘how do we learn?’ and ‘what mechanisms cause individual differences?’ cannot be answered thoroughly by using only behavioural approaches. In the last three decades, the advances of neuroimaging technologies and computational power have allowed researchers to investigate these questions beyond behavioural measures that provide complementary knowledge about human brain.Aim: One of the most recent neuroimaging techniques that holds much promise for use in educational settings is functional near-infrared spectroscopy (fNIRS). This article aims to introduce the fNIRS technique to educational researchers interested in neurocognitive mechanisms of academic learning and achievements to further promote the growing field of Educational Neuroscience.Method: We present the properties of the fNIRS device, its basic principles and important considerations when planning an fNIRS study.Results: Functional near-infrared spectroscopy is a portable, cost-effective and easy-to-handle neuroimaging device that allows experimentation in naturalistic settings such as in the school.Conclusion: Even though several articles describe different applications and technical features of the fNIRS technique, there is still a need for materials with a more accessible language for those unfamiliar with neuroscientific and technical terms.