Inferências sobre o acoplamento de redes cerebrais intersujeitos em crianças durante tarefas cooperativas: um estudo de espectroscopia no infravermelho próximo

Resumo

Hyperscanning is a cutting-edge innovation in functional near-infrared spectroscopy (fNIRS) experimentation (Scholkman et al., 2014). This technique makes it possible to monitor the brain hemodynamic states of two subjects simultaneously and investigate the functional substrates of human interactions. In addition, since fNIRS acquisition can be acquired during more flexible paradigms, it is also possible to analyze changes in cortical oxy/deoxyhemoglobin concentration when the subjects are performing tasks involving movement and cognition. We have already explored the potential of hyperscanning in a preliminary study from our group involving two violinists playing a musical excerpt together (Balardin et al., 2017). One of the main conceptual frameworks in current developmental neuroscience and educational literature is the social interaction between infants. In contrast, the methodological literature regarding the experimental design and data analysis of fNIRS hyperscanning experiments is still scarce. Thus, this proposal combines functional brain networks and graph theory to develop novel hyperscanning data analysis methods and provide illustrations of child-child interactions in a cooperative task.In this subproject, we aim to explore the feasibility of using fNIRS and functional network analysis to investigate the neural substrates of children's interaction when performing cooperative tasks. Considering the previous results from our research group, the expected results are two. First, it is indeed feasible to conduct fNIRS naturalistic cooperative tasks involving both motor and cognitive processing, even in children below 10 years old. Second, we expect to find greater intersubject-brain coupling based on functional network analysis in accordance with the scope of the current Thematic Project. We believe these expected findings will be of impact since they are very relevant to advance the current knowledge on experimental design and data analysis and also to provide a deeper understanding of the limitations and promises of fNIRS technology when combined with statistics in graphs. (AU)