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Inferences on interbrain networks using functional near-infrared spectroscopy: investigating child-adult interactions

Grant number: 19/17907-5
Support Opportunities:Scholarships in Brazil - Doctorate (Direct)
Effective date (Start): October 01, 2019
Effective date (End): August 31, 2022
Field of knowledge:Interdisciplinary Subjects
Principal Investigator:João Ricardo Sato
Grantee:Amanda Yumi Ambriola Oku
Host Institution: Centro de Matemática, Computação e Cognição (CMCC). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil
Associated research grant:18/21934-5 - Network statistics: theory, methods, and applications, AP.TEM


The advances in functional Near-Infrared Spectroscopy (fNIRS) acquisition systems make it possible to collect the data of hemodynamic states in severalbrain regions simultaneously in two subjects, while they are interacting. This approach is named hyperscanning and is one of the hot topics in social Neuroscience. The interactions between an adult and child in which there isan affective bond (e.g.: mother and child; teacher and student) is of particular interest in developing Neuroscience and the Sciences of Learning. Studies investigating the neurobiological underpinning of both interaction and affective bond with hyperscanning are scarce. Thus, little is known about the brain functional mechanisms and regions involved. In this project, we collect the data of hyperscanning fNIRS from 20 pairs of achild (3-5 years old) and an adult (mothers and teachers) recruited from the local community. The experiments will be based on activities in which the adult will read (and interpret, and explain the figures) of an infant book, playand sing to the child, in an attempt to capture his/her interest and to keep him/her engaged. Any volunteer matching any neurological or psychiatric diagnostic criteria willbe excluded as a participant. In agreement with the Federal laws, there search will be conducted under the approval of the local ethics review board. All parents or guardians will provide written consent for the participation of their children. All volunteers will provide verbal assent inparticipating in this study. The fNIRS signal will be acquired using a NIRS with 8 emitters and 8 detectors for each subject, in a simultaneous acquisition. The opto desmontage will cover prefrontal and parietal brain regions based on the EEG 10-20 system and a cap. The voltage signals captured by the detectors will be converted to hemodynamic states (oxy and deoxyhemoglobin concentrations) and exported to ASCII to be preprocessed and analyzed in a specialized computational platform such as R, Python or Matlab. Functional connectivity estimates between each channel inter and intra-subject will be carried out using Spearman Correlation coefficient. These functional connections will then be used to model a graph, composed of two subgraphs (adult and child). In the following, we will use the statistical network methods developed in this thematic project to analyze this inter subject network. We believe the analysis of these metrics will provide further insight son the neurobiological underpinnings of interaction and affective bond. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
OKU, AMANDA YUMI AMBRIOLA; SATO, JOAO RICARDO. Predicting Student Performance Using Machine Learning in fNIRS Data. FRONTIERS IN HUMAN NEUROSCIENCE, v. 15, . (19/17907-5, 18/04654-9, 18/21934-5)
AMBRIOLA OKU, AMANDA YUMI; ZIMEO MORAIS, GUILHERME AUGUSTO; ARANTES BUENO, ANA PAULA; FUJITA, ANDRE; SATO, JOAO RICARDO. Potential Confounders in the Analysis of Brazilian Adolescent's Health: A Combination of Machine Learning and Graph Theory. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, v. 17, n. 1, . (19/17907-5, 18/21934-5, 16/19376-9, 18/04654-9)

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