Applications of graph theory to the analysis of fNIRS data in hyperscanning paradigms

Amanda Yumi Ambriola Oku; Candida Barreto; Guilherme Bruneri; Guilherme Brockington; Andre Fujita; João Ricardo Sato

doi:10.3389/fncom.2022.975743

Applications of graph theory to the analysis of fNIRS data in hyperscanning paradigms

Front Comput Neurosci. 2022 Sep 14:16:975743. doi: 10.3389/fncom.2022.975743. eCollection 2022.

Authors

Amanda Yumi Ambriola Oku¹, Candida Barreto^{1

2}, Guilherme Bruneri³, Guilherme Brockington⁴, Andre Fujita⁵, João Ricardo Sato¹

Affiliations

¹ Center of Mathematics, Computing and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil.
² NRF South Africa Chair: Integrated Studies of Learning Language, Science and Mathematics in the Primary School, University of Johannesburg, Johannesburg, South Africa.
³ Physics Department, Aeronautics Institute of Technology, São José dos Campos, Brazil.
⁴ Center for Natural and Human Sciences, Universidade Federal do ABC, Santo André, Brazil.
⁵ Computer Science Department, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil.

Abstract

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.

Keywords: degree centrality; eigenvector centrality and modularity; fNIRS; graph theory; neuroscience.