A person-centered, network-based approach to mother-infant neural synchrony with functional near infrared spectroscopy

• In Event: Parent-Child Biobehavioral Synchrony at Multiple Levels of Analysis

Abstract

Synchrony in the parent-child dyad is considered an instrumental mechanism allowing for a child to learn from a caregiver (Bell, 2020), and may have implications in the development of self-regulation (DePasquale, 2021). Where synchrony may occur across many domains, here we focus upon neural synchrony in the prefrontal cortex (PFC), a brain region critical in regulatory processes. Characterizing parent-child neural synchrony in infancy is an important step in understanding mechanisms for adaptive development.
In this study, we measured neural synchrony in the PFC during an unstructured free play using functional near infrared spectroscopy (fNIRS) hyperscanning. Data collection is ongoing. Participants to date in this analysis were 12 mother-infant dyads, Minfant age = 7.29 months, 8 female, 77% White non-Hispanic. The parent was instructed to play with their child as they usually would, while seated in a chair in front of their infant, who was in a highchair. fNIRS signal was recorded from 10 frontal sites each on mother and infant (Figure 1).
Traditional approaches to assessing synchrony in fNIRS data include Wavelet Transform Coherence, which uses time series data from homologous optodes across social partners to determine the degree of interpersonal synchrony at cortical locations. Here, we used Group Iterative Multiple Model Estimation (GIMME) to construct person-specific networks between regions of interest (ROIs). From these networks we derive data-driven subgroups based on network connectivity. GIMME differs from correlational approaches by assessing contemporaneous and lagged relations between all ROIs, as well as autoregressive paths. Considering autoregressive paths both establishes Granger causality and minimizes the rate of false positives (Gates & Molenaar, 2012). Moreover, in fMRI data GIMME may present orthogonally to traditional correlation approaches (Gunther et al., 2022). This is the first study to our knowledge using GIMME to analyze interpersonal synchrony in fNIRS data. Thus, this approach may offer a novel perspective on parent-child neural synchrony.
GIMME yielded two data-driven subgroups for dyadic connectivity. Subgroup 1 was characterized by the absence of connections between mother and infant ROIs, while subgroup 2 was characterized by the presence of these connections (Figure 2). A correlation table was used to describe differences in these subgroups based on measures related to developmental risk for psychopathology. We included measures of infant negative affect (Infant Behavior Questionnaire – short form; Putnam et al, 2014), maternal anxiety (GAD-7; Spitzer et al., 2006), and maternal depression (PHQ-8, Kroenke et al., 2001), as well as infant age. We found that infants in subgroup 2, characterized by recovered connections between mother and infant ROIs, had significantly lower negative affect than infants in subgroup 1, b=-0.72, p=.02. Mothers from dyads in subgroup 2 also had significantly fewer anxiety symptoms than mothers in subgroup 2, b=-0.66, p=.04. There were no significant differences in subgroups in terms of infant age and maternal depression. These preliminary findings suggest that GIMME detects heterogeneity in neural synchrony between members of a dyad, and these differences in synchrony may relate to infant negative affect and maternal anxiety. Future research should seek to understand the mechanisms driving these group differences.

Authors

• Kelley E Gunther, Yale University
  Presenting Author

• Berenice Anaya, Pennsylvania State University, University Park

• Zachary Fisher

• Morgan E Jones, Pennsylvania State University, University Park

• Daniel Petrie

• Christina Olena Hlutkowsky, The Pennsylvania State University

• Koraly Elisa Perez-Edgar, Pennsylvania State University, University Park