Emotional valence modulates the topology of the parent-infant inter-brain network

Abstract

Emotional communication between parents and children is crucial during early life, yet little is known about its neural underpinnings. Here, we adopt a dual-brain connectivity approach to assess how emotional valence modulates the parent-infant neural network.Fifteen mothers modelled positive and negative emotions toward pairs of objects during social interaction with their infants (aged 10.3 months) whilst their neural activity was concurrently measured using dual-EEG. Intra-brain and inter-brain network connectivity in the 6-9 Hz (infant Alpha) range was computed during maternal expression of positive and negative emotions using directed (partial directed coherence) and non-directed (phaselocking value) connectivity metrics. Graph theoretical metrics were used to quantify differences in network topology as a function of emotional valence. Inter-brain network indices (Density, Strength and Divisibility) consistently revealed that the integration of parents' and childrens' neural processes was significantly stronger during maternal demonstrations of positive than negative emotions. Further, directed inter-brain metrics indicated that mother-to-infant directional influences were stronger during the expression of positive than negative emotions. These results suggest that the parent-infant inter-brain network is modulated by the emotional quality and tone of dyadic social interactions, and that inter-brain graph metrics may be successfully applied to examine these changes in interpersonal network topology. (200/200 words) Keywords:EEG hyperscanning, network connectivity, graph theory, emotional expression, mother-infant interaction atypical patterns of EEG asymmetry, commonly showing higher right frontal EEG activity than controls (Gotlib et al., 1998). Recent research has also started to examine intraindividual network topology during emotion processing using graph-theoretic measures. For example, a recent study with adults showed that EEG graph-theoretic features performed better than traditionally used EEG features (such as spectral power and asymmetry) on the automatic classification of affective neural states (Gupta et al. , 2016).Behavioral and neuroimaging studies into early development suggest that the neural architecture for the detection and prioritized processing of emotional expressions, such as fear, emerges sometime during the first year of life (Hoehl, 2013; Hoehl et al.

Towards a Personalized Multi-Domain Digital Neurophenotyping Model for the Detection and Treatment of Mood Trajectories

“…Furthermore, in the fast-advancing field of social neuroscience, dual-EEG (concurrent neural recording from two interacting individuals) is increasingly being used in naturalistic settings to track dynamic changes in interpersonal neural coupling, even between infants and adult caregivers [ 39 , 40 , 41 ]. Although there are unique technical challenges associated with the collection and interpretation of naturalistic dual-EEG data, particularly with infant participants [ 42 ], the mother–infant interpersonal neural network has been found to be exquisitely sensitive to changes in maternal emotional state [ 43 ] or parenting stress [ 44 ], and also predicts the likelihood of infant social learning from their parent [ 41 ]. Dual-EEG studies with adults have demonstrated that interpersonal neural coupling may also index empathy, differentiate the emotional tone of the conversation [ 45 ], and signify the degree of cooperation between interacting members of a dyad [ 46 , 47 ].…”

Towards a Personalized Multi-Domain Digital Neuro-phenotyping Model for the Detection and Treatment of Mood Trajectories

“…Furthermore, in the fast-advancing field of social neuroscience, dual-EEG (concurrent neural recording from two interacting individuals) is increasingly being used in naturalistic settings to track dynamic changes in interpersonal neural coupling, even between infants and adult caregivers (Leong et al, 2017;Wass et al, 2020;Leong et al, in revision). Although there are unique technical challenges associated with the collection and interpretation of naturalistic dual-EEG data, particularly with infant participants (Noreika et al, 2020;Georgieva et al, 2020), the mother-infant interpersonal neural network has been found to be exquisitely sensitive to changes in maternal emotional state (Santamaria et al, 2019), and also predicts the likelihood of infant social learning from their parent (Leong et al, in revision). Other dual-EEG studies with adults have demonstrated that interpersonal neural coupling may also index empathy (Astolfi et al, 2015) and differentiate the emotional tone of the conversation (Astolfi et al, 2010).…”

Interacting brains revisited: A cross-brain network neuroscience perspective

“…Such pairwise comparisons of region pairs may not be sufficient to fully capture INS, given the large number of dynamic dependencies between brain regions as well as individual variability in functional networks. While the first few, mostly electroencephalography (EEG)-based, hyperscanning studies have adopted a graph analytic approach to study INS [e.g., ( 8, 9 )], graph analysis for hyperscanning data is still in its infancy ( 10 ). Here, we posit a comprehensive analytical framework for inference and prediction based on bipartite interbrain graphs.…”

“…From such a bipartite graph, both global and nodal properties can be derived. Previous graph-based hyperscanning studies have often focused on global metrics [e.g., ( 8, 9 )]. A global metric, such as the global efficiency, is a single metric (scalar) that aggregates a specific graph topological property across the entire graph ( 11 ).…”