Quantitative Assessment of Socio-Affective Dynamics in Autism Using Interpersonal Physiology

Background: Children with Autism Spectrum Disorder (ASD) often have great difficulty interpreting and using nonverbal communication, understanding and navigating social relationships, and making sense of their own and others’ emotions. Many of these impairments relate to deficits in social reciprocity (SR), the ability to recognize and understand the mental states of others and respond appropriately. In addition, deficits in emotion regulation (ER), the ability to modulate emotional response to a situation to achieve a goal, are implicated in several core features of ASD, including socioemotional problems and challenging behaviors (Cohen, et al., 2011; Gross, 2013; Gulsrud, Jahromi, & Kasari, 2010; Mazefsky et al., 2013). One possible explanation for these findings is that early SR deficits interfere with effective infant-caregiver co-regulation, caregiver-driven regulation crucial for developing effective self-driven ER and an important target for early intervention (Gulsrud, Jahromi, & Kasari, 2010). SR and ER are under-studied in ASD, and existing methods for assessing co-regulation mainly rely on labor-intensive and potentially subjective behavioral observation. Physiological measures offer a complimentary means for objectively evaluating SR and ER simultaneously in dyads that include an individual with ASD and a partner (e.g., peer, caregiver, teacher), shedding light on biological processes that may underlie observable behavior. However, dyadic physiological data is complex to analyze and interpret, and to-date only two published studies have examined such data in children with ASD (Baker et. al., 2015; Chaspari et. al., 2014). 

Objectives: Develop a novel analytic procedure for modeling interpersonal physiological dynamics and evaluate that model on pilot data collected from minimally-verbal (MV) children with ASD and their therapists during an empirically validated intervention focused on joint engagement and co-regulation (Kasari, Freeman, & Paparella, 2006). Using dynamical systems models, our analytical method provides clear effect sizes for levels of physiological interdependence (i.e., synchrony) and shows consistency with existing behavioral coding data.                                                                                                                                

Methods: Electrodermal activity (EDA) data was wirelessly recorded from six MV children with ASD and their therapists during intervention sessions. Using a windowed time-series approach, we applied a dynamical systems model of self- and co-regulation. For each child-therapist dyad we extracted the percentage of variance explained by their partner’s physiology via hierarchical regression. Subsequently, we assessed correspondence of these interpersonal physiological parameters with expert-coded behavioral measures of SR using a mixed-effects model to account for the nested structure of the data. 

Results: Our dynamical systems model explained significant variance attributable to interpersonal influence (R² range: 0.0 – 0.67), and showed correspondence with behavioral coding of SR-relevant behaviors (F(2,61)=4.21, p<.05, R²=0.10).

Conclusions: These data confirm the co-regulatory nature of the child and therapist physiology, and correspond to behavioral ratings, while providing greater temporal specificity on co-regulatory dynamics. To our knowledge, this is the first time interpersonal physiological measures using dynamical systems models have been applied to dyadic interactions in children with ASD. The utility of physiological measures for evaluating interpersonal functioning, and our new analytic technique, shows promise for allowing more efficient, objective, reproducible, and sensitive indices of SR and ER to study developmental underpinnings of socio-affective dynamics in ASD.