A Latent Change Score Approach to Understanding Autonomic Coordination

Children’s self-regulation has long been considered a key component of child development. Over the past two decades, physiological indices of self-regulation, particularly the autonomic nervous system (ANS), have garnered increased attention as an informative level of analysis in regulation research. The ANS is comprised of excitatory sympathetic and inhibitory parasympathetic branches, which serve to control core adaptive systems. Cardiography supports the simultaneous examination of both ANS branches across periods of rest, reactivity, and recovery via measures of pre-ejection period (PEP) and respiratory sinus arrythmia (RSA) as indicators of sympathetic and parasympathetic activity, respectively. However, despite their heavily intertwined functions, research examining autonomic coordination across sympathetic and parasympathetic systems is scarce. Moreover, extant research has favored static, mean level reactivity analyses, despite the dynamic nature of ANS regulation and the availability of analytic tools that can model these processes in real-time. This dissertation drew on a sample of 198 six-year-old children from a diverse community sample (49.5% female, 43.9% Latinx) to examine autonomic coordination by using bivariate latent change score modeling to evaluate bidirectional influences of sympathetic and parasympathetic activity over the course of a challenging puzzle completion task. Results indicated that children evidenced reciprocal sympathetic activation (i.e., PEP shortening and RSA withdrawal) across the challenge task, and these regulatory responses were driven by the leading influence of PEP on lagging changes in RSA. The current findings advance our understanding of children’s sympathetic and parasympathetic autonomic coordination while illustrating a novel analytic technique to support ongoing efforts to understand the etiology and developmental significance of children’s physiological self-regulation