Adolescents’ Emotion System Dynamics:Network-based Analysis of Physiological and Emotional Experience

• In Event: 2-020 - Dynamics of Physiological Processes in Interpersonal Contexts: Correlates and Outcomes

Integrative Statement

Emotional challenges are prominent in adolescence (Allen & Sheeber, 2008; Dahl, 2001; Hollenstein & Lougheed, 2013). This phase of the lifespan is characterized by increases in emotional intensity, more negativity, and less positivity (Hardhavala et al., in press; Larson & Ham, 1993; Rosenblum & Lewis, 2003) – characteristics of emotional processing that are also related to several adolescent-onset psychopathologies. Viewed from a developmental perspective, phenotypic differences that manifest as anxiety or depression are both shaped by and shape the structural organization of the emotion system.

Built from a decade of adaptation, the structural organization of an adolescent’s emotion system shapes the ongoing moment-by-moment rise and fall of emotions. Prior experience and transactions with the environment coalesce into system structures that may promote higher reactivity to or better regulation of stressful events, and differential development of long-lasting interindividual differences (traits, personality, psychopathology). This study examines how differences in structure of individuals’ on-going emotional processes and system reactivity are associated with differences in trait anxiety.

In this study, second-by-second psychophysiological time-series data were collected from 130 adolescents (52% female, age 12.00 to 16.67 years, 73% White) completing a three-minute social stress-inducing speech task, during which each adolescents’ trivariate psychophysiological time-series data (skin conductance level, SCL; respiratory sinus arrhythmia, RSA; and self-rated distress). These data, as shown in Figure 1a, were modeled using unified structural equation models (Gates et al., 2010) to obtain structural organization of the emotion system, illustrated as person-specific networks like the one shown in Figure 1b. Then, impulse response analysis (Lütkepohl, 2005) was used to simulate how each component of the system responds to perturbation. The asymptotic level of equilibrium of each trajectory after perturbation, as shown in Figure 1c, is defined as the reactivity derived from the dynamics. In a final step, differences in adolescent’s psychophysiological reactivity profiles were described and examined in relation to individual differences in trait anxiety.

Results indicate substantial heterogeneity in the coordination patterns of adolescents’ psychophysiological reactivity. Of the 119 valid networks, 29 networks (24.4%) contained at least one negative feedback loop (regulatory process), 56 networks (47.1%) had only unidirectional edges, 32 networks (26.9%) had no cross-component temporal relation (completely independent components), and 2 networks (1.7%) contained at least one positive feedback loop (concordant process). More importantly, as shown in Table 1, greater reactivity of RSA to perturbation of RSA, R(RSA→RSA), was associated with higher levels of trait anxiety (β_5 = 0.079, p = 0.01), even after controlling for mean level of SCL, RSA, and distress during the task. This study highlights how structural organization of adolescents’ emotion systems, as captured through analysis of real-time biological, behavioral, cognitive, and emotional dynamics, can explain interindividual differences of emotional development.  

Authors

• Xiao Yang, Penn State University
  Presenting Author

• Nilam Ram, Penn State University
  Non-Presenting Author

• Jessica P. Lougheed, Purdue University
  Non-Presenting Author

• Peter Molenaar, The Pennsylvania State University
  Non-Presenting Author

• Tom Hollenstein, Queen’s University
  Non-Presenting Author