A Monte Carlo evaluation of growth mixture modeling.

Growth mixture modeling (GMM) and its variants, which group individuals based on similar longitudinal growth trajectories, are quite popular in developmental and clinical science. However, research addressing the validity of GMM-identified latent subgroupings is limited. This Monte Carlo simulation tests the efficiency of GMM in identifying known subgroups (k = 1-4) across various combinations of distributional characteristics, including skew, kurtosis, sample size, intercept effect size, patterns of growth (none, linear, quadratic, exponential), and proportions of observations within each group. In total, 1,955 combinations of distributional parameters were examined, each with 1,000 replications (1,955,000 simulations). Using standard fit indices, GMM often identified the wrong number of groups. When one group was simulated with varying skew and kurtosis, GMM often identified multiple groups. When two groups were simulated, GMM performed well only when one group had steep growth (whether linear, quadratic, or exponential). When three to four groups were simulated, GMM was effective primarily when intercept effect sizes and sample sizes were large, an uncommon state of affairs in real-world applications. When conditions were less ideal, GMM often underestimated the correct number of groups when the true number was between two and four. Results suggest caution in interpreting GMM results, which sometimes get reified in the literature.

Using automated computer vision and machine learning to code facial expressions of affect and arousal: Implications for emotion dysregulation research.

As early as infancy, caregivers' facial expressions shape children's behaviors, help them regulate their emotions, and encourage or dissuade their interpersonal agency. In childhood and adolescence, proficiencies in producing and decoding facial expressions promote social competence, whereas deficiencies characterize several forms of psychopathology. To date, however, studying facial expressions has been hampered by the labor-intensive, time-consuming nature of human coding. We describe a partial solution: automated facial expression coding (AFEC), which combines computer vision and machine learning to code facial expressions in real time. Although AFEC cannot capture the full complexity of human emotion, it codes positive affect, negative affect, and arousal-core Research Domain Criteria constructs-as accurately as humans, and it characterizes emotion dysregulation with greater specificity than other objective measures such as autonomic responding. We provide an example in which we use AFEC to evaluate emotion dynamics in mother-daughter dyads engaged in conflict. Among other findings, AFEC (a) shows convergent validity with a validated human coding scheme, (b) distinguishes among risk groups, and (c) detects developmental increases in positive dyadic affect correspondence as teen daughters age. Although more research is needed to realize the full potential of AFEC, findings demonstrate its current utility in research on emotion dysregulation.

Respiratory sinus arrhythmia reactivity across empirically based structural dimensions of psychopathology: A meta-analysis.

Low resting respiratory sinus arrhythmia (RSA) is observed in many mental health conditions, including anxiety disorders, mood disorders, schizophrenia spectrum disorders, disruptive behavior disorders, and nonsuicidal self-injury, among others. Findings for RSA reactivity are more mixed. We evaluate associations between RSA reactivity and empirically derived structural categories of psychopathology-including internalizing, externalizing, and thought problems-among physically healthy adults. We searched multiple electronic databases for studies of RSA among participants who were assessed either dimensionally using well-validated measures or diagnostically using structured interviews. Strict inclusion criteria were used to screen 3,605 published reports, which yielded 37 studies including 2,347 participants and 76 effect sizes. We performed a meta-analysis, with meta-analytic regressions of potential moderators, including psychopathology subtypes. The sample-wide meta-analytic association between RSA reactivity and psychopathology was quite small, but heterogeneity was considerable. Moderation analyses revealed significant RSA reactivity (withdrawal) specifically in externalizing samples. Additional moderators included (a) stimulus conditions used to elicit RSA reactivity (only negative emotion inductions were effective), (b) sex (women showed greater RSA reactivity than men), and (c) adherence to established methodological guidelines (e.g., higher electrocardiographic sampling rates yielded greater RSA reactivity). These findings indicate that associations between RSA reactivity and psychopathology are complex and suggest that future studies should include more standardized RSA assessments to increase external validity and decrease measurement error.

Improvements in Negative Parenting Mediate Changes in Children's Autonomic Responding Following a Preschool Intervention for ADHD.

Abnormal patterns of sympathetic- and parasympathetic- linked cardiac activity and reactivity are observed among externalizing children, and mark deficiencies in central nervous system regulation of behavior and emotion. Although changes in these biomarkers have been observed following treatment, mechanisms remain unexplored. We used MEMORE-a new approach to analyzing intervening variable effects-to evaluate improvements in parenting as mediators of changes in SNS- and PNS-linked cardiac activity and reactivity among 99 preschoolers with attention-deficit hyperactivity disorder who were treated using an empirically supported intervention. Decreases in negative parenting (criticism, negative commands, physical intrusions) were associated with increases in resting RSA and PEP reactivity to incentives from pre- to post-intervention. Increases in positive parenting were not associated with changes in autonomic function. These findings suggest socially-induced plasticity in peripheral biomarkers of behavior and emotion regulation, and underscore the importance of reducing aversive interactions between parents and children when treating externalizing behavior.

Quantifying respiratory sinus arrhythmia: Effects of misspecifying breathing frequencies across development.

Low resting respiratory sinus arrhythmia (RSA), and to a lesser extent excessive RSA reactivity to emotion evocation, are observed in many psychiatric disorders characterized by emotion dysregulation, including syndromes spanning the internalizing and externalizing spectra, and other conditions such as nonsuicidal self-injury. Nevertheless, some inconsistencies exist. For example, null outcomes in studies of RSA-emotion dysregulation relations are sometimes observed among younger participants. Such findings may derive from use of age inappropriate frequency bands in calculating RSA. We combine data from five published samples (N = 559) spanning ages 4 to 17 years, and reanalyze RSA data using age-appropriate respiratory frequencies. Misspecifying respiratory frequencies results in overestimates of resting RSA and underestimates of RSA reactivity, particularly among young children. Underestimates of developmental shifts in RSA and RSA reactivity from preschool to adolescence were also observed. Although correlational analyses revealed weak negative associations between resting RSA and aggression, those with clinical levels of externalizing exhibited lower resting RSA than their peers. No associations between RSA reactivity and externalizing were observed. Results confirm that age-corrected frequency bands should be used when estimating RSA, and that literature-wide overestimates of resting RSA, underestimates of RSA reactivity, and underestimates of developmental shifts in RSA and RSA reactivity may exist.