Dynamical Systems Modeling of Couple Interaction: a New Method for Assessing Intervention Impact Across the Transition to Parenthood

Multilevel Latent Differential Structural Equation Model with Short Time Series and Time-Varying Covariates: A Comparison of Frequentist and Bayesian Estimators

Continuous-time modeling using differential equations is a promising technique to model change processes with longitudinal data. Among ways to fit this model, the Latent Differential Structural Equation Modeling (LDSEM) approach defines latent derivative variables within a structural equation modeling (SEM) framework, thereby allowing researchers to leverage advantages of the SEM framework for model building, estimation, inference, and comparison purposes. Still, a few issues remain unresolved, including performance of multilevel variations of the LDSEM under short time lengths (e.g., 14 time points), particularly when coupled multivariate processes and time-varying covariates are involved. Additionally, the possibility of using Bayesian estimation to facilitate the estimation of multilevel LDSEM (M-LDSEM) models with complex and higher-dimensional random effect structures has not been investigated. We present a series of Monte Carlo simulations to evaluate three possible approaches to fitting M-LDSEM, including: frequentist single-level and two-level robust estimators and Bayesian two-level estimator. Our findings suggested that the Bayesian approach outperformed other frequentist approaches. The effects of time-varying covariates are well recovered, and coupling parameters are the least biased especially using higher-order derivative information with the Bayesian estimator. Finally, an empirical example is provided to show the applicability of the approach.

A Dynamical Systems Investigation of the Co-regulation between Perceived Daily Parental Warmth and Adolescent Attention-deficit/hyperactivity Disorder Symptoms

Longitudinal research demonstrates that child ADHD symptoms and behaviors exhibit reciprocal associations with parenting behaviors over time. However, minimal research has investigated these associations and their dynamic links at the daily level. Intensive longitudinal data can disentangle stable between-person differences from within-person fluctuations and reveal nuanced short-term family dynamics on a micro timescale. Using 30-day daily diary data from a community sample of 86 adolescents (Mage = 14.5, 55% female, 56% White, 22% Asian) and latent differential equation modeling, this study examined the links between perceived daily parental warmth and ADHD symptoms as coupled dynamical systems. The results show that the magnitude of fluctuations in perceived daily parental warmth generally remains stable, while elevated ADHD symptoms return to their normal level over time. Perceived parental warmth is sensitive to change in ADHD symptoms such that adolescents feel that their parents will fine-tune their warmth with gradual changes when adolescents demonstrate heightened symptoms. There are substantial between-family differences in these regulating system dynamics. Among families with more baseline parental non-harsh discipline, both perceived parental warmth and ADHD symptoms tend to be more stable and fluctuate less often. Intensive longitudinal data and dynamical systems approaches offer a new lens to uncover short-term family dynamics and adolescent adjustment at a refined micro level. Future research should explore antecedents and consequences of between-family differences in these short-term family dynamics on multiple timescales.

Dysregulated biodynamics in metabolic attractor systems precede the emergence of amyotrophic lateral sclerosis

Evolutionarily conserved mechanisms maintain homeostasis of essential elements, and are believed to be highly time-variant. However, current approaches measure elemental biomarkers at a few discrete time-points, ignoring complex higher-order dynamical features. To study dynamical properties of elemental homeostasis, we apply laser ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS) to tooth samples to generate 500 temporally sequential measurements of elemental concentrations from birth to 10 years. We applied dynamical system and Information Theory-based analyses to reveal the longest-known attractor system in mammalian biology underlying the metabolism of nutrient elements, and identify distinct and consistent transitions between stable and unstable states throughout development. Extending these dynamical features to disease prediction, we find that attractor topography of nutrient metabolism is altered in amyotrophic lateral sclerosis (ALS), as early as childhood, suggesting these pathways are involved in disease risk. Mechanistic analysis was undertaken in a transgenic mouse model of ALS, where we find similar marked disruptions in elemental attractor systems as in humans. Our results demonstrate the application of a phenomological analysis of dynamical systems underlying elemental metabolism, and emphasize the utility of these measures in characterizing risk of disease.

The metabolism of essential elements in early life is essential to healthy growth and development. Elemental homeostasis is typically studied by characterizing distributions of elemental concentrations at the level of the population. Here, we introduce a new method of characterizing elemental metabolism at the level of the individual. Using tooth-based biomarkers, we tracked the longitudinal trajectory of essential elements throughout childhood at weekly temporal resolution from birth through approximately 10 years of life. We analyzed these trajectories to identify the formation of stable dynamic states (attractors) and transitions between these states throughout development. We found that metabolic dynamics were specific to discrete elemental pathways; copper metabolism typically involved the formation of multiple discrete states throughout childhood, whereas other elements, such as zinc, tended to persist in a single stable dynamic throughout development. Next, we compared elemental biodynamics in neurologically healthy cases and subjects that were later diagnosed with amyotrophic lateral sclerosis (ALS). We found these patterns were dysregulated in ALS, and also found similar results in a mouse model of ALS. Overall, our results provide a novel approach to characterize elemental biodynamics throughout development, and emphasize that the dysregulation of these processes may be predictive of later onset of disease.

Using Network Analysis for Examining Interpersonal Emotion Dynamics

Several contemporary models conceptualize emotion as inherently interpersonal. We demonstrate how network analysis, a class of statistical methods often used to examine intrapersonal dynamic processes, provides a potential avenue for parameterizing interpersonal emotion dynamics (and interpersonal dynamics in general). We claim that this method allows (a) observing interpersonal dynamics at various temporal levels; (b) examining interpersonal dynamics occurring through various emotional pathways; and (c) capturing variations in interpersonal networks, which can subsequently be used to predict changes in outcomes. To demonstrate the potential of this method, we used dyadic daily diary data on emotion dynamics from two samples; Sample 1 involved couples in their routine daily lives, whereas Sample 2 involved couples in their transition to parenthood. Graphical Multilevel-Vector-Autoregressive modeling was used to estimate partners' emotional networks, whereas in a second step, LASSO was used to test the predictive value of couple-level differences of the obtained networks. The analysis revealed several patterns. For example, the between-couple network of Sample 1 was more interpersonally dense, but couple-level differences in the networks' interpersonal associations were predictive of partners' relationship satisfaction over time only in Sample 2. We also include commented code implementing a new dyadmlvar R package developed for conducting this analysis.

The Inconsolable Doll Task: Prenatal coparenting behavioral dynamics under stress predicting child cognitive development at 18 months

Studies have demonstrated that coparenting can be assessed prenatally through playful observational conditions, including simulated baby enactments. Regrettably, there is a lack of empirical research examining how prenatal coparenting under the emotional stress elicited by the distress of a simulated infant predicts children's cognitive development. The current longitudinal study introduces a novel procedure-the Inconsolable Doll Task-to assess prenatal coparenting behavioral dynamics under the stress of a non-responsive doll simulator, and examines the extent to which prenatal interaction patterns predict the child's cognitive development at 18 months. The sample consists of 105 community-based, co-living, expectant fathers and mothers. Data were collected prenatally, at three, six, and 18 months in home and lab visits. Results indicate that the prenatal coparenting dynamic of negative escalation explains a unique variance in children's cognitive development at 18 months. This effect is evident even when accounting for both prenatal and postnatal assessments of low-stress coparenting behavioral patterns or self-reported coparenting perceptions, and when controlling for parental education. These findings are discussed in terms of their methodical, empirical, and clinical implications.