Examining timing effects in the intergenerational transmission of anxiety and depressive symptoms: A genetically informed study.

The present study examined genetic, prenatal, and postnatal environmental pathways in the intergenerational transmission of anxiety and depressive symptoms from parents to early adolescents (when these symptoms start to increase), while considering timing effects of exposure to parent anxiety and depressive symptoms postnatally. The sample was from the Early Growth and Development Study, including 561 adopted children (57% male, 55% White, 13% Black/African American, 11% Hispanic/Latine, 20% multiracial, 1% other; 407 provided data in early adolescence) and their birth (BP) and adoptive parents (AP). Using a trait-state-occasion model with eight assessments from child ages 9 months to 11 years, we partitioned trait-like AP anxiety and depressive symptoms from time-specific fluctuations of AP anxiety and depressive symptoms. Offspring anxiety and depressive symptoms were assessed at 11 years (while controlling for similar symptoms at 4.5 years). Results suggested that time-specific fluctuations of AP1 (mostly mothers) anxiety/depressive symptoms in infancy (9 months) were indirectly associated with offspring anxiety/depressive symptoms at 11 years via offspring anxiety/depressive symptoms at 4.5 years; time-specific fluctuations of AP1 anxiety/depressive symptoms at child age 11 years were concurrently associated with offspring anxiety/depressive symptoms at 11 years. AP2 (mostly fathers) anxiety/depressive symptoms were not associated with offspring symptoms. Genetic and prenatal influences measured by BP internalizing problems were not associated with offspring symptoms. Results suggested infancy and early adolescence as developmental periods when children are susceptible to influences of parent anxiety and depressive symptoms. Preventive interventions should consider time-specific fluctuations in parent anxiety and depressive symptoms during these developmental periods. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Subgrouping with Chain Graphical VAR Models.

Recent years have seen the emergence of an "idio-thetic" class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic processes by pooling intraindividual information across individuals to inform group-level inference or vice versa. The current work introduces a novel "idio-thetic" model: the subgrouped chain graphical vector autoregression (scGVAR). The scGVAR is unique in its ability to identify subgroups of individuals who share common dynamic network structures in both lag(1) and contemporaneous effects. Results from Monte Carlo simulations indicate that the scGVAR shows promise over similar approaches when clusters of individuals differ in their contemporaneous dynamics and in showing increased sensitivity in detecting nuanced group differences while keeping Type-I error rates low. In contrast, a competing approach-the Alternating Least Squares VAR (ALS VAR) performs well when groups were separated by larger distances. Further considerations are provided regarding applications of the ALS VAR and scGVAR on real data and the strengths and limitations of both methods.

Evaluating Discrete Time Methods for Subgrouping Continuous Processes.

Rapid developments over the last several decades have brought increased focus and attention to the role of time scales and heterogeneity in the modeling of human processes. To address these emerging questions, subgrouping methods developed in the discrete-time framework-such as the vector autoregression (VAR)-have undergone widespread development to identify shared nomothetic trends from idiographic modeling results. Given the dependence of VAR-based parameters on the measurement intervals of the data, we sought to clarify the strengths and limitations of these methods in recovering subgroup dynamics under different measurement intervals. Building on the work of Molenaar and collaborators for subgrouping individual time-series by means of the subgrouped chain graphical VAR (scgVAR) and the subgrouping option in the group iterative multiple model estimation (S-GIMME), we present results from a Monte Carlo study aimed at addressing the implications of identifying subgroups using these discrete-time methods when applied to continuous-time data. Results indicate that discrete-time subgrouping methods perform well at recovering true subgroups when the measurement intervals are large enough to capture the full range of a system's dynamics, either via lagged or contemporaneous effects. Further implications and limitations are discussed therein.

Estimating both directed and undirected contemporaneous relations in time series data using hybrid-group iterative multiple model estimation.

Researchers across varied fields increasingly are collecting and analyzing intensive longitudinal data (ILD) to examine processes across time at the individual level. Two types of relations are typically examined: lagged and contemporaneous. Lagged relations capture how variables at a prior time point can be used to explain variance in variables at a later time point. These are always modeled using auto- and cross-regressions by means of vector autoregression (VAR). By contrast, there are two types of relations commonly used to model the contemporaneous relations, which model how variables relate instantaneously. Until now, researchers must opt to either model contemporaneous relations as undirected relations among residuals (e.g., partial or full correlations) or as directed relations among the variables (e.g., paths or regressions). The choice for how to model contemporaneous relations has implications for inferences as well as the potential to introduce bias in the VAR lagged relations if the wrong type of relation is used. This article introduces a novel data-driven method, hybrid-group iterative multiple model estimation (GIMME), that provides a solution to the problem of having to choose one or the other type of contemporaneous relation to model. The modeling framework utilized in hybrid-GIMME allows for both types of contemporaneous relations in addition to the standard VAR relations. Both simulated and empirical data were used to test the performance of hybrid-GIMME. Results suggest this is a robust method for recovering contemporaneous relations in an exploratory manner, particularly with an ample number of time points per person. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Control Theory Forecasts of Optimal Training Dosage to Facilitate Children's Arithmetic Learning in a Digital Educational Application.

Education can be viewed as a control theory problem in which students seek ongoing exogenous input-either through traditional classroom teaching or other alternative training resources-to minimize the discrepancies between their actual and target (reference) performance levels. Using illustrative data from [Formula: see text] Dutch elementary school students as measured using the Math Garden, a web-based computer adaptive practice and monitoring system, we simulate and evaluate the outcomes of using off-line and finite memory linear quadratic controllers with constraintsto forecast students' optimal training durations. By integrating population standards with each student's own latent change information, we demonstrate that adoption of the control theory-guided, person- and time-specific training dosages could yield increased training benefits at reduced costs compared to students' actual observed training durations, and a fixed-duration training scheme. The control theory approach also outperforms a linear scheme that provides training recommendations based on observed scores under noisy and the presence of missing data. Design-related issues such as ways to determine the penalty cost of input administration and the size of the control horizon window are addressed through a series of illustrative and empirically (Math Garden) motivated simulations.

A Square-Root Second-Order Extended Kalman Filtering Approach for Estimating Smoothly Time-Varying Parameters.

Researchers collecting intensive longitudinal data (ILD) are increasingly looking to model psychological processes, such as emotional dynamics, that organize and adapt across time in complex and meaningful ways. This is also the case for researchers looking to characterize the impact of an intervention on individual behavior. To be useful, statistical models must be capable of characterizing these processes as complex, time-dependent phenomenon, otherwise only a fraction of the system dynamics will be recovered. In this paper we introduce a Square-Root Second-Order Extended Kalman Filtering approach for estimating smoothly time-varying parameters. This approach is capable of handling dynamic factor models where the relations between variables underlying the processes of interest change in a manner that may be difficult to specify in advance. We examine the performance of our approach in a Monte Carlo simulation and show the proposed algorithm accurately recovers the unobserved states in the case of a bivariate dynamic factor model with time-varying dynamics and treatment effects. Furthermore, we illustrate the utility of our approach in characterizing the time-varying effect of a meditation intervention on day-to-day emotional experiences.

On Standardized Measurement in Behavioral Science.

That standardized measurement procedures are a sine qua non of "good" science is generally not questioned. Here we examine the meaning and use of standardized measurement in behavioral science. Procedures and methods of measurement that have served the physical sciences so well should not blindly be assumed to work in the same manner and with the same effectiveness in behavioral science. There seems to be general agreement that social/behavioral science is "different" among the sciences. Problems arising from how behavioral science is "different" begin, we believe, with measurement. We put forward the argument that the source of the difference is unique to animate objects and is first evident at the stage of measuring the behavioral attributes of interest. It is at that point in conducting scientific inquiry that the matters raised might be resolved by developing and applying alternatives to standardized measurement. One such alternative discussed is the idiographic filter (Nesselroade, Gerstorf, Hardy, & Ram, 2007).

Describing and Controlling Multivariate Nonlinear Dynamics: A Boolean Network Approach.

We introduce a discrete-time dynamical system method, the Boolean network method, that may be useful for modeling, studying, and controlling nonlinear dynamics in multivariate systems, particularly when binary time-series are available. We introduce the method in three steps: inference of the temporal relations as Boolean functions, extraction of attractors and assignment of desirability based on domain knowledge, and design of network control to direct a psychological system toward a desired attractor. To demonstrate how the Boolean network can describe and prescribe control for emotion regulation dynamics, we applied this method to data from a study of how children use bidding to an adult and/or distraction to regulate their anger during a frustrating task (N = 120, T = 480 seconds). Network control strategies were designed to move the child into attractors where anger is OFF. The sample shows heterogeneous emotion regulation dynamics across children in 22 distinct Boolean networks, and heterogeneous control strategies regarding which behavior to perturb and how to perturb it. The Boolean network method provides a novel method to describe nonlinear dynamics in multivariate psychological systems and is a method with potential to eventually inform the design of interventions that can guide those systems toward desired goals.

Detecting Task-Dependent Functional Connectivity in Group Iterative Multiple Model Estimation with Person-Specific Hemodynamic Response Functions.

Introduction: Group iterative multiple model estimation (GIMME) has proven to be a reliable data-driven method to arrive at functional connectivity maps that represent associations between brain regions across time in groups and individuals. However, to date, GIMME has not been able to model time-varying task-related effects. This article introduces HRF-GIMME, an extension of GIMME that enables the modeling of the direct and modulatory effects of a task on functional magnetic resonance imaging data collected by using event-related designs. Critically, hemodynamic response function (HRF)-GIMME incorporates person-specific modeling of the HRF to accommodate known variability in onset delay and shape. Methods: After an introduction of the technical aspects of HRF-GIMME, the performance of HRF-GIMME is evaluated via both a simulation study and application to empirical data. The simulation study assesses the sensitivity and specificity of HRF-GIMME by using data simulated from one slow and two rapid event-related designs, and HRF-GIMME is then applied to two empirical data sets from similar designs to evaluate performance in recovering known neural circuitry. Results: HRF-GIMME showed high sensitivity and specificity across all simulated conditions, and it performed well in the recovery of expected relations between convolved task vectors and brain regions in both simulated and empirical data, particularly for the slow event-related design. Conclusion: Results from simulated and empirical data indicate that HRF-GIMME is a powerful new tool for obtaining directed functional connectivity maps of intrinsic and task-related connections that is able to uncover what is common across the sample as well as crucial individual-level path connections and estimates. Impact statement Group iterative multiple model estimation (GIMME) is a reliable method for creating functional connectivity maps of the connections between brain regions across time, and it is able to detect what is common across the sample and what is shared between subsets of participants, as well as individual-level path estimates. However, historically, GIMME does not model task-related effects. The novel HRF-GIMME algorithm enables the modeling of direct and modulatory task effects through individual-level estimation of the hemodynamic response function (HRF), presenting a powerful new tool for assessing task effects on functional connectivity networks in functional magnetic resonance imaging data.

A Tribute to the Mind, Methodology and Mentoring of Wayne Velicer.

Wayne Velicer is remembered for a mind where mathematical concepts and calculations intrigued him, behavioral science beckoned him, and people fascinated him. Born in Green Bay, Wisconsin on March 4, 1944, he was raised on a farm, although early influences extended far beyond that beginning. His Mathematics BS and Psychology minor at Wisconsin State University in Oshkosh, and his PhD in Quantitative Psychology from Purdue led him to a fruitful and far-reaching career. He was honored several times as a high-impact author, was a renowned scholar in quantitative and health psychology, and had more than 300 scholarly publications and 54,000+ citations of his work, advancing the arenas of quantitative methodology and behavioral health. In his methodological work, Velicer sought out ways to measure, synthesize, categorize, and assess people and constructs across behaviors and time, largely through principal components analysis, time series, and cluster analysis. Further, he and several colleagues developed a method called Testing Theory-based Quantitative Predictions, successfully applied to predicting outcomes and effect sizes in smoking cessation, diet behavior, and sun protection, with the potential for wider applications. With $60,000,000 in external funding, Velicer also helped engage a large cadre of students and other colleagues to study methodological models for a myriad of health behaviors in a widely applied Transtheoretical Model of Change. Unwittingly, he has engendered indelible memories and gratitude to all who crossed his path. Although Wayne Velicer left this world on October 15, 2017 after battling an aggressive cancer, he is still very present among us.

Problems with Centrality Measures in Psychopathology Symptom Networks: Why Network Psychometrics Cannot Escape Psychometric Theory.

Understanding patterns of symptom co-occurrence is one of the most difficult challenges in psychopathology research. Do symptoms co-occur because of a latent factor, or might they directly and causally influence one another? Motivated by such questions, there has been a surge of interest in network analyses that emphasize the putatively direct role symptoms play in influencing each other. In this critical paper, we highlight conceptual and statistical problems with using centrality measures in cross-sectional networks. In particular, common network analyses assume that there are no unmodeled latent variables that confound symptom co-occurrence. The traditions of clinical taxonomy and test development in psychometric theory, however, greatly increase the possibility that latent variables exist in symptom data. In simulations that include latent variables, we demonstrate that closeness and betweenness are vulnerable to spurious covariance among symptoms that connect subgraphs (e.g., diagnoses). We further show that strength is redundant with factor loading in several cases. Finally, if a symptom reflects multiple latent causes, centrality metrics reflect a weighted combination, undermining their interpretability in empirical data. Our results suggest that it is essential for network psychometric approaches to examine the evidence for latent variables prior to analyzing or interpreting patterns at the symptom level. Failing to do so risks identifying spurious relationships or failing to detect causally important effects. Altogether, we argue that centrality measures do not provide solid ground for understanding the structure of psychopathology when latent confounding exists.

Affect and Personality: Ramifications of Modeling (Non-)Directionality in Dynamic Network Models.

The use of dynamic network models has grown in recent years. These models allow researchers to capture both lagged and contemporaneous effects in longitudinal data typically as variations, reformulations, or extensions of the standard vector autoregressive (VAR) models. To date, many of these dynamic networks have not been explicitly compared to one another. We compare three popular dynamic network approaches-GIMME, uSEM, and LASSO gVAR-in terms of their differences in modeling assumptions, estimation procedures, statistical properties based on a Monte Carlo simulation, and implications for affect and personality researchers. We found that all three approaches dynamic networks provided yielded group-level empirical results in partial support of affect and personality theories. However, individual-level results revealed a great deal of heterogeneity across approaches and participants. Reasons for discrepancies are discussed alongside these approaches' respective strengths and limitations.

Dynamics among borderline personality and anxiety features in psychotherapy outpatients: An exploration of nomothetic and idiographic patterns.

Borderline personality disorder (BPD) involves instability in self-concept, emotions, and behavior. However, the dynamic, longitudinal relations among BPD symptoms and between these symptoms and other problematic emotional experiences are poorly understood. It is also unclear whether these dynamics are the same across persons (including across diagnostic boundaries), specific to individuals with BPD, or idiographic. The current study uses ecological momentary assessment and group iterative multiple model estimation, a novel, data-driven approach to identifying dynamic patterns in time-series data at group, subgroup, and individual levels, to investigate the dynamic connections among select features of BPD (anger, impulsivity, and identity disturbance) and anxiety-related experiences. Forty-two psychiatric outpatients diagnosed with BPD (n = 27) or with an anxiety disorder, but not BPD (n = 15), rated their anger, identity disturbance, impulsivity, anxiety, stress, and calmness states 6 times per day for 21 days, providing a total of 4,699 surveys. Only 1 dynamic link between symptoms was identified that applied at the group level, and group iterative multiple model estimation did not reveal stable subgroups of individuals with distinct symptom dynamics. Instead, these dynamics differed from individual to individual. These results suggest that connections among these BPD and anxiety symptoms do not depend on diagnosis and are somewhat idiographic. Case examples are used to illustrate the clinical utility of within-person symptom models as a supplement to traditional diagnostic information. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Adolescents' emotion system dynamics: Network-based analysis of physiological and emotional experience.

An individual's emotions system can be conceived of as a synchronized, coordinated, and/or emergent combination of physiology, experience, and behavioral components. Together, the interplay among these components produce emotional experiences through coordinated excitatory positive feedback (i.e., the mutual amplification of emotion concordance) and/or inhibitory negative feedback (i.e., the damping of emotion regulation) processes. Different system configurations produce differential psychophysiological reactivity profiles, and by implication, differential moment-to-moment emotional experience and long-term development. Applying dynamic systems models to second-by-second psychophysiological and experience time-series data collected from 130 adolescents (age 12.0 to 16.7 years) completing a social stress-inducing speech task, we describe the configuration of adolescents' emotion systems, and examine how differences in the dynamic outputs of those systems (psychophysiological reactivity profile) are related to individual differences in trait anxiety. We found substantial heterogeneity in the coordination patterns of these adolescents. Some individuals' emotion systems were characterized by negative feedback loops (emotion regulation processes), many by unidirectionally connected or independent components, and a few by positive feedback loops (emotion concordance). The reactivity dynamics of respiratory sinus arrhythmia were related to adolescents' level of trait anxiety. Results highlight how dynamic systems models may contribute to our understanding of interindividual and developmental differences. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Evaluating the organizational structure and specificity of network topology within the face processing system.

There is increasing appreciation that network-level interactions among regions produce components of face processing previously ascribed to individual regions. Our goals were to use an exhaustive data-driven approach to derive and quantify the topology of directed functional connections within a priori defined nodes of the face processing network and evaluate whether the topology is category-specific. Young adults were scanned with fMRI as they viewed movies of faces, objects, and scenes. We employed GIMME to model effective connectivity among core and extended face processing regions, which allowed us to evaluate all possible directional connections, under each viewing condition (face, object, place). During face processing, we observed directional connections from the right posterior superior temporal sulcus to both the right occipital face area and right fusiform face area (FFA), which does not reflect the topology reported in prior studies. We observed connectivity between core and extended regions during face processing, but this limited to a feed-forward connection from the FFA to the amygdala. Finally, the topology of connections was unique to face processing. These findings suggest that the pattern of directed functional connections within the face processing network, particularly in the right core regions, may not be as hierarchical and feed-forward as described previously. Our findings support the notion that topologies of network connections are specialized, emergent, and dynamically responsive to task demands.

The impact of trait mindfulness on relational outcomes in novice yoga practitioners participating in an academic yoga course.

OBJECTIVE: This study examined the role of dispositional mindfulness in impacting the relational benefits of yoga in novice practitioners in an undergraduate academic yoga course. PARTICIPANTS: 21 college students enrolled in a yoga course throughout a 15-week academic semester (January-April 2016). METHODS: Participants attended lab assessments at the beginning and end of the semester and were asked to complete eight consecutive Internet-based daily surveys across six separate bursts, yielding 48 data points for each participant. RESULTS: Multivariate time-series analyses revealed within-person subgroup differences such that in general, those in the high trait mindfulness group gleaned benefits from yoga practice with respect to relational outcomes. CONCLUSIONS: This study demonstrated trait mindfulness impacts relational outcomes in novice yoga practitioners, suggesting those low in trait mindfulness may not garner immediate relational benefits from yoga and could benefit from additional strategies to bolster against any negative influences of initiating yoga practice.

Granger Causality Testing with Intensive Longitudinal Data.

The availability of intensive longitudinal data obtained by means of ambulatory assessment opens up new prospects for prevention research in that it allows the derivation of subject-specific dynamic networks of interacting variables by means of vector autoregressive (VAR) modeling. The dynamic networks thus obtained can be subjected to Granger causality testing in order to identify causal relations among the observed time-dependent variables. VARs have two equivalent representations: standard and structural. Results obtained with Granger causality testing depend upon which representation is chosen, yet no criteria exist on which this important choice can be based. A new equivalent representation is introduced called hybrid VARs with which the best representation can be chosen in a data-driven way. Partial directed coherence, a frequency-domain statistic for Granger causality testing, is shown to perform optimally when based on hybrid VARs. An application to real data is provided.

Socioemotional Dynamics of Emotion Regulation and Depressive Symptoms: A Person-Specific Network Approach.

Socioemotional processes engaged in daily life may afford and/or constrain individuals' emotion regulation in ways that affect psychological health. Recent findings from experience sampling studies suggest that persistence of negative emotions (emotion inertia), the strength of relations among an individual's negative emotions (density of the emotion network), and cycles of negative/aggressive interpersonal transactions are related to psychological health. Using multiple bursts of intensive experience sampling data obtained from 150 persons over one year, person-specific analysis, and impulse response analysis, this study quantifies the complex and interconnected socioemotional processes that surround individuals' daily social interactions and on-going regulation of negative emotion in terms of recovery time. We also examine how this measure of regulatory inefficiency is related to interindividual differences and intraindividual change in level of depressive symptoms. Individuals with longer recovery times had higher overall level of depressive symptoms. As well, during periods where recovery time of sadness was longer than usual, individuals' depressive symptoms were also higher than usual, particularly among individuals who experienced higher overall level of stressful life events. The findings and analysis highlight the utility of a person-specific network approach to study emotion regulation, how regulatory processes change over time, and potentially how planned changes in the configuration of individuals' systems may contribute to psychological health.

The Impact of Variation in Twin Relatedness on Estimates of Heritability and Environmental Influences.

By taking advantage of the natural variation in genetic relatedness among identical (monozygotic: MZ) and fraternal (dizygotic: DZ) twins, twin studies are able to estimate genetic and environmental contributions to complex human behaviors. Recently concerns have been raised about the accuracy of twin studies in light of findings of genetic and epigenetic changes in twins. One of the concerns raised is that MZ twins are not 100% genetically and epigenetically similar because they show variations in their genomes and epigenomes leading to inaccurate estimates of heritability. This article presents findings from a simulation study that examined the degree of bias in estimates of heritability and environmentality when the genetic and epigenetic similarity of MZ twins differs from 1.00 and when the genetic and epigenetic similarity of DZ twins differs from 0.50. The findings suggest that in the standard biometric model when MZ or DZ twin similarity differs from 1.00 or 0.50, respectively, the variance that should be attributed to genetic influences is instead attributed to nonshared environmental influences, thus deflating the estimates of genetic influences and inflating the estimates of nonshared environmental influences. Although estimates of genetic and nonshared environmental influences from the standard biometric model were found to deviate from "true" values, the bias was usually smaller than 10% points indicating that the interpretations of findings from previous twin studies are mostly correct.