Intergenerational relations in childhood anxiety: A network approach.

OBJECTIVE: Family factors are assumed to play a central role in the development of childhood anxiety disorders. How child and parental anxiety symptoms are intertwined on a symptom and family level has not yet been examined. Such knowledge may lead to a more detailed understanding of the intergenerational relation in anxiety problems. The current study investigated the relation between anxiety in children and their parents at a symptom level using a network approach. METHOD: Parents of 1,452 clinically referred children in the Netherlands completed questionnaires on anxiety about their children and themselves. We examined relations on a symptom level both within persons and between parents and children. In addition, we also compared the relations between parental and child anxiety symptoms in families with children with an anxiety disorder (n = 350) versus families with children who displayed other psychiatric diagnoses (n = 1,102). RESULTS: Anxiety symptom relations within persons were more intertwined than the symptom relations between family members. Between-person relations were found among similar anxiety symptoms, suggesting specific intergenerational relations. The feeling of being fearful was found to be a central and connecting symptom in all family members (fathers, mothers, and children). The relations between parental and child anxiety symptoms were more specific (i.e., among similar symptoms) in families with children with an anxiety disorder than in families with children with other types of psychopathologies. CONCLUSIONS: This study found that anxiety symptom associations are present within the family on a detailed (symptom) level. This stresses the importance of future studies to examine factors responsible for this family-anxiety transmission.

Comparing network structures on three aspects: A permutation test.

Network approaches to psychometric constructs, in which constructs are modeled in terms of interactions between their constituent factors, have rapidly gained popularity in psychology. Applications of such network approaches to various psychological constructs have recently moved from a descriptive stance, in which the goal is to estimate the network structure that pertains to a construct, to a more comparative stance, in which the goal is to compare network structures across populations. However, the statistical tools to do so are lacking. In this article, we present the network comparison test (NCT), which uses resampling-based permutation testing to compare network structures from two independent, cross-sectional data sets on invariance of (a) network structure, (b) edge (connection) strength, and (c) global strength. Performance of NCT is evaluated in simulations that show NCT to perform well in various circumstances for all three tests: The Type I error rate is close to the nominal significance level, and power proves sufficiently high if sample size and difference between networks are substantial. We illustrate NCT by comparing depression symptom networks of males and females. Possible extensions of NCT are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The search for causality: A comparison of different techniques for causal inference graphs.

Estimating causal relations between two or more variables is an important topic in psychology. Establishing a causal relation between two variables can help us in answering that question of why something happens. However, using solely observational data are insufficient to get the complete causal picture. The combination of observational and experimental data may give adequate information to properly estimate causal relations. In this study, we consider the conditions where estimating causal relations might work and we show how well different algorithms, namely the Peter and Clark algorithm, the Downward Ranking of Feed-Forward Loops algorithm, the Transitive Reduction for Weighted Signed Digraphs algorithm, the Invariant Causal Prediction (ICP) algorithm and the Hidden Invariant Causal Prediction (HICP) algorithm, determine causal relations in a simulation study. Results showed that the ICP and the HICP algorithms perform best in most simulation conditions. We also apply every algorithm to an empirical example to show the similarities and differences between the algorithms. We believe that the combination of the ICP and the HICP algorithm may be suitable to be used in future research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Obsessive-compulsive symptoms in eating disorders: A network investigation.

OBJECTIVE: Eating disorders (EDs) are complex, heterogeneous, and severe psychiatric syndromes. They are highly comorbid with obsessive-compulsive disorder (OCD) which exacerbates the course of illness and impedes treatment. However, the direct functional relations between EDs and OCD symptoms remain largely unexplored. Hence, using network analysis, we investigated the relationship between ED and OCD at the level of symptoms in a heterogeneous clinical sample. METHOD: We used cross sectional data of 303 treatment-seeking patients with clinically relevant ED and OCD pathology. We constructed a regularized partial correlation network that featured both ED and OCD symptoms as nodes. To determine each symptom's influence, we calculated expected influence (EI) as an index of symptom centrality (i.e., "importance"). Bridge symptoms (i.e., symptoms from one syndromic cluster that have strong connections to symptoms of another syndromic cluster) were identified by computing bridge expected influence metrics. RESULTS: Fear of weight gain and dietary restraint were especially important among the ED symptoms. Interference due to obsessions was the key feature of OCD. ED and OCD clustered distinctly with few potential bridges between clusters. DISCUSSION: This study underscores the importance of cognitive symptoms for both ED and OCD although direct functional links between the two clusters are missing. Potentially, a network incorporating nodes capturing features of personality may account for diagnostic comorbidity better than specific symptoms of EDs or features of OCD do.

Applying a Dynamical Systems Model and Network Theory to Major Depressive Disorder.

Mental disorders like major depressive disorder can be modeled as complex dynamical systems. In this study we investigate the dynamic behavior of individuals to see whether or not we can expect a transition to another mood state. We introduce a mean field model to a binomial process, where we reduce a dynamic multidimensional system (stochastic cellular automaton) to a one-dimensional system to analyse the dynamics. Using maximum likelihood estimation, we can estimate the parameter of interest which, in combination with a bifurcation diagram, reflects the expectancy that someone has to transition to another mood state. After numerically illustrating the proposed method with simulated data, we apply this method to two empirical examples, where we show its use in a clinical sample consisting of patients diagnosed with major depressive disorder, and a general population sample. Results showed that the majority of the clinical sample was categorized as having an expectancy for a transition, while the majority of the general population sample did not have this expectancy. We conclude that the mean field model has great potential in assessing the expectancy for a transition between mood states. With some extensions it could, in the future, aid clinical therapists in the treatment of depressed patients.

Emotional and Behavioral Symptom Network Structure in Elementary School Girls and Association With Anxiety Disorders and Depression in Adolescence and Early Adulthood: A Network Analysis.

Importance: The onset of adult psychopathologic disorders can be traced to behavioral or emotional symptoms observed in childhood, which could be targeted in early interventions to prevent future mental disorders. The network perspective is a novel conceptualization of psychopathologic disorders that could help to identify target symptoms with a distinct role in the emergence of mental illness. Objective: To assess whether the network structure of emotional and behavioral symptoms among elementary school girls is associated with anxiety disorders or major depression in early adulthood. Design, Setting, and Participants: The Quebec Longitudinal Study of Kindergarten Children is an ongoing, prospective, population-based study of kindergarten children attending French-speaking state schools in the Canadian province of Quebec in 1986-1988. This study included 932 girls whose parents completed the Social Behavior Questionnaire when the girls were ages 6 (baseline), 8, and 10 years; 780 participants were interviewed to assess the presence of mental disorders at age 15 and/or 22 years. Data analysis was conducted from December 2016 to April 2018. Main Outcomes and Measures: Gaussian graphical models were estimated for 33 symptoms (eg, internalizing, externalizing, and prosocial behaviors) assessed using the Social Behavior Questionnaire to evaluate the temporal stability of the symptom network through childhood. At follow-up time points, mental disorders were assessed using the DSM-III-R, and symptom networks were reestimated at ages 6 to 10 years, this time including a variable indicative of future diagnosis. Results: At baseline, the mean (SD) age of the 932 girls was 6.0 (0.3) years. Among the 780 women assessed at follow-up, 270 (34.6%) and 128 (16.4%) had developed anxiety disorders and major depression, respectively. Symptoms clustered in internalizing and externalizing communities. Five symptoms-irritable, blames others, not liked by others, often cries, and solitary-emerged as bridge symptoms between the disruptive and internalizing communities. These symptoms were those that were connected with the highest regularized edge weights (from 0.015 to 0.076) to future anxiety disorders once added to the network. Bootstrapped 95% CIs ranged from (95% CI, -0.063 to 0.068) to (95% CI, 0.561 to 0.701) for positive edges and from (95% CI, -0.156 to 0.027) to (95% CI, -0.081 to 0.078) for negative edges included in the regularized network. Conclusions and Relevance: Bridge symptoms between disruptive and internalizing communities are identified for the first time in childhood, and these findings suggest that these symptoms could be central in indicating probable later anxiety disorders. The study suggests that bridge symptoms should be investigated further as potential early targets in disease-prevention interventions.

Longitudinal network structure of depression symptoms and self-efficacy in low-income mothers.

Maternal depression was recently conceptualized as a network of interacting symptoms. Prior studies have shown that low self-efficacy, as an index of maternal functioning, is one important source of stress that worsens depression. We have limited information, however, on the specific relationships between depression symptoms and self-efficacy. In this study, we used regularized partial correlation networks to explore the multivariate relationships between maternal depression symptoms and self-efficacy over time. Depressed mothers (n = 306) completed the Center for Epidemiological Studies Depression (CES-D) scale at four time points, between four and eight weeks apart. We estimated (a) the network structure of the 20 CES-D depression symptoms and self-efficacy for each time point, (b) determined the centrality or structural importance of all variables, and (c) tested whether the network structure changed over time. In the resulting networks, self-efficacy was mostly negatively connected with depression symptoms. The strongest relationships among depression symptoms were 'lonely-sleep difficulties' and 'inability to get going-crying'. 'Feeling disliked' and 'concentration difficulty' were the two most central symptoms. In comparing the network structures, we found that the network structures were moderately stable over time. This is the first study to investigate the network structure and their temporal stability of maternal depression symptoms and self-efficacy in low-income depressed mothers. We discuss how these findings might help future research to identify clinically relevant symptom-to-symptom relationships that could drive maternal depression processes, and potentially inform tailored interventions. We share data and analytical code, making our results fully reproducible.

Pitching Emotions: The Interpersonal Effects of Emotions in Professional Baseball.

Sports games are inherently emotional situations, but surprisingly little is known about the social consequences of these emotions. We examined the interpersonal effects of emotional expressions in professional baseball. Specifically, we investigated whether pitchers' facial displays influence how pitches are assessed and responded to. Using footage from the Major League Baseball World Series finals, we isolated incidents where the pitcher's face was visible before a pitch. A pre-study indicated that participants consistently perceived anger, happiness, and worry in pitchers' facial displays. An independent sample then predicted pitch characteristics and batter responses based on the same perceived emotional displays. Participants expected pitchers perceived as happy to throw more accurate balls, pitchers perceived as angry to throw faster and more difficult balls, and pitchers perceived as worried to throw slower and less accurate balls. Batters were expected to approach (swing) when faced with a pitcher perceived as happy and to avoid (no swing) when faced with a pitcher perceived as worried. Whereas previous research focused on using emotional expressions as information regarding past and current situations, our work suggests that people also use perceived emotional expressions to predict future behavior. Our results attest to the impact perceived emotional expressions can have on professional sports.

The application of a network approach to Health-Related Quality of Life (HRQoL): introducing a new method for assessing HRQoL in healthy adults and cancer patients.

PURPOSE: Health-Related Quality of Life (HRQoL) research has typically adopted either a formative approach, in which HRQoL is the common effect of its observables, or a reflective approach--defining HRQoL as a latent variable that determines observable characteristics of HRQoL. Both approaches, however, do not take into account the complex organization of these characteristics. The objective of this study was to introduce a new approach for analyzing HRQoL data, namely a network model (NM). An NM, as opposed to traditional research strategies, accounts for interactions among observables and offers a complementary analytic approach. METHODS: We applied the NM to samples of Dutch cancer patients (N = 485) and Dutch healthy adults (N = 1742) who completed the 36-item Short Form Health Survey (SF-36). Networks were constructed for both samples separately and for a combined sample with diagnostic status added as an extra variable. We assessed the network structures and compared the structures of the two separate samples on the item and domain levels. The relative importance of individual items in the network structures was determined using centrality analyses. RESULTS: We found that the global structure of the SF-36 is dominant in all networks, supporting the validity of questionnaire's subscales. Furthermore, results suggest that the network structure of both samples was highly similar. Centrality analyses revealed that maintaining a daily routine despite one's physical health predicts HRQoL levels best. CONCLUSIONS: We concluded that the NM provides a fruitful alternative to classical approaches used in the psychometric analysis of HRQoL data.