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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.

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.

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.