Publications

Psychopathology is often classified according to diagnostic categories or scale scores. These ignore potentially important information about associations between specific symptoms and, consequently, lead to heterogeneous constructs that may mask relevant individual differences. Network analyses focus on these specific symptom associations, providing the opportunity to explore the complex structure of psychopathology in more detail. We examined the empirical network structure of 95 emotional and behavioral problems of the Youth Self-Report (YSR) to explore how well this structure reflected the predefined YSR domains. The study was conducted in a large community sample (N = 2,175) of preadolescents (mean age = 11.1, SD = 0.6 years), and the network structure was determined by means of the recently developed network analysis technique, eLasso. Although problems within the same domain, in general, showed more and stronger connections than problems belonging to different domains, some problems showed substantially more or stronger associations than others; consequently, problems cannot be considered interchangeable indicators of their domain. Furthermore, no sharp boundaries were found between the domains as specific symptom pairs of different domains showed strong connections. Taken together, our findings indicate that network models provide a promising addition to the more traditional way of distinguishing diagnoses or scale scores. (PsycINFO Database Record

Although multiple differences between typically and atypically developing children have been robustly observed in empirical studies, we lack a unified theoretical framework on how these observed differences in cognitive phenotypes might arise. Here, we introduce a first step to-wards such a framework based on an extension of the mutualistic network model, which conceptualizes cognitive development in terms of reciprocal interactions between cognitive processes that promote each other’s growth. We extend the mutualistic network model with dy-namic mechanisms of competition for limited resources, such as time and environmental factors. By doing so, the modelling framework defines both an empirical and theoretical research agenda. We show that this relatively simple model generates patterns of development that potentially explain five established phenomena in cognitive development: (1) developmental phases, (2) slower cognitive development in atypical cohorts, (3) lack of early indicators for atypical development, (4) developmental regression in the autistic profile, and (5) "quasi-autism"caused by extreme environmental deprivation. Theoretically, the presented framework fits a generic movement towards formal theory con-struction. The model can easily be reproduced and developed further through our openly available code, and we offer several paths forward in doing so.

Identifying the different influences of symptoms in dynamic psychopathology models may hold promise for increasing treatment efficacy in clinical applications. Dynamic psychopathology models study the behavioral patterns of symptom networks, where symptoms mutually enforce each other. Interventions could be tailored to specific symptoms that are most effective at lowering symptom activity or that hinder the further development of psychopathology. Simulating interventions in psychopathology network models fits in a novel tradition where symptom-specific perturbations are used as in silico interventions. Here, we present the NodeIdentifyR algorithm (NIRA) to identify the projected most efficient, symptom-specific intervention target in a network model (i.e., the Ising model). We implemented NIRA in a freely available R package. The technique studies the projected effects of symptom-specific interventions by simulating data while symptom parameters (i.e., thresholds) are systematically altered. The projected effect of these interventions is defined in terms of the expected change in overall symptom activity across simulations. With this algorithm, it is possible to study (1) whether symptoms differ in their projected influence on the behavior of the symptom network and, if so, (2) which symptom has the largest projected effect in lowering or increasing overall symptom activation. As an illustration, we apply the algorithm to an empirical dataset containing Post-Traumatic Stress Disorder symptom assessments of participants who experienced the Wenchuan earthquake in 2008. The most important limitations of the method are discussed, as well as recommendations for future research, such as shifting towards modeling individual processes to validate these types of simulation-based intervention methods.

Network approaches to psychopathology have become increasingly common in mental health research, with many theoretical and methodological developments quickly gaining traction. This article illustrates contemporary practices in applying network analytical tools, bridging the gap between network concepts and their empirical applications. We explain how we can use graphs to construct networks representing complex associations among observable psychological variables. We then discuss key network models, including dynamic networks, time‐varying networks, network models derived from panel data, network intervention analysis, latent networks, and moderated models. In addition, we discuss Bayesian networks and their role in causal inference with a focus on cross‐sectional data. After presenting the different methods, we discuss how network models and psychopathology theories can meaningfully inform each other. We conclude with a discussion that summarizes the insights each technique can provide in mental health research.

This study examines how broad attitude networks are affected by tailored interventions aimed at variables selected based on their connectiveness with other variables. We first computed a broad attitude network based on a large-scale cross-sectional COVID-19 survey (N = 6,093). Over a period of approximately 10 weeks, participants were invited five times to complete this survey, with the third and fifth wave including interventions aimed at manipulating specific variables in the broad COVID-19 attitude network. Results suggest that targeted interventions that yield relatively strong effects on variables central to a broad attitude network have downstream effects on connected variables, which can be partially explained by the variables the interventions were aimed at. We conclude that broad attitude network structures can reveal important relations between variables that can help to design new interventions.

Observing exclusively positive associations among a set of variables (i.e., a positive manifold) is a robust finding in many areas in psychology. These positive associations can be explained by positing an underlying common cause or, alternatively, through positive direct effects among the variables. Recently, the Kruis-Maris model has been proposed as a third alternative mechanism to explain a positive manifold. In the Kruis-Maris model, observed variables are causes of a latent variable, such that conditioning on the latent variable induces associations between the observed variables. In this paper, we analyze the difference between the Kruis-Maris model and the more familiar causal indicator model. Both are examples of common effect models, but they imply different relations between the response variables and the effect variable. Whereas conditioning on the common effect in the Kruis-Maris model implies a positive manifold among the response variables, we show that conditioning on the common effect in the causal indicator model implies a negative manifold. We demonstrate this result for two specific cases: (1) in which both the response variables and the effect variable are normally distributed variables, and (2) in which the effect variable is normally distributed but the response variables are dichotomous variables.

In the proposed symptom network approach to psychopathology, psychiatric disorders are assumed to result from the (causal) interplay between symptoms. By implementing this approach we explored whether individual feedback on symptom dynamics complements current categorical classification and treatment. The aim of this proof-of-principle case-study was to explore the feasibility, acceptability and usability of this transdiagnostic approach.A female patient, aged 67, suffering from treatment resistant anxious and depressive symptoms was treated in our tertiary outpatient clinic for old age psychiatry. She participated in ecological momentary assessments (EMA), which involved intensive repeated measurements of mood and context-related items during two weeks. Visualizations of the interplay between the items were provided by network graphs and were discussed with the patient.Network graphs were discussed with the patient. For example, it was hypothesized and discussed with the patient that feeling relaxed increased physical activity, causing physical discomfort in the following hours. Physical discomfort caused stress as its symptoms resembled her feared somatic anxiety symptoms. This increased the patient's insight that stress, expressed as somatic symptoms, played a central role in her panic disorder. This started a dialogue on how to cope with stress caused by somatic (anxiety) symptoms and provided a rationale for the patient to start an interoceptive exposure intervention she had repeatedly refused before.The observed symptom dynamics may not be generalizable to any other random two weeks.Personalized diagnosis of psychopathology incorporating complex symptom dynamics is feasible and a promising addition to current categorical diagnostic systems and could guide intervention selection. This merits further exploration.

For a very long time in the COVID-19 crisis, behavioural change leading to physical distancing behaviour was the only tool at our disposal to mitigate virus spread. In this large-scale naturalistic experimental study we show how we can use behavioural science to find ways to promote the desired physical distancing behaviour. During seven days in a supermarket we implemented different behavioural interventions: (i) rewarding customers for keeping distance; (i) providing signage to guide customers; and (iii) altering shopping cart regulations. We asked customers to wear a tag that measured distances to other tags using ultra-wide band at 1Hz. In total N = 4, 232 customers participated in the study. We compared the number of contacts (< 1.5 m, corresponding to Dutch regulations) between customers using state-of-the-art contact network analyses. We found that rewarding customers and providing signage increased physical distancing, whereas shopping cart regulations did not impact physical distancing. Rewarding customers moreover reduced the duration of remaining contacts between customers. These results demonstrate the feasibility to conduct large-scale behavioural experiments that can provide guidelines for policy. While the COVID-19 crisis unequivocally demonstrates the importance of behaviour and behavioural change, behaviour is integral to many crises, like the trading of mortgages in the financial crisis or the consuming of goods in the climate crisis. We argue that by acknowledging the role of behaviour in crises, and redefining this role in terms of the desired behaviour and necessary behavioural change, behavioural science can open up new solutions to crises and inform policy. We believe that we should start taking advantage of these opportunities.

<h3>Importance</h3> 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. <h3>Objective</h3> 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. <h3>Design, Setting, and Participants</h3> 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. <h3>Main Outcomes and Measures</h3> 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<i>DSM-III-R</i>, and symptom networks were reestimated at ages 6 to 10 years, this time including a variable indicative of future diagnosis. <h3>Results</h3> 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. <h3>Conclusions and Relevance</h3> 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.

With the emergence of online publishing, opportunities to maximize transparency of scientific research have grown considerably. However, these possibilities are still only marginally used. We argue for the implementation of (1) peer-reviewed peer review, (2) transparent editorial hierarchies, and (3) online data publication. First, peer-reviewed peer-review entails a community-wide review system in which reviews are published online and rated by peers. Reviewers with higher average ratings are weighted more heavily in publication decisions, thereby ensuring academic quality of reviews. Second, reviewers who write many highly regarded reviews may move to higher editorial positions. This leads to a transparent basis for editorial hierarchies and prevents politically inspired editorial appointments. Third, online publication of data ensures the possibility of independent verification of inferential claims in published papers. This counters statistical cherry-picking and data massaging to obtain favorable results. We illustrate the benefits of these strategies by discussing an example in which the classical publication system has gone awry, namely controversial IQ research. We argue that this case would have likely been avoided using more transparent publication practices. We argue that the proposed system leads to better reviews, meritocratic editorial hierarchies, and a higher degree of replicability of statistical analyses. Therefore it leads to a publication system that more fully benefits from the advantages and possibilities offered by online, open access publication.