The network approach to psychopathology posits that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms. This approach, first posited in 2008, has ...grown substantially over the past decade and is now a full-fledged area of psychiatric research. In this article, we provide an overview and critical analysis of 363 articles produced in the first decade of this research program, with a focus on key theoretical, methodological, and empirical contributions. In addition, we turn our attention to the next decade of the network approach and propose critical avenues for future research in each of these domains. We argue that this program of research will be best served by working toward two overarching aims: (a) the identification of robust empirical phenomena and (b) the development of formal theories that can explain those phenomena. We recommend specific steps forward within this broad framework and argue that these steps are necessary if the network approach is to develop into a progressive program of research capable of producing a cumulative body of knowledge about how specific mental disorders operate as causal systems.
Statistical network models describing multivariate dependency structures in psychological data have gained increasing popularity. Such comparably novel statistical techniques require specific ...guidelines to make them accessible to the research community. So far, researchers have provided tutorials guiding the estimation of networks and their accuracy. However, there is currently little guidance in determining what parts of the analyses and results should be documented in a scientific report. A lack of such reporting standards may foster researcher degrees of freedom and could provide fertile ground for questionable reporting practices. Here, we introduce reporting standards for network analyses in cross-sectional data, along with a tutorial and two examples. The presented guidelines are aimed at researchers as well as the broader scientific community, such as reviewers and journal editors evaluating scientific work. We conclude by discussing how the network literature specifically can benefit from such guidelines for reporting and transparency.
Translational Abstract
In recent years, network models have become increasingly popular in the field of psychology. Such comparably novel statistical techniques require specific guidelines to make them accessible to the research community. So far, researchers have provided tutorials guiding how network analysis can be applied to psychological data. However, there is currently little guidance in determining what parts of the analyses and results should be documented in a scientific report. A lack of such reporting standards may result in researchers being confronted with too much choice in reporting their results, which in turn might provide fertile ground for questionable reporting practices. Here, we introduce reporting standards for network analyses in cross-sectional data, along with a tutorial and two examples. The presented guidelines are aimed at researchers as well as the broader scientific community, such as reviewers and journal editors evaluating scientific work. We conclude by discussing how the network literature specifically can benefit from such guidelines for reporting and transparency.
One hypothesis flowing from the network theory of psychopathology is that symptom network structure is associated with psychopathology severity and in turn, one may expect that individual network ...structure changes with the level of psychopathology severity. However, this expectation has rarely been addressed directly. This study aims to examine (1) the stability of individual contemporaneous symptom networks over a one-year period and (2) whether network stability is associated with a change in psychopathology. We used daily diary data of n = 66 individuals, located along the psychosis severity continuum, from two separate 90-day periods, one year apart (t = 180). Based on the newly developed Individual Network Invariance Test (INIT) to assess symptom-network stability, participants were divided into two groups with stable and unstable networks and we tested whether these groups differed in their absolute change in psychopathology severity. The majority of the sample (n = 51, 77.3%) showed a stable network over time while most individuals showed a decrease in psychopathological severity. We found no significant association between a change in psychopathology severity and individual network stability. Our results call for further critical evaluation of the association between networks and psychopathology to optimize the implementation of clinical applications based on current methods.
The use of idiographic research techniques has gained popularity within psychological research and network analysis in particular. Idiographic research has been proposed as a promising avenue for ...future research, with differences between idiographic results highlighting evidence for radical heterogeneity. However, in the quest to address the individual in psychology, some classic statistical problems, such as those arising from sampling variation and power limitations, should not be overlooked. This article aims to determine to what extent current tools to compare idiographic networks are suited to disentangle true from illusory heterogeneity in the presence of sampling error. To this end, we investigate the performance of tools to inspect heterogeneity (visual inspection, comparison of centrality measures, investigating standard deviations of random effects, and GIMME) through simulations. Results show that power limitations hamper the validity of conclusions regarding heterogeneity and that the power required to assess heterogeneity adequately is often not realized in current research practice. Of the tools investigated, inspecting standard deviations of random effects and GIMME proved the most suited. However, all tools evaluated leave the door wide open to misinterpret all observed variability in terms of individual differences. Hence, the current paper calls for caution in the use and interpretation of new time-series techniques when it comes to heterogeneity.
The comparison of idiographic network structures to determine the presence of heterogeneity is a challenging endeavor in many applied settings. Previously, researchers eyeballed idiographic networks, ...computed correlations, and used techniques that make use of the multilevel structure of the data (e.g., group iterative multiple model estimation and multilevel vector autoregressive) to investigate individual differences. However, these methods do not allow for testing the (in)equality of idiographic network structures directly. In this article, we propose the Individual Network Invariance Test (INIT), which we implemented in the R package INIT. INIT extends common model comparison practices in structural equation modeling to idiographic network structures to test for (in)equality between idiographic networks. In a simulation study, we evaluated the performance of INIT on both saturated and pruned idiographic network structures by inspecting the rejection rate of the χ² difference test and model selection criteria, such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC). Results show INIT performs adequately when
= 100 per individual. When applying INIT on saturated networks, the AIC performed best as a model selection criterion, while the BIC showed better results when applying INIT on pruned networks. In an empirical example, we highlight the possibilities of this new technique, illustrating how INIT provides researchers with a means of testing for (in)equality between idiographic network structures and within idiographic network structures over time. To conclude, recommendations for empirical researchers are provided. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
International guidelines present overall symptom severity as the key dimension for clinical characterisation of major depressive disorder (MDD). However, differences may reside within severity levels ...related to how symptoms interact in an individual patient, called symptom dynamics.
To investigate these individual differences by estimating the proportion of patients that display differences in their symptom dynamics while sharing the same overall symptom severity.
Participants with MDD (
= 73; mean age 34.6 years, s.d. = 13.1; 56.2% female) rated their baseline symptom severity using the Inventory for Depressive Symptomatology Self-Report (IDS-SR). Momentary indicators for depressive symptoms were then collected through ecological momentary assessments five times per day for 28 days; 8395 observations were conducted (average per person: 115; s.d. = 16.8). Each participant's symptom dynamics were estimated using person-specific dynamic network models. Individual differences in these symptom relationship patterns in groups of participants sharing the same symptom severity levels were estimated using individual network invariance tests. Subsequently, the overall proportion of participants that displayed differential symptom dynamics while sharing the same symptom severity was calculated. A supplementary simulation study was conducted to investigate the accuracy of our methodology against false-positive results.
Differential symptom dynamics were identified across 63.0% (95% bootstrapped CI 41.0-82.1) of participants within the same severity group. The average false detection of individual differences was 2.2%.
The majority of participants within the same depressive symptom severity group displayed differential symptom dynamics. Examining symptom dynamics provides information about person-specific psychopathological expression beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may be a promising new dimension for clinical characterisation, warranting replication in independent samples. To inform personalised treatment planning, a next step concerns linking different symptom relationship patterns to treatment response and clinical course, including patterns related to spontaneous recovery and forms of disorder progression.
Objective
Eating disorder (ED) symptoms and transdiagnostic vulnerability characteristics play a crucial role in the aetiology and maintenance of EDs. Over the last decade, researchers have started ...to model complex interrelations between symptoms using network models, but the literature is limited in that it has focused solely on symptoms and investigated‐specific disorders while ignoring transdiagnostic aspects of mental health.
Method
This study tackles these challenges by investigating network relations among core ED symptoms, comorbid clinical symptoms (depression and anxiety) and empirically supported vulnerability and protective mechanisms (personality traits, maladaptive cognitive schemata, perfectionism and resilience) in a sample of 2302 treatment‐seeking ED patients. We estimated a regularized partial correlation network to obtain conditional dependence relations among all variables. We estimated node centrality (interconnectivity) and node predictability (the overall magnitude of symptom inter‐relationships).
Results
The findings indicate a central role of overvigilance, excessive focus on inhibiting emotions and feelings, interoceptive awareness and perfectionism.
Conclusions
These results suggest that excessive control of bodily aspects by dietary restraint (possibly through inhibition) and interoceptive awareness may be important constructs that warrant future research in understanding vulnerability in EDs. We provide all code and data via the Open Science Framework.
Highlights
This network analysis revealed a relationship between body dissatisfaction and transdiagnostic vulnerability factors in ED patients
The results indicate a central role of personal standards (perfectionism), overvigilance and inhibition (maladaptive schemata), and the specific ED symptoms, ineffectiveness and interoceptive awareness
Using motivational strategies for bridging ED symptoms, adequate methods of self‐improvement and adaptive emotion regulation were recommended in the treatment of ED patients
Many studies have found that depressive complaints are associated with the regulation of affect while facing stress. Individuals inclined towards the experience of negative affect are more vulnerable ...to developing depressive complaints, while frequent experiences of positive affect buffer the development of such complaints. To better understand the dynamic mechanisms between affect and depression in detail, this paper investigates how different evaluations of depressive complaints over a prolonged period of stress relate to fluctuations in affect. We included assessments of affect (Positive and Negative Affect Scale) and depressive complaints (Patient Health Questionnaire) in 228 participants who completed at least 20 assessments spanning between 9-14 weeks. We (i) explored affect trajectories for different evolutions of depressive complaints, (ii) estimated longitudinal multilevel network models to examine the direct interplay between affect and depressive complaints in detail, and (iii) investigated how person-specific network density relates to changes in depressive complaints over time. When separating affect trajectories based on depressive complaints, we identified that individuals consistently experiencing depressive complaints (PHQ > 4) report higher negative affect levels than positive affect. Contrary, individuals consistently reporting no depressive complaints (PHQ ≤4) showed the opposite pattern. Furthermore, the longitudinal networks included many and strong relations between the affects and depressive complaints variables. Lastly, we found a strong correlation between the density of person-specific networks and their change (aggravation or alleviation) in depressive complaints. We conclude that affect fluctuations and evolutions of depressive complaints are directly related both within- and across individuals over time.
We present a dataset on participants' attitudes towards the consumption of meat (N = 30). Participants were presented with a baseline questionnaire entailing 11 statements. After a baseline ...measurement, we perturbed the participant's opinion on one of the 11 items, after which the participant completed the same questionnaire. By repeating this procedure for each of the 11 items, we measured to what extent the perturbation changed the participant's baseline score.In addition, we asked participants to draw the influence of a specific item onto the other items in a network format. The data are suitable for various purposes, like causal inference and the malleability of attitudes.Keywords: Attitude, perturbation, causality
We outline a network method to synthesize a literature overview from search results obtained by multiple team members. Several network statistics are used to create a single representativeness ...ranking. We illustrate the method with the dispersed literature on a common misinterpretation of analysis of covariance (ANCOVA). The network method yields a top ten list of the most relevant articles that students and researchers can take as a point of departure for a more detailed study on this topic. The proposed methodology is implemented in Shiny, an open-source R package.
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