Psychological networks in clinical populations: investigating the consequences of Berkson's bias

Autistic Symptoms, Irritability, and Executive Dysfunctions: Symptom Dynamics from Multi-Network Models

Socio-cognitive difficulties in individuals with autism spectrum disorder (ASD) are heterogenuous and often co-occur with irritability symptoms, such as angry/grouchy mood and temper outbursts. However, the specific relations between individual symptoms are not well-represented in conventional methods analyzing aggregated autistic symptoms and ASD diagnosis. Moreover, the cognitive-behavioral mechanisms linking ASD to irritability are largely unknown. This study investigated the dynamics between autistic (Social Responsiveness Scale) and irritability (Affective Reactivity Index) symptoms and executive functions (Cambridge Neuropsychological Test Automated Battery) in a sample of children and adolescents with ASD, their unaffected siblings, and neurotypical peers (N = 345, aged 6-18 years, 78.6% male). Three complementary networks across the entire sample were computed: (1) Gaussian graphical network estimating the conditional correlations between symptom nodes; (2) Relative importance network computing relative influence between symptoms; (3) Bayesian directed acyclic graph estimating predictive directionality between symptoms. Networks revealed numerous partial correlations within autistic (rs = .07-.56) and irritability (rs = .01-.45) symptoms and executive functions (rs = -.83 to .67) but weak connections between clusters. This segregated pattern converged in all directed and supplementary networks. Plausible predictive paths were found between social communication difficulties to autism mannerisms and between "angry frequently" to "lose temper easily." Autistic and irritability symptoms are two relatively independent families of symptoms. It is unlikely that executive dysfunctions explain elevated irritability in ASD. Findings underscore the need for researching other mood and cognitive-behavioral bridge symptoms, which may inform individualized treatments for co-occurring irritability in ASD.

A Note on Ising Network Analysis with Missing Data

The Ising model has become a popular psychometric model for analyzing item response data. The statistical inference of the Ising model is typically carried out via a pseudo-likelihood, as the standard likelihood approach suffers from a high computational cost when there are many variables (i.e., items). Unfortunately, the presence of missing values can hinder the use of pseudo-likelihood, and a listwise deletion approach for missing data treatment may introduce a substantial bias into the estimation and sometimes yield misleading interpretations. This paper proposes a conditional Bayesian framework for Ising network analysis with missing data, which integrates a pseudo-likelihood approach with iterative data imputation. An asymptotic theory is established for the method. Furthermore, a computationally efficient Pólya-Gamma data augmentation procedure is proposed to streamline the sampling of model parameters. The method's performance is shown through simulations and a real-world application to data on major depressive and generalized anxiety disorders from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC).

Cumulative comorbidity between neurodevelopmental, internalising, and externalising disorders in childhood: a network approach

Cumulative comorbidity of mental disorders is common, but the extent and patterns of comorbid psychopathology in childhood are not well established. The current study aimed to elucidate the emergent patterns of cumulative mental disorder comorbidity in children using network analysis of diagnoses recorded between birth and age 12 years. Participants were 90,269 children (mean age 12.7 years; 51.8% male) within the New South Wales Child Development Study (NSW-CDS)-a longitudinal record-linkage cohort study of Australian children born in NSW between 2002 and 2005. Binary indicators for eight types of mental disorder were derived from administrative health records. Patterns of conditional association between mental disorders were assessed utilising network analysis. Of 90,269 children, 2268 (2.5%) had at least one mental disorder by age 12 years; of the 2268 children who had at least one mental disorder by age 12 years, 461 (20.3%) were diagnosed with two or more different disorders out of the eight disorder types included in analyses. All disorders were either directly or indirectly interconnected, with childhood affective and emotional disorders and developmental disorders being most central to the network overall. Mental disorder nodes aggregated weakly (modularity = 0.185) into two communities, representative of internalising and externalising disorders, and neurodevelopmental and sleep disorders. Considerable sex differences in the structure of the mental disorder comorbidity networks were also observed. Developmental and childhood affective and emotional disorders appear to be key to mental disorder comorbidity in childhood, potentially reflecting that these disorders share symptoms in common with many other disorders.

Network Inference With the Lasso

Calculating confidence intervals and p-values of edges in networks is useful to decide their presence or absence and it is a natural way to quantify uncertainty. Since lasso estimation is often used to obtain edges in a network, and the underlying distribution of lasso estimates is discontinuous and has probability one at zero when the estimate is zero, obtaining p-values and confidence intervals is problematic. It is also not always desirable to use the lasso to select the edges because there are assumptions required for correct identification of network edges that may not be warranted for the data at hand. Here, we review three methods that either use a modified lasso estimate (desparsified or debiased lasso) or a method that uses the lasso for selection and then determines p-values without the lasso. We compare these three methods with popular methods to estimate Gaussian Graphical Models in simulations and conclude that the desparsified lasso and its bootstrapped version appear to be the best choices for selection and quantifying uncertainty with confidence intervals and p-values.

Network analysis of additional clinical features of (Internet) gaming disorder

OBJECTIVES

There are dozens of screening instruments purporting to measure the (Internet) gaming disorder (IGD/GD). The two prominent diagnostic manuals, DSM-5 and ICD-11, list several additional diagnostic or clinical features and problems (e.g., neglect of sleep, neglect of daily duties, health deterioration) that should co-occur or be caused by the IGD/GD. It remains unclear how specific IGD/GD operationalizations (different screening scales) are related to these functional impairments.

METHODS

To explore this, data on six measures of IGD/GD (IGDS9-SF, GDSS, GDT, GAMES test, two self-assessments) and 18 additional diagnostic features were collected from a sample of 1009 players who play digital games at least 13 h per week. A network approach was utilized to determine which operationalization is most strongly associated with functional impairment.

RESULTS

In most of the networks, IGD/GD consistently emerged as the most central node.

CONCLUSION

The similar centrality of IGD/GD, irrespective of its definition (DSM-5 or ICD-11) or operationalization, provides support for the valid comparison or synthesis of results from studies that used instruments coming from both DSM-5 and ICD-11 ontologies, but only if the goal is to evaluate IGD/GD relationships to other phenomena, not the relationships between the symptoms themselves.

Symptom networks in glioma patients: understanding the multidimensionality of symptoms and quality of life

PURPOSE

To comprehend the complex relationship between symptoms and health-related quality of life (HRQoL) in patients with diffuse glioma, we applied symptom network analysis to identify patterns of associations between depression, cognition, brain tumor-related symptoms, and HRQoL. Additionally, we aimed to compare global strength between symptom networks to understand if symptoms are more tightly connected in different subgroups of patients.

METHODS

We included 256 patients and stratified the sample based on disease status (preoperative vs. postoperative), tumor grade (grade II vs. III/IV), and fatigue status (non-fatigued vs. fatigued). For each subgroup of patients, we constructed a symptom network. In these six networks, each node represented a validated subscale of a questionnaire and an edge represented a partial correlation between two nodes. We statistically compared global strength between networks.

RESULTS

Across the six networks, nodes were highly correlated: fatigue severity, depression, and social functioning in particular. We found no differences in GS between the networks based on disease characteristics. However, global strength was lower in the non-fatigued network compared to the fatigued network (5.51 vs. 7.49, p < 0.001).

CONCLUSIONS

Symptoms and HRQoL are highly interrelated in patients with glioma. Interestingly, nodes in the network of fatigued patients were more tightly connected compared to non-fatigued patients.

IMPLICATIONS FOR CANCER SURVIVORS

We introduce symptom networks as a method to understand the multidimensionality of symptoms in glioma. We find a clear association between multiple symptoms and HRQoL, which underlines the need for integrative symptom management targeting fatigue in particular.

Depressive symptomatology in older adults treated with behavioral activation: A network perspective

BACKGROUND

Late-life depression is a serious mental health problem. Behavioral Activation (BA) is an effective, accessible psychotherapeutic treatment for older adults. However, little is known about which symptoms decrease and how associations between depressive symptoms change during BA treatment.

METHODS

Using data from a cluster-randomized trial for older adults with late-life depression, we estimated a partial correlation network and a relative importance network of depressive symptoms before and after 8 weeks of BA treatment in primary care (n = 96). Networks were examined with measures of network structure, connectivity, centrality as well as stability.

RESULTS

The most central symptoms at baseline and post-treatment were anhedonia, fatigue, and feeling depressed. In contrast, sleeping problems had the lowest centrality. The post-treatment network was significantly more interconnected than at baseline. Moreover, all symptoms were significantly more central at post-treatment.

CONCLUSION

Our findings highlight the utility of the network approach to better understand symptom networks of depressed older adults before and after BA treatment. Results show that network connectivity and centrality of all symptoms increased after treatment. Future studies should investigate longitudinal idiographic networks to explore symptom dynamics within individuals over time.

A Network Study of Family Affect Systems in Daily Life

Adolescence is a time period characterized by extremes in affect and increasing prevalence of mental health problems. Prior studies have illustrated how affect states of adolescents are related to interactions with parents. However, it remains unclear how affect states among family triads, that is adolescents and their parents, are related in daily life. This study investigated affect state dynamics (happy, sad, relaxed, and irritated) of 60 family triads, including 60 adolescents (Mage = 15.92, 63.3% females), fathers and mothers (Mage = 49.16). The families participated in the RE-PAIR study, where they reported their affect states in four ecological momentary assessments per day for 14 days. First, we used multilevel vector-autoregressive network models to estimate affect dynamics across all families, and for each family individually. Resulting models elucidated how family affect states were related at the same moment, and over time. We identified relations from parents to adolescents and vice versa, while considering family variation in these relations. Second, we evaluated the statistical performance of the network model via a simulation study, varying the percentage missing data, the number of families, and the number of time points. We conclude with substantive and statistical recommendations for future research on family affect dynamics.

Variation in global network properties across risk factors for adolescent internalizing symptoms: evidence of cumulative effects on structure and connectivity

BACKGROUND

Identifying adolescents at risk of internalizing problems is a key priority. However, studies have tended to consider such problems in simple ways using diagnoses, or item summaries. Network theory and methods instead allow for more complex interaction between symptoms. Two key hypotheses predict differences in global network properties for those at risk: altered structure and increased connectivity.

METHODS

The current study evaluated these hypotheses for nine risk factors (e.g. income deprivation and low parent/carer support) individually and cumulatively in a large sample of 12-15 year-olds (N = 34 564). Recursive partitioning and bootstrapped networks were used to evaluate structural and connectivity differences.

RESULTS

The pattern of network interactions was shown to be significantly different via recursive partitioning for all comparisons across risk-present/absent groups and levels of cumulative risk, except for income deprivation. However, the magnitude of differences appeared small. Most individual risk factors also showed relatively small effects for connectivity. Exceptions were noted for gender and sexual minority risk groups, as well as low parent/carer support, where larger effects were evident. A strong linear trend was observed between increasing cumulative risk exposure and connectivity.

CONCLUSIONS

A robust approach to considering the effect of risk exposure on global network properties was demonstrated. Results are consistent with the ideas that pathological states are associated with higher connectivity, and that the number of risks, regardless of their nature, is important. Gender/sexual minority status and low parent/carer support had the biggest individual impacts on connectivity, suggesting these are particularly important for identification and prevention.

Pathological network changes in patients with social anxiety disorder before and after an Internet-based CBT

A network perspective may shed light on the understanding of Internet-based CBT efficacy for social anxiety disorder. Previous cross-sectional evidence revealed a densely interconnected network for individuals with social anxiety. Yet, longitudinal network changes before and after ICBT are lacking. This study aimed to investigate pathological network changes with Graphical Gaussian Model among patients with social anxiety disorder (n = 249). Social phobia scale (SPS) and Social interaction anxiety scale (SIAS) were measured before and after 8 weeks Internet-based CBT. Results revealed the connection between symptom tension when speaking and symptom awkward when being watched was the most robust edges during ICBT interventions. The pathological network benefited from ICBT and exhibited modification in several prominent interconnections. The overall network connectivity continues to exhibit comparable strength after ICBT. This study represents the first examination of social anxiety network changes after patients with SAD completed a systematic ICBT. Changes in critical edges and nodes provide valuable insights for the design and efficacy assessment of ICBT interventions.

Symptom Network Analysis Tools for Applied Researchers With Cross-Sectional and Panel Data - A Brief Overview and Multiverse Analysis

In recent years, there has been a growing interest in utilizing symptom-network models to study psychopathology and relevant risk factors, such as cognitive and physical health. Various methodological approaches can be employed by researchers analyzing cross-sectional and panel data (i.e., several time points over an extended period). This paper provides an overview of some commonly used analytical tools, including moderated network models, network comparison tests, cross-lagged network analysis, and panel graphical vector-autoregression (VAR) models. Using an easily accessible dataset (easySHARE), this study demonstrates the use of different analytical approaches when investigating (a) the association between mental health and cognitive functioning, and (b) the role of chronic disease in mediating or moderating this association. This multiverse analysis showcases both converging and diverging evidence from different analytical avenues. These findings underscore the importance of multiverse investigations to increase transparency and communicate the extent to which conclusions depend on analytical choices.

Comorbidity among depression, anxiety and stress symptoms in naturalistic clinical samples: A cross-cultural network analysis

Comorbidity between depression and anxiety is well-established across various settings and cultures. We approached comorbidity from the network psychopathology perspective and examined the depression, anxiety/autonomic arousal and stress/tension symptoms in naturalistic clinical samples from Serbia, Italy and Croatia. This was a multisite study in which regularized partial correlation networks of the symptoms, obtained via self-reports on the Depression Anxiety and Stress Scales-21 (DASS-21) in three cross-cultural, clinical samples (total N = 874), were compared with respect to centrality, edge weights, community structure and bridge centrality. A moderate degree of similarity in a number of network indices across the three networks was observed. While negative mood emerged to be the most central node, stress/tension nodes were the most likely bridge symptoms between depressive and anxiety/autonomic arousal symptoms. We demonstrated that the network structure and features in mixed clinical samples were similar across three different languages and cultures. The symptoms such as agitation, restlessness and inability to relax functioned as bridges across the three symptom communities explored in this study. Important theoretical and clinical implications were derived.

Implications of the Network Theory for the Treatment of Mental Disorders: A Secondary Analysis of a Randomized Clinical Trial

Importance

Conceptualizing mental disorders as latent entities has been challenged by the network theory of mental disorders, which states that psychological problems are constituted by a network of mutually interacting symptoms. While the implications of the network approach for planning and evaluating treatments have been intensively discussed, empirical support for the claims of the network theory regarding treatment effects is lacking.

Objective

To assess the extent to which specific hypotheses derived from the network theory regarding the (interindividual) changeability of symptom dynamics in response to treatment align with empirical data.

Design, Setting, and Participants

This secondary analysis entails data from a multisite randomized clinical trial, in which 254 patients with chronic depression reported on their depressive symptoms at every treatment session. Data collection was conducted between March 5, 2010, and October 14, 2013, and this analysis was conducted between November 1, 2021, and May 31, 2022.

Intervention

Thirty-two sessions of either disorder-specific or nonspecific psychotherapy for chronic depression.

Main Outcomes and Measures

Longitudinal associations of depressive symptoms with each other and change of these associations through treatment estimated by a time-varying longitudinal network model.

Results

In a sample of 254 participants (166 [65.4%] women; mean [SD] age, 44.9 [11.9] years), symptom interactions changed through treatment, and this change varied across treatments and individuals. The mean absolute (ie, valence-ignorant) strength of symptom interactions (logarithmic odds ratio scale) increased from 0.40 (95% CI, 0.36-0.44) to 0.60 (95% CI, 0.52-0.70) during nonspecific psychotherapy and to 0.56 (95% CI, 0.48-0.64) during disorder-specific psychotherapy. In contrast, the mean raw (ie, valence-sensitive) strength of symptom interactions decreased from 0.32 (95% CI, 0.28-0.36) to 0.26 (95% CI, 0.20-0.32) and to 0.09 (95% CI, 0.02-0.16), respectively. Changing symptom severity could be explained to a large extent by symptom interactions.

Conclusions and Relevance

These findings suggest that specific treatment-related hypotheses of the network theory align well with empirical data. Conceptualizing mental disorders as symptom networks and treatments as measures that aim to change these networks is expected to give further insights into the working mechanisms of mental health treatments, leading to the improvement of current and the development of new treatments.

Trial Registration

ClinicalTrials.gov Identifier: NCT00970437.

Identifying the bridge between depression and mania: A machine learning and network approach to bipolar disorder

OBJECTIVES

Although the cyclic nature of bipolarity is almost by definition a network system, no research to date has attempted to scrutinize the relationship of the two bipolar poles using network psychometrics. We used state-of-the-art network and machine learning methodologies to identify symptoms, as well as relations thereof, that bridge depression and mania.

METHODS

Observational study that used mental health data (12 symptoms for depression and 12 for mania) from a large, representative Canadian sample (the Canadian Community Health Survey of 2002). Complete data (N = 36,557; 54.6% female) were analysed using network psychometrics, in conjunction with a random forest algorithm, to examine the bidirectional interplay of depressive and manic symptoms.

RESULTS

Centrality analyses pointed to symptoms relating to emotionality and hyperactivity as being the most central aspects of depression and mania, respectively. The two syndromes were spatially segregated in the bipolar model and four symptoms appeared crucial in bridging them: sleep disturbances (insomnia and hypersomnia), anhedonia, suicidal ideation, and impulsivity. Our machine learning algorithm validated the clinical utility of central and bridge symptoms (in the prediction of lifetime episodes of mania and depression), and suggested that centrality, but not bridge, metrics map almost perfectly onto a data-driven measure of diagnostic utility.

CONCLUSIONS

Our results replicate key findings from past network studies on bipolar disorder, but also extend them by highlighting symptoms that bridge the two bipolar poles, while also demonstrating their clinical utility. If replicated, these endophenotypes could prove fruitful targets for prevention/intervention strategies for bipolar disorders.

Networks of Neurodevelopmental Traits, Socioenvironmental Factors, Emotional Dysregulation in Childhood, and Depressive Symptoms Across Development in Two U.K. Cohorts

OBJECTIVE

Previous population-based studies have identified associations between childhood neurodevelopmental traits and depression in childhood, adolescence, and young adulthood. However, neurodevelopmental traits are highly correlated with each other, which could confound associations when traits are examined in isolation. The authors sought to identify unique associations between multiple neurodevelopmental traits in childhood and depressive symptoms across development, while taking into account co-occurring difficulties, in multivariate analyses.

METHODS

Data from two U.K. population-based cohorts, the Twins Early Development Study (TEDS) (N=4,407 independent twins) and the Avon Longitudinal Study of Parents and Children (ALSPAC) (N=10,351), were independently analyzed. Bayesian Gaussian graphical models were estimated to investigate pairwise conditional associations between neurodevelopmental traits (autism and ADHD symptoms and general cognitive, learning, and communication abilities), socioenvironmental stressors (academic performance and peer relations), and emotional dysregulation in childhood (ages 7-11) and depressive symptoms across development (ages 12, 16, and 21).

RESULTS

In both cohorts, bivariate correlations indicated several associations between neurodevelopmental traits and depressive symptoms across development. However, based on replicated findings across cohorts, these pairs of variables were mostly conditionally independent, and none were conditionally associated, after accounting for socioenvironmental stressors and emotional dysregulation. In turn, socioenvironmental stressors and emotional dysregulation were conditionally associated with both neurodevelopmental traits and depressive symptoms. Based on replicated findings across cohorts, neurodevelopmental traits in childhood could be associated only indirectly with depressive symptoms across development.

CONCLUSIONS

This study indicates that associations between childhood neurodevelopmental traits and depressive symptoms across development could be explained by socioenvironmental stressors and emotional dysregulation. The present findings could inform future research aimed at the prevention of depression in youths with neurodevelopmental disorders.

Anxiety and depression played a central role in the COVID-19 mental distress: A network analysis

INTRODUCTION

Psychological, socio-demographics, and clinical factors play an important role in patients with COVID-19, but their relationship is complex. The network approach might be used to disentangle complex interactions in different systems. Using data from a multicentre, cross-sectional, survey among patients with COVID-19 in Spain (July-November 2020), we investigated the network structure of mental disorders symptoms, social support, and psychological resilience, and changes in network structures according to the presence of a pre-existing mental disorder or hospitalization for COVID-19.

METHODS

Subjects completed a survey to evaluate sociodemographic characteristics, COVID-19 infection status, resilience, social support, and symptoms of depression, anxiety disorders, post-traumatic stress disorder, panic attacks, and substance use disorder. 2084 patients with COVID-19 were included in the analysis. Network analysis was conducted to evaluate network and bridge centrality, and the network properties were compared between COVID-19 patients with and without a history of lifetime mental disorder, and between hospitalized and non-hospitalized patients.

LIMITATIONS

Generalization of our findings may be difficult since differences in network connectivity may exist in different populations or samples.

RESULTS

Anxiety and depression showed high centrality in patients with COVID-19 and anxiety showed the highest bridge influence in the network. Resilience and social support showed a low influence on mental disorder symptoms. Global network estimations show no statistically significant changes between patients with and without pre-existing mental disorders or between hospitalized and non-hospitalized patients.

CONCLUSIONS

Anxiety might be a key treatment target in patients with COVID-19 since its treatment might prevent other mental health adverse outcomes.

Using network models to explore the associations between posttraumatic stress disorder symptoms and subjective cognitive functioning

Several studies have identified relationships between posttraumatic stress disorder (PTSD) and cognitive functioning. Here, we aimed to elucidate the nature of this relationship by investigating cross-sectional associations between subjective cognitive functioning (SCF) and 1) the PTSD sum score, 2) symptom domains, and 3) individual symptoms. We also investigated temporal stability by testing whether results replicated over a 3-year period. We estimated partial correlation networks of DSM-5 PTSD symptoms (at baseline) and SCF (at baseline and follow-up, respectively), using data from the National Health and Resilience in Veterans Study (NHRVS; N = 1484; Mdn = 65 years). The PTSD sum score was negatively associated with SCF. SCF was consistently negatively associated with the PTSD symptom domains 'marked alterations in arousal and reactivity' and 'negative alterations in cognitions and mood', and showed robust relations with the specific symptoms 'having difficulty concentrating' and 'trouble experiencing positive feelings'. Results largely replicated at the 3-year follow-up, suggesting that some PTSD symptoms both temporally precede and are statistically associated with the development or maintenance of reduced SCF. We discuss the importance of examining links between specific PTSD domains and symptoms with SCF-relations obfuscated by focusing on PTSD diagnoses or sum scores-as well as investigating mechanisms underlying these relations. Registration Number: 37069 (https://aspredicted.org/n5sw7.pdf).

The impact of ordinal scales on Gaussian mixture recovery

Gaussian mixture models (GMMs) are a popular and versatile tool for exploring heterogeneity in multivariate continuous data. Arguably the most popular way to estimate GMMs is via the expectation-maximization (EM) algorithm combined with model selection using the Bayesian information criterion (BIC). If the GMM is correctly specified, this estimation procedure has been demonstrated to have high recovery performance. However, in many situations, the data are not continuous but ordinal, for example when assessing symptom severity in medical data or modeling the responses in a survey. For such situations, it is unknown how well the EM algorithm and the BIC perform in GMM recovery. In the present paper, we investigate this question by simulating data from various GMMs, thresholding them in ordinal categories and evaluating recovery performance. We show that the number of components can be estimated reliably if the number of ordinal categories and the number of variables is high enough. However, the estimates of the parameters of the component models are biased independent of sample size. Finally, we discuss alternative modeling approaches which might be adopted for the situations in which estimating a GMM is not acceptable.

Investigating the Physical and Mental Health Nexus: a Network Analysis of Depression, Cardiometabolic Health, Bone Mass, and Perceived Health Status Among Filipino Domestic Workers

BACKGROUND

Migrant domestic workers are vulnerable to physical and mental health problems given the many challenges they experience while working abroad. Using network analysis, this study examined the structure of depression, cardiometabolic health indicators (BMI, waist-hip ratio (WHR), blood pressure, and heart rate), bone mass, and perceived health status in this population. The network model allowed for an examination of central symptoms or symptoms with the most direct connections with other symptoms; bridge symptoms, or symptoms that link two or more communities; and edges, or relationships among symptoms.

METHOD

Cross-sectional data were gathered from 1375 Filipino domestic workers in Macao (SAR), China. Data from a subsample of 510 participants who met a cutoff indicating depression were analyzed. Anthropometric measurements and surveys were used to collect data, which was analyzed using R statistical software.

RESULTS

Results showed four community clusters: three communities consisted of at least two depression symptoms each and the fourth community included physical health indicators. Strong edges were formed between BMI-bone mass, psychomotor-concentration, BMI-WHR, and sad mood-anhedonia. The node with the highest expected influence was BMI. There were three bridges: worthlessness, psychomotor difficulties, and concentration difficulties.

CONCLUSION

The link between depression, cardiometabolic indicators, bone mass, and poor perceived health reinforces the need to address multimorbidity within migrant populations. Health promotion interventions that address mental and physical health may improve the health of this population.

A house is not a home: a network model perspective on the dynamics between subjective quality of living conditions, social support, and mental health of refugees and asylum seekers

BACKGROUND

Providing adequate living conditions for forcibly displaced people represents a significant challenge for host countries such as Germany. This study explores refugee mental health's reciprocal, dynamic relationship with post-migration living conditions and social support.

METHODS

The study sample included 325 Arabic- or Farsi-speaking asylum seekers and refugees residing in Germany since 2014 and seeking mental health treatment. Associations between reported symptoms of post-traumatic stress and depression and the subjective quality of living conditions and perceived social support were analyzed using a two-level approach including multiple linear regression and network analyses.

RESULTS

Post-migration quality of living conditions and perceived social support were significantly associated with negative mental health outcomes on both levels. In the network, both post-migration factors were negatively connected with overlapping symptoms of psychiatric disorders, representing potential target symptoms for psychological treatment.

CONCLUSION

Post-migration quality of living conditions and social support are important factors for refugee mental health and should be targeted by various actors fostering mental well-being and integration.

Silence and related symptoms in children and adolescents: a network approach to selective mutism

Background

Silence in certain situations represents the core symptom of selective mutism (SM). However, it is unclear what additional symptoms are part of this disorder. Although knowledge of symptoms is essential for diagnostics and intervention, to date, only scarce research exists on circumscribed symptoms of SM. Given the large overlap between SM and social anxiety disorder (SAD), it remains also unclear which symptoms can differentiate both disorders.

Methods

A network analysis of potential symptoms of SM was performed based on a mixed sample of N = 899 children and adolescents with and without indication of SM (n = 629 with silence in certain situations). In a preliminary analysis, we demonstrated that children with and without silence in certain situations do not differ with respect to their network structure, justifying an analysis on the entire mixed sample. Possible communities (symptom clusters) within the network and thus potential latent variables were examined, and symptoms were analyzed in terms of their centrality (the extent to which they are associated with other symptoms in the network). To investigate the differentiability of symptoms of the SM network from symptoms of SAD, we computed a network that additionally contains symptoms of SAD.

Results

In the resulting network on symptoms of SM, silence was, as expected, the symptom with the highest centrality. We identified two communities (symptom cluster): (1) symptoms associated with the fear response of freezing, (2) symptoms associated with speech production and avoidance. SM network symptoms and SAD symptoms largely formed two separate symptom clusters, with only selectivity of speaking behavior (more talkative at home and taciturn or mute outside the home) falling into a common cluster with SAD symptoms.

Conclusions

Silence appears to have been confirmed by analysis as a core symptom of SM. Additional anxiety-related symptoms, such as avoidance behavior or motor inhibition associated with freezing, seem to co-occur with silence. The two communities of SM potentially indicate different mechanisms of silence. The symptoms of SM appear to be distinguishable from those of SAD, although there seems to be overlap in terms of difficulty speaking in situations outside the home.

Quantifying and addressing the impact of measurement error in network models

Network psychometric models are often estimated using a single indicator for each node in the network, thus failing to consider potential measurement error. In this study, we investigate the impact of measurement error on cross-sectional network models. First, we conduct a simulation study to evaluate the performance of models based on single indicators as well as models that utilize information from multiple indicators per node, including average scores, factor scores, and latent variables. Our results demonstrate that measurement error impairs the reliability and performance of network models, especially when using single indicators. The reliability and performance of network models improves substantially with increasing sample size and when using methods that combine information from multiple indicators per node. Second, we use empirical data from the STAR*D trial (n = 3,731) to further evaluate the impact of measurement error. In the STAR*D trial, depression symptoms were assessed via three questionnaires, providing multiple indicators per symptom. Consistent with our simulation results, we find that when using sub-samples of this dataset, the discrepancy between the three single-indicator networks (one network per questionnaire) diminishes with increasing sample size. Together, our simulated and empirical findings provide evidence that measurement error can hinder network estimation when working with smaller samples and offers guidance on methods to mitigate measurement error.

Does the network structure of obsessive-compulsive symptoms at treatment admission identify patients at risk for non-response?

Exposure and response prevention is the gold-standard treatment for obsessive compulsive disorder (OCD), yet up to half of patients do not adequately respond. Thus, different approaches to identifying and intervening with non-responders are badly needed. One approach would be to better understand the functional connections among aspects of OCD symptoms and, ultimately, how to target those associations in treatment. In a large sample of patients who completed intensive treatment for OCD and related disorders (N = 1343), we examined whether differences in network structure of OCD symptom aspects existed at baseline between treatment responders versus non-responders. A network comparison test indicated a significant difference between OCD network structure for responders versus non-responders (M = 0.19, p = .02). Consistent differences emerged between responders and non-responders in how they responded to emotional distress. This pattern of associations suggests that non-responders may have been more reactive to their distress by performing compulsions, thereby worsening their functioning. By examining the association between baseline distress intolerance with other symptom aspects that presumably maintain the disorder (e.g., ritualizing), clinicians can more effectively target those associations in treatment.

Childhood exposure to interpersonal violence is associated with greater transdiagnostic integration of psychiatric symptoms

BACKGROUND

Childhood exposure to interpersonal violence (IPV) may be linked to distinct manifestations of mental illness, yet the nature of this change remains poorly understood. Network analysis can provide unique insights by contrasting the interrelatedness of symptoms underlying psychopathology across exposed and non-exposed youth, with potential clinical implications for a treatment-resistant population. We anticipated marked differences in symptom associations among IPV-exposed youth, particularly in terms of 'hub' symptoms holding outsized influence over the network, as well as formation and influence of communities of highly interconnected symptoms.

METHODS

Participants from a population-representative sample of youth (n = 4433; ages 11-18 years) completed a comprehensive structured clinical interview assessing mental health symptoms, diagnostic status, and history of violence exposure. Network analytic methods were used to model the pattern of associations between symptoms, quantify differences across diagnosed youth with (IPV+) and without (IPV-) IPV exposure, and identify transdiagnostic 'bridge' symptoms linking multiple disorders.

RESULTS

Symptoms organized into six 'disorder' communities (e.g. Intrusive Thoughts/Sensations, Depression, Anxiety), that exhibited considerably greater interconnectivity in IPV+ youth. Five symptoms emerged in IPV+ youth as highly trafficked 'bridges' between symptom communities (11 in IPV- youth).

CONCLUSION

IPV exposure may alter mutually reinforcing symptom co-occurrence in youth, thus contributing to greater psychiatric comorbidity and treatment resistance. The presence of a condensed and unique set of bridge symptoms suggests trauma-enriched nodes which could be therapeutically targeted to improve outcomes in violence-exposed youth.

Are Autistic and Alexithymic Traits Distinct? A Factor-Analytic and Network Approach

Despite the heterogeneity in autism, socioemotional difficulties are often framed as universal. Increasing evidence, however, suggests that socioemotional difficulties may be explained by alexithymia, a distinct yet frequently co-occurring condition. If, as some propose, autistic traits are responsible for socioemotional impairments, then alexithymia may itself be a symptom of autism. We aimed to determine whether alexithymia should be considered a product of autism or regarded as a separate condition. Using factor-analytic and network approaches, we provide evidence that alexithymic and autistic traits are distinct. We argue that: (1) models of socioemotional processing in autism should conceptualise difficulties as intrinsic to alexithymia; and (2) assessment of alexithymia is crucial for diagnosis and personalised interventions.

Network analysis of PGD, PTSD and insomnia symptoms in Chinese shidu parents with PGD

Background

Chinese shidu parents (bereaved parents over the age of 49 who have lost their only child) are potentially at a high risk of prolonged grief disorder (PGD), posttraumatic stress disorder (PTSD) and insomnia.

Objective

The current study aimed to estimate three network models in 310 shidu parents who met the ICD-11 criteria for PGD: (1) a PGD network to identify central symptoms; (2) a comorbidity network to explore bridge symptoms between PGD and PTSD; (3) a comorbidity network to examine the associations between PGD and insomnia symptoms.

Methods

The R-packages bootnet, qgraph and networktools were used to investigate the structure of network models and centrality indices of symptoms. In addition, robustness and significance analyses for the edge weights and the order of centrality were performed.

Results

Emotional pain and numbness emerged as the most central symptoms in the PGD network. In the PGD-PTSD comorbidity network, the highest bridge strength symptoms were inability to trust others (PGD) and feeling upset (PTSD). Inability to trust others (PGD), avoidance (PGD), and impairment of life quality (insomnia) were possible bridge symptoms connecting PGD and insomnia.

Conclusions

Reducing emotional pain and numbness may be a viable target in PGD interventions for shidu parents. Additionally, findings suggest that future studies could examine the role of inability to trust others and avoidance in PGD comorbidities.

HIGHLIGHTS

• Emotional pain and numbness were the most influential symptoms in shidu parents with PGD. The role of PGD symptoms of inability to trust others and avoidance in the comorbidities of PGD with PTSD and insomnia might be worthy of further study.

Resolving the paradox of posttraumatic growth and event centrality in trauma survivors

When a traumatic experience is central to an individual's identity and worldview, it can result in either severe posttraumatic stress disorder (PTSD) symptoms, perceived posttraumatic growth (PTG), or, paradoxically, both. To resolve this apparent paradox, we used network analytic methods to estimate the relations among components of event centrality (EC), PTSD symptoms, and PTG in 1,136 undergraduates who had experienced trauma. Participants completed surveys on their experiences with traumatic events as well as the degree to which they experienced PTSD symptoms, components of EC, and components of PTG. We performed network analysis to examine EC, PTSD, and PTG and identify which components of EC were most conducive to its associations with PTSD versus those with PTG. We found that the components of EC most associated with PTSD, the extent to which trauma serves as a script for the future, were markedly distinct from the components associated with PTG, the extent to which trauma is seen as a turning point in one's life. The combined findings suggest that EC may be a catalyst for subsequent positive or negative effects contingent upon how an individual interprets the centrality of their traumatic experience.

Depressive symptom complexes of community-dwelling older adults: a latent network model

Late-life depression has multiple, heterogeneous clinical presentations. The aim of the study was to identify higher-order homogeneous clinical features (symptom complexes), while accounting for their potential causal interactions within the network approach to psychopathology. We analyzed cross-sectional data from community-dwelling adults aged 65-85 years recruited by the European MentDis_ICF65+ study (n = 2623, mean age 74, 49% females). The severity of 33 depressive symptoms was derived from the age-adapted Composite International Diagnostic Interview. Symptom complexes were identified using multiple detection algorithms for symptom networks, and their fit to data was assessed with latent network models (LNMs) in exploratory and confirmatory analyses. Sensitivity analyses included the Partial Correlation Likelihood Test (PCLT) to investigate the data-generating structure. Depressive symptoms were organized by the Walktrap algorithm into eight symptom complexes, namely sadness/hopelessness, anhedonia/lack of energy, anxiety/irritability, self-reproach, disturbed sleep, agitation/increased appetite, concentration/decision making, and thoughts of death. An LNM adequately fit the distribution of individual symptoms' data in the population. The model suggested the presence of reciprocal interactions between the symptom complexes of sadness and anxiety, concentration and self-reproach and between self-reproach and thoughts of death. Results of the PCLT confirmed that symptom complex data were more likely generated by a network, rather than a latent-variable structure. In conclusion, late-life depressive symptoms are organized into eight interacting symptom complexes. Identification of the symptom complexes of late-life depression may streamline clinical assessment, provide targets for personalization of treatment, and aid the search for biomarkers and for predictors of outcomes of late-life depression.

Modeling alcohol use disorder as a set of interconnected symptoms - Assessing differences between clinical and population samples and across external factors

Alcohol use disorder is argued to be a highly complex disorder influenced by a multitude of factors on different levels. Common research approaches fail to capture this breadth of interconnecting symptoms. To address this gap in theoretical assumptions and methodological approaches, we used a network analysis to assess the interplay of alcohol use disorder symptoms. We applied the exploratory analysis to two US-datasets, a population sample with 23,591 individuals and a clinical sample with 483 individuals seeking treatment for alcohol use disorder. Using a Bayesian framework, we first investigated differences between the clinical and population sample looking at the symptom interactions and underlying structure space. In the population sample the time spent drinking alcohol was most strongly connected, whereas in the clinical sample loss of control showed most connections. Furthermore, the clinical sample demonstrated less connections, however, estimates were too unstable to conclude the sparsity of the network. Second, for the population sample we assessed whether the network was measurement invariant across external factors like age, gender, ethnicity and income. The network differed across all factors, especially for age subgroups, indicating that subgroup specific networks should be considered when deriving implications for theory building or intervention planning. Our findings corroborate known theories of alcohol use disorder stating loss of control as a central symptom in alcohol dependent individuals.

Anticipating the direction of symptom progression using critical slowing down: a proof-of-concept study

BACKGROUND

As complex dynamic systems approach a transition, their dynamics change. This process, called critical slowing down (CSD), may precede transitions in psychopathology as well. This study investigated whether CSD may also indicate the direction of future symptom transitions, i.e., whether they involve an increase or decrease in symptoms.

METHODS

In study 1, a patient with a history of major depression monitored their mental states ten times a day for almost eight months. Study 2 used data from the TRAILS TRANS-ID study, where 122 young adults at increased risk of psychopathology (mean age 23.64±0.67 years, 56.6% males) monitored their mental states daily for six consecutive months. Symptom transitions were inferred from semi-structured diagnostic interviews. In both studies, CSD direction was estimated using moving-window principal component analyses.

RESULTS

In study 1, CSD was directed towards an increase in negative mental states. In study 2, the CSD direction matched the direction of symptom shifts in 34 individuals. The accuracy of the indicator was higher in subsets of individuals with larger absolute symptom transitions. The indicator's accuracy exceeded chance levels in sensitivity analyses (accuracy 22.92% vs. 11.76%, z=-2.04, P=.02) but not in main analyses (accuracy 27.87% vs. 20.63%, z=-1.32, P=.09).

CONCLUSIONS

The CSD direction may predict whether upcoming symptom transitions involve remission or worsening. However, this may only hold for specific individuals, namely those with large symptom transitions. Future research is needed to replicate these findings and to delineate for whom CSD reliably forecasts the direction of impending symptom transitions.

Network Analysis of Psychopathology: Controversies and Challenges

Empirical publications inspired by the network approach to psychopathology have increased exponentially in the twenty-first century. The central idea that an episode of mental disorder arises from causal interactions among its symptomatic elements has especially resonated with those clinical scientists whose disenchantment with traditional categorical and dimensional approaches to mental illness has become all too apparent. As the field has matured, conceptual and statistical concerns about the limitations of network approaches to psychopathology have emerged, inspiring the development of novel methods to address these concerns. Rather than reviewing the vast empirical literature, I focus instead on the issues and controversies regarding this approach and sketch directions where the field might go next.

Inflammatory phenotype of depression symptom structure: A network perspective

BACKGROUND

There has been increasing interest in classifying inflammatory phenotypes of depression. Most investigations into inflammatory phenotypes only have tested whether elevated inflammation is associated with elevated levels of depression symptoms, or risk for a diagnosis. This study expanded the definition of phenotype to include the structure of depression symptoms as a function of inflammation.

METHODS

Network models of depression symptoms were estimated in a sample of 4157 adults (mean age = 47.6, 51% female) from the 2015-2016 National Health and Nutrition Examination Survey (NHANES). Analyses included comparisons of networks between those with elevated (C-reactive protein (CRP) values ≥ 3.0 mg/L; N = 1696) and non-elevated CRP (N = 2841) as well as moderated network models with CRP group status and raw CRP values moderating the associations between depression symptoms.

RESULTS

Differences emerged at all levels of analysis (global, symptom-specific, symptom-symptom associations). Specifically, the elevated CRP group had greater symptom connectivity (stronger total associations between symptoms). Further, difficulty concentrating and psychomotor difficulties had higher expected influence (concordance with other symptoms) in the elevated CRP group. Finally, there was evidence that several symptom-symptom associations were moderated by CRP.

CONCLUSIONS

This study provides consistent evidence that the structure of depression symptoms varies as a function of CRP levels. Greater symptom connectivity might contribute to why elevated CRP is associated with treatment-resistant depression. Additionally, differences in symptom structure might highlight different maintenance mechanisms and treatment targets for individuals with compared to those without elevated CRP. Finally, differences in symptom structure as a function of CRP highlight a potential misalignment of standard depression measures (the structure of which are evaluated on groups unselected for CRP levels) and the presentation of depression symptoms in those with elevated CRP.

How handling extreme C-reactive protein (CRP) values and regularization influences CRP and depression criteria associations in network analyses

Increasingly, it has been recognized that analysis at the symptom, rather than diagnostic, level will drive progress in the field of immunopsychiatry. Network analysis offers a useful tool in this pursuit with the ability to identify associations between immune markers and individual symptoms, independent of all other variables modeled. However, investigation into how methodological decisions (i.e., including vs. excluding participants with C-reactive protein (CRP) >10 mg/L, regularized vs. nonregularized networks) influence results is necessary to establish best practices for the use of network analysis in immunopsychiatry. In a sample of 3,464 adult participants from the 2015-2016 National Health and Nutrition Examination Survey dataset, this study found consistent support for associations between CRP and fatigue and changes in appetite and some support for additional CRP-criterion associations. Methodologically, results consistently demonstrated that including individuals with CRP >10 mg/L and estimating nonregularized networks provided better estimates of these associations. Thus, we recommend considering the use of nonregularized networks in immunopsychiatry and inclusion of cases with CRP values >10 mg/L when testing the association between CRP and depression criteria, unless contraindicated by the research question being tested. Additionally, results most consistently suggest that CRP is uniquely related to fatigue and changes in appetite, supporting their inclusion in an immunometabolic phenotype of depression. Finally, these associations suggest that fatigue and changes in appetite might be particularly receptive to anti-inflammatory treatments. However, future research with more nuanced measures is necessary to parse out whether appetite increases or decreases drive this association. Further, longitudinal research is an important next step to test how these relationships manifest over time.

A Narrative Review of Network Studies in Depression: What Different Methodological Approaches Tell Us About Depression

The network theory of psychopathology proposes that mental disorders arise from direct interactions between symptoms. This theory provides a promising framework to understand the development and maintenance of mental disorders such as depression. In this narrative review, we summarize the literature on network studies in the field of depression. Four methodological network approaches are distinguished: (i) studies focusing on symptoms at the macro-level vs. (ii) on momentary states at the micro-level, and (iii) studies based on cross-sectional vs. (iv) time-series (dynamic) data. Fifty-six studies were identified. We found that different methodological approaches to network theory yielded largely inconsistent findings on depression. Centrality is a notable exception: the majority of studies identified either positive affect or anhedonia as central nodes. To aid future research in this field, we outline a novel complementary network theory, the momentary affect dynamics (MAD) network theory, to understand the development of depression. Furthermore, we provide directions for future research and discuss if and how networks might be used in clinical practice. We conclude that more empirical network studies are needed to determine whether the network theory of psychopathology can indeed enhance our understanding of the underlying structure of depression and advance clinical treatment.

PTSD networks of veterans with combat versus non-combat types of index trauma

BACKGROUND

Network analysis has become popular among PTSD researchers for studying causal structure or interrelationships among symptoms. However, some have noted that results do not seem to be consistent across studies. Preliminary evidence suggests that trauma type may be one source of variability.

METHODS

The current study sought to examine the PTSD networks of veterans with combat versus non-combat index trauma. Participants included 944 veterans who completed the PTSD Checklist for DSM-5 at intake at two VA PTSD clinics.

RESULTS

There were many similarities between the combat and non-combat trauma networks, including strong edges between symptoms that were theoretically related or similar (e.g., avoidance) and negative emotion being a highly central symptom. However, correlations of edge weights (0.509) and node centrality (0.418) across networks suggested moderate correspondence, and there appeared to be some differences associated with certain symptoms. Detachment was relatively more central and the connections of negative emotion with blame and lack of positive emotion with reckless behavior were stronger for veterans with combat-related index trauma.

LIMITATIONS

The data were cross-sectional, which limits the ability to infer directional relationships between symptoms. In addition, the sample was likely not large enough to directly test for differences between networks via network comparison tests.

CONCLUSIONS

Although there were many similarities, results also suggested some variability in PTSD networks associated with combat versus non-combat index trauma that could have implications for conceptualizing and treating PTSD among veterans.

Transdiagnostic vulnerability factors in eating disorders: A network analysis

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.

On the validity of the centrality hypothesis in cross-sectional between-subject networks of psychopathology

BACKGROUND

In the network approach to psychopathology, psychiatric disorders are considered networks of causally active symptoms (nodes), with node centrality hypothesized to reflect symptoms' causal influence within a network. Accordingly, centrality measures have been used in numerous network-based cross-sectional studies to identify specific treatment targets, based on the assumption that deactivating highly central nodes would proliferate to other nodes in the network, thereby collapsing the network structure and alleviating the overall psychopathology (i.e., the centrality hypothesis).

METHODS

Here, we summarize three types of evidence pertaining to the centrality hypothesis in psychopathology. First, we discuss the validity of the theoretical assumptions underlying the centrality hypothesis in psychopathology. We then summarize the methodological aspects of extant studies using centrality measures as predictors of symptom change following treatment, while delineating their main findings and several of their limitations. Finally, using a specific dataset of 710 treatment-seeking patients with posttraumatic stress disorder (PTSD) as an example, we empirically examine node centrality as a predictor of therapeutic change, replicating the approach taken by previous studies, while addressing some of their limitations. Specifically, we investigated whether three pre-treatment centrality indices (strength, predictability, and expected influence) were significantly correlated with the strength of the association between a symptom's change and the change in the severity of all other symptoms in the network from pre- to post-treatment (Δnode-Δnetwork association). Using similar analyses, we also examine the predictive validity of two simple non-causal node properties (mean symptom severity and infrequency of symptom endorsement).

RESULTS

Of the three centrality measures, only expected influence successfully predicted how strongly changes in nodes/symptoms were associated with change in the remainder of the nodes/symptoms. Importantly, when excluding the amnesia node, a well-documented outlier in the phenomenology of PTSD, none of the tested centrality measures predicted symptom change. Conversely, both mean symptom severity and infrequency of symptom endorsement, two standard non-network-derived indices, were found to be more predictive than expected influence and remained significantly predictive also after excluding amnesia from the network analyses.

CONCLUSIONS

The centrality hypothesis in its current form is ill-defined, showing no consistent supporting evidence in the context of cross-sectional, between-subject networks.

Risk factors associated with mental illness in hospital discharged patients infected with COVID-19 in Wuhan, China

The coronavirus disease 2019 (COVID-19) pandemic can have a profound impact on the mental health of patients who survived the illness. However, little is known about the prevalence rate of mental health disorders among hospital discharged COVID-19 patients and its associated factors. A cross-sectional survey of hospital discharged patients was conducted April 11-22, 2020 in Wuhan, China (where the pandemic began). 675 participants completed the survey, including 90 (13.3%) medical staff (physicians and nurses who had been ill). We used Fisher's exact test and multivariable logistic regression methods to explore the risk factors associated with mental health problems (anxiety, depression, and PTSD symptoms associated with COVID-19 hospitalization). Adverse mental health effects of COVID-19 are evident after discharge from the hospital, with sleep difficulties highlighted as a central issue. As we found that perceived discrimination was a central predictor of mental illness, preventing and addressing social stigma associated with COVID-19 may be crucial for improving mental health for recovered patients.

The association between insight and depressive symptoms in schizophrenia: Undirected and Bayesian network analyses

Background.

Greater levels of insight may be linked with depressive symptoms among patients with schizophrenia, however, it would be useful to characterize this association at symptom-level, in order to inform research on interventions.

Methods.

Data on depressive symptoms (Calgary Depression Scale for Schizophrenia) and insight (G12 item from the Positive and Negative Syndrome Scale) were obtained from 921 community-dwelling, clinically-stable individuals with a DSM-IV diagnosis of schizophrenia, recruited in a nationwide multicenter study. Network analysis was used to explore the most relevant connections between insight and depressive symptoms, including potential confounders in the model (neurocognitive and social-cognitive functioning, positive, negative and disorganization symptoms, extrapyramidal symptoms, hostility, internalized stigma, and perceived discrimination). Bayesian network analysis was used to estimate a directed acyclic graph (DAG) while investigating the most likely direction of the putative causal association between insight and depression.

Results.

After adjusting for confounders, better levels of insight were associated with greater self-depreciation, pathological guilt, morning depression and suicidal ideation. No difference in global network structure was detected for socioeconomic status, service engagement or illness severity. The DAG confirmed the presence of an association between greater insight and self-depreciation, suggesting the more probable causal direction was from insight to depressive symptoms.

Conclusions.

In schizophrenia, better levels of insight may cause self-depreciation and, possibly, other depressive symptoms. Person-centered and narrative psychotherapeutic approaches may be particularly fit to improve patient insight without dampening self-esteem.

Better insight seems associated with depressive symptoms in schizophrenia.

Network analyses were used to explore this association in a large sample.

Insight was associated with self-depreciation, guilt, and suicidal ideation.

Although cross-sectional, data suggest causal direction from insight to depression.

Network structure of depression symptomology in participants with and without depressive disorder: the population-based Health 2000-2011 study

PURPOSE

Putative causal relations among depressive symptoms in forms of network structures have been of recent interest, with prior studies suggesting that high connectivity of the symptom network may drive the disease process. We examined in detail the network structure of depressive symptoms among participants with and without depressive disorders (DD; consisting of major depressive disorder (MDD) and dysthymia) at two time points.

METHODS

Participants were from the nationally representative Health 2000 and Health 2011 surveys. In 2000 and 2011, there were 5998 healthy participants (DD-) and 595 participants with DD diagnosis (DD+). Depressive symptoms were measured using the 13-item version of the Beck Depression Inventory (BDI). Fused Graphical Lasso was used to estimate network structures, and mixed graphical models were used to assess network connectivity and symptom centrality. Network community structure was examined using the walktrap-algorithm and minimum spanning trees (MST). Symptom centrality was evaluated with expected influence and participation coefficients.

RESULTS

Overall connectivity did not differ between networks from participants with and without DD, but more simple community structure was observed among those with DD compared to those without DD. Exploratory analyses revealed small differences between the samples in the order of one centrality estimate participation coefficient.

CONCLUSIONS

Community structure, but not overall connectivity of the symptom network, may be different for people with DD compared to people without DD. This difference may be of importance when estimating the overall connectivity differences between groups with and without mental disorders.