Replicability and Generalizability of Posttraumatic Stress Disorder (PTSD) Networks: A Cross-Cultural Multisite Study of PTSD Symptoms in Four Trauma Patient Samples

On the Importance of Estimating Parameter Uncertainty in Network Psychometrics: A Response to Forbes et al. (2019)

In their recent paper, Forbes et al. (2019; FWMK) evaluate the replicability of network models in two studies. They identify considerable replicability issues, concluding that "current 'state-of-the-art' methods in the psychopathology network literature […] are not well-suited to analyzing the structure of the relationships between individual symptoms". Such strong claims require strong evidence, which the authors do not provide. FWMK identify low replicability by analyzing point estimates of networks; contrast low replicability with results of two statistical tests that indicate higher replicability, and conclude that these tests are problematic. We make four points. First, statistical tests are superior to the visual inspection of point estimates, because tests take into account sampling variability. Second, FWMK misinterpret the statistical tests in several important ways. Third, FWMK did not follow established recommendations when estimating networks in their first study, underestimating replicability. Fourth, FWMK draw conclusions about methodology, which does not follow from investigations of data, and requires investigations of methodology. Overall, we show that the "poor replicability "observed by FWMK occurs due to sampling variability and use of suboptimal methods. We conclude by discussing important recent simulation work that guides researchers to use models appropriate for their data, such as nonregularized estimation routines.

Quantifying the Reliability and Replicability of Psychopathology Network Characteristics

Pairwise Markov random field networks-including Gaussian graphical models (GGMs) and Ising models-have become the "state-of-the-art" method for psychopathology network analyses. Recent research has focused on the reliability and replicability of these networks. In the present study, we compared the existing suite of methods for maximizing and quantifying the stability and consistency of PMRF networks (i.e., lasso regularization, plus the bootnet and NetworkComparisonTest packages in R) with a set of metrics for directly comparing the detailed network characteristics interpreted in the literature (e.g., the presence, absence, sign, and strength of each individual edge). We compared GGMs of depression and anxiety symptoms in two waves of data from an observational study (n = 403) and reanalyzed four posttraumatic stress disorder GGMs from a recent study of network replicability. Taken on face value, the existing suite of methods indicated that overall the network edges were stable, interpretable, and consistent between networks, but the direct metrics of replication indicated that this was not the case (e.g., 39-49% of the edges in each network were unreplicated across the pairwise comparisons). We discuss reasons for these apparently contradictory results (e.g., relying on global summary statistics versus examining the detailed characteristics interpreted in the literature) and conclude that the limited reliability of the detailed characteristics of networks observed here is likely to be common in practice, but overlooked by current methods. Poor replicability underpins our concern surrounding the use of these methods, given that generalizable conclusions are fundamental to the utility of their results.

On Unreplicable Inferences in Psychopathology Symptom Networks and the Importance of Unreliable Parameter Estimates

We recently wrote an article comparing the conclusions that followed from two different approaches to quantifying the reliability and replicability of psychopathology symptom networks. Two commentaries on the article have raised five core criticisms, which are addressed in this response with supporting evidence. 1) We did not over-generalize about the replicability of symptom networks, but rather focused on interpreting the contradictory conclusions of the two sets of methods we examined. 2) We closely followed established recommendations when estimating and interpreting the networks. 3) We also closely followed the relevant tutorials, and used examples interpreted by experts in the field, to interpret the bootnet and NetworkComparisonTest results. 4) It is possible for statistical control to increase reliability, but that does not appear to be the case here. 5) Distinguishing between statistically significant versus substantive differences makes it clear that the differences between the networks affect the inferences we would make about symptom-level relationships (i.e., the basis of the purported utility of symptom networks). Ultimately, there is an important point of agreement between our article and the commentaries: All of these applied examples of cross-sectional symptom networks are demonstrating unreliable parameter estimates. While the commentaries propose that the resulting differences between networks are not genuine or meaningful because they are not statistically significant, we propose that the unreplicable inferences about the symptom-level relationships of interest fundamentally undermine the utility of the symptom networks.

Psychological State Among the General Chinese Population Before and During the COVID-19 Epidemic: A Network Analysis

Background: The infectious disease Coronavirus Disease 2019 (COVID-19) outbroke in 2019 spread to multiple countries. The quick spread of the virus and isolation strategies may trigger psychological problems. Our aim was to explore the dynamic network structure of the psychological state before and during the epidemic. Methods: A web-based survey was conducted in two stages: the T1 stage (1 January 2019 to 31 December 2019) and the T2 stage (1 February 2020 to 8 March 2020). In both stages, the Patient Health Questionnaire-9, General Anxiety Disorder-7, and Pittsburgh Sleep Quality Index were used to assess depression, anxiety, and sleep, respectively. Results: We matched the data based on IP addresses. We included 1,978, 1,547, and 2,061 individuals who completed the depression, anxiety, and sleep assessments, respectively, at both stages. During epidemics, psychomotor agitation/retardation, inability to relax, restless behavior, and the frequency of using medicine had high centrality. Meanwhile, the network structure of psychological symptoms becomes stronger than before the epidemic. Conclusion: Symptoms of psychomotor agitation/retardation, inability to relax, and restless behavior should be treated preferentially. It is necessary to provide mental health services, including timely and effective early psychological intervention. In addition, we should also pay attention to the way patients use medicines to promote sleep quality.

A network perspective on suicidal behavior: Understanding suicidality as a complex system

BACKGROUND

Suicidal behavior is the result of complex interactions between many different factors that change over time. A network perspective may improve our understanding of these complex dynamics. Within the network perspective, psychopathology is considered to be a consequence of symptoms that directly interact with one another in a network structure. To view suicidal behavior as the result of such a complex system is a good starting point to facilitate moving away from traditional linear thinking.

OBJECTIVE

To review the existing paradigms and theories and their application to suicidal behavior.

METHODS

In the first part of this paper, we introduce the relevant concepts within network analysis such as network density and centrality. Where possible, we refer to studies that have applied these concepts within the field of suicide prevention. In the second part, we move one step further, by understanding the network perspective as an initial step toward complex system theory. The latter is a branch of science that models interacting variables in order to understand the dynamics of complex systems, such as tipping points and hysteresis.

RESULTS

Few studies have applied network analysis to study suicidal behavior. The studies that do highlight the complexity of suicidality. Complexity science offers potential useful concepts such as alternative stable states and resilience to study psychopathology and suicidal behavior, as demonstrated within the field of depression. To date, one innovative study has applied concepts from complexity science to better understand suicidal behavior. Complexity science and its application to human behavior are in its infancy, and it requires more collaboration between complexity scientists and behavioral scientists.

CONCLUSIONS

Clinicians and scientists are increasingly conceptualizing suicidal behavior as the result of the complex interaction between many different biological, social, and psychological risk and protective factors. Novel statistical techniques such as network analysis can help the field to better understand this complexity. The application of concepts from complexity science to the field of psychopathology and suicide research offers exciting and promising possibilities for our understanding and prevention of suicide.

Changes in the Network Structure of Post-traumatic Stress Disorder Symptoms Among Earthquake Exposed Adolescents in China: A 2-Year Longitudinal Study

Despite considerable studies focused on the symptoms of posttraumatic stress disorder (PTSD), little is understood about how symptoms of PTSD naturalistically change over time. Using network analyses approaches, the current study aimed to understand the nature of the association between PTSD symptoms at different time points among adolescents who experienced an earthquake. This study enrolled 900 youth survivors who completed 3 assessments with the Child PTSD Symptom Scale at 1 year, 1.5 years, and 2 years after the Wenchuan earthquake. A graphical Gaussian model (GGM) was used to investigate how symptom networks changed across these time points and to identify the symptoms that were the most central within the network. Results from GGM indicated that different symptoms were observed to have highest centrality at different time points. Feeling distant or cut off from others, avoid thoughts and feelings about the trauma, and feeling irritable or having angry outbursts appeared as the node with highest centrality at 1 year (T1), 1.5 years (T2), and 2 years (T3) post-earthquake, respectively.

Deconstructing PTSD: Trauma and emotion among Mexican immigrant women

The symptomatology for Post-Traumatic Stress Disorder (PTSD) narrowly focuses on particular diagnostic frames and a single triggering event. Such narrow definitions of trauma and recovery have been heavily critiqued by anthropologists and cultural psychiatrists for overlooking cultural complexity as well as the effects of multiple and overlapping events that may cause someone to become "traumatized" and thereby affect recovery. This article investigates how subjective reporting of traumatic experience in life history narratives relates to depressive and PTSD symptomatology, cultural idioms, and repeated traumatic experiences among low-income Mexican immigrant women in Chicago. We interviewed 121 Mexican immigrant women and collected life history narratives and psychiatric scales for depression and PTSD. Most women spoke of the detrimental effects of repeated traumatic experiences, reported depressive (49%) and PTSD (38%) symptoms, and described these experiences through cultural idioms. These data complicate the PTSD diagnosis as a discrete entity that occurs in relation to a single acute event. Most importantly, these findings reveal the importance of cumulative trauma and cultural idioms for the recognition of suffering and the limitation of diagnostic categories for identifying the needs of those who experience multiple social and psychological stressors.

Investigating the Interrelations Between Systems of Support in 13- to 18-Year-Old Adolescents: A Network Analysis of Resilience Promoting Systems in a High and Middle-Income Country

Adolescents' ability to function well under adversity relies on a network of interrelated support systems. This study investigated how consecutive age groups differ in the interactions between their support systems. A secondary data analysis of cross-sectional studies that assessed individual, caregiver, and contextual resources using the Child and Youth Resilience Measure (Ungar & Liebenberg, 2005) in 13- to 18-year-olds in Canada (N = 2,311) and South Africa (N = 3,039) was conducted applying network analysis. Individual and contextual systems generally showed the highest interconnectivity. While the interconnectivity between the individual and caregiver system declined in the Canadian sample, a u-shaped pattern was found for South Africa. The findings give first insights into cross-cultural and context-dependent patterns of interconnectivity between fundamental resource systems during adolescence.

Sex differences in depressive symptoms and their networks in a treatment-seeking population - a cross-sectional study

BACKGROUND

The higher prevalence of major depressive disorder (MDD) in females relative to males is well-established. Some authors have posited this difference arises to divergent symptom profiles in females vs. males. However, empirical tests of this hypothesis have yielded equivocal results. Here, we investigate sex differences in MDD of individual symptoms and symptom networks in a treatment-seeking sample.

METHODS

We assessed depressive symptoms using Hamilton Depression Rating Scale (HDRS-17) in 590 treatment-seeking adults with MDD (300 females). We examined group differences in symptom endorsement. We investigated symptom networks and estimated Gaussian Graphical Models. Finally, we compared the female and male networks using the Network Comparison Test.

RESULTS

Females scored significantly higher in psychological anxiety (p <0.001; rB = -0.155), somatic anxiety (p = .001; rB = -0.150) and feelings of guilt (p = .002; rB = -0.139). Male and female patients did not differ in depression sum scores. There were no sex differences in network structure or global strength.

LIMITATIONS

Our study was sufficiently powered to detect only medium sized symptom differences. The generalizability of our study is limited to clinical samples and further studies are needed to investigate if findings also translate to outpatient samples.

CONCLUSION

Females reported elevated anxiety symptoms and guilt. Clinicians should assess these symptom differences and tailor treatment to individual symptom profiles. No differences between sexes emerged in MDD network structures, indicating that features may be more similar than previously assumed. Sex differences in psychopathological features of MDD are important for future research and personalized treatment.

Suicide ideation as a symptom of adolescent depression. a network analysis

INTRODUCTION

According to the network perspective, psychopathology is the result of interactions between symptoms. A previous study used network analysis to identify central symptoms of adolescent depression. The aim of the current study was replicate and extend this study by including suicide ideation as a symptom of depression and evaluating which depression symptoms are contributing factors to suicide ideation in adolescents.

METHOD

A large community sample (N = 5,888) of adolescents aged 11-16 years completed the Children's Depression Inventory (CDI-2). Network analysis was used to identify the network structure of the CDI-2 and which symptoms were directly related to suicide ideation in the network. Additionally, the network structure of adolescents who did and did not experience suicide ideation were compared.

RESULTS

Results pertaining the depression network were highly similar to the study we aimed to replicate. The most central symptoms in the depression network were loneliness, sadness, self-hatred, fatigue, self-deprecation and crying. Loneliness explained most variance of suicide ideation. Adolescents who experience suicide ideation had a similar network structure as those who do not. Adolescents with suicide ideation scored higher on all depression symptoms.

LIMITATIONS

The use of cross-sectional data indicates that only undirected networks and results based on between-subject data could be estimated.

CONCLUSIONS

Loneliness was a central factor for depression networks and also the most contributing factor of suicide ideation. Preventative efforts should consider taking experiences of loneliness into account as these are especially prevalent in adolescents. Suicide ideation seems more representative of depression symptom severity in adolescents.

Phenotype Network and Brain Structural Covariance Network of Major Depression

Phenotype networks enable clinicians to elucidate the patterns of coexistence and interactions among the clinical symptoms, negative cognitive styles , neurocognitive performance, and environmental factors in major depressive disorder (MDD). Results of phenotype network approach could be used in finding the target symptoms as these are tightly connected or associated with many other phenomena within the phenotype network of MDD specifically when comorbid psychiatric disorder(s) is/are present. Further, by comparing the differential patterns of phenotype networks before and after the treatment, changing or enduring patterns of associations among the clinical phenomena in MDD have been deciphered.Brain structural covariance networks describe the inter-regional co-varying patterns of brain morphologies, and overlapping findings have been reported between the brain structural covariance network and coordinated trajectories of brain development and maturation. Intra-individual brain structural covariance illustrates the degrees of similarities among the different brain regions for how much the values of brain morphological features are deviated from those of healthy controls. Inter-individual brain structural covariance reflects the degrees of concordance among the different brain regions for the inter-individual distribution of brain morphologic values. Estimation of the graph metrics for these brain structural covariance networks uncovers the organizational profile of brain morphological variations in the whole brain and the regional distribution of brain hubs.

Lifestyle and Symptom Management Needs: A Network Analysis of Family Caregiver Needs of Cancer Patients

Previous research on the needs of family cancer caregivers (FCCs) have not elucidated associations between specific caregiving needs. Network analysis, a statistical approach that allows the estimation of complex relationship patterns, helps facilitate the understanding of associations between needs and provides the opportunity to identify and direct interventions at relevant and specific targets. No studies to date, have applied network analysis to FCC populations. The aim of the study is to explore the network structure of FCC needs in a cohort of caregivers in Singapore. FCCs (N = 363) were recruited and completed a self-report questionnaire on socio-demographic data, medical data on their loved ones, and the Needs Assessment of Family Caregivers-Cancer scale. The network was estimated using state-of-the-art regularized partial correlation model. The most central needs were having to deal with lifestyle changes and managing care-recipients cancer-related symptoms. The strongest associations were between (1) having enough insurance coverage and understanding/navigating insurance coverage, (2) managing cancer-related pain and managing cancer-related symptoms, (3) being satisfied with relationships and having intimate relationships, and (4) taking care of bills and paying off medical expenses. Lifestyle changes, living with cancer, and symptom management are central to FCCs in Singapore. These areas deserve special attention in the development of caregiver support systems. Our findings highlight the need to improve access to social and medical support to help FCCs in their transition into the caregiving role and handle cancer-related problems.

Bayesian Network Structure and Predictability of Autistic Traits

The aim of this work is to explore the construct of autistic traits through the lens of network analysis with recently introduced Bayesian methods. A conditional dependence network structure was estimated from a data set composed of 649 university students that completed an autistic traits questionnaire. The connectedness of the network is also explored, as well as sex differences among female and male subjects in regard to network connectivity. The strongest connections in the network are found between items that measure similar autistic traits. Traits related to social skills are the most interconnected items in the network. Sex differences are found between female and male subjects. The Bayesian network analysis offers new insight on the connectivity of autistic traits as well as confirms several findings in the autism literature.

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.

Resilience and Stress in Later Life: A Network Analysis Approach Depicting Complex Interactions of Resilience Resources and Stress-Related Risk Factors in Older Adults

BACKGROUND

Emerging systemic approaches on resilience propose that a person's or group's adaptability to significant stress relies on a network of interdependent resources. However, little knowledge exists on systemic resilience in older survivors of early-life adversity (ELA) and how ELA affects their resource network in later life.

OBJECTIVE

This study investigated how ELA may be linked to the interplay of resources and stress-related risk factors in later life.

RESEARCH DESIGN AND METHODS

Data from N = 235 older adults (M age = 70.43 years; 46.40% female) were assessed. Half the participants were affected by ELA through compulsory social measures and placements in childhood, and/or adolescence ("risk group"). The other half were age-matched, non-affected participants ("control group"). Using psychometric instruments, a set of resilience-supporting resources in later life and current stress indices were assessed. Regularized partial correlation networks examined the interplay of resources in both groups, whilst also considering the impact of stress.

RESULTS

Both groups demonstrated only positive resource interrelations. Although the control group showed more possible resource connections, the groups did not significantly differ in the overall strength of connections. While group-specific resource interrelations were identified, self-esteem was observed to be the most important resource for the network interconnectedness of both groups. The risk group network showed a higher vulnerability to current stress.

DISCUSSION AND IMPLICATIONS

Network analysis is a useful approach in the examination of the complex interrelationships between resilience resources and stress-related risk factors in older adulthood.

Emotion Network Analysis During COVID-19 Quarantine - A Longitudinal Study

Introduction: The coronavirus disease 2019 (COVID-19) emergency has imposed important challenges in the lives of individuals, particularly since the restriction of free movement. In Spain, this mandatory home confinement started on March 14, 2020. In this scenario, some calls have been made to better understand the exact impact of the quarantine on the emotional status of individuals across time. Materials and Methods: On the first day that the Spanish government imposed the quarantine, our team launched an online longitudinal study to monitor emotional responses to the COVID-19 emergency over time. For 2 weeks, 187 people have responded to a daily diary on emotion functioning. An emotion network analysis was performed to study the network structure of 30 mood states and its changes during the first 2 weeks of the quarantine. Results: The emotional network showed critical changes in the interactions of emotions over time. An analysis of mean emotional levels did not show statistically significant changes in mood over time. Interestingly, two different network patterns were found when the sample was divided between those with favorable responses and those with unfavorable responses. Discussion: This new approach to the study of longitudinal changes of the mood state network of the population reveals different adaptation strategies reflected on the sample's emotional network. This network approach can help identify most fragile individuals (more vulnerable to external stressors) before they develop clear and identifiable psychopathology and also help identify anti-fragile individuals (those who improve their functioning in the face of external stressors). This is one of the first studies to apply an emotional network approach to study the psychological effects of pandemics and might offer some clues to psychologists and health administrators to help people cope with and adjust to this critical situation.

Cross-cultural generalizability of the ICD-11 PGD symptom network: Identification of central symptoms and culturally specific items across German-speaking and Chinese bereaved

BACKGROUND

Prolonged grief disorder (PGD) is a newcomer to psychopathology and the new ICD-11 diagnostic criteria are conceptualized with an eye towards global applicability. Yet, previous network studies have not used official ICD-11 criteria nor tested whether network structures generalize across cultural groups even though much current research relies on ICD-11 PGD criteria.

METHODS

To overcome these limitations, the present study used data from 539 German-speaking (n = 214) and Chinese (n = 325) bereaved individuals to investigate similarities and differences in network structures of ICD-11 PGD criteria. In addition, network structures were investigated for an expanded supplementary questionnaire of culturally-bound grief symptoms hypothesized to be of relevance in each cultural context.

RESULTS

Results suggested both similarities and differences in network structures between the two samples. Across cultural groups, intense feelings of sorrow and inability to experience joy or satisfaction since the death emerged as most central symptoms. Compared to the standard PGD network, the expanded network showed a better average predictability for Chinese participants, but no improvement for the German-speaking context. Unhealthy behavior change was the most central symptom for Chinese bereaved when additional grief symptoms were included.

CONCLUSIONS

Results of the present study suggest there are culturally-bound symptoms of grief which are not included in the current ICD-11 PGD criteria. These findings provide areas of special clinical attention concerning screening and treatment and present a first step towards a more cultural-sensitive understanding of grief.

CLINICAL TRIALS

NCT03568955.

Understanding the Relationship Between Complicated Grief Symptoms and Patterns of Personality Disorders in a Substance Users' Sample: A Network Analysis Approach

BACKGROUND

The presence of personality disorders is greatly prevalent among substance users. Personality disorders could increase vulnerability to complicated grief symptoms. Bereavement is commonly overlooked among substance users. We used network analysis to estimate the structure of relations between patterns of personality disorders and complicated grief symptoms among a bereaved substance-using population.

METHODS

Complicated grief and personality disorders were assessed in a sample of 196 bereaved substance users. We use the graphical least absolute shrinkage selection operator (LASSO) to compute a regularized partial correlation network revealing associations among different patterns of personality disorders and complicated grief symptoms.

RESULTS

In a network involving nodes for personality disorders and symptomatology of complicated grief, patterns of depressive and paranoid personality disorder showed small relationships to complicated grief symptoms. All other personality disorders showed negligible to no relationship to complicated grief symptoms. Further, in the overall network, complicated grief showed the lowest level of centrality, suggesting that it is independent of personality disorders, whereas depressive and paranoid personality disorder symptoms showed the highest centrality.

CONCLUSION

Network analysis can be used to understand the relationships among higher-level constructs such as disorders. We found that complicated grief is largely independent of patterns of personality disorders with the exception of depressive and paranoid. Findings have implications for assessment and appropriate treatment of complicated grief symptoms and substance use disorder.

Symptom structure of PTSD and co-morbid depressive symptoms - a network analysis of combat veteran patients

BACKGROUND

Despite extensive research, symptom structure of posttraumatic stress disorder (PTSD) is highly debated. The network approach to psychopathology offers a novel method for understanding and conceptualizing PTSD. However, extant studies have mainly used small samples and self-report measures among sub-clinical populations, while also overlooking co-morbid depressive symptoms.

METHODS

PTSD symptom network topology was estimated in a sample of 1489 treatment-seeking veteran patients based on a clinician-rated PTSD measure. Next, clinician-rated depressive symptoms were incorporated into the network to assess their influence on PTSD network structure. The PTSD-symptom network was then contrasted with the network of 306 trauma-exposed (TE) treatment-seeking patients not meeting full criteria for PTSD to assess corresponding network differences. Finally, a directed acyclic graph (DAG) was computed to estimate potential directionality among symptoms, including depressive symptoms and daily functioning.

RESULTS

The PTSD symptom network evidenced robust reliability. Flashbacks and getting emotionally upset by trauma reminders emerged as the most central nodes in the PTSD network, regardless of the inclusion of depressive symptoms. Distinct clustering emerged for PTSD and depressive symptoms within the comorbidity network. DAG analysis suggested a key triggering role for re-experiencing symptoms. Network topology in the PTSD sample was significantly distinct from that of the TE sample.

CONCLUSIONS

Flashbacks and psychological reactions to trauma reminders, along with their strong connections to other re-experiencing symptoms, have a pivotal role in the clinical presentation of combat-related PTSD among veterans. Depressive and posttraumatic symptoms constitute two separate diagnostic entities, but with meaningful between-disorder connections, suggesting two mutually-influential systems.

Comparing Gaussian graphical models with the posterior predictive distribution and Bayesian model selection

Gaussian graphical models are commonly used to characterize conditional (in)dependence structures (i.e., partial correlation networks) of psychological constructs. Recently attention has shifted from estimating single networks to those from various subpopulations. The focus is primarily to detect differences or demonstrate replicability. We introduce two novel Bayesian methods for comparing networks that explicitly address these aims. The first is based on the posterior predictive distribution, with a symmetric version of Kullback-Leibler divergence as the discrepancy measure, that tests differences between two (or more) multivariate normal distributions. The second approach makes use of Bayesian model comparison, with the Bayes factor, and allows for gaining evidence for invariant network structures. This overcomes limitations of current approaches in the literature that use classical hypothesis testing, where it is only possible to determine whether groups are significantly different from each other. With simulation we show the posterior predictive method is approximately calibrated under the null hypothesis (α = .05) and has more power to detect differences than alternative approaches. We then examine the necessary sample sizes for detecting invariant network structures with Bayesian hypothesis testing, in addition to how this is influenced by the choice of prior distribution. The methods are applied to posttraumatic stress disorder symptoms that were measured in 4 groups. We end by summarizing our major contribution, that is proposing 2 novel methods for comparing Gaussian graphical models (GGMs), which extends beyond the social-behavioral sciences. The methods have been implemented in the R package BGGM. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Anti-roma Bias (Stereotypes, Prejudice, Behavioral Tendencies): A Network Approach Toward Attitude Strength

The Roma have been and still are a target of prejudice, marginalization, and social exclusion across Europe, especially in East-Central European countries. This paper focuses on a set of stereotypical, emotional, and behavioral evaluative responses toward Roma people selected as representing the underlying components of anti-Roma bias. Employing network analysis, we investigated if attitude strength is associated with stronger connectivity in the networks of its constituent elements. The findings from representative surveys carried out in Hungary, Romania, Slovakia, France, and Ireland supported our assumption, as high attitude strength toward the Roma resulted in stronger connectivity in all pairs of high- versus low-attitude-strength networks. Our finding yields a solid theoretical framework for targeting the central variables-those with the strongest associations with other variables-as a potentially effective attitude change intervention strategy. Moreover, perceived threat to national identity, sympathy, and empathy were found to be the most central variables in the networks.

Developing thinking around mental health science: the example of intrusive, emotional mental imagery after psychological trauma

INTRODUCTION

One route to advancing psychological treatments is to harness mental health science, a multidisciplinary approach including individuals with lived experience and end users (e.g., Holmes, E. A., Craske, M. G., & Graybiel, A. M. (2014). Psychological treatments: A call for mental-health science. Nature, 511(7509), 287-289. doi:10.1038/511287a). While early days, we here illustrate a line of research explored by our group-intrusive imagery-based memories after trauma.

METHOD/RESULTS

We illustrate three possible approaches through which mental health science may stimulate thinking around psychological treatment innovation. First, focusing on single/specific target symptoms rather than full, multifaceted psychiatric diagnoses (e.g., intrusive trauma memories rather than all of posttraumatic stress disorder). Second, investigating mechanisms that can be modified in treatment (treatment mechanisms), rather than those which cannot (e.g., processes only linked to aetiology). Finally, exploring novel ways of delivering psychological treatment (peer-/self-administration), given the prevalence of mental health problems globally, and the corresponding need for effective interventions that can be delivered at scale and remotely for example at times of crisis (e.g., current COVID-19 pandemic).

CONCLUSIONS

These three approaches suggest options for potential innovative avenues through which mental health science may be harnessed to recouple basic and applied research and transform treatment development.

Central and Peripheral Symptoms in Network Analysis are Differentially Heritable A Twin Study of Anxious Misery

BACKGROUND

Evidence suggests that depression and anxiety disorders are genetically based. Although symptoms of these internalizing disorders tend to correlate, the degree to which the related symptoms are heritable is unclear. This overlap has been conceptualized as Anxious Misery and existing research examining similar constructs of negative affect has revealed moderate heritability. However, it is unclear if some symptoms that characterize these constructs are more heritable than others. Modeling the symptom structure of Anxious Misery and examining which symptoms are most heritable may have implications for etiological models of internalizing disorders. Accordingly, the present study employed network analysis to explore the relationships across symptoms of Anxious Misery and to test if central symptoms in the network, compared to more peripheral symptoms, differ in their heritabilities.

METHODS

Twin pairs (N = 1,344 pairs) with a mean age of 39 years (SD = 16 years) completed measures of anxiety and neuroticism to represent the Anxious Misery network.

RESULTS

Panic-related symptoms were the most central in the network and were the most heritable, with genetic factors accounting for up to 59% of phenotypic variance. Peripheral symptoms were less heritable, accounting for as little as 21% of phenotypic variance. The degree of symptom heritability was strongly correlated with the degree of centrality of a symptom in the network (r = .53).

LIMITATIONS

Reliance on two self-report measures to represent Anxious Misery limits the generalizability of the findings.

CONCLUSIONS

Central and peripheral symptoms of an Anxious Misery network may differ in degree of heritability.

Exploring the interrelatedness of risk factors for child maltreatment: A network approach

BACKGROUND

Theories on the etiology of child maltreatment generally focus on the interaction between multiple risk and protective factors. Moreover, the quadratic model of cumulative risk describes a threshold at which the risk of child maltreatment increases exponentially, suggesting a synergistic effect between risk factors.

OBJECTIVE

This study explored the interrelatedness of risk factors for child maltreatment.

PARTICIPANTS AND SETTING

The sample consisted of risk assessments performed for both high-risk families (n = 2,399; child protection services) and lower risk families (n = 1,904; community outreach services).

METHODS

Network analyses were performed on parental risk factors. Three networks were constructed: a cross-sample network, a high-risk network, and a lower risk network. The relations between risk factors were examined, as well as the centrality of each risk factor in these networks. Additionally, the networks of the two samples were compared.

RESULTS

The networks revealed that risk factors for child maltreatment were highly interrelated, which is consistent with Belsky's multi-dimensional perspective on child maltreatment. As expected, risk factors were generally stronger related to each other in the high-risk sample than in the lower risk sample. Centrality analyses showed that the following risk factors play an important role in the development of child maltreatment: "Caregiver was maltreated as a child", "History of domestic violence", and "Caregiver is emotionally absent".

CONCLUSIONS

We conclude that studying the interrelatedness of risk factors contributes to knowledge on the etiology of child maltreatment and the improvement of both risk assessment procedures and interventions for child maltreatment.

Network Analyses of Maternal Pre- and Post-Partum Symptoms of Depression and Anxiety

BACKGROUND

Maternal mental health problems often develop prenatally and predict post-partum mental health. However, the circumstances before and following childbirth differ considerably. We currently lack an understanding of dynamic variation in the profiles of depressive and anxiety symptoms over the perinatal period.

METHODS

Depressive and anxiety symptoms were self-reported by 980 women at 26-week pregnancy and 3 months post-partum. We used network analysis of depressive and anxiety symptoms to investigate if the symptoms network changed during and after pregnancy. The pre- and post-partum depressive-anxiety symptom networks were assessed for changes in structure, unique symptom-symptom interactions, central and bridging symptoms. We also assessed if central symptoms had stronger predictive effect on offspring's developmental outcomes outcomes at birth and 24, 54, and 72 months old than non-central symptoms. Bridging symptoms between negative and positive mental health were also assessed.

RESULTS

Though the depressive-anxiety network structures were stable during and after pregnancy, the post-partum network was more strongly connected. The central depressive-anxiety symptoms were also different between prenatal and post-partum networks. During pregnancy, central symptoms were mostly related to feeling worthless or useless; after pregnancy, central symptoms were mostly related to feeling overwhelmed or being punished. Central symptoms during pregnancy were associated with poorer developmental outcomes for the child. Anxiety symptoms were strongest bridging symptoms during and after pregnancy. The interactions between negative and positive mental health symptoms were also different during and after pregnancy.

CONCLUSIONS

The differences between pre- and post-partum networks suggest that the presentation of maternal mental health problems varies over the peripartum period. This variation is not captured by traditional symptom scale scores. The bridging symptoms also suggest that anxiety symptoms may precede the development of maternal depression. Interventions and public health policies should thus be tailored to specific pre- and post-partum symptom profiles.

A network model of affective odor perception

The affective appraisal of odors is known to depend on their intensity (I), familiarity (F), detection threshold (T), and on the baseline affective state of the observer. However, the exact nature of these relations is still largely unknown. We therefore performed an observer experiment in which participants (N = 52) smelled 40 different odors (varying widely in hedonic valence) and reported the intensity, familiarity and their affective appraisal (valence and arousal: V and A) for each odor. Also, we measured the baseline affective state (valence and arousal: BV and BA) and odor detection threshold of the participants. Analyzing the results for pleasant and unpleasant odors separately, we obtained two models through network analysis. Several relations that have previously been reported in the literature also emerge in both models (the relations between F and I, F and V, I and A; I and V, BV and T). However, there are also relations that do not emerge (between BA and V, BV and I, and T and I) or that appear with a different polarity (the relation between F and A for pleasant odors). Intensity (I) has the largest impact on the affective appraisal of unpleasant odors, while F significantly contributes to the appraisal of pleasant odors. T is only affected by BV and has no effect on other variables. This study is a first step towards an integral study of the affective appraisal of odors through network analysis. Future studies should also include other factors that are known to influence odor appraisal, such as age, gender, personality, and culture.

Network Structures of Symptoms From the Zung Depression Scale

The Self-rating Depression Scale (SDS) is a psychometric tool composed of 20 items used to assess depression symptoms. The aim of this work is to perform a network analysis of this scale in a large sample composed of 1090 French-speaking Belgian university students. We estimated a regularized partial correlation network and a Directed Acyclic Graph for the 20 items of the questionnaire. Node predictability (shared variance with surrounding nodes in the network) was used to assess the connectivity of items. The network comparison test was performed to compare networks from female and male students. The network composed of items from the SDS is overall positively connected, although node connectivity varies. Item 11 ("My mind is as clear as it used to be") is the most interconnected item. Networks from female and male students did not differ. DAG reported directed edges among items. Network analysis is a useful tool to explore depression symptoms and offers new insight as to how they interact. Further studies may endeavor to replicate our findings in different samples, including clinical samples to replicate the network structures and determine possible viable targets for clinical intervention.

The Centrality of Doubting and Checking in the Network Structure of Obsessive-Compulsive Symptom Dimensions in Youth

OBJECTIVE

Obsessive-compulsive disorder (OCD) is a heterogeneous condition with well-established symptom dimensions across the lifespan. The objective of the present study was to use network analysis to investigate the internal structure of these dimensions in unselected schoolchildren and in children with OCD.

METHOD

We estimated the network structure of OCD symptom dimensions in 6,991 schoolchildren and 704 children diagnosed with OCD from 18 sites across 6 countries. All participants completed the Obsessive-Compulsive Inventory-Child Version.

RESULTS

In both the school-based and clinic-based samples, the OCD dimensions formed an interconnected network with doubting/checking emerging as a highly central node, that is, having strong connections to other symptom dimensions in the network. The centrality of the doubting/checking dimension was consistent across countries, sexes, age groups, clinical status, and tic disorder comorbidity. Network differences were observed for age and sex in the school-based but not the clinic-based samples.

CONCLUSION

The centrality of doubting/checking in the network structure of childhood OCD adds to classic and recent conceptualizations of the disorder in which the important role of doubt in disorder severity and maintenance is highlighted. The present results suggest that doubting/checking is a potentially important target for further research into the etiology and treatment of childhood OCD.

Comparison of the touch-screen and traditional versions of the Corsi block-tapping test in patients with psychosis and healthy controls

BACKGROUND

Working memory (WM) refers to the capacity system for temporary storage and processing of information, which is known to depend on the integrity of the prefrontal cortex. Impairment in working memory is a core cognitive deficit among individuals with psychotic disorders. The Corsi block-tapping test is a widely-used instrument to assess visuospatial working memory. The traditional version is composed of 9 square blocks positioned on a physical board. In recent years, the number of digital instruments has increased significantly; several advantages might derive from the use of a digital version of the Corsi test.

METHODS

This study aimed to compare the digital and traditional versions of the Corsi test in 45 patients with psychotic disorders and 45 healthy controls. Both groups completed a neuropsychological assessment involving attention and working memory divided into the two conditions.

RESULTS

Results were consistent between the traditional and digital versions of the Corsi test. The digital version, as well as the traditional version, can discriminate between patients with psychosis and healthy controls. Overall, patients performed worse with respect to the healthy comparison group. The traditional Corsi test was positively related to intelligence and verbal working memory, probably due to a more significant effort to execute the test.

CONCLUSIONS

The digital Corsi might be used to enhance clinical practice diagnosis and treatment.The digital version can be administered in a natural environment in real-time. Further, it is easy to administer while ensuring a standard procedure.

An exploration of the DSM-5 posttraumatic stress disorder symptom latent variable network

BACKGROUND

Both the latent variable model and the network model have been widely used to conceptualize mental disorders. However, it has been pointed out that there is no clear dichotomy between the two models, and a combination of these two model could enable a better understanding of psychopathology. The recently proposed latent network model (LNM) has provided a statistical framework to enable this combination. Evidence has shown that posttraumatic stress disorder (PTSD) could be a suitable candidate disorder to study the combined model. In the current study, we initiated the first investigation of the latent network of PTSD symptoms.

METHODS

The latent network of DSM-5 PTSD symptoms was estimated in 1196 adult survivors of China's 2008 Wenchuan earthquake. Validation testing of the latent network was conducted in a replication sample of children and adolescent who experienced various trauma types. PTSD symptoms were measured by the PTSD Checklist for DSM-5 (PCL-5). The latent network was estimated using the seven-factor hybrid model of DSM-5 PTSD symptoms, analysed using the R package lvnet.

RESULTS

The latent network model demonstrated good fit in both samples. A strong weighted edge between the intrusion and avoidance dimensions was identified (regularized partial correlation = 0.75). The externalizing behaviour dimension demonstrated the highest centrality in the latent network.

CONCLUSIONS

This study is the first to investigate the latent network of DSM-5 PTSD symptoms. Results suggest that both latent symptom dimension and associations between the dimensions should be considered in future PTSD studies and clinical practices.

Back to the basics: Rethinking partial correlation network methodology

The Gaussian graphical model (GGM) is an increasingly popular technique used in psychology to characterize relationships among observed variables. These relationships are represented as elements in the precision matrix. Standardizing the precision matrix and reversing the sign yields corresponding partial correlations that imply pairwise dependencies in which the effects of all other variables have been controlled for. The graphical lasso (glasso) has emerged as the default estimation method, which uses ℓ1 -based regularization. The glasso was developed and optimized for high-dimensional settings where the number of variables (p) exceeds the number of observations (n), which is uncommon in psychological applications. Here we propose to go 'back to the basics', wherein the precision matrix is first estimated with non-regularized maximum likelihood and then Fisher Z transformed confidence intervals are used to determine non-zero relationships. We first show the exact correspondence between the confidence level and specificity, which is due to 1 minus specificity denoting the false positive rate (i.e., α). With simulations in low-dimensional settings (p ≪ n), we then demonstrate superior performance compared to the glasso for detecting the non-zero effects. Further, our results indicate that the glasso is inconsistent for the purpose of model selection and does not control the false discovery rate, whereas the proposed method converges on the true model and directly controls error rates. We end by discussing implications for estimating GGMs in psychology.

Embracing data preservation, sharing, and re-use in traumatic stress research

This editorial argues that it is time for the traumatic stress field to join the growing international movement towards Findable, Accessible, Interoperable, and Re-usable (FAIR) research data, and that we are well-positioned to do so. The field has a huge, largely untapped resource in the enormous number of rich potentially re-usable datasets that are not currently shared or preserved. We have several promising shared data resources created via international collaborative efforts by traumatic stress researchers, but we do not yet have common standards for data description, sharing, or preservation. And, despite the promise of novel findings from data sharing and re-use, there are a number of barriers to researchers' adoption of FAIR data practices. We present a vision for the future of FAIR traumatic stress data, and a call to action for the traumatic stress research community and individual researchers and research teams to help achieve this vision.

Extending our understanding of the association between posttraumatic stress disorder and positive emotion dysregulation: A network analysis approach

Posttraumatic stress disorder (PTSD) has empirically-established associations with positive emotion dysregulation. Extending existing research, we utilized a network approach to examine relations between PTSD symptom clusters (intrusions, avoidance, negative alterations in cognitions and mood [NACM], alterations in arousal and reactivity [AAR]) and positive emotion dysregulation dimensions (nonacceptance, impulse control, goal-directed behavior). We identified (1) differential relations of PTSD symptom clusters with positive emotion dysregulation, and (2) central symptoms accounting for the PTSD and positive emotion dysregulation inter-group interconnections. Participants were 371 trauma-exposed community individuals (M_age = 43.68; 70.9 % females; 34.5 % white). We estimated a regularized Gaussian Graphic Model comprising four nodes representing the PTSD symptom clusters and three nodes representing positive emotion dysregulation dimensions. Study results indicated the key role of AAR and intrusions clusters in the PTSD group and impulse control difficulties in the positive emotion dysregulation group. Regarding cross-group connectivity patterns, findings indicate the pivotal role of (1) AAR in its link with positive emotion dysregulation dimensions, and (2) nonacceptance of positive emotions and impairment in goal-directed behavior in the context of positive emotions in their link to PTSD symptom clusters. Thus, the current study indicates the potentially central role of particular PTSD symptom clusters and positive emotion dysregulation dimensions, informing assessment and treatment targets.

Exploring network structure and central items of the Narcissistic Personality Inventory

OBJECTIVES

The aim of this work is to explore the Narcissistic Personality Inventory (NPI) using network analysis in a dataset of 942 university students from the French-speaking part of Belgium.

METHODS

We estimated an Ising Model for the forty items in the questionnaire and explored item interconnectedness with strength centrality. We provide in the supplementary materials the dataset used for the analyses as well as the full code to ensure the reproducibility of our results.

RESULTS

The NPI is presented as an overall positively connected network with items from entitlement, authority and superiority reporting the highest centrality estimates.

CONCLUSIONS

Network analysis highlights new properties of items from the NPI. Future studies should endeavor to replicate our findings in other samples, both clinical and non-clinical.

Mothers' emotions after pediatric burn injury: Longitudinal associations with posttraumatic stress and depressive symptoms 18 months postburn

BACKGROUND

Various emotions are implicated in posttraumatic stress disorder (PTSD). Longitudinal studies examining temporal associations between emotions and posttraumatic stress may reveal who is at risk of chronic psychological problems. This study examined the longitudinal relationships of mothers' trauma-related emotions with posttraumatic stress and depressive symptoms after pediatric burn injury.

METHODS

Data from two cohort studies were used (n = 296). Mothers reported the intensity of burn-related emotions within the first month (T1) and 12 months postburn (T2). The Impact of Event Scale (IES) and the Hospital and Anxiety Depression Scale (HADS-D; depression subscale) were administered at T1 and 18 months postburn (T3).

RESULTS

Based on two exploratory factor analyses, emotion variables were combined into acute and long-term basic emotions (fear, sadness, horror, anger) and self-conscious emotions (guilt, shame). The path model showed a positive relationship between acute and long-term basic emotions. Higher long-term basic emotions were related to persistence of posttraumatic stress and depressive symptoms. Acute self-conscious emotions showed associations with posttraumatic stress and depressive symptoms at T1 and were longitudinally related to depressive, but not posttraumatic stress, symptoms.

LIMITATIONS

The posttraumatic stress measure was not based on DSM-5 PTSD criteria and results require replication using these criteria.

CONCLUSIONS

This study suggests that mothers' acute self-conscious and long-term basic emotions in relation to their child's burn injury are involved in the development of posttraumatic stress and depressive symptoms. Clinically, assessing and monitoring parents' early posttraumatic stress, depressive symptoms and burn-related emotions may be useful to identify parents at risk.

The use of machine learning techniques in trauma-related disorders: a systematic review

Establishing the diagnosis of trauma-related disorders such as Acute Stress Disorder (ASD) and Posttraumatic Stress Disorder (PTSD) have always been a challenge in clinical practice and in academic research, due to clinical and biological heterogeneity. Machine learning (ML) techniques can be applied to improve classification of disorders, to predict outcomes or to determine person-specific treatment selection. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with ASD or PTSD. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to May 2019. We found 806 abstracts and included 49 studies in our review. Most of the included studies used multiple levels of biological data to predict risk factors or to identify early symptoms related to PTSD. Other studies used ML classification techniques to distinguish individuals with ASD or PTSD from other psychiatric disorder or from trauma-exposed and healthy controls. We also found studies that attempted to define outcome profiles using clustering techniques and studies that assessed the relationship among symptoms using network analysis. Finally, we proposed a quality assessment in this review, evaluating methodological and technical features on machine learning studies. We concluded that etiologic and clinical heterogeneity of ASD/PTSD patients is suitable to machine learning techniques and a major challenge for the future is to use it in clinical practice for the benefit of patients in an individual level.

Commentary on the Special Issue on Network Analysis: Assessment, Intervention, Theory, and the Nature of Reality: Actualizing the Potential of Network Perspectives on Posttraumatic Stress Disorder

This commentary on the Journal of Traumatic Stress special issue on network analysis explores the network perspective on posttraumatic stress disorder (PTSD), emphasizing the advances in research made in this collection of articles. The commentary is organized around the following themes related to actualizing the perspective's methodological, assessment, and intervention potential and the potential shift in the theoretical underpinnings of mental disorders that networks models imply. First, extant data using network analysis suggest that reactions to traumatic stress are more complicated than once thought but that this complexity does not mean efficient, relatively simple heuristics to aid assessment and intervention do not exist. Attention to methodological issues in symptom assessment may help move this aspect of the research forward. Second, the extant research is largely correlational and has not yet established causal linkages, although temporal associations underlying network models are being identified. Prospective and intervention studies employing network analysis are critical. Third, the network perspective of PTSD symptoms may advance research on the mechanisms of risk and resilience (e.g., neurodevelopmental, cognitive behavioral, emotional, and social models) by helping link symptoms to theoretical causal processes. A developmental framework that views the effect of traumatic stress in terms of temporal cascades of reactions with both negative and potentially positive cognitive, behavioral, social, and emotional outcomes fits the network analysis model. Fourth, network models call into question some of the fundamental assumptions underlying the conceptualization of mental disorders, leaving several ontological questions and implications currently unanswered; research examining the implications of the new assumptions is needed.

Posttraumatic Stress Disorder Symptom Network Structures: A Comparison Between Men and Women

This study estimated gender differences in the posttraumatic stress disorder (PTSD) symptom network structure (i.e., the unique associations across symptoms) using network analysis in a Latin American sample. Participants were 1,104 adults, taken from epidemiological studies of mental health following natural disasters and accidents in Mexico and Ecuador. Symptoms of DSM-IV PTSD were measured dichotomously with the Spanish version of the Composite International Diagnostic Interview. We estimated the PTSD symptom network of the full sample and in male and female subsamples as well as indices of centrality, the stability and accuracy of the modeled networks, and communities of nodes within each network. The male and female networks were compared statistically using the Network Comparison Test (NCT). Results indicated strength centrality was the only stable centrality measure, with correlation stability (CS) coefficients of .59, .28, and .44 for the full, male, and female networks, respectively. We found the most central symptoms, measured by strength centrality, were loss of interest and flashbacks for men; and concentration impairment, avoiding thoughts/feelings, and physiological reactivity for women. The NCT revealed that the global structure (M = 0.84), p = .704, and global strength (S = 5.04), p = .556, of the male and female networks did not differ significantly. Although some gender differences in the most central symptoms emerged, thus offering some evidence for gender differences pending replication in larger samples, on the whole, our results suggest that once PTSD develops, the way the symptoms are associated does not differ substantially between men and women.

Examining the boundaries between ICD-11 PTSD/CPTSD and depression and anxiety symptoms: A network analysis perspective

BACKGROUND

Two newly identified sibling disorders - ICD-11 PTSD and CPTSD - have been well validated in the last few years. Although these trauma-related disorders are suggested to be neatly separated from depression and anxiety, no study has used a network analysis to examine those definitions' construct validity when they also interplay with symptoms of depression and anxiety. Additionally, no research has focused upon the specific boundaries between these four disorders' symptoms, the bridges between them, and the ways they influence each other among clinical populations.

METHODS

A sample of 234 men drawn randomly from a national sample of 1,600 Jewish men receiving treatment for domestic violence in Israel completed the ICD-11 International Trauma Questionnaire (ITQ) and Brief Symptom Inventory (BSI).

RESULTS

The ICD-11 CPTSD, depression and anxiety clustering network results revealed, within the EGA, a four-cluster solution in which PTSD and CPTSD symptoms are differentiated from two other distinct clusters of anxiety and depression symptoms. Feelings of worthlessness and avoiding internal reminders of the experience were the most central symptoms.

LIMITATIONS

Due to the use of a cross-sectional design, causal interpretation of the network correlation between symptoms should be made cautiously.

CONCLUSIONS

These findings strengthen the approach that ICD-11 PTSD and CPTSD have a distinct construct; however, they also reflect a strong positive connection to anxiety and depression symptoms and no clear boundaries between disorders. Specifically, dysphoria/avoidance-related symptoms act as a bridge between the disorders, which may be important targets for specific assessments and related interventions.

The network approach to psychopathology: a review of the literature 2008-2018 and an agenda for future research

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.

A Cross-Cultural Comparison of ICD-11 Complex Posttraumatic Stress Disorder Symptom Networks in Austria, the United Kingdom, and Lithuania

The 11th revision of the World Health Organization's International Classification of Diseases (ICD-11) includes a new disorder, complex posttraumatic stress disorder (CPTSD). The network approach to psychopathology enables investigation of the structure of disorders at the symptom level, which allows for analysis of direct symptom interactions. The network structure of ICD-11 CPTSD has not yet been studied, and it remains unclear whether similar networks replicate across different samples. We investigated the network models of four different trauma samples that included a total of 879 participants (M age = 47.17 years, SD = 11.92; 59.04% women) drawn from Austria, Lithuania, and Scotland and Wales in the United Kingdom. The International Trauma Questionnaire was used to assess symptoms of ICD-11 CPTSD in all samples. The prevalence of PTSD and CPTSD ranged from 23.7% to 37.3% and from 9.3% to 53.1%, respectively. Regularized partial correlation networks were estimated and the resulting networks compared. Despite several differences in the symptom presentation and cultural background, the networks across the four samples were considerably similar, with high correlations between symptom profiles (ρs = .48-.87), network structures (ρs = .69-.75), and centrality estimates (ρs = .59-.82). These results support the replicability of CPTSD network models across different samples and provide further evidence about the robust structure of CPTSD. The most central symptom in all four sample-specific networks and the overall network was "feelings of worthlessness." Implications of the network approach in research and practice are discussed.

Dynamic Network Analysis of Negative Emotions and DSM-5 Posttraumatic Stress Disorder Symptom Clusters During Conflict

Investigating dynamic associations between specific negative emotions and PTSD symptom clusters may provide novel insights into the ways in which PTSD symptoms interact with, emerge from, or are reinforced by negative emotions. The present study estimated the associations among negative emotions and the four DSM-5 PTSD symptom clusters (intrusions, avoidance, negative alterations in cognitions and mood [NACM], and arousal) in a sample of Israeli civilians (n = 96) during the Israel-Gaza War of July-August 2014. Data were collected using experience sampling methodology, with participants queried via smartphone about PTSD symptoms and negative emotions twice a day for 30 days. We used a multilevel vector auto-regression model to estimate temporal and contemporaneous temporal networks. Contrary to our hypothesis, in the temporal network, PTSD symptom clusters were more predictive of negative emotions than vice versa, with arousal emerging as the strongest predictor that negative emotions would be reported at the next measurement point; fear and sadness were also strong predictors of PTSD symptom clusters. In the contemporaneous network, negative emotions exhibited the strongest associations with the NACM and arousal PTSD symptom clusters. The negative emotions of sadness, stress, fear, and loneliness had the strongest associations to the PTSD symptom clusters. Our findings suggest that arousal has strong associations to both PTSD symptoms and negative emotions during ongoing trauma and highlights the potentially relevant role of arousal for future investigations in primary or early interventions.