Evidence that psychopathology symptom networks have limited replicability

Problems with Centrality Measures in Psychopathology Symptom Networks: Why Network Psychometrics Cannot Escape Psychometric Theory

Understanding patterns of symptom co-occurrence is one of the most difficult challenges in psychopathology research. Do symptoms co-occur because of a latent factor, or might they directly and causally influence one another? Motivated by such questions, there has been a surge of interest in network analyses that emphasize the putatively direct role symptoms play in influencing each other. In this critical paper, we highlight conceptual and statistical problems with using centrality measures in cross-sectional networks. In particular, common network analyses assume that there are no unmodeled latent variables that confound symptom co-occurrence. The traditions of clinical taxonomy and test development in psychometric theory, however, greatly increase the possibility that latent variables exist in symptom data. In simulations that include latent variables, we demonstrate that closeness and betweenness are vulnerable to spurious covariance among symptoms that connect subgraphs (e.g., diagnoses). We further show that strength is redundant with factor loading in several cases. Finally, if a symptom reflects multiple latent causes, centrality metrics reflect a weighted combination, undermining their interpretability in empirical data. Our results suggest that it is essential for network psychometric approaches to examine the evidence for latent variables prior to analyzing or interpreting patterns at the symptom level. Failing to do so risks identifying spurious relationships or failing to detect causally important effects. Altogether, we argue that centrality measures do not provide solid ground for understanding the structure of psychopathology when latent confounding exists.

Investigating the Utility of Fixed-margin Sampling in Network Psychometrics

Steinley, Hoffman, Brusco, and Sher (2017) proposed a new method for evaluating the performance of psychological network models: fixed-margin sampling. The authors investigated LASSO regularized Ising models (eLasso) by generating random datasets with the same margins as the original binary dataset, and concluded that many estimated eLasso parameters are not distinguishable from those that would be expected if the data were generated by chance. We argue that fixed-margin sampling cannot be used for this purpose, as it generates data under a particular null-hypothesis: a unidimensional factor model with interchangeable indicators (i.e., the Rasch model). We show this by discussing relevant psychometric literature and by performing simulation studies. Results indicate that while eLasso correctly estimated network models and estimated almost no edges due to chance, fixed-margin sampling performed poorly in classifying true effects as "interesting" (Steinley et al. 2017, p. 1004). Further simulation studies indicate that fixed-margin sampling offers a powerful method for highlighting local misfit from the Rasch model, but performs only moderately in identifying global departures from the Rasch model. We conclude that fixed-margin sampling is not up to the task of assessing if results from estimated Ising models or other multivariate psychometric models are due to chance.

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.

Metacognition and emotion regulation as treatment targets in binge eating disorder: a network analysis study

BACKGROUND

This study aims to examine the underlying associations between eating, affective and metacognitive symptoms in patients with binge eating disorder (BED) through network analysis (NA) in order to identify key variables that may be considered the target for psychotherapeutic interventions.

METHODS

A total of 155 patients with BED completed measures of eating psychopathology, affective symptoms, emotion regulation and metacognition. A cross-sectional network was inferred by means of Gaussian Markov random field estimation using graphical LASSO and the extended Bayesian information criterion (EBIC-LASSO), and central symptoms of BED were identified by means of the strength centrality index.

RESULTS

Impaired self-monitoring metacognition and difficulties in impulse control emerged as the symptoms with the highest centrality. Conversely, eating and affective features were less central. The centrality stability coefficient of strength was above the recommended cut-off, thus indicating the stability of the network.

CONCLUSIONS

According to the present NA findings, impaired self-monitoring metacognition and difficulties in impulse control are the central nodes in the psychopathological network of BED whereas eating symptoms appear marginal. If further studies with larger samples replicate these results, metacognition and impulse control could represent new targets of psychotherapeutic interventions in the treatment of BED. In light of this, metacognitive interpersonal therapy could be a promising aid in clinical practice to develop an effective treatment for BED.

Evaluating the Viability of Neurocognition as a Transdiagnostic Construct Using Both Latent Variable Models and Network Analysis

The relational structure of psychological symptoms and disorders is of crucial importance to mechanistic and causal research. Methodologically, factor analytic approaches (latent variable modeling) and network analyses are two dominant approaches. Amidst some debate about their relative merits, use of both methods simultaneously in the same data set has rarely been reported in child or adolescent psychopathology. A second issue is that the nosological structure can be enriched by inclusion of transdiagnostic constructs, such as neurocognition (e.g., executive functions and other processes). These cut across traditional diagnostic boundaries and are rarely included even though they can help map the mechanistic architecture of psychopathology. Using a sample enriched for ADHD (n = 498 youth ages 6 to 17 years; M = 10.8 years, SD = 2.3 years, 55% male), both approaches were used in two ways: (a) to model symptom structure and (b) to model seven neurocognitive domains hypothesized as important transdiagnostic features in ADHD and associated disorders. The structure of psychopathology domains was similar across statistical approaches with internalizing, externalizing, and neurocognitive performance clusters. Neurocognition remained a distinct domain according to both methods, showing small to moderate associations with internalizing and externalizing domains in latent variable models and high connectivity in network analyses. Overall, the latent variable and network approaches yielded more convergent than discriminant findings, suggesting that both may be complementary tools for evaluating the utility of transdiagnostic constructs for psychopathology research.

Deterministic blockmodelling of signed and two-mode networks: A tutorial with software and psychological examples

Deterministic blockmodelling is a well-established clustering method for both exploratory and confirmatory social network analysis seeking partitions of a set of actors so that actors within each cluster are similar with respect to their patterns of ties to other actors (or, in some cases, other objects when considering two-mode networks). Even though some of the historical foundations for certain types of blockmodelling stem from the psychological literature, applications of deterministic blockmodelling in psychological research are relatively rare. This scarcity is potentially attributable to three factors: a general unfamiliarity with relevant blockmodelling methods and applications; a lack of awareness of the value of partitioning network data for understanding group structures and processes; and the unavailability of such methods on software platforms familiar to most psychological researchers. To tackle the first two items, we provide a tutorial presenting a general framework for blockmodelling and describe two of the most important types of deterministic blockmodelling applications relevant to psychological research: structural balance partitioning and two-mode partitioning based on structural equivalence. To address the third problem, we developed a suite of software programs that are available as both Fortran executable files and compiled Fortran dynamic-link libraries that can be implemented in the R software system. We demonstrate these software programs using networks from the literature.

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.

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.

The Architecture of Cognitive Vulnerability to Depressive Symptoms in Adolescence: A Longitudinal Network Analysis Study

Rates of subclinical symptoms and full-blown depression significantly increase during adolescence. Hence, understanding how multiple cognitive risk factors are related to depression in adolescence is of major importance. For this purpose, we simultaneously considered multiple cognitive vulnerabilities, as proposed by three major cognitive theories for depression, namely Beck’s cognitive theory, hopelessness theory, and response style theory. In this four-wave study, we investigated the architecture, interplay, and stability of cognitive vulnerability mechanisms, depressive symptoms, and stressors in a large group of adolescents over a period of one year (n = 469; mean age = 15 years; 64% female). Network analysis was used to shed light on the structure of cognitive vulnerabilities in a data-driven fashion. Analyses revealed that different cognitive vulnerabilities were intertwined and automatic thoughts played the role of hub node in the network. Moreover, the interplay among cognitive vulnerabilities and depressive symptoms was already markedly stable in adolescence and did not change over a 12-month period. Finally, no evidence was found that cognitive vulnerabilities interacted with stressors, as proposed by diathesis-stress models. These findings advance our understanding of multiple cognitive risk factors for depression in adolescence.

Applying network analysis to investigate the links between dimensional schizotypy and cognitive and affective empathy

BACKGROUND

Although impairment in empathy has been reported in schizophrenia spectrum disorders, little is known about the relationship between empathy and schizotypal traits. This study examines this relationship by applying network analysis to a large sample collected at 18-months follow-up in a longitudinal dataset.

METHODS

One thousand four hundred and eighty-six college students were recruited and completed a set of self-reported questionnaires on empathy, schizotypy, depression, anxiety and stress. Networks were constructed by taking the subscale scores of these measures as nodes and partial correlations between each pair of nodes as edges. Network Comparison Tests were performed to investigate the differences between individuals with high and low schizotypy.

RESULTS

Cognitive and affective empathy were strongly connected with negative schizotypy in the network. Physical and social anhedonia showed high centrality measured by strength, closeness and betweenness while anxiety and stress showed high expected influence. Predictability ranged from 22.4% (personal distress) to 79.9% (anxiety) with an average of 54.4%. Compared with the low schizotypy group, the high schizotypy group showed higher global strength (S = 0.813, p < 0.05) and significant differences in network structure (M = 0.531, p < 0.001) and strength of edges connecting empathy with schizotypy (adjusted ps < 0.05).

LIMITATIONS

Only self-rating scales were used, and disorganized schizotypy was not included.

CONCLUSIONS

Our findings suggest that the cognitive and affective components of empathy and dimensions of schizotypy are closely related in the general population and their network interactions may play an important role in individuals with high schizotypy.

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.

Comorbidity Between Non-suicidal Self-Injury Disorder and Borderline Personality Disorder in Adolescents: A Graphical Network Approach

In 2013, DSM-5 urged for further research on non-suicidal self-injury (NSSI) and defined NSSI disorder (NSSI-D) for the first time separate from borderline personality disorder (BPD). However, research on the comorbidity between NSSI-D and BPD symptoms is still scarce, especially in adolescent populations. The current study selected 347 adolescents who engaged at least once in NSSI (78.4% girls, M age = 15.05) and investigated prevalence, comorbidity, gender differences, and bridge symptoms of NSSI-D and BPD. Network analysis allowed us to visualize the comorbidity structure of NSSI-D and BPD on a symptom-level and revealed which bridge symptoms connected both disorders. Our results supported NSSI-D as significantly distinct from, yet closely related to, BPD in adolescents. Even though girls were more likely to meet the NSSI-D criteria, our findings suggested that the manner in which NSSI-D and BPD symptoms were interconnected, did not differ between girls and boys. Furthermore, loneliness, impulsivity, separation anxiety, frequent thinking about NSSI, and negative affect prior to NSSI were detected as prominent bridge symptoms between NSSI-D and BPD. These bridge symptoms could provide useful targets for early intervention in and prevention of the development of comorbidity between NSSI-D and BPD. Although the current study was limited by a small male sample, these findings do provide novel insights in the complex comorbidity between NSSI-D and BPD symptoms in adolescence.

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.

Deriving symptom networks from digital phenotyping data in serious mental illness

BACKGROUND

Symptoms of serious mental illness are multidimensional and often interact in complex ways. Generative models offer value in elucidating the underlying relationships that characterise these networks of symptoms.

AIMS

In this paper we use generative models to find unique interactions of schizophrenia symptoms as experienced on a moment-by-moment basis.

METHOD

Self-reported mood, anxiety and psychosis symptoms, self-reported measurements of sleep quality and social function, cognitive assessment, and smartphone touch screen data from two assessments modelled after the Trail Making A and B tests were collected with a digital phenotyping app for 47 patients in active treatment for schizophrenia over a 90-day period. Patients were retrospectively divided up into various non-exclusive subgroups based on measurements of depression, anxiety, sleep duration, cognition and psychosis symptoms taken in the clinic. Associated transition probabilities for the patient cohort and for the clinical subgroups were calculated using state transitions between adjacent 3-day timesteps of pairwise survey domains.

RESULTS

The three highest probabilities for associated transitions across all patients were anxiety-inducing mood (0.357, P < 0.001), psychosis-inducing mood (0.276, P < 0.001), and anxiety-inducing poor sleep (0.268, P < 0.001). These transition probabilities were compared against a validation set of 17 patients from a pilot study, and no significant differences were found. Unique symptom networks were found for clinical subgroups.

CONCLUSIONS

Using a generative model using digital phenotyping data, we show that certain symptoms of schizophrenia may play a role in elevating other schizophrenia symptoms in future timesteps. Symptom networks show that it is feasible to create clinically interpretable models that reflect the unique symptom interactions of psychosis-spectrum illness. These results offer a framework for researchers capturing temporal dynamics, for clinicians seeking to move towards preventative care, and for patients to better understand their lived experience.

Clusters of Trauma Types as Measured by the Life Events Checklist for DSM-5

Experiences of potentially traumatic events (PTE), commonly assessed with the Life Events Checklist for DSM-5 (LEC-5), can be both varied in pattern and type. An understanding of LEC-assessed PTE type clusters and their relation to psychopathology can enhance research feasibility (e.g., address low base rates for certain PTE types), research communication/comparisons via the use of common terminology, and nuanced trauma assessments/treatments. To this point, the current study examined (1) clusters of PTE types assessed by the LEC-5; and (2) differential relations of these PTE type clusters to mental health correlates (i.e., posttraumatic stress disorder [PTSD] severity, depression severity, emotion dysregulation, reckless and self-destructive behaviors [RSDBs]). A trauma-exposed community sample of 408 participants was recruited via Amazon's Mechanical Turk (M age = 35.90 years; 56.50% female). Network analyses indicated three PTE type clusters: Accidental/Injury Traumas (LEC-5 items 1, 2, 3, 4, 12), Victimization Traumas (LEC-5 items 6, 8, 9), and Predominant Death Threat Traumas (LEC-5 items 5, 7, 10, 11, 13-16). Multiple regression analyses indicated that the Victimization Trauma Cluster significantly predicted PTSD severity (β = .23, p <.001), depression severity (β = .20, p =.001), and negative emotion dysregulation (β = .22, p <.001); and the Predominant Death Threat Trauma Cluster significantly predicted engagement in RSDBs (β = 31, p <.001) and positive emotion dysregulation (β = .26, p <.001), accounting for the influence of other PTE Clusters. Results support three PTE type classifications assessed by the LEC-5, with important clinical and research implications.

How to Compare Psychometric Factor and Network Models

In memory of Dr. Dennis John McFarland, who passed away recently, our objective is to continue his efforts to compare psychometric networks and latent variable models statistically. We do so by providing a commentary on his latest work, which he encouraged us to write, shortly before his death. We first discuss the statistical procedure McFarland used, which involved structural equation modeling (SEM) in standard SEM software. Next, we evaluate the penta-factor model of intelligence. We conclude that (1) standard SEM software is not suitable for the comparison of psychometric networks with latent variable models, and (2) the penta-factor model of intelligence is only of limited value, as it is nonidentified. We conclude with a reanalysis of the Wechlser Adult Intelligence Scale data McFarland discussed and illustrate how network and latent variable models can be compared using the recently developed R package Psychonetrics. Of substantive theoretical interest, the results support a network interpretation of general intelligence. A novel empirical finding is that networks of intelligence replicate over standardization samples.

Personalized models of personality disorders: using a temporal network method to understand symptomatology and daily functioning in a clinical sample

BACKGROUND

An ongoing challenge in understanding and treating personality disorders (PDs) is a significant heterogeneity in disorder expression, stemming from variability in underlying dynamic processes. These processes are commonly discussed in clinical settings, but are rarely empirically studied due to their personalized, temporal nature. The goal of the current study was to combine intensive longitudinal data collection with person-specific temporal network models to produce individualized symptom-level structures of personality pathology. These structures were then linked to traditional PD diagnoses and stress (to index daily functioning).

METHODS

Using about 100 daily assessments of internalizing and externalizing domains underlying PDs (i.e. negative affect, detachment, impulsivity, hostility), a temporal network mapping approach (i.e. group iterative multiple model estimation) was used to create person-specific networks of the temporal relations among domains for 91 individuals (62.6% female) with a PD. Network characteristics were then associated with traditional PD symptomatology (controlling for mean domain levels) and with daily variation in clinically-relevant phenomena (i.e. stress).

RESULTS

Features of the person-specific networks predicted paranoid, borderline, narcissistic, and obsessive-PD symptom counts above average levels of the domains, in ways that align with clinical conceptualizations. They also predicted between-person variation in stress across days.

CONCLUSIONS

Relations among behavioral domains thought to underlie heterogeneity in PDs were indeed associated with traditional diagnostic constructs and with daily functioning (i.e. stress) in person-specific networks. Findings highlight the importance of leveraging data and models that capture person-specific, dynamic processes, and suggest that person-specific networks may have implications for precision medicine.

Network structure reveals clusters of associations between childhood adversities and development outcomes

Exposure to childhood adversity is common and associated with a host of negative developmental outcomes. The most common approach used to examine the consequences of adversity exposure is a cumulative risk model. Recently, we have proposed a novel approach, the dimensional model of adversity and psychopathology (DMAP), where different dimensions of adversity are hypothesized to impact health and well-being through different pathways. We expect deprivation to primarily disrupt cognitive processing, whereas we expect threat to primarily alter emotional reactivity and automatic regulation. Recent hypothesis-driven approaches provide support for these differential associations of deprivation and threat on developmental outcomes. However, it is not clear whether these patterns would emerge using data-driven approaches. Here we use a network analytic approach to identify clusters of related adversity exposures and outcomes in an initial study (Study 1: N = 277 adolescents aged 16-17 years; 55.1% female) and a replication (Study 2: N = 262 children aged 8-16 years; 45.4% female). We statistically compare our observed clusters with our hypothesized DMAP model and a clustering we hypothesize would be the result of a cumulative stress model. In both samples we observed a network structure consistent with the DMAP model and statistically different than the hypothesized cumulative stress model. Future work seeking to identify in the pathways through which adversity impacts development should consider multiple dimensions of adversity.

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.

Inter-relationships among psychopathology, premorbid adjustment, cognition and psychosocial functioning in first-episode psychosis: a network analysis approach

BACKGROUND

Better understanding of interplay among symptoms, cognition and functioning in first-episode psychosis (FEP) is crucial to promoting functional recovery. Network analysis is a promising data-driven approach to elucidating complex interactions among psychopathological variables in psychosis, but has not been applied in FEP.

METHOD

This study employed network analysis to examine inter-relationships among a wide array of variables encompassing psychopathology, premorbid and onset characteristics, cognition, subjective quality-of-life and psychosocial functioning in 323 adult FEP patients in Hong Kong. Graphical Least Absolute Shrinkage and Selection Operator (LASSO) combined with extended Bayesian information criterion (BIC) model selection was used for network construction. Importance of individual nodes in a generated network was quantified by centrality analyses.

RESULTS

Our results showed that amotivation played the most central role and had the strongest associations with other variables in the network, as indexed by node strength. Amotivation and diminished expression displayed differential relationships with other nodes, supporting the validity of two-factor negative symptom structure. Psychosocial functioning was most strongly connected with amotivation and was weakly linked to several other variables. Within cognitive domain, digit span demonstrated the highest centrality and was connected with most of the other cognitive variables. Exploratory analysis revealed no significant gender differences in network structure and global strength.

CONCLUSION

Our results suggest the pivotal role of amotivation in psychopathology network of FEP and indicate its critical association with psychosocial functioning. Further research is required to verify the clinical significance of diminished motivation on functional outcome in the early course of psychotic illness.

Steps Towards a Unified Theory of Psychopathology: The Phase Space of Meaning Model

The hypothesis of a general psychopathology factor (p factor) has been advanced in recent years. It is an innovation with breakthrough potential, in the perspective of a unified view of psychopathology; however, what remains a controversial topic is how its nature might be conceptualized. The current paper outlines a semiotic, embodied and psychoanalytic conceptualization of psychopathology - the Phase Space of Meaning (PSM) model - aimed at providing ontological grounds to the p factor hypothesis. Framed within a more general model of how the mind works, the PSM model maintains that the p factor can be conceived as the empirical marker of the degree of rigidity of the meaning-maker's way of interpreting experience, namely of the dimensions of meanings used to map the environment's variability. As to the clinical implications, two main aspects are outlined. First, according PSM model, psychopathology is not an invariant condition, and does not have a set dimensionality, but is able to vary it locally, in order to address the requirement of situated action. Second, psychopathology is conceived as one of the mind's modes of working, rather than the manifestation of its disruption. Finally, the puzzling issue of the interplay between stability and variability in the evolutionary trajectories of patients along with their life events is addressed and discussed.

A facet atlas: Visualizing networks that describe the blends, cores, and peripheries of personality structure

We created a facet atlas that maps the interrelations between facet scales from 13 hierarchical personality inventories to provide a practically useful, transtheoretical description of lower-level personality traits. We generated this atlas by estimating a series of network models that visualize the correlations among 268 facet scales administered to the Eugene-Springfield Community Sample (Ns = 571-948). As expected, most facets contained a blend of content from multiple Big Five domains and were part of multiple Big Five networks. We identified core and peripheral facets for each Big Five domain. Results from this study resolve some inconsistencies in facet placement across instruments and highlight the complexity of personality structure relative to the constraints of traditional hierarchical models that impose simple structure. This facet atlas (also available as an online point-and-click app at tedschwaba.shinyapps.io/appdata/) provides a guide for researchers who wish to measure a domain with a limited set of facets as well as information about the core and periphery of each personality domain. To illustrate the value of a facet atlas in applied and theoretical settings, we examined the network structure of scales measuring impulsivity and tested structural hypotheses from the Big Five Aspect Scales inventory.

Main Symptomatic Treatment Targets in Suspected and Early Psychosis: New Insights From Network Analysis

The early detection and intervention in psychoses prior to their first episode are presently based on the symptomatic ultra-high-risk and the basic symptom criteria. Current models of symptom development assume that basic symptoms develop first, followed by attenuated and, finally, frank psychotic symptoms, though interrelations of these symptoms are yet unknown. Therefore, we studied for the first time their interrelations using a network approach in 460 patients of an early detection service (mean age = 26.3 y, SD = 6.4; 65% male; n = 203 clinical high-risk [CHR], n = 153 first-episode psychosis, and n = 104 depression). Basic, attenuated, and frank psychotic symptoms were assessed using the Schizophrenia Proneness Instrument, Adult version (SPI-A), the Structured Interview for Psychosis-Risk Syndromes (SIPS), and the Positive And Negative Syndrome Scale (PANSS). Using the R package qgraph, network analysis of the altogether 86 symptoms revealed a single dense network of highly interrelated symptoms with 5 discernible symptom subgroups. Disorganized communication was the most central symptom, followed by delusions and hallucinations. In line with current models of symptom development, the network was distinguished by symptom severity running from SPI-A via SIPS to PANSS assessments. This suggests that positive symptoms developed from cognitive and perceptual disturbances included basic symptom criteria. Possibly conveying important insight for clinical practice, central symptoms, and symptoms "bridging" the association between symptom subgroups may be regarded as the main treatment targets, in order to prevent symptomatology from spreading or increasing across the whole network.

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.

Can network analysis shed light on predictors of lithium response in bipolar I disorder?

OBJECTIVE

To undertake a large-scale clinical study of predictors of lithium (Li) response in bipolar I disorder (BD-I) and apply contemporary multivariate approaches to account for inter-relationships between putative predictors.

METHODS

We used network analysis to estimate the number and strength of connections between potential predictors of good Li response (measured by a new scoring algorithm for the Retrospective Assessment of Response to Lithium Scale) in 900 individuals with BD-I recruited to the Consortium of Lithium Genetics.

RESULTS

After accounting for co-associations between potential predictors, the most important factors associated with the good Li response phenotype were panic disorder, manic predominant polarity, manic first episode, age at onset between 15-32 years and family history of BD. Factors most strongly linked to poor outcome were comorbid obsessive-compulsive disorder, alcohol and/or substance misuse, and/or psychosis (symptoms or syndromes).

CONCLUSIONS

Network analysis can offer important additional insights to prospective studies of predictors of Li treatment outcomes. It appears to especially help in further clarifying the role of family history of BD (i.e. its direct and indirect associations) and highlighting the positive and negative associations of different subtypes of anxiety disorders with Li response, particularly the little-known negative association between Li response and obsessive-compulsive disorder.

Depicting the associations between different forms of psychopathology in trauma-exposed adolescents

Psychiatric comorbidity in traumatized youth is prevalent, but such associations between two disorders may be confounded with other comorbid conditions. Few studies have examined the unique relationships among multiple disorders. Which disorders maximally explain the relationships between others and whether such disorders differ by sex remain largely unknown. Using a construct-level network approach, this study characterized the independent associations among nine prevalent emotional and behavioral disorders/problems evaluated by the PTSD Checklist for DSM-5, the Revised Children's Anxiety and Depression Scale, and the Youth Self-Report in a sample of 1181 disaster-exposed adolescents (53.9% girls; a mean age of 14.3 ± 0.8 years). The associations were strong among the seven internalizing problems and between the two externalizing ones, but weaker between these two spectra of psychopathology. Major depressive disorder (MDD) was most strongly connected with others, maximally accounting for the associations, especially those between the two spectra. Overall and individual association strength and the connecting role of MDD were generally equivalent across sex. These findings highlight the necessity of MDD in linking comorbid forms of psychopathology in traumatized youth, and suggest MDD as a potential intervention priority in this population.

The current status of temporal network analysis for clinical science: Considerations as the paradigm shifts?

OBJECTIVE

Network analysis in psychology has ushered in a potentially revolutionary way of analyzing clinical data. One novel methodology is in the construction of temporal networks, models that examine directionality between symptoms over time. This paper provides context for how these models are applied to clinically-relevant longitudinal data.

METHODS

We provide a survey of statistical and methodological issues involved in temporal network analysis, providing a description of available estimation tools and applications for conducting such analyses. Further, we provide supplemental R code and discuss simulations examining temporal networks that vary in sample size, number of variables, and number of time points.

RESULTS

The following packages and software are reviewed: graphicalVAR, mlVAR, gimme, SparseTSCGM, mgm, psychonetrics, and the Mplus dynamic structural equation modeling module. We discuss the utility each procedure has for specific design considerations.

CONCLUSION

We conclude with notes on resources for estimating these models, emphasizing how temporal networks best approximate network theory.

The Network Structure of Personality Pathology in Adolescence With the 100-Item Personality Inventory for DSM-5 Short-Form (PID-5-SF)

There is currently a lack of understanding of the structure of personality disorder (PD) trait facets. The network approach may be useful in providing additional insights, uncovering the unique association of each PD trait facet with every other facet. A unique feature of network analysis is centrality, which indicates the importance of the role a trait facet plays in the context of other trait facets. Using data from 1,940 community Dutch adolescents, we applied network analysis to the 25 trait facets from the 100-item Personality Inventory for DSM-5 Short-Form (PID-5-SF) to explore their associations. We found that some trait facets only seem to be core indicators of their pre-ordained domains, whereas we observed that other trait facets were strongly associated with trait facets outside of their hypothesized domains. Importantly, anxiousness and callousness were identified as highly central facets, being uniquely associated with many other trait facets. Future longitudinal network studies could therefore further examine the possibility of anxiousness and callousness as risk marker trait facets among other PD trait facets.

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.

Eating disorder psychopathology in adults and adolescents with anorexia nervosa: A network approach

OBJECTIVE

The aim of this study was to assess and compare eating disorder feature networks in adult and adolescent patients with anorexia nervosa.

METHODS

Patients seeking treatment for anorexia nervosa in inpatient and outpatient settings were consecutively recruited from January 2008 to September 2019. Body mass index was measured, and each patient completed the Eating Disorder Examination Questionnaire.

RESULTS

The sample comprised 547 adolescent and 724 adult patients with anorexia nervosa. Network analysis showed that in both adults and adolescents, the most central and highly interconnected nodes in the network were related to shape overvaluation and desiring weight loss. The network comparison test identified similar global strength and network invariance, confirming the similarity of the two network structures.

DISCUSSION

The network structures in adult and adolescent patients with anorexia nervosa are similar, and lend weight to the cognitive behavioral theory that overvaluation of shape and weight is the core feature of anorexia nervosa psychopathology.

Association Rule Learning Is an Easy and Efficient Method for Identifying Profiles of Traumas and Stressors that Predict Psychopathology in Disaster Survivors: The Example of Sri Lanka

Research indicates that psychopathology in disaster survivors is a function of both experienced trauma and stressful life events. However, such studies are of limited utility to practitioners who are about to go into a new post-disaster setting as (1) most of them do not indicate which specific traumas and stressors are especially likely to lead to psychopathology; and (2) each disaster is characterized by its own unique traumas and stressors, which means that practitioners have to first collect their own data on common traumas, stressors and symptoms of psychopathology prior to planning any interventions. An easy-to-use and easy-to-interpret data analytical method that allows one to identify profiles of trauma and stressors that predict psychopathology would be of great utility to practitioners working in post-disaster contexts. We propose that association rule learning (ARL), a big data mining technique, is such a method. We demonstrate the technique by applying it to data from 337 survivors of the Sri Lankan civil war who completed the Penn/RESIST/Peradeniya War Problems Questionnaire (PRPWPQ), a comprehensive, culturally-valid measure of experienced trauma, stressful life events, anxiety and depression. ARL analysis revealed five profiles of traumas and stressors that predicted the presence of some anxiety, three profiles that predicted the presence of severe anxiety, four profiles that predicted the presence of some depression and five profiles that predicted the presence of severe depression. ARL allows one to identify context-specific associations between specific traumas, stressors and psychological distress, and can be of great utility to practitioners who wish to efficiently analyze data that they have collected, understand the output of that analysis, and use it to provide psychosocial aid to those who most need it in post-disaster settings.

The Symptom and Domain Structure of Psychotic Disorders: A Network Analysis Approach

Little is understood about the symptom network structure of psychotic disorders. In the current study, we aimed to examine the network structure of psychotic symptoms in a broad and transdiagnostic sample of subjects with psychotic disorders (n = 2240) and to determine whether network structure parameters vary across demographic, sampling method and clinical variables. Gaussian graphical models were estimated for 73 psychotic symptoms assessed using the Comprehensive Assessment of Symptoms and History. A 7-cluster solution (reality distortion, disorganization, catatonia, diminished expressivity, avolition/anhedonia, mania, and depression) best explained the underlying symptom structure of the network. Symptoms with the highest centrality estimates pertained to the disorganization and, to a lesser extent, negative domains. Most bridge symptoms pertained to the disorganization domain, which had a central position within the network and widespread connections with other psychopathological domains. A comparison of networks in subgroups of subjects defined by premorbid adjustment levels, treatment response, and course pattern significantly influenced both network global strength and network structure. The sampling method and diagnostic class influenced network structure but not network global strength. Subgroups of subjects with less densely connected networks had poorer outcomes or more illness severity than those with more densely connected networks. The network structure of psychotic features emphasizes the importance of disorganization symptoms as a central domain of psychopathology and raises the possibility that interventions that target these symptoms may prove of broad use across psychopathology. The network structure of psychotic disorders is dependent on the sampling method and important clinical variables.

A Network Analysis of Hoarding Symptoms, Saving and Acquiring Motives, and Comorbidity

Hoarding disorder is marked by strong attachments to everyday objects, extreme difficulties discarding, and impairing levels of clutter. We examined the associations between hoarding symptoms and associated clinical features using network analysis in a large sample of individuals with established hoarding disorder (n=217) and matched healthy controls (n=130). Network nodes included the three core features of hoarding (difficulties discarding, clutter, and acquiring), along with comorbid symptoms, impairment, and saving and acquiring motives. Models showed hoarding and comorbid symptoms as separate syndromes. Healthy and patient networks differed significantly in both global network strength and structure. For the hoarding patient network, the comorbidity and hoarding clusters were connected by acquiring and anxiety, which served as bridge symptoms. Clutter was the only hoarding node associated with impairment. Hoarding beliefs were not central to the model, and only difficulties discarding was associated with saving and acquiring motives, including emotional attachment and wastefulness beliefs. Our findings indicate that the network approach to mental disorders provides a new and complementary way to improve our understanding of the etiological model of hoarding, and may present novel hypotheses to examine in treatment development research.

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.

Using Network Analysis for Examining Interpersonal Emotion Dynamics

Several contemporary models conceptualize emotion as inherently interpersonal. We demonstrate how network analysis, a class of statistical methods often used to examine intrapersonal dynamic processes, provides a potential avenue for parameterizing interpersonal emotion dynamics (and interpersonal dynamics in general). We claim that this method allows (a) observing interpersonal dynamics at various temporal levels; (b) examining interpersonal dynamics occurring through various emotional pathways; and (c) capturing variations in interpersonal networks, which can subsequently be used to predict changes in outcomes. To demonstrate the potential of this method, we used dyadic daily diary data on emotion dynamics from two samples; Sample 1 involved couples in their routine daily lives, whereas Sample 2 involved couples in their transition to parenthood. Graphical Multilevel-Vector-Autoregressive modeling was used to estimate partners' emotional networks, whereas in a second step, LASSO was used to test the predictive value of couple-level differences of the obtained networks. The analysis revealed several patterns. For example, the between-couple network of Sample 1 was more interpersonally dense, but couple-level differences in the networks' interpersonal associations were predictive of partners' relationship satisfaction over time only in Sample 2. We also include commented code implementing a new dyadmlvar R package developed for conducting this analysis.

Personalized models of psychopathology as contextualized dynamic processes: An example from individuals with borderline personality disorder

OBJECTIVE

Psychopathology research has relied on discrete diagnoses, which neglects the unique manifestations of each individual's pathology. Borderline personality disorder combines interpersonal, affective, and behavioral regulation impairments making it particularly ill-suited to a "one size fits all" diagnosis. Clinical assessment and case formulation involve understanding and developing a personalized model for each patient's contextualized dynamic processes, and research would benefit from a similar focus on the individual.

METHOD

We use group iterative multiple model estimation, which estimates a model for each individual and identifies general or shared features across individuals, in both a mixed-diagnosis sample (N = 78) and a subsample with a single diagnosis (n = 24).

RESULTS

We found that individuals vary widely in their dynamic processes in affective and interpersonal domains both within and across diagnoses. However, there was some evidence that dynamic patterns relate to transdiagnostic baseline measures. We conclude with descriptions of 2 person-specific models as an example of the heterogeneity of dynamic processes.

CONCLUSIONS

The idiographic models presented here join a growing literature showing that the individuals differ dramatically in the total patterning of these processes, even as key processes are shared across individuals. We argue that these processes are best estimated in the context of person-specific models, and that so doing may advance our understanding of the contextualized dynamic processes that could identify maintenance mechanisms and treatment targets. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Network analysis of mood symptoms in adolescents with or at high risk for bipolar disorder

OBJECTIVES

Network analyses of psychopathology examine the relationships between individual symptoms in an attempt to establish the causal interactions between symptoms that may give rise to episodes of psychiatric disorders. We conducted a network analysis of mood symptoms in adolescents with or at risk for bipolar spectrum disorders.

METHODS

The sample consisted of 272 treatment-seeking adolescents with or at high risk for bipolar disorder who had at least subsyndromal depressive or (hypo)manic symptoms. Based on symptom scores assessed via semi-structured interviews, we constructed the network of depressive and manic symptoms and identified the most central symptoms and symptom communities within the network. We used bootstrapping analyses to determine the reliability of network parameters.

RESULTS

Symptoms within the depressive and manic mood poles were more related to each other than to symptoms of the opposing mood pole. Four communities were identified, including a depressive symptom community and three manic symptom communities. Fatigue and depressed mood were the strongest individual symptoms within the overall network (ie the most highly correlated with other symptoms), followed by motor hyperactivity. Mood lability and irritability were found to be "bridge" symptoms that connected the two mood poles.

CONCLUSIONS

Symptoms of activity/energy (ie fatigue and hyperactivity) and depressed mood are the most prominent mood symptoms among youth with bipolar spectrum disorders. Mood lability and irritability represent potential warning signs of emergent episodes of either polarity. Targeting these central and bridge symptoms would lead to more efficient assessments and therapeutic interventions for bipolar disorder.

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.

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.

Network Analysis of Posttraumatic Stress Disorder Symptoms in a National Sample of U.S. Adults: Implications for the Phenotype and the ICD-11 Model of PTSD

The phenotype for posttraumatic stress disorder (PTSD) in the fifth edition of the Diagnostic and Statistical Manual of Mental Diseases (DSM-5) includes 20 symptoms in four clusters. In contrast, the PTSD model in the 11th revision of the International Classification of Diseases (ICD-11) includes six symptoms in three clusters. Whether those six symptoms are, in fact, the most central symptoms of the PTSD phenotype remains an open question. In a previous network analysis of DSM-5 PTSD symptoms, Mitchell and colleagues (2017) reported limited overlap between central PTSD symptoms and those in the ICD-11 model in a national sample of U.S. veterans. The present study sought to replicate and extend upon these findings in a large national sample of U.S. adults (N = 2,953). Centrality statistics from both a replication sample (i.e., participants with DSM-5 PTSD, n = 173) and an extension sample (i.e., participants who had been exposed to potentially traumatic events, n = 2,468) were moderately strongly convergent with the findings reported by Mitchell et al., rs = .54-.73. Additionally, only three of the six most central symptoms in both the replication and extension samples overlapped with the ICD-11 model, indicating that the ICD-11 model (a) failed to include network-central symptoms of the PTSD phenotype and (b) included extra symptoms that were not network-central. Several symptoms from the DSM-5 Criterion D cluster (negative alterations in cognition and mood) that were excluded in ICD-11 were found to be among the most central PTSD symptoms.

The replicability and generalizability of internalizing symptom networks across five samples

The popularity of network analysis in psychopathology research has increased exponentially in recent years. Yet, little research has examined the replicability of cross-sectional psychopathology network models, and those that have used single items for symptoms rather than multiitem scales. The present study therefore examined the replicability and generalizability of regularized partial correlation networks of internalizing symptoms within and across 5 samples (total N = 2,573) using the Inventory for Depression and Anxiety Symptoms, a factor analytically derived measure of individual internalizing symptoms. As different metrics may yield different conclusions about the replicability of network parameters, we examined both global and specific metrics of similarity between networks. Correlations within and between nonclinical samples suggested considerable global similarities in network structure (rss = .53-.87) and centrality strength (rss = .37-.86), but weaker similarities in network structure (rss = .36-.66) and centrality (rss = .04-.54) between clinical and nonclinical samples. Global strength (i.e., connectivity) did not significantly differ across all 5 networks and few edges (0-5.5%) significantly differed between networks. Specific metrics of similarity indicated that, on average, approximately 80% of edges were consistently estimated within and between all 5 samples. The most central symptom (i.e., dysphoria) was consistent within and across samples, but there were few other matches in centrality rank-order. In sum, there were considerable similarities in network structure, the presence and sign of individual edges, and the most central symptom within and across internalizing symptom networks estimated from nonclinical samples, but global metrics suggested network structure and symptom centrality had weak to moderate generalizability from nonclinical to clinical samples. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Pretreatment Posttraumatic Stress Disorder Symptom Network Metrics Predict the Strength of the Association Between Node Change and Network Change During Treatment

Network analysis has been increasingly applied in an effort to understand complex interactions among symptoms in posttraumatic stress disorder (PTSD). Although methods that initially focused on identifying central symptoms in cross-sectional networks have been extended to longitudinal data that can reveal the relative roles of acute symptoms in the emergence of the PTSD syndrome, the association between network metrics and symptom change during treatment have yet to be explored in PTSD. To address this gap, we estimated pretreatment PTSD symptom networks in a sample of patients from a multisite clinical trial for women with full or subthreshold PTSD and substance use. We tested the hypothesis that node metrics calculated in the pretreatment network would be predictive of the strength of the association between a symptom's change and the change in the severity of all other symptoms through the course of treatment. A symptom node's strength and predictability in the pretreatment network were each strongly correlated with the association between that symptom's change and overall change across the symptom network, r(15) = .79, p < .001 and r(15) = .75, p < .001, respectively, whereas a symptom's mean severity at pretreatment was not, r(15) = .27, p = .292. These findings suggest that a node's centrality prior to treatment engagement is a predictor of its association with overall symptom change throughout the treatment process.