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

Topological properties of psychopathological networks of healthy and disordered individuals across mental disorders

The identification of psychopathological markers has been the focus of several scientific fields. The results were inconsistent due to lack of a clear nosology. Network analysis, focusing on the interactions between symptoms, provided important insights into the nosology of mental disorders. These interactions originate several topological properties that could constitute markers of psychopathology. One of these properties is network connectivity, which has been explored in recent years. However, the results have been inconsistent, and the topological properties of psychopathological networks remain largely unexplored and unknown. We compared several topological properties (i.e., connectivity, average path length, assortativity, average degree, modularity, global clustering) of psychopathological networks of healthy and disordered participants across depression (N = 2830), generalized anxiety (N = 13,463), social anxiety (N = 12,814), and obsessive-compulsive disorder (N = 16,426). Networks were estimated using Bayesian Gaussian Graphical Models. The Janson-Shannon measure of divergence was used to identify differences between the network properties. Network connectivity distinguished healthy and disordered participants' networks in all disorders. However, in depression and generalized anxiety, network connectivity was higher in healthy participants. The presence and number of motifs also distinguished the networks of healthy and disordered participants. Other topological properties (i.e., modularity, clustering, average path length and average degree) seem to be disorder-specific. The psychopathological significance of network connectivity must be clarified. Some topological properties of psychopathological networks are promising markers of psychopathology and may contribute to clarifying the nosology of mental disorders.

A Network Approach to Understanding the Role of Executive Functioning and Alpha Oscillations in Inattention and Hyperactivity-Impulsivity Symptoms of ADHD

OBJECTIVE

ADHD is a prevalent neurodevelopmental disorder characterized by symptoms of inattention and hyperactivity-impulsivity. Impairments in executive functioning (EF) are central to models of ADHD, while alpha-band spectral power event-related decreases (ERD) have emerged as a putative electroencephalography (EEG) biomarker of EF in ADHD. Little is known about the roles of EF and alpha ERD and their interactions with symptoms of ADHD.

METHOD

We estimated network models of ADHD symptoms and integrated alpha ERD measures into the symptom network.

RESULTS

EF emerges as a bridge network node connecting alpha ERD and the hyperactivity/impulsivity and inattention symptoms. We found that EF most closely relates to a subset of symptoms, namely the motoric symptoms, "seat" (difficulty staying seated), and "runs" (running or climbing excessively).

CONCLUSIONS

EF functions as a bridge node connecting alpha ERD and the ADHD symptom network. Motoric-type symptoms and EF deficits may constitute important nodes in the interplay between behavior/symptoms, cognition, and neurophysiological markers of ADHD.

Longitudinal relationship between oppositional defiant disorder symptoms and attention-deficit/hyperactivity disorder symptoms in Chinese children: insights from cross-lagged panel network analyses

Oppositional defiant disorder (ODD) and attention-deficit/hyperactivity disorder (ADHD) are two of the most common childhood mental disorders, and they have substantial comorbidity. The developmental precursor model has long been widely used to explain the mechanisms of comorbidity between ODD and ADHD, however whether it is equally effective at the symptomatic level is unclear. Therefore, this study aimed to (a) examine the stability of the ODD and ADHD comorbidity network in a longitudinal sample of high-risk children in China; and (b) examine the longitudinal relationship between the ODD and ADHD symptom networks based on a developmental precursor model. Two hundred sixty-three Chinese children aged 6 to 13 years with ODD and/or ADHD were assessed for symptoms of ODD and ADHD in two surveys conducted 1 year apart. We used data from these two time points to construct two cross-sectional networks and a cross-lagged panel network (CLPN) to explore the symptom network for comorbidity of ODD and ADHD. The analysis shows that: (1) the two cross-sectional networks are highly similar in terms of structure, existence of edges, centrality estimates, and the invariance test shows that there is no significant difference between them. The symptoms "follow through", "interrupts/intrudes", "difficulty playing quietly" and "concentration" had the highest expected influence centrality at both time points. (2) Combined with the results of the cross-sectional and cross-lagged networks, we found that "annoy" and "blame" are potential bridge symptoms between the ODD and ADHD symptom networks. The symptom "annoy" forms a reciprocal predictive relationship with "interrupts/intrudes", while "blame" unidirectionally predicts "close attention". In addition, we found that "vindictive" predicted numerous ADHD symptoms, whereas "angry" was predicted by numerous ADHD symptoms. The findings emphasize the broad predictive relationship between ODD and ADHD symptoms with each other, and that ODD symptoms may lead to activation of the ADHD symptom network and vice versa. These findings suggest that the developmental precursor model at the symptom level may partially explain the comorbidity mechanisms of ODD and ADHD, and future studies should further investigate the underlying multiple mechanisms.

A Network Analysis of Perinatal Depression, Anxiety, and Temperaments in Women in the First, Second, and Third Trimesters of Pregnancy

Background/Objectives: Although depression and anxiety are found to be affected by temperaments, little research has studied these relationships in pregnancy. The present study explored the associations among perinatal depression (PD), anxiety dimensions (state, trait, and generalized anxiety disorder (GAD)), and temperaments between women in the three trimesters of pregnancy through a network analysis approach. Moreover, differences in the severity of PD and anxiety between women in the three trimesters were evaluated. Methods: Women in first (N = 31), second (N = 184), and third (N = 54) trimesters of pregnancy were recruited in the present cross-sectional study. The network analysis included PD, anxiety dimensions, and temperaments. Three network models were estimated, and ANOVAs evaluated the differences in the severity of PD and anxiety, including trimesters as a between-subject factor. Results: PD and GAD were the nodes most strongly connected across the three groups. Cyclothymic, depressive, and anxious temperaments were most frequently associated with PD and GAD. Hyperthymic temperament was in the periphery of the three networks. Lastly, women in the first trimester had the highest severity of PD and GAD. Conclusions: PD and GAD showed the strongest associations. Anxiety dimensions had positive associations with PD and GAD, suggesting their role as possible risk factors. Temperaments were differently associated within the network between the three groups. Clinical interventions during pregnancy should target the central variables, considering their direct and indirect relationships.

Methodological and Statistical Practices of Using Symptom Networks to Evaluate Mental Health Interventions: A Review and Reflections

The network approach to psychopathology, which assesses associations between individual symptoms, has recently been applied to evaluate treatments for mental disorders. While various options for conducting network analyses in intervention research exist, an overview and an evaluation of the various approaches are currently missing. Therefore, we conducted a review on network analyses in intervention research. Studies were included if they constructed a symptom network, analyzed data that were collected before, during or after treatment of a mental disorder, and yielded information about the treatment effect. The 56 included studies were reviewed regarding their methodological and analytic strategies. About half of the studies based on data from randomized trials conducted a network intervention analysis, while the other half compared networks between treatment groups. The majority of studies estimated cross-sectional networks, even when repeated measures were available. All but five studies investigated networks on the group level. This review highlights that current methodological practices limit the information that can be gained through network analyses in intervention research. We discuss the strength and limitations of certain methodological and analytic strategies and propose that further work is needed to use the full potential of the network approach in intervention research.

Contemporaneous and temporal network analysis of complex Posttraumatic stress disorder among Chinese college students with Childhood adversity: A longitudinal study

OBJECTIVE

To enhance understanding of the longitudinal progression of complex posttraumatic stress disorder (CPTSD) symptoms, this longitudinal study examined how CPTSD symptoms interact over time in Chinese college students with childhood trauma.

METHODS

From 18,933 college students who took part in two surveys 12 months apart, 4,006 participants who reported adverse childhood experiences were screened. Cross-sectional network comparisons and cross-lagged panel network (CLPN) analysis characterized interactions among CPTSD symptoms.

RESULTS

In the cross-sectional networks, feeling like a failure and avoid activities reminiscent of the trauma were the central symptoms. Takes long time to calm down and exaggerated startle are important bridge symptoms in the two networks respectively. The comparison of cross-sectional networks indicates that the global network strength was stable. The findings of the CLPN model reveal that feel worthless and feel like a failure had the highest "out" expected influence; exaggerated startle and avoid thoughts and feelings about the trauma had the highest "in" expected influence.

CONCLUSIONS

By conducting cross-sectional network analyses, the study illuminated the attributes of CPTSD networks across various time points. Additionally, the CLPN analysis uncovered the longitudinal patterns of CPTSD symptoms.

The Complexity of Borderline Personality Disorder: Network Analysis of Personality Factors and Defense Styles in the Context of Borderline Personality Organization

OBJECTIVE

Borderline personality disorder (BPD) is known to share characteristics with a variety of personality disorders (PDs) and exhibits diverse patterns of defense mechanisms. To enhance our understanding of BPD, it's crucial to shift our focus from traditional categorical diagnostics to the dimensional traits shared with other PDs, as the borderline personality organization (BPO) model suggests. This approach illuminates the nuanced spectrum of BPD characteristics, offering deeper insights into its complexity. While studies have investigated the comorbidity of BPD with other PDs, research exploring the relationship between various personality factors and defense mechanisms within BPD itself has been scarce. The present study was undertaken to investigate the complex interrelationships between various personality factors and defense styles in individuals diagnosed with BPD.

METHODS

Using a network analysis approach, data from 227 patients diagnosed with BPD were examined using the Defense Style Questionnaire and Personality Disorder Questionnaire-4+ for assessment.

RESULTS

Intricate connections were observed between personality factors and defense styles. Significant associations were identified between various personality factors and defense styles, with immature defense styles, such as maladaptive and image-distorting being particularly prominent in BPD in the centrality analysis. The maladaptive defense style had the highest expected influence centrality. Furthermore, the schizotypal, dependent, and narcissistic personality factors demonstrated relatively high centrality within the network.

CONCLUSION

Network analysis can effectively delineate the complexity of various PDs and defense styles. These findings are expected to facilitate a deeper understanding of why BPD exhibits various levels of organization and presents with heterogeneous characteristics, consistent with the perspectives proposed by the BPO.

Prolonged grief disorder symptomology in three African countries: A network analysis and comparison

Background

Bereavement is a globally prevalent life stressor, but in some instances, it may be followed by a persistent condition of grief and distress, codified within the 11th edition of the International Classification of Diseases (ICD-11) as prolonged grief disorder (PGD). Network analysis provides a valuable framework for understanding psychological disorders at a nuanced symptom-based level.

Aim

This study novelly explores the network structure of ICD-11 PGD symptomology in a non-Western sample and assesses the replication of this across three African country sub-samples in these data.

Methodology

Network models were estimated using the "Inventory of Complicated Grief-Revised" in a sample of trauma-exposed individuals who experienced bereavement throughout life (N = 1,554) from three African countries (Ghana, n = 290; Kenya, n = 619; Nigeria, n = 645). These networks were statistically evaluated using the network comparison test.

Results

It was found that "Feelings of Loss" and "Difficulty moving on" were the most central symptoms in the combined sample network. These findings were largely consistent for the Ghana and Nigeria sub-samples, however, network structure differences were noted in the Kenya sub-sample.

Conclusion

The identified PGD network highlights particular indicators and associations across three African samples. Implications for the assessment and treatment of PGD in these cultural contexts warrant consideration.

Psychological flexibility and cognitive-affective processes in young adults' daily lives

Psychological flexibility plays a crucial role in how young adults adapt to their evolving cognitive and emotional landscapes. Our study investigated a core aspect of psychological flexibility in young adults: adaptive variability and maladaptive rigidity in the capacity for behavior change. We examined the interplay of these elements with cognitive-affective processes within a dynamic network, uncovering their manifestation in everyday life. Through an Ecological Momentary Assessment design, we collected intensive longitudinal data over 3 weeks from 114 young adults ages 19 to 32. Using a dynamic network approach, we assessed the temporal dynamics and individual variability in flexibility in relation to cognitive-affective processes in this sample. Rigidity exhibited the strongest directed association with other variables in the temporal network as well as highest strength centrality, demonstrating particularly strong associations to other variables in the contemporaneous network. In conclusion, the results of this study suggest that rigidity in young adults is associated with negative affect and cognitions at the same time point and the immediate future.

eHealth tools use and mental health: a cross-sectional network analysis in a representative sample

eHealth tools usage is vital for health care systems and increased significantly after the COVID-19 pandemic, which aggravated mental health issues. This cross-sectional study explored whether sociodemographic characteristics and mental health indices (stress and symptoms of anxiety and depression) were linked to the behavioral intention to use eHealth tools and eHealth tools usage in a representative sample from Poland using a network approach. Measurements were conducted in March 2023 among 1000 participants with a mean age of 42.98 (18-87) years, with 51.50% women. The measures included the behavioral intention to use eHealth tools (BI) based on the UTUAT2; eHealth tool use frequency (use behavior) including ePrescription, eSick leave, eReferral, electronic medical documentation (EMD), Internet Patient Account (IKP), telephone consultation, video consultation, mobile health applications, and private and public health care use; and the PSS-4, GAD-2, and PHQ-2. Furthermore, sociodemographic factors (sex, age, children, relationship status, education, and employment) were included in the research model. Network analysis revealed that mental health indices were weakly related to eHealth tools use. Higher stress was positively linked with mobile health application use but negatively linked to video consultation use. Use of various eHealth tools was intercorrelated. Sociodemographic factors were differentially related to the use of the eight specific eHealth tools. Although mental health indices did not have strong associations in the eHealth tools use network, attention should be given to anxiety levels as the factor with the high expected influence.

Centrality measures in psychological networks: A simulation study on identifying effective treatment targets

The network theory of psychopathology suggests that symptoms in a disorder form a network and that identifying central symptoms within this network might be important for an effective and personalized treatment. However, recent evidence has been inconclusive. We analyzed contemporaneous idiographic networks of depression and anxiety symptoms. Two approaches were compared: a cascade-based attack where symptoms were deactivated in decreasing centrality order, and a normal attack where symptoms were deactivated based on original centrality estimates. Results showed that centrality measures significantly affected the attack's magnitude, particularly the number of components and average path length in both normal and cascade attacks. Degree centrality consistently had the highest impact on the network properties. This study emphasizes the importance of considering centrality measures when identifying treatment targets in psychological networks. Further research is needed to better understand the causal relationships and predictive capabilities of centrality measures in personalized treatments for mental disorders.

Changes in the network structure of well-being components in adolescents in the school context: A 2-year longitudinal study

Disentangling the connections between subjective and psychological well-being may help practitioners identify effective targets of intervention to promote mental health in school settings. Based on theoretical foundations of well-being, the present study utilized psychometric network analysis to explore prospective associations between the subjective and psychological well-being of adolescents over 2 years. To this end, a cross-sectional network was estimated at Time 1 (n = 560) and Time 2 (n = 281), followed by a longitudinal network incorporating individual changes across time points in each component (n = 235). The networks included different indicators of subjective (e.g., life satisfaction, positive affect, negative affect) and psychological well-being measured by means of self-reported questionnaires. The results revealed direct connections between indicators of subjective and psychological well-being over time. Positive affect, especially feeling happy and satisfied, exhibited most of these connections. Only one negative longitudinal association emerged, which involved negative affect (e.g., feeling worried) and psychological well-being. The suitability of the network approach to represent the structure of subjective and psychological well-being can be used to widen research on adolescents' well-being. Considering the longitudinal associations identified, the present study makes an exploratory hypothesis to propose specific connectors between subjective and psychological well-being as potential targets for interventions aimed at promoting adolescents' mental health.

To Use or Not to Use: Exploring Therapists' Experiences with Pre-Treatment EMA-Based Personalized Feedback in the TheraNet Project

BACKGROUND

Using idiographic network models in psychotherapy has been a growing area of interest. However, little is known about the perceived clinical utility of network models. The present study aims to explore therapists' experiences with network model-based feedback within the context of the TheraNet Project.

METHODS

In total, 18 therapists who had received network-based feedback for at least 1 patient at least 2 months prior were invited to retrospective focus groups. The focus group questions related to how participation in the study influenced the therapeutic relationship, how the networks were used, and what might improve their clinical utility. The transcribed focus groups were analyzed descriptively using qualitative content analysis.

RESULTS

Most therapists mentioned using the feedback to support their existingtheir case concept, while fewer therapists discussed the feedback directly with the patients. Several barriers to using the feedback were discussed, as well as various suggestions for how to make it more clinically useful. Many therapists reported skepticism with regards to research in the outpatient training center in general, though they were also all pleasantly surprised by being involved, having their opinions heard, and showing a readiness to adapt research to their needs/abilities.

CONCLUSIONS

This study highlights the gap between researchers' and therapists' perceptions about what useful feedback should look like. The TheraNet therapists' interest in adapting the feedback and building more informative feedback systems signals a general openness to the implementation of clinically relevant research. We provide suggestions for future implementations of network-based feedback systems in the outpatient clinical training center setting.

Estimating the prognostic value of cross-sectional network connectivity for treatment response in depression

BACKGROUND

Tightly connected symptom networks have previously been linked to treatment resistance, but most findings come from small-sample studies comparing single responder v. non-responder networks. We aimed to estimate the association between baseline network connectivity and treatment response in a large sample and benchmark its prognostic value against baseline symptom severity and variance.

METHODS

N = 40 518 patients receiving treatment for depression in routine care in England from 2015-2020 were analysed. Cross-sectional networks were constructed using the Patient Health Questionnaire-9 (PHQ-9) for responders and non-responders (N = 20 259 each). To conduct parametric tests investigating the contribution of PHQ-9 sum score mean and variance to connectivity differences, networks were constructed for 160 independent subsamples of responders and non-responders (80 each, n = 250 per sample).

RESULTS

The baseline non-responder network was more connected than responders (3.15 v. 2.70, S = 0.44, p < 0.001), but effects were small, requiring n = 750 per group to have 85% power. Parametric analyses revealed baseline network connectivity, PHQ-9 sum score mean, and PHQ-9 sum score variance were correlated (r = 0.20-0.58, all p < 0.001). Both PHQ-9 sum score mean (β = -1.79, s.e. = 0.07, p < 0.001), and PHQ-9 sum score variance (β = -1.67, s.e. = 0.09, p < 0.001) had larger effect sizes for predicting response than connectivity (β = -1.35, s.e. = 0.12, p < 0.001). The association between connectivity and response disappeared when PHQ-9 sum score variance was accounted for (β = -0.28, s.e. = 0.19, p = 0.14). We replicated these results in patients completing longer treatment (8-12 weeks, N = 22 952) and using anxiety symptom networks (N = 70 620).

CONCLUSIONS

The association between baseline network connectivity and treatment response may be largely due to differences in baseline score variance.

Central psychological symptoms from a network analysis of patients with anxiety, somatoform or personality disorders before psychotherapy

BACKGROUND

Cross-sectional network analysis examines the relationships between symptoms to explain how they constitute disorders. Up to now, research focuses mostly on depression, posttraumatic stress disorder, and rarely assesses larger networks of various symptoms measured with instruments independent of classifications. Studies on large groups of psychotherapy patients are also rare.

METHODS

Analyzing triangulated maximally filtered graph (TMFG) networks of 62 psychological symptoms reported by 4616 consecutive nonpsychotic adults in 1980-2015.

RESULTS

Case-dropping and nonparametric bootstrap proved the accuracy, stability and reliability of networks in patients' sex-, age-, and time of visit divided subgroups. Feeling that others are prejudiced against the patient was the most central symptom, followed by catastrophic fears, feeling inferior and underestimated. Sadness, panic, and sex-related complaints were less central than we expected. All analysed symptoms were connected, and we found only small sex-related differences between subsamples' networks. No differences were observed for time of visit and age of patients.

LIMITATION

Analyses were cross-sectional and retrospective, not allowing examination of directionality or causality. Further, data are at the between-person level; thus, it is unknown whether the network remains constant for any person over time. One self-report checklist and building binary network method may bias results. Our results indicate how symptoms co-occured before psychotherapy, not longitudinally. Our sample included public university hospital patients, all White-Europeans, predominantly females and university students.

CONCLUSIONS

Hostile projection, catastrophic fears, feeling inferior and underestimated were the most important psychological phenomena reported before psychotherapy. Exploring these symptoms would possibly lead to enhancement of treatments.

A Network Analysis of Brief Measure of Perceived Courtesy and Affiliate Stigma During COVID-19 in Hubei China

PURPOSE

The current study aims to test perceived affiliate and courtesy stigma in Hubei province China during the early periods of COVID-19 by using network analysis.

METHOD

In this study, 4,591 participants (3,034 female, mean age = 26.64) from the Hubei Province of China were recruited to conduct network analysis.

RESULTS

The network analysis found network connections between Estranged - Blamed, Shamed - No Strong Point, and Rejected - Plague were the strongest. The most important stigma features (nodes) of COVID-19 (i.e. Plague, No Strong Point, Discriminated, and Disgusting).

DISCUSSION AND CONCLUSIONS

This study uncovered the most central features of perceived affiliate and courtesy stigma on COVID-19, proposing these features (and associations between features) could be prioritized for anti-stigma interventions for the COVID-19 pandemic.

How attachment style, mentalization and preparedness for death are associated with pre-loss grief symptoms' severity: A network analysis study in caregivers of terminally ill cancer patients

Several psychological dimensions influence the psychological adjustment of terminally ill cancer patients' caregivers, during the end-of-life phase. The present study explored the associations between attachment styles, mentalization, preparedness for death, and the severity of pre-loss grief symptoms in 102 caregivers of terminal cancer patients. The results of the network analysis showed that insecure attachment dimensions were positively associated with several central pre-loss grief symptoms. Mentalization and preparedness for death showed negative associations with several pre-loss grief symptoms. Interestingly, bitterness showed a negative association with need for approval and a positive association with mentalization. The results provided insight into the grieving process for palliative care providers to implement effective caregiver support interventions.

Network analysis of acute stress reaction in a sample of Chinese male military college students

Background

Acute stress reaction (ASR) following a stressful event is associated with stress-related mental disorders. However, no studies have investigated the relationships between ASR symptom clusters. The present study aimed to provide a fine-grained understanding of the complex relationships among symptom clusters and identify the central symptom clusters of ASR using network analysis.

Methods

The Acute Stress Reaction Scale (ASRS) was used to investigate the network structure of ASR in 1792 Chinese male military college students who were about to participate in an important physical fitness test. We calculated the weights of the edges connecting different symptom clusters and the central indices of 25 symptom clusters in the final network.

Results

There were five strongest edges with significantly higher weights than most other edge weights, including the edges between "Less communication" and "Isolated from others." The symptom clusters of "Somatic symptoms," "Hypoprosexia," and "Anxiety" were found to be the central nodes with the highest expected influences (primary centrality index).

Conclusion

The present study explored the network structure of ASR, revealed complex connections between symptom clusters, and identified central clusters. These findings have important clinical implications, and it is suggested that the three central symptom clusters may be potential targets for effective interventions for ASR.

Attention allocation in OCD: A systematic review and meta-analysis of eye-tracking-based research

INTRODUCTION

Cognitive models of obsessive-compulsive disorder (OCD) implicate heightened attention allocation to stimuli related to one's obsessions in the disorder. Recently, to overcome several limitations of reaction time-based measures, eye-tracking methodology has been increasingly used in attentional research.

METHODS

A meta-analysis of studies examining attention allocation towards OCD-related vs. neutral stimuli, using eye-tracking methodology and a group-comparison design, was conducted conforming to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Separate meta-analyses were performed for attentional vigilance (both latency and location of first fixations) and maintenance (total dwell time and total fixation count, conjointly). Each meta-analysis was conducted twice - once including all studies (main analysis) and once only including studies using the free-viewing paradigm (secondary analysis).

RESULTS

The systematic search yielded a total of nine studies. Of those, eight provided the needed data to be included in the meta-analysis. No evidence emerged for vigilance via latency to first fixation. Vigilance reflected via first fixation location emerged in the main analysis, but not in the secondary one. Evidence for attentional maintenance was found only when analyzing free-viewing studies exclusively (the secondary analysis).

LIMITATIONS

To increase the accuracy of the research question, correlational studies were excluded, resulting in a small number of available studies.

CONCLUSIONS

OCD may be characterized by vigilance, but mainly in tasks entailing specific demands and/or goals. Conversely, attentional maintenance may be evident only when using tasks that pose no requirements or demands for participants.

Borderline Personality Disorder (BPD) and Complex Posttraumatic Stress Disorder (CPTSD): A Network Analysis in a Highly Traumatized Clinical Sample

Whether complex posttraumatic stress disorder (CPTSD) and borderline personality disorder (BPD) diagnoses differ substantially enough to warrant separate diagnostic classifications has been a subject of controversy for years. To contribute to the nomological network of cumulative evidence, the main goal of the present study was to explore, using network analysis, how the symptoms of ICD-11 PTSD and disturbances in self-organization (DSO) are interconnected with BPD in a clinical sample of polytraumatized individuals (N = 330). Participants completed measures of life events, CPTSD, and BPD. Overall, our study suggests that BPD and CPTSD are largely separated. The bridges between BPD and CPTSD symptom clusters were scarce, with "Affective Dysregulation" items being the only items related to BPD. The present study contributes to the growing literature on discriminant validity of CPTSD and supports its distinctiveness from BPD. Implications for treatment are discussed.

Network-based validation of the psychometric questionnaire EDI-3 for the assessment of eating disorders

Assessing the validity of a psychometric test is fundamental to ensure a reliable interpretation of its outcomes. Few attempts have been made recently to complement classical approaches (e.g., factor models) with a novel technique based on network analysis. The objective of the current study is to carry out a network-based validation of the Eating Disorder Inventory 3 (EDI-3), a questionnaire designed for the assessment of eating disorders. Exploiting a reliable, open source sample of 1206 patients diagnosed with an eating disorder, we set up a robust validation process encompassing detection and handling of redundant EDI-3 items, estimation of the cross-sample psychometric network, resampling bootstrap procedure and computation of the median network of the replica samples. We then employed a community detection algorithm to identify the topological clusters, evaluated their coherence with the EDI-3 subscales and replicated the full validation analysis on the subpopulations corresponding to patients diagnosed with either anorexia nervosa or bulimia nervosa. Results of the network-based analysis, and particularly the topological community structures, provided support for almost all the composite scores of the EDI-3 and for 2 single subscales: Bulimia and Maturity Fear. A moderate instability of some dimensions led to the identification of a few multidimensional items that should be better located in the intersection of multiple psychological scales. We also found that, besides symptoms typically attributed to eating disorders, such as drive for thinness, also non-specific symptoms like low self-esteem and interoceptive deficits play a central role in both the cross-sample and the diagnosis-specific networks. Our work adds insights into the complex and multidimensional structure of EDI-3 by providing support to its network-based validity on both mixed and diagnosis-specific samples. Moreover, we replicated previous results that reinforce the transdiagnostic theory of eating disorders.

The structure of eating disorder and somatic symptoms

Individuals with eating disorders (EDs) often present with somatic concerns in treatment, such as bloating, fullness, and feeling tight clothes on skin. However, most research generally focuses on general interoception (e.g., heartbeat) rather than sensations relevant to EDs (e.g., sensations related to the gastrointestinal system or body movement). In the current study (N = 181), we used network analysis to model the structure of ED symptoms and somatic concerns among individuals with anorexia nervosa, bulimia nervosa, and other specified feeding and eating disorder. Results showed that heightened sensitivity to somatic concerns had the highest strength centrality within a symptom network comprising ED and somatic symptoms. Exploratory graph analysis identified four symptom dimensions: cognitive-affective ED symptoms, behavioral ED symptoms, general interoception, and ED-specific proprioception. Findings suggest that heightened sensitivity to somatic concerns may maintain ED symptoms and mutually reinforce other somatic concerns. Implications concerning assessment and treatment of EDs are discussed.

A network analysis of anxiety, depressive, and psychotic symptoms and functioning in children and adolescents at clinical high risk for psychosis

Objective

Youths at clinical high risk for psychosis (CHR-P) are characterized by a high prevalence of anxiety and depressive disorders. The present study aimed at developing and analyzing a network structure of CHR-P symptom domains (i.e., positive, negative, disorganization, and general subclinical psychotic symptoms), depressive and anxiety symptoms, and general functioning.

Methods

Network analysis was applied to data on 111 CHR-P children and adolescents (M age = 14.1), who were assessed using the Structured Interview for Prodromal Syndromes, the Children's Depression Inventory, the Children's Global Assessment Scale, and the Multidimensional Anxiety Scale for Children.

Results

In the network, negative and disorganization symptoms showed the strongest association (r = 0.71), and depressive and anxiety symptoms showed dense within-domain connections, with a main bridging role played by physical symptoms of anxiety. The positive symptom cluster was not associated with any other node. The network stability coefficient (CS) was slightly below 0.25, and observed correlations observed ranged from 0.35 to 0.71.

Conclusion

The lack of association between subclinical positive symptoms and other network variables confirmed the independent nature of subclinical positive symptoms from comorbid symptoms, which were found to play a central role in the analyzed network. Complex interventions should be developed to target positive and comorbid symptoms, prioritizing those with the most significant impact on functioning and the most relevance for the young individual, through a shared decision-making process. Importantly, the results suggest that negative and disorganization symptoms, as well as depressive and anxiety symptoms, may be targeted simultaneously.

Character strengths and mental health as complex systems: a network analysis to identify bridge strengths

The network approach poses an alternative focus to understand psychological constructs as emerging from mutual interactions among indicators. Network psychometrics has been applied to psychopathology to unravel the connections between symptoms, but it can also be applied to the study of well-being. The role of character strengths in mental health is at the forefront of research attention. Previous findings suggest that heart character strengths are more predictive of mental health than mind character strengths. Nevertheless, researchers have rarely applied the network approach in this context. The present study examines, from the network approach, the connections between heart and mind character strengths and mental health. Building upon the dual-factor model of mental health, positive (i.e., happiness and life satisfaction) and negative indicators (i.e., depression) were included in the assessment of this construct. A sample of 597 Spanish undergraduates (M = 23.52; SD = 5.25; 75.6% females) completed cross-sectional self-report measures. Network analysis was used to estimate a network composed of two communities: character strengths and mental health. We used centrality analysis to calculate the importance of each node and bridge centrality to examine the interactions between the communities. The results indicated that the heart strengths of love, zest, hope, and gratitude reported the highest bridge strength centrality, suggesting that they played an intermediary role activating and deactivating components of mental health. Adopting the network approach to explore the connections between character strengths and mental health can help design focused intervention strategies in psychology.

Cross-sectional and longitudinal AUD symptom networks: They tell different stories

Modern theoretical models of Alcohol Use Disorder (AUD) highlight the different functional roles played by various mechanisms associated with different symptoms. Symptom network models (SNMs) offer one approach to modeling AUD symptomatology in a way that could reflect these processes and provide important information on the progression and persistence of disorder. However, much of the research conducted using SNMs relies on cross-sectional data, which has raised questions regarding the extent they reflect dynamic processes. The current study aimed to (a) examine symptom networks of AUD and (b) compare the extent to which cross-sectional network models had similar structures and interpretations as longitudinal network models. 17,360 participants from Wave 1 (2001-2002) and Wave 2 (2003-2004) of the National Epidemiological Survey on Alcohol and Related Conditions (NESARC) were used to model cross-sectional and longitudinal AUD symptom networks. The cross-sectional analyses demonstrate high replicability across waves and central symptoms consistent with other cross-sectional studies on addiction networks. The longitudinal network shared much less similarity than the cross-sectional networks and had a substantially different structure. Given the increasing attention given to the network perspective in psychopathology research, the results of this study raise concerns about interpreting cross-sectional symptom networks as representative of temporal changes occurring within a psychological disorder. We conclude that the psychological symptom network literature should be bolstered with additional research on longitudinal network models.

A network approach can improve eating disorder conceptualization and treatment

Eating disorders are severe mental illnesses with the second highest mortality rate of all psychiatric illnesses. Eating disorders are exceedingly deadly because of their complexity. Specifically, eating disorders are highly comorbid with other psychiatric illnesses (up to 95% of individuals with an eating disorder have at least one additional psychiatric illness), have extremely heterogeneous presentations, and individuals often migrate from one specific eating disorder diagnosis to another. In this Perspective, we propose that understanding eating disorder comorbidity and heterogeneity via a network theory approach offers substantial benefits for both conceptualization and treatment. Such a conceptualization, strongly based on theory, can identify specific pathways that maintain psychiatric comorbidity, how diagnoses vary across individuals, and how specific symptoms and comorbidities maintain illness for one individual, thereby paving the way for personalized treatment.

Understanding the Association Between Intolerance of Uncertainty and Problematic Smartphone Use: A Network Analysis

BACKGROUND

Existing research has demonstrated that intolerance of uncertainty (IU) is associated with problematic smartphone use (PSU). However, little is known about how different IU components such as uncertainty-related beliefs, emotions, and behaviors may impact on different PSU symptoms.

METHODS

Extending previous research, the current study examined the specific associations between IU components and PSU symptoms via a symptom-level network approach. A regularized partial correlation network consisting of different IU components and PSU symptoms was estimated among 1,849 Chinese university students. We examined pathways and influential nodes (i.e. central components/symptoms and bridge components/symptoms) within the IU-PSU network.

RESULTS

The strongest pathway linking IU and PSU was between emotional reactions to uncertainty and coping-motivated smartphone use. Importantly, emotional reactions toward not having enough information (a reflection of emotional reactions to uncertainty) may act as both a central and a bridge component in maintaining the whole IU-PSU network.

CONCLUSIONS

The results are in line with the I-PACE model and highlight that PSU may be a coping response for negative emotions derived from uncertainty. Finally, the current findings highlight the potential of interventions targeting intolerance of uncertainty for reducing PSU.

Examining the impact of the COVID-19 pandemic through the lens of the network approach to psychopathology: Analysis of the Brazilian Longitudinal Study of Health (ELSA-Brasil) cohort over a 12-year timespan

Cohort studies have displayed mixed findings on changes in mental symptoms severity in 2020, when the COVID-19 pandemic outbreak started. Network approaches can provide additional insights by analyzing the connectivity of such symptoms. We assessed the network structure of mental symptoms in the Brazilian Longitudinal Study of Health (ELSA-Brasil) in 3 waves: 2008-2010, 2017-2019, and 2020, and hypothesized that the 2020 network would present connectivity changes. We used the Clinical Interview Scheduled-Revised (CIS-R) questionnaire to evaluates the severity of 14 common mental symptoms. Networks were graphed using unregularized Gaussian models and compared using centrality and connectivity measures. The predictive power of centrality measures and individual symptoms were also estimated. Among 2011 participants (mean age: 62.1 years, 58% females), the pandemic symptom 2020 network displayed higher overall connectivity, especially among symptoms that were related to general worries, with increased local connectivity between general worries and worries about health, as well as between anxiety and phobia symptoms. There was no difference between 2008 and 2010 and 2017-2019 networks. According to the network theory of mental disorders, external factors could explain why the network structure became more densely connected in 2020 compared to previous observations. We speculate that the COVID-19 pandemic and its innumerous social, economical, and political consequences were prominent external factors driving such changes; although further assessments are warranted.

Associations between PTSD symptoms and other psychiatric symptoms among college students exposed to childhood sexual abuse: a network analysis

Background: Childhood sexual abuse (CSA) is one of the prevalent forms of trauma experienced during childhood and adolescence. Previous research underscores its associations with depression, anxiety, post-traumatic stress disorder (PTSD), and psychosis. Objective: This study examined symptom connections between depression, anxiety, PTSD, and psychosis while simultaneously investigating whether these connections differed by gender among CSA survivors. Methods: A large-scale, cross-sectional study among 96,218 college students was conducted in China. Participants' CSA was measured by the Childhood Trauma Questionnaire-Short Form (CTQ-SF). Participants' PTSD, psychosis, depression, and anxiety were measured by the Trauma Screening Questionnaire (TSQ), the Psychosis Screener (PS), the nine-item Patient Health Questionnaire (PHQ-9), and the 7-item Generalized Anxiety Disorder Scale (GAD-7), respectively. Network analysis was used to explore the potential associations between these symptoms and to compare the sex differences in the symptoms model. Results: Among participants who suffered from CSA, females were more likely from left-behind households, while males were more likely from households with a high annual income (P < .001, Cohen's W = 0.07). In addition, compared to male victims, female victims were more likely to report depression, anxiety, and PTSD (P < .001, Cohen's d≈0.2), while male victims were more likely to report psychosis (P < .001, Cohen's d = 0.36). Results from network estimation showed that psychosis, depression, anxiety, and PTSD symptoms were positively correlated. Moreover, psychosis had a stronger connection with PTSD symptoms, including hypervigilance, intrusive thoughts, and physiological and emotional reactivity. Conclusions: The current study explores the associations between PTSD symptoms and psychiatric symptoms among college students exposed to CSA using a network analysis approach. These crucial symptoms of PTSD may have potential connections to psychosis. Target intervention and strategy should be developed to improve mental health and quality of life among these CSA victims. Furthermore, longitudinal studies are warranted to advance our understanding of PTSD and psychosis.

Co-occurrence of severe PTSD, somatic symptoms and dissociation in a large sample of childhood trauma inpatients: a network analysis

Co-occurrence of mental disorders including severe PTSD, somatic symptoms, and dissociation in the aftermath of trauma is common and sometimes associated with poor treatment outcomes. However, the interrelationships between these conditions at symptom level are not well understood. In the present study, we aimed to explore direct connections between PTSD, somatic symptoms, and dissociation to gain a deeper insight into the pathological processes underlying their comorbidity that can inform future treatment plans. In a sample of 655 adult inpatients with a diagnosis of severe PTSD following childhood abuse (85.6% female; mean age = 47.57), we assessed symptoms of PTSD, somatization, and dissociation. We analyzed the comorbidity structure using a partial correlation network with regularization. Mostly positive associations between symptoms characterized the network structure. Muscle or joint pain was among the most central symptoms. Physiological reactivation was central in the full network and together with concentrations problems acted as bridge between symptoms of PTSD and somatic symptoms. Headaches connected somatic symptoms with others and derealization connected dissociative symptoms with others in the network. Exposure to traumatic events has a severe and detrimental effect on mental and physical health and these consequences worsen each other trans-diagnostically on a symptom level. Strong connections between physiological reactivation and pain with other symptoms could inform treatment target prioritization. We recommend a dynamic, modular approach to treatment that should combine evidence-based interventions for PTSD and comorbid conditions which is informed by symptom prominence, readiness to address these symptoms and preference.

Excessive Worrying as a Central Feature of Anxiety during the First COVID-19 Lockdown-Phase in Belgium: Insights from a Network Approach

Since the WHO declared the COVID-19 pandemic on March 11, 2020, the novel coronavirus, SARS-CoV-2, has profoundly impacted public health and the economy worldwide. But there are not the only ones to be hit. The COVID-19 pandemic has also substantially altered mental health, with anxiety symptoms being one of the most frequently reported problems. Especially, the number of people reporting anxiety symptoms increased significantly during the first lockdown-phase compared to similar data collected before the pandemic. Yet, most of these studies relied on a unitary approach to anxiety, wherein its different constitutive features (i.e., symptoms) were tallied into one sum-score, thus ignoring any possibility of interactions between them. Therefore, in this study, we seek to map the associations between the core features of anxiety during the first weeks of the first Belgian COVID-19 lockdown-phase (n = 2,829). To do so, we implemented, in a preregistered fashion, two distinct computational network approaches: a Gaussian graphical model and a Bayesian network modelling approach to estimate a directed acyclic graph. Despite their varying assumptions, constraints, and computational methods to determine nodes (i.e., the variables) and edges (i.e., the relations between them), both approaches pointed to excessive worrying as a node playing an especially influential role in the network system of the anxiety features. Altogether, our findings offer novel data-driven clues for the ongoing field's larger quest to examine, and eventually alleviate, the mental health consequences of the COVID-19 pandemic.

Symptom networks of COVID-19-related versus other potentially traumatic events in a global sample

The potential mental health consequences of the coronavirus disease 2019 (COVID-19) pandemic are widely acknowledged; however, limited research exists regarding the nature and patterns of stress responses to COVID-19-related potentially traumatic events (PTEs) and the convergence/divergence with responses to other (non-COVID-19-related) PTEs. Network analysis can provide a useful method for evaluating and comparing these symptom structures. The present study includes 7034 participants from 86 countries who reported on mental health symptoms associated with either a COVID-19-related PTE (n = 1838) or other PTE (n = 5196). Using network analysis, we compared the centrality and connections of symptoms within and between each group. Overall, results show that the COVID-19-related network includes transdiagnostic symptom associations similar to networks tied to PTEs unrelated to the pandemic. Findings provide evidence for a shared centrality of depression across networks and theoretically consistent connections between symptoms. Network differences included stronger connections between avoidance-derealization and hypervigilance-depression in the COVID-19 network. Present findings support the conceptualization of psychological responses to pandemic-related PTEs as a network of highly interconnected symptoms and support the use of a transdiagnostic approach to the assessment and treatment of mental health challenges related to the COVID-19 pandemic.

Intervening on psychopathology networks: Evaluating intervention targets through simulations

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

The network of stress-related states and depression and anxiety symptoms during the COVID-19 lockdown

BACKGROUND

The coronavirus (COVID-19) pandemic and the social distancing protocols used to impede the spread of the virus may have severe mental health consequences. The purpose of this study was to investigate the network of components of pandemic-related negative psychological states (i.e., fear of infection, financial worries, loneliness) and symptoms of major depressive disorder (MDD) and generalized anxiety disorder (GAD).

METHODS

Data from 10,061 Norwegian adults recruited through an online survey during a period of strict social distancing protocols were analyzed by cross-sectional network methods.

RESULTS

Of the infection fears, fear of being infected, fear of dying from the coronavirus and fear of significant others dying from it had notable connections to the GAD symptoms anxiety and/or fear of awful events. The financial worry component worry about personal economy was connected to the MDD symptom sleep problems and to the GAD symptom generalized worry. Each of the loneliness components was connected to a specific MDD symptom. Depressed mood, low energy and worthlessness had the highest strength centrality among the MDD symptoms; generalized worry, uncontrollability of worry, and trouble relaxing among the GAD symptoms; fear of dying from the virus among the fear of infection components; and feeling isolated among the loneliness components.

LIMITATIONS

Full random sampling was not conducted, although the sample turned out to be relatively representative of the Norwegian population.

CONCLUSIONS

Some components of the pandemic-related distressing states of fear of infection, financial worry and loneliness seem to be associated with specific symptoms of MDD and GAD.

Bridging late-life depression and chronic somatic diseases: a network analysis

The clinical presentation of late-life depression is highly heterogeneous and likely influenced by the co-presence of somatic diseases. Using a network approach, this study aims to explore how depressive symptoms are interconnected with each other, as well as with different measures of somatic disease burden in older adults. We examined cross-sectional data on 2860 individuals aged 60+ from the Swedish National Study on Aging and Care in Kungsholmen, Stockholm. The severity of sixteen depressive symptoms was clinically assessed with the Comprehensive Psychopathological Rating Scale. We combined data from individual clinical assessment and health-registers to construct eight system-specific disease clusters (cardiovascular, neurological, gastrointestinal, metabolic, musculoskeletal, respiratory, sensory, and unclassified), along with a measure of overall somatic burden. The interconnection among depressive symptoms, and with disease clusters was explored through networks based on Spearman partial correlations. Bridge centrality index and network loadings were employed to identify depressive symptoms directly connecting disease clusters and depression. Sadness, pessimism, anxiety, and suicidal thoughts were the most interconnected symptoms of the depression network, while somatic symptoms of depression were less interconnected. In the network integrating depressive symptoms with disease clusters, suicidal thoughts, reduced appetite, and cognitive difficulties constituted the most consistent bridge connections. The same bridge symptoms emerged when considering an overall measure of somatic disease burden. Suicidal thoughts, reduced appetite, and cognitive difficulties may play a key role in the interconnection between late-life depression and somatic diseases. If confirmed in longitudinal studies, these bridging symptoms could constitute potential targets in the prevention of late-life depression.

COVID-19 lockdown has altered the dynamics between affective symptoms and social isolation among older adults: results from a longitudinal network analysis

The COVID-19 lockdown has drastically limited social interactions and brought about a climate of fear and uncertainty. These circumstances not only increased affective symptoms and social isolation among community dwelling older adults but also alter the dynamics between them. Using network analyses, we study the changes in these dynamics before and during the lockdown. Community-dwelling older adults (N = 419) completed questionnaires assessing depression, anxiety, and social isolation, before the COVID-19 pandemic, as part of a cohort study, and during the lockdown period. The total scores of these questionnaires were compared across time. For the network analyses, partial correlation networks were constructed using items in the questionnaires as nodes, separately at both timepoints. Changes in edges, as well as nodal and bridge centrality were examined across time. Depression and anxiety symptoms, and social isolation had significantly increased during the lockdown. Significant changes were observed across time on several edges. Greater connectivity between the affective and social isolation nodes at lockdown was observed. Depression symptoms have become more tightly coupled across individuals, and so were the anxiety symptoms. Depression symptoms have also become slightly decoupled from those of anxiety. These changing network dynamics reflect the greater influence of social isolation on affective symptoms across individuals and an increased vulnerability to affective disorders. These findings provide novel perspectives and translational implications on the changing mental health context amidst a COVID-19 pandemic situation.

Increased attention allocation to socially threatening faces in social anxiety disorder: A replication study

BACKGROUND

Threat-related attention bias has been implicated in the etiology and maintenance of social anxiety disorder (SAD), with attentional research increasingly using eye-tracking methodology to overcome the poor psychometric properties of response-time-based tasks and measures. Yet, extant eye-tracking research in social anxiety has mostly failed to report on psychometrics and attempts to replicate past results are rare. Therefore, we attempted to replicate a previously published eye-tracking study of gaze patterns in socially anxious and nonanxious participants as they viewed social threatening and neutral faces, while also exploring the psychometric properties of the attentional measures used.

METHODS

Gaze was monitored as participants freely viewed 60 different matrices comprised of eight socially-threatening and eight neutral faces, presented for 6000 ms each. Gaze patterns directed at threat and neutral areas of interest (AOIs) were compared by group. Internal consistency and test-retest reliability were also evaluated.

RESULTS

Relative to healthy controls, socially anxious patients dwelled significantly longer on threat faces, replicating prior findings with the same task. Internal consistency of total dwell time on threat and neutral AOIs was high, and two-week test-retest reliability was acceptable.

LIMITATIONS

Test-retest reliability was only examined for the control group, which had a small sample size.

CONCLUSION

Increased dwell time on socially threatening stimuli is a reliable, stable, and generalizable measure of attentional bias in adults with social anxiety.

The importance of transdiagnostic symptom level assessment to understanding prognosis for depressed adults: analysis of data from six randomised control trials

BACKGROUND

Depression is commonly perceived as a single underlying disease with a number of potential treatment options. However, patients with major depression differ dramatically in their symptom presentation and comorbidities, e.g. with anxiety disorders. There are also large variations in treatment outcomes and associations of some anxiety comorbidities with poorer prognoses, but limited understanding as to why, and little information to inform the clinical management of depression. There is a need to improve our understanding of depression, incorporating anxiety comorbidity, and consider the association of a wide range of symptoms with treatment outcomes.

METHOD

Individual patient data from six RCTs of depressed patients (total n = 2858) were used to estimate the differential impact symptoms have on outcomes at three post intervention time points using individual items and sum scores. Symptom networks (graphical Gaussian model) were estimated to explore the functional relations among symptoms of depression and anxiety and compare networks for treatment remitters and those with persistent symptoms to identify potential prognostic indicators.

RESULTS

Item-level prediction performed similarly to sum scores when predicting outcomes at 3 to 4 months and 6 to 8 months, but outperformed sum scores for 9 to 12 months. Pessimism emerged as the most important predictive symptom (relative to all other symptoms), across these time points. In the network structure at study entry, symptoms clustered into physical symptoms, cognitive symptoms, and anxiety symptoms. Sadness, pessimism, and indecision acted as bridges between communities, with sadness and failure/worthlessness being the most central (i.e. interconnected) symptoms. Connectivity of networks at study entry did not differ for future remitters vs. those with persistent symptoms.

CONCLUSION

The relative importance of specific symptoms in association with outcomes and the interactions within the network highlight the value of transdiagnostic assessment and formulation of symptoms to both treatment and prognosis. We discuss the potential for complementary statistical approaches to improve our understanding of psychopathology.