The Study of Psychopathology from the Network Analysis Perspective: A Systematic Review

Severity of depressive and anxious symptoms and its association with birth outcomes among pregnant women during the COVID-19 pandemic: a prospective case-control study

AIM

Comparing the anxiety and depression severity and their impact on subsequent birth outcomes in pregnant women before and during Omicron wave in Shanghai in 2022.

METHODS

The depression-anxiety symptoms networks were compared between the pregnant women during the outbreak period (outbreak group; n = 783) and a matched control group of pregnant women before the outbreak (pre-outbreak group; n = 783). The impact of baseline mental state on follow-up pregnancy and neonatal outcomes was also explored by logistic regression.

FINDINGS

Levels of depression and anxiety between the two groups were not significant different. Network analysis showed that central symptom "trouble relaxing" and bridge symptom "depressed mood" shared by both groups. Different symptom associations in different periods of the pandemic. Total scores and sub-symptom scores of prenatal depressive and anxious severities increased the odds ratios of maternal and neonatal syndromes. The influence of mental state on gestational and neonatal outcomes differed across different pandemic periods.

CONCLUSION

The Omicron wave did not have a significant negative impact on the depressive and anxious mood in pregnant women. Targeting central and bridge symptoms intervention may be effective in reducing their adverse effects on co-occurring of anxious and depressive mood and birth outcomes.

Problematic pornography use and novel patterns of escalating use: A cross-sectional network analysis with two independent samples

Modern internet pornography allows users to harness sexual novelty in numerous ways, which can be used to overcome desensitisation through increasing volume of use (quantitative tolerance), progressing to more stimulating genres (qualitative escalation), skipping between stimuli (tab-jumping), delaying orgasm ('edging'), and engaging in pornographic binges. However, existing research has not yet evaluated how these potentially reciprocal consumption patterns relate to problematic pornography use (PPU). To this end, we recruited two independent samples of male pornography users (N1 = 1,356, Mage = 36.86, SD = 11.26; N2 = 944, Mage = 38.69, SD = 12.26) and examined the relationships between these behavioural dimensions and self-reported difficulties in controlling one's pornography use. Data were analysed through the network analysis approach (using Gaussian graphical models). As hypothesised, i) quantitative tolerance was centrally placed within the overall network, and ii) acted as a statistical bridge node between other patterns of pornography use (e.g., pornographic binges), and all measured facets of PPU. Our results are consistent with other emerging literature suggesting that tolerance, pornographic binges, tab-jumping, and edging behaviours as relevant features ofPPU, and that upscaling overall usage may connect broader patterns of use with problematic engagement. Clinical and theoretical implications, as well as future research directions, are discussed.

Integrating Intra- and Interindividual Phenomena in Psychological Theories

Psychological science is divided into two distinct methodological traditions. One tradition seeks to understand how people function at the individual level, while the other seeks to understand how people differ from each other. Methodologies that have grown out of these traditions typically rely on different sources of data. While both use statistical models to understand the structure of the data, and these models are often similar, Molenaar (2004) showed that results from one type of analysis rarely transfer to the other, unless unrealistic assumptions hold. This raises the question how we may integrate these approaches. In this paper, we argue that formalized theories can be used to connect intra- and interindividual levels of analysis. This connection is indirect, in the sense that the relationship between theory and data is best understood through the intermediate level of phenomena: robust statistical patterns in empirical data. To illustrate this, we introduce a distinction between intra- and interindividual phenomena, and argue that many psychological theories will have implications for both types of phenomena. Formalization provides us with a methodological tool for investigating what kinds of intra- and interindividual phenomena we should expect to find if the theory under consideration were true.

Identification of the Core Characteristics of Vulnerable/Hypersensitive Narcissism and its Association with the Dark Triad in a Large International Sample: A Network Analysis Study

The current paper aimed to investigate the network structure and centrality indexes of hypersensitive narcissism using the hypersensitive Narcissism Scale (HSN). Additionally, we aimed to explore its relationships with dark triad personality aspects. A globally diverse sample of "53,981" participants (47.9% non-United States responders) completed the HSN and Dark Triad Dirty Dozen scale (DTD). We estimated the network structure across genders to determine the core characteristics of hypersensitive narcissism. Additionally, bridge and central nodes (characteristics) were identified. All analyses were performed using R-Studio programming software. The network comparison test indicated significant differences in the network structures between males and females (Network-Invariance: 0.0489, P < 0.01; Global Strength Invariance: 0.101, P < 0.01). In the network of HSN for male participants, characteristics with the highest strength centrality were "Highly affected by criticism" (HSN.2, strength = 1.08) and "Self-absorbed in personal pursuits" (HSN.8, strength = 1.28). For female participants, "Self-absorbed in personal pursuits" (HSN.8, strength = 1.32) and "privately annoyed by others' needs" (HSN.10, strength = 1.21) were the highest central characteristics. The assessment of bridge strength indicated that nodes HSN.2 (Highly sensitive to criticism), scoring 0.42, and DTD.1 (Tendency to manipulate for gain, a component of Machiavellianism), scoring 0.428, showed the highest bridge strength values. The current study identified core characteristics of hypersensitive narcissism and its correlation with dark triad personality, revealing gender-specific patterns and bridging symptoms between the two constructs. These findings showed that focusing on these core characteristics may be advantageous in treating individuals exhibiting elevated levels of narcissism.

A network perspective on posttraumatic stress disorder and comorbid borderline personality disorder symptoms

Background: Comorbidity between posttraumatic stress disorder (PTSD) and borderline personality disorder (BPD) is surrounded by diagnostic controversy and although various effective treatments exist, dropout and nonresponse are high.Objective: By estimating the network structure of comorbid PTSD and BPD symptoms, the current study illustrates how the network perspective offers tools to tackle these challenges.Method: The sample comprised of 154 patients with a PTSD diagnosis and BPD symptoms, assessed by clinician-administered interviews. A regularised partial correlation network was estimated using the GLASSO algorithm in R. Central symptoms and bridge symptoms were identified. The reliability and accuracy of network parameters were determined through bootstrapping analyses.Results: PTSD and BPD symptoms largely clustered into separate communities. Intrusive memories, physiological cue reactivity and loss of interest were the most central symptoms, whereas amnesia and suicidal behaviour were least central.Conclusions: Present findings suggest that PTSD and BPD are two distinct, albeit weakly connected disorders. Treatment of the most central symptoms could lead to an overall deactivation of the network, while isolated symptoms would need more specific attention during therapy. Further experimental, longitudinal research is needed to confirm these hypotheses.Trial registration: ClinicalTrials.gov identifier: NCT03833453.

Inter-relationships of depression and insomnia symptoms with life satisfaction in stroke and stroke-free older adults: Findings from the Health and Retirement Study based on network analysis and propensity score matching

BACKGROUND

Depression and insomnia are common co-occurring psychiatric problems among older adults who have had strokes. Nevertheless, symptom-level relationships between these disorders remain unclear.

OBJECTIVES

In this study, we compared inter-relationships of depression and insomnia symptoms with life satisfaction among older stroke patients and stroke-free peers in the United States.

METHODS

The study included 1026 older adults with a history of stroke and 3074 matched controls. Data were derived from the US Health and Retirement Study. Depression, insomnia and life satisfaction were assessed. Propensity score matching was employed to identify demographically-similar groups of stroke patients and controls. Central and bridge symptoms were assessed using Expected influence (EI) and bridge EI, respectively.

RESULTS

The prevalence of depression in the stroke group (25.0 %) was higher than that of controls (14.3 %, P < 0.001). In stroke group, "Feeling depressed" (CESD1; EI: 5.80), "Feeling sad" (CESD7; EI: 4.67) and "Not enjoying life" (CESD6; EI: 4.51) were the most central symptoms, while "Feeling tired in the morning" (JSS4; BEI: 1.60), "Everything was an effort" (CESD2; BEI: 1.21) and "Waking up during the night" (JSS2; BEI: 0.98) were key bridge symptoms. In controls, the most central symptoms were "Lack of happiness" (CESD4; EI: 6.45), "Feeling depressed" (CESD1; EI: 6.17), and "Feeling sad" (CESD7; EI: 6.12). Furthermore, "Feeling tired in the morning" (JSS4; BEI: 1.93), "Everything was an effort" (CESD2; BEI: 1.30), and "Waking up too early" (JSS3; BEI: 1.12) were key bridge symptoms. Life satisfaction had the most direct associations with "Not enjoying life" (CESD6) and "Feeling lonely" (CESD5) in the two groups, respectively.

CONCLUSION

Older adults with stroke exhibited more severe depression and insomnia symptoms. Interventions targeting central and bridge symptoms may help to mitigate the co-occurrence of these symptoms.

Examining network structure of impulsivity and depression in adolescents and young adults: A two-sample study

BACKGROUND

Depression ranks as one of the top five contributors to ill health in youth, the most formative period in life. Extensive research has highlighted the significant role of impulsivity in understanding depression. However, there has been limited exploration into how each dimension of impulsivity uniquely affect depressive symptoms, especially across crucial developmental stages like adolescence and young adulthood.

METHODS

This study investigates the unique relationships between impulsivity (assessed by the short UPPS-P scale) and depression (assessed by the Patient Health Questionnaire-9) via network analysis. We analysed data from a total of 2296 participants, comprising 858 adolescents aged 14-17 years and 1438 young adults aged 18-25 years, to estimate both a combined network and age-group specific networks. Key features of the networks, including their structure, global connectivity, and bridge nodes, were compared.

RESULTS

The results indicated that age differentially impacts individual depression symptoms, both directly and indirectly, via impulsivity dimensions. The comparison test revealed consistent network structures between the two age groups, with several robust pathways, such as lack of perseverance to concentration difficulties, sensation seeking to suicidal ideation, and negative urgency to feelings of worthlessness. Negative urgency and lack of perseverance were identified as bridge nodes across the two networks.

LIMITATIONS

The study employed a cross-sectional design, which limits the ability to estimate causal or temporal relationships.

CONCLUSIONS

The current findings highlight the significance of tailoring intervention strategies to individual symptom profiles and assessing negative urgency and lack of perseverance as potential early targets for depression among youth.

A network analysis of exhaustion disorder symptoms throughout treatment

BACKGROUND

Stress-induced Exhaustion Disorder (ED) is associated with work absenteeism and adverse health outcomes. Currently, little is known regarding how the symptoms of ED are interrelated and whether the patterns of symptoms influence treatment outcomes. To this end, the current study applied network analyses on ED patients participating in a multimodal intervention.

METHODS

The first aim of the study was to explore the internal relationships between exhaustion symptoms and identify symptoms that were more closely related than others. A second aim was to examine whether the baseline symptom network of non-responders to treatment was more closely connected than the baseline symptom networks of responders, by comparing the sum of all absolute partial correlations in the respective groups' symptom network. This comparison was made based on the hypothesis that a more closely connected symptom network before treatment could indicate poorer treatment outcomes. Network models were constructed based on self-rated ED symptoms in a large sample of patients (n = 915) participating in a 24-week multimodal treatment program with a 12-month follow-up.

RESULTS

The internal relations between self-rated exhaustion symptoms were stable over time despite markedly decreased symptom levels throughout participation in treatment. Symptoms of limited mental stamina and negative emotional reactions to demands were consistently found to be the most closely related to other ED symptoms. Meanwhile, sleep quality and irritability were weakly related to other exhaustion symptoms. The symptom network for the full sample became significantly more closely connected from baseline to the end of treatment and 12-month follow-up. The symptom network of non-responders to treatment was not found to be more closely connected than the symptom network of responders at baseline.

CONCLUSIONS

The results of the current study suggest symptoms of limited mental stamina and negative emotional reactions to demands are central ED symptoms throughout treatment, while symptoms of irritability and sleep quality seem to have a weak relation to other symptoms of ED. The implications of these findings are discussed in relation to the conceptualization, assessment, and treatment of ED.

TRIAL REGISTRATION

The clinical trial was registered on Clinicaltrials.gov 2017-12-02 (Identifier: NCT03360136).

Testing Conditional Independence in Psychometric Networks: An Analysis of Three Bayesian Methods

Network psychometrics uses graphical models to assess the network structure of psychological variables. An important task in their analysis is determining which variables are unrelated in the network, i.e., are independent given the rest of the network variables. This conditional independence structure is a gateway to understanding the causal structure underlying psychological processes. Thus, it is crucial to have an appropriate method for evaluating conditional independence and dependence hypotheses. Bayesian approaches to testing such hypotheses allow researchers to differentiate between absence of evidence and evidence of absence of connections (edges) between pairs of variables in a network. Three Bayesian approaches to assessing conditional independence have been proposed in the network psychometrics literature. We believe that their theoretical foundations are not widely known, and therefore we provide a conceptual review of the proposed methods and highlight their strengths and limitations through a simulation study. We also illustrate the methods using an empirical example with data on Dark Triad Personality. Finally, we provide recommendations on how to choose the optimal method and discuss the current gaps in the literature on this important topic.

Exploring the differential effects of psychological resilience and social support in mitigating post-traumatic psychiatric symptoms: real-world network analysis of front-line rescuers

BACKGROUND

Although both psychological resilience and social support are widely believed to be effective in alleviating post-traumatic psychiatric symptoms in individuals with traumatic events, there has been a lack of comparative analysis of their intervention effects on different post-traumatic psychiatric symptoms. Furthermore, previous studies have mostly failed to control for potential confounding effects caused by different traumatic events.

AIMS

We used the novel network analysis approach to examine the differential moderating effects of psychological resilience and social support on post-traumatic psychiatric symptoms, controlling for the confounding effects of traumatic events.

METHOD

We recruited 264 front-line rescuers who experienced the same traumatic event. Quantified edge weights and bridge expected influence (BEI) were applied to compare the alleviating effects of psychological resilience and social support.

RESULTS

Our study revealed distinct correlations in a sample of front-line rescuers: social support negatively correlates more with psychosomatic symptoms, notably fatigue in depressive networks and sleep disturbance in post-traumatic stress disorder (PTSD) networks, whereas psychological resilience shows fewer such correlations. Quantitative analysis using BEI indicated that psychological resilience more effectively suppresses depressive and anxiety symptom networks, whereas social support more significantly inhibits PTSD symptom networks.

CONCLUSIONS

The current study represents the first attempt to examine the differential effects of psychological resilience and social support on post-traumatic outcomes in real-world emergency rescuers, controlling for the confounding effect of traumatic events. Our results can act as the theoretical reference for future precise and efficient post-trauma psychological interventions.

Recent Trends in Plastic Surgery: A Network Analysis of the Abstract Titles of the Largest German Plastic Surgery Congress 2023

BACKGROUND

Every year, German-speaking experts in plastic, reconstructive, and aesthetic surgery gather to discuss the latest developments at Germany's largest conference for plastic surgery, the joint annual meeting of the German Society of Plastic, Reconstructive and Aesthetic Surgery (DGPRÄC) and the Association of German Aesthetic Plastic Surgeons (VDÄPC). Since the topics of the conference have a lasting impact on the practice and research of plastic surgery, an examination of the presented content provides insight into the driving developments in plastic surgery in Germany.

MATERIALS AND METHODS

We conducted a retrospective network analysis of all abstract titles from the DGPRÄC and VDÄPC annual meeting in 2023. Data were extracted regarding titles, language, author, and place of origin, and the titles were categorized into the four pillars of the specialty. The titles were standardized and subjected to network analysis.

RESULTS

A total of 299 titles from 281 lectures and 18 instructional courses were analyzed. After preprocessing the data, 2463 words with 9384 connections qualified for network analysis. The most frequently mentioned keywords throughout the congress were 'Surgery', 'Breast', 'Reconstruction', 'Flap', 'Patient', 'Tissue', and 'Therapy'. Locations contributing the highest number of abstracts were Ludwigshafen, Hanover, Leipzig, and Munich.

CONCLUSION

In the era of big data, network analysis provides the ability to identify underlying structures and nodes in multidimensional, complex datasets. This study demonstrates the useful application of network analysis to identify thematic focuses and connections at the current DGPRÄC and VDÄPC annual meeting. Sites of intensified research could thus be identified.

Unraveling the core symptoms of mental health in senior grade three students- a network analysis

Background

Adolescence is not only an important transitional period of many developmental challenges, but also a high risk period for mental health problems. Psychotherapy is recommended for mental health problems in adolescents, but its effectiveness is not always satisfactory. One possible contributing factor may be the lack of clarity surrounding core symptoms.

Methods

In this study, we investigated the mental health status of senior grade three students, a group of adolescents facing college entrance exams, by the Middle School Student Mental Health Test (MHT) and analyzed the core symptoms by network analysis. This study was conducted through an online survey platform (www.xiaodongai.com) from 15 February 2023 to 28 March 2024. The subjects scanned a QR code with their mobile phone to receive the questionnaire.

Results

The mean age of these 625 students were 18.11 ± 2.90 years. There are 238 male participants and 387 female participants. 107 individuals scored above 56 (107/461, 23.2%), with individual scale scores over 8 up to over 60% of participating students. Notably, the top three prominent symptoms were "academic anxiety", "allergic tendency" and "somatic symptoms". However, upon conducting network analysis, it became evident that three strongest edges in this network were "somatic symptoms" and "impulsive tendency", "academic anxiety" and "social anxiety" as well as "social anxiety" and "Loneliness tendency". "somatic symptoms", "social anxiety" and "self-blame tendency" exerted the highest expected influence. This suggests that, statistically speaking, these three symptoms exhibited the strongest interconnections within the network.

Limitation

Cross-sectional analysis; Bias in self-reported variables.

Conclusion

These findings can deepen the knowledge of mental health among senior grade three students and provide some implications (i.e., targeting symptoms having highest expected influence) for clinical prevention and intervention to address the mental health needs of this particular group.

Brazilian Scale for Evaluation of Mental Health Care Needs: Additional Evidence

OBJECTIVE

To investigate validity evidence of the Brazilian Scale for Evaluation of Mental Health Care Needs (CuidaSM).

METHODS

This is a psychometric study, which seeks additional evidence of internal structure. Data collection was carried out in 11 Primary Health Care (PHC) services , which implement the Health Care Planning (HCP) methodology, distributed across the five Brazilian regions. The preliminary version of CuidaSM, containing a block self-referred by the user and another block evaluated by PHC professionals, was applied to users aged 18 or over who attended the PHC services for consultation with a higher education professional. The techniques of confirmatory factor analysis and network analysis were used to investigate validity evidence. For the primary data of the confirmatory factor analysis, the factorial loads and the item's predictive power (R2) were used. Six model adjustment indices were adopted and reliability was measured by three indicators using Bayesian estimation.

RESULTS

A total of 879 users participated in the study. By confirmatory factor analysis, factorial loads ranged from 0.43 to 0.99 and R2 from 0.19 to 0.98. Both the primary indicators and the model adequacy indices were established at satisfactory and consistent levels. The network analysis showed that the items were appropriately associated with their peers, respecting the established dimensions, which again indicates the sustainability and stability of the proposed model.

CONCLUSIONS

The study findings confirm a consistent and reliable model of the instrument, through a combination of techniques. Considering the importance of using solid instruments in clinical practice, CuidaSM is a promising tool for population-based management and network care organization, aligned with HCP proposals.

Hazardous drinking in young adults with co-occurring PTSD and psychosis symptoms: A network analysis

BACKGROUND

Existing literature suggests the co-occurrence of post-traumatic stress disorder (PTSD) and psychosis among young adults is related to hazardous drinking. However, the influencing mechanisms among these co-occurrences are inconclusive. Thus, this study aimed to investigate the symptomatic associations between PTSD, psychosis, and hazardous drinking.

METHODS

This study included 96,218 young Chinese adults, divided into three groups (PTSD, Psychosis, and co-occurring PTSD-Psychosis). PTSD, psychosis, and hazardous drinking were measured by the ten-item Trauma Screening Questionnaire, the seven-item Psychosis Screener Scale, and the four-item Alcohol Use Disorders Identification Test, respectively. Network analysis was utilized to explore and compare the symptomatic correlation between PTSD, psychosis, and hazardous drinking.

RESULTS

In this study, the most crucial symptom (both central and bridge) was "delusion of control" among the three networks. Hazardous drinking was another main bridge symptom. Compared to the Psychosis group and the co-occurring PTSD-Psychosis group, "Delusion of reference or persecution" to "Grandiose delusion" was the strongest edge in "the network structure of the PTSD group".

LIMITATIONS

The cross-sectional study cannot determine the causal relationship. Applying self-reporting questionnaires may cause inherent bias. Young adult participants limited the generalization of the results to other groups.

CONCLUSIONS

Among the three network structures, delusion of control was the most crucial symptom, and hazardous drinking was another bridge symptom; the edge of delusion of reference or persecution and grandiose delusion was strongest in the PTSD group's network. Efforts should be taken to develop diverse targeted interventions for these core symptoms to relieve PTSD, psychosis, and hazardous drinking in young adults.

What time-varying network models based on functional analysis tell us about the course of a patient's problem

Background: Relations among psychological variables are assumed to be complex and to vary over time. Personalized networks can model multivariate complex interactions. The development of time-varying networks allows to model the variation of parameters over time. Objectives: We aimed to determine the value of time-varying networks for clinical practice. Methods: We applied time-varying mixed graphical models (TV-MGM) and time-varying vector autoregressive models (TV-VAR) to intensive longitudinal data of nine participants with depressive symptoms (n = 6) or anxiety (n = 3). Results: Most of the participants showed temporal changes in network topology within the assessment period of 30 days. Time-varying networks of participants with small, medium, and large time variability in edge parameters clearly show the different temporal evolvements of dynamic interactions between variables. The case example indicates clinical utility but also limitations to the application of time-varying networks in clinical practice. Conclusion: Time-varying network models provide a data-driven and exploratory approach that could complement current diagnostic standards by reflecting interacting, often mutually reinforcing processes of mental health problems and by accounting for variation over time. They can be used to generate hypotheses for further confirmatory and clinical testing.

Multilayer depressive symptom networks in adults with bodily pain living in precarious housing or homelessness

Housing insecurity is associated with co-occurring depression and pain interfering with daily activities. Network analysis of depressive symptoms along with associated risk or protective exposures may identify potential targets for intervention in patients with co-occurring bodily pain. In a community-based sample of adults (n = 408) living in precarious housing or homelessness in Vancouver, Canada, depressive symptoms were measured by the Beck Depression Inventory; bodily pain and impact were assessed with the 36-item Short Form Health Survey. Network and bootstrap permutation analyses were used to compare depressive symptoms endorsed by Low versus Moderate-to-Severe (Mod + Pain) groups. Multilayer networks estimated the effects of risk and protective factors. The overall sample was comprised of 78% men, mean age 40.7 years, with 53% opioid use disorder and 14% major depressive disorder. The Mod + Pain group was characterized by multiple types of pain, more persistent pain, more severe depressive symptoms and a higher rate of suicidal ideation. Global network connectivity did not differ between the two pain groups. Suicidal ideation was a network hub only in the Mod + Pain group, with high centrality and a direct association with exposure to lifetime trauma. Antidepressant medications had limited impact on suicidal ideation. Guilt and increased feelings of failure represented symptoms from two other communities of network nodes, and completed the shortest pathway from trauma exposure through suicidal ideation, to the non-prescribed opioid exposure node. Interventions targeting these risk factors and symptoms could affect the progression of depression among precariously housed patients.

The relations of psychotic-like experiences (PLEs) and depressive symptoms and the bias of depressive symptoms during the clustering among Chinese adolescents: Findings from the network perspective

BACKGROUND

There are rare studies about the network structure of psychotic-like experiences (PLEs) and depressive symptoms among adolescents. Studies have widely acknowledged that PLEs in adolescents confer a higher risk of depressive symptoms, but the complex interactions remain inadequately understood. Our study aimed to examine the hierarchy and inter-associations of PLEs and depressive symptoms in a large adolescent sample from the network analysis perspective.

METHODS

A total of 5008 Chinese adolescents were enrolled in our sample. Community Assessment of Psychic Experiences-42 (CAPE-42) was applied to build the network. Centrality indexes were calculated to represent the significance of nodes in the network. Community detection was conducted to figure out the specific clustering of nodes. Demographic information was collected for the sub-network comparisons. Accuracy and stability of the network were also tested.

RESULTS

"Failure", "External control", and "Lack of activity" were the most central nodes. The main bridge nodes linking PLEs and depressive symptoms were "Failure", "Guilty", and "No future". Positive PLE "Odd looks" and negative PLE "Unable to terminate" are the two PLEs that were most relevant to depressive nodes. Community detection further demonstrated the bias of depressive nodes in the data-driven clustering. Comparative sub-network analysis suggested that age was the only demographic factor related to the current network.

CONCLUSION

In this study of a large adolescent sample, we first demonstrated the network structure and specific clustering preference of PLEs and depressive symptoms. Our findings may enhance the understanding of the relationship between PLE and depressive symptoms.

The Connection Between Sleep Problems and Emotional and Behavioural Difficulties in Autistic Children: A Network Analysis

The interactions between sleep problems, autism symptoms and emotional and behavioural difficulties were explored using network analysis in 240 autistic children (mean age: 8.8 years, range 5-13 years) with moderate to severe sleep problems. Findings revealed a highly connected and interpretable network, with three separate clusters identified of the modelled variables. Depression, anxiety and behavioural difficulties were the most central variables of the network. Depression, anxiety and restricted repetitive and stereotyped patterns behaviours (RRBs) were the strongest bridging variables in the network model, transmitting activation both within and between other symptom clusters. The results highlight that depression and anxiety were highly connected symptoms within the network, suggesting support in these areas could be helpful, as well as future research.

A network analysis of ICD-11 Complex PTSD, emotional processing, and dissociative experiences in the context of psychological trauma at different developmental stages

Objective

Traumatic experiences are a significant risk factor for psychological disturbances, including disorders such as complex posttraumatic stress disorder, emotion-processing problems, and trauma-related dissociative experiences. The present investigation examined the coexistence of these symptoms using a network analysis model.

Method

This study included a sample of 406 people referred to comprehensive health centers in Tehran from September to December 2023 with psychopathological syndromes. Variables were assessed using The International Trauma Questionnaire, International Measurement of Exposure to Traumatic Event checklist, Baker Emotional Processing Questionnaire, and Dissociative Experiences. A regularized partial correlation network and Glasso algorithm, in combination with Extended Bayesian information criteria, were applied to estimate the network structure.

Results

Signs of unprocessed emotions and disturbance in self-organization symptoms were the most important symptoms in the symptom network, forming strong connections with other nodes. Thereby, these two symptoms can be regarded as the most important clinical manifestations in the symptom network following traumatic experiences. Three distinct symptom communities were identified: the community of traumatic experiences (childhood, adolescence, adulthood), the community of dissociative experiences (amnesia, depersonalization/derealization, and absorption), and the community of emotional processing (suppression, unpleasant emotional experience, Signs of unprocessed emotions, avoidance, and emotional control, posttraumatic stress disorder symptoms and disturbance in self-organization symptoms). The strongest edges observed were between childhood trauma-adolescence trauma (0.473) in the community of traumatic experiences, between amnesia and depersonalization/derealization (0.644) in the community of dissociative experiences, and between disturbance in self-organization symptoms and unprocessed emotions (0.324) in the community of emotional processing, indicating the recurrent occurrence of these symptoms.

Conclusion

In this study, disturbance in self-organization symptoms was identified as the central psychopathologic symptom in individuals experiencing traumas at different developmental stages. It seems that adolescent trauma and not childhood trauma plays a more decisive role in the symptoms that a person manifests after traumatic experiences. Also, posttraumatic stress disorder symptoms and disturbance in self-organization symptoms were recognized in the cluster of emotional processing symptoms and can have substantial roles in prioritizing therapeutic measures.

A network model of depressive and anxiety symptoms: a statistical evaluation

BACKGROUND

Although network analysis studies of psychiatric syndromes have increased in recent years, most have emphasized centrality symptoms and robust edges. Broadening the focus to include bridge symptoms within a systematic review could help to elucidate symptoms having the strongest links in network models of psychiatric syndromes. We conducted this systematic review and statistical evaluation of network analyses on depressive and anxiety symptoms to identify the most central symptoms and bridge symptoms, as well as the most robust edge indices of networks.

METHODS

A systematic literature search was performed in PubMed, PsycINFO, Web of Science, and EMBASE databases from their inception to May 25, 2022. To determine the most influential symptoms and connections, we analyzed centrality and bridge centrality rankings and aggregated the most robust symptom connections into a summary network. After determining the most central symptoms and bridge symptoms across network models, heterogeneity across studies was examined using linear logistic regression.

RESULTS

Thirty-three studies with 78,721 participants were included in this systematic review. Seventeen studies with 23 cross-sectional networks based on the Patient Health Questionnaire (PHQ) and Generalized Anxiety Disorder (GAD-7) assessments of clinical and community samples were examined using centrality scores. Twelve cross-sectional networks based on the PHQ and GAD-7 assessments were examined using bridge centrality scores. We found substantial variability between study samples and network features. 'Sad mood', 'Uncontrollable worry', and 'Worrying too much' were the most central symptoms, while 'Sad mood', 'Restlessness', and 'Motor disturbance' were the most frequent bridge centrality symptoms. In addition, the connection between 'Sleep' and 'Fatigue' was the most frequent edge for the depressive and anxiety symptoms network model.

CONCLUSION

Central symptoms, bridge symptoms and robust edges identified in this systematic review can be viewed as potential intervention targets. We also identified gaps in the literature and future directions for network analysis of comorbid depression and anxiety.

Examining the validity of the Mini-Mental State Examination (MMSE) and its domains using network analysis

BACKGROUND

The Mini-Mental State Examination (MMSE) is the most widely used standardised screener for impairments across a range of cognitive domains. However, the degree to which its domains (orientation, registration, attention, recall, language, and visuospatial) capture cognitive functioning measured using standardised neuropsychological tests is unclear.

METHOD

A longitudinal research design with four biannual assessments over a 6-year period was used with an initial sample of 1037 older adults (aged above 70 years). Participants completed MMSE and neuropsychological tests at each assessment. Network analysis was utilised to investigate unique associations among the MMSE and its domains and neuropsychological test performance at each time point.

RESULTS

The total MMSE and two of its domains, language and recall, were associated with neuropsychological memory performance. The MMSE orientation, registration and visuospatial domains did not have any unique associations with neuropsychological performance. No stable internal interconnections between MMSE domains were found over time. The association of total MMSE as well as its recall domain with neuropsychological memory performance remained very similar over the 6-year period.

CONCLUSIONS

The present study adds evidence to the validity of the MMSE and supports the clinical usage of the MMSE, whereby the total score is used for screening patients with or without cognitive impairments, with repeated administration to monitor cognitive changes over time, to inform intervention. However, the tool is not able to diagnose the cases for changes in specific cognitive domains and as such, should not replace a complete neuropsychological assessment.

The association between team cohesion and performance: A network analysis of nurses

Team cohesion, as a necessary condition for the cooperation and development of a team, has been shown to have a strong association with team performance. However, the mechanism of this internal correlation is unclear and more in-depth studies are lacking. The study aimed to explore the complex links between the dimensions of team cohesion and performance in nurses. A total of 1639 practice nurses from 118 nursing teams were included in this cross-sectional study. Data were collected using the Team Cohesion Scale (including consistency of affection, behavior, and cognition) and the Team Effectiveness Scale (including cooperation satisfaction, and task performance). Using network analysis, the team cohesion and performance network was constructed, and the strength and bridge strength of nodes were calculated. The results showed that the edges between team cohesion and performance dimensions were all positively correlated. Cooperation satisfaction and consistency of affection are the core variables in the network. Interventions targeting cooperation satisfaction and consistency of affection need to be developed at the team level to maximize team cohesion and performance among nurses.

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.

Machine Learning and Bayesian Network Analyses Identifies Psychiatric Disorders and Symptom Associations with Insomnia in a national sample of 31,285 Treatment-Seeking College Students

Background

A better understanding of the structure of relations among insomnia and anxiety, mood, eating, and alcohol-use disorders is needed, given its prevalence among young adults. Supervised machine learning provides the ability to evaluate the discriminative accuracy of psychiatric disorders associated with insomnia. Combined with Bayesian network analysis, the directionality between symptoms and their associations may be illuminated.

Methods

The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. Firstly, an elastic net regularization model was trained to predict, via repeated 10-fold cross-validation, which psychiatric disorders were associated with insomnia severity. Seven disorders were included: major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder. Secondly, using a Bayesian network approach, completed partially directed acyclic graphs (CPDAG) built on training and holdout samples were computed via a Bayesian hill-climbing algorithm to determine symptom-level interactions of disorders most associated with insomnia [based on SHAP (SHapley Additive exPlanations) values)] and were evaluated for stability across networks.

Results

Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .449 (.016); RMSE = 5.00 [.081]), with comparable performance in accounting for variance explained in the holdout sample [R2 = .33; RMSE = 5.47). SHAP indicated the presence of any psychiatric disorder was associated with higher insomnia severity, with major depressive disorder demonstrated to be the most associated disorder. CPDAGs showed excellent fit in the holdout sample and suggested that depressed mood, fatigue, and self-esteem were the most important depression symptoms that presupposed insomnia.

Conclusion

These findings offer insights into associations between psychiatric disorders and insomnia among college students and encourage future investigation into the potential direction of causality between insomnia and major depressive disorder.

Trial registration

Trial may be found on the National Institute of Health RePORTER website: Project Number: R01MH115128-05.

The Underlying Structure of Preventive Behaviors and Related Factors During the COVID-19 Pandemic: A Comprehensive Network Analysis

BACKGROUND

Various strategies against COVID-19 have been adopted in different countries, with vaccination and mask-wearing being widely used as self-preventive interventions. However, the underlying structure of these behaviors and related factors remain unclear.

PURPOSE

In this study, we aimed to explore the network structure of preventive behaviors during the COVID-19 pandemic and their underlying factors, incorporating age and sex in the network.

METHODS

We used a multi-center sample of 20,863 adults who were vaccinated against COVID-19 in China between April 1, 2021, and June 1, 2021. Networks were estimated using unregularized partial correlation models. We also estimated the accuracy and stability of the network.

RESULTS

The preventive behaviors related to network factors revealed that self-initiated vaccination was more connected with cognition factors, and mask-wearing was more connected with personal profiles. The two clusters were linked through information-seeking and political beliefs. Moreover, self-initiated vaccination was negatively connected with vaccine hesitancy and concerns about COVID-19 vaccines and positively connected with trust in the vaccines, pandemic-related altruism, political beliefs, and being married. Mask-wearing was negatively connected with being a professional/white collar worker and higher education level and positively connected with regular physical examination, self-rated health, migration, being married, and better family relationships. Incorporation of age and sex into the network revealed relevant associations between age and mask-wearing and age and self-initiated vaccination. The network was highly accurately estimated. The subset bootstrap showed that the order of node strength centrality, betweenness, and closeness were all stable. The correlation stability coefficient (CS-coefficient) also showed the stability of this estimate, with 0.75 for node strength, 0.75 for betweenness, and 0.67 for closeness.

CONCLUSIONS

The internal structures of vaccination and mask-wearing behaviors were quite different, the latter of which were mainly affected by socioeconomic status and health-related behaviors and the former by knowledge about vaccines and political beliefs. Information-seeking and family relationships were the bridge factors connecting these two self-preventive behavior clusters, suggesting the direction of future efforts.

The dynamics of psychological attributes and symptomatic comorbidity of depression in children and adolescents

PURPOSE

This investigation aimed to explore attribute dynamics and symptomatic comorbidity of depression with internalizing, externalizing, and other personal-contextual problems in children and adolescents from a network analysis.

METHODS

We tested an attribute network of regularized partial correlations, standard and alternative centrality measures, and comorbidity bridge symptoms according to centrality bridge measures.

RESULTS

Regularized partial correlation network and a centrality measures graph shown the prominent position of social problems and anxiety-depression. Minimum spanning tree (MST) found a hierarchical dynamics between attributes where mixed anxiety-depression was identified as the core and the other attributes were hierarchically connected to it by being positioned in six branches that are differentiated according to their theoretical contents. The most central connections are established with the attributes of their own community or theoretical groups, and 37 bridge symptoms were identified in all networks.

CONCLUSIONS

A significant role of mixed anxiety depression as an activator and intermediary of psychopathologies was supported as a central attribute of internalizing problems. Aggressive behavior as part of the broad externalizing dimension was one of the constructs that most intensively activate the network, and social problems were also distinguished as a relevant factor not only in terms of connections and central attributes but also in terms of bridge symptoms and comorbidity. This framework extends to the study of symptomatic "comorbidity."

The network structure of psychopathological and resilient responses to the pandemic: A multicountry general population study of depression and anxiety

Commonly identified patterns of psychological distress in response to adverse events are characterized by resilience (i.e., little to no distress), delayed (i.e., distress that increases over time), recovery (i.e., distress followed by a gradual decrease over time), and sustained (i.e., distress remaining stable over time). This study aimed to examine these response patterns during the COVID-19 pandemic. Anxiety and depressive symptom data collected across four European countries over the first year of the pandemic were analyzed (N = 3,594). Participants were first categorized into groups based on the four described patterns. Network connectivity and symptom clustering were then estimated for each group and compared. Two thirds (63.6%) of the sample displayed a resilience pattern. The sustained distress network (16.3%) showed higher connectivity than the recovery network (10.0%) group, p = .031; however, the resilient network showed higher connectivity than the delayed network (10.1%) group, p = .016. Regarding symptom clustering, more clusters emerged in the recovery network (i.e., three) than the sustained network (i.e., two). These results replicate findings that resilience was the most common mental health pattern over the first pandemic year. Moreover, they suggest that high network connectivity may be indicative of a stable mental health response over time, whereas fewer clusters may be indicative of a sustained distress pattern. Although exploratory, the network perspective provides a useful tool for examining the complexity of psychological responses to adverse events and, if replicated, could be useful in identifying indicators of protection against or vulnerability to future psychological distress.

Network analysis of depressive and anxiety symptoms in older Chinese adults with diabetes mellitus

Background

The move away from investigating mental disorders as whole using sum scores to the analysis of symptom-level interactions using network analysis has provided new insights into comorbidities. The current study explored the dynamic interactions between depressive and anxiety symptoms in older Chinese adults with diabetes mellitus (DM) and identified central and bridge symptoms in the depression-anxiety network to provide potential targets for prevention and intervention for depression and anxiety.

Methods

This study used a cross-sectional design with data from the 2017-2018 wave of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). A regularized partial correlation network for depressive and anxiety symptoms was estimated based on self-reported scales completed by 1685 older adults with DM aged 65 years or older. Depressive and anxiety symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) and the Seven-Item Generalized Anxiety Disorder Scale (GAD-7), respectively. Expected influence (EI) and bridge expected influence (BEI) indices were calculated for each symptom.

Results

According to cutoff scores indicating the presence of depression and anxiety, the prevalences of depression and anxiety in our sample were 52.9% and 12.8%, respectively. The comorbidity rate of depression and anxiety was 11.5%. The six edges with the strongest regularized partial correlations were between symptoms from the same disorder. "Feeling blue/depressed", "Nervousness or anxiety", "Uncontrollable worry", "Trouble relaxing", and "Worry too much" had the highest EI values. "Nervousness or anxiety" and "Everything was an effort" exhibited the highest BEI values.

Conclusion

Central and bridge symptoms were highlighted in this study. Targeting these symptoms may be effective in preventing the comorbidity of depressive and anxiety symptoms and facilitate interventions in older Chinese adults with DM who are at risk for or currently have depressive and anxiety symptoms.

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.

A comparison of psychiatric symptoms between mental health professionals with and without post-infection sequelae of COVID-19

Post-infection sequelae of COVID-19 (PISC) have raised public health concerns. However, it is not clear whether infected mental health professionals (MHPs) with PISC have experienced more psychiatric symptoms than MHPs without PISC do. This study examined differences in the prevalence of self-reported depression, anxiety, insomnia and suicidality as well as the network structures of these symptoms between these two groups. Participants completed questionnaire measures of psychiatric symptoms and demographics. Expected influence was used to measure centrality of symptoms and network comparison tests were adopted to compare differences in the two network models. The sample comprised 2,596 participants without PISC and 2,573 matched participants with PISC. MHPs with PISC had comparatively higher symptom levels related to depression (55.2% vs. 23.5 %), anxiety (32.0% vs. 14.9 %), insomnia (43.3% vs. 17.3 %), and suicidality (9.6% vs. 5.3 %). PHQ4 ("Fatigue"), PHQ6 ("Guilt"), and GAD2 ("Uncontrollable Worrying") were the most central symptoms in the "without PISC" network model. Conversely, GAD3 ("Worry too much"), GAD5 ("Restlessness"), and GAD4 ("Trouble relaxing") were more central in the "with PISC" network model. In sum, MHPs with PISC experienced comparatively more psychiatric symptoms and related disturbances. Network results provide foundations for the expectation that MHPs with PISC may benefit from interventions that address anxiety-related symptoms, while those without PISC may benefit from interventions targeting depression-related symptoms.

Understanding of Depressive Symptomatology across Major Depressive Disorder and Bipolar Disorder: A Network Analysis

Background and Objectives: Depressive symptoms are prominent in both major depressive disorder (MDD) and bipolar disorder (BD). However, comparative research on the network structure of depressive symptoms in these two diagnostic groups has been limited. This study aims to compare the network structure of depressive symptoms in MDD and BD, providing a deeper understanding of the depressive symptomatology of each disorder. Materials and Methods: The Zung Self-Rating Depressive Scale, a 20-item questionnaire, was administered to assess the depressive symptoms in individuals with MDD (n = 322) and BD (n = 516). A network analysis was conducted using exploratory graph analysis (EGA), and the network structure was analyzed using regularized partial correlation models. To validate the dimensionality of the Zung SDS, principal component analysis (PCA) was adopted. Centrality measures of the depressive symptoms within each group were assessed, followed by a network comparison test between the two groups. Results: In both diagnostic groups, the network analysis revealed four distinct categories, aligning closely with the PCA results. "Depressed affect" emerged as the most central symptom in both MDD and BD. Furthermore, non-core symptoms, "Personal devaluation" in MDD and "Confusion" in BD, displayed strong centrality. The network comparison test did not reveal significant differences in the network structure between MDD and BD. Conclusions: The absence of significant differences in the network structures between MDD and BD suggests that the underlying mechanisms of depressive symptoms may be similar across these disorders. The identified central symptoms, including "Depressed affect", in both disorders and the distinct non-core symptoms in each highlight the complexity of the depressive symptomatology. Future research should focus on validating these symptoms as therapeutic targets and incorporate various methodologies, including non-metric dimension reduction techniques or canonical analysis.

Workaholism, work engagement, and burnout among academics in Montenegro: A psychometric network approach

BACKGROUND

The academic environment is known for its high demands in research, teaching, and administration, that along with increasing publish or perish culture can lead to reduced psychological well-being and mental health issues.

OBJECTIVE

This study aimed to investigate the associations between workaholism, work engagement, and burnout among academics in Montenegro.

METHODS

A cross-sectional design was used to develop anonymous online survey. Data was collected from 131 participants employed as teaching and research staff at public and private universities. To measure the variables of interest we used: ultra-short Utrecht Work Engagement Scale (UWES-3), the work-related burnout subscale from the Copenhagen Burnout Inventory (CBI-7) and the Dutch Work Addiction Scale (DUWAS-10). Psychometric network analysis was employed to examine the relationships among variables.

RESULTS

The findings revealed two distinct clusters: the first containing the dimensions of work engagement and the second containing burnout and the dimensions of workaholism. The two clusters were connected with the dimensions of dedication - burnout having the strongest edge (-0.25 and -0.40). In the cross-sample network the strongest connection was burnout -working excessively (.35). No significant differences in network density (0.80 (12/15 edges)) and global strength (p = 0.159) in the networks of public and private universities were found.

CONCLUSION

Results of the network centrality and the edge strength analyses suggest that the interventions focused at increasing dedication while not fostering a work environment that encourages working excessively might be the key to preventing and reducing burnout in academia across contexts of public and private universities.

The Predictive Role of Tolerance and Health Problems in Problem Gambling: A Cross-Sectional and Cross-Lagged Network Analyses

The existing symptomatic networks of problem gambling are all based on cross-sectional data. Thus, there is a need to explore longitudinal symptom networks of problem gambling. Moreover, the replicability of cross-sectional symptom networks can be limited; therefore, further research should assess the convergence between cross-sectional networks of problem gambling symptoms. The present study aimed (i) to examine cross-sectional networks of problem gambling symptoms and evaluate their replicability and (ii) to examine a longitudinal cross-lagged network of problem gambling symptoms. The study included a representative sample of young adult gamblers (born between 1984 and 2000) from the first two waves of the Budapest Longitudinal Study (original sample: N = 2777; final sample: N = 335). The Problem Gambling Severity Index was used to assess symptoms of problem gambling. Cross-sectional symptom networks showed differences in the centrality of nodes. Correlations between the two cross-sectional networks were low in the presence vs. absence of edges, rank order of edge weights, and centrality estimates. However, network invariance tests indicated non-significant differences between them. The cross-lagged network revealed that the symptoms of tolerance and health problems could predict the subsequent presence of multiple problem gambling symptoms. Overall, limited evidence demonstrated the replicability of cross-sectional symptom networks of problem gambling. Future research needs to explore the utility of cross-sectional networks of problem gambling and assess more precisely causal relationships between problem gambling symptoms by distinguishing within- and between-subject effects.

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

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

Network analysis of depression and anxiety symptoms in Chinese rheumatoid arthritis patients

Background

Rheumatoid arthritis (RA) patients are susceptible to comorbid anxiety and depression. From the network model perspective, comorbidity is due to direct interactions between depression and anxiety symptoms. The objective of this study was to assess the network structure of depression and anxiety symptoms in Chinese RA patients and identify the central and bridge symptoms as well as how depression and anxiety symptoms are related to quality of life (QoL) in the network.

Methods

A total of 402 Chinese RA patients were included in this study. Depression and anxiety symptoms were measured by the Hospital Anxiety and Depression Scale (HADS). R software was used to estimate the network. Specifically, we computed the predictability, expected influence (EI) and bridge expected influence (BEI) for each symptom and showed a flow network of "QoL".

Results

Our network revealed that the strongest edge was D2 "See the bad side of things" and D3 "Not feeling cheerful" across the whole network. For centrality indices, D3 "Not feeling cheerful" and D6 "Feeling down" had the highest EI values in the network, while A4 "Trouble relaxing" and D6 "Feeling down" had the highest BEI values of their respective community. As to "QoL", the strongest direct edge related to it was A1 "Nervousness".

Conclusions

"Feeling down" and "Not feeling cheerful" emerged as the strongest central symptoms, while "Trouble relaxing" and "Feeling down" were bridge symptoms in the anxiety-depression network of RA patients. Intervention on depression and anxiety symptoms in nurses should prioritize these symptoms.

The network structure of mania symptoms differs between people with and without binge eating

OBJECTIVES

People with bipolar disorder who also report binge eating have increased psychopathology and greater impairment than those without binge eating. Whether this co-occurrence is related to binge eating as a symptom or presents differently across full-syndrome eating disorders with binge eating is unclear.

METHODS

We first compared networks of 13 lifetime mania symptoms in 34,226 participants from the United Kingdom's National Institute for Health and Care Research BioResource with (n = 12,104) and without (n = 22,122) lifetime binge eating. Second, in the subsample with binge eating, we compared networks of mania symptoms in participants with lifetime anorexia nervosa binge-eating/purging (n = 825), bulimia nervosa (n = 3737), and binge-eating disorder (n = 3648).

RESULTS

People with binge eating endorsed every mania symptom significantly more often than those without binge eating. Within the subsample, people with bulimia nervosa most often had the highest endorsement rate of each mania symptom. We found significant differences in network parameter statistics, including network structure (M = 0.25, p = 0.001) and global strength (S = 1.84, p = 0.002) when comparing the binge eating with no binge-eating participants. However, network structure differences were sensitive to reductions in sample size and the greater density of the latter network was explained by the large proportion of participants (34%) without mania symptoms. The structure of the anorexia nervosa binge-eating/purging network differed from the bulimia nervosa network (M = 0.66, p = 0.001), but the result was unstable.

CONCLUSIONS

Our results suggest that the presence and structure of mania symptoms may be more associated with binge eating as a symptom rather than any specific binge-type eating disorder. Further research with larger sample sizes is required to confirm our findings.

Effects of Bullying on Anxiety, Depression, and Posttraumatic Stress Disorder Among Sexual Minority Youths: Network Analysis

BACKGROUND

Bullying victimization is highly prevalent among sexual minority youths, particularly in educational settings, negatively affecting their mental health. However, previous studies have scarcely explored the symptomatic relationships among anxiety, depression, and posttraumatic stress disorder (PTSD) among sexual minority youths who experienced bullying on college campuses.

OBJECTIVE

The objectives of our study were to (1) characterize the anxiety-depression-PTSD network structures of gay or lesbian, bisexuals, and other sexual minority youths previously bullied on college campuses; and (2) compare symptomatic associations in the anxiety-depression-PTSD networks among bullied sexual minority youths and heterosexual youths' groups.

METHODS

This cross-sectional study recruited college participants from Jilin Province, China. Data were analyzed using a subset of the data extracted after screening for sexual orientation and history of bullying victimization. Sexual minority youths were then divided into 3 subgroups: gay or lesbian (homosexual), bisexual, and other. Mental health symptom severity was assessed using scales: the 7-item Generalized Anxiety Disorder Scale measuring anxiety, the 9-item Patient Health Questionnaire measuring depression, and the 10-item Trauma Screening Questionnaire measuring PTSD symptoms. Combining the undirected and Bayesian network analyses, the anxiety-depression-PTSD networks were compared among sexual minority youths subgroups, and the difference between heterosexual youths and sexual minority youths was investigated. Chi-square tests were used to compare the difference in categorical variables, while independent-sample t tests were run on continuous variables.

RESULTS

In this large-scale sample of 89,342 participants, 12,249 identified as sexual minority youths, of which 1603 (13.1%, 95% CI 12.5%-13.7%) reported being bullied on college campuses in the past year. According to the expected influence (EI) and bridge expected influence (bEI) index, in the global network structure of anxiety, depression, and PTSD, sad mood (EI=1.078, bEI=0.635) and irritability (EI=1.077, bEI=0.954) were identified as central and bridge symptoms; emotional cue reactivity (EI=1.015) was a central symptom of PTSD in this global network. In the anxiety-depression-PTSD Bayesian network, anhedonia had the highest prediction priority for activating other symptoms; and feeling afraid linked symptoms from anxiety to the PTSD community. Compared to their heterosexual counterparts, sexual minority youths exhibited a stronger association between difficulty concentrating and appetite. The "sad mood-appetite" edge was strongest in the gay or lesbian network; the "irritability-exaggerated startle response" edge was strongest in the bisexual network.

CONCLUSIONS

For the first time, this study identified the most central and bridge symptoms (sad mood and irritability) within the depression-anxiety-PTSD network of sexual minority youths with past bullying-victim experiences on college campuses. Emotional cue reactivity, anhedonia, and feeling afraid were other vital symptoms in the comorbid network. Symptomatic relationships existed showing heterogeneity in bullied heterosexual youths and sexual minority youth networks, which also was present within the sexual minority youth subgroups. Consequently, refined targeted interventions are required to relieve anxiety, depression, and PTSD symptoms.

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.

Revealing clinically relevant specific IgE sensitization patterns in Hymenoptera venom allergy with dimension reduction and clustering

Background

Immunoglobulin E (IgE) blood tests are used to detect sensitizations and potential allergies. Recent studies suggest that specific IgE sensitization patterns due to molecular interactions affect an individual's risk of developing allergic symptoms.

Objective

The aim of this study was to reveal specific IgE sensitization patterns and investigate their clinical implications in Hymenoptera venom allergy.

Methods

In this cross-sectional study, 257 hunters or fishers with self-filled surveys on previous Hymenoptera stings were analyzed. Blood samples were taken to determine Hymenoptera IgE sensitization levels. Using dimensionality reduction and clustering, specific IgE for 10 Hymenoptera venom allergens were evaluated for clinical relevance.

Results

Three clusters were unmasked using novel dimensionality reduction and clustering methods solely based on specific IgE levels to Hymenoptera venom allergens. These clusters show different characteristics regarding previous systemic reactions to Hymenoptera stings.

Conclusion

Our study was able to unmask non-linear sensitization patterns for specific IgE tests in Hymenoptera venom allergy. We were able to derive risk clusters for anaphylactic reactions following hymenoptera stings and pinpoint relevant allergens (rApi m 10, rVes v 1, whole bee, and wasp venom) for clustering.

The relationships between anxiety and suicidal ideation and between depression and suicidal ideation among Chinese college students: A network analysis

Background

Suicide is a worldwide public health problem. Evidence from previous studies has confirmed the relationship among anxiety, depression and suicidal ideation. However, the complex psychopathological pathways between anxiety and suicidal ideation and between depression and suicidal ideation require further study.

Methods

A total of 505 college students from Shanghai during COVID-19 pandemic were investigated in an online study. Anxiety, depression and suicidal ideation of the participants were investigated. R software was used to construct the anxiety-suicidal ideation and depression-suicidal ideation networks and to evaluate the bridge expected influences.

Results

The anxiety-suicidal ideation network had 28 cross-community edges, the strongest one was A7 "Afraid something will happen"-S7 "Unable to solve personal problem"; A5 "Restlessness" and S3 "Hopelessness and suicide thoughts" had the highest bridge expected influences. The depression-suicidal ideation network had 36 cross-community edges, and the strongest one was D9 "Thoughts of death"-S5 "Unable to accomplish something important"; D9 "Thoughts of death" and S3 "Hopelessness and suicide thoughts" had the highest bridge expected influences.

Conclusion

Intricate psychopathological pathways exist between anxiety and suicidal ideation and between depression and suicidal ideation. "Restlessness", "Thoughts of death" and "Hopelessness and suicide thoughts" are considered targets for suicidal ideation interventions. The present study enriches the theory of symptoms and mental disorders and provides a reliable reference for the intervention practice of suicidal ideation.

Anxiety and Depression Symptoms among Youth Survivors of Childhood Sexual Abuse: A Network Analysis

BACKGROUND

Previous studies have frequently reported a high prevalence of co-occurring anxiety and depression among people who experienced stressful events in childhood. However, few have noted the symptomatic relationship of this comorbidity among childhood sexual abuse (CSA) survivors. Therefore, this study's objectives were as follows: (1) to examine the relationship across symptoms between anxiety and depression among CSA survivors; (2) to compare differences between male and female network structures among CSA survivors.

METHODS

A total of 63 Universities and Colleges in Jilin Province, China, covered 96,218 participants in this study, a sub-set data of which met the criteria of CSA was analyzed with the network analysis. The Childhood Trauma Questionnaire-Short Form (CTQ-SF), measured CSA. Anxiety was measured by the seven-item Generalized Anxiety Disorder Scale (GAD-7), and depression was measured by the Patient Health Questionnaire (PHQ-9). The sex difference between anxiety and depression among CSA survivors was compared.

RESULTS

3,479 college students reported the experience of CSA (CTQ-SF total scores ≥ 8), with a prevalence of 3.62% (95% CI: 3.50-3.73%). Among CSA survivors, control worry, sad mood, and energy were central and bridge symptoms of the anxiety and depression network. Meanwhile, male CSA survivors appeared to have a stronger correlation between guilt and suicide, but female CSA survivors seemed to have a stronger correlation between control worry and suicide. Moreover, the edge of control worry-relax-afraid was stronger in the male network, while the edge of restless-relax was stronger in the female network.

CONCLUSION

Control worry, sad mood, and energy are crucial to offer targeted treatment and to relieve anxiety and depression symptoms for CSA survivors. Guilt needs more attention for male CSA survivors, while control worry remains more important for female CSA survivors to reduce suicidal ideation and suicide attempts.

Exploring the structure of the university-students obsessive-compulsive tendency scale in Iranian university students: a network analysis study

INTRODUCTION

A risk factor for developing obsessive-compulsive disorder (OCD) in non-clinical samples is obsessive-compulsive tendencies (OCT). An OCT scale has recently been developed for university students (UOC) and showed promising psychometric properties. However, no validated Persian language scale evaluates OCT in non-clinical samples. Accordingly, this study aimed to validate the Persian version of the UOC in Iranian university students.

METHODS

Three hundred sixty-eight university students (54.6% females, mean ages: 22.4 ± 4.51) entered the study. The Persian version of UOC was evaluated concerning the structure of Exploratory Factor Analysis (EFA), Confirmatory factor analysis (CFA), and Exploratory graph analysis (EGA). Regarding the construct validity, the concurrent validity was assessed between the UOC and The Obsessive-Compulsive Inventory-Revised (OCI-R), Kessler Psychological Distress Scale (K10), and Yale-Brown Obsessive-Compulsive Scale (Y-BOCS). We calculated Cronbach's alpha to evaluate the reliability of the UOC. All statistical calculations were done in R programming language (in R-Studio Desktop version 4.2.1).

RESULTS

The Persian version of UOC showed a convenient internal consistency with Cronbach's alpha coefficient for the total scale 0.88. UOC scores were significantly correlated with OCI-R, K-10, and YBOCS. The EFA and EGA showed four and three-factor solutions with 25 and 28 items, respectively. Also, CFA showed that these two solutions were reliable, and the three-factors solution showed higher fit indexes. Finally, the results showed that item-27 was the most central item in the UOC network structure.

CONCLUSION

The findings from the present study indicated that the Persian version of UOC has acceptable psychometric properties. So, this scale can be used for examining obsessive-compulsive tendencies in Iranian university students.

Network of burnout, depression, anxiety, and dropout intention in medical undergraduates

BACKGROUND

Burnout, depression, and anxiety are highly prevalent among medical students, which often leads to their attrition. We aim to assess the inter-relationships of depression, burnout, and anxiety symptoms with dropout intention among Chinese medical undergraduates using the network analysis.

METHOD

A total of 3,648 Chinese medical undergraduates were recruited through snowball sampling. Learning burnout scale, 9-item Patient Health Questionnaire (PHQ-9), and General Anxiety Disorder Scale (GAD-7) was used to assess burnout, depression, and anxiety symptoms, respectively. We used the EBICglasso model to estimate the network. We compared the network based on gender, study phase, and clinical experience.

RESULTS

After removing repeated submissions and incorrect responses to the trap question, 3,536 participants were included in the final analysis. The prevalence of burnout, depression, anxiety, and dropout intention was 38, 62.7, 38.4, and 39% respectively, which is consistent with previous findings. Network analysis suggested that anxiety and depression items clustered together and displayed several strong bridge connections, while burnout items formed another cluster. All the strongest edges were within the respective distress. Cynicism symptoms 'I am fed up with study' and 'I want to study but I feel that studying is boring' were the most central symptoms, while 'fatigue' and 'worthless' were the bridge symptoms within the burnout-depression-anxiety network. Other central symptoms included 'worthless', 'I can handle my courses', 'nervous', and 'uncontrollable worry'. Cynicism symptoms 'I am interested in my major' and 'I feel that the knowledge I have learned is useless' were mostly related to dropout intention. Gender, study phase, and clinical experience didn't affect the global strength of the burnout-depression-anxiety network.

CONCLUSION

Our results indicated the predominance of cynicism symptoms within the burnout-depression-anxiety network and its substantial impact on dropout intention, suggesting that early detection and intervention for cynicism symptoms in Chinese medical students are in urgent need. Other central and bridge symptoms might also serve as potential targets for the prevention and treatment of burnout, depression, and anxiety among medical students. For example, studies suggest cognitive-behavioral therapy could quickly improve 'worthless', which might be beneficial in treating burnout, depression, and anxiety in medical students.

Stress response and experiential avoidance among firefighters: Preliminary insights from network analyses

Firefighters are at increased risk of developing posttraumatic stress disorder (PTSD) due to exposure to potentially traumatic events during their careers. However, little is known about the prevalence of PTSD among this population, particularly when taking moderating variables into account. Using Gaussian Graphical Models and Directed Acyclic Graphs, we conducted network analyses to examine the interactions between clusters of PTSD symptoms, perceived stress, hardiness, and experiential avoidance among 187 firefighters. The data and code are published with the paper. Experiential avoidance, as part of psychological inflexibility, was found to be the only variable that interacted with PTSD symptomatology. Strong positive associations were observed between experiential avoidance and the "negative mood and cognitions" subscale of the PTSD Checklist for DSM-5 (PCL-5). Through this association, other PTSD symptoms were activated, particularly avoidance and arousal. Our findings suggest that experiential avoidance and negative mood and cognition symptoms are particularly important in the expression of PTSD symptomatology in firefighters. In addition, experiential avoidance may be used as a coping strategy to reduce perceived stress during potentially traumatic events. Therefore, experiential avoidance may be a prime target for future interventions and training focused on flexible self-regulation strategies in this population.

A Network Analysis Approach on the Psychological Flexibility/Inflexibility Model

Acceptance and Commitment Therapy (ACT) is purported to work via targeting six interrelated processes summarized as the Psychological Inflexibility/Psychological Flexibility (PI/PF) model. However, the theoretical structure and interconnections of this model have not been sufficiently explored. Lacking are examinations of the interrelations among its components. Network Analysis (NA) can model PI/PF as a system of interconnected variables. We aimed at exploring the role and associations of the PI/PF model's components using NA in two different samples and sets of scales, and compare its structure across sub-samples. Sample 1 consisted of 501 individuals, who completed an online battery of questionnaires including the Multidimensional Psychological Flexibility Inventory, and Sample 2 consisted of 428 people, who completed an online set of six ACT measures, each assessing a component of the PI/PF model. NA could not verify the six ACT dimensions as distinct components. Values and Committed Action components were found to be strongly associated and combined in a group in both sets of measures and samples. Interestingly, Acceptance and Defusion were not the most central components as purported in some ACT conceptualizations, whereas Self-as-Context had a key role on both sets of measures and its items were often merged with Present Moment Awareness items. No significant differences were found in the comparison of networks across different subsamples and sets of scales. After combining different sets of scales, the six ACT components could not be completely verified as distinct entities, which might reflect problems with the theoretical model, or with the scales used. All components had critical roles in the model, particularly Self-as-Context, which reflects the need to redirect research towards this understudied construct. Findings point towards considerations of a triflex instead of a hexaflex ACT model.

Psychometric Properties of the Peruvian Version of the MOS Scale for Social Support in Cancer Patients and Convergent Network with Quality of Life

Introduction

One of the key psychosocial factors that impact mental and emotional health is social support. While much research has been conducted on the role of social support in the lives of cancer patients, there is a lack of studies that consider populations who need specific tools to assess this concept.

Purpose

The aim of this study was to evaluate the psychometric properties of the MOS Social Support Scale (1991) in 499 Peruvian cancer patients between the ages of 18 and 87 (M= 46.30, SD = 15.747).

Methods

In this study, three models of the MOS were analyzed based on 19-item versions (four factor, second order and bifactor model).

Results

The results showed a better psychometric fit in the 19-item bifactor model with optimal fit indices through the structural equation method (SB-χ2/df = 1.94, CFI = 0.966, TLI= 0.955, SRMR = 0.038 and RMSEA = 0.058). Additionally, there was evidence of configural, metric and scalar invariance with this instrument according to the gender of the surveyed cancer patients. Convergent validity using a network analysis approach revealed positive associations between social support dimensions and quality of life.

Conclusion

Emotional support and positive interactions dimensions were found to be important interconnections in the overall network, as indicated by their greater centralities. Therefore, this instrument could be suggested as a reliable way to evaluate cancer patients and their perceived support.

The interplay of personality traits, anxiety, and depression in Chinese college students: a network analysis

Background

Anxiety and depression are among the greatest contributors to the global burden of diseases. The close associations of personality traits with anxiety and depression have been widely described. However, the common practice of sum scores in previous studies limits the understanding of the fine-grained connections between different personality traits and anxiety and depression symptoms and cannot explore and compare the risk or protective effects of personality traits on anxiety and depression symptoms.

Objective

We aimed to determine the fine-grained connections between different personality traits and anxiety and depression symptoms and identify the detrimental or protective effects of different personality traits on anxiety and depression symptoms.

Methods

A total of 536 college students from China were recruited online, and the average age was 19.98 ± 1.11. The Chinese version of the Ten-Item Personality Inventory, Generalized Anxiety Disorder-7, and Patient Health Questionnaire-9 was used to investigate the personality traits and symptoms of anxiety and depression of participants after they understood the purpose and filling method of the survey and signed the informed consent. The demographic characteristics were summarized, and the scale scores were calculated. The network model of personality traits and symptoms of anxiety and depression was constructed, and bridge expected influence (BEI) was measured to evaluate the effect of personality traits on anxiety and depression. The edge accuracy and BEI stability were estimated, and the BEI difference and the edge weight difference were tested.

Results

In the network, 29 edges (indicating partial correlations between variables) bridged the personality community and the anxiety and depression community, among which the strongest correlations were extraversion-fatigue, agreeableness-suicidal ideation, conscientiousness-uncontrollable worry, neuroticism-excessive worry, neuroticism-irritability, and openness-feelings of worthlessness. Neuroticism had the highest positive BEI value (0.32), agreeableness had the highest negative BEI value (-0.27), and the BEI values of neuroticism and agreeableness were significantly different from those of most other nodes (p < 0.05).

Conclusion

There are intricate correlations between personality traits and the symptoms of anxiety and depression in college students. Neuroticism was identified as the most crucial risk trait for depression and anxiety symptoms, while agreeableness was the most central protective trait.

A network analysis of the long-term quality of life and mental distress of COVID-19 survivors 1 year after hospital discharge

Objectives

COVID-19 survivors suffer from persistent mental distress and impaired quality of life (QOL) after recovery from the infection. However, the symptom-symptom interaction between these psychological variables remained unexplored. The present study aimed to determine the symptom network of mental distress (depression, anxiety, sleep disturbance, fatigue, and post-traumatic stress disorder) and their association with QOL among 535 COVID-19 survivors 1 year after hospital discharge.

Methods

9-item Patient Health Questionnaire, 7-item Generalized Anxiety Disorder Scale, Chalder fatigue scale, Impact of Event Scale-Revised, Pittsburgh Sleep Quality Index, and 36-Item Short-Form Health Survey were applied to measure depression, anxiety, fatigue, PTSD, sleep disturbances, and QOL, respectively. Two networks were estimated using Gaussian graphical model. Network 1 consisted of mental symptoms to determine the central and bridge symptoms. Network 2 additionally included QOL to determine which mental symptoms were mostly related to QOL.

Results

60% of the COVID-19 survivors experienced mental distress 1 year after hospital discharge. Uncontrollable and excessive worry, psychomotor symptoms, intrusion, and daytime dysfunction were the most central symptoms. Daytime dysfunction and fatigue (especially mental fatigue and loss of energy) served as the bridge symptoms across the mental distress network and exhibited the most substantial association with QOL.

Conclusion

Our study demonstrated several key symptoms that played a vital role in mental distress and QOL among COVID-19 survivors. Prompt screening and targeted interventions for these symptoms might hold great promise in preventing mental distress and improving QOL in COVID-19 survivors.

Relations between PTSD symptom clusters and pain in three trauma-exposed samples with pain

OBJECTIVES

Little is known about how the individual PTSD symptom clusters relate to intensity and interference of pain and whether these relationships differ across clinical groups. The present study examines relations between PTSD symptom clusters and pain in three trauma-exposed, unique clinical groups: 1) adults seeking treatment for chronic pain with current symptoms of PTSD, 2) trauma affected refugees seeking treatment for PTSD and chronic pain; and 3) individuals identified at admission to the emergency ward after whiplash injury.

METHODS

Network analysis was used to assess unique relations between pain intensity, pain interference, re-experiencing, avoidance, numbing, hyperarousal, depression, and anxiety separately in each sample. Links between PTSD clusters and pain were then compared within and between samples.

RESULTS

No within-group differences were identified for the links between pain and any of PTSD clusters in the chronic pain and refugee groups. In the whiplash group, hyperarousal was more strongly related to pain than re-experiencing, avoidance, and numbing. Between group comparisons revealed a more pronounced relationship between hyperarousal and pain in the whiplash group, with no between-group differences between the chronic pain and refugee groups.

CONCLUSIONS

The findings suggest that when depression and anxiety are accounted for, few unique associations are found between pain and the PTSD symptom clusters in trauma-exposed samples with pain, with the exception of a link between pain and hyperarousal in individuals with whiplash-related PTSD symptoms.