Evidence that psychopathology symptom networks have limited replicability

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.

Network analysis of social anxiety and problematic mobile phone use in Chinese adolescents: A longitudinal study

Social anxiety is a common comorbid factor of problematic mobile phone use, but the relationship between them from the inside structure has been underexplored. This two-wave longitudinal study aims to identify the central symptoms of problematic mobile phone use and social anxiety, respectively, as well as the bridge symptoms between the two disorders. Furthermore, this study examined the time and gender differences. A total of 3,294 Chinese adolescents were recruited during two waves (T1: January 2023; T2: August 2023), and completed the questionnaires of social anxiety and problematic mobile phone use. Through network analysis, the results showed that 1) the central symptom of problematic mobile phone use was "loss of control" both at T1 and T2, 2) the central symptom of social anxiety was "fear of negative evaluation" both at T1 and T2, and 3) the bridge symptom between social anxiety and problematic mobile phone use was "withdrawal" both at T1 and T2. Moreover, the network comparison test indicated that the nodes of social anxiety were more strongly associated with "craving", "loss of control", and "negative life consequences" of problematic mobile phone use at T2 than at T1. The nodes of social anxiety were more strongly associated with "negative life consequences" of problematic mobile phone use for boys than for girls, while the "fear of negative evaluation" was more strongly associated with "loss of control" for girls than for boys. These findings provide new scientific basis for interventions targeting adolescents who are susceptible to social anxiety and problematic mobile phone use.

Core features of callous-unemotional traits: a cross-cultural comparison of youth in four countries

With considerable debate concerning the impact of culture on the expression of callous-unemotional (CU) traits, it is unclear whether the core features of CU traits generalize to youth across cultures. This study aimed to examine whether cultural differences are reflected in the core features of CU traits and the associations among these features. Network analysis was employed to identify the core features and to examine the network structure of CU traits operationalized by the Inventory of Callous Unemotional traits (ICU) in four community youth samples from different nations (Australia, N = 190; the UK, N = 437; the USA, N = 330; China, N = 503). The item "Apologizes to people" was identified as a cross-cultural core feature in the ICU network with a greater centrality of this item compared to others in all four samples. In addition, some items were identified as culture-specific core features in the network, differing in their centrality across samples. The network structures of the youth self-report ICU items were moderately similar across samples, while the structures of parent-report items showed substantial differences. These findings have important implications for cross-cultural research on CU traits as well as practical implications for screening and treatment. The core features of ICU appear to be generalizable in youth across cultures, although cultural-specific manifestations should be noted.

Mapping of the network connection between sleep quality symptoms, depression, generalized anxiety, and burnout in the general population of Peru and El Salvador

BACKGROUND

A meta-analysis of randomized controlled trials has suggested a bidirectional relationship between sleep problems and mental health issues. Despite these findings, there is limited conclusive evidence on the relationship between sleep quality, depression, anxiety, and burnout.

OBJECTIVE

The current study aimed to evaluate the relationships between sleep quality symptoms, anxiety, depression, and burnout in samples of adult individuals from two Latin American countries, Peru and El Salvador, through network analysis and to identify key symptoms that reinforce the correlation and intensify the syndromes.

METHODS

A total of 1012 individuals from El Salvador and Peru participated, with an average age of 26.5 years (SD = 9.1). Symptom networks were constructed for both countries based on data from the Jenkins Sleep Scale, Patient Health Questionnaire-2, General Anxiety Disorder-2, and a single burnout item.

RESULTS

The results indicated that Depressed Mood, Difficulty Falling Asleep, and Nervousness were the most central symptoms in a network in the participating countries. The strongest conditional associations were found between symptoms belonging to the same construct, which were similar in both countries. Thus, there is a relationship between Nervousness and Uncontrollable Worry, Anhedonia and Depressed Mood, and Nighttime Awakenings and Difficulty in Staying Asleep. It was observed that burnout is a bridge symptom between both countries and presents stronger conditional associations with Tiredness on Awakening, Depressed Mood, and Uncontrollable Worry. Other bridge symptoms include a Depressed Mood and Nervousness. The network structure did not differ between the participants from Peru and El Salvador.

CONCLUSION

The networks formed by sleep quality, anxiety, depression, and burnout symptoms play a prominent role in the comorbidity of mental health problems among the general populations of Peru and El Salvador. The symptom-based analytical approach highlights the different diagnostic weights of these symptoms. Treatments or interventions should focus on identifying central and bridge symptoms.

Identifying symptom clusters and temporal interconnections in patients with lung tumors after CT-guided microwave ablation: A network analysis

PURPOSE

To explore symptom clusters and interrelationships using a network analysis approach among symptoms in patients with lung tumors who underwent computed tomography (CT)-guided microwave ablation (MWA).

METHODS

A longitudinal study was conducted, and 196 lung tumor patients undergoing MWA were recruited and were measured at 24 h, 48 h, and 72 h after MWA. The Chinese version of the MD Anderson Symptom Inventory and the Revised Lung Cancer Module were used to evaluate symptoms. Network analyses were performed to explore the symptom clusters and interrelationships among symptoms.

RESULTS

Four stable symptom communities were identified within the networks. Distress, weight loss, and chest tightness were the central symptoms. Distress, and weight loss were also the most key bridge symptoms, followed by cough. Three symptom networks were temporally stable in terms of symptom centrality, global connectivity, and network structure.

CONCLUSION

Our findings identified the central symptoms, bridge symptoms, and the stability of symptom networks of patients with lung tumors after MWA. These network results will have important implications for future targeted symptom management intervention development. Future research should focus on developing precise interventions for targeting central symptoms and bridge symptoms to promote patients' health.

Network analysis of pandemic fatigue symptoms in samples from five South American countries

BACKGROUND

Pandemic fatigue generates low motivation or the ability to comply with protective behaviors to mitigate the spread of COVID-19.

AIMS

This study aimed to analyze the symptoms of pandemic fatigue through network analysis in individuals from five South American countries.

METHOD

A total of 1,444 individuals from Argentina, Bolivia, Paraguay, Peru, and Uruguay participated and were evaluated using the Pandemic Fatigue Scale. The networks were estimated using the ggmModSelect estimation method and a polychoric correlation matrix was used. Stability assessment of the five networks was performed using the nonparametric resampling method based on the case bootstrap type. For the estimation of network centrality, a metric based on node strength was used, whereas network comparison was performed using a permutation-based approach.

RESULTS

The results showed that the relationships between pandemic fatigue symptoms were strongest in the demotivation dimension. Variability in the centrality of pandemic fatigue symptoms was observed among participating countries. Finally, symptom networks were invariant and almost identical across participating countries.

CONCLUSIONS

This study is the first to provide information on how pandemic fatigue symptoms were related during the COVID-19 pandemic.

Centrality statistics of symptom networks of schizophrenia: a systematic review

The network theory of psychological disorders posits that systems of symptoms cause, or are associated with, the expression of other symptoms. Substantial literature on symptom networks has been published to date, although no systematic review has been conducted exclusively on symptom networks of schizophrenia, schizoaffective disorder, and schizophreniform (people diagnosed with schizophrenia; PDS). This study aims to compare statistics of the symptom network publications on PDS in the last 21 years and identify congruences and discrepancies in the literature. More specifically, we will focus on centrality statistics. Thirty-two studies met the inclusion criteria. The results suggest that cognition, and social, and occupational functioning are central to the network of symptoms. Positive symptoms, particularly delusions were central among participants in many studies that did not include cognitive assessment. Nodes representing cognition were most central in those studies that did. Nodes representing negative symptoms were not as central as items measuring positive symptoms. Some studies that included measures of mood and affect found items or subscales measuring depression were central nodes in the networks. Cognition, and social, and occupational functioning appear to be core symptoms of schizophrenia as they are more central in the networks, compared to variables assessing positive symptoms. This seems consistent despite heterogeneity in the design of the studies.

The structure of depressive symptoms using CES-D and ZDS in outpatients in a general hospital in Lima, Peru

BACKGROUND

Depression represents one of the leading causes of disability due to illness worldwide. Previous studies have demonstrated the significant heterogeneity of the diagnosis of depression, making it necessary to develop new diagnostic approaches. Network analysis is a perspective that considers symptoms as constituents of the psychiatric disorder itself. The objective was to determine the structure of depressive symptoms using the CES-D and ZDS depression scales.

METHODS

Cross-sectional study of secondary analysis of 194 patients using the CES-D and ZDS scales. Correlation matrices and regularised partial correlation networks were constructed from the database. Centrality measures were estimated, and a network stability analysis was performed.

RESULTS

On the CES-D scale, the most central item was "Sad"; while on the ZDS scale, the most central items were "Sad" and "Live". On the CES-D scale, the connection between "Enjoy" and "Happy" was the strongest. On the ZDS scale, the strongest connection was between the items "Live" with "Useful". The item "Morning" was the least connected on the ZDS.

CONCLUSIONS

The most central symptom from the CES-D scale was sadness, while from the ZDS scale, was sadness and anhedonia.

Mapping the Pathways Between Posttraumatic Stress Disorder, Depression, and Alcohol and Cannabis Use: A Network Analysis

Objective: The present study examines the network structure and, using Bayesian network analysis, estimates the directional pathways among symptoms of posttraumatic stress disorder (PTSD), major depressive disorder (MDD), and levels of alcohol and cannabis use. Method: A sample of 1471 adults in the United States, who reported at least one potentially traumatic event, completed the PTSD Checklist (PCL-5), Patient Health Questionnaire (PHQ-9), and the Alcohol/Cannabis Use Disorders Identification Test (AUDIT/CUDIT). A regularized partial correlation network provided estimates of symptoms clusters and connections. Directional pathways within the network were then estimated using a directed acyclic graph (DAG). Results: Symptoms clustered in theoretically consistent ways. Risky behavior demonstrated the highest strength centrality and bridge strength. Neither alcohol nor cannabis use appeared central in the network, and DAG results suggested that MDD and PTSD symptoms are more likely to lead to substance use than the other way around. Conclusions: Results suggest that cannabis use is largely connected to alcohol use. Consistent with prior research, risky behavior appeared to be the primary bridge between substance use and PTSD. The direction of associations between substance use and psychological symptoms requires further attention.

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.

Which Symptoms Bridge Symptoms of Depression and Symptoms of Eating Disorders?: A Network Analysis

ABSTRACT

Depression is a common comorbid mental illness in eating disorders (EDs). Network theory focuses on interactions between symptoms, but findings from network analyses of EDs and depression need to be replicated to make reliable claims about the nature of symptomatic interplay. We used cross-sectional data of 366 online-recruited participants with clinically elevated ED symptomatology and constructed a regularized partial correlation network with ED and depression symptoms. To determine each symptom's influence, we calculated expected influence (EI) and bridge EI to identify symptoms that bridged symptoms of depression and ED. Concerns that others see one eat, fear of weight gain, and fear of loss of control over eating were especially important among the ED symptoms. Loss of interest and feeling sad were the key depression symptoms. Eating in secret and low self-esteem emerged as potential bridge symptoms between clusters. These findings regarding bridge symptoms partially overlap with prior network analyses in nonclinical and clinical samples. Future studies that investigate symptom interplay via a longitudinal design to deduce causality are needed.

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.

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.

Exploring symptom-level associations between anxiety and depression across developmental stages of adolescence: a network analysis approach

BACKGROUND

Anxiety and depression often co-occur during adolescence, but the associations between symptoms of these two disorders in this developmental period are not yet fully understood. Network analysis provides a valuable approach to uncover meaningful associations among symptoms and offers insights for prevention and intervention strategies. This study aimed to investigate symptom-level associations between anxiety and depression using network analysis and to identify core symptoms, bridge symptoms, and differences in network structure across different stages of adolescence.

METHODS

The cross-sectional study was conducted in March 2022 in Shenzhen, China. Participants completed the Generalized Anxiety Disorder Scale-7 and Patient Health Questionnaire Depression Scale, along with demographic questionnaires assessing age and gender. Chinese adolescents aged 10 to 17 who were in Grades 5 or 6 of elementary school, Grades 1 or 2 of middle school, or Grades 1 or 2 of high school, and who could comprehensively understand and read Chinese were recruited as participants. Students in Grade 3 of middle and high schools were excluded due to their upcoming high school or college entrance examinations. Based on age, participants were categorized into early, middle, and late developmental stages of adolescence.

RESULTS

"Loss of control" was among the most central symptoms in the comorbidity network throughout all three developmental stages; "excessive worry" and "anhedonia" emerged as the core symptoms in early adolescence, and "restlessness" as the core symptom in late adolescence. "Anhedonia," "sad mood," and "fatigue" were identified as bridge symptoms between anxiety and depression across all three developmental stages of adolescence. The global strength of the network in middle adolescence was significantly higher compared to the other two stages.

CONCLUSION

These findings highlight the core and bridge symptoms that require special attention and intervention at each stage of adolescence. Moreover, significantly higher network connectivity in middle adolescence suggests this is a critical period for intervention to prevent the development of comorbid mental disorders.

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.

Mapping PTSD, depression, and anxiety: A network analysis of co-occurring symptoms in treatment-seeking first responders

First responders are at high risk for a range of co-occurring mental health conditions due to their repeated exposure to traumatic events. When first responders present for treatment, their complex presentation of symptoms including posttraumatic stress disorder (PTSD), depression, and generalized anxiety disorder (GAD) can prove challenging to differentiate for clinical purposes. Network analysis provides a means to identify the nuanced associations between the symptoms of these conditions and to identify groups of related symptoms. In this study, a treatment-seeking sample of first responders (N = 432) completed self-report measures of PTSD, depression, and GAD. Network analysis was used to identify symptom clusters within the sample. Our cross-sectional data yielded six empirically distinct communities: depression symptoms, GAD symptoms, and four communities comprising PTSD symptoms - intrusion and avoidance; irritability and aggression; negative affect; and arousal and sleep. Network associations underscore the heterogeneity of PTSD and also highlight overlapping and diverging symptoms of depression and GAD. These findings are discussed within the context of existing research on first responders, and recommendations for further study and treatment interventions are provided.

The unique role of smartphone addiction and related factors among university students: a model based on cross-sectional and cross-lagged network analyses

Smartphone addiction is a global problem affecting university students. Previous studies have explored smartphone addiction and related factors using latent variables. In contrast, this study examines the role of smartphone addiction and related factors among university students using a cross-sectional and cross-lagged panel network analysis model at the level of manifest variables. A questionnaire method was used to investigate smartphone addiction and related factors twice with nearly six-month intervals among 1564 first-year university students (M = 19.14, SD = 0.66). The study found that procrastination behavior, academic burnout, self-control, fear of missing out, social anxiety, and self-esteem directly influenced smartphone addiction. Additionally, smartphone addiction predicted the level of self-control, academic burnout, social anxiety, and perceived social support among university students. Self-control exhibited the strongest predictive relationship with smartphone addiction. Overall, self-control, self-esteem, perceived social support, and academic burnout were identified as key factors influencing smartphone addiction among university students. Developing prevention and intervention programs that target these core influencing factors would be more cost-effective.

The relationship of food addiction with binge eating disorder and obesity: A network analysis study

Food addiction (FA) has been associated with binge eating disorder (BED) and obesity at varying levels of severity and treatment outcomes. Despite much debate and scientific interest in FA, the mechanisms that underlie its co-occurrence with both conditions are not yet well understood. In order to understand this relationship, this study explores FA in a clinical sample of individuals with BED and obesity using network analysis (NA). A total of 303 patients (151 with BED and 152 with obesity) completed a battery of tests that investigated eating psychopathology, eating behaviours, emotional dysregulation, depression and FA. Two different NAs were conducted to investigate the interaction between these variables and FA. The BED and obesity groups were comparable in age (38 ± 14 vs. 42 ± 13 years), body mass index (38.8 ± 8.5 vs 42.4 ± 7.8), sex and demographics. According to the expected influence values, binge eating severity and depression were identified as the central nodes in both networks. In the BED group, binge eating severity was the central node and showed strong connections to both FA and grazing. In contrast, in the obesity group, depression was the central node, but its connections were weak, with only marginal associations to FA. These results suggest that FA represents an important and distinct construct of the two populations. In patients with BED, FA is intimately connected to other loss-of-control-related eating behaviours, such as binge eating and grazing. Conversely, in those with obesity, depression explains the relationship of FA with pathological eating behaviours. The presence of FA seems to be a distinguishing characteristic in the psychopathology of patients suffering from obesity with and without BED, and this could have implications for the prevention, treatment and management of these disorders.

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.

A comparison of logistic regression methods for Ising model estimation

The Ising model has received significant attention in network psychometrics during the past decade. A popular estimation procedure is IsingFit, which uses nodewise l1-regularized logistic regression along with the extended Bayesian information criterion to establish the edge weights for the network. In this paper, we report the results of a simulation study comparing IsingFit to two alternative approaches: (1) a nonregularized nodewise stepwise logistic regression method, and (2) a recently proposed global l1-regularized logistic regression method that estimates all edge weights in a single stage, thus circumventing the need for nodewise estimation. MATLAB scripts for the methods are provided as supplemental material. The global l1-regularized logistic regression method generally provided greater accuracy and sensitivity than IsingFit, at the expense of lower specificity and much greater computation time. The stepwise approach showed considerable promise. Relative to the l1-regularized approaches, the stepwise method provided better average specificity for all experimental conditions, as well as comparable accuracy and sensitivity at the largest sample size.

Network analysis of physical activity and depressive and affective symptoms during COVID-19 home confinement

INTRODUCTION

The aim of this study was to analyze the network structure of physical activity, frequency, depressive, and affective symptoms in people under home isolation due to COVID-19.

METHOD

A longitudinal study was conducted in two phases (beginning (March 19, 2020) and end of home confinement (April 8, 2020)). The sample consisted of 579 participants from Spain (250 men and 329 women) aged 16 to 92 years (overall sample: M = 47.06, SD = 14.52). A network analysis was performed.

RESULTS

Four clusters emerged (PA, depressive symptoms, positive affect, and negative affect). A higher frequency of physical activity was related to better-sustained attention, increased alertness, and enthusiasm. In addition, feelings of guilt and shame were mitigated, and confinement distress and irritability were reduced. Physical activity also mitigated fatigue in women, whereas feelings of unhappiness were reduced in men.

CONCLUSION

Physical activity seems to be an effective option for mitigating the negative effects of the COVID-19 pandemic. Public health policymakers should develop programs to promote physical activity in order to be able to cope with confinement or similar scenarios in the future.

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.

Network analysis applied to post-concussion symptoms in two mild traumatic brain injury samples

Objective

A latent disease explanation cannot exclusively explain post-concussion symptoms after mild traumatic brain injury (mTBI). Network analysis offers an alternative form of explanation for relationships between symptoms. The study aimed to apply network analysis to post-concussion symptoms in two different mTBI cohorts; an acute treatment-seeking sample and a sample 10 years post-mTBI.

Method

The treatment-seeking sample (n = 258) were on average 6 weeks post-injury; the 10 year post mTBI sample (n = 193) was derived from a population-based incidence and outcomes study (BIONIC). Network analysis was completed on post-concussion symptoms measured using the Rivermead Post-Concussion Questionnaire.

Results

In the treatment-seeking sample, frustration, blurred vision, and concentration difficulties were central to the network. These symptoms remained central in the 10 year post mTBI sample. A Network Comparison Test revealed evidence of a difference in network structure across the two samples (p = 0.045). However, the only symptoms that showed significant differences in strength centrality across samples were irritability and restlessness.

Conclusion

The current findings suggest that frustration, blurred vision and concentration difficulties may have an influential role in the experience and maintenance of post-concussion symptoms. The impact of these symptoms may remain stable over time. Targeting and prioritising the management of these symptoms may be beneficial for mTBI rehabilitation.

Heterogeneity in Individual Network Analysis: Reality or Illusion?

The use of idiographic research techniques has gained popularity within psychological research and network analysis in particular. Idiographic research has been proposed as a promising avenue for future research, with differences between idiographic results highlighting evidence for radical heterogeneity. However, in the quest to address the individual in psychology, some classic statistical problems, such as those arising from sampling variation and power limitations, should not be overlooked. This article aims to determine to what extent current tools to compare idiographic networks are suited to disentangle true from illusory heterogeneity in the presence of sampling error. To this end, we investigate the performance of tools to inspect heterogeneity (visual inspection, comparison of centrality measures, investigating standard deviations of random effects, and GIMME) through simulations. Results show that power limitations hamper the validity of conclusions regarding heterogeneity and that the power required to assess heterogeneity adequately is often not realized in current research practice. Of the tools investigated, inspecting standard deviations of random effects and GIMME proved the most suited. However, all tools evaluated leave the door wide open to misinterpret all observed variability in terms of individual differences. Hence, the current paper calls for caution in the use and interpretation of new time-series techniques when it comes to heterogeneity.

Anhedonia as a transdiagnostic symptom across psychological disorders: a network approach

BACKGROUND

Anhedonia is apparent in different mental disorders and is suggested to be related to dysfunctions in the reward system and/or affect regulation. It may hence be a common underlying feature associated with symptom severity of mental disorders.

METHODS

We constructed a cross-sectional graphical Least Absolute Shrinkage and Selection Operator (LASSO) network and a relative importance network to estimate the relationships between anhedonia severity and the severity of symptom clusters of major depressive disorder (MDD), anxiety sensitivity (AS), attention-deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD) in a sample of Dutch adult psychiatric patients (N = 557).

RESULTS

Both these networks revealed anhedonia severity and depression symptom severity as central to the network. Results suggest that anhedonia severity may be predictive of the severity of symptom clusters of MDD, AS, ADHD, and ASD. MDD symptom severity may be predictive of AS and ADHD symptom severity.

CONCLUSIONS

The results suggest that anhedonia may serve as a common underlying transdiagnostic psychopathology feature, predictive of the severity of symptom clusters of depression, AS, ADHD, and ASD. Thus, anhedonia may be associated with the high comorbidity between these symptom clusters and disorders. If our results will be replicated in future studies, it is recommended for clinicians to be more vigilant about screening for anhedonia and/or depression severity in individuals diagnosed with an anxiety disorder, ADHD and/or ASD.

Gender differences in anxiety and depressive symptomatology determined by network analysis in panic disorder

BACKGROUND

It has been suggested that gender differences in anxiety and depressive symptoms characterize panic disorder (PD) in terms of vulnerability to stressful life events, anxiety, depressive symptom patterns, and brain structure. However, few studies have investigated the gender differences in PD using a network approach.

METHODS

This study included 619 participants with PD (313 men). The Panic Disorder Severity Scale, Albany Panic and Phobia Questionnaire, and Beck Depression Inventory-II were used to evaluate symptomatology. To investigate the PD-related white matter (WM) neural correlates, tract-based spatial statistics were used. The PD-related clinical scales and WM neural correlates were included in the network analysis to identify associations between variables. To evaluate network differences between genders, network comparison tests were conducted.

RESULTS

Our findings revealed that agoraphobia in men was the strongest central symptom. In addition, loss of pleasure, and not anxiety or panic symptoms, was the strongest central symptom in women with PD. The network comparison test revealed that the bridge strength score was higher in agoraphobia and tiredness in men and in self-criticalness in women. Furthermore, in the network that includes neural correlates of WM, the bridge strength score was higher in the cingulate gyrus WM in men and the cingulum hippocampus in women.

LIMITATIONS

Since this is a cross-sectional network study of PD patients, the causal relationship between interactions in this network analysis for both genders may not be accurately determined.

CONCLUSION

Network structures of anxiety and depressive symptomatology and related WM neural correlates can differ according to gender in PD patients.

A Network Analysis of Cigarette Craving

INTRODUCTION

Craving is considered a central process to addictive behavior including cigarette smoking, although the clinical utility of craving relies on how it is defined and measured. Network analysis enables examining the network structure of craving symptoms, identifying the most central symptoms of cigarette craving, and improving our understanding of craving and its measurement.

AIMS AND METHODS

This study used network analysis to identify the central symptoms of self-reported cigarette craving as measured by the Craving Experience Questionnaire, which assesses both craving strength and craving frequency. Data were obtained from baseline of a randomized controlled trial of mindfulness training for smoking cessation.

RESULTS

The most central symptoms in an overall cigarette craving network were the frequency of imagining its smell, imagining its taste, and intrusive thoughts. The most central symptoms of both craving frequency and craving strength sub-networks were imagining its taste, the urge to have it, and intrusive thoughts.

CONCLUSIONS

The most central craving symptoms reported by individuals in treatment for cigarette smoking were from the frequency domain, demonstrating the value of assessing craving frequency along with craving strength. Central craving symptoms included multisensory imagery (taste, smell), intrusive thoughts, and urge, providing additional evidence that these symptoms may be important to consider in craving measurement and intervention. Findings provide insight into the symptoms that are central to craving, contributing to a better understanding of cigarette cravings, and suggesting potential targets for clinical interventions.

IMPLICATIONS

This study used network analysis to identify central symptoms of cigarette craving. Both craving frequency and strength were assessed. The most central symptoms of cigarette craving were related to craving frequency. Central symptoms included multisensory imagery, intrusive thoughts, and urge. Central symptoms might be targeted by smoking cessation treatment.

A Network Investigation of Core Mindfulness Processes

Objective

Mindfulness has been associated with improved psychological well-being and health, although outcomes from mindfulness-based interventions are mixed. One challenge is a limited understanding about which specific processes are core to mindfulness. Network analysis offers a method to characterize the core processes of mindfulness.

Methods

This study used network analysis to identify which processes are central to mindfulness (have the strongest connectivity with other mindfulness processes) as measured by the Five Facets Mindfulness Questionnaire- Short Form, analyzed at the item-level. Data were obtained from baseline of a randomized clinical trial of smartphone app-based mindfulness training for smoking cessation.

Results

The most central processes in the mindfulness network included, "I think some of my emotions are bad or inappropriate and I shouldn't feel them," an aspect of Nonjudgment/acceptance; as well as "I can easily put my beliefs, opinions, and expectations into words," and "It's hard for me to find the words to describe what I'm thinking," aspects of Describing.

Conclusions

Findings help to clarify which processes are to mindfulness, contributing to a better understanding of the definition of mindfulness, and suggest factors that may be promising to target in mindfulness-based interventions. Future research should examine if mindfulness-based interventions may be improved by targeting these core mindfulness processes.

Annual Research Review: Stability of psychopathology: lessons learned from longitudinal population surveys

BACKGROUND

Psychopathology has been long recognized as a fluctuating process with various expressions over time, which can only be properly understood if we follow individuals and their social context from childhood up until adulthood. Longitudinal population-based studies have yielded powerful data to analyze this process. However, the resulting publications have not been reflected upon with regard to (a) the homotypic and heterotypic stability of internalizing and externalizing problems and (b) how transactions between psychopathology and environmental factors shape its development.

METHODS

In this narrative review, we primarily focused on population-based studies that followed cohorts repeatedly from an early age (<18 years) onwards, across multiple stages of development, using statistical methods that permit inferences about within-person bidirectional associations between internalizing and externalizing problems or psychopathology-environment transactions.

RESULTS

There is robust evidence that mental health problems in childhood or adolescence predict psychiatric problems later in development. In terms of the broadband domains internalizing and externalizing problems, homotypic stability greatly exceeds heterotypic stability and transitions from purely internalizing to purely externalizing problems or vice versa are rare. Homotypic rank-order stabilities seem to increase over time. Findings regarding transactions with environmental factors are less robust, due to widely varying research topics and designs, and a scarcity of studies that separated between-person differences from within-person changes. In general, however, the literature shows little consistent evidence for substantial mutual prospective influences between psychopathology and environmental factors.

CONCLUSIONS

Longitudinal surveys have strongly augmented insight into homotypic and heterotypic stability and change. Attempts to unravel the myriad of risk and protective factors that place individuals on particular pathways or deflect them from these pathways are still in a pioneering phase and have not yet generated robust findings. As a way forward, we propose to join forces and develop a common risk factor taxonomy.

Disentangling heterogeneity of psychosis expression in the general population: sex-specific moderation effects of environmental risk factors on symptom networks

BACKGROUND

Psychosis expression in the general population may reflect a behavioral manifestation of the risk for psychotic disorder. It can be conceptualized as an interconnected system of psychotic and affective experiences; a so-called 'symptom network'. Differences in demographics, as well as exposure to adversities and risk factors, may produce substantial heterogeneity in symptom networks, highlighting potential etiological divergence in psychosis risk.

METHODS

To explore this idea in a data-driven way, we employed a novel recursive partitioning approach in the 2007 English National Survey of Psychiatric Morbidity (N = 7242). We sought to identify 'network phenotypes' by explaining heterogeneity in symptom networks through potential moderators, including age, sex, ethnicity, deprivation, childhood abuse, separation from parents, bullying, domestic violence, cannabis use, and alcohol.

RESULTS

Sex was the primary source of heterogeneity in symptom networks. Additional heterogeneity was explained by interpersonal trauma (childhood abuse and domestic violence) in women and domestic violence, cannabis use, ethnicity in men. Among women, especially those exposed to early interpersonal trauma, an affective loading within psychosis may have distinct relevance. Men, particularly those from minority ethnic groups, demonstrated a strong network connection between hallucinatory experiences and persecutory ideation.

CONCLUSION

Symptom networks of psychosis expression in the general population are highly heterogeneous. The structure of symptom networks seems to reflect distinct sex-related adversities, etiologies, and mechanisms of symptom-expression. Disentangling the complex interplay of sex, minority ethnic group status, and other risk factors may help optimize early intervention and prevention strategies in psychosis.

Depression networks: a systematic review of the network paradigm causal assumptions

The network paradigm for psychiatric disorder nosology was proposed based on the hypothesis that mental disorders are caused by networks of symptoms that are themselves causally related. Researchers have widely applied and integrated this paradigm to examine a variety of mental disorders, particularly depression. Existing studies generally focus on the correlation structure of symptoms, inferring causal relationships. Thus, presumption of causality may not be justified. The goal of this review was to examine the assumptions necessary for causal inference in network studies of depression. Specifically, we examined whether and how network studies address common violations of causal assumptions (i.e. no measurement error, exchangeability, and positivity). Of the 41 studies reviewed, five (12%) studies discussed sources of confounding unrelated to measurement error; none discussed positivity; and five conducted post-hoc analysis for measurement error. Depression network studies, in principle, are conducted under the assumption that symptom relationships are causal. Yet, in practice, studies seldomly discussed or adequately tested assumptions required to infer causality. Researchers continue to design studies that are unable to support the credibility of the network paradigm for the study of depression. There is a critical need to ensure scientific efforts cease to perpetuate problematic designs and findings to a potentially unsubstantiated paradigm.

Network analysis of body-related complaints in patients with neurotic or personality disorders referred to psychotherapy

Background

Psychopathology theory and clinical practice require the most complex knowledge about patients' complaints. In patients seeking for psychotherapy, body-related symptoms often complicate treatment.

Aim

This study aimed at examining connections between body-related symptoms, and identification of symptoms which may be responsible for emergency and sustaining of anxiety, somatoform and personality disorders with the use of network analysis.

Methods

In our retrospective research we used data from a sample of 4616 patients of the Department of Psychotherapy, University Hospital in Cracow, diagnosed with anxiety, somatoform or personality disorders. We constructed the Triangulated Maximally Filtered Graph (TMFG) networks of 44 somatoform symptoms endorsed in the symptom checklist "O" (SCL-O) and identified the most central symptoms within the network for all patients and in subgroups of women vs. men, older vs. younger, and diagnosed in 1980-2000 vs. 2000-2015. We used bootstrap to determine the accuracy and stability of five networks' parameters: strength, expected influence, eigenvector, bridge strength and hybrid centrality.

Results

The most central symptoms within the overall network, and in six subnetworks were dyspnea and migratory pains. We identified some gender-related differences, but no differences were observed for the age and time of diagnosis.

Conclusions

Self-reported dyspnea and migratory pains are potential important targets for treatment procedures.

Childhood anxious/withdrawn behaviour and later anxiety disorder: a network outcome analysis of a population cohort

BACKGROUND

Several previous studies have identified a continuity between childhood anxiety/withdrawal and anxiety disorder (AD) in later life. However, not all children with anxiety/withdrawal problems will experience an AD in later life. Previous studies have shown that the severity of childhood anxiety/withdrawal accounts for some of the variability in AD outcomes. However, no studies to date have investigated how variation in features of anxiety/withdrawal may relate to continuity prognoses. The present research addresses this gap.

METHODS

Data were gathered as part of the Christchurch Health and Development Study, a 40-year population birth cohort of 1265 children born in Christchurch, New Zealand. Fifteen childhood anxiety/withdrawal items were measured at 7-9 years and AD outcomes were measured at various interviews from 15 to 40 years. Six network models were estimated. Two models estimated the network structure of childhood anxiety/withdrawal items independently for males and females. Four models estimated childhood anxiety/withdrawal items predicting adolescent AD (14-21 years) and adult AD (21-40 years) in both males and females.

RESULTS

Approximately 40% of participants met the diagnostic criteria for an AD during both the adolescent (14-21 years) and adult (21-40 years) outcome periods. Outcome networks showed that items measuring social and emotional anxious/withdrawn behaviours most frequently predicted AD outcomes. Items measuring situation-based fears and authority figure-specific anxious/withdrawn behaviour did not consistently predict AD outcomes. This applied across both the male and female subsamples.

CONCLUSIONS

Social and emotional anxious/withdrawn behaviours in middle childhood appear to carry increased risk for AD outcomes in both adolescence and adulthood.

Eating disorder psychopathology and resilience in Iranian college students: A network analysis

OBJECTIVE

Eating disorder (ED) psychopathology is common among Iranian college students. Resilience (i.e., the ability to bounce back and adapt in the face of adverse and stressful conditions) has been found to be a protective factor against ED psychopathology in the West. However, no research to date has examined resilience as a protective factor against ED psychopathology in Iran. The current study used network analysis to examine an ED and resilience network in an Iranian sample.

METHOD

Participants were Iranian college students (N = 478) who completed the Farsi-Eating Disorder Examination-Questionnaire and Farsi-Brief Resilience Scale. We estimated a network of ED symptoms and resilience processes and identified central and bridge symptoms.

RESULTS

Central ED and resilience nodes were discomfort in seeing one's own body, feeling guilty about eating due to shape/weight, and thinking about shape and weight making it difficult to concentrate. Having a hard time making it through stressful events bridged with binge eating and fear of losing control over eating.

CONCLUSION

Processes related to managing stress and binge eating appear to maintain the association between ED symptoms and resilience processes.

Psychopathological networks in psychosis: Changes over time and clinical relevance. A long-term cohort study of first-episode psychosis

BACKGROUND

First-episode psychosis is a critical period for early interventions to reduce the risk of poor outcomes and relapse as much as possible. However, uncertainties about the long-term outcomes of symptomatology remain to be ascertained.

METHODS

The aim of the present study was to use network analysis to investigate first-episode and long-term stages of psychosis at three levels of analysis: micro, meso and macro. The sample was a cohort of 510 patients with first-episode psychoses from the SEGPEP study, who were reassessed at the long-term follow-up (n = 243). We used the Comprehensive Assessment of Symptoms and History for their assessments and lifetime outcome variables of clinical relevance.

RESULTS

Our results showed a similar pattern of clustering between first episodes and long-term follow-up in seven psychopathological dimensions at the micro level, 3 and 4 dimensions at the meso level, and one at the macro level. They also revealed significant differences between first-episode and long-term network structure and centrality measures at the three levels, showing that disorganization symptoms have more influence in long-term stabilized patients.

CONCLUSIONS

Our findings suggest a relative clustering invariance at all levels, with the presence of two domains of disorganization as the most notorious difference over time at micro level. The severity of disorganization at the follow-up was associated with a more severe course of the psychosis. Moreover, a relative stability in global strength of the interconnections was found, even though the network structure varied significantly in the long-term follow-up. The macro level was helpful in the integration of all dimensions into a common psychopathology factor, and in unveiling the strong relationships of psychopathological dimensions with lifetime outcomes, such as negative with poor functioning, disorganization with high antipsychotic dose-years, and delusions with poor adherence to treatment. These results add evidence to the hierarchical, dimensional and longitudinal structure of psychopathological symptoms and their clinical relevance in first-episode psychoses.

Unraveling the central and bridge psychological symptoms of people living with HIV: A network analysis

Background

People living with HIV (PLWH) experience multiple psychological symptoms. Few studies have provided information on central and bridge psychological symptoms among PLWH. This information has implications for improving the efficiency and efficacy of psychological interventions. Our study aimed to identify the central and bridge psychological symptoms of PLWH and to explore the interconnectedness among symptoms and clusters.

Methods

Our study used data from the HIV-related Symptoms Monitoring Survey, a multisite, cross-sectional study conducted during 2017-2021. We used R to visualize the network of 16 symptoms and analyzed the centrality and predictability indices of the network. We further analyzed the bridge symptoms among the three symptom clusters.

Results

A total of 3,985 participants were included in the analysis. The results suggested that sadness had the highest strength (r S = 9.69) and predictability (70.7%) compared to other symptoms. Based on the values of bridge strength, feeling unsafe (r bs = 0.94), uncontrollable worry (r bs = 0.82), and self-abasement (r bs = 0.81) were identified as bridge symptoms. We also found a strong correlation between sadness and self-abasement (r = 0.753) and self-loathing and self-blame (r = 0.744).

Conclusion

We found that sadness was the central psychological symptom of PLWH, indicating that sadness was the center of the psychological symptom network from a mechanistic perspective and could be a target for intervention. Deactivating bridge symptoms, including "feeling unsafe," "self-abasement," and "uncontrollable worry," could be more effective in preventing symptom activation from spreading (e.g., one symptom activating another).

Using network analysis to examine connections between Acceptance and Commitment Therapy (ACT) processes, internalizing symptoms, and well-being in a sample of undergraduates

BACKGROUND

Acceptance and Commitment Therapy (ACT) has been shown to be effective in treating internalizing symptoms. Understanding which ACT processes are most closely linked to certain symptoms may help develop targeted treatments. Network analysis an approach to gain insight into the interconnection between processes and the downstream benefits of targeting a particular process. However, limited work to date has explored networks involving ACT processes specifically.

METHODS

Undergraduate students (N = 447; 76.5 % female; 89.5 % White/Non-Hispanic) completed online questionnaires. The ACT processes assessed included experiential avoidance (AAQ-II), openness, awareness, and engagement (CompACT), and tacting ability (TOF), and internalizing symptoms/well-being (IDAS-II). Zero-order and partial correlation networks were examined as well as resulting communities.

RESULTS

In the association network, dysphoria and experiential avoidance, and suicidality (in the concentration network only) were central nodes. In community analyses, experiential avoidance had the strongest influence in the association network, whereas well-being had the strongest influence in the concentration network. Auto-detected communities were also evaluated.

LIMITATIONS

The present study was cross-sectional and included a largely White, female, undergraduate sample. This limits generalizability to more diverse, clinical, or general community populations. Potential concerns about data are also noted including low reliability on the TOF and two skewed domains on the IDAS-II which may impact stability of centrality metrics.

CONCLUSIONS

Well-being, dysphoria, and suicidality may be important process-based treatment targets. Further work is needed with diverse samples and using longitudinal designs to examine within person change of the associations between ACT processes and internalizing symptoms.

Network analysis of internet addiction and depression among Chinese college students during the COVID-19 pandemic: A longitudinal study

Background

There has been growing evidence of comorbidity between internet addiction and depression in youth during the COVID-19 period. According to the network theory, this may arise from the interplay of symptoms shared by these two mental disorders. Therefore, we examined this underlying process by measuring the changes in the central and bridge symptoms of the co-occurrence networks across time.

Methods

A total of 852 Chinese college students were recruited during two waves (T1: August 2020; T2: November 2020), and reported their internet addiction symptoms and depressive symptoms. Network analysis was utilized for the statistical analysis.

Results

The internet addiction symptoms "escape" and "irritable," and depression symptoms "energy" and "guilty" were the central symptoms for both waves. At the same time, "guilty" and "escape" were identified as bridge symptoms. Notably, the correlation between "anhedonia" and "withdrawal" significantly increased, and that between "guilty" and "escape" significantly decreased over time.

Conclusions

This study provides novel insights into the central features of internet addiction and depression during the two stages. Interestingly, "guilty" and "escape," two functions of the defense mechanism, are identified as bridge symptoms. These two symptoms are suggested to activate the negative feedback loop and further contribute to the comorbidity between internet addiction and depression. Thus, targeting interventions on these internalized symptoms may contribute to alleviating the level of comorbidity among college students.

Intersecting identities and adolescent depression: Patterns of depressed mood and anhedonia in the past decade

BACKGROUND

Research suggests adolescent depression is increasing and certain adolescents may be uniquely vulnerable. However, limited conceptualizations of identity and time, as well as the reliance on unitary conceptualizations of depression, inhibits a nuanced perspective on these trends. In response, we examined how adolescent depressive symptoms, depressed mood, and anhedonia, vary across intersecting identities over time.

METHODS

Secondary data analysis on the National Survey on Drug Use and Health between 2009 and 2017 was conducted. In total 145,499 nationally representative adolescents (ages 12-17) completed a diagnostic assessment for depression. Lifetime and past year reports of depressive symptoms, depressed mood, and anhedonia were treated as separate variables. A novel, mixed-level model in which participants were nested within identity (defined by one's age, gender, race/ethnicity, poverty level) and time was used to test our aims.

RESULTS

Overall, the relation between depression outcomes and identity did not vary over time (p > .01). Further, identity's impact on depression was approximately ten-fold that of temporal effects. Multiracial, late adolescent, female adolescents were at particular risk. Findings concerning depressed mood and anhedonia were similar across analyses.

LIMITATIONS

All facets of identity (e.g., sexual identity) were not included in the model and a unidimensional measure of poverty may have underestimated its depressogenic influence.

CONCLUSION

Adolescent depression outcomes are mostly consistent across criterial symptom subtypes and time, but vary as a function of identity. Prevention protocols that highlight mechanisms of risk tethered to social identity, and include salient experiences of females, late adolescents, and multiracial youth in particular, need to be prioritized in mental health initiatives.

Analysis of dynamic psychological processes to understand and promote physical activity behaviour using intensive longitudinal methods: a primer

Physical activity behaviour displays temporal variability, and is influenced by a range of dynamic psychological processes (e.g., affect) and shaped by various co-occurring events (e.g., social/environmental factors, interpersonal dynamics). Yet, most physical activity research tends not to examine the dynamic psychological processes implicated in adopting and maintaining physical activity. Intensive longitudinal methods (ILM) represent one particularly salient means of studying the complex psychological dynamics that underlie and result from physical activity behaviour. With the increased recent interest in using intensive longitudinal data to understand specific dynamic psychological processes, the field of exercise and health psychology is well-positioned to draw from state-of-the-art measurement and statistical approaches that have been developed and operationalised in other fields of enquiry. The purpose of this review is to provide an overview of some of the fundamental dynamic measurement and modelling approaches applicable to the study of physical activity behaviour change, as well as the dynamic psychological processes that contribute to such change.

Idiographic Ising and Divide and Color Models: Encompassing Networks for Heterogeneous Binary Data

The Ising model is a graphical model that has played an essential role in network psychometrics. It has been used as a theoretical model to conceptualize psychological concepts and as a statistical model to analyze psychological data. Using graphical models such as the Ising model to analyze psychological data has been heavily critiqued since these data often come from cross-sectional applications. An often voiced concern is the inability of the Ising model to express heterogeneity in the population. The idiographic approach has been posed as an alternative and aims to infer individual network structures. While idiographic networks overcome population heterogeneity, it is unclear how they aggregate into established cross-sectional phenomena. This paper establishes a formal bridge between idiographic and cross-sectional network approaches of the Ising model. We ascertain unique topological structures that characterize individuals and aggregate into an Ising model cross-sectionally. This new formulation supports population heterogeneity while being consistent with cross-sectional phenomena. The proposed theory also establishes a new statistical framework for analyzing populations of idiographic networks for binary variables. The Ising model and the divide and color model are special cases of this new framework. We introduce a Gibbs sampling algorithm to estimate models from this new framework.

Externalizing psychopathology from childhood to early adolescence: Psychometric evaluation using latent variable and network modeling

Applying both latent variable and network frameworks, we conducted a comprehensive psychometric evaluation of the diverse array of symptoms from three externalizing dimensions, including attention problems, aggressive behavior, and delinquency/rule-breaking of the Child Behavior Checklist (Achenbach, 1991) across six time points from childhood to early adolescence. We also examined sex differences. Participants (N = 1,339) were drawn from two multisite longitudinal studies: Fast Track and the Child Development Project. Parents reported on externalizing psychopathology in kindergarten and Grades 1, 2, 4, 5, and 7. Using exploratory structural equation modeling, we found almost uniformly excellent fit across time and samples. However, we also observed multiple cross-loadings and heterogeneity in terms of which symptoms cross-loaded across time points. Alternatively, using network modeling, we observed that symptoms of attention problems and aggressive behavior had stronger connections, relative to delinquency/rule-breaking, across time and samples. Significant differences in overall connectivity were found at early (kindergarten vs. Grade 1, Grade 1 vs. Grade 2) and late (Grade 5 vs. Grade 7) time points for the combined sample and only late time points for the male sample. In addition, the items impulsive and lies or cheats consistently displayed the greatest bridge strength, that is, symptom from one dimension that connects to symptoms from another dimension, across time and samples. Our results illustrate how two methods-latent variable and network modeling-provide important and complementary information on multidimensional constructs. Findings also inform understanding of externalizing psychopathology through childhood to early adolescence by identifying key symptoms, critical transition points, and possible transdiagnostic liabilities. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Relations between Networks, Regression, Partial Correlation, and the Latent Variable Model

The Gaussian graphical model (GGM) has become a popular tool for analyzing networks of psychological variables. In a recent article in this journal, Forbes, Wright, Markon, and Krueger (FWMK) voiced the concern that GGMs that are estimated from partial correlations wrongfully remove the variance that is shared by its constituents. If true, this concern has grave consequences for the application of GGMs. Indeed, if partial correlations only capture the unique covariances, then the data that come from a unidimensional latent variable model ULVM should be associated with an empty network (no edges), as there are no unique covariances in a ULVM. We know that this cannot be true, which suggests that FWMK are missing something with their claim. We introduce a connection between the ULVM and the GGM and use that connection to prove that we find a fully-connected and not an empty network associated with a ULVM. We then use the relation between GGMs and linear regression to show that the partial correlation indeed does not remove the common variance.

Generalized network psychometrics of eating-disorder psychopathology

OBJECTIVE

As network models of eating disorder (ED) psychopathology become increasingly popular in modeling symptom interconnectedness and identifying potential treatment targets, it is necessary to contextualize their performance against other methods of modeling ED psychopathology and to evaluate potential ways to optimize and capitalize on their use. To accomplish these goals, we used generalized network psychometrics to estimate and compare latent variable models and network models, as well as hybrid models.

METHOD

We tested the structure of the Eating Pathology Symptoms Inventory (EPSI) and Eating Disorder Examination-Questionnaire (EDE-Q) in Recovery Record, Inc. mobile phone application users (N = 6856).

RESULTS

Although all models fit well, results favored a hybrid latent variable and network framework, which showed that ED symptoms fit best when modeled as higher-order constructs, rather than direct symptom-to-symptom connections, and when the relationships between those constructs are described as a network. Hybrid models in which latent factors were modeled as nodes within a network showed that EPSI Purging, Binge Eating, Cognitive Restraint, Body Dissatisfaction, and Excessive Exercise had high importance in the network. EDE-Q Eating Concern and Shape Concern were also important nodes. Results showed that the EPSI network was highly stable and replicable, whereas the EDE-Q network was not.

DISCUSSION

Integrating latent variable and network model frameworks enables tests of centrality to identify important latent variables, such as purging, that may promote the spread of ED psychopathology throughout a network, allowing for the identification of future treatment targets.

Network-based methods for psychometric data of eating disorders: A systematic review

BACKGROUND

Network science represents a powerful and increasingly promising method for studying complex real-world problems. In the last decade, it has been applied to psychometric data in the attempt to explain psychopathologies as complex systems of causally interconnected symptoms. One category of mental disorders, relevant for their severity, incidence and multifaceted structure, is that of eating disorders (EDs), serious disturbances that negatively affect a person's eating behavior.

AIMS

We aimed to review the corpus of psychometric network analysis methods by scrutinizing a large sample of network-based studies that exploit psychometric data related to EDs. A particular focus is given to the description of the methodologies for network estimation, network description and network stability analysis providing also a review of the statistical software packages currently used to carry out each phase of the network estimation and analysis workflow. Moreover, we try to highlight aspects with potential clinical impact such as core symptoms, influences of external factors, comorbidities, and related changes in network structure and connectivity across both time and subpopulations.

METHODS

A systematic search was conducted (February 2022) on three different literature databases to identify 57 relevant research articles. The exclusion criteria comprehended studies not based on psychometric data, studies not using network analysis, studies with different aims or not focused on ED, and review articles.

RESULTS

Almost all the selected 57 papers employed the same analytical procedures implemented in a collection of R packages specifically designed for psychometric network analysis and are mostly based on cross-sectional data retrieved from structured psychometric questionnaires, with just few exemptions of panel data. Most of them used the same techniques for all phases of their analysis. In particular, a pervasive use of the Gaussian Graphical Model with LASSO regularization was registered for in network estimation step. Among the clinically relevant results, we can include the fact that all papers found strong symptom interconnections between specific and nonspecific ED symptoms, suggesting that both types should therefore be addressed by clinical treatment.

CONCLUSIONS

We here presented the largest and most comprehensive review to date about psychometric network analysis methods. Although these methods still need solid validation in the clinical setting, they have already been able to show many strengths and important results, as well as great potentials and perspectives, which have been analyzed here to provide suggestions on their use and their possible improvement.

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

Objective

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

Methods

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

Results

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

Conclusion

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

Symptom Structure of Depression in Older Adults on the Qinghai-Tibet Plateau: A Network Analysis

Previous studies have confirmed that depression among residents in high-altitude areas is more severe, and that depression may be more persistent and disabling in older adults. This study aims to identify the symptom structure of depression among older adults on the Qinghai-Tibet Plateau (the highest plateau in the world) from a network perspective. This cross-sectional study enrolled 507 older adults (ages 60-80 years old) from the Yushu Prefecture, which is on the Qinghai-Tibet Plateau, China. Depressive symptoms were self-reported using the shortened Center for Epidemiological Studies-Depression Scale (CES-D-10). Then, a Gaussian graphical model (GGM) of depression was developed. Poor sleep, fear, and hopelessness about the future exhibited high centrality in the network. The strongest edge connections emerged between unhappiness and hopelessness about the future, followed by hopelessness about the future and fear; hopelessness about the future and poor sleep; fear and unhappiness; and then poor sleep and unhappiness in the network. The findings of this current study add to the small body of literature on the network structure and complex relationships between depressive symptoms in older adults in high-altitude areas.

The tangled webs we wreak: Examining the structure of aggressive personality using psychometric networks

OBJECTIVE

Trait aggression is a prominent construct in the psychological literature, yet little work has sought to situate trait aggression among broader frameworks of personality. Initial evidence suggests that trait aggression may be best couched within the nomological network of the Five-Factor Model (FFM). The current work sought to locate the most appropriate home for trait aggression among the FFM.

METHOD

We applied a preregistered regimen of psychometric network analyses to three datasets (combined N = 2927) that contained self-reports of trait aggression and the FFM traits.

RESULTS

Trait aggression was highly central in the factor-level networks, which contained associations consistent with the conceptualization of this construct as a lower-order component of low agreeableness. The facet-level networks revealed that the behavioral facets of trait aggression reflected low agreeableness, but that the anger and hostility facets reflected high neuroticism. The item-level network suggested that the intent to initiate aggressive encounters was the primary bridge that empirically linked trait aggression to agreeableness.

CONCLUSIONS

Our results indicate that trait aggression is primarily a lower-order facet of agreeableness, advance our understanding of trait aggression, integrate it with broader frameworks of personality, and suggest future directions to refine this complex dispositional tendency.

Natural Language Processing and Psychosis: On the Need for Comprehensive Psychometric Evaluation

BACKGROUND AND HYPOTHESIS

Despite decades of "proof of concept" findings supporting the use of Natural Language Processing (NLP) in psychosis research, clinical implementation has been slow. One obstacle reflects the lack of comprehensive psychometric evaluation of these measures. There is overwhelming evidence that criterion and content validity can be achieved for many purposes, particularly using machine learning procedures. However, there has been very little evaluation of test-retest reliability, divergent validity (sufficient to address concerns of a "generalized deficit"), and potential biases from demographics and other individual differences.

STUDY DESIGN

This article highlights these concerns in development of an NLP measure for tracking clinically rated paranoia from video "selfies" recorded from smartphone devices. Patients with schizophrenia or bipolar disorder were recruited and tracked over a week-long epoch. A small NLP-based feature set from 499 language samples were modeled on clinically rated paranoia using regularized regression.

STUDY RESULTS

While test-retest reliability was high, criterion, and convergent/divergent validity were only achieved when considering moderating variables, notably whether a patient was away from home, around strangers, or alone at the time of the recording. Moreover, there were systematic racial and sex biases in the model, in part, reflecting whether patients submitted videos when they were away from home, around strangers, or alone.

CONCLUSIONS

Advancing NLP measures for psychosis will require deliberate consideration of test-retest reliability, divergent validity, systematic biases and the potential role of moderators. In our example, a comprehensive psychometric evaluation revealed clear strengths and weaknesses that can be systematically addressed in future research.