Estimating psychopathological networks: Be careful what you wish for

The network structures of depressive and insomnia symptoms among cancer patients using propensity score matching: Findings from the Health and Retirement Study (HRS)

OBJECTIVE

Both depression and insomnia are found to be more prevalent in cancer patients compared to the general population. This study compared the network structures of depression and insomnia among cancer patients versus cancer-free participants (controls hereafter).

METHOD

The 8-item Center for Epidemiological Studies Depression Scale (CESD-8) and the 4-item Jenkins Sleep Scale (JSS-4) were used to measure depressive and insomnia symptoms, respectively. Propensity score matching (PSM) was used to construct the control group using data from the Health and Retirement Study (HRS). In total, a sample consisting of 2216 cancer patients and 2216 controls was constructed. Central (influential) and bridge symptoms were estimated using the expected influence (EI) and bridge expected influence (bridge EI), respectively. Network stability was assessed using the case-dropping bootstrap method.

RESULT

The prevalence of depression (CESD-8 total score ≥ 4) in cancer patients was significantly higher compared to the control group (28.56 % vs. 24.73 %; P = 0.004). Cancer patients also had more severe depressive symptoms relative to controls, but there was no significant group difference for insomnia symptoms. The network structures of depressive and insomnia symptoms were comparable between cancer patients and controls. "Felt sadness" (EI: 6.866 in cancer patients; EI: 5.861 in controls), "Felt unhappy" (EI: 6.371 in cancer patients; EI: 5.720 in controls) and "Felt depressed" (EI: 6.003 in cancer patients; EI: 5.880 in controls) emerged as the key central symptoms, and "Felt tired in morning" (bridge EI: 1.870 in cancer patients; EI: 1.266 in controls) and "Everything was an effort" (bridge EI: 1.046 in cancer patients; EI: 0.921 in controls) were the key bridge symptoms across both groups.

CONCLUSION

Although cancer patients had more frequent and severe depressive symptoms compared to controls, no significant difference was observed in the network structure or strength of the depressive and insomnia symptoms. Consequently, psychosocial interventions for treating depression and insomnia in the general population could be equally applicable for cancer patients who experience depression and insomnia.

Sleep Health Analysis Through Sleep Symptoms in 35,808 Individuals Across Age and Sex Differences: Comparative Symptom Network Study

BACKGROUND

Sleep health is a multidimensional construct that includes objective and subjective parameters and is influenced by individual sleep-related behaviors and sleep disorders. Symptom network analysis allows modeling of the interactions between variables, enabling both the visualization of relationships between different factors and the identification of the strength of those relationships. Given the known influence of sex and age on sleep health, network analysis can help explore sets of mutually interacting symptoms relative to these demographic variables.

OBJECTIVE

This study aimed to study the centrality of symptoms and compare age and sex differences regarding sleep health using a symptom network approach in a large French population that feels concerned about their sleep.

METHODS

Data were extracted from a questionnaire provided by the Réseau Morphée health network. A network analysis was conducted on 39 clinical variables related to sleep disorders and sleep health. After network estimation, statistical analyses consisted of calculating inferences of centrality, robustness (ie, testifying to a sufficient effect size), predictability, and network comparison. Sleep clinical variable centralities within the networks were analyzed by both sex and age using 4 age groups (18-30, 31-45, 46-55, and >55 years), and local symptom-by-symptom correlations determined.

RESULTS

Data of 35,808 participants were obtained. The mean age was 42.7 (SD 15.7) years, and 24,964 (69.7%) were women. Overall, there were no significant differences in the structure of the symptom networks between sexes or age groups. The most central symptoms across all groups were nonrestorative sleep and excessive daytime sleepiness. In the youngest group, additional central symptoms were chronic circadian misalignment and chronic sleep deprivation (related to sleep behaviors), particularly among women. In the oldest group, leg sensory discomfort and breath abnormality complaint were among the top 4 central symptoms. Symptoms of sleep disorders thus became more central with age than sleep behaviors. The high predictability of central nodes in one of the networks underlined its importance in influencing other nodes.

CONCLUSIONS

The absence of structural difference between networks is an important finding, given the known differences in sleep between sexes and across age groups. These similarities suggest comparable interactions between clinical sleep variables across sexes and age groups and highlight the implication of common sleep and wake neural circuits and circadian rhythms in understanding sleep health. More precisely, nonrestorative sleep and excessive daytime sleepiness are central symptoms in all groups. The behavioral component is particularly central in young people and women. Sleep-related respiratory and motor symptoms are prominent in older people. These results underscore the importance of comprehensive sleep promotion and screening strategies tailored to sex and age to impact sleep health.

Learning Bayesian Networks: A Copula Approach for Mixed-Type Data

Estimating dependence relationships between variables is a crucial issue in many applied domains and in particular psychology. When several variables are entertained, these can be organized into a network which encodes their set of conditional dependence relations. Typically however, the underlying network structure is completely unknown or can be partially drawn only; accordingly it should be learned from the available data, a process known as structure learning. In addition, data arising from social and psychological studies are often of different types, as they can include categorical, discrete and continuous measurements. In this paper, we develop a novel Bayesian methodology for structure learning of directed networks which applies to mixed data, i.e., possibly containing continuous, discrete, ordinal and binary variables simultaneously. Whenever available, our method can easily incorporate known dependence structures among variables represented by paths or edge directions that can be postulated in advance based on the specific problem under consideration. We evaluate the proposed method through extensive simulation studies, with appreciable performances in comparison with current state-of-the-art alternative methods. Finally, we apply our methodology to well-being data from a social survey promoted by the United Nations, and mental health data collected from a cohort of medical students. R code implementing the proposed methodology is available at https://github.com/FedeCastelletti/bayes_networks_mixed_data .

A network analysis of mechanisms of change during exposures over the course of intensive OCD treatment

Exposure and response prevention (ERP) is an evidence-based treatment for obsessive-compulsive disorder (OCD). Theories for how it works vary in their emphasis on active mechanisms of change. The current study aimed to clarify mechanisms of change in ERP for OCD using network analysis, comparing ERP networks at the start and end of intensive treatment (partial hospital and residential). In our sample of 182 patients, the most central node in both networks was engagement with exposure, which was consistently related to greater understanding of ERP rationale, higher willingness, and less ritualization, accounting for all other variables in the network. There were no significant differences in networks between the start and end of treatment. These results suggest that nonspecific parameters like facilitating engagement in exposures without ritualizing and providing a clear rationale to clients may be key to effective treatment. As such, it may be useful for clinicians to spend adequate time underscoring the need to eliminate rituals to fully engage in exposure tasks and explaining the rationale for ERP prior to doing exposures, regardless of theoretical orientation. Nonetheless, findings represent group-level statistics and more fine-grained idiographic analyses may reveal individual-level differences with respect to central mechanisms of change. Other limitations include demographic homogeneity of our sample.

Prevalence and network structure of depression, insomnia and suicidality among mental health professionals who recovered from COVID-19: a national survey in China

Psychiatric syndromes are common following recovery from Coronavirus Disease 2019 (COVID-19) infection. This study investigated the prevalence and the network structure of depression, insomnia, and suicidality among mental health professionals (MHPs) who recovered from COVID-19. Depression and insomnia were assessed with the Patient Health Questionnaire (PHQ-9) and Insomnia Severity Index questionnaire (ISI7) respectively. Suicidality items comprising suicidal ideation, suicidal plan and suicidal attempt were evaluated with binary response (no/yes) items. Network analyses with Ising model were conducted to identify the central symptoms of the network and their links to suicidality. A total of 9858 COVID-19 survivors were enrolled in a survey of MHPs. The prevalence of depression and insomnia were 47.10% (95% confidence interval (CI) = 46.09-48.06%) and 36.2% (95%CI = 35.35-37.21%), respectively, while the overall prevalence of suicidality was 7.8% (95%CI = 7.31-8.37%). The key central nodes included "Distress caused by the sleep difficulties" (ISI7) (EI = 1.34), "Interference with daytime functioning" (ISI5) (EI = 1.08), and "Sleep dissatisfaction" (ISI4) (EI = 0.74). "Fatigue" (PHQ4) (Bridge EI = 1.98), "Distress caused by sleep difficulties" (ISI7) (Bridge EI = 1.71), and "Motor Disturbances" (PHQ8) (Bridge EI = 1.67) were important bridge symptoms. The flow network indicated that the edge between the nodes of "Suicidality" (SU) and "Guilt" (PHQ6) showed the strongest connection (Edge Weight= 1.17, followed by "Suicidality" (SU) - "Sad mood" (PHQ2) (Edge Weight = 0.68)). The network analysis results suggest that insomnia symptoms play a critical role in the activation of the insomnia-depression-suicidality network model of COVID-19 survivors, while suicidality is more susceptible to the influence of depressive symptoms. These findings may have implications for developing prevention and intervention strategies for mental health conditions following recovery from COVID-19.

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.

Simulation-Based Performance Evaluation of Missing Data Handling in Network Analysis

Network analysis has gained popularity as an approach to investigate psychological constructs. However, there are currently no guidelines for applied researchers when encountering missing values. In this simulation study, we compared the performance of a two-step EM algorithm with separated steps for missing handling and regularization, a combined direct EM algorithm, and pairwise deletion. We investigated conditions with varying network sizes, numbers of observations, missing data mechanisms, and percentages of missing values. These approaches are evaluated with regard to recovering population networks in terms of loss in the precision matrix, edge set identification and network statistics. The simulation showed adequate performance only in conditions with large samples (n ≥ 500 ) or small networks (p = 10). Comparing the missing data approaches, the direct EM appears to be more sensitive and superior in nearly all chosen conditions. The two-step EM yields better results when the ratio of n/p is very large - being less sensitive but more specific. Pairwise deletion failed to converge across numerous conditions and yielded inferior results overall. Overall, direct EM is recommended in most cases, as it is able to mitigate the impact of missing data quite well, while modifications to two-step EM could improve its performance.

Symptom patterns in the co-occurrence of depressive and generalized anxiety symptoms: A network analysis of a Canadian nationally representative sample

BACKGROUND

This study aimed to investigate the symptom patterns of major depressive disorder (MDD) and generalized anxiety disorder (GAD) in a matched nationally representative sample of the Canadian population. We also tested whether childhood maltreatment (CM) exposures and sex would be linked with different symptom patterns.

METHODS

A total of 3296 participants from the Canadian Community Health Survey-Mental Health with complete information on MDD and GAD symptoms and being matched on the studied sociodemographic characteristics were included in the current study. Network analysis was performed to examine the MDD-GAD symptom network, network stability and centrality indices were also estimated. Finally, network comparison in connectivity patterns was conducted to explore the impact of maltreatment experience and sex differences in the MDD-GAD symptom networks.

RESULTS

The CM group had stronger network connections and showed differences in the network structures from the non-CM group. In the CM group, depressed mood and diminished interest were central symptoms and strongly connected with other symptoms. Additionally, females had stronger connections in the MDD-GAD symptom network than males, and sleep disturbance was a central symptom for females, alongside depressed mood and diminished interest.

LIMITATIONS

The cross-sectional design restricts our capacity to establish longitudinal or causal connections between symptoms.

CONCLUSIONS

Depressed mood was the most central node that was strongly connected with other symptoms in the network. Distinct MDD-GAD symptom networks were discovered in the CM and the female group when compared to their counterparts. Noteworthy, individuals with CM had a stronger correlation between worry and suicidal ideation. Clinical management and intervention efforts should pay close attention to these core symptoms to yield optimal treatment effects, particularly for females and individuals with CM.

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.

Unpacking the overlap between Autism and ADHD in adults: A multi-method approach

The overlap between Autism and Attention-Deficit Hyperactivity Disorder (ADHD) is widely observed in clinical settings, with growing interest in their co-occurrence in neurodiversity research. Until relatively recently, however, concurrent diagnoses of Autism and ADHD were not possible. This has limited the scope for large-scale research on their cross-condition associations, further stymied by a dearth of open science practices in the neurodiversity field. Additionally, almost all previous research linking Autism and ADHD has focused on children and adolescents, despite them being lifelong conditions. Tackling these limitations in previous research, 5504 adults - including a nationally representative sample of the UK (Study 1; n = 504) and a large pre-registered study (Study 2; n = 5000) - completed well-established self-report measures of Autism and ADHD traits. A series of network analyses unpacked the associations between Autism and ADHD at the individual trait level. Low inter-item connectivity was consistently found between conditions, supporting the distinction between Autism and ADHD as separable constructs. Subjective social enjoyment and hyperactivity-impulsivity traits were most condition-specific to Autism and ADHD, respectively. Traits related to attention control showed the greatest Bridge Expected Influence across conditions, revealing a potential transdiagnostic process underlying the overlap between Autism and ADHD. To investigate this further at the cognitive level, participants completed a large, well-powered, and pre-registered study measuring the relative contributions of Autism and ADHD traits to attention control (Study 3; n = 500). We detected age- and sex-related effects, however, attention control did not account for the covariance between Autism and ADHD traits. We situate our findings and discuss future directions in the cognitive science of Autism, ADHD, and neurodiversity, noting how our open datasets may be used in future research.

Emotion dysregulation links pathological eating styles and psychopathological traits in bariatric surgery candidates

Objectives

Approximately one-third of bariatric surgery patients experience weight regain or suboptimal weight loss within five years post-surgery. Pathological eating styles and psychopathological traits (e.g., emotion dysregulation) are recognized as potential hindrances to sustain weight loss efforts and are implicated in obesity development. A comprehensive understanding of these variables and their interplays is still lacking, despite their potential significance in developing more effective clinical interventions for bariatric patients. We investigate the prevalence of and interactions between pathological eating styles and psychopathological traits in this population.

Materials and methods

110 bariatric surgery candidates were characterized using the Binge Eating Scale (BES), Hamilton Depression/Anxiety Scales (HAM-D/A), Barratt Impulsiveness Scale (BIS-11), Experiences in Close Relationships (ECR), Difficulties in Emotion Regulation Scale (DERS). We analyzed these variables with multiple logistic regression analyses and network analysis.

Results

Patients with pathological eating styles showed more pronounced anxiety/depressive symptoms and emotion dysregulation. Network analysis revealed strong connections between BES and DERS, with DERS also displaying robust connections with HAM-A/D and ECR scales. DERS and attention impulsivity (BIS-11-A) emerged as the strongest nodes in the network.

Discussion

Our findings demonstrate the mediating role of emotion dysregulation between pathological eating styles and psychopathological traits, supporting existing literature on the association between psychopathological traits, insecure attachment styles, and pathological eating behaviors. This research emphasizes the significance of emotion regulation in the complex network of variables contributing to obesity, and its potential impact on bariatric surgery outcomes. Interventions focusing on emotion regulation may thus lead to improved clinical outcomes for bariatric patients.

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.

Comparing community detection algorithms in psychometric networks: A Monte Carlo simulation

Identifying the correct number of factors in multivariate data is fundamental to psychological measurement. Factor analysis has a long tradition in the field, but it has been challenged recently by exploratory graph analysis (EGA), an approach based on network psychometrics. EGA first estimates a network and then applies the Walktrap community detection algorithm. Simulation studies have demonstrated that EGA has comparable or better accuracy for recovering the same number of communities as there are factors in the simulated data than factor analytic methods. Despite EGA's effectiveness, there has yet to be an investigation into whether other sparsity induction methods or community detection algorithms could achieve equivalent or better performance. Furthermore, unidimensional structures are fundamental to psychological measurement yet they have been sparsely studied in simulations using community detection algorithms. In the present study, we performed a Monte Carlo simulation using the zero-order correlation matrix, GLASSO, and two variants of a non-regularized partial correlation sparsity induction methods with several community detection algorithms. We examined the performance of these method-algorithm combinations in both continuous and polytomous data across a variety of conditions. The results indicate that the Fast-greedy, Louvain, and Walktrap algorithms paired with the GLASSO method were consistently among the most accurate and least-biased overall.

Pain trajectories and their associations with cognition among older adults: a 10-year cohort study from network perspective

BACKGROUND

Few studies have examined the associations between pain trajectories and cognitive function in older adults. This study explored the associations between pain trajectories and different cognitive domains in older adults from a network perspective.

METHODS

Data on pain trajectories were derived from the Health and Retirement Study between 2010 and 2020 using latent class growth analyses. Measurements of key cognition domains, including memory, attention, calculation, orientation and language, were included. Linear regression and network analysis were performed to evaluate the associations between different pain trajectories and cognition.

RESULTS

A total of 9,551 older adults were included in this study and three trajectories of pain were identified. After controlling for the covariates, persistent severe pain trajectory was associated with poorer overall cognition, memory and calculation ability when compared to mild or non-persistent pain trajectory. In the pain and cognition network model, memory (expected influence (EI) = 0.62), language (EI = 0.58) and calculation (EI = 0.41) were the most central domains.

CONCLUSIONS

Pain trajectories appeared stable over time among older adults in this study. Severity of persistent pain was an important risk factor for poor cognition, especially in relation to memory and calculation domains. Interventions targeting memory, language and calculation domains might be useful in addressing cognitive decline in older adults with persistent pain.

Interactive changes in depression and loneliness symptoms prior to and during the COVID-19 pandemic: A longitudinal network analysis

OBJECTIVES

Depression and loneliness co-occur frequently. This study examined interactive changes between depression and loneliness among older adults prior to and during the COVID-19 pandemic from a longitudinal network perspective.

METHODS

This network study was based on data from three waves (2016-2017, 2018-2019, and 2020) of the English Longitudinal Study of Ageing (ELSA). Depression and loneliness were measured with the eight-item version of the Center for Epidemiologic Studies Depression Scale (CESD-8) and three item version of the University of California Los Angeles (UCLA) Loneliness Scale, respectively. A network model was constructed using an Ising Model while network differences were assessed using a Network Comparison Test. Central symptoms were identified via Expected Influence (EI).

RESULTS

A total of 4,293 older adults were included in this study. The prevalence and network of depression and loneliness did not change significantly between the baseline and pre-pandemic assessments but increased significantly from the pre-pandemic assessment to during COVID-19 assessment. The central symptom with the strongest increase from pre-pandemic to pandemic assessments was "Inability to get going" (CESD8) and the edge with the highest increase across depression-loneliness symptom communities was "Lack companionship" (UCLA1) - "Inability to get going" (CESD8). Finally, "Feeling depressed" (CESD1) and "Everything was an effort" (CESD2) were the most central symptoms over the three assessment periods.

CONCLUSIONS

The COVID-19 pandemic was associated with significant changes in the depression-loneliness network model. The most changed symptoms and edges could be treatment targets for reducing the risk of depression and loneliness in older adults.

Metabolites and depressive symptoms: Network- and longitudinal analyses from the Finnish Depression and Metabolic Syndrome in Adults (FDMSA) Study

BACKGROUND

Depression is associated with metabolic abnormalities linked to metabolic syndrome and tissue inflammation, but the interplay between metabolic markers and their association with subsequent depression is unknown. Therefore, we aimed to describe the network of metabolites and their prospective association with depressive symptoms.

METHODS

The Finnish Depression and Metabolic Syndrome in Adults (FDMSA) cohort, originally a prospective case-control study, comprised a group with Beck Depression Inventory (BDI)-I scores ≥10 at baseline, and controls (n = 319, BDI-I < 10); mean (sd) follow-up time: 7.4 (0.7) years. Serum metabolic biomarkers were determined by proton nuclear magnetic resonance (NMR), and depressive symptoms sum-score by using the BDI-I. We examined the prospective associations between metabolites at baseline and BDI score at follow-up utilizing multivariate linear regression, parsimonious predictions models and network analysis.

RESULTS

Some metabolites tended to be either negatively (e.g. histidine) or positively associated (e.g. glycoprotein acetylation, creatinine and triglycerides in very large high density lipoproteins [XL-HDL-TG]) with depressive symptoms. None of the associations were significant after correction for multiple testing. The network analysis suggested high correlation among the metabolites, but that none of the metabolites directly influenced subsequent depressive symptoms.

LIMITATIONS

Although the sample size may be considered satisfactory in a prospective context, we cannot exclude the possibility that our study was underpowered.

CONCLUSIONS

Our results suggest that the investigated metabolic biomarkers are not a driving force in the development of depressive symptoms. These findings should be confirmed in studies with larger samples and studies that account for the heterogeneity of depressive disorders.

Addressing the Complex Links between Psychopathy and Childhood Maltreatment, Emotion Regulation, and Aggression-A Network Analysis in Adults

Childhood maltreatment is the strongest predictor of psychopathology and personality disorders across the lifespan and is strongly associated with a variety of psychological problems, namely, mood and anxiety disorders, behavioral and personality disorders, substance abuse, aggression, and self-harm. In this study, we aim to provide a comprehensive picture of the interplay between different traits of psychopathy and distinct dimensions of childhood maltreatment, emotion regulation, and aggression. Using a cross-sectional design, we employed correlational network analysis to explore the nomological network of psychopathy and provide a sample-based estimate of the population parameters reflecting the direction, strength, and patterns of relationships between variables. The sample consisted of 846 adults (71% females) who completed questionnaires measuring psychopathy, childhood maltreatment, emotion regulation, and aggression. The results highlight that disinhibition traits of psychopathy are the closest attributes of early experiences of abuse (but not neglect) in childhood and correlate with all dimensions of emotion regulation difficulties, being specifically associated with reactive aggression. Neglect was a unique attribute in the nomological network of meanness, with widespread correlations with emotion regulation difficulties but also an increased ability to engage in goal-directed behavior. Physical abuse was the only dimension of childhood adversity that was found to be intercorrelated with boldness and increased emotional regulation was found in this psychopathic trait. No significant associations were found between boldness, meanness, and aggression once shared variance with disinhibition was controlled. These results are discussed in terms of their implication for research and clinical practice.

The inner workings of anger: A network analysis of anger and emotion regulation

OBJECTIVE

This study aimed to investigate the interrelations between emotion regulation strategies and different types of anger using network analysis.

METHOD

Data were drawn from a cross-sectional sample of 538 adults (55% females; mean age = 39.8 years, SD = 12.3) seeking treatment for anger. Data were collected between March and November 2019 in Sweden. Participants completed measures of anger problems (anger expression, anger suppression, angry reactions, anger rumination, trait anger, hostility, physical aggression, and verbal aggression) and emotion regulation (cognitive reappraisal, expressive suppression, anger relaxation, and five mindfulness strategies). To determine whether distinct clusters of anger nodes would emerge, exploratory graph analysis was employed. Based on clustering of nodes, we estimated separate networks including all measures of emotion regulation.

RESULTS

Two clusters emerged: one consisting primarily of cognitive components of anger, and another of behavioral. Across networks, anger nodes were strongly interconnected, and anger rumination and anger suppression were especially influential. Several direct links were found between specific emotion regulation strategies and cognitive components of anger, whereas most strategies were only indirectly related to angry behavior. Cognitive reappraisal showed no direct link with any of the anger nodes.

CONCLUSIONS

Our findings reveal potential pathways by which different emotion regulation strategies may influence different types of anger, which could serve as therapeutic targets.

Depression and anxiety among caregivers of psychiatric patients during the late stage of the COVID-19 pandemic: A perspective from network analysis

BACKGROUND

Depressive and anxiety symptoms (depression and anxiety hereafter) are common among psychiatric patients and their caregivers during the COVID-19 pandemic. Network analysis is a novel method to assess the associations between psychiatric syndromes/disorders at the symptom level. This study examined depression and anxiety among caregivers of psychiatric inpatients during the late stage of the COVID-19 pandemic from the perspective of network analysis.

METHODS

A total of 1101 caregivers of psychiatric inpatients were included in this study. The severity of depression was assessed using the nine-item Patient Health Questionnaire (PHQ-9), while anxiety was assessed with the seven-item Generalized Anxiety Disorder Scale (GAD-7). The expected index (EI) and bridge EI index were used to identify the central and bridge symptoms, respectively. The stability of the network was evaluated via a case-dropping bootstrap procedure.

RESULTS

The prevalence of depression and anxiety were 32.4 % (95%CI: 29.7 %-35.3 %) and 28.0 % (95%CI: 25.4 %-30.7 %), respectively while the prevalence of comorbid depression and anxiety was 24.9 % (95%CI: 22.4 %-27.6 %). The most central symptom was "Fatigue", followed by "Trouble Relaxing" and "Restlessness". The highest bridge symptom was "Restlessness", followed by "Uncontrollable worry" and "Suicide ideation". The bootstrap test indicated that the whole network model was stable, and no network difference was detected between genders and between different education levels.

CONCLUSIONS

Depression, anxiety, and comorbid depression and anxiety were common among caregivers of psychiatric inpatients during the late stage of the COVID-19 pandemic. Central and bridge symptoms identified in this network analysis should be considered key target symptoms to address in caregivers of patients.

The interrelatedness of cognitive abilities in very preterm and full-term born children at 5.5 years of age: a psychometric network analysis approach

BACKGROUND

Very preterm (VP) birth is associated with a considerable risk for cognitive impairment, putting children at a disadvantage in academic and everyday life. Despite lower cognitive ability on the group level, there are large individual differences among VP born children. Contemporary theories define intelligence as a network of reciprocally connected cognitive abilities. Therefore, intelligence was studied as a network of interrelated abilities to provide insight into interindividual differences. We described and compared the network of cognitive abilities, including strength of interrelations between and the relative importance of abilities, of VP and full-term (FT) born children and VP children with below-average and average-high intelligence at 5.5 years.

METHODS

A total of 2,253 VP children from the EPIPAGE-2 cohort and 578 FT controls who participated in the 5.5-year-follow-up were eligible for inclusion. The WPPSI-IV was used to measure verbal comprehension, visuospatial abilities, fluid reasoning, working memory, and processing speed. Psychometric network analysis was applied to analyse the data.

RESULTS

Cognitive abilities were densely and positively interconnected in all networks, but the strength of connections differed between networks. The cognitive network of VP children was more strongly interconnected than that of FT children. Furthermore, VP children with below average IQ had a more strongly connected network than VP children with average-high IQ. Contrary to our expectations, working memory had the least central role in all networks.

CONCLUSIONS

In line with the ability differentiation hypothesis, children with higher levels of cognitive ability had a less interconnected and more specialised cognitive structure. Composite intelligence scores may therefore mask domain-specific deficits, particularly in children at risk for cognitive impairments (e.g., VP born children), even when general intelligence is unimpaired. In children with strongly and densely connected networks, domain-specific deficits may have a larger overall impact, resulting in lower intelligence levels.

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.

Unique Variable Analysis: A Network Psychometrics Method to Detect Local Dependence

The local independence assumption states that variables are unrelated after conditioning on a latent variable. Common problems that arise from violations of this assumption include model misspecification, biased model parameters, and inaccurate estimates of internal structure. These problems are not limited to latent variable models but also apply to network psychometrics. This paper proposes a novel network psychometric approach to detect locally dependent pairs of variables using network modeling and a graph theory measure called weighted topological overlap (wTO). Using simulation, this approach is compared to contemporary local dependence detection methods such as exploratory structural equation modeling with standardized expected parameter change and a recently developed approach using partial correlations and a resampling procedure. Different approaches to determine local dependence using statistical significance and cutoff values are also compared. Continuous, polytomous (5-point Likert scale), and dichotomous (binary) data were generated with skew across a variety of conditions. Our results indicate that cutoff values work better than significance approaches. Overall, the network psychometrics approaches using wTO with graphical least absolute shrinkage and selector operator with extended Bayesian information criterion and wTO with Bayesian Gaussian graphical model were the best performing local dependence detection methods overall.

Childhood maltreatment and major depressive disorder in well-being: a network analysis of a longitudinal community-based cohort

BACKGROUND

Little has been done to comprehensively study the relationships between multiple well-being constructs at a time. Even less is known about whether child maltreatment and major depressive disorder (MDD) impact different well-being constructs. This study aims to examine whether maltreated or depressed individuals have specific impacts on well-being structures.

METHODS

Data analyzed were from the Montreal South-West Longitudinal Catchment Area Study (N = 1380). The potential confounding of age and sex was controlled by propensity score matching. We used network analysis to assess the impact of maltreatment and MDD on well-being. The centrality of nodes was estimated with the 'strength' index and a case-dropping bootstrap procedure was used to test network stability. Differences in the structure and connectivity of networks between different studied groups were also examined.

RESULTS

Autonomy and daily life and social relations were the most central nodes for the MDD and maltreated groups [MDD group: strength coefficient (SC) autonomy = 1.50; SCdaily life and social relations = 1.34; maltreated group: SCautonomy = 1.69; SCdaily life and social relations = 1.55]. Both maltreatment and MDD groups had statistical differences in terms of the global strength of interconnectivity in their networks. Network invariance differed between with and without MDD groups indicating different structures of their networks. The non-maltreatment and MDD group had the highest level of overall connectivity.

CONCLUSIONS

We discovered distinct connectivity patterns of well-being outcomes in maltreatment and MDD groups. The identified core constructs could serve as potential targets to maximize the effectiveness of clinical management of MDD and also advance prevention to minimize the sequelae of maltreatment.

Network analysis of cold cognition and depression in middle-aged and elder population: the moderation of grandparenting

Background

Cognitive decline and negative emotions are common in aging, especially decline in cold cognition which often co-occurred with depression in middle-aged and older adults. This study analyzed the interactions between cold cognition and depression in the middle-aged and elder populations using network analysis and explored the effects of grandparenting on the cold cognition-depression network.

Methods

The data of 6,900 individuals (≥ 45 years) from the China Health and Retirement Longitudinal Study (CHARLS) were used. The Minimum Mental State Examination (MMSE) and the Epidemiology Research Center Depression Scale-10 (CESD-10) were used to assess cold cognition and depressive symptoms, respectively. Centrality indices and bridge centrality indices were used to identify central nodes and bridge nodes, respectively.

Results

Network analysis showed that nodes "language ability" and "depressed mood" were more central nodes in the network of cold cognition and depression in all participants. Meantime, nodes "attention," "language ability" and "hopeless" were three key bridge nodes connecting cold cognition and depressive symptoms. Additionally, the global connectivity of the cold cognition and depression network was stronger in the non-grandparenting than the grandparenting.

Conclusion

The findings shed a light on the complex interactions between cold cognition and depression in the middle-aged and elder populations. Decline in language ability and depressed mood can serve as predictors for the emergence of cold cognitive dysfunction and depression in individuals during aging. Attention, language ability and hopelessness are potential targets for psychosocial interventions. Furthermore, grandparenting is effective in alleviating cold cognitive dysfunction and depression that occur during individual aging.

Prospective network analysis of proinflammatory proteins, lipid markers, and depression components in midlife community women

BACKGROUND

Vulnerability theories propose that suboptimal levels of lipid markers and proinflammatory proteins predict future heightened depression. Scar models posit the reverse association. However, most studies that tested relationships between non-specific immune/endocrine markers and depression did not separate temporal inferences between people and within-person and how different immunometabolism markers related to unique depression symptoms. We thus used cross-lagged prospective network analyses (CLPN) to investigate this topic.

METHODS

Community midlife women (n = 2224) completed the Center for Epidemiologic Studies-Depression scale and provided biomarker samples across five time-points spanning 9 years. CLPN identified significant relations (edges) among components (nodes) of depression (depressed mood, somatic symptoms, interpersonal issues), lipid markers [insulin, fasting glucose, triglycerides, low-density lipoprotein-cholesterol (LDL), high-density lipoprotein-cholesterol (HDL)], and proinflammatory proteins [C-reactive protein (CRP), fibrinogen], within and across time-points. All models adjusted for age, estradiol, follicle-stimulating hormone, and menopausal status.

RESULTS

In within-person temporal networks, higher CRP and HDL predicted all three depression components (d = 0.131-2.112). Increased LDL preceded higher depressed mood and interpersonal issues (v. somatic symptoms) (d = 0.251-0.327). Elevated triglycerides predicted more somatic symptoms (v. depressed mood and interpersonal problems) (d = 0.131). More interpersonal problems forecasted elevated fibrinogen and LDL levels (d = 0.129-0.331), and stronger somatic symptoms preceded higher fibrinogen levels (d = 0.188).

CONCLUSIONS

Results supported both vulnerability and scar models. Long-term dysregulated immunometabolism systems, social disengagement, and related patterns are possible mechanistic accounts. Cognitive-behavioral therapies that optimize nutrition and physical activity may effectively target depression.

Socioemotional profiles of autism spectrum disorders, attention deficit hyperactivity disorder, and disinhibited and reactive attachment disorders: a symptom comparison and network approach

Children with autism spectrum disorders (ASDs), attention deficit hyperactivity disorder (ADHD) and disinhibited and reactive attachment disorders (RAD/DAD) often experience socioemotional problems. Elucidating a clear picture of these profiles is essential. Strengths and Difficulties Questionnaires (SDQs) were analysed from cohort of children with ASD (n = 1430), ADHD (n = 1193), and RAD/DAD (n = 39). Kruskal-Wallis Tests and network analytic techniques were used to investigate symptom profiles. Children with ASD experienced more emotional problems, peer problems and fewer prosocial behaviours. Children with ADHD and RAD/DAD had higher levels of hyperactivity and conduct problems. Overall, ASD and ADHD networks were highly correlated (rs = 0.82), and we did not observe a statistically significant difference in terms of global Strength.

Weight Bias Internalization and Eating Disorder Psychopathology in Treatment-Seeking Patients with Obesity

This study aimed to investigate the relationship between weight bias internalization and eating disorder psychopathology in treatment-seeking patients with severe obesity using a network approach. Two thousand one hundred and thirteen patients with obesity were consecutively admitted to a specialist clinical unit for obesity and were recruited from January 2016 to February 2023. Body mass index was measured, and each patient completed the Weight Bias Internalization Scale (WBSI) and the Eating Disorder Examination Interview (EDE). Network analysis showed that the most central and highly interconnected nodes in the network were related to the EDE items exposure avoidance, dissatisfaction with shape, and wanting an empty stomach. Bridge nodes were found, but the bootstrap difference test on expected bridge influence indicated non-significant centrality differences. Nevertheless, the eating disorder psychopathology and weight bias internalization network structure in patients seeking treatment for obesity indicate the prominent roles of body dissatisfaction and control of eating and weight in these psychological constructs. This finding, if replicated, could pave the way for a new understanding of the psychological mechanisms operating in patients with obesity.

Internet addiction and residual depressive symptoms among clinically stable adolescents with major psychiatric disorders during the COVID-19 pandemic: a network analysis perspective

To assess the inter-relationships between residual depressive symptoms (RDS) and Internet addiction (IA) using network analysis among clinically stable adolescents with major psychiatric disorders during the COVID-19 pandemic. RDS and IA were assessed using the Patient Health Questionnaire-9 (PHQ-9) and the Internet Addiction Test (IAT), respectively. Central symptoms and bridge symptoms in the network model were examined. A total of 1,454 adolescents met the study criteria and were included in the analyses. The prevalence of IA was 31.2% (95% CI: 28.8%-33.6%). In the network analysis, the nodes IAT15 ("Preoccupation with the Internet"), PHQ2 ("Sad mood"), and PHQ1 ("Anhedonia") were the most central symptoms in the IA-RDS network model. Bridge symptoms included IAT10 ("Sooth disturbing about your Internet use"), PHQ9 ("Suicide ideation"), and IAT3 ("Prefer the excitement online to the time with others"). Additionally, PHQ2 ("Sad mood") was the main node linking "Anhedonia" to other IA clusters. Internet addiction was common among clinically stable adolescents with major psychiatric disorders during the COVID-19 pandemic. Core and bridge symptoms identified in this study could be prioritized as targets for the prevention and treatment of IA in this population.

Network connectivity between anxiety, depressive symptoms and psychological capital in Chinese university students during the COVID-19 campus closure

BACKGROUND

In the context of the outbreak of COVID-19 within mainland China, to understand the mental health status of university students during campus closure, this study analyzes the relationship between anxiety, depressive symptoms, and psychological capital and to reveals their central symptoms.

METHODS

A total of 12,945 university students were included in this study from April 10 to 19, 2022. Anxiety and depressive symptoms were measured by the seven-item Generalized Anxiety Disorder Scale (GAD-7) and two-item Patient Health Questionnaires (PHQ-2). Psychological capital was measured using the Psychological Capital Questionnaire (PCQ-24). The centrality and bridge centrality indexes were used to identify central and bridge symptoms, respectively. Network Comparison Test (NCT) was also administered to check whether network traits differed by gender and place of residence.

RESULTS

The most influential node in this study was Trouble relaxing (GAD4), followed by Uncontrollable worry (GAD2) and Excessive worry (GAD3). The main bridging symptoms were Depressed mood (PHQ2), Psychological capital. There are no differences in the network structure of students by place of residence, while there are more significant differences in the network structure of students by gender.

CONCLUSION

Central and bridging symptoms may be the core symptoms that trigger or maintain the development of anxiety and depression among university students during the COVID-19 campus closure. Timely and reasonable interventions targeting these symptoms may help reduce depression and anxiety in this population. In addition, improving university students' psychological capital may likewise contribute to the development of their good mental health.

Depression and anxiety among Macau residents during the COVID-19 outbreak: A network analysis perspective

Background

The 2019 novel coronavirus disease (COVID-19) outbreak affected people's lifestyles and increased their risk for depressive and anxiety symptoms (depression and anxiety, respectively hereafter). We assessed depression and anxiety in residents of Macau during "the 6.18 COVID-19 outbreak" period and explored inter-connections of different symptoms from the perspective of network analysis.

Methods

In this cross-sectional study, 1,008 Macau residents completed an online survey comprising the nine-item Patient Health Questionnaire (PHQ-9) and seven-item Generalized Anxiety Disorder Scale (GAD-7) to measure depression and anxiety, respectively. Central and bridge symptoms of the depression-anxiety network model were evaluated based on Expected Influence (EI) statistics, while a bootstrap procedure was used to test the stability and accuracy of the network model.

Results

Descriptive analyses indicated the prevalence of depression was 62.5% [95% confidence interval (CI) = 59.47-65.44%], the prevalence of anxiety was 50.2% [95%CI = 47.12-53.28%], and 45.1% [95%CI = 42.09-48.22%] of participants experienced comorbid depression and anxiety. "Nervousness-Uncontrollable worry" (GADC) (EI = 1.15), "Irritability" (GAD6) (EI = 1.03), and "Excessive worry" (GAD3) (EI = 1.02) were the most central symptoms, while "Irritability" (GAD6) (bridge EI = 0.43), "restlessness" (GAD5) (bridge EI = 0.35), and "Sad Mood" (PHQ2) (bridge EI = 0.30) were key bridge symptoms that emerged in the network model.

Conclusion

Nearly half of residents in Macau experienced comorbid depression and anxiety during the 6.18 COVID-19 outbreak. Central and bridge symptoms identified in this network analysis are plausible, specific targets for treatment and prevention of comorbid depression and anxiety related to this outbreak.

The core symptom in multiple myeloma patients undergoing chemotherapy: a network analysis

BACKGROUND

During chemotherapy for multiple myeloma, symptoms include those related to the disease, as well as adverse effects of the treatment. Few studies have explored the relationships between these symptoms. Network analysis could identify the core symptom in the symptom network.

OBJECTIVE

The aim of this study was to explore the core symptom in multiple myeloma patients undergoing chemotherapy.

METHODS

This was a cross-sectional study in which sequential sampling was used to recruit 177 participants from Hunan, China. Demographic and clinical characteristics were surveyed using a self-developed instrument. The symptoms of chemotherapy-treated multiple myeloma, including pain, fatigue, worry, nausea, and vomiting, were measured using a questionnaire with good reliability and validity. The mean ± SD, frequency, and percentages were used as descriptive statistics. Network analysis was used to estimate the correlation between symptoms.

RESULTS

The results showed that 70% of multiple myeloma patients using chemotherapy exhibited pain. In the network analysis, worrying was the dominant symptom, and the strongest relationship was between nausea and vomiting in chemotherapy-treated multiple myeloma patients' symptoms.

CONCLUSION

Worrying is the core symptom of multiple myeloma patients. Interventions could be most effective if there is a symptom management focus on worrying when providing care to chemotherapy-treated multiple myeloma patients. Nausea combined with vomiting could be better managed, which would decrease the cost of health care. Understanding the relationship between the symptoms of multiple myeloma patients undergoing chemotherapy is beneficial for precise symptom management.

IMPLICATIONS FOR PRACTICE

Nurses and health care teams should be a priority to intervene in the worrying for chemotherapy-treated multiple myeloma patients to maximize the effectiveness of an intervention. Except, nausea and vomiting should be managed together in a clinical setting.

A Network Analysis of Multiple Preconception Health Behaviors in Chinese Women

BACKGROUND

A healthy preconception lifestyle, consisting of multiple health behaviors, is crucial for preventing adverse health outcomes in mothers and offspring. Although knowledge about the pattern of inter-behavior relations may provide insights for nudging multiple health behavior changes, this has not been adequately explored in the existing literature. Adopting a network perspective, the present study conceptualized multiple health behaviors as a behavior network (i.e., behaviors as nodes, inter-behavior relations as edges) and utilized network analysis to investigate the pattern of interrelations of preconception health behaviors in a large sample of Chinese women.

METHOD

We used the data of a population-based cohort study in China to estimate the behavior network. An analytic sample included 41,127 Chinese women who were surveyed about their adoptions of multiple health behaviors during the preconception period.

RESULTS

Network analysis revealed a relatively dense behavior network and visualized the network structure of multiple preconception health behaviors. Subsequent centrality analysis identified three central behaviors (i.e., avoiding second- or third-hand smoke, reducing psychosocial stress, and reducing alcohol) that had distinctively stronger connections to other behaviors.

CONCLUSIONS

Preconception health behaviors were strongly interconnected, and certain behaviors had stronger influences than others within the behavior network. Our findings highlight the strong inter-relatedness of preconception health behaviors. This study also encourages targeting the three central behaviors in preconception lifestyle promotions because this may bring more secondary improvements on other non-targeted behaviors and thereby achieve comprehensive lifestyle change.

Network analysis of eating disorder symptoms in women in perimenopause and early postmenopause

OBJECTIVE

Eating disorders (EDs) are often stereotyped as disorders of adolescence and young adulthood; however, they can occur at any age. Prevalence of EDs at midlife are approximately 3.5% and specific symptoms at midlife can have prevalences as high as 29.3%. Studies also inconsistently suggest that EDs and related symptoms may be more prevalent in midlife aged women during perimenopause compared with midlife aged women at pre-menopause. To date few studies have examined the structure of and associations between ED symptoms in women specifically during perimenopause and early postmenopause. Thus, the purpose of the current study is to investigate the structure of ED symptoms specifically during perimenopause and early postmenopause.

METHODS

Participants included 36 participants (45-61 y old) in a larger clinical trial who completed the Eating Disorder Examination Questionnaire (EDE-Q) at a baseline study visit. Network analysis statistical models were used to examine the structure of and associations between ED symptoms assessed via the EDE-Q.

RESULTS

Shape dissatisfaction and weight dissatisfaction were the top 2 central symptoms in the network.

CONCLUSIONS

Results corroborate previous studies and indicate that, similar to young adult samples, dissatisfaction with body image is a core feature of ED pathology across the lifespan.

Suicide ideation and anhedonia among clinically stable adolescents with the recurrent depressive disorder during the COVID-19 pandemic: A network perspective

BACKGROUND

Anhedonia is a suicide risk factor among adolescent patients with recurrent depressive disorder (depression hereafter). This study examined associations between suicidal ideation (SI) and residual depressive symptoms (RSD), including anhedonia, among clinically stable adolescents with depression.

METHOD

A network analysis was performed to examine the association between RDS and SI among adolescents with depression. Node-specific predictive betweenness was computed to examine short paths between anhedonia and SI. Additionally, a Network Comparison Test (NCT) was conducted to examine gender differences in derived network model characteristics.

RESULTS

The network analysis identified close associations of PHQ9 ("Suicide ideation") with PHQ1 ("Anhedonia") as well as some other RDS including PHQ6 ("Guilt"), PHQ2 ("Sad mood") and PHQ8 ("Motor disturbances"). Additionally, PHQ2 ("Sad mood") and PHQ4 ("Fatigue") were the main bridge nodes linking anhedonia and SI. Comparisons of network models did not find significant differences in network global strength or edge weights.

LIMITATION

Causal relations between anhedonia and SI could not be determined due to the cross-sectional study design.

CONCLUSIONS

SI was directly related to Anhedonia in addition to Guilt, Sad mood and Motor disturbances. Sad mood and Fatigue were the main bridge nodes linking Anhedonia and SI. To reduce the risk of SI among clinically stable adolescents with depression during the COVID-19 pandemic, specific RDS including Anhedonia, Guilt, Sad mood, Motor disturbances and Fatigue should be targeted in interventions.

Inter-relationships of depressive and anxiety symptoms with suicidality among adolescents: A network perspective

BACKGROUND

Persons with suicidality including suicidal ideation (SI), suicide plans (SP) and/or suicide attempts (SA) are at higher risk for future suicide than those without suicidality. To reduce the risk of future suicide, it is important to understand symptoms of emotional distress that have the strongest links with SI, SP and SA. This network analysis examined item-level relations of depressive and anxiety symptoms with suicidality among adolescents during the COVID-19 pandemic.

METHODS

Adolescents between 12 and 20 years of age were assessed with the Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder Scale (GAD-7), and individual binary reponse (no/yes) items assessing SI, SP, and SA during the pandemic. The structure of depressive symptoms, anxiety symptoms and suicidality was characterized using "Expected Influence" and "Bridge Expected Influence" as centrality indices in the symptom network. Network stability was tested using a case-dropping bootstrap procedure. Node-specific predictive betweenness was computed to examine short paths of anhedonia, other depressive symptoms and anxiety symptoms with suicidality. A Network Comparison Test (NCT) was conducted to examine whether network characteristics differed based on gender.

RESULTS

Prevalence rates of depressive symptoms, anxiety symptoms, and suicidality were 44.60 % (95% confidence interval (CI) = 41.53-47.67 %), 31.12 % (95%CI = 28.26-33.98 %), and 16.95 % (95%CI = 14.63-19.26 %), respectively, in the study sample. The network analysis identified GAD3 ("Worry too much") as the most central symptom, followed by GAD6 ("Irritability") and PHQ6 ("Guilt") in the sample. Additionally, PHQ6 ("Guilt"), GAD6 ("Irritability"), and PHQ2 ("Sad mood") were bridge nodes linking depressive and anxiety symptoms with suicidality. A flow network indicated that the connection between S ("Suicidality") and PHQ6 ("Guilt") reflected the strongest connection, followed by connections of S ("Suicidality") with GAD2 ("Uncontrollable worrying"), and S ("Suicidality") with PHQ2 ("Sad mood"). Finally, PHQ2 ("Sad mood") was the main bridge node linking anhedonia with other depressive and anxiety symptoms and suicidality in the sample.

CONCLUSIONS

Findings highlight the potential importance of reducing specific depressive and anxiety symptoms as possible means of reducing suicidality among adolescents during the pandemic. Central symptoms and key bridge symptoms identified in this study should be targeted in suicide prevention for at-risk adolescents.

Racial Differences in C-Reactive Protein, Depression Symptoms, and Social Relationships in Older Adults: A Moderated Network Analysis

INTRODUCTION

We introduce moderated network analysis as an integrative approach to assess the moderation effects of race on the relationship between C-reactive protein (CRP) and depression symptoms in older adults. This study further explores how the observed relationships differ adjusting for social relationships.

METHODS

This secondary analysis of cross-sectional data from the National Social Life, Health, and Aging Project (2010-2011) includes 2,880 older adults. We used different depression symptom domains (depressed affect, low positive affect, somatic symptoms, and interpersonal problems) from the Center for Epidemiologic Studies-Depression Scale. Social relationships were assessed with measures of social integration, social support, and social strain. The moderated networks were constructed using the R-package mgm. The racial moderator was coded as White/African American racial groups.

RESULTS

In the moderated networks of CRP and depression symptoms, CRP-"interpersonal problems" edge was present only among African Americans. CRP-"somatic symptoms" edge was present in both racial groups with equal edge weights. After adjusting for social relationships, the aforementioned patterns remained the same, but the edge weights were attenuated. We additionally observed CRP-social strain and social integration-"depressed affect" edges only in African Americans.

DISCUSSION

Race may moderate the relationship between the CRP and depression symptoms in older adults and social relationships might be important covariates to consider while analyzing them. This study as an initiation point; future network investigations would benefit from leveraging more contemporary cohorts of older adults, gaining a large sample size with diverse racial/ethnic backgrounds, and important covariates. Several important methodological issues of the current study are addressed.

Social Interpretation Inflexibility and Functioning: Associations with Symptoms and Stress

Introduction: Interpretation inflexibility has been implicated in a range of mental health problems, including depression, social anxiety, and paranoia. Inflexible interpretation of social situations may be particularly important as it can set the stage for problems in social functioning, a symptom cutting across all three groups of disorders. Methods: This study aimed to examine the interrelations among interpretation inflexibility, social functioning impairment, and affective and psychotic symptoms. The study also explored the potential moderating effects of COVID-related preoccupation, as an example of a major stressor, on these relationships. Results: Based on a sample recruited from the general population (N = 247), interpretation inflexibility was found to be associated with social functioning impairment, with affective symptoms and paranoia as statistical mediators of the association. These relationships were magnified by ambient stress during the COVID-19 pandemic—a moderated mediation that was found only in relation to affective symptoms but not paranoia. A parallel network analysis further confirmed the moderating effects of COVID-related preoccupation on the relation between interpretation inflexibility and depression. Limitations: Measuring ambient stress with a self-report question on COVID-related preoccupation may not be representative of the amount of distress an individual experienced during the pandemic. Also, our mediation models were performed on cross-sectional data, thus not necessarily implying a feed-forward causal mediational relationship. Conclusions: These findings highlight the importance of examining social functioning as a crucial outcome, as well as the differential role of stress in modulating social interpretation flexibility with respect to affective vs. psychotic symptoms.

A house is not a home: a network model perspective on the dynamics between subjective quality of living conditions, social support, and mental health of refugees and asylum seekers

BACKGROUND

Providing adequate living conditions for forcibly displaced people represents a significant challenge for host countries such as Germany. This study explores refugee mental health's reciprocal, dynamic relationship with post-migration living conditions and social support.

METHODS

The study sample included 325 Arabic- or Farsi-speaking asylum seekers and refugees residing in Germany since 2014 and seeking mental health treatment. Associations between reported symptoms of post-traumatic stress and depression and the subjective quality of living conditions and perceived social support were analyzed using a two-level approach including multiple linear regression and network analyses.

RESULTS

Post-migration quality of living conditions and perceived social support were significantly associated with negative mental health outcomes on both levels. In the network, both post-migration factors were negatively connected with overlapping symptoms of psychiatric disorders, representing potential target symptoms for psychological treatment.

CONCLUSION

Post-migration quality of living conditions and social support are important factors for refugee mental health and should be targeted by various actors fostering mental well-being and integration.

Network analysis to evaluate complexities in relationships among fermentation variables measured within continuous culture experiments

The objective of this study was to leverage a frequentist (ELN) and Bayesian learning (BLN) network analyses to summarize quantitative associations among variables measured in 4 previously published dual-flow continuous culture fermentation experiments. Experiments were originally designed to evaluate effects of nitrate, defaunation, yeast, and/or physiological shifts associated with pH or solids passage rates on rumen conditions. Measurements from these experiments that were used as nodes within the networks included concentrations of individual volatile fatty acids, mM and nitrate, NO3-,%; outflows of non-ammonia nitrogen (NAN, g/d), bacterial N (BN, g/d), residual N (RN, g/d), and ammonia N (NH3-N, mg/dL); degradability of neutral detergent fiber (NDFd, %) and degradability of organic matter (OMd, %); dry matter intake (DMI, kg/d); urea in buffer (%); fluid passage rate (FF, L/d); total protozoa count (PZ, cells/mL); and methane production (CH4, mmol/d). A frequentist network (ELN) derived using a graphical LASSO (least absolute shrinkage and selection operator) technique with tuning parameters selected by Extended Bayesian Information Criteria (EBIC) and a BLN were constructed from these data. The illustrated associations in the ELN were unidirectional yet assisted in identifying prominent relationships within the rumen that were largely consistent with current understanding of fermentation mechanisms. Another advantage of the ELN approach was that it focused on understanding the role of individual nodes within the network. Such understanding may be critical in exploring candidates for biomarkers, indicator variables, model targets, or other measurement-focused explorations. As an example, acetate was highly central in the network suggesting it may be a strong candidate as a rumen biomarker. Alternatively, the major advantage of the BLN was its unique ability to imply causal directionality in relationships. Because the BLN identified directional, cascading relationships, this analytics approach was uniquely suited to exploring the edges within the network as a strategy to direct future work researching mechanisms of fermentation. For example, in the BLN acetate responded to treatment conditions such as the source of N used and the quantity of substrate provided, while acetate drove changes in the protozoal populations, non-NH3-N and residual N flows. In conclusion, the analyses exhibit complementary strengths in supporting inference on the connectedness and directionality of quantitative associations among fermentation variables that may be useful in driving future studies.

A network approach to climate change anxiety and its key related features

Research has pointed to startling worldwide rates of people reporting considerable anxiety vis-à-vis climate change. Yet, uncertainties remain regarding how climate anxiety's cognitive-emotional features and daily life functional impairments interact with one another and with climate change experience, pro-environmental behaviors, and general worry. In this study, we apply network analyses to examine the associations among these variables in an international community sample (n = 874). We computed two network models, a graphical Gaussian model to explore network structure, potential communities, and influential nodes, and a directed acyclic graph to examine the probabilistic dependencies among the variables. Both network models pointed to the cognitive-emotional features of climate anxiety as a potential hub bridging general worry, the experience of climate change, pro-environmental behaviors, and the functional impairments associated with climate anxiety. Our findings offer data-driven clues for the field's larger quest to establish the foundations of climate anxiety.

Individual and dyadic network analyses of depressive symptoms in Chinese postpartum couples: A cross-sectional study

BACKGROUND

The network approach to psychopathology is symptom oriented and may open new possibilities for intervention development and health care practices in postpartum depression.

OBJECTIVE

To investigate the individual and dyadic symptom network of postpartum depression in Chinese mothers and fathers in the very early postpartum period.

DESIGN

A cross-sectional study was conducted.

SETTINGS AND PARTICIPANTS

A total of 457 couples in the 2∼3 days postpartum period was recruited consecutively from a hospital in Guangzhou, China from September 2020 to April 2021.

METHODS

The Edinburgh Postnatal Depression Scale and socio-demographic and obstetric data sheet were used to collect data. We estimated the individual symptom networks of postpartum mothers and fathers separately and a dyadic symptom network that consisted of symptoms of both spouses. Network characteristics including global strength and node centralities were analyzed and systematically compared.

RESULTS

Strength centralities in the individual networks showed acceptable stability [Correlation stability coefficient (CS) for mothers = 0.60; CS for fathers = 0.52]. The central depressive symptoms in mothers were Crying (Z_strength = 1.32), Overwhelmed (Z_strength = 1.01) and Sad mood (Z_strength = 0.93). The central depressive symptom in fathers was Sad Mood (Z_strength = 1.35). The symptom "Crying" had a distinctive link to thoughts of self-harm in fathers. The symptom network of mothers (global strength = 4.15) was more interconnected than that of fathers (global strength = 3.74). There was a statistically significant but unstable within-couple connection of thoughts of self-harm (CS = 0.21).

CONCLUSIONS

Postpartum mothers are more vulnerable to activation spreads of depressive symptoms than postpartum fathers. Symptoms including "Sad mood", "Overwhelmed" and "Crying" warrant the attention of health care providers. Investigations with larger sample sizes and gender-sensitive instruments are needed to further unfold the individual and dyadic symptom dynamics of postpartum depression.

Emotion Regulation in the Classroom: A Network Approach to Model Relations among Emotion Regulation Difficulties, Engagement to Learn, and Relationships with Peers and Teachers

Emotion regulation is theorized to shape students' engagement in learning activities, but the specific pathways via which this occurs remain unclear. This study examined how emotion regulation mechanisms are related to behavioral and emotional engagement as well as relations with peers and teachers. The sample included 136 secondary school students (59,7% girls; M_age = 14.93, SD_age = 1.02, range: 13-18 years). Psychometric network models revealed that difficulties in emotional awareness, emotional clarity, and access to emotion regulation strategies were differentially related to behavioral and emotional engagement, establishing an indirect link with teacher and/or peer relations. Nonacceptance of emotional responses, emotional awareness, and impulse control difficulties were uniquely related to teacher and/or peer relations, establishing an indirect link with student engagement. Causal discovery analysis suggested that student emotional engagement is an empirically-plausible direct cause of increased access to emotion regulation strategies. These findings uncover potential pathways through which emotion regulation hampers or facilitates learning at school, providing information useful for the design of school curricula and teacher training programs.

On the interplay of borderline personality features, childhood trauma severity, attachment types, and social support

BACKGROUND

Adverse childhood experiences (ACE) have consistently been associated with borderline personality disorder (BPD). Still, it is not yet entirely understood if and how different types of ACE (emotional, physical, sexual abuse, neglect) relate to different BPD subdomains (affective instability, identity disturbance, negative relationships, self-harm). Insecure attachment and lower perceived social support are associated with both ACE and BPD and may therefore contribute to their relationship. No study so far integrated all these variables in one model, while accounting for their mutual influence on each other. We investigated the interplay of BPD subdomains, ACE, attachment, and perceived social support using a graph-theoretical approach.

METHODS

An international sample of 1692 participants completed the Childhood Trauma Questionnaire (CTQ), the Borderline Feature Scale from the Personality Assessment Inventory (PAI-BOR), the Adult Attachment Scale (AAS), and Multidimensional Scale of Perceived Social Support (MSPSS) via an online survey. We estimated a partial correlation network including subscales of the CTQ and the PAI-BOR as nodes. We extended the network by including subscales of the AAS and MSPSS as additional nodes.

RESULTS

Emotional abuse was the most central node in both networks and a bridge between other types of ACE and BPD features. All domains of BPD except affective instability were associated with emotional abuse. Identity disturbances was the most central node in the BPD network. The association between ACE and BPD features was partly but not fully explained by attachment and social support.

CONCLUSION

Our findings suggest that emotional abuse is an important link in the association between ACE and BPD features, also when taking attachment and social support into account. Findings further suggest an outstanding role of identity disturbance, linking emotional abuse to affective instability and being strongly associated with attachment anxiety.

COVID-19 vaccine acceptance and perceived stigma in patients with depression: a network perspective

The association between coronavirus disease (COVID-19) vaccine acceptance and perceived stigma of having a mental illness is not clear. This study examined the association between COVID-19 vaccine acceptance and perceived stigma among patients with recurrent depressive disorder (depression hereafter) using network analysis. Participants were 1149 depressed patients (842 men, 307 women) who completed survey measures of perceived stigma and COVID-19 vaccine attitudes. T-tests, chi-square tests, and Kruskal-Wallis tests were used to compare differences in demographic and clinical characteristics between depressed patients who indented to accepted vaccines and those who were hesitant. Hierarchical multiple regression analyses assessed the unique association between COVID-19 vaccine acceptance and perceived stigma, independent of depression severity. Network analysis examined item-level relations between COVID-19 vaccine acceptance and perceived stigma after controlling for depressive symptoms. Altogether, 617 depressed patients (53.7%, 95 confidence intervals (CI) %: 50.82-56.58%) reported they would accept future COVID-19 vaccination. Hierarchical multiple regression analyses indicated higher perceived stigma scores predicted lower levels of COVID-19 vaccination acceptance (β = -0.125, P < 0.001), even after controlling for depression severity. In the network model of COVID-19 vaccination acceptance and perceived stigma nodes, "Feel others avoid me because of my illness", "Feel useless", and "Feel less competent than I did before" were the most influential symptoms. Furthermore, "COVID-19 vaccination acceptance" had the strongest connections with illness stigma items reflecting social rejection or social isolation concerns ("Employers/co-workers have discriminated", "Treated with less respect than usual", "Sense of being unequal in my relationships with others"). Given that a substantial proportion of depressed patients reported hesitancy with accepting COVID-19 vaccines and experiences of mental illness stigma related to social rejection and social isolation, providers working with this group should provide interventions to reduce stigma concerns toward addressing reluctance in receiving COVID-19 vaccines.

A Network Approach to Trauma, Dissociative Symptoms, and Psychosis Symptoms in Schizophrenia Spectrum Disorders

BACKGROUND

Dissociative experiences commonly occur in schizophrenia spectrum disorders (SSD). Yet little is known about how dissociative experiences in SSD are related to SSD symptoms. Accordingly, we investigated the relations between dissociative experiences and SSD symptoms, focusing on symptoms bridging these 2 symptom clusters as well as their relation to reported trauma history.

STUDY DESIGN

Network analyses were conducted on the responses of 248 individuals with an SSD who enrolled from multiple mental health centers in The Netherlands. Dissociative experiences were assessed via the Dissociative Experience Scale, SSD symptoms using the Positive and Negative Syndrome Scale, and trauma history through the Trauma History Questionnaire.

STUDY RESULTS

The results indicated that dissociative symptoms in SSD are mostly independent of other symptoms, but that emotional distress bridges between the dissociative and SSD symptom clusters. Furthermore, results revealed associations between positive and negative SSD symptoms and trauma through emotional distress, whereas dissociative symptoms remained relatively isolated.

CONCLUSION

Because SSD symptoms and dissociative experiences clustered relatively independent from each other, our findings promote the idea of tailored treatment approaches for individuals with an SSD with frequent dissociative experiences, specifically targeting these symptoms.

Neuropsychological networks in cognitively healthy older adults and dementia patients

Neuropsychological tests have commonly been used to determine the organization of cognitive functions by identifying latent variables. In contrast, an approach which has seldom been employed is network analysis. We characterize the network structure of a set of representative neuropsychological test scores in cognitively healthy older adults and MCI and dementia patients using network analysis. We employed the neuropsychological battery from the National Alzheimer's Coordinating Center which included healthy controls (n = 7623), mild cognitive impairment patients (n = 5981) and dementia patients (n = 2040), defined according to the Clinical Dementia Rating. The results showed that, according to several network analysis measures, the most central cognitive function is executive function followed by attention, language, and memory. At the test level, the most central test was the Trail Making Test B, which measures cognitive flexibility. Importantly, these results and most other network measures, such as the community organization and graph representation, were similar across the three diagnostic groups. Therefore, network analysis can help to establish a ranking of cognitive functions and tests based on network centrality and suggests that this organization is preserved in dementia. Central nodes might be particularly relevant both from a theoretical and clinical point of view, as they are more associated with other nodes, and their disruption is likely to have a larger effect on the overall network than peripheral nodes. The present analysis may provide a proof of principle for the application of network analysis to cognitive data.

Changes in specific and nonspecific psychopathology network structure after intensive cognitive behavior therapy in patients with anorexia nervosa

OBJECTIVE

This study aimed to compare eating disorder-specific and nonspecific clinical features in patients with anorexia nervosa before and after intensive enhanced cognitive behavior therapy (CBT-E) via network analysis.

METHODS

All consecutive patients admitted to intensive CBT-E were eligible, and the sample comprised patients aged ≥16 years who completed a 20-week intensive CBT-E program. Body mass index (BMI), Eating Disorder Examination Questionnaire and Brief Symptoms Inventory responses were gathered at baseline and end of treatment, and used to generate statistical networks of the connections between symptoms (nodes) and the strength and centrality thereof.

RESULTS

A total of 214 patients were included. Most nodes had relatively similar centrality compared to other nodes in the networks. "Eating concern" and "phobic anxiety" showed the greatest bridge centrality at both time points. No differences were found between baseline and the end of treatment in either global network or individual connection strengths.

CONCLUSION

These findings suggest that some clinical expressions not specific to eating-disorder psychopathology remain strongly connected in the generalized network of patients with anorexia nervosa after CBT-E. Future research should examine whether additional procedures specifically designed to target these symptoms should be integrated into this and other treatments.

The dynamic network associations of food craving, restrained eating, hunger and negative emotions

OBJECTIVE

Food craving, restrained eating, hunger, and negative emotions may predict and reinforce one another. However, less is known about how they interact together as a complex system in daily life. Therefore, we used a dynamic network approach to examine the associations between food craving, restrained eating, hunger and negative emotions in daily life.

METHODS

Food craving, restrained eating, hunger and negative emotions were measured using ecological momentary assessment three times a day over ten days in a community sample in Israel (n = 123). A two-step multilevel vector auto-regression network analysis was used to estimate temporal, contemporaneous and between-persons networks.

RESULTS

In the temporal network, restrained eating was the most central predictor of eating behaviors and negative emotions, predicting food craving and hunger as well as sadness and loneliness. Food craving was also predicted by hunger and stress, and hunger predicted loneliness. In the contemporaneous network, food craving was associated with hunger and feeling bored, and higher anger was associated with lower restrained eating. Stress and sadness were central negative emotions in the models.

DISCUSSION

This study suggests possible temporal and contemporaneous relationships between food craving, restrained eating, hunger and negative emotions, emphasizing their complex interactions in daily life. Restrained eating and stress should be investigated as potential targets for interventions addressing food craving and overeating.

Examining the Interrelationships Among Suicide Cognitions, Suicidal Ideation, and Theoretically Derived Protective Factors

INTRODUCTION

Several protective factors for mitigating suicidal ideation (SI) such as positive affect, reasons for living, purpose in life, meaning in life, gratitude, grit, optimism, social support, and hope have been identified and received empirical support. However, few studies have examined the interrelationships of these protective factors and the identification of protective factors most closely linked to lower levels of SI may be useful for both theory-building initiatives and improvement of suicide-specific interventions. Network analysis offers an approach for testing the relation among these constructs, SI, and suicide risk factors.

METHODS

A sample N = 557 undergraduate students oversampled for lifetime SI completed a cross-sectional, online survey. The data was used to estimate an undirected, cross-sectional network of the aforementioned protective factors.

RESULTS

The resulting inferred network implicates strong negative influence of suicide cognitions, but not recent SI, and the strong positive influence of presence of meaning in life, trait hope, and low negative affect.

CONCLUSIONS

Implications for dimensionality of SI versus suicide cognitions, targeting presence of meaning in life, trait hope, and negative affect in treatment, and cross-cultural variations in reasons for living are discussed. The study is limited by the cross-sectional and convenience sampling methodology.HighlightsProtective factors may have less direct influence on suicidal ideationSuicide cognitions and the suicidal mode may be of phenomenological importancePresence of meaning and trait hope may be primary targets for suicide interventions.