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

Exploring network relations between healthcare access and utilisation in individuals with rare diseases

Background

Rare diseases affect fewer than one in 2000 people and impact approximately 400 million individuals globally. High costs, uncoordinated care, and inadequate provider knowledge pose challenges to rare disease care. We aimed to examine the relationship between healthcare access and utilisation among rare disease patients in Switzerland.

Study design

A cross-sectional survey was conducted with 314 individuals with a rare disease.

Methods

Participants completed the Perception of Access to Healthcare Questionnaire (PAHQ) and provided data on healthcare utilisation (institutional and provider levels). Network analysis assessed nodes were based on expected influence (EI), predictability, and bridge centrality (BC).

Results

Four PAHQ subscales (acceptability, availability, adequacy, and awareness) exhibited higher EI and predictability. Conversely, accessibility and affordability of healthcare services had lower EI and predictability scores. In terms of healthcare utilisation, hospitals, private practices, general practictioners (GPs), mental health professionals, and emergency services demonstrated elevated EI and predictability. Specialists and holistic healthcare providers exhibited lower EI and predictability. Affordability, disease course, as well as hospital, and GP utilisation had elevated BC values and emerged as key connectors between access and utilisation.

Conclusion

This study illuminates the intricate dynamics of healthcare experiences for patients with rare diseases. This work validates network analysis as a valuable tool for examining healthcare systems. Findings can inform policies that address challenges faced by this vulnerable population, namely care integration for individuals with an unstable disease course.

Psychometric properties of the Chinese version of the M.D. Anderson symptom Inventory-Multiple Myeloma Module: a translation and validation study

BACKGROUND

The reliability of the M.D. Anderson Symptom Inventory-Multiple Myeloma Module (MDASI-MM) was evaluated through its Chinese translation among Chinese patients with multiple myeloma.

METHODS

The MDASI-MM scale underwent translation into Chinese following Brislin's two-way translation paradigm, incorporating ortho-translation, back-translation, pre-surveying, and cultural adaptation. The scale's validity and reliability were assessed using a sample of five hundred multiple myeloma patients from three tertiary general hospitals in Zhejiang Province, China, selected through convenience sampling based on inclusion and exclusion criteria. Descriptive statistics provided demographic information, while item analysis evaluated scale components. The scale's validity was assessed through content, construct, discriminant, convergent, and criterion validity analyses. Reliability was evaluated using internal consistency and split-half reliability measures, while responsiveness was assessed using the Mann-Whitney U test.

RESULTS

The Chinese version of the MDASI-MM scale consists of 26 items, including 13 core symptom items, seven multiple myeloma-specific symptom items, and six interference items. The item-level content validity index ranges from 0.889 to 1.000, the scale-level content validity index/universal agreement is 0.846, and the scale-level content validity index/average is 0.983. Validated factor analysis showed good model fit with χ2/df = 1.687, GFI = 0.879, RMSEA = 0.053, CFI = 0.913, NFI = 0.813, TLI = 0.899, IFI = 0.915, AGFI = 0.848. The combination reliability (CR) values ranged from 0.747 to 0.865, and the average variance extracted (AVE) ranged from 0.529 to 0.643. Each dimension's correlation coefficient with other dimensions was lower than the corresponding AVE's square root. The total scale demonstrated a Cronbach's alpha of 0.908 (range: 0.856-0.889) and split-half reliability of 0.890 (range: 0.873-0.916).

CONCLUSIONS

The Chinese version of the MDASI-MM scale demonstrates robust validity and reliability for evaluating clinical features in Chinese patients with multiple myeloma. This comprehensive symptom assessment tool enables healthcare professionals to examine disease characteristics thoroughly, providing a foundation for developing targeted and effective symptom management strategies.

What is empathy for laypeople? - A replication study of Hall, Schwartz, and Duong (2021)

To understand how laypeople define empathy, Hall, Schwartz, and Duong (2021) asked U.S. participants to rate how well items from various empathy measures matched their own definitions. The current paper (N = 549) is a replication of Hall, Schwartz, and Duong (2021, Study 2) using a highly similar study procedure, with a small extension consisting of items from an emotional contagion scale. We conducted a multi-group CFA to test the replicability of Hall et al.'s model, but the factor structure was not replicated. As an extension, we conducted an exploratory graph analysis (EGA), that revealed a similar factor structure, though some items were discarded due to poor fit. Additionally, the ranking of the items (i.e. what the participants saw as closest to their definition of empathy) shows the same pattern as in the original study. We consider this to be a successful partial replication of Hall et al.'s (2021) findings.

Psychological consistency network characteristics and influencing factors in patients after percutaneous coronary intervention treatment

BACKGROUND

A psychological sense of coherence (SOC) in percutaneous coronary intervention (PCI) patients is important for disease prognosis, and there is considerable variation between their symptoms. In contrast, network analysis provides a new approach to gaining insight into the complex nature of symptoms and symptom clusters and identifying core symptoms.

AIM

To explore the psychological coherence of symptoms experienced by PCI patients, we aim to analyze differences in their associated factors and employ network analysis to characterize the symptom networks.

METHODS

A total of 472 patients who underwent PCI were selected for a cross-sectional study. The objective was to investigate the association between general patient demographics, medical coping styles, perceived stress status, and symptoms of psychological coherence. Data analysis was conducted using a linear regression model and a network model to visualize psychological coherence and calculate a centrality index.

RESULTS

Post-PCI patients exhibited low levels of psychological coherence, which correlated with factors such as education, income, age, place of residence, adherence to medical examinations, perceived stress, and medical coping style. Network analysis revealed that symptoms within the sense of psychological coherence were strongly interconnected, particularly with SOC2 and SOC8, demonstrating the strongest correlations. Among these, SOC10 emerged as the symptom with the highest intensity, centrality, and proximity, identifying it as the most central symptom.

CONCLUSION

The network model has strong explanatory power in describing the psychological consistency symptoms of patients after PCI, identifying the central SOC symptoms, among which SOC10 is the key to overall SOC enhancement, and there is a strong positive correlation between SOC2 and SOC8, emphasizing the need to consider the synergistic effect of symptoms in intervention measures.

Revised network loadings

Psychometric assessment is the foundation of psychological research, where the accuracy of outcomes and their interpretations depend on measurement. Due to the widespread application of factor models, factor loadings are fundamental to modern psychometric assessment. Recent advances in network psychometrics introduced network loadings which aim to provide network models with a metric similar to factor loadings to assess measurement quality when the data are generated from a factor model. Our study revisits and refines the original network loadings to account for properties of (regularized) partial correlation networks, such as the reduction of partial correlation size as the number of variables increase, that were not considered previously. Using a simulation study, the revised network loadings demonstrated greater congruence with the simulated factor loadings across conditions relative to the original formulation. The simulation also evaluated how well correlations between factors can be captured by scores estimated with network loadings. The results show that not only can these network scores adequately estimate the simulated correlations between factors, they can do so without the need for rotation, a standard requirement for factor loadings. The consequence is that researchers do not need to choose a rotation with the revised network loadings, reducing the analytic degrees of freedom and eliminating this common source of variability in factor analysis. We discuss the interpretation of network loadings when data are believed to be generated from a network model and how they may fit into a network theory of measurement.

The relationship between resilience and mental health: mobile phone dependence and its differences across levels of parent-child conflict among left-behind adolescents: a cross-sectional network analysis

BACKGROUND

Mobile phone dependence and mental health problems have become increasingly prominent among left-behind adolescents in China. In recent years, some studies have focused on the important role of parent-child relationship and psychological resilience. Therefore, this study aims to explore the multidimensional relationships among resilience, mental health, and mobile phone dependence among left-behind adolescents, and to assess the impact of parent-child conflict level on these relationships.

METHODS

The Brief Symptom Inventory (BSI-18), the Chinese version of the Mobile Phone Addiction Index (MPAI), the Resilience Scale for Children and Adolescents (RSCA), and the Parent-Child Conflict Scale were used to investigate 2,100 left-behind adolescents in Sichuan Province, and R was run to make network analysis and network comparison.

RESULTS

(1) A structurally stable network relationship exists between left-behind adolescents' resilience, mental health, and mobile phone dependence; (2) BSI3 (Anxiety) is the most important node of the network model, followed by MPAI1 (the inability to control cravings subscale); (3) MPAI1 (the inability to control cravings subscale) and RSCA4 (family support) are key to connect resilience, mental health, and smartphone addiction in the study sample; (4) There was a significant difference in the network structure between the high- and low-level groups of parent-child conflict, no significant difference in the global strength of the network, and a significant difference in the centrality of strength and the centrality of bridge strength.

CONCLUSIONS

Chinese left-behind adolescents' resilience and mental health, mobile phone dependence are both independent and interact with each other to some extent. Specifically, high centrality dimensions such as anxiety, the inability to control cravings, and family support can be prioritised for intervention in related treatments, or reducing parent-child conflict and enhancing resilience to mitigate mobile phone dependence among left-behind adolescents, thus improving their mental health.

Schizotypal Traits, Psychopathology, and Reflective Functioning Impairments During Adolescence: A Bayesian Network Approach

BACKGROUND

New theoretical and measurement models related to Bayesian networks can usefully be implemented to enrich our understanding of psychosis risk. The present study aims to explore, using a directed acyclic graph (DAG), the putative causal relationship within schizotypal facets, as well as between schizotypal dimensions, psychopathology, and reflective functioning (RF) impairments, in a representative sample of non-clinical adolescents.

STUDY DESIGN

A sample of 1476 adolescents from the general population participated in a cross-sectional survey. The Oviedo Schizotypy Assessment Questionnaire-Revised, the Strengths and Difficulties Questionnaire, and the Reflective Functioning Questionnaire (RFQ) were used.

STUDY RESULTS

Schizotypal traits were positively associated with psychopathology and hypomentalizing. Putative causal relationships are presented between Reality distortion, Social disorganization, and Anhedonia. In addition, estimated DAG suggests that schizotypal dimensions influence psychopathology and RF impairments.

CONCLUSIONS

The findings suggest different pathways connecting schizotypal traits, mental health problems, and RF impairments during adolescence. The use of probabilistic DAG may allow us to make more robust conclusions about the direction of causation and to unravel potentially complex causal chains in the study of psychosis risk.

Core and bridge symptoms in self-perceived aging, depression, and anxiety among the elderly with multiple chronic conditions in Chinese communities: a network analysis perspective

BACKGROUND

Previous research has overlooked the role of self-perceived aging in the psychological well-being of older adults with multiple chronic conditions, and few studies have analyzed specific symptom interactions from a symptom network perspective. Our study aimed to explore the structure of the network among self-perceived aging, depression, and anxiety in community-dwelling older adults with multiple chronic conditions.

METHODS

This was a cross-sectional survey conducted using convenience sampling from four prefecture-level cities in Jiangsu Province, China, between November 2022 and May 2023. A total of 478 participants were included in the analysis. The Brief Ageing Perceptions Questionnaire (B-APQ) and the Depression Anxiety and Stress Scales-21 (DASS-21) were used to assess self-perceived aging, depression, and anxiety among older adults. Network analysis was performed using R to explore the interrelationships among symptoms in the network and identify the core symptoms and bridge symptoms.

RESULTS

Network analysis revealed that, after controlling for covariates, the node S5 ('Emotional-Representations') had the highest strength, followed by D7 ('Meaningless'), S2 ('Consequences-Positive'), S1 ('Consequences and Control Negative'), and D6 ('Worthless'). Furthermore, based on the bridge strength values, A5 ('Panic'), D7 ('Meaningless'), and S5 ('Emotional-Representations') were identified as bridge symptoms connecting self-perceived aging, depression, and anxiety. The study also identified several strong edge weight, most of which were linked to core symptoms and bridge symptoms.

CONCLUSION

The study suggests that targeting "Emotional-Representations" as a core symptom can be effective in addressing psychological issues in older adults with multiple chronic conditions. Furthermore, preventing and inhibiting bridge symptoms such as "panic," "Meaningless," and "Emotional-Representations" could be potentially effective prevent widespread activation of symptoms (e.g., from self-perceived aging to depression).

Relationship Between Schizotypal Traits, Emotion Regulation, and Negative Affect in Children: A Network Analysis

BACKGROUND AND HYPOTHESIS

Evidence suggests that emotion regulation is related to schizotypal traits and negative affect in adults. Few studies examined the interplay among these constructs in school-aged children. We examined the complex relationship between schizotypal traits, emotion regulation, and negative affect in children aged 9-12 years.

STUDY DESIGN

One-thousand-and-nineteen children completed the Schizotypal Personality Questionnaire-children (SPQ-C), the Depression Anxiety Stress Scales (DASS-21), and the Emotion Regulation Questionnaire for Children and Adolescence (ERQ-CA). Using subscales of these measures as nodes, we estimated a partial correlation network. We estimated a Directed Acyclic Graph to explore the putative directional relationship between schizotypal traits, emotion regulation, and negative affect. Node and bridge centrality indices were estimated.

RESULTS

We found positive correlations between schizotypal dimensions and negative affect (depressed mood, anxiety, and stress) in the network. Emotion suppression was positively correlated with interpersonal and disorganized schizotypal dimensions, and negative affect. Emotion reappraisal was positively correlated with the cognitive-perceptual dimension and negatively correlated with interpersonal schizotypal traits, depressed mood, and stress. Stress showed higher strength than all nodes except depressed mood, and stress showed the highest expected influence (EI). The Bayesian network revealed that schizotypal traits appeared to be driven by stress. Network comparisons preliminarily showed higher EI for emotion reappraisal in girls' than boys' networks, and significant impacts of age and schizotypy levels on network patterns.

CONCLUSION

Children with higher levels of schizotypal traits may have more negative affect and suppression. Stress appears to drive schizotypal traits.

Multiple pathways to suicide: A network analysis based on three components of psychological pain

BACKGROUND

Psychological pain is a multidimensional structure that has long been recognized as an important risk factor for suicidal ideation and behavior. The roles of interactions among psychological pain subfactors at different stages of suicidality remain unclear.

METHODS

A relatively large sample of outpatients with major depressive disorder (N = 501) was recruited to complete the Three-Dimensional Psychological Pain Scale (TDPPS). Exploratory graphical analysis (EGA) was conducted to explore the factor structure of TDPPS, thereby ensuring that the psychological pain subfactor was measured accurately. Network analysis included all TDPPS items, depression, passive suicidal ideation (PSI), active suicidal ideation (ASI), and history of suicidal action (SA) to identify key loops of suicidality.

RESULTS

EGA disclosed a three-factor structure of TDPPS comprising cognitive, affective, and motivational factors. Network analysis revealed that items of motivational factors, but none of the items of cognitive and affective factors, were directly linked to PSI, ASI, and SA. Furthermore, three communities were identified by a "walktrap" algorithm. Depression and cognitive factor coalesced into a 'cognitive' community, affective factor constituted an 'affective' community, and motivational factor, PSI, ASI, and SA comprised a 'suicidal' community.

LIMITATIONS

This study used a cross-sectional design that cannot provide information on causal relationships among variables in the network.

CONCLUSIONS

Psychological pain avoidance may be a direct driver of suicidality, and therefore its assessment and intervention in clinical practice is necessary.

Identifying candidate mechanisms of comorbidity in disordered eating and posttraumatic stress disorder symptoms among U.S. veterans: A network analytic approach

OBJECTIVE

Comorbidity between posttraumatic stress disorder (PTSD) and disordered eating (DE) symptoms is common, reflecting a possible reciprocal relationship between these disorders. Network analysis may reveal candidate mechanisms underlying their comorbidity and highlight important treatment targets.

METHOD

Two national samples of U.S. veterans endorsing trauma exposure self-reported PTSD and DE symptoms. The discovery sample included veterans from all service eras (n = 434). The validation sample included recently separated post-9/11 veterans (n = 507). We fit graphical lasso models to evaluate the network structure of PTSD factors based on the seven-factor "hybrid" model and DE symptoms within each sample. We used strength scores to identify the most central symptoms within the networks and identified bridge symptoms connecting PTSD and DE features. We tested for network invariance between self-identified men and women within each sample and across the studies.

RESULTS

PTSD and DE symptoms clustered as expected within networks for each sample. The strongest nodes in the networks included both PTSD and DE features. The strongest bridge symptoms in both studies included overevaluation of shape and weight, negative affect, and avoidance. Networks were invariant across men and women in each sample and largely invariant across samples.

CONCLUSIONS

Cross-sectional network models of PTSD and DE symptoms largely replicated across national samples of U.S. veterans and between men and women within samples. Cognitive features of both disorders, along with avoidance, may partially underlie comorbidity and represent potential treatment targets. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Interaction among negative mood, sleep, and diet habits in adolescents with non-suicidal self-injury: A cross-sectional network analysis

BACKGROUND

Non-suicidal self-injury (NSSI) is a serious public health problem that can lead to adverse effects such as cognitive impairment, poor interpersonal relationships and violent crime. The progress of preventing and treating NSSI in adolescents depends on other changeable risk factors, such as negative mood, sleep, and diet habits. However, the risk factors for adolescent NSSI remain unclear. Therefore, this study explores the key factors influencing adolescents' NSSI behavior through the use of network analysis.

METHODS

The study recruited 4040 middle school students aged 12-18 years old. From November 1st, 2023 to January 1st, 2024, paper questionnaires were utilized to investigate the participants' basic conditions, negative mood, NSSI, sleep conditions, and diet habits. Network analysis was used to explore the interrelationship among risk factors and discover the core factor.

RESULTS

In the network structure of adolescents with NSSI behavior, the most correlated nodes were negative emotions (CDI2) and low self-esteem (CDI3). Compared with adolescents without NSSI behavior, adolescents with NSSI behavior showed prominent anxiety symptoms related to injury avoidance (MASC1) and social anxiety (MASC2). Anhedonia (CDI1) was both the central node and the bridge node of the network.

LIMITATION

The cross-sectional design of this study couldn't clarify the causal inference between independent variables and results.

CONCLUSION

Anhedonia is the key factor affecting adolescents' emotion regulation. Furthermore, we hypothesize that adolescent NSSI is an emotional disorder, and anhedonia plays an important role in preventing and intervening in adolescents' NSSI behavior.

Symptom clusters and networks analysis in acute-phase stroke patients: a cross-sectional study

The symptoms of stroke jeopardize patients' health and increase the burden on society and caregivers. Although the traditional symptom cluster research paradigm can enhance management efficiency, it fails to provide targets for intervention, thereby hindering the development of patient-centered precision medicine. However, the symptom network paradigm, as a novel research approach, addresses the limitations of traditional symptom management by identifying core symptoms and determining intervention targets, thereby enhancing the efficiency and precision of symptom management. This study. aims to explore the symptom network and core symptoms of acute-phase stroke patients. A convenience sample of 505 stroke patients was selected for this study. Symptoms were assessed by the Stroke Symptom Experience Scale.Exploratory factor analysis was utilized to extract symptom clusters, and network analysis was conducted to construct the symptom network and characterize its nodes. In this study, four symptom clusters were extracted through exploratory factor analysis. Based on the results of node predictability(re) and node centrality such as strength centrality (rs), it was found that the symptoms of "No interest in surroundings" (rs = 1.299, re = 1.081), "Be disappointed about future" (rs = 0.922, re = 0.901), and "Unable to maintain body balance" (rs = 0.747, re = 0.744) had the highest centrality and predictability values, indicating their core positions within the symptom network. No interest in surroundings, Be disappointed about future, and Unable to maintain body balance are core symptoms in the symptom network. In the future, intervention methods for core symptoms can be constructed and validated for their intervention effects to further demonstrate the benefits of core symptoms.

Gender differences in sexual satisfaction through network analysis in young cisgender peruvians

Gender differences related to sexual satisfaction (SS) have been discussed for decades. However, these subtle differences in SS indicators and their associations have not been fully understood. This study aims to analyze differences in the network structures of the items of an SS scale between men and women. The participants included 205 men (M_age = 21.6 ± 2.13) and 219 women (M_age = 22.1 ± 2.21) from Peru. Using network analysis, we identified the central elements in young men and women and compared the network structures based on gender. Results showed that "concentration during sexual activity" is an important and characteristic aspect of men's SS, whereas "variety of sexual activities" is the most relevant aspect for women to perceive themselves as sexually satisfied. Differences and similarities in the elements that contribute to SS are described for males and females, highlighting that strategies designed to promote SS for both groups should take these into account.

PTSD symptom networks during treatment among residents in domestic violence shelters

Little is known about how the interrelationships among posttraumatic stress disorder (PTSD) symptoms change throughout the treatment. We examined changes in PTSD symptoms among women who experienced intimate partner violence and received one of two evidence-based interventions. We conducted a secondary analysis of a completed randomized, double-blind clinical trial, which demonstrated efficacy in reducing PTSD symptoms. We used cross-lagged panel network analysis to identify the influential PTSD symptoms among women who had completed either the Helping to Overcome PTSD through Empowerment or an adapted version of person-centered therapy. We examined if a symptom's expected influence, a metric of interconnectedness, would predict overall PTSD symptom reductions at baseline, postshelter, posttreatment, and 6- and 12-months posttreatment. Women who showed more significant decreases in feeling upset and avoidance also demonstrated greater decreases in their overall PTSD symptoms at postshelter, posttreatment, and 6 and 12 months posttreatment. Findings indicate that changes in symptoms with high centrality result in larger PTSD network changes observed at both adjacent and future time points. Identifying and targeting symptoms with influential associations produce therapeutic cascades, resulting in symptom reductions. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

The differences in symptom networks of depression, anxiety, and sleep in college students with different stress levels

BACKGROUND

Stress is closely related to depression, anxiety, and sleep problems. However, few studies have explored the complex symptom-level relationships among these variables at different stress levels among college students.

METHODS

From March to April 2024, a survey was conducted using a convenience sampling method in three universities in Daqing City, Heilongjiang Province. A total of 7,845 participants (2,447 males and 5,398 females) were assessed using the Psychological Stress Tolerance Index (PSTR), the General Health Questionnaire-20 (GHQ-20), and the Self-Rating Scale of Sleep (SRSS). Based on the GHQ-20 scores, college students were categorized into low, medium, and high-stress levels. Non-parametric tests and Post-hoc tests were conducted to explore the impact of stress levels on depression, anxiety, and sleep. Network analysis methods were used to reveal the differences in the symptom networks of depression, anxiety, and sleep among college students at different stress levels.

RESULTS

Non-parametric test results indicate significant differences in depression, anxiety, and sleep scores among high, medium, and low-stress groups. Post-hoc tests reveal that the high-stress group scores significantly higher in depression, anxiety, and sleep than the medium and low-stress groups. The medium-stress group scored significantly higher than the low-stress group. Network analysis shows that the core symptoms in the low-stress group are "Difficulty falling asleep", "Anxious and restless", and "Taking sleeping pills", with bridging symptoms including "Hopeless future", "Feeling useless", "Life is a battlefield", and "Anxious and restless". For the medium-stress group, the core symptoms are "Difficulty falling asleep", "Easily awakened after sleeping", and "Life is hopeless", with bridging symptoms including "Feeling useless", "Life is a battlefield", "Anxious and restless", and "Taking sleeping pills". In the high-stress group, the core symptoms are "Difficulty falling asleep", "Feeling useless", and "Anxious and resless", with bridging symptoms including "Feeling useless", "Life is a battlefield", "Anxious and restless", and "Stress hinders tasks".

CONCLUSION

Stress exacerbates depression, anxiety, and sleep problems among college students, with differences in core symptoms and bridging symptoms of depression, anxiety, and sleep disturbances at varying levels of stress. Therefore, precise interventions can be implemented based on the core and bridge symptoms of the three networks, further improving university students' physical and mental health.

Comparing NIRA and Traditional Network Approaches: A Study Case With Antisocial Personality Disorder Traits

OBJECTIVE

This study explores the NodeIdentifyR algorithm (NIRA) as a novel network analysis method for examining Antisocial Personality Disorder (ASPD) traits.

METHODS

Using a sample of 2230 Brazilian adults (aged 18-73 years) who responded to ASPD-related factors of the Personality Inventory for DSM-5 (PID-5), we applied NIRA to an ASPD network and compared its results with traditional network analysis methods.

RESULTS

Our findings revealed that deceitfulness emerged as the most central trait across both methodologies. NIRA provided additional insights, indicating that simulated decreases in the likelihood of irresponsibility reduced the presence of other traits, while a simulated increase in deceitfulness amplified the likelihood of other ASPD pathological traits.

CONCLUSIONS

Our results suggest that traditional network centrality measures converge with NIRA's simulated increase results, but NIRA's simulated decrease provides additional information not captured by traditional centrality estimates. We recommend further research to validate these findings across different psychopathologies and refine NIRA use in clinical settings. The insights from this study could serve as a foundation for developing targeted interventions and enhancing our understanding of ASPD trait dynamics.

Correlation between benefit finding and caregiving abilities among family caregivers of patients with lung cancer: a network analysis

Objective

This study aimed to construct a network analysis model for benefit finding (BF) and caregiving abilities to clarify the interrelationships and mutual influences among different dimensions and propose nursing strategies.

Methods

Convenience sampling was used to select 272 family caregivers of patients with lung cancer admitted to a tertiary hospital in Guangzhou City, China, from April 2023 to November 2023. Socio-demographic and disease characteristics questionnaire, the revised BF Scale, and the Family Caregiver Task Inventory (FCTI) were used for data collection. The R software was used to construct networks for BF and caregiving abilities, and network analysis methods were employed to identify network characteristics, core nodes, and bridge nodes.

Results

In the overall network, the social relationship dimension (rs = 3.04) exhibited the highest strength centrality index, followed by the family relationship dimension (rs = 2.94). In addition, the social relationship dimension (rbs = 0.30) had the highest bridge strength centrality index, followed by the dimension of addressing personal emotional needs (rbs = 0.26).

Conclusion

This study provides a new perspective on exploring the underlying mechanisms of interaction among different dimensions of BF and caregiving abilities in family caregivers of patients with lung cancer using network analysis. The findings suggest that healthcare professionals can improve family caregivers' social relationships, family relationships and address emotion regulation to enhance BF and caregiving abilities. Specific nursing strategies are proposed, offering new intervention targets for enhancing BF and caregiving abilities among family caregivers of patients with lung cancer.

Unravelling inter-relations within and between psychodynamic constructs and psychopathology using network analysis

Psychodynamic constructs and psychopathology are closely inter-related, but more detailed insight is needed. We investigated these complex inter-relations using network analysis. A Gaussian graphical model in a sample of N = 2232 psychotherapeutic inpatients was estimated. Self-administered questionnaires to assess interpersonal relations (Inventory of Interpersonal Problems-32), psychodynamic conflicts (Operationalized Psychodynamic Diagnosis-Conflict Questionnaire), personality functioning (Operationalized Psychodynamic Diagnosis-Structure Questionnaire-Short Form, Inventory of Personality Organization-Short Form), and psychopathology (Brief Symptom Inventory) were utilized. We investigated the network structure, identified the most inter-related psychodynamic constructs and the psychodynamic constructs with the strongest inter-relations to psychopathology, and explored the clustering of all included constructs. Active and passive conflict processing modes were negatively inter-related in most conflicts. Passive conflict processing modes were more strongly related to psychopathology than active ones in all conflicts, apart from the care versus autarky conflict. Identity diffusion shared the strongest inter-relations within psychodynamic constructs. The psychodynamic constructs that were most strongly related to psychopathology were impairments in self-perception and the passive self-worth conflict. Psychopathology and psychodynamic constructs formed distinct clusters. Our results emphasize the relevance of personality functioning within psychodynamic constructs and in relation to psychopathology.

Longitudinal panel networks of risk and protective factors for early adolescent suicidality in the ABCD sample

Rates of youth suicidal thoughts and behaviors (STBs) are rising, and younger age at onset increases vulnerability to negative outcomes. However, few studies have investigated STBs in early adolescence (ages 10-13), and accurate prediction of youth STBs remains poor. Network analyses that can examine pairwise associations between many theoretically relevant variables may identify complex pathways of risk for early adolescent STBs. The present study applied longitudinal network analysis to examine interrelations between STBs and several previously identified risk and protective factors. Data came from 9,854 youth in the Adolescent Brain Cognitive Development Study cohort (Mage = 9.90 ± .62 years, 63% white, 53% female at baseline). Youth and their caregivers completed an annual measurement battery between ages 9-10 through 11-12 years. Panel Graphical Vector Autoregressive models evaluated associations between STBs and several mental health symptoms, socioenvironmental factors, life stressors, and substance use. In the contemporaneous and between-subjects networks, direct associations were observed between STBs and internalizing symptoms, substance use, family conflict, lower parental monitoring, and lower school protective factors. Potential indirect pathways of risk for STBs were also observed. Age-specific interventions may benefit from prioritizing internalizing symptoms and early substance use, as well as promoting positive school and family support.

An information-theoretic approach to build hypergraphs in psychometrics

Psychological network approaches propose to see symptoms or questionnaire items as interconnected nodes, with links between them reflecting pairwise statistical dependencies evaluated on cross-sectional, time-series, or panel data. These networks constitute an established methodology to visualise and conceptualise the interactions and relative importance of nodes/indicators, providing an important complement to other approaches such as factor analysis. However, limiting the representation to pairwise relationships can neglect potentially critical information shared by groups of three or more variables (higher-order statistical interdependencies). To overcome this important limitation, here we propose an information-theoretic framework to assess these interdependencies and consequently to use hypergraphs as representations in psychometrics. As edges in hypergraphs are capable of encompassing several nodes together, this extension can thus provide a richer account on the interactions that may exist among sets of psychological variables. Our results show how psychometric hypergraphs can highlight meaningful redundant and synergistic interactions on either simulated or state-of-the-art, re-analysed psychometric datasets. Overall, our framework extends current network approaches while leading to new ways of assessing the data that differ at their core from other methods, enriching the psychometrics toolbox, and opening promising avenues for future investigation.

Nurse burnout: deep connections and solutions revealed by network analysis

BACKGROUND

Burnout is very important to nurses' physical and mental health and career development. Current approaches to assessing nurse burnout tend to use a total score modeling paradigm to explore the impact of external factors on burnout. The purpose of this study is to delve into the item-level relationship between nurse burnout and its influencing factors at both the social and psychological levels using a network analysis approach.

METHODS

This study was conducted in June 2023 and 1,005 nurses from 4 hospitals out of 8 tertiary care hospitals in Harbin were selected to participate in this study using whole cluster sampling method. Measurements included a general demographic questionnaire, Trait coping styles questionnaire, Organizational commitment questionnaire, Work-family conflict scale, Transformational leadership questionnaire, and Maslach Burnout Inventory-General Survey.

RESULTS

Our results suggest that "C1 Time-Based work interference with family" showed the strongest centrality and bridging in the overall network. This finding underscores its centrality to burnout. Other strongest bridge symptoms included "E2 Reduced personal accomplishment" and "A1 Positive coping styles" indicating their strongest connections to other clusters.

CONCLUSIONS

Nurse administrators should be encouraged to pay more attention to nurses' work situations and family distress, and to help nurses in a flexible way.

Dissociative Experiences, Borderline Personality Disorder Features, and Childhood Trauma: Generating Hypotheses from Data-Driven Network Analysis in an International Sample

Dissociation is a multifaceted phenomenon that occurs in various mental disorders, including borderline personality disorder (BPD), but also in non-clinical populations. Severity of childhood trauma (abuse, neglect) plays an important role in the development of dissociation and BPD. However, the complex interplay of different dissociative symptoms, BPD features, and self-reported childhood trauma experiences is not yet fully understood. Graph-theoretical network analysis can help to better understand such multivariate interrelations. Objective: This study aimed to investigate associations between self-reported dissociation, BPD features, and childhood trauma experiences using a graph-theoretical approach. Data was collected online via international mental health platforms and research sites. N = 921 individuals (77.4% female) were included; 40% reported pathological levels of dissociation. Variables were assessed with established psychometric scales (Dissociative Experiences Scale; Personality Assessment Inventory Borderline Features Scale; Childhood Trauma Questionnaire) and analyzed within a partial correlation network. Positive bivariate correlations between all variables were found. When accounting for their mutual influence on each other, dissociation was predominantly connected to BPD features with effect sizes between rp = .028 and rp = .126, while still showing a slight unique relationship with physical neglect (rp = .044). Findings suggest close associations between dissociative experiences and BPD features. While childhood trauma plays an important role in the development of dissociation and BPD, its recall may not fully explain their current co-occurrence. Prospective studies are needed to shed more light on causal pathways to better understand which factors contribute to dissociation and its link to BPD (features).

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

BACKGROUND

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

AIMS

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

METHOD

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

RESULTS

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

CONCLUSIONS

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

Psychometric network analysis of the Patient Health Questionnaire-4 (PHQ-4) in Paraguayan general population

BACKGROUND

Depression and anxiety are two of the most prevalent and disabling mental disorders worldwide, both in the general population and in outpatient clinical settings.

OBJECTIVE

This study aimed to analyze the psychometric properties of the Patient Health Questionnaire-4 (PHQ-4) based on network analysis metrics.

METHODS

A total of 911 Paraguayans (23.71% women and 76.29% men; mean age 31.25 years, SD = 10.63), selected by non-probabilistic convenience sampling, participated in the study. Network analysis was used to evaluate the internal structure, reliability, and measurement invariance between men and women.

RESULTS

The results revealed that the PHQ-4 is a unidimensional measure through Exploratory Graph Analysis (EGA). Reliability, through structural consistency, identified that 100% of the time, only a single dimension was obtained, and all items remained stable, as they were always replicated within the empirical dimension. The unidimensional structure has shown evidence of configural invariance; therefore, the network structure functioned equally among the different sex groups.

CONCLUSION

The PHQ-4 presented optimal preliminary evidence of validity based on its internal structure, reliability, and invariance between sexes. Therefore, it may be useful as an accurate and brief measure of anxiety and depressive symptoms in the Paraguayan context.

Cross-Informant Compatibility of Depression Symptoms in Children: A Network Approach

Utilizing multiple informants to assess children's depressive symptoms increases diagnostic accuracy, reliability, and validity of inferences. However, previous studies have found low to moderate agreement among informants. We applied network statistics to gain insight into children and their mothers' differential perceptions of depressive symptoms. The sample included children and mother dyads (n = 185) who applied to psychotherapy services at an outpatient university clinic. Mothers filled out the Child Behavior Checklist, which includes a depression subscale, and children filled out the Children's Depression Inventory. We computed association networks for thirteen depressive symptoms separately for children and mothers using the graphical LASSO. Sadness had the highest strength centrality in the networks of both children and mothers, but the pattern of connectivity and centrality of other symptoms differed. We discussed our findings within the framework of network theory.

A network approach to subjective cognitive decline: Exploring multivariate relationships in neuropsychological test performance across Alzheimer's disease risk states

Subjective cognitive decline (SCD) is characterized by subjective concerns of cognitive change despite test performance within normal range. Although those with SCD are at higher risk for developing further cognitive decline, we still lack methods using objective cognitive measures that reliably distinguish SCD from cognitively normal aging at the group level. Network analysis may help to address this by modeling cognitive performance as a web of intertwined cognitive abilities, providing insight into the multivariate associations determining cognitive status. Following previous network studies of mild cognitive impairment (MCI) and Alzheimer's dementia (AD), the current study centered upon the novel visualization and analysis of the SCD cognitive network compared to cognitively normal (CN) older adult, MCI, and AD group networks. Cross-sectional neuropsychological data from CIMA-Q and COMPASS-ND cohorts were used to construct Gaussian graphical models for CN (n = 122), SCD (n = 207), MCI (n = 210), and AD (n = 79) groups. Group networks were explored in terms of global network structure, prominent edge weights, and strength centrality indices. CN and SCD group networks were contrasted using the Network Comparison Test. Results indicate that CN and SCD groups did not differ in univariate cognitive performance or global network structure. However, measures of strength centrality, principally in executive functioning and processing speed, showed a CN-SCD-MCI gradient where subtle differences within the SCD network suggest that SCD is an intermediary between CN and MCI stages. Additional results may indicate a distinctiveness of network structure in AD, a reversal in network influence between age and general cognitive status as clinical impairment increases, and potential evidence for cognitive reserve. Together, these results provide evidence that network-specific metrics are sensitive to cognitive performance changes across the dementia risk spectrum and can help to objectively distinguish SCD group cognitive performance from that of the CN group.

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.

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

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

Identifying the core residual symptom in patients with major depressive disorder using network analysis and illustrating its association with prognosis: A study based on the national cohorts in China

OBJECTIVE

To identify the core residual symptom of MDD and assess its relationship with patients' long-term outcomes.

METHOD

All patients were administered antidepressants during the acute phase and treated continuously. The 521 patients remitted at month 6 of a multicenter prospective project were included. Remission was defined as a Quick Inventory of Depressive Symptoms-Self-Report total score of ≤5. Functional impairments were measured with the Sheehan Disability Scale, quality of life with the Quality of Life Enjoyment and Satisfaction Questionnaire - short form, and family burden with the Family Burden Scale of Disease. Visits were scheduled at baseline, weeks 2, 8, 12, and month 6.

RESULTS

Difficulty with concentration/decision making was the core residual symptom of MDD, determined with the centrality measure of network analysis. It was positively associated with functional impairments and family burden (r = 0.35, P < 0.01 and r = 0.31, P < 0.01, respectively) and negatively associated with life satisfaction (r = -0.29, P < 0.01). The exhibition of this residual symptom was associated with a family history of psychiatric disorders (OR = 2.610 [1.242-5.485]).

CONCLUSIONS

The core residual symptom of MDD, difficulty with concentration/decision making, is associated with poorer social functioning, heavier family burden, and lower life satisfaction. Early detection and intervention of this symptom may be beneficial.

CLINICAL TRIALS REGISTRATION NUMBER

(Chinese Clinical Trials.gov identifier) ChiCTR-OOC-17012566 and ChiCTR-INR-17012574.

Variation in global network properties across risk factors for adolescent internalizing symptoms: evidence of cumulative effects on structure and connectivity

BACKGROUND

Identifying adolescents at risk of internalizing problems is a key priority. However, studies have tended to consider such problems in simple ways using diagnoses, or item summaries. Network theory and methods instead allow for more complex interaction between symptoms. Two key hypotheses predict differences in global network properties for those at risk: altered structure and increased connectivity.

METHODS

The current study evaluated these hypotheses for nine risk factors (e.g. income deprivation and low parent/carer support) individually and cumulatively in a large sample of 12-15 year-olds (N = 34 564). Recursive partitioning and bootstrapped networks were used to evaluate structural and connectivity differences.

RESULTS

The pattern of network interactions was shown to be significantly different via recursive partitioning for all comparisons across risk-present/absent groups and levels of cumulative risk, except for income deprivation. However, the magnitude of differences appeared small. Most individual risk factors also showed relatively small effects for connectivity. Exceptions were noted for gender and sexual minority risk groups, as well as low parent/carer support, where larger effects were evident. A strong linear trend was observed between increasing cumulative risk exposure and connectivity.

CONCLUSIONS

A robust approach to considering the effect of risk exposure on global network properties was demonstrated. Results are consistent with the ideas that pathological states are associated with higher connectivity, and that the number of risks, regardless of their nature, is important. Gender/sexual minority status and low parent/carer support had the biggest individual impacts on connectivity, suggesting these are particularly important for identification and prevention.

Individual-Specific Animated Profiles of Mental Health

How important is the timing of the pretreatment evaluation? If we consider mental health to be a relatively fixed condition, the specific timing (e.g., day, hour) of the evaluation is immaterial and often determined on the basis of technical considerations. Indeed, the fundamental assumption underlying the vast majority of psychotherapy research and practice is that mental health is a state that can be captured in a one-dimensional snapshot. If this fundamental assumption, underlying 80 years of empirical research and practice, is incorrect, it may help explain why for decades psychotherapy failed to rise above the 50% efficacy rate in the treatment of mental-health disorders, especially depression, a heterogeneous disorder and the leading cause of disability worldwide. Based on recent studies suggesting within-individual dynamics, this article proposes that mental health and its underlying therapeutic mechanisms have underlying intrinsic dynamics that manifest across dimensions. Computational psychotherapy is needed to develop individual-specific pretreatment animated profiles of mental health. Such individual-specific animated profiles are expected to improve the ability to select the optimal treatment for each patient, devise adequate treatment plans, and adjust them on the basis of ongoing evaluations of mental-health dynamics, creating a new understanding of therapeutic change as a transition toward a more adaptive animated profile.

The novel subtype of major depressive disorder characterized by somatic symptoms is associated with poor treatment efficacy and prognosis: A data-driven cluster analysis of a prospective cohort in China

BACKGROUND

There is not yet a valid and evidence-based system to classify patients with MDD into more homogeneous subtypes based on their clinical features. This study aims to identify symptom-based subtypes of MDD and investigate whether the treatment outcomes of those subtypes would be different.

METHOD

The cohort was established at 12 densely populated cities of China. A total of 1487 patients were enrolled. All participants were 18-65 years old and diagnosed with MDD. Participants were followed up at baseline, weeks 4, 8, and 12, and months 4 and 6. K-means algorithm was used to cluster patients with MDD according to clinical symptoms. The network analysis was adopted to characterize and compare the symptom patterns in the clusters. We also examined the associations between the clusters and the clinical outcomes.

RESULTS

The optimal number of the clusters was determined to be 2. Each cluster's maximum Jaccard Co-efficient was calculated to be >0.5 (cluster1 = 0.53, cluster 2 = 0.67). The symptom "depressed mood" and some other affective symptoms were the most prominent in cluster 1. Somatic symptoms, such as weight loss and general somatic symptoms, had the greatest expected influence in cluster 2. Compared with the response rates of the patients in the "somatic cluster", those of the patients in the "affective cluster" were significantly higher (P < 0.05).

CONCLUSIONS

Patients with MDD might be classified into two symptom-based subtypes featured with affective symptoms or somatic symptoms. The treatment efficacy and prognosis of the subtype featured with somatic symptoms may be worse.

Dyslexia, the Amsterdam Way

The current aim is to illustrate our research on dyslexia conducted at the Developmental Psychology section of the Department of Psychology, University of Amsterdam, in collaboration with the nationwide IWAL institute for learning disabilities (now RID). The collaborative efforts are institutionalized in the Rudolf Berlin Center. The first series of studies aimed at furthering the understanding of dyslexia using a gamified tool based on an artificial script. Behavioral measures were augmented with diffusion modeling in one study, and indices derived from the electroencephalogram were used in others. Next, we illustrated a series of studies aiming to assess individuals who struggle with reading and spelling using similar research strategies. In one study, we used methodology derived from the machine learning literature. The third series of studies involved intervention targeting the phonics of language. These studies included a network analysis that is now rapidly gaining prominence in the psychopathology literature. Collectively, the studies demonstrate the importance of letter-speech sound mapping and word decoding in the acquisition of reading. It was demonstrated that focusing on these abilities may inform the prediction, classification, and intervention of reading difficulties and their neural underpinnings. A final section examined dyslexia, conceived as a neurobiological disorder. This analysis converged on the conclusion that recent developments in the psychopathology literature inspired by the focus on research domain criteria and network analysis might further the field by staying away from longstanding debates in the dyslexia literature (single vs. a multiple deficit, category vs. dimension, disorder vs. lack of skill).

Maintenance in relationships, satisfaction, jealousy, and violence in young couples: a network analysis

BACKGROUND

The study explores the associations among Relationship Maintenance, Satisfaction, Jealousy, and Violence in young Peruvian couples, particularly in a post-pandemic context, using a network analysis.

METHODS

Eight hundred thirty-two participants aged 18-30 (M = 20.94, SD = 2.29), with 645 females (77.50%) and 187 males (22.50%), were involved. The study aimed to discern relationships among network nodes, emphasizing the link between Relationship Maintenance dimensions and Jealousy and Violence. The research also sought the central node in the network and examined gender-specific node connections, using the SMOTE algorithm for gender data balance.

RESULTS

Findings revealed a direct connection between Complementarity and Jealousy, implying intense shared interests can lead to unhealthy dependence. An inverse relationship was seen between Companionship and Violence. Satisfaction was pivotal, showcasing its importance in romantic relationship success. Additionally, the study shows men prioritize Companionship and Sharing, possibly due to cultural norms, while women focus on the Companionship-Complementarity bond, indicating mutual support.

CONCLUSIONS

The research emphasizes the critical role of maintenance variables in determining Satisfaction, Jealousy, and Violence in relationships. The pandemic's influence on romantic dynamics is evident, emphasizing the importance of Satisfaction. Future studies should focus on gender equity and further explore these relationships.

Symptom Network Analysis Tools for Applied Researchers With Cross-Sectional and Panel Data - A Brief Overview and Multiverse Analysis

In recent years, there has been a growing interest in utilizing symptom-network models to study psychopathology and relevant risk factors, such as cognitive and physical health. Various methodological approaches can be employed by researchers analyzing cross-sectional and panel data (i.e., several time points over an extended period). This paper provides an overview of some commonly used analytical tools, including moderated network models, network comparison tests, cross-lagged network analysis, and panel graphical vector-autoregression (VAR) models. Using an easily accessible dataset (easySHARE), this study demonstrates the use of different analytical approaches when investigating (a) the association between mental health and cognitive functioning, and (b) the role of chronic disease in mediating or moderating this association. This multiverse analysis showcases both converging and diverging evidence from different analytical avenues. These findings underscore the importance of multiverse investigations to increase transparency and communicate the extent to which conclusions depend on analytical choices.

Depression and loneliness symptoms in Brazilian older people during the COVID-19 pandemic: a network approach

OBJECTIVES

Depression and loneliness in older people were a global challenge during the COVID-19 pandemic. Causal mechanisms to trigger depression might vary across different life events. We aimed to apply network analysis in a sample of Brazilian older people during the first wave of the COVID-19 pandemic, to investigate if loneliness and depression symptoms were connected within a psychological network. We explored how symptoms manifested and interacted, to discuss possible interventions that could mitigate late-life depression and loneliness symptoms in face of the COVID-19 pandemic.

METHOD

We collected data from 384 Brazilian older people who answered an online protocol to assess sociodemographic data, loneliness symptoms (assessed by the short version of UCLA-BR), and depression symptoms (assessed by the PHQ-2).

RESULTS

'Lack of companionship' was the bridge symptom connecting loneliness and depression communities. 'I feel shut out and excluded by others' and 'People are around me, but not with me' were the most predictable symptoms of loneliness.

CONCLUSION

Interventions aimed to promote older people's social participation and skills, combined with strategies to expand their sources of social support and combat ageism, might be relevant to mitigate symptoms of loneliness and depression in older people during a crisis like the COVID-19 pandemic.

Comparison of latent variable and psychological network models in PROMIS data: output metrics and factor structure

PURPOSE

Much research is still needed to compare traditional latent variable models such as confirmatory factor analysis (CFA) to emerging psychometric models such as the Gaussian graphical model (GGM). Previous comparisons of GGM centrality indices with factor loadings from CFA have discovered redundancies, and investigations into how well a GGM-based alternative to exploratory factor analysis (i.e., exploratory graph analysis, or EGA) is able to recover the hypothesized factor structure show mixed results. Importantly, such comparisons have not typically been examined in real mental and physical health symptom data, despite such data being an excellent candidate for the GGM. Our goal was to extend previous work by comparing the GGM and CFA using data from Wave 1 of the Patient Reported Outcomes Measurement Information System (PROMIS).

METHODS

Models were fit to PROMIS data based on 16 test forms designed to measure 9 mental and physical health domains. Our analyses borrowed a two-stage approach for handling missing data from the structural equation modeling literature.

RESULTS

We found weaker correspondence between centrality indices and factor loadings than found by previous research, but in a similar pattern of correspondence. EGA recommended a factor structure discrepant with PROMIS domains in most cases yet may be taken to provide substantive insight into the dimensionality of PROMIS domains.

CONCLUSION

In real mental and physical health data, the GGM and EGA may provide complementary information to traditional CFA metrics.

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.

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

PURPOSE

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

METHOD

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

RESULTS

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

DISCUSSION AND CONCLUSIONS

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

Neurocognitive and self-reported psychosocial and behavioral functioning in siblings of individuals with neurodevelopmental conditions: a study using remote self-administered testing

OBJECTIVE

This study compared and explored the neurocognitive profiles of siblings of persons with and without neurodevelopmental conditions (NDCs) and associations between objective test performance and self-reported psychosocial functioning.

METHODS

Siblings of persons with and without NDCs (64 NDC and 64 control siblings; mean age 19.88 years, range 11-27 years, 73.44% female, 75.78% White Caucasian) completed self-report questionnaires and self-administered computerized neurocognitive tests of executive functioning (EF). Using Bayesian analyses, we examined cross-sectional associations between self-reported psychosocial functioning and cognitive test performance, and predictors of EF over 15 months.

RESULTS

NDC siblings had poorer working memory, inhibition, attention, and shifting compared to controls, as measured by experimental paradigms on the backward Corsi span, N-Back 2-back task, Stop Signal Task, Sustained Attention to Response Task, and the Wisconsin Card Sorting Test (effect size δ ranging 0.49 to 0.64). Bayesian cross-sectional networks revealed negative emotion reactivity and working memory difficulties were central to the NDC sibling network. Over 15 months, poorer EF (k low test scores) was predicted by negative emotion reactivity, sleep problems, and anxiety, over and above effects of age and subclinical autistic and ADHD traits. Siblings of autistic individuals and persons with fetal alcohol spectrum disorder had higher rates of neurocognitive and psychiatric difficulties than other NDCs and controls (Bayes factors >20).

CONCLUSIONS

Neurocognitive difficulties were associated with transdiagnostic vulnerability to poorer wellbeing in NDC siblings. These findings demonstrate the feasibility of remote online cognitive testing and highlight the importance of individualized prevention and intervention for NDC siblings.

The relationship between alexithymia, depression, anxiety, and stress in elderly with multiple chronic conditions in China: a network analysis

Objective

This study aimed to construct a network structure to investigate the connections between alexithymia, depression, anxiety, and stress in Chinese older adults with multiple chronic conditions (MCC), identifying core and bridge symptoms, and comparing the network structure across different levels of alexithymia.

Methods

This study used a cross-sectional survey design and convenience sampling to recruit participants from six cities in Jiangsu Province. The study assessed the levels of alexithymia, depression, anxiety, and stress in older adults with MCC using the Toronto Alexithymia Scale (TAS-20) and the Depression Anxiety and Stress Scale-21 (DASS-21). Network analysis was performed using R language to identify core and bridge symptoms in the network and compare the network structure across different levels of alexithymia.

Results

A total of 662 participants were included in the analysis, including 395 men and 267 women. The mean age was 70.37 ± 6.92 years. The finding revealed that the "Difficulty Identifying Feelings" (DIF) node had the highest strength centrality (strength = 2.49) and predictability (rp = 0.76) in the network. The next highest strength centrality was observed for "Meaningless" (strength = 1.50), "Agitated" (strength = 1.47), "Scared" (strength = 1.42), and "No look forward" (strength = 0.75). They were identified as core symptoms. The bridge strength analysis identified "Panic," "Scared," "No wind down," "No initiative," and "No positive" as the bridge symptoms. There were notable differences in the overall network structure and specific connections between the groups with and without alexithymia (p < 0.05).

Conclusion

"DIF" is a core node in the network of older adults with MCC, indicating its significance as a potential target for psychological interventions in clinical practice. Preventing and mitigating bridge symptoms such as "panic," "Scared," "No wind down," "No initiative," and "No positive" can effectively impede the spread of symptom activation, thereby interrupting or severing the connections among comorbidities in older adults. Additionally, compared to non-alexithymia individuals, the psychological issues of older adults with alexithymia require prioritized intervention from healthcare professionals.

Heterogeneity in Individual Network Analysis: Reality or Illusion?

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

A network analysis of subjective well-being in Chinese high school students

BACKGROUND

The psychological situation of high school students during adolescence is not promising, and the most obvious manifestation is the lack of subjective well-being (SWB). This network analysis presents a model of the interaction and correlation between different items of SWB, identifying the most central items for high school students.

METHODS

Through offline and online surveys, 4,378 questionnaires were sent out and finally 4,282 Chinese high school students were available. The response rate was 97.807%. The study used the eLASSO method to estimate the network structure and centrality measures. This algorithm used the EBIC to select the best neighbor factor for each node.

RESULTS

The average age for high school students was 16.320 years old and the average SWB score was 76.680. The distribution of SWB between male and female students was significant different (P < 0.001). S8 (Have you been anxious, worried, or upset) was the node with the highest strength and expected influence. The network structure and centrality remained stable after discarding 75% of the sample at random. Except for S15 (How concerned or worried about your health have you been), all nodes were positively correlated with each other (P < 0.01). The network structure of SWB was similar for female and male students (network strength: 8.482 for male participants; 8.323 for female participants; P = 0.159), as well as for rural and urban students (network strength: 8.500 for rural students; 8.315 for urban students; P = 0.140).

CONCLUSION

Targeting S8 (Have you been anxious, worried, or upset) as a potential intervention target may increase high school students' SWB effectively.

A network approach to understanding distance learners' experience of stress and mental distress whilst studying

Research has shown that learners' stress and mental distress are linked to poorer academic outcomes. A better understanding of stress and mental distress experiences during study could foster more nuanced course and intervention design which additionally teaches learners how to navigate through to protect their academic performance. The current study draws on data collected via validated self-reported questionnaires completed by final year undergraduate students (n = 318) at a large distance education university. We examined how common features of stress, depression and anxiety link to each other using a network analysis of reported symptoms. The results included findings demonstrating the symptoms with the greatest relative importance to the network. Specifically, these included the stress symptom 'I found it difficult to relax' and the depression symptom 'I was unable to become enthusiastic about anything'. The findings could help institutions design interventions that directly correspond to common features of students' stress and distress experiences.

The Questionable Practice of Partialing to Refine Scores on and Inferences About Measures of Psychological Constructs

Partialing is a statistical approach researchers use with the goal of removing extraneous variance from a variable before examining its association with other variables. Controlling for confounds through analysis of covariance or multiple regression analysis and residualizing variables for use in subsequent analyses are common approaches to partialing in clinical research. Despite its intuitive appeal, partialing is fraught with undesirable consequences when predictors are correlated. After describing effects of partialing on variables, we review analytic approaches commonly used in clinical research to make inferences about the nature and effects of partialed variables. We then use two simulations to show how partialing can distort variables and their relations with other variables. Having concluded that, with rare exception, partialing is ill-advised, we offer recommendations for reducing or eliminating problematic uses of partialing. We conclude that the best alternative to partialing is to define and measure constructs so that it is not needed.

Love, jealousy, satisfaction and violence in young couples: A network analysis

In recent years, couples have been affected by health measures related to COVID-19, a circumstance that forces us to examine couple interactions in terms of crucial variables of their functioning. In this sense, the present study aimed to examine the association between love, jealousy, satisfaction, and violence in young couples through network analysis. A total of 834 young people and adults between 18 and 38 years of age (Mean = 20.97, SD = 2.39) participated; 646 women (77.50%) and 188 men (22.50%), who completed the Sternberg's love scale (STLS-R), Brief Jealousy Scale (BJS), Relationship Assessment Scale (RAS) and Woman Abuse Screening Tool (WAST-2). A partial unregularized network was estimated using the ggmModSelect function. The Bridge Strength index was calculated because the aim was to identify the bridge nodes between the variables under study. The results reveal that two nodes of the love variable, Commitment, and Intimacy, had a direct and moderate relationship with the Satisfaction node. The latter is the central node in the network. However, in the male group, the most intense associations are in Satisfaction-Intimacy, Violence-Passion, Jealousy-Commitment. It is concluded that there are relevant connections between the nodes of the network, which invite further research on couple relationships after the COVID-19 pandemic.

Using network analysis to explore the validity and influential items of the Parkinson's Disease Questionnaire-39

Quality of life (QoL) in people with Parkinson´s disease (PD) is commonly measured with the PD questionnaire-39 (PDQ-39), but its factor structure and construct validity have been questioned. To develop effective interventions to improve QoL, it is crucial to understand the connection between different PDQ-39 items and to assess the validity of PDQ-39 subscales. With a new approach based on network analysis using the extended Bayesian Information Criterion Graphical Least Absolute Shrinkage and Selection Operator (EBICglasso) followed by factor analysis, we mostly replicated the original PDQ-39 subscales in two samples of PD patients (total N = 977). However, model fit was better when the "ignored" item was categorized into the social support instead of the communication subscale. In both study cohorts, "depressive mood", "feeling isolated", "feeling embarrassed", and "having trouble getting around in public/needing company when going out" were identified as highly connected variables. This network approach can help to illustrate the relationship between different symptoms and direct interventional approaches in a more effective manner.

The relationship between irritability, depression and anxiety among Chinese college students during the COVID-19 pandemic: A network analysis

Introduction

Irritability, a common symptom included in the 5th Edition of Diagnostic and Statistical Manual of Mental Disorders (DSM-5), is thought to be associated with multiple emotional disorders. It is commonly seen among college students in isolation during the COVID-19 pandemic. However, its relation with anxiety and depression remains unclear. We aim to study the relation of irritability, anxiety and depression in Chinese college students during the COVID-19 pandemic by using network analysis to understand the co-occurrence of these three disorders.

Methods

During the COVID-19 pandemic, we recruited 1516 college students from five general universities in China to complete the Irritability, Depression and Anxiety Scale (IDA-S) to analyze the symptom network of irritability, depression and anxiety. Specifically, we assessed the indices of strength centrality and bridge strength for each node in the network.

Results

Some strongest linkages were found among anxiety symptoms "nervous" and "panic" (weight = 0.36), depression symptoms "sad mood" and "amused" (weight = 0.32), inward irritability items "self-hurt" and "self-harm" (weight = 0.32) and outward items "rough" and "aggressive" (weight = 0.28). The anxiety symptom "panic" had the highest strength value, followed by the inward irritability symptom "annoyed". The nodes "ease" and "sleep" had the lowest strength value. In addition, the anxiety symptom "relax" had the highest bridge strength value, followed by inward irritability symptom "annoyed".

Conclusion

This study explored the characteristics of a network of irritability, depression and anxiety symptoms among Chinese college students during the COVID-19 pandemic. We found that anxiety and irritability symptoms played an important role in the network. The findings provide evidence for prevention and intervention for college students' mental health problems during the COVID-19 pandemic.