The Differential Role of Central and Bridge Symptoms in Deactivating Psychopathological Networks

The bridge relationships of PTSD and depression symptoms among snakebite victims: a cross-sectional community-based survey

BACKGROUND

The incidence of comorbid depression and post-traumatic stress disorder (PTSD) symptoms is higher in snakebite victims. However, the present state and contributing factors of depression and PTSD among Chinese snakebite victims remain unclear.

METHODS

A representative sample of 6837 snakebite victims were assessed with the Post-traumatic Stress Disorder Checklist (Civilian Version) and The Center for Epidemiologic Studies Depression Scale. Multivariate analyses, including network analysis, evaluated the contributing factors of PTSD and depression symptoms caused by snake bites, as well as the bridge symptoms of comorbidity networks.

RESULTS

Among 6,837 snakebite victims, 79.5% reported PTSD symptoms and 81.4% reported depression symptoms. Comorbidity of PTSD and depression symptoms was found in 75.1%. Key factors included the presence sequelae after snakebite (ORPTSD = 2.31, ORDepression = 1.89), time to medical facilities (6-8 h: ORPTSD = 3.17, ORDepression = 2.46), and marital status (divorced/widowed: ORPTSD = 1.78, ORDepression = 1.76). Symptoms I1 ("Repeated disturbing memories") and D1 ("Bothered by things that don't usually bother me") bridged PTSD and depression networks.

CONCLUSION

The primary psychological challenges for snakebite victims in China are PTSD and depression symptoms, which is concerning. Standardized diagnosis and treatments, timely medical care, and stable marital relationships can reduce risks. Additional psychological support and management of negative memories, especially for those with severe bridge symptoms, can be beneficial. Further research should concentrate on understanding victims' psychological states and developing effective interventions.

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

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

Symptom network connectivity and interaction among people with HIV in China: secondary analysis based on a cross-sectional survey

BACKGROUND

The symptom burden in people with HIV (PWH) is considerable. Nonetheless, the identification of a central symptom, or bridge symptom, among the myriad symptoms experienced by PWH remains unclear. This study seeks to establish networks of symptom experiences within different clusters and investigate the relationships and interconnectedness between these symptoms in PWH.

METHODS

A multicenter, cross-sectional descriptive design was carried out in China over two periods: November 2021 to January 2022 and April 2022 to May 2022. A total of 711 PWH completed online questionnaires, providing information on demographics and the 27-item Self-Report Symptom Scale. The symptom network was analyzed using Network/Graph theory, allowing for the exploration of connections between physical, cognitive, and psychological symptoms. This analysis was based on data from a subset of 493 individuals out of the total 711 PWH.

RESULTS

A total of 493 PWH who exhibited symptoms out of a total of 711 PWH were analyzed. The average number of symptoms reported was 5.367. The most prevalent symptom was sleep disturbance (37.98%). In the node centrality analysis, a cognitive symptom, 'becoming confusing', emerged as the most central symptom with significant values for node centrality (strength = 1.437, betweenness = 140.000, closeness = 0.003). Fever was identified as the bridge symptom with the highest bridge strength (0.547), bridge closeness (0.053), lower bridge betweenness (23.000), and bridge expectedinfluence (0.285). Overall, our network displayed good accuracy and stability.

CONCLUSION

Early identification and assessment of the central or bridge symptoms should be emphasized in clinical practice. According to the findings from network analysis, healthcare providers should proactively explore intervention strategies or bundle care to alleviate the burden of symptoms and enable anticipatory care.

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

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

Investigating the network structure and causal relationships among bridge symptoms of comorbid depression and anxiety: A Bayesian network analysis

BACKGROUND

The network analysis method emphasizes the interaction between individual symptoms to identify shared or bridging symptoms between depression and anxiety to understand comorbidity. However, the network analysis and community detection approach have limitations in identifying causal relationships among symptoms. This study aims to address this gap by applying Bayesian network (BN) analysis to investigate potential causal relationships.

METHOD

Data were collected from a sample of newly enrolled college students. The network structure of depression and anxiety was estimated using the Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder (GAD-7) Scale measures, respectively. Shared symptoms between depression and anxiety were identified through network analysis and clique percolation (CP) method. The causal relationships among symptoms were estimated using BN.

RESULTS

The strongest bridge symptoms, as indicated by bridge strength, include sad mood (PHQ2), motor (PHQ8), suicide (PHQ9), restlessness (GAD5), and irritability (GAD6). These bridge symptoms formed a distinct community using the CP algorithm. Sad mood (PHQ2) played an activating role, influencing other symptoms. Meanwhile, restlessness (GAD5) played a mediating role with reciprocal influences on both anxiety and depression symptoms. Motor (PHQ8), suicide (PHQ9), and irritability (GAD6) assumed recipient positions.

CONCLUSION

BN analysis presents a valuable approach for investigating the complex interplay between symptoms in the context of comorbid depression and anxiety. It identifies two activating symptoms (i.e., sadness and worry), which serve to underscore the fundamental differences between these two disorders. Additionally, psychomotor symptoms and suicidal ideations are recognized as recipient roles, being influenced by other symptoms within the network.

Network Analysis of Behavioral Activation/Inhibition Systems and Brain Volume in Individuals With and Without Major Depressive Disorder or Social Anxiety Disorder

BACKGROUND

Social anxiety disorder (SAD) and major depressive disorder (MDD) are characterized by behavioral abnormalities in motivational systems, namely the behavioral inhibition system (BIS) and behavioral activation system (BAS). Limited studies indicate brain volume in regions that support emotion, learning/memory, reward, and cognitive functions relate to BIS/BAS. To increase understanding of BIS/BAS, the current study used a network approach.

METHODS

Patients with SAD (n = 59), patients with MDD (n = 64), and healthy control participants (n = 36) completed a BIS/BAS questionnaire and structural magnetic resonance imaging scans; volumetric regions of interest comprised cortical and limbic structures based on previous BIS/BAS studies. A Bayesian Gaussian graphical model was used for each diagnostic group, and groups were compared. Among network metrics, bridge centrality was of primary interest. Analysis of variance evaluated BIS/BAS behaviors between groups.

RESULTS

Bridge centrality showed hippocampus positively related to BAS, but not to BIS, in the MDD group; no findings were observed in the SAD or control groups. Yet, network density (i.e., overall strength of relationships between variables) and degree centrality (i.e., overall relationship between one variable to all other variables) showed that cortical (e.g., precuneus, medial orbitofrontal) and subcortical (e.g., amygdala, hippocampus) regions differed between diagnostic groups. Analysis of variance results showed BAS was lower in the MDD/SAD groups compared with the control group, while BIS was higher in the SAD group relative to the MDD group, which in turn was higher than the control group.

CONCLUSIONS

Preliminary findings indicate that network-level aberrations may underlie motivational abnormalities in MDD and SAD. Evidence of BIS/BAS differences builds on previous work that points to shared and distinct motivational differences in internalizing psychopathologies.

Symptom networks in glioma patients: understanding the multidimensionality of symptoms and quality of life

PURPOSE

To comprehend the complex relationship between symptoms and health-related quality of life (HRQoL) in patients with diffuse glioma, we applied symptom network analysis to identify patterns of associations between depression, cognition, brain tumor-related symptoms, and HRQoL. Additionally, we aimed to compare global strength between symptom networks to understand if symptoms are more tightly connected in different subgroups of patients.

METHODS

We included 256 patients and stratified the sample based on disease status (preoperative vs. postoperative), tumor grade (grade II vs. III/IV), and fatigue status (non-fatigued vs. fatigued). For each subgroup of patients, we constructed a symptom network. In these six networks, each node represented a validated subscale of a questionnaire and an edge represented a partial correlation between two nodes. We statistically compared global strength between networks.

RESULTS

Across the six networks, nodes were highly correlated: fatigue severity, depression, and social functioning in particular. We found no differences in GS between the networks based on disease characteristics. However, global strength was lower in the non-fatigued network compared to the fatigued network (5.51 vs. 7.49, p < 0.001).

CONCLUSIONS

Symptoms and HRQoL are highly interrelated in patients with glioma. Interestingly, nodes in the network of fatigued patients were more tightly connected compared to non-fatigued patients.

IMPLICATIONS FOR CANCER SURVIVORS

We introduce symptom networks as a method to understand the multidimensionality of symptoms in glioma. We find a clear association between multiple symptoms and HRQoL, which underlines the need for integrative symptom management targeting fatigue in particular.

Post-stroke Depressive Symptoms and Cognitive Performances: A Network Analysis

OBJECTIVE

To examine the relationships between post-stroke depression and cognition using network analysis. In particular, we identified central depressive symptoms, central cognitive performances, and bridge components that connect these 2 constructs.

DESIGN

An observational study. We applied network analysis to analyze baseline data to visualize and quantify the relationships between depression and cognition.

SETTING

Home and Community.

PARTICIPANTS

202 participants with mild-to-moderate stroke (N=202; mean age: 59.7 years; 55% men; 55% Whites; 90% ischemic stroke).

INTERVENTION

Not applicable.

MAIN OUTCOME MEASURES

Patient Health Questionnaire (PHQ-8) for depressive symptoms and the NIH Toolbox Cognitive Battery for cognitive performances.

RESULTS

Depressive symptoms were positively intercorrelated with the network, with symptoms from similar domains clustered together. Mood (expected influence=1.58), concentration (expected influence=0.67), and guilt (expected influence=0.63) were the top 3 central depressive symptoms. Cognitive performances also showed similar network patterns, with executive function (expected influence=0.89), expressive language (expected influence=0.68), and processing speed (expected influence=0.48) identified as the top 3 central cognitive performances. Psychomotor functioning (bridge expected influence=2.49) and attention (bridge expected influence=1.10) were the components connecting depression and cognition.

CONCLUSIONS

The central and bridge components identified in this study might serve as targets for interventions against these deficits. Future trials are needed to compare the effectiveness of interventions targeting the central and bridge components vs general interventions treating depression and cognitive impairment as a homogenous clinical syndrome.

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

Background

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

Aim

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

Methodology

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

Results

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

Conclusion

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

Symptom networks in older adults with cancer: A network analysis

INTRODUCTION

Due to aging, older adults with cancer (OAC) may be confronted with a complex interplay of multiple age-related issues; coupled with receiving cancer treatment, OAC may experience multiple concurrent symptoms that require the identification of the core symptom for effective management. Constructing symptom networks will help in the identification of core symptoms and help achieve personalized and precise interventions. Currently, few studies have used symptom networks to identify core symptoms in OAC. Our objectives were to construct symptom networks of OAC, explore the core symptoms, and compare the differences in symptom networks among various subgroups.

MATERIALS AND METHODS

Secondary analysis was performed using data from 485 OAC collected in 2021 from a cross-sectional survey named the Shanghai CANcer Survivor (SCANS) Report. The MD Anderson Symptom Inventory (MDASI) was used to assess the incidence and severity of cancer-related symptoms. We used the R package to construct symptom networks and identify the centrality indices. The network comparison test was used to compare network differences among the subgroups.

RESULTS

The most common and severe symptoms reported were fatigue, disturbed sleep, and difficulty remembering. The network density was 0.718. Vomiting (rs = 1.81, rb = 2.13), fatigue (rs = 1.54, rb = 1.93), and sadness (rs = 0.81, rb = 0.69) showed the highest strength values, which suggested that these symptoms were more likely to co-occur with other symptoms. The network comparison tests showed significant differences in symptom network density between the subgroups categorized as survival "< 5 years" and survival "≥ 5 years" (p = 0.002), as well as between the those with comorbidities and those without comorbidities (p = 0.037).

DISCUSSION

Our study identified symptom networks in 485 OAC. Vomiting, fatigue, and sadness were important symptoms in the symptom networks of OAC. The symptom networks differed among populations with different survival durations and comorbidities. Our network analysis provides a reference for future targeted symptom management and interventions in OAC. In the future, conducting dynamic research on symptom networks will be crucial to explore interaction mechanisms and change trends between symptoms.

Looking at individual symptoms: the dynamic network structure of depressive symptoms in cancer survivors and their preferences for psychological care

PURPOSE

The majority of depressed cancer survivors do not receive psychological care, possibly because offered care does not align with their experiences and preferences. We examined (1) which depressive symptoms cancer survivors would like to receive psychological care for; (2) how distinct depressive symptoms are related to each other in the contemporaneous and temporal network of depressive symptoms; and (3) whether survivors' care needs correspond to the interconnectedness of these specific symptoms.

METHOD

Fifty-two cancer survivors suffering from at least mild depressive symptoms and were not receiving psychological care filled out a baseline questionnaire about their care needs for distinct depressive symptoms, followed by ecological momentary assessments (EMA) assessing depressive symptoms (14 days, five times a day). Multi-level vector autoregression analysis was used to estimate associations between distinct depressive symptoms as well as their centrality within the network.

RESULTS

Cancer survivors most strongly preferred to receive care for fatigue, feeling down, little enjoyment, and sleep problems. Fatigue, together with worry and lack of concentration, most strongly predicted the onset of other symptoms. Little enjoyment and feeling down were two of the most central symptoms (i.e., strongly connected to other symptoms) in the contemporaneous network and were most strongly influenced by other symptoms in the temporal network.

CONCLUSIONS

Clinicians can offer specific interventions that target fatigue, as these played an important role in the onset of symptoms and would align with survivors' needs.

IMPLICATIONS FOR CANCER SURVIVORS

Offering such symptom-specific care may increase the uptake of psychological interventions in cancer survivors.

Network analysis of suicide ideation and depression-anxiety symptoms among Chinese adolescents

Background

The co-occurrence of depression and anxiety among adolescents is typically associated with suicide ideation.

Aims

The study aimed to investigate the symptom-level relationship between suicide ideation and the comorbidity of depression and anxiety.

Methods

1501 adolescents aged 12-19 years were assessed using the Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder Scale, and 716 adolescents who scored ≥5 on both scales were selected as participants. Network analysis was used to identify the network structure of depressive symptoms and anxiety symptoms. Participants were categorised into either the suicide ideation or non-suicide ideation groups based on their scoring on the suicide-related item in PHQ-9. A comparison was made between the depression-anxiety symptom networks of the two groups.

Results

'Restlessness', 'sad mood' and 'trouble relaxing' were the most prominent central symptoms in the depression-anxiety symptom network, and 'restlessness', 'nervousness' and 'reduced movement' were the bridge symptoms in this network. 'Sad mood' was found to be directly related to 'suicide ideation' with the highest variance. The network structure was significantly different in properties between the suicide ideation group and the non-suicide ideation group, with 'restlessness' and 'sad mood' exhibiting significantly higher influence in the network of the suicide ideation group than that in the non-suicide ideation group.

Conclusion

Restlessness and sad mood could be targeted for the intervention of depression-anxiety symptoms among adolescents with suicide ideation.

Fear of disease in patients with epilepsy - a network analysis

Background

Disease-related fear among patients with epilepsy has significantly impacted their quality of life. The Disease-Related Fear Scale (D-RFS), comprising three dimensions, serves as a relatively well-established tool for assessing fear in these patients. However, certain problems potentially exist within the D-RFS's attribution of items, and its internal structure is still unclear. To establish an appropriate dimensional structure and gain deeper comprehension of its internal structure-particularly its core variables-is vital for developing more effective interventions aimed at alleviating disease-related fear among patients with epilepsy.

Methods

This study employed a cross-sectional survey involving 609 patients with epilepsy. All participants underwent assessment using the Chinese version of the D-RFS. We used exploratory network analysis to discover a new structure and network analysis to investigate the interrelationships among fear symptom domains. In addition to the regularized partial correlation network, we also estimated the node and bridge centrality index to identify the importance of each item within the network. Finally, it was applied to analyze the differences in network analysis outcomes among epilepsy patients with different seizure frequencies.

Results

The research findings indicate that nodes within the network of disease-related fear symptoms are interconnected, and there are no isolated nodes. Nodes within groups 3 and 4 present the strongest centrality. Additionally, a tight interconnection exists among fear symptoms within each group. Moreover, the frequency of epileptic episodes does not significantly impact the network structure.

Conclusion

In this study, a new 5-dimension structure was constructed for D-RFS, and the fear of disease in patients with epilepsy has been conceptualized through a network perspective. The goal is to identify potential targets for relevant interventions and gain insights for future research.

Understanding the Risk of Sexual Reoffending in Adult Men: A Network-Based Model

The predominant approach to understand dynamic risk factors of sexual reoffending has been referred to as the Propensities Model (Thornton, 2016). According to this model, dynamic risk factors can be conceptualized as latent constructs whose change alters the risk of sexual reoffending. Despite its strengths and contributions to research, this model does not offer answers to the question of how dynamic risk factors contribute to the risk of sexual reoffending, or of how sustained change in risk might take place. In this paper we introduce the Network-Based Model of Risk of Sexual Reoffending (NBM-RSR), which addresses several limitations and constraints of the Propensities Model and offers empirically testable propositions regarding the nature and development of the risk of sexual reoffending. The NBM-RSR considers risk of sexual reoffending to involve a self-sustaining network of causally connected dynamic risk factors. Consistent with this, an increased risk of sexual reoffending is characterized through a network that contains more and stronger interconnected dynamic risk factors with a higher strength. Sustained change in risk of sexual reoffending occurs when activity in the network exceeds a critical point resulting in a new self-sustaining network. Propositions based on the NBM-RSR are introduced and translated into testable hypotheses. These propositions revolve around (a) risk of sexual reoffending resulting from the construction of a network of causally connected dynamic risk factors, (b) network stability, sudden changes, and critical transitions, and (c) dynamic risk factors' relative influence on risk of sexual reoffending.

Symptoms experienced after transcatheter arterial chemoembolization in patients with primary liver cancer: A network analysis

Objective

This study aimed to establish a symptom network for patients with primary liver cancer posttranscatheter arterial chemoembolization (TACE), identifying core and bridge symptoms. The goal is to provide a foundation for precise and comprehensive nursing interventions.

Methods

A total of 1207 post-TACE patients were included using a consecutive sampling method. Data collection involved a general information questionnaire, the Anderson Symptom Assessment Scale, and a primary liver cancer-specific symptom module. The symptom network was constructed using the R language.

Results

In the overall network, distress exhibited the highest strength (rs = 1.31) and betweenness (rb = 62). Fatigue had the greatest closeness (rc = 0.0043), while nausea and vomiting (r = 0.76 ± 0.02) had the highest marginal weights. Nausea had the highest bridge strength (rbs = 5.263). In the first-time TACE-treated symptom network, sadness (rbs = 5.673) showed the highest bridge strength, whereas in the non-first-time symptom network, fever (rbs = 3.061) had the highest bridge strength.

Conclusions

Distress serves as a core symptom, and nausea acts as a bridge symptom after TACE treatment in liver cancer patients. Interventions targeting bridge symptoms should be tailored based on the number of treatments, enhancing the quality of symptom management.

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

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

Core and bridge symptoms of demoralization in Chinese female cancer patients: a network analysis

Objective

In this study, we explore the core and bridge symptoms of demoralization in female cancer patients in China, and provide a basis for precise psychological intervention among female cancer patients.

Methods

This study used a cross-sectional survey. Participants were recruited from three third-class hospitals in Jiangsu Province from June 2022 to June 2023 using the convenience sampling method. The severity of each symptom of demoralization was investigated in female cancer patients using the Demoralization Scale (DS). Network analysis was performed using the R language to identify core and bridge symptoms in the network and further explore some characteristic edge connections in the network.

Results

The network structure model of demoralization had strong accuracy and stability. In the network, the symptoms with the highest strength centrality were "Discouragement" (C3, strength=2.19), "No self-worth" (A3, strength=1.21), "Don't want to live" (A5, strength=1.20), "Hopeless" (D4, strength=0.81), and "Vulnerability" (B3, strength=0.74), respectively. The bridge strength analysis identified "Hopeless" (D4, bridge strength=0.92), "Discouragement" (C3, bridge strength=0.85), "No self-worth" (A3, bridge strength=0.75), "Poor spirits" (E2, bridge strength=0.71), and "Vulnerability" (B3, bridge strength=0.69) as the bridge symptoms. The strongest edge connections of all dimensions were "No self-worth" and "Worthless" (A3-E6, edge weighting=0.27), "Poor spirits" and "Loss of emotional control" (E2-D1, edge weighting=0.22), "Discouragement" and "Vulnerability" (C3-B3, edge weighting=0.14), and "Hopeless" and "No meaning of survival" (D4-A4, edge weighting=0.12).

Conclusion

"Discouragement (C3)", "No self-worth (A3)", "Hopeless (D4)", and "Vulnerability (B3)" are both core symptoms and bridge symptoms. These symptoms can not only trigger a patient's demoralization but also stimulate more severe symptom clusters through interactions. The early recognition of and intervention regarding these symptoms could be important for the prevention and treatment of demoralization among female cancer patients.

Network analysis of mental health problems among adults in Addis Ababa, Ethiopia: a community-based study during the COVID-19 pandemic

OBJECTIVE

COVID-19 has negatively impacted mental health of adults globally with increased rates of psychiatric comorbidities. However, network analysis studies to examine comorbidities and correlations between symptoms of different mental disorders are uncommon in low-income countries. This study aimed to investigate the network structure of depression, anxiety and perceived stress among adults in Addis Ababa and identify the most central and bridge symptoms within the depressive-anxiety-perceived symptoms network model.

DESIGN

Community-based cross-sectional study.

SETTING

This study was carried out on a sample of the general population in Addis Ababa during the first year of the COVID-19 pandemic. A total of 1127 participants were included in this study, of which 747 (66.3%) were females, and the mean age was 36 years.

PRIMARY AND SECONDARY OUTCOME MEASURES

Symptoms of depression, anxiety and stress were measured using the Patient Health Questionnaire, Generalized Anxiety Disorder Scale and the Perceived Stress Scale, respectively.Network analysis was conducted to investigate the network structure. The centrality index expected influence (EI) and bridge EI (1-step) were applied to determine the central and bridge symptoms. Case-dropping procedure was used to examine the network stability.

RESULT

The sad mood (EI=1.52) was the most central and bridge symptom in the depression, anxiety and perceived stress network model. Irritability (bridge EI=1.12) and nervousness and stressed (bridge EI=1.33) also served as bridge symptoms. The strongest edge in the network was between nervousness and uncontrollable worry (weight=0.36) in the anxiety community. The network had good stability and accuracy. The network structure was invariant by gender and age based on the network structure invariance test.

CONCLUSIONS

In this study, the sad mood was the core and bridge symptom. This and the other central and bridge symptoms identified in the study should be targeted to prevent mental health disorders and comorbidities among adults.

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

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

Bridge symptoms between parenting styles and proximal psychological risk factors associated with adolescent suicidal thoughts: a network analysis

BACKGROUND

Parenting styles and the associated proximal psychological factors are suggested to increase suicidal risks in adolescents. However, how the two factors interact and confer risks on the emergence of adolescent suicidal thoughts remains unclear. Herein, we used a network approach to investigate their interrelationship and explore whether the network properties predict adolescent suicidal thoughts.

METHODS

Self-report questionnaires were completed by 1171 students aged 12-16. Network analyses were performed by Gaussian graphical models estimating the adolescent psychosocial network structure of parenting styles and psychological variables including depression, anxiety, affective lability, rumination, and resilience. Furthermore, we re-examined the network by adding a variable measuring active suicidal thoughts. Moreover, we conducted linear regressions to examine the predictive utility of bridge symptoms for adolescent suicidal thoughts.

RESULTS

Resilience, Afraid, Rumination, Concentration, and affective lability (Anger) had the highest bridge strengths in the adolescent psychosocial network. Among the identified bridge symptoms, Resilience was negatively correlated with active suicidal thoughts (regularized edge weights = -0.181, bootstrapped 95% CIs: [-0.043, -0.155]), whereas affective lability (from Anxiety to Depression, Anger), Rumination, and Afraid were positively correlated with active suicidal thoughts, with edge weights (bootstrapped 95% CIs) ranging from 0.057 (0.001, 0.112) to 0.081(0.026, 0.136). Regression analysis showed that bridge strength was significantly correlated with active suicidal thoughts (R2 = 0.432, P = 0.001).

CONCLUSION

Negative parenting styles may drive and maintain suicidal thoughts by modifying the key proximal psychological variables. Our findings highlight the important role of bridge symptoms, which may serve as vital targets for triggering adolescent suicide.

Sex differences in the symptom network structure of depression: Findings from a nationwide sample of the Spanish adult population

BACKGROUND

Sex differences in the prevalence and clinical features of depression have been widely described. However, some authors argue that categorical diagnostic systems do not adequately capture the complexity of depression. The aim of this study was to examine sex differences in the symptom network structure of depressive symptoms among individuals with a major depressive episode.

METHODS

The study sample consisted of 510 participants (age 62.17 ± 14.43, 71.96 % women) from a nationwide study of the Spanish non-institutionalised adult population (Edad con Salud). To estimate the presence of a 12-month major depressive episode according to DSM-IV criteria, participants were administered an adapted version of the Composite International Diagnostic Interview (CIDI 3.0). A network analysis was carried out to determine possible interrelationships between different depressive symptoms by sex.

RESULTS

Men and women showed a similar overall structure and network strength. However, sex-specific variations emerged in relation to individual symptom associations and symptom centrality. Specifically, for individual symptom associations "loss of confidence" and "suicide attempts" were more strongly related in women, and "suicidal ideation" and "impaired thinking" in men. For symptom centrality, "anxiety" played a central role in men's symptomatology, whereas "hopelessness", "loss of confidence", "distress" and "slowness of movement" were the most central symptoms in the women's group.

LIMITATIONS

Reliance on cross-sectional data precludes us from determining the direction and temporality of the association between different symptoms.

CONCLUSIONS

This study suggests that specific symptoms should be prioritised in the prevention, diagnosis assessment and treatment of depressed patients based on sex.

Multidomain concussion symptoms in adolescents: A network analysis

Concussion is a heterogeneous injury involving symptoms and impairment that represent multiple domains (e.g., anxiety, cognitive, vestibular). Network analysis, a modeling technique that estimates relationships among symptoms, provides a statistically sound and clinically practical method for evaluating these interrelationships. The purpose of this study was to examine, using network analysis, relationships among clinical assessments and multidomain symptom report within a sample of adolescent patients following a concussion. Participants included 326 patients (49.7% female) aged 10-21 years presenting to a concussion specialty clinic within 28 days of a diagnosed concussion. Participants completed the Post-Concussion Symptom Scale (PCSS) and Vestibular-Ocular Motor Screening (VOMS) tool at initial visit. Network models were applied to PCSS symptoms initially, and then applied to VOMS and PCSS symptom data together. Dizziness (Expected influence (EI) = 1.10) and sadness (EI = 1.91) were most central (i.e., highest cumulative partial correlations) to the symptom network. Numerous interdomain relationships were supported, including irritability with mental fogginess (edgeweight = 0.12), dizziness with headache (edgeweight = 0.16), and dizziness with vision problems (edgeweight = 0.13). Community analyses resulted in VOMS groupings by domain (e.g., vestibular) and symptom (e.g., dizziness). The findings suggest a more direct focus on symptom interrelationships, such as how dizziness contributes to emotional symptoms, may help guide and better target treatments. Also, results suggest grouping VOMS assessment by symptom (e.g., dizziness) and item (e.g., vestibular-ocular reflex) may better reflect underlying impairments reflected by these symptom-item combinations.

Longitudinal relationships in the psychopathology of depressive symptoms in middle-aged and older adults in China

OBJECTIVES

To develop symptom networks and examine the longitudinal relationships of depressive symptoms among middle-aged and older adults in China.

METHOD

This study used three-wave data from the China Health and Retirement Longitudinal Study (2013 (T1), 2015 (T2), and 2018 (T3)). Depressive symptoms were measured by the 10-item Center for Epidemiologic Studies Depression Scale (CES-D). A multilevel vector autoregression model (VAR) was used to identify ten depressive symptoms dynamically interacting with each other over time.

RESULTS

A total of 3,558 participants were included in the final analysis. The strongest direct effects were 'D10: felt fearful' -> 'D6: felt everything I did was an effort' (β = 0.14). 'D10: felt fearful' reported the largest value of out-predictability (r = 0.064) and out-strength (r = 0.635). 'D3: felt depressed' reported the largest value of in-predictability (r = 0.077) and in-strength (r = 0.545). Substantial heterogeneity in the network may stem from an individual's sex and place of residence.

CONCLUSIONS

'Felt fearful' was the strongest predictor compared to the other nine depressive symptoms based on node centrality. Our study suggests that, after understanding the causes of fear, strategies to reduce fear should be incorporated into multimodal interventions for middle-aged and older adults with depressive symptoms.

Posttraumatic stress symptoms in healthcare workers during the COVID-19 pandemic: A four-wave longitudinal study

BACKGROUND

Posttraumatic stress symptoms of healthcare workers have become a significant public concern in the healthcare system that have long COVID-19. It is less known how the pandemic impacts the HCWs' PTSS longitudinally and long-term risk factors for it.

METHODS

Four consecutive surveys were conducted among healthcare workers in China from 2019 to 2023 COVID-19 outbreaks. Multilevel mixed-effect models were used to examine longitudinal changes and risk factors. Network analysis was utilized to explore network centrality changes in PTSS symptoms.

RESULTS

HCWs' PTSS symptoms were increased over time during the COVID-19 pandemic. Being female, being nurse, working in the emergency department, working longer hours, less frequently going back home and having COVID-19 infection are risk factors of PTSS for HCWs; unmarried is the protective factor. Significant interaction between symptom changes and profession exists. PTSS networks showed that Avoidance of thoughts, Emotional-cue activity, Exaggerated startle response and Hypervigilance were the central symptoms during four waves. The global strength of the PTSS network grows over time, and nodal strength of Avoidance of thoughts, Loss of interest and Negative beliefs increased by COVID-19.

CONCLUSION

The pandemic's impacts on healthcare workers vary by professions. PTSS symptoms exacerbate, reinforce each other, and persists with recurring waves.

Network analysis of neuropsychiatric symptoms in Alzheimer's disease

BACKGROUND

Neuropsychiatric symptoms due to Alzheimer's disease (AD) and mild cognitive impairment (MCI) can decrease quality of life for patients and increase caregiver burden. Better characterization of neuropsychiatric symptoms and methods of analysis are needed to identify effective treatment targets. The current investigation leveraged the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDS) to examine the network structure of neuropsychiatric symptoms among symptomatic older adults with cognitive impairment.

METHODS

The network relationships of behavioral symptoms were estimated from Neuropsychiatric Inventory Questionnaire (NPI-Q) data acquired from 12,494 older adults with MCI and AD during their initial visit. Network analysis provides insight into the relationships among sets of symptoms and allows calculation of the strengths of the relationships. Nodes represented individual NPI-Q symptoms and edges represented the pairwise dependency between symptoms. Node centrality was calculated to determine the relative importance of each symptom in the network.

RESULTS

The analysis showed patterns of connectivity among the symptoms of the NPI-Q. The network (M = .28) consisted of mostly positive edges. The strongest edges connected nodes within symptom domain. Disinhibition and agitation/aggression were the most central symptoms in the network. Depression/dysphoria was the most frequently endorsed symptom, but it was not central in the network.

CONCLUSIONS

Neuropsychiatric symptoms in MCI and AD are highly comorbid and mutually reinforcing. The presence of disinhibition and agitation/aggression yielded a higher probability of additional neuropsychiatric symptoms. Interventions targeting these symptoms may lead to greater neuropsychiatric symptom improvement overall. Future work will compare neuropsychiatric symptom networks across dementia etiologies, informant relationships, and ethnic/racial groups, and will explore the utility of network analysis as a means of interrogating treatment effects.

A Distinction Without a Difference? A Multi-Method Approach to Understanding PTSD and Depression Symptom Overlap Among Disaster-Exposed Adolescents

Among adolescents exposed to trauma, posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) commonly co-occur. Despite the prevalence of comorbidity, the question of how PTSD and MDD are related and appropriate conceptual models for understanding their linkage in adolescence remains unclear. This study applies a multi-methodological approach to advance conceptual and theoretical understanding of the overlap between PTSD and MDD diagnoses/symptoms. We tested three methodological approaches with three distinct theoretical underpinnings on the structure of each disorder proposed in the literature: confirmatory factor analysis (CFA) with dimensional constructs, latent class analysis (LCA) with person-based categorical constructs, and network analysis with symptom-to-symptom associations. Across the three different analytical approaches, there was a significant overlap between PTSD and MDD. Overall, there was no compelling evidence for distinct boundaries between disorders among trauma-exposed adolescents. Instead, we found considerable evidence that the typical latent-construct-based conceptualizations, whether categorical or dimensional, may need revision.

The Longitudinal Features of Depressive Symptoms During the COVID-19 Pandemic Among Chinese College Students: A Network Perspective

There is substantial evidence that the Corona Virus Disease 2019 (COVID-19) pandemic increased the risk of depressive symptoms among college students, but the long-term features of depressive symptoms on a symptom level have been poorly described. The current study investigated interaction patterns between depressive symptoms via network analysis. In this longitudinal study, participants included 860 Chinese college students (65.8% female; M_age = 20.6, SD_age = 1.8, range: 17-27) who completed a questionnaire at three-time points three months apart. Results demonstrated that fatigue was the most influential symptom, and the occurrence of fatigue could give rise to other depressive symptoms. In addition to predicting other symptoms, fatigue could be predicted by other symptoms in the measurement. The network structures were similar across time, suggesting that the overall interaction pattern of depressive symptoms was stable over the longitudinal course. These findings suggest that depressive symptoms during the COVID-19 period are associated with the presence of fatigue.

An online solution focused brief therapy for adolescent anxiety : A randomized controlled trial

In this randomized clinical trial, we investigated the efficacy of an online solution focused brief therapy (SFBT) for adolescents' anxiety symptoms during the COVID-19 period. Eligible participants were between the ages of 11 and 18 years, scored a 10 or above on the Generalized Anxiety Disorder-7 (GAD-7). The results found that compared to adolescents who did not receive any treatment, the intervention yielded significant results in alleviating adolescents' anxiety and depressive symptoms while promoting problem oriented coping strategies at immediate post-intervention. The therapeutic benefit has persisted, as shown in our results from the 1-month follow-up.

Associations among psychosis, mood, anxiety, and posttraumatic stress symptoms: A network analysis

The associations among psychotic experiences (i.e., hallucinations and delusions), trauma exposure, and posttraumatic stress symptoms are complex and multidirectional. Using network analysis to understand how psychotic experiences and symptoms of posttraumatic stress disorder (PTSD) relate to one another may identify new interventional targets to treat comorbidity and its underlying pathological processes. This study aimed to use network analysis to examine the associations among psychotic experiences; negative symptoms of psychosis; and symptoms of PTSD, anxiety, and depression. In this population-based cohort study, 4,472 participants (36.7% male) were assessed for psychotic experiences, negative symptoms of psychosis, PTSD, anxiety, and depression at age 23 (M = 23.86 years, SD = 0.520) or 24 years (M = 24.03, SD = 0.848). Associations among symptoms were assessed via network analysis. Exploratory graph analysis identified three clusters of densely connected symptoms within the overall network: psychotic experiences; PTSD symptoms; and depressive and anxiety symptoms and negative symptoms of psychosis. Psychotic experiences had the strongest associations with other symptoms in the network, and symptoms of anxiety played a key role in bridging psychotic experiences, symptoms of PTSD, and depressive symptoms. Consistent with the stress reactivity and affective models for psychotic experiences, the results suggest that symptoms of anxiety and emotional distress (e.g., hyperarousal, panic) may have a key role in the development and maintenance of psychotic experiences and symptoms of PTSD. Targeting these symptoms may ameliorate symptom burden transdiagnostically.

Characterizing the Network Structure of Post-Concussion Symptoms

OBJECTIVE

Assessment of post-concussion symptoms is implemented at secondary, post-secondary, and professional levels of athletics. Network theory suggests that disorders can be viewed as a set of interacting symptoms that amplify, reinforce, and maintain one another. Examining the network structure of post-concussion symptoms may provide new insights into symptom comorbidity and may inform targeted treatment. We used network analysis to examine the topology of post-concussion symptoms using the Post-Concussion Symptom Scale (PCSS) in high school athletes with recent suspected sport-related concussion.

METHOD

Using a cross-sectional design, the network was estimated from Post Concussion Symptom Scale scores from 3,292 high school athletes, where nodes represented symptoms and edges represented the association between symptoms. Node centrality was calculated to determine the relative importance of each symptom in the network.

RESULTS

The network consisted of edges within and across symptom domains. "Difficulty concentrating" and "dizziness" were the most central symptoms in the network. Although not highly central in the network, headaches were the highest rated symptom.

CONCLUSIONS

The interconnectedness among symptoms supports the notion that post-concussion symptoms are interrelated and mutually reinforcing. Given their central role in the network, "difficulty concentrating" and "dizziness" are expected to affect the activation and persistence of other post-concussion symptoms. Interventions targeting difficulties with concentration and dizziness may help alleviate other symptoms. Our findings could inform the development of targeted treatment with the aim of reducing overall symptom burden. Future research should examine the trajectory of post-concussion symptom networks to advance the clinical understanding of post-concussive recovery.

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

Background

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

Methods

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

Results

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

Conclusion

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

Relationships between satisfaction with life, posttraumatic growth, coping strategies, and resilience in cancer survivors: A network analysis approach

OBJECTIVE

Cancer survivors' satisfaction with life should be seen through the psychological factors related to a person's capabilities to face and handle the situation. This study aimed to (1) examine the relationships of satisfaction with life, posttraumatic growth, resilience and coping strategies in a global network model, (2) find the bridge indicators between satisfaction with life and the other constructs, and (3) test for the invariance of the network structures across several moderating variables.

METHODS

In a heterogeneous sample of 696 cancer survivors (69% female; mean age = 53.1 ± 15.44 years; median time from being diagnosed = 4 years; breast cancer was the most frequent type of cancer) their satisfaction with life, resilience, coping strategies and posttraumatic growth was measured. In order to account for their complexity, the relationships between the constructs were explored using a network analysis approach.

RESULTS

The network analysis shows that satisfaction with life is strongly connected to resilience, moderately connected to coping strategies, and has a weak connection with posttraumatic growth. In the separate networks, the relationships between the psychological constructs were examined in greater detail. Besides some exceptions observed in the degree of disability, the networks were invariant across gender, age, years since being diagnosed, cancer type and treatment type.

CONCLUSION

The findings suggest that interventions focused on cancer survivors' coping strategies and resilience could help increase their satisfaction with life. However, further replication of the proposed and/or modified model is needed.

The nosographic structure of posttraumatic stress symptoms across trauma types: An exploratory network analysis approach

The nosographic structure of posttraumatic stress disorder (PTSD) remains unclear, and attempts to determine its symptomatic organization have been unsatisfactory. Several explanations have been suggested, and the impact of trauma type is receiving increasing attention. As little is known about the differential impact trauma type in the nosographic structure of PTSD, we explored the nosology of PTSD and the effect of trauma type on its symptomatic organization. We reanalyzed five cross-sectional psychopathological networks involving different trauma types, encompassing a broad range of traumatic events in veterans, war-related trauma in veterans, sexual abuse, terrorist attacks, and various traumatic events in refugees. The weighted topological overlap was used to estimate the networks and attribute weights to their links. Coexpression differential network analysis was used to identify the common and specific network structures of the connections across different trauma types and to determine the importance of symptoms across the networks. We found a set of symptoms with more common connections with other symptoms, suggesting that these might constitute the prototypical nosographic structure of PTSD. We also found a set of symptoms that had a high number of specific connections with other symptoms; these connections varied according to trauma type. The importance of symptoms across the common and specific networks was ascertained. The present findings offer new insights into the symptomatic organization of PTSD and support previous research on the impact of trauma type on the nosology of this disorder.

Exploring bridge symptoms in HIV-positive people with comorbid depressive and anxiety disorders

BACKGROUND

The prevalence of comorbid depressive and anxiety disorders in people living with HIV (PLWH) is high. However, it is unclear which symptom is the bridge symptom between depression and anxiety in PLWH. This study aimed to develop symptom networks for depression and anxiety and explore the bridge symptoms and interconnectedness between these disorders in PLWH with comorbid depressive and anxiety disorders.

METHODS

A multisite, hospital-based cross-sectional study was conducted from March 2020 to November 2021. Depression and anxiety were measured with the Hospital Anxiety and Depression Scale. We visualized the symptom network using the qgraph package and computed the bridge expected influence of each node. The GLASSO layout was used to generate undirected association networks.

RESULTS

A total of 2016 individuals were included in the analysis. In the anxiety cluster, "not feeling relaxed" had the highest bridge expected influence and strength (r_{bridge expected influence} = 0.628, r_strength = 0.903). In the depression cluster, "not feeling cheerful" was identified as having a high bridge expected influence (r_{bridge expected influence} = 0.385). "Not feeling cheerful" and "not feeling relaxed" were the strongest edges across the depression and anxiety clusters (r = 0.30 ± 0.02).

CONCLUSIONS

Healthcare professionals should take note when PLWH report severe bridge symptoms. To enhance the levels of perceived cheerfulness and relaxation, positive psychology interventions could be implemented.

Role of polygenic and environmental factors in the co-occurrence of depression and psychosis symptoms: a network analysis

Depression and psychosis are often comorbid; they also have overlapping genetic and environmental risk factors, including trauma and area-level exposures. The present study aimed to advance understanding of this comorbidity via a network approach, by (1) identifying bridge nodes that connect clusters of lifetime depression and psychosis symptoms and (2) evaluating the influence of polygenic and environmental risk factors in these symptoms. This study included data from European ancestry participants in UK Biobank, a large population-based sample (N = 77,650). In Step 1, a network model identified bridge nodes between lifetime symptoms of depression and psychosis and functional impairment. In Step 2, genetic and environmental risk factors were incorporated to examine the degree to which symptoms associated with polygenic risk scores for depression and schizophrenia, lifetime exposure to trauma and area-level factors (including deprivation, air pollution and greenspace). Feelings of worthlessness, beliefs in unreal conspiracy against oneself, depression impairment and psychosis impairment emerged as bridges between depression and psychosis symptoms. Polygenic risk scores for depression and schizophrenia were predominantly linked with depression and psychosis impairment, respectively, rather than with specific symptoms. Cumulative trauma emerged as a bridge node associating deprivation with feelings of worthlessness and beliefs in unreal conspiracy, indicating that the experience of trauma is prominently linked with the co-occurrence of depression and psychosis symptoms related to negative views of oneself and others. These key symptoms and risk factors provide insights into the lifetime co-occurrence of depression and psychosis.

Symptom-level networks of youth- and parent-reported depression and anxiety in a transdiagnostic clinical sample

Depression and anxiety disorders confer a significant public health concern for youth and their co-occurrence places youth at a higher risk for poorer psychosocial outcomes. In the present study, we use network analysis to investigate the role of and interactions among individual depression and anxiety symptoms in a treatment-seeking clinical sample.

METHODS

We estimate regularized partial correlation networks for youth- and parent reported symptoms in a transdiagnostic sample of youth (N = 417, ages 8-18). We examined features of the symptom-level networks such as network stability, centrality, bridge symptoms, and communities in both youth- and parent-reported networks.

RESULTS

Results indicate stable networks with disorder-specific clustering, such that symptoms were more interconnected within compared to between disorders. Symptoms related to self-comparison to peers and negative views of the future were most central in both networks. Symptoms of depression and anxiety were connected by worries for the future and hopelessness in the youth-reported network, whereas self-comparison to peers and low self-efficacy were bridge symptoms in the parent network. Distinct symptom clusters emerged in the parent- and youth-reported networks.

CONCLUSIONS

Our findings indicate that negative self-evaluation, negative views of the future, and repetitive negative thinking more generally are influential symptoms in the presentation and co-occurrence of depression and anxiety and as such may be promising targets in the treatment and prevention of depression and anxiety in youth.

Enhancing Informed Decisions for Coastal Groundwater Sustainability: A Network Analysis of Water-Related Indicator Results from 122 Cities

In many places around the globe, groundwater has been threatened by various pressures, which calls for better management strategies for groundwater sustainability. In this study, we suggest a novel framework for identifying factors critical to coastal groundwater based on results from City Blueprint (CB) assessments. By compiling the 5856 indicator results of the City Blueprint Approach (CBA) from 122 cities and analyzing the correlation between these indicators, we constructed City Blueprint networks (CBN) by using a complex network modeling approach for three groups of cities: all 122 cities, 40 coastal, and 82 non-coastal cities. These networks were then analyzed for their node centralities to identify major factors that influence coastal groundwater management. Interestingly, our analysis revealed that groundwater has various indirect but important links with the factors that are typically unexplored in the literature. We also assessed the CB of the two largest coastal cities in South Korea. By combining the results of network analysis and CB assessment of the two cities, we could identify the indicators that are potentially at risk regarding coastal groundwater. We propose the CBN as a novel approach to unveil underestimated or hidden factors related to the target system (e.g., groundwater), which allows extensive options for sustainable groundwater management.

Seeking bridge symptoms of anxiety, depression, and sleep disturbance among the elderly during the lockdown of the COVID-19 pandemic-A network approach

BACKGROUND

Besides physical changes, elderly adults are prone to have mental disorders such as anxiety, depression, and sleep disturbance, and the pandemic of COVID-19 worsened the situation. However, internal relationships and co-occurrence of psychopathologies were scarcely examined. Therefore, in the current study, through network analysis, we inspected relationships among symptoms of depression, anxiety, and sleep disturbance and identified key symptoms that espoused the disease.

METHODS

We asked 1,302 elderly adults to fill in Patient Health Questionnaire-2 (depressive symptoms), the Generalized Anxiety Disorder-2 (anxiety symptoms), and the Youth Self-rating Insomnia Scale (sleep disturbance) and then constructed three networks for elderly adults, male elderly, and female elderly. Via network analysis, we accomplished four goals. First, we identified symptom with the highest centrality (i.e., strength) index for each network; then, we found the strongest correlation (i.e., edges) in each network; thirdly, we confirmed specific nodes that could bridge anxiety, depression, and sleep disturbance; the last was to compare networks based on genders. Network stability and accuracy tests were performed.

RESULTS

Networks of elderly adults, male elderly, and female elderly were stable, accurate, and intelligible. Among all networks, "Nervousness"- "Excessive worry" (GAD-1- GAD-2) had the strongest correlation, and "Nervousness" (GAD-1) had the highest strength and bridge strength value. When we made a comparison between female elderly's and male elderly's networks, except for the significant difference in the mean value of "Difficulty initiating sleep" (YSIS-3), the findings showed that the two networks were similar. Network stability and accuracy proved to be reliable.

CONCLUSIONS

In networks of anxiety, depression, and sleep disturbance, anxiety played a conspicuous role in comorbidity, which could be a target for practical intervention and prevention.

Intervening on psychopathology networks: Evaluating intervention targets through simulations

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

Using network analysis to compare diagnosis-specific and age-specific symptom networks in eating disorders

OBJECTIVE

The network theory of mental disorders conceptualizes eating disorders (EDs) as networks of interacting symptoms. Network analysis studies in EDs mostly have examined transdiagnostic and/or mixed age samples. The aim of our study was to investigate similarities and differences of networks in adolescents and adults with anorexia nervosa (AN) or bulimia nervosa (BN).

METHOD

Participants were 2,535 patients (n = 991 adults with AN, n = 821 adolescents with AN, n = 473 adults with BN, and n = 250 adolescents with BN) who completed the Eating Disorder Inventory-2. Twenty-seven items were selected. Cross-sectional networks were estimated via Joint Graphical Lasso. Core symptoms were identified using strength centrality. Spearman correlations and network comparison tests (NCTs) were used to compare groups.

RESULTS

Across diagnoses and ages, feeling ineffective, desire to be thinner, worries that feelings will get out of control, guilt after overeating as well as doing things perfectly emerged as most central symptoms. There were moderate to high correlations between symptom profiles (0.62-0.97, mean: 0.78) as well as high correlations between network structures (0.83-0.93, mean: 0.87) and network strengths (0.73-0.95, mean: 0.85). Global strength significantly differed in two of the six NCTs, and 2.5-10% of edges differed between networks.

DISCUSSION

Considerable similarities in network structures and strengths across diagnoses and ages speak in favor of the transdiagnostic approach to EDs. Besides drive for thinness, ineffectiveness, emotion regulation difficulties, and perfectionism might be the most consistent factors in ED networks. These symptoms as well as their symptom connections should be especially focused in treatment regardless of age and diagnosis.

How does neighbourhood socio-economic status affect the interrelationships between functioning dimensions in first episode of psychosis? A network analysis approach

The links between psychosis and socio-economic disadvantage have been widely studied. No previous study has analysed the interrelationships and mutual influences between functioning dimensions in first episode of psychosis (FEP) according to their neighbourhood household income, using a multidimensional and transdiagnostic perspective. 170 patients and 129 controls, participants in an observational study (AGES-CM), comprised the study sample. The WHO Disability Assessment Schedule (WHODAS 2.0) was used to assess functioning, whereas participants' postcodes were used to obtain the average household income for each neighbourhood, collected by the Spanish National Statistics Institute (INE). Network analyses were conducted with the aim of defining the interrelationships between the different dimensions of functioning according to the neighbourhood household income. Our results show that lower neighbourhood socioeconomic level is associated with lower functioning in patients with FEP. Moreover, our findings suggest that "household responsibilities" plays a central role in the disability of patients who live in low-income neighbourhoods, whereas "dealing with strangers" is the most important node in the network of patients who live in high-income neighbourhoods. These results could help to personalize treatments, by allowing the identification of potential functioning areas to be prioritized in the treatment of FEP according to the patient's neighbourhood characteristics.

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

BACKGROUND

In the network approach to psychopathology, psychiatric disorders are considered networks of causally active symptoms (nodes), with node centrality hypothesized to reflect symptoms' causal influence within a network. Accordingly, centrality measures have been used in numerous network-based cross-sectional studies to identify specific treatment targets, based on the assumption that deactivating highly central nodes would proliferate to other nodes in the network, thereby collapsing the network structure and alleviating the overall psychopathology (i.e., the centrality hypothesis).

METHODS

Here, we summarize three types of evidence pertaining to the centrality hypothesis in psychopathology. First, we discuss the validity of the theoretical assumptions underlying the centrality hypothesis in psychopathology. We then summarize the methodological aspects of extant studies using centrality measures as predictors of symptom change following treatment, while delineating their main findings and several of their limitations. Finally, using a specific dataset of 710 treatment-seeking patients with posttraumatic stress disorder (PTSD) as an example, we empirically examine node centrality as a predictor of therapeutic change, replicating the approach taken by previous studies, while addressing some of their limitations. Specifically, we investigated whether three pre-treatment centrality indices (strength, predictability, and expected influence) were significantly correlated with the strength of the association between a symptom's change and the change in the severity of all other symptoms in the network from pre- to post-treatment (Δnode-Δnetwork association). Using similar analyses, we also examine the predictive validity of two simple non-causal node properties (mean symptom severity and infrequency of symptom endorsement).

RESULTS

Of the three centrality measures, only expected influence successfully predicted how strongly changes in nodes/symptoms were associated with change in the remainder of the nodes/symptoms. Importantly, when excluding the amnesia node, a well-documented outlier in the phenomenology of PTSD, none of the tested centrality measures predicted symptom change. Conversely, both mean symptom severity and infrequency of symptom endorsement, two standard non-network-derived indices, were found to be more predictive than expected influence and remained significantly predictive also after excluding amnesia from the network analyses.

CONCLUSIONS

The centrality hypothesis in its current form is ill-defined, showing no consistent supporting evidence in the context of cross-sectional, between-subject networks.