Bridging Symptoms Research Articles

Edge-centric connectome-genetic markers of bridging factor to comorbidity between depression and anxiety

Depression-anxiety comorbidity is commonly attributed to the occurrence of specific symptoms bridging the two disorders. However, the significant heterogeneity of most bridging symptoms presents challenges for psychopathological interpretation and clinical applicability. Here, we conceptually established a common bridging factor (cb factor) to characterize a general structure of these bridging symptoms, analogous to the general psychopathological p factor. We identified a cb factor from 12 bridging symptoms in depression-anxiety comorbidity network. Moreover, this cb factor could be predicted using edge-centric connectomes with robust generalizability, and was characterized by connectome patterns in attention and frontoparietal networks. In an independent twin cohort, we found that these patterns were moderately heritable, and identified their genetic connectome-transcriptional markers that were associated with the neurobiological enrichment of vasculature and cerebellar development, particularly during late-childhood-to-young-adulthood periods. Our findings revealed a general factor of bridging symptoms and its neurobiological architectures, which enriched neurogenetic understanding of depression-anxiety comorbidity.

A network analysis on the relationship between depression symptoms and loneliness in elderly Peruvians

ABSTRACT Loneliness and depression are the most prevalent mental health issues among older adults, and their relationship has been documented in studies using reflective psychopathological models based on the total scores. However, mental health problems should be investigated at the level of individual symptoms in order to develop intervention strategies aimed at mitigating the adverse impact of both variables. The goal was to examine the extent to which symptoms of loneliness relate to symptoms of depression in elderly Peruvians using the network method in 328 Peruvian adults with an average age of 68.44 years (SD = 7.64). The participants were selected through non-probability convenience sampling, which may limit the generalizability of the results. The Patient Health Questionnaire-2 (PHQ-2) and three-item UCLA Loneliness Scale were used in this study. Network analysis was used to identify central and bridging symptoms within the network of loneliness and depressive symptoms. Furthermore, the invariance in symptom networks between men and women was estimated. The results indicated that ‘feeling excluded’ was the most central symptom. Furthermore, the symptoms ‘feeling excluded’ and ‘anhedonia’ have the strongest relationship and can be considered as bridge symptoms between loneliness and depressive disorders. Symptom networks were invariant between men and women (M = .26; p = .42; S = .09; p = .38). In conclusion, the network structure provides valuable insights into the relationship between the symptoms of loneliness and depression in older Peruvian adults. The results suggest that the symptoms ‘feeling excluded’ and ‘anhedonia’ could serve as potential targets for psychological interventions aimed at efficiently reducing loneliness and depression.

Hierarchical network structure: A novel approach to conceptualising ICD-11 Complex PTSD using a general population sample from Africa

BackgroundInvestigations have sought to model the structure of ICD-11 Complex PTSD (CPTSD) using factor analytic models, finding support for higher-order domains representing PTSD and Disturbances in Self Organisation (DSO). Network analysis has alternatively modelled CPTSD through dimensional symptom associations. MethodologyThis study investigated the structure of CPTSD leveraging a novel approach, Hierarchical Exploratory Graph Analysis, using African general population samples (N = 2524). ResultsThe hierarchical graph model was estimated identifying a structure comprising six lower-order communities, indicative of ICD-11 CPTSD symptom domains, and two higher-order communities, indicative of PTSD and DSO. Results indicate the superiority of the hierarchical model, confirming the conceptualisation of ICD-11 PTSD and CPTSD symptom groupings. LimitationsThe cross-sectional nature of these data, and novelty of the methods used prompt calls for additional investigation to support these findings. ConclusionsHierarchical Exploratory Graph Analysis may offer a valuable means to better understanding the complexity of CPTSD symptomology through a novel network modelling approach. Relationships between Sense of Threat and Affect Dysregulation may serve as bridging symptoms between PTSD and DSO difficulties. These may be prioritised as the therapeutic targets for CPTSD. This pioneering approach using EGA, offers new insights into the intricate structure of CPTSD, potentially informing the use of assessments and interventions across diverse populations.

The Symptom Structure and Causal Relationships of Comorbid Anxiety and Depression Among Chinese Primary and Middle School Teachers: A Network Analysis.

In China, as educational reforms progress, the characteristics of teachers' work have undergone significant changes, resulting in extremely high levels of stress that can trigger anxiety and depression. Anxiety and depression often co-occur, with two mainstream theories explaining this co-existence: the tripartite model and the diathesis-stress model. However, systematic research focusing on this population is relatively scarce, and the applicability of these models has not been thoroughly tested. This study aims to use network analysis methods to examine the interactions between symptoms and analyze the co-existence of anxiety and depression, thereby expanding the research on teachers. Data were provided by the Science Database of People Mental Health, which includes 1670 teachers with a mean age of 30.01. The Self-Rating Anxiety Scale and Self-Rating Depression Scale were used to estimate the network structures of anxiety and depression, respectively. Shared symptoms between depression and anxiety were identified using network analysis and clique percolation methods. Bayesian Networks was used to estimate causal relationships between symptoms. Data were analyzed using R packages. Network structure was constructed with the qgraph package, node centrality and bridge symptoms were evaluated using the networktools package, and network stability was measured via the bootnet package. The Clique Percolation method was implemented with the CliqurPercolation package, and Bayesian network modeling was performed via the Bnlearn package. Dizziness and Easy Fatigability & Weakness were central symptoms in the network. Bridging strength results showed that, the important bridging symptoms included Tachycardia, Depressed Affect, Fatigue, Crying Spell, Easy Fatigability & Weakness, Nightmares, Face Flushing, and Sweating were the strong bridging symptoms. Additionally, Sleep Disturbance played a key mediating role. Depressed Affect and Dissatisfaction were activation symptoms for anxiety-depression co-existence. Using network analysis, this study elucidated core, bridging, and shared symptoms, as well as potential causal pathways between anxiety and depression. Specifically, somatic symptoms are crucial in maintaining and developing the anxiety-depression network among teachers. Sleep disturbance serves as the sole gateway for mild symptoms to develop into other communities. The Bayesian network identified two key activating symptoms within the teacher anxiety-depression network, validating the applicability of the tripartite model among teachers.

Network analysis of Tourette syndrome and attention-deficit/hyperactivity disorder symptoms in children and adolescents

BackgroundWhile Tourette syndrome (TS) and attention-deficit/hyperactivity disorder (ADHD) often co-occur, the nature of the relationship between their symptoms is not well understood. Network analysis of psychopathology allow for detailed examinations of symptom interactions, providing an effective approach to explore the patterns of comorbidity between TS and ADHD symptoms.MethodsThis study included 3,958 participants (male/female = 3,004/954, age mean ± SD = 8.60 ± 2.25 years). We collected data on TS symptoms using the Motor Tic, Obsessions and Compulsions, Vocal Tic Evaluation Survey (MOVES), and ADHD symptoms using the Swanson, Nolan, and Pelham Rating Scale-IV (SNAP-IV). Network analysis was employed to construct a combined network of TS and ADHD symptoms at the symptom level. We utilized the expected influence (EI) and bridge EI metrics to explore the core and bridge symptoms within the network.ResultsThe network structure demonstrated a moderate number of non-zero connections between TS and ADHD symptoms, constituting 23.06% of all potential connections. Core symptoms in the comorbidity network included “Often has difficulty sustaining attention in tasks or play activities,” “Certain bad words or thoughts keep going through my mind,” and “Words come out that I can’t stop or control.” Bridging symptoms identified were “Words come out that I can’t stop or control,” “I do certain things like jumping or clapping over and over,” “I can’t control all my movements,” and “Often talks excessively.”ConclusionThe core and bridging symptoms identified in this study serve as potential therapeutic targets for the treatment of TS and ADHD comorbidity in clinical children and adolescents.

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.

Perceived functions and importance of digital media use and adolescent depression and suicidal ideation.

Although social media (SM) use is nearly ubiquitous among adolescents, there is a lack of clarity concerning the relationship between SM use and mental health outcomes like depression and suicidal ideation (SI), which increase during adolescence. Much of the previous literature has focused on the frequency of SM use; however, the current study examined the relationship between the perceived importance of three types of interactions and functions of SM use and depressive symptoms and suicidal ideation. A sample of 4057 adolescents (M age = 14.6; 47.0% girls; 69.0% Non-Hispanic/Latine White) were recruited via an online survey manager between February and March 2019. Participants completed the Adolescent Digital Technology Interactions and Importance (ADTI) Scale, which assessed the perceived importance of bridging online and offline experiences (bridging), going outside one's identity or offline experience (identity), and facilitating social connections (social). Participants also completed measures of depressive symptoms and SI and a self-report measure of SM use. A series of path analyses were conducted to examine the relationship between bridging, identity, social, and depressive symptoms and SI. After accounting for covariates (gender identity, racial identity, age, and SM use), bridging was negatively associated with depressive symptoms, whereas identity was positively associated with depressive symptoms. Bridging and social connection were negatively associated with SI, whereas identity was positively associated with SI. Gender moderated these relationships; however, racial identity did not. The results highlight the importance of understanding adolescent SM use beyond screen time in relation to depression and SI.

The comorbid network characteristics of anxiety and depressive symptoms among Chinese college freshmen.

This study aimed to investigate the interplay between anxiety and depressive symptoms in Chinese college freshmen using the causal system perspective (CSP), which differs from the traditional common cause perspective (CCP) by providing an alternative explanation by attributing comorbidity to direct interactions among symptoms. A convenience sample of 2,082 Chinese college freshmen (39.51% male, Mage = 18.61) from a normal university completed the Generalized Anxiety Disorder 7-Item Scale (GAD-7) and the Patient Health Questionnaire (PHQ-9). Network analysis was conducted and evaluated as to centrality, stability, node predictability, and bridging features. Moreover, the moderated network model (MNM) was utilized to detect the moderation effects of gender in the comorbidity network. The network of anxiety and depressive symptoms exhibited stability, characterized by the core symptoms of "restlessness", "lack of energy", and "excessive worry about control", as well as the bridging symptoms of "fearfulness", "sad mood", and "irritability". Notably, the nodes representing "uncontrollable worry" and "difficulty in relaxation" demonstrated the highest predictive power. Gender did not exert any moderating effects on the anxiety and depressive symptom network. These results reinforce that certain anxiety or depressive symptoms are more central than others, and thus play a more vital role in the comorbid network. These findings highlight underlying potential targeting symptoms to consider in future interventions.

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

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. 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. 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. 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.

Relationships between psychopathological symptoms, pandemic-related stress, perceived social support, and COVID-19 infection history: a network analysis in Chinese college students.

Previous coronavirus, 2019 (COVID-19) research has applied network analysis to examine relationships between psychopathological symptoms but rarely extended to potential risk and protective factors or the influence of COVID-19 infection history. This study examined complex inter-relationships between psychopathological symptoms, COVID-19-related stressors, perceived social support, and COVID-19 infection history among Chinese university/college students during the peak of fifth pandemic wave using a network analysis approach. A Least Absolute Shrinkage and Selection Operator-regularized partial correlation network using Gaussian graphical model was constructed in 1,395 Chinese university/college students in Hong Kong who completed a survey between 15 March and 3 April, 2022. Depressive, anxiety, and acute/traumatic stress symptoms were measured by Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and Impact of Event Scale-6, respectively. COVID-19-related stressors and perceived social support were measured. Network differences by COVID-19 infection history (COVID-network vs. no_COVID-network) and network communities were examined. Our results showed that the most influential nodes were depressed mood, uncontrollable worries, and uncontrollable thoughts about COVID-19. The main bridging symptoms were concentration problems and psychomotor problems. The COVID-network, comprising participants with a history of COVID-19 infection only, was significantly stronger than the no_COVID-network. Perceived social support and stress from conflicts with family/friends formed a unique community with negative cognition and suicidal idea in the COVID-network only. Our findings indicate that specific interventions targeting interpersonal conflicts and concentration problems as well as facilitating stress buffering effects of social support may represent effective strategies to reduce psychological distress in university/college students during COVID-19 and should be considered for future pandemic preparedness.

Uncovering the symptom relationship between anxiety, depression, and internet addiction among left-behind children: A large-scale purposive sampling network analysis

Facing long-term separation from their parents, left-behind children are at risk of the co-occurrence of internalizing and externalizing problems. Although previous research has gained substantial information examining the relationship between anxiety, depression, and internet addiction at the aggregate level of variables, little is known about the heterogeneity and interactions between these components at the symptom level with a large-scale purposive sample. Adopting the network approach, two network pathways, depression and anxiety, and associations between these variables and internet addiction were constructed. Our sample included 5367 left-behind children (Mage = 13.57; SDage = 1.37; 50.07% females). Relevant bridging, central symptoms, and network stability were identified. Two relatively stable networks were obtained. For the network of anxiety and depression, sleep problems and tachycardia were vital bridging symptoms. Central symptoms, including tachycardia, restlessness, fatigue, and emptiness, were symptoms of depression. For the network of symptoms of anxiety, depression, and internet addiction, the bridging symptoms remained the same, and the central symptoms included tachycardia, restlessness, loss of control, and emptiness. By identifying relevant bridging and central symptoms, those with higher levels of these symptoms could be regarded as intervention targets, providing a reference for the current issue of valuing diagnosis over prevention in left-behind children.

Understanding Comorbidity Between Non-Suicidal Self-Injury and Depressive Symptoms in a Clinical Sample of Adolescents: A Network Analysis.

Non-suicidal self-injury (NSSI) and depression often co-occur among adolescents with more severe clinical symptoms. This study examined the network structures of NSSI and depressive symptoms in adolescents. Participants were recruited in the psychiatric outpatient clinics of three tertiary hospitals between April 10 and July 10, 2023. All participants been already found with self-injury behaviors in outpatient when enrolled. NSSI diagnostic criteria and Patient Health Questionnaire-9 (PHQ-9) were utilized to collect NSSI and depressive symptoms separately. We performed a network analysis to visualize the correlation between each symptom and to identify core and bridging symptoms in comorbidities. A total of 248 patients were enrolled in the study, with a mean age of 15.48 (SD = 1.62). Based on the PHQ-9 scores and grades, our results showed that the incidence of depression in adolescents with non-suicidal self-injury behavior was relatively high (N=235, 94.76%), with the majority having severe depression. The network analysis revealed that nodes D-6 "feeling bad, failing or letting yourself or your family down", D-1 "little interest or pleasure" and D-4 "feeling tired" were the most vital and most central symptoms. The most crucial bridging symptom is the node NSSI-8 "frequent thinking about self-injury", which connects the NSSI to the depression comorbid network. This study offers a significant symptom-level conceptualization of the association between NSSI and depressive symptoms in a clinical sample of adolescents, which not only enhances our understanding of the comorbid but also identifies potential treatment targets to prevent and treat comorbidity between adolescent NSSI and depression.

A network analysis of depressive symptoms, psychosocial factors, and suicidal ideation in 8686 adolescents aged 12–20 years

Suicide has aroused global concern, and a better understanding of the complex interactions between suicide ideation and various psychopathological features is critical. We aimed to explore the complex interplays among adolescents. This study adopted a multistage stratified cluster sampling method and recruited adolescents aged 12 to 20 year-old from 8 middle and high schools between December 2020 and September 2021 in Guangzhou, China. We assessed loneliness, social support, bullying victimization, depressive symptoms, and suicidal ideation. We used network analysis to examine the network structure of the correlates of suicidal ideation and identify central symptoms and bridge symptoms. We used case-drop bootstrapping and correlation stability coefficients to examine the stability of the network. Among 8686 adolescents, 347 (4 %) adolescents reported suicidal ideation in the past two weeks. Network analyses identified ‘hopeless’, ‘psychomotor’, and ‘failure’ were the three strongest edges linked to suicidal ideation. The most central nodes were identified as ‘hopeless’ being the most central node, followed by loneliness and verbal bullying victimization, while sexual bullying victimization, sex, and relational bullying were the strongest bridging symptoms. The findings shed light on the complexity of associations of suicidal ideation and could provide insight into school-based suicide risk assessment and prevention for adolescents.

Bridge connection between depression and anxiety symptoms and lifestyles in Chinese residents from a network perspective.

Lifestyle habits are vital components of the culture of mental health treatment settings. We examined the bridge connection between depressive and anxiety symptoms and lifestyles from a network perspective using a population-based study. Face-to-face interviews were conducted with a provincially representative sample of 13,768 inhabitants from the Guangdong Sleep and Psychosomatic Health Survey based on standardized evaluation techniques. We identified the central symptoms by expected influence. The interconnection between depression and anxiety symptoms, as well as the bridge connectivity linking depression-anxiety symptoms and lifestyle factors, were assessed using the bridge centrality index. Network stability and sensibility analyses were performed using a case-dropping bootstrap procedure. The core symptom that exhibited the highest expected influence was fatigue or little energy, followed by uncontrollable worry, trouble relaxing, and sad mood in the depression-anxiety symptoms network, while guilt was the most interconnected symptom and had the highest bridge strength. Surrounding nodes of each node explained an average variance of 57.63%. Additionally, suicidal thoughts were recognized as collective bridging symptoms connecting lifestyle variables in the network integrating depression-anxiety symptoms with lifestyle factors. Current tobacco and alcohol consumption were positively associated with suicidal thoughts and irritability. Habitual diet rhythm and physical exercise frequency were linked to suicidal thoughts, guilt, and poor appetite or overeating. Suicidal thoughts, irritability, and guilt indicated the greatest connectivity with lifestyle factors. All networks had high stability and accuracy. These highlighted core and bridge symptoms could serve as latent targets for the prevention and intervention of comorbid depression and anxiety. It might be crucial for clinical practitioners to design effective and targeted treatment and prevention strategies aiming at specific lifestyles and behaviors.

The influence of changes in the Chinese COVID-19 prevention and control policies on mental health of medical staff: A network analysis

BackgroundShortly after the first outbreak of COVID-19 in Wuhan, the disease spread rapidly around the world. Previous findings reported an increase in mental health problems among Chinese medical staff, but there was a lack of research following changes in COVID-19 prevention and control policies. MethodsMedical staff were recruited separately in China from 15 to 16 December 2022 (N = 765, wave 1) and from 5 to 8 January 2023 (N = 690, wave 2). All participants completed the assessments of Generalized Anxiety Disorder-7, the Patient Health Questionnaire-9 and the Euthymia Scale. Network analysis was used to explore the relationships between symptoms both within and across depression, anxiety and euthymia. ResultsMedical staff showed worse anxiety, depression and euthymia at wave 2 than at wave 1. Depression, motor, restlessness and uncontrollable worrying showed high centrality (i.e., strength, expected influence, closeness) at wave 1, but higher at peak. Meanwhile, motor symptoms and restlessness showed the strongest connection between different mental disorders at both wave 1 and wave 2. The network structure was stable over time after the relaxation of the infection policy.Limitations.Our participants were not a random sample and the assessments were based on self-reports. ConclusionsThis study indicated the changes in central and bridging symptoms in medical staff at different stages after lifting of restrictions and the withdrawal of testing requirements, which provided management suggestions for the Chinese government and hospitals, as well as clinical guidance for psychological interventions.

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

BackgroundIn the context of the outbreak of COVID-19 within mainland China, to understand the mental health status of university students during campus closure, this study analyzes the relationship between anxiety, depressive symptoms, and psychological capital and to reveals their central symptoms. MethodsA total of 12,945 university students were included in this study from April 10 to 19, 2022. Anxiety and depressive symptoms were measured by the seven-item Generalized Anxiety Disorder Scale (GAD-7) and two-item Patient Health Questionnaires (PHQ-2). Psychological capital was measured using the Psychological Capital Questionnaire (PCQ-24). The centrality and bridge centrality indexes were used to identify central and bridge symptoms, respectively. Network Comparison Test (NCT) was also administered to check whether network traits differed by gender and place of residence. ResultsThe most influential node in this study was Trouble relaxing (GAD4), followed by Uncontrollable worry (GAD2) and Excessive worry (GAD3). The main bridging symptoms were Depressed mood (PHQ2), Psychological capital. There are no differences in the network structure of students by place of residence, while there are more significant differences in the network structure of students by gender. ConclusionCentral and bridging symptoms may be the core symptoms that trigger or maintain the development of anxiety and depression among university students during the COVID-19 campus closure. Timely and reasonable interventions targeting these symptoms may help reduce depression and anxiety in this population. In addition, improving university students' psychological capital may likewise contribute to the development of their good mental health.

A network analysis of difficulties in emotion regulation, anxiety, and depression for adolescents in clinical settings

BackgroundDifficulties in emotion regulation (DER) are widely considered to underlie anxiety and depression. Given the prevalence of anxiety and depression in adolescents and the fact that adolescence is a key period for the development of emotion regulation ability, it is important to examine how DER is related to anxiety and depression in adolescents in clinical settings.MethodsIn the present study, we assessed 209 adolescents in clinical settings using the Difficulties in Emotion Regulation Scale (DERS) and the Hospital Anxiety and Depression Scale (HADS) and examined the associations between six components of DER and 14 symptoms of anxiety and depression. We used network analysis, constructed circular and multidimensional scaling (MDS) networks, and calculated network centrality, bridge centrality, and stability of centrality indices.ResultsThe results showed that: (1) The global centrality index shows that the Strategy component (i.e., lack of access to strategies) is the center in the whole network, ranking highest in strength, closeness, betweenness, and expected influence. (2) The MDS network showed a closeness of anxiety and depression symptoms, while Awareness component (i.e., lack of emotional awareness) stayed away from other DER components, but Awareness is close to some depression symptoms. (3) The bridge nodes of three groups, Strategy from DERS, Worry and Relax from anxiety symptoms, and Cheerful and Slow from depression symptoms, had the strongest relationships with the other groups.ConclusionLack of access to strategies remains in the center not only in DER but also in the DER-anxiety-depression network, while lack of awareness is close to depression but not to anxiety. Worrying thoughts and inability to relax are the bridging symptoms for anxiety, while lack of cheerful emotions and slowing down are the bridging symptoms for depression. These findings suggest that making emotion regulation strategies more accessible to patients and reducing these bridging symptoms may yield the greatest rewards for anxiety and depression therapy.

More than the aggregation of its components: Unveiling the associations between anxiety, depression, and suicidal behavior in adolescents from a network perspective

BackgroundFacing multiple changes, adolescents are prone to have anxiety and depression concurrently, which would accompany a particularly high risk for suicide. However, most previous studies have ignored the heterogeneity of the components and used latent variable methods to explore the associations between these core variables, resulting in a lack of component-level discussions. MethodUsing a large sample of 9300 adolescents (Meanage = 13.51; SDage = 1.33; 49.82 % females), two network pathways of anxiety and depression and the associations between these variables and suicidal behavior were constructed. The central components and the stability of both networks were also identified. ResultsConsidering the network of anxiety and depression, there were two strong bridging symptoms of sleep problems and palpitation or tachycardia. The symptoms of depression showed a more vital centrality than anxiety, and the central symptoms were tachycardia, worthlessness, fatigue, and feeling of choking. For the network of suicidal behavior and symptoms of anxiety and depression, besides sleep problems, the edge linking lifetime suicide ideation and attempt and the frequency of suicide ideation in the past year was also a strong edge. Worthlessness connected symptoms of anxiety and depression with suicidal behavior. The central components were tachycardia, worthlessness, the frequency of suicidal ideation over the past year, and fatigue. Additionally, both networks had higher stability in terms of edge and centrality. ConclusionBased on the identified relevant strong bridging and central components, effective therapies would target these components first, which would lead to the alleviating effects on other components.

Anxiety, depression, stress, worry about COVID-19 and fear of loneliness during COVID-19 lockdown in Peru: A network analysis approach.

This study aims to examine the relationships between symptoms of anxiety, depression, stress, worry about COVID-19 and fear of loneliness during COVID-19 lockdown in Peru using network analysis. There were 854 participants aged 18 to 50 years (Mean = 36.54; SD = 9.23); 634 females (74.20%) and 220 males (25.80%), who completed the Generalized Anxiety Disorder Scale (GAD-7), Patient Health Questionnaire (PHQ-9), Perceived Stress Scale (PSS-10), Preoccupation with COVID-19 Contagion (PRE-COVID-19), Brief Scale of Fear of Loneliness (BSFL). A partial unregularized network was estimated through the ggmModSelect function. Expected influence (EI) and bridging EI values were calculated to identify central symptoms and bridging symptoms respectively. The results reveal those two symptoms of depression—stress and anxiety—were the most central symptoms in the network. Depressive symptoms are at the same time the most comorbid and it is shown that there are no differences in the network when compared between those who left home and those who did not leave home during lockdown. Depressive symptoms are concluded to be central and bridging in the network and interconnected with some symptoms of stress and anxiety. These findings may be important to understand the experience of COVID-19 lockdown in Peru.

The association between family relationships and depressive symptoms among pregnant women: A network analysis.

BackgroundDepression of pregnant women has been a growing concern in recent years, and previous research has found that family relationships are strongly associated with depression. From a network perspective, family relationships and depression can be conceptualized as the result of interactions between individual symptoms. This research approach can elucidate the structure and mechanisms of the relationship between individual symptoms within the two groups.MethodsA total of 990 participants were recruited from the obstetrics outpatient clinic of Maternal and Child Health Hospital in Huai'an through a randomized whole-group sampling. Respondents' depressive symptoms and family relationships were self-reported using questionnaire, and the structure of the family relationship-depressive symptoms network and related centrality indicators were examined for this sample.ResultsThe results of the network analysis suggested that the most influential symptoms in the network of family relationship-depressive symptoms were worry, feeling worthless, equal status with husband and couple relationship. And equal status with husband was the most prominent bridging symptoms in this study. The whole network was robust in both stability and accuracy tests.LimitationsInformation was obtained from subjects' self-reports, which may be subject to information bias. As a cross-sectional study, no causal link between family relationships and depressive symptoms can be established.ConclusionWorry, feeling worthless, equal status with husband and couple relationship are central symptoms of the family relationship-depressive symptoms network structure in pregnant women. Timely and systematic multilevel interventions targeting the central symptoms may be effective in alleviating the onset of depressive symptoms in women during this period.