Forcibly displaced people, such as refugees and asylum-seekers (RAS), are at higher risk of mental disorders, mainly post-traumatic stress disorder (PTSD), depression and anxiety. Little is known about the complex relationships between these mental disorders among culturally and linguistically diverse RAS. To investigate this, the present study applied a novel network analytical approach to examine and compare the central and bridge symptoms within and between PTSD, depression and anxiety among Afghan and Syrian RAS in Türkiye.

A large-scale online survey study with 785 Afghan and 798 Syrian RAS in Türkiye was conducted in 2021. Symptoms of PTSD (the short form of Post-Traumatic Stress Disorders Checklist [PCL-5]), depression and anxiety (Hopkins Symptoms Checklist-25) [HSCL-25]) were measured via self-administrated validated instruments. We conducted network analysis to identify symptoms that are most strongly connected with other symptoms (central symptoms) and those that connect the symptoms of different disorders (bridge symptoms) in R Studio using the qgraph package.

Overall, Afghans and Syrians differed in terms of network structure, but not in network strength. Results showed that feeling blue, feeling restless and spells of terror or panic were the most central symptoms maintaining the overall symptom structure of common mental disorders among Afghan participants. For Syrian participants, worrying too much, feeling blue and feeling tense were identified as the central symptoms. For both samples, anger and irritability and feeling low in energy acted as a bridge connecting the symptoms of PTSD, depression and anxiety.

The current findings provide insights into the interconnectedness within and between the symptoms of common mental disorders and highlight the key symptoms that can be potential targets for psychological interventions for RAS. Addressing these symptoms may aid in tailoring existing evidence-based interventions and enhance their effectiveness. This contributes to reducing the overall mental health burden and improving well-being in this population.

Hungarians exhibit more negative attitudes toward help-seeking for mental health problems compared to other European countries. However, research on help-seeking in Hungary is limited, and it is unclear how stigma relates to help-seeking when considering demographic and clinical characteristics. We used a network analytic approach to simulate a stigma model using hypothesized constructs in a sizable sample of Hungarian adults.

Participants were 345 adults recruited from nine primary care offices across Hungary. Participants completed self-report measures assessing public stigma, self-stigma, experiential avoidance (EA), attitudes toward seeking professional psychological help, anxiety, depression, demographics, prior use of mental health services, and whether they have a family member or friend with a mental health condition.

EA and anxiety were the most central nodes in the network. The network also revealed associations between greater EA with greater public stigma, anxiety, depression, and having a family member or friend with a mental health condition. More positive attitudes toward seeking help were associated with lower self-stigma, public stigma, and having received psychological treatment in their lifetime. Being female was associated with lower income, higher education, and having received psychological treatment in their lifetime. Finally, having a family member or friend with a mental health condition was associated with having received psychological treatment in their lifetime and greater public stigma.

The strength centrality and associations of EA with clinical covariates and public stigma implicate its importance in stigma models. Findings also suggest that while some aspects of existing stigma models are retained in countries like Hungary, other aspects may diverge.

Depression is one of the most prevalent mental health conditions in the world. However, the heterogeneity of depression has presented obstacles for research concerning disease mechanisms, treatment indication, and personalization. The current study used network analysis to analyze and compare profiles of depressive symptoms present in community samples, considering the relationship between symptoms.

Cross-sectional measures of depression using the Patient Health Questionnaire – 9 items (PHQ-9) were collected from community samples using data from participants scoring above a clinical threshold of ≥10 points (N = 2,023; 73.9% female; mean age 49.87, SD = 17.40). Data analysis followed three steps. First, a profiling algorithm was implemented to identify all possible symptom profiles by dichotomizing each PHQ-9 item. Second, the most prevalent symptom profiles were identified in the sample. Third, network analysis for the most prevalent symptom profiles was carried out to identify the centrality and covariance of symptoms.

Of 382 theoretically possible depression profiles, only 167 were present in the sample. Furthermore, 55.6% of the symptom profiles present in the sample were represented by only eight profiles. Network analysis showed that the network and symptoms’ relationship varied across the profiles.

Findings indicate that the vast number of theoretical possible ways to meet the criteria for major depressive disorder (MDD) is significantly reduced in empirical samples and that the most common profiles of symptoms have different networks and connectivity patterns. Scientific and clinical consequences of these findings are discussed in the context of the limitations of this study.

Although both psychological resilience and social support are widely believed to be effective in alleviating post-traumatic psychiatric symptoms in individuals with traumatic events, there has been a lack of comparative analysis of their intervention effects on different post-traumatic psychiatric symptoms. Furthermore, previous studies have mostly failed to control for potential confounding effects caused by different traumatic events.

We used the novel network analysis approach to examine the differential moderating effects of psychological resilience and social support on post-traumatic psychiatric symptoms, controlling for the confounding effects of traumatic events.

We recruited 264 front-line rescuers who experienced the same traumatic event. Quantified edge weights and bridge expected influence (BEI) were applied to compare the alleviating effects of psychological resilience and social support.

Our study revealed distinct correlations in a sample of front-line rescuers: social support negatively correlates more with psychosomatic symptoms, notably fatigue in depressive networks and sleep disturbance in post-traumatic stress disorder (PTSD) networks, whereas psychological resilience shows fewer such correlations. Quantitative analysis using BEI indicated that psychological resilience more effectively suppresses depressive and anxiety symptom networks, whereas social support more significantly inhibits PTSD symptom networks.

The current study represents the first attempt to examine the differential effects of psychological resilience and social support on post-traumatic outcomes in real-world emergency rescuers, controlling for the confounding effect of traumatic events. Our results can act as the theoretical reference for future precise and efficient post-trauma psychological interventions.

Both impulsivity and compulsivity have been identified as risk factors for problematic use of the internet (PUI). Yet little is known about the relationship between impulsivity, compulsivity and individual PUI symptoms, limiting a more precise understanding of mechanisms underlying PUI.

The current study is the first to use network analysis to (a) examine the unique association among impulsivity, compulsivity and PUI symptoms, and (b) identify the most influential drivers in relation to the PUI symptom community.

We estimated a Gaussian graphical model consisting of five facets of impulsivity, compulsivity and individual PUI symptoms among 370 Australian adults (51.1% female, mean age = 29.8, s.d. = 11.1). Network structure and bridge expected influence were examined to elucidate differential associations among impulsivity, compulsivity and PUI symptoms, as well as identify influential nodes bridging impulsivity, compulsivity and PUI symptoms.

Results revealed that four facets of impulsivity (i.e. negative urgency, positive urgency, lack of premeditation and lack of perseverance) and compulsivity were related to different PUI symptoms. Further, compulsivity and negative urgency were the most influential nodes in relation to the PUI symptom community due to their highest bridge expected influence.

The current findings delineate distinct relationships across impulsivity, compulsivity and PUI, which offer insights into potential mechanistic pathways and targets for future interventions in this space. To realise this potential, future studies are needed to replicate the identified network structure in different populations and determine the directionality of the relationships among impulsivity, compulsivity and PUI symptoms.

The fifth version of the Diagnostic and Statistical Manual for Mental Disorders (DSM-5) and its revised version (DSM-5-TR) propose severity levels for gambling disorder (GD) based on the number of criteria met. However, this taxonomy has some limitations. We aimed to assess the centrality of each criterion and its relationship by conducting a network analysis while considering sex differences.

We performed a network analysis with the DSM-5 criteria for GD with data from 4,203 treatment-seeking patients (3,836 men and 367 women) diagnosed with GD who sought for treatment in a general tertiary hospital which has a unit specialized in behavioral addictions.

The withdrawal criterion (“Restless or irritable when attempting to cut down or stop gambling”) showed the highest centrality values in both sexes. In men, the second most central criterion was the tolerance criterion (“Needs to gamble with increasing amounts of money in order to achieve the desired excitement”); while among women, the second was the chasing losses criterion (“After losing money gambling, often returns another day to get even”).

The most central criteria identified are associated with compulsivity-driven behaviors of the addictive process. Taking into account the high relevance and transitive capacity of withdrawal in both men and women, as well as tolerance in men, and chasing losses in women, the recognition and understanding of these symptoms are fundamental for the accurate diagnosis and severity assessment of GD.

This study aimed to estimate networks of depressive symptoms among Irish adults with and without diabetes at two time points and compare between the two groups at each time point using data from the Irish Longitudinal Study on Ageing (TILDA).

Participants were from Wave 1 (2009–2011) and Wave 4 (2016) of TILDA, with n = 639 participants with diabetes and n = 7,837 without diabetes at Wave 1, and n = 1,151 with diabetes and n = 4,531 without diabetes at Wave 4. Depressive symptoms were measured using the 8 items of the Center for Epidemiologic Studies Depression Scale. Network psychometric analysis was used to examine symptom centrality, symptom-level associations, and network comparisons at each time point.

Stable, strongly connected networks emerged for people with and without diabetes at both time points. The symptoms of feeling depressed, feeling like everything’s an effort, not enjoying life, feeling sad, and couldn’t get going were the most central nodes in all networks, which did not differ between people with and without diabetes. However, for people with diabetes, the network was more densely connected at Wave 4, when the sample was predominately people with newly diagnosed diabetes. Furthermore, the relationship between ‘felt lonely’ and ‘couldn’t get going’ and between ‘not enjoying life’ and ’sad’ was significantly stronger for people with diabetes than for those without.

This study provides a more detailed understanding of the structure of depressive symptoms at two time points in older Irish adults with and without type 1 or type 2 diabetes.