A network perspective on comorbid depression in adolescents with obsessive-compulsive disorder

The Global and Local Patterns of Reading-Related Cognitive and Ecological Variables in Chinese First-Grade Children: A Cross-Sectional Network Analysis

In the current study, we tested a network model of reading difficulty by using state-of-the-art psychological network analysis. Four hundred and fifty-three Chinese first-grade children (about 38% female, mean age = 7.00, SD = 0.41) were divided into good (n = 154), competent (n = 147), and struggling readers (n = 152) based on their scores of Chinese character reading. The Extended Bayesian Information Criterion graphical lasso (EBICglasso) method was applied to estimate cross-sectional networks for the three groups. Each network included four cognitive nodes (homophone awareness, morphological structure awareness, phonological awareness, and vocabulary) and two ecological nodes (family socioeconomic status and the number of books at home). Chronological age and nonverbal intelligence were also included in the estimated networks. The global (i.e., global structure and global connectivity) and local patterns (i.e., the most important edges and nodes) in each network were reported. The network comparison results showed that global connectivity was significantly lower among struggling readers than for good readers, implying that a holistic impairment of bidirectional connections among multiple variables relates to the difficulty in learning to read. The theoretical and empirical implications and the significance of applying the network approach to reading research are discussed.

Mapping network connection and direction among symptoms of depression and anxiety in patients with chronic gastritis

Regarding neurophysiological and developmental findings, anxiety and depression are usual comorbidities of gastritis patients. However, research related to anxiety and depression among chronic gastritis patients was conducted on the disease level while ignoring symptoms. Hence, we rendered the network approach to reveal the symptoms of anxiety and depression among chronic gastritis patients. Three hundred and sixty-nine chronic gastritis patients (female = 139, Mage = 55.87 years) were asked to complete the Self-Rating Anxiety Scale and Self-Rating Depression Scale. Three symptom networks and one directed acyclic graph (DAG) network were formed. First, in the anxiety network of chronic gastritis patients, dizziness was the most influential symptom. In the depression network of chronic gastritis patients, depressed affect and psychomotor retardation were the influential symptoms. Second, panic, easy fatiguability, weakness, palpitation, depressed affect, tachycardia, fatigue, and psychomotor agitation bridged the anxiety-depression network of chronic gastritis patients. Third, DAG networks showed that anxiousness and hopelessness could trigger other symptoms in the anxiety-depression networks of chronic gastritis patients. The current study provided insightful information on patients with chronic gastritis by examining the structures of symptoms.

The psychological side of menopause: evidence from the comorbidity network of menopausal, anxiety, and depressive symptoms

OBJECTIVE

Numerous studies have uncovered a correlation between menopausal, anxiety, and depressive symptoms. How these symptoms interrelate and influence each other, however, remains unclear. This study aimed to identify the associations between menopausal, anxiety, and depressive symptoms using network analysis.

METHODS

The participants comprised 423 women ( Mage = 49.21 ± 4.01 y; range, 40-60 y) recruited from a menopause clinic at a tertiary hospital in Beijing, China. Demographic characteristics and menopausal, anxiety, and depressive symptoms were obtained through self-report questionnaires. Two networks were established: a partial correlation network and a Bayesian network.

RESULTS

The menopausal symptom of nervousness exhibited a robust association with anxiety symptoms in both networks. Within the partial correlation network, the depressive symptom of tiredness emerged as a pivotal symptom, facilitating the co-occurrence of menopausal and depressive symptoms. Bayesian network analysis exhibited that the depressive symptom of a loss of interest was related to certain menopausal symptoms through depressive symptoms of tiredness and motor problems, both serving as critical links between menopausal symptoms and depression. Notably, four menopausal symptoms-arthralgia/myalgia, formication, sexual complaints, and urinary tract infection-appeared independent of other menopausal, anxiety, and depressive symptoms.

CONCLUSIONS

Both psychological (eg, fatigue) and somatic (eg, hot flashes, headaches, and dizziness) menopausal symptoms demonstrate strong associations with depression. In providing optimal support for women's health during menopause, psychological interventions aimed at depression, particularly among those experiencing a loss of interest or pleasure in activities, should complement conventional therapies.

Using Bayesian Networks to Investigate Psychological Constructs: The Case of Empathy

Network analysis is an emerging field for the study of psychopathology that considers constructs as arising from the interactions among their constituents. Pairwise effects among psychological components are often investigated by using this framework. Few studies have applied Bayesian networks, models that include directed interactions to perform causal inference on psychological constructs. Directed graphical models may be less straightforward to interpret in case the construct at hand does not contain symptoms but instead psychometric items from self-report measures. However, they may be useful in validating specific research questions that arise while using standard pairwise network models. In this study, we use Bayesian networks to investigate a well-known psychological construct, empathy from the Interpersonal Reactivity Index, in large two samples of 1973 university students from Belgium. Overall, our results support the hypotheses emphasizing empathic concern (i.e., sympathy) as causally important in the construct of empathy, and overall attribute the primacy of emotional components of empathy over their intellectual counterparts. Bayesian networks help researchers identify the plausible causal relationships in psychometric data, to gain new insight on the psychological construct under examination, help generate new hypotheses and provide evidence relevant to old ones.

Developing a transdiagnostic Ecological Momentary Assessment protocol for psychopathology

OBJECTIVES

The network approach to psychopathology posits that mental disorders emerge from dynamic interactions among psychopathology-relevant variables. Ecological Momentary Assessment (EMA) is frequently used to assess these variables in daily life. Considering the transdiagnostic nature of the network approach to psychopathology, this study describes the development of a transdiagnostic EMA protocol for psychopathology.

METHODS

First, 96 clinicians completed an online survey, providing three EMA constructs for up to three disorders they specialize in, and three EMA constructs relevant across disorders (transdiagnostic constructs). Second, 12 focus groups were conducted with clinical experts for specific types of diagnoses (e.g., mood disorders, anxiety disorders). Finally, a selection of items was reached by consensus. Two raters independently coded the online survey responses with an inter-rater agreement of 87.3%.

RESULTS

Jaccard indices showed up to 52.6% overlap in EMA items across types of diagnoses. The most frequently reported transdiagnostic constructs were mood, sleep quality, and stress. A final set of EMA items is created based on items' frequency and informativeness, ensuring completeness across diagnoses and minimizing burden.

CONCLUSIONS

The described procedure resulted in a feasible EMA protocol to examine psychopathology transdiagnostically. Feasibility was helped by the overlap in mentioned symptoms across disorders. Such overlap raises questions about the validity of DSM categories.

Network analysis of anxiety and cognitive impairment among mental healthcare workers

Introduction

With the rising demand for medical services and the associated burden, work-related stress and mental health issue have garnered increased attention among healthcare workers. Anxiety, cognitive impairment, and their comorbidities severely impact the physical and mental health as well as the work status of healthcare workers. The network analysis method was used to identify the anxiety and cognitive impairment among mental healthcare workers using the Generalized Anxiety Disorder Scale (GAD-7) and the Perceived Deficit Questionnaire for Depression (PDQ-D). We sought to identify the core symptoms associated with the comorbidity of anxiety and cognitive impairment in mental healthcare workers.

Methods

The study was conducted by Shandong Daizhuang Hospital and Qingdao Mental Health Center in China from September 13, 2022, to October 25, 2022, involving a total of 680 healthcare workers as participants. GAD-7 and PDQ-D were utilized to assess anxiety and cognitive impairment, respectively. Regularized partial correlation network analysis was employed to examing the expected influence and predictability of each item within the network. Statistical analysis and visualization of the network were performed using R software.

Results

The mean total score for anxiety was 3.25, while the mean total score for cognitive symptoms was 15.89. PDQ17 "Remembering numbers", PDQ12 "Trouble get started" and PDQ20 "Trouble make decisions" emerged as central symptoms in the anxiety-cognition network. GAD6 "Irritable", GAD5 "Restlessness" and GAD1 "Nervousness or anxiety" were identified as the most critical bridge symptoms connecting anxiety and cognition. Gender was found to be unrelated to the global strength of the network, edge weight distribution, or individual edge weights.

Conclusion

Utilizing central and bridge symptoms (i.e., Remembering numbers, Trouble get started, Trouble make decisions, Irritable, Restlessness and Nervousness or anxiety) as primary intervention points may aid in mitigating the serious health consequences of anxiety, cognitive impairment, and comorbidities anxiety and cognitive impairment for mental healthcare workers.

Psychopathology symptoms of frontline nurses under sudden public health crisis: A network analysis

Public health crises can significantly impact the emotional well-being of healthcare workers. Network analysis is a novel approach to exploring interactions between mental disorders at the symptom level. This study aimed to elucidate the characteristics of post-traumatic stress disorder (PTSD), anxiety, depression, and insomnia symptoms network among frontline nurses under sudden public health crisis. A cross-sectional survey was conducted online among 556 frontline nurses through convenience sampling in Hubei Province, China, from 21 February 2020, to 10 March 2020. Symptoms of PTSD, anxiety, depression, and insomnia were assessed by the Post-Traumatic Stress Disorder Checklist (PCL-5), Generalized Anxiety Disorder scale (GAD-7), Patient Health Questionnaire (PHQ-9) and Insomnia Severity Index, respectively. Central symptoms (the most important symptoms, activation has the strongest influence the other nodes) and bridge symptoms (nodes where deactivation can prevent activation from spreading from one disorder to another) were identified via centrality and bridge centrality indices, respectively. Network stability was examined using the case-dropping procedure. We found that the correlation between PHQ-9 item 9 'suicidal thoughts' and PCL-5 item 16 'reckless or self-destructive behaviour' was the strongest. Moreover, 'reckless or self-destructive behaviour' was the strongest central symptom, and PHQ-9 item 3 'sleep problems' was the most important bridge symptom. Other major symptoms included GAD-7 item 6 'uncontrollable anxiety' and PHQ-9 item 2 'depressed or sad mood'. Timely, systemic targeting interventions on central symptoms and bridge symptoms may effectively alleviate co-occurring experiences of psychopathological symptoms among frontline nurses.

Network analysis of the relationship between non-suicidal self-injury, depression, and childhood trauma in adolescents

BACKGROUND

Non-suicidal self-injury seriously harm the physical and mental health of adolescents. The aim of the current study was to explore the relationship between non-suicide self-injury, depression, and childhood trauma from the perspective of symptoms in adolescents.

METHODS

A cross-sectional survey was conducted in four junior high middle schools and collected 2640 valid questionnaires. There were 1329 male students and 1311 female students. The age of the participants ranged from 11 to 17 years old, with a mean age of 13.3 (± 0.94) years. Non-suicidal self-injury (NSSI), depressive symptoms, and childhood trauma were assessed using the Adolescent Self-Harm Scale, the Childhood Depression Scale, and the Childhood Trauma Questionnaire, respectively. A network analysis was performed.

RESULTS

In the network, NSSI, depressive symptoms, and childhood trauma were closely related. Negative self-esteem in the depressive symptoms and emotional abuse in childhood were the most central nodes. Negative self-esteem and negative mood were directly connected to NSSI, other nodes of depressive symptoms appeared to be indirectly connected to NSSI through these two nodes. Emotional abuse was the only node in childhood trauma categories directly connected to NSSI. Nodes of other categories of childhood trauma (physical neglect, physical abuse, emotional neglect, and sexual abuse) were indirectly connected to NSSI through emotional abuse.

CONCLUSIONS

NSSI, depression, and childhood trauma of teenagers were closely related. Individuals who have suffered emotional abuse in childhood were more likely to have depressive symptoms and NSSI. Improving negative self-esteem and negative emotions and reducing emotional abuse may be beneficial in alleviating depression and reducing NSSI in adolescents.

A study on why foreign-born East Asians but not US-born East Asians are underrepresented in leadership attainment in the U.S

A recent study investigated the impact of culture of Asian groups on leadership attainment in the U.S. It revealed that East Asians (EAs) are less likely than South Asians (SAs) and white people (WP) to attain leadership positions, and SAs may even surpass WP in leadership attainment. The study explained that the underrepresentation of EAs in leadership positions in the U.S. (the so called bamboo ceiling) is partly because EAs communicate less assertively. Specifically, EA cultures value collectivism (e.g., humility and harmony), which are at odds with western cultures that value individualism (e.g., extraversion and assertiveness), whereas SA cultures are congruent with western cultures. However, the study did not distinguish the different impact of home culture (i.e., EA cultures) and host culture (i.e., western cultures) on US-born EAs versus foreign-born EAs. We argue that for US-born EAs (i.e., second generation EAs), host culture plays a more important role than home culture in their growth and they may not be underrepresented in leadership attainment compared to WP. The bamboo ceiling effect is mostly demonstrated among foreign-born EAs (i.e., first generation EAs) who are shaped mainly under the home culture. We support the argument by conducting analysis on one of the datasets in the original study and a new dataset from Fortune's 40-under-40 list. Our study suggests that when studying the underrepresentation of leadership attainment for EAs, US-born EAs and foreign-born EAs should not be aggregated in one category. Considering the ethnic EA group, the bamboo ceiling phenomenon may exist mainly among foreign-born EAs because US-born EAs, with a median age of 21.3, are much younger than the general American population, who may not be experienced enough to be considered for leadership positions in established large companies.

Tinnitus: A Dimensionally Segregated, yet Perceptually Integrated Heterogeneous Disorder

OBJECTIVES

Tinnitus subtypes are proposed to lie on a continuum of different symptom dimensions rather than be categorical. However, there is no comprehensive empirical data showing this complex relationship between different tinnitus symptoms. The objective of this study is to provide empirical evidence for the dimensional nature of tinnitus and how different auditory and non-auditory symptoms interact with each other through complex interactions. We do this using graph theory, a mathematical tool that empirically maps this complex interaction. This way, graph theory can be utilised to highlight a new and possibly important outlook on how we can understand the heterogeneous nature of tinnitus.

DESIGN

In the current study, we use the screening databases of the Treatment Evaluation of Neuromodulation for Tinnitus-Stage A1 (TENT-A1) and A2 (TENT-A2) randomised trials to delineate the dimensional relationship between different clinical measures of tinnitus as a secondary data analysis. We first calculate the empirical relationship by computing the partial correlation. Following this, we use different measures of centrality to describe the contribution of different clinical measures to the overall network. We also calculate the stability of the network and compare the similarity and differences between TENT-A1 and TENT-A2.

RESULTS

Components of the auditory subnetwork (loudness discomfort level, sound sensitivity, average hearing loss and high frequency hearing loss) are highly inter-connected in both networks with sound sensitivity and loudness discomfort level being highly influential with high measures of centrality. Furthermore, the relationship between the densely connected auditory subnetwork with tinnitus-related distress seems to vary at different levels of distress, hearing loss, duration and age of the participants.

CONCLUSION

Our findings provide first-time evidence for tinnitus varying in a dimensional fashion illustrating the heterogeneity of this phantom percept and its ability to be perceptually integrated, yet behaviourally segregated on different symptomatic dimensions.

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

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

Improving the Walktrap Algorithm Using K-Means Clustering

The walktrap algorithm is one of the most popular community-detection methods in psychological research. Several simulation studies have shown that it is often effective at determining the correct number of communities and assigning items to their proper community. Nevertheless, it is important to recognize that the walktrap algorithm relies on hierarchical clustering because it was originally developed for networks much larger than those encountered in psychological research. In this paper, we present and demonstrate a computational alternative to the hierarchical algorithm that is conceptually easier to understand. More importantly, we show that better solutions to the sum-of-squares optimization problem that is heuristically tackled by hierarchical clustering in the walktrap algorithm can often be obtained using exact or approximate methods for K-means clustering. Three simulation studies and analyses of empirical networks were completed to assess the impact of better sum-of-squares solutions.

Identification of central symptoms of children depression and development of two short version of Children's Depression Inventory: Based on network analysis and machine learning

BACKGROUND

Using network analysis to study the central symptoms is important for understanding the mechanism of depression symptoms and selecting items for the short version depression screening scale. This study aimed to identify the central symptoms of depression and develop the short and effective depression screening tools for Chinese rural children.

METHODS

Firstly, the 2458 individuals (Mage = 10.74; SDage = 1.64; 51.2 % were female) were recruited from the rural children's mental health database. Children's Depression Inventory (CDI) was used to assess depression symptoms. Then, network analysis was used to identify the central symptoms of depression. The accuracy, stability, and gender invariance of the depression symptoms network were tested. Finally, a short version of CDI with central symptoms (CDI-SC) and a new CDI-10 (CDI-10-N) were developed by network analysis and feature selection techniques to optimize the existing CDI-10. Their performances in screening depression symptoms were validated by the cutoff threshold and machine learning.

RESULTS

The central symptoms of Chinese rural children's depression were sadness, self-hatred, loneliness and self-deprecation. This result was accurate and stable and depression symptoms network has gender invariance. The AUC values of CDI-10-N and CDI-SC are over 0.9. The CDI-10-N has a higher AUC than CDI-10. The optimal cutoff thresholds for CDI-10-N and CDI-SC are 6 and 1. The performance of machine learning on AUC generally outperforms those of cutoff threshold.

CONCLUSIONS

The central symptoms identified in this study should be highlighted in screening depression symptoms, and CDI-10-N and CDI-SC are effective tools for screening depression symptoms in Chinese rural children.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Mapping network connection and direction between anxiety and depression symptoms across the early, middle, and late adolescents: Insights from a large Chinese sample

Depression and anxiety are two mental disorders prevailing among adolescents. However, issues regarding the trajectory of depression and anxiety are still controversial on both the disease and symptom dimensions. The novel method of network analysis was used to provide insight into the symptom dimension. 20,544 adolescents (female = 10,743, 52.3%) aged between 14 and 24 years (age mean ± sd = 16.9 ± 2.94) were divided into three subgroups according to age so that the course of depression and anxiety could be traced. Network analysis and the Bayesian network model were used in the current study. The results indicated that uncontrollable worry - excessive worry was the most significant edge for all adolescents, whereas concentration - motor had the highest edge weights for early adolescents, and anhedonia - energy was the most critical pairwise symptom for middle and late adolescents. Irritability can bridge anxiety and depression in the early and middle stages of adolescence, while suicide plays a bridging role in the early and late stages of adolescence. Restlessness and guilt can bridge anxiety and depression in middle- and late-stage adolescents, and feeling afraid plays a unique role in middle-stage adolescents. Except for sad mood, which can trigger middle adolescents' anxiety and depression, the other three subgroups were mainly triggered by nervousness. In addition, all results in our current study were shown to be stable and accurate. In treatment, targeting central and triggering symptoms at different stages of adolescence may be critical to alleviating the comorbidity of anxiety and depression.

Regional homogeneity in patients with obsessive-compulsive disorder and depression: A resting state functional magnetic resonance imaging study

OBJECTIVE

To explore the brain functional impairment of patients with obsessive-compulsive disorder (OCD) with and without depressive symptoms and analyze the correlation between the degree of impairment and the severity of symptoms.

METHOD

Fourteen patients with OCD who met the ICD-10 diagnostic criteria for OCD were included. The group having OCD with depression (OCDd) consisted of 15 patients, and 17 healthy controls (HC) matched for age and education were also included. The Yale-Brown OCD Scale (Y-BOCS) and the 24-item Hamilton Assessment of Depression Scale (HAMD) were administered to the OCD and OCDd groups. Resting-state functional brain magnetic resonance imaging was performed in the three groups of participants.

RESULT

The OCDd group had lower scores on the HAMD, Y-BOCS, and obsessive-compulsive thinking subscales compared with the OCD group (P < 0.05). The scores on the OCDd subscale were negatively correlated with the HAMD scores (R =  - 0.568, P = 0.027). The OCDd group had higher regional homogeneity (ReHo) values in the lingual gyrus than the OCD group. The OCDd group had higher ReHo values in the lingual gyrus than the HC group, and the OCDd group had higher ReHo values than the HC group. These differences were statistically significant (P < 0.05). After correction for multiple comparisons, significant difference was observed between the OCDd and HC groups (P＜0.05). In the OCD group, the ReHo value of the lingual gyrus was negatively correlated with the Y-BOCS total score and the compulsive behavior subscale score (R =  - 0.609, -0.552; P = 0.016, 0.033).

CONCLUSION

Abnormal ReHo values in the lingual gyrus and right medial superior frontal gyrus were found in the patients with OCDd. In the OCDd group, the ReHo values of the lingual gyrus were negatively correlated with the scores on the Y-BOCS total and obsessive-compulsive subscales, suggesting that abnormal local coherence of the lingual gyrus may be related to the severity of OCD.

The longitudinal relationship between bullying victimization and depressive symptoms for middle school students: A cross-lagged panel network analysis

BACKGROUND

Previous research about the relationship between bullying victimization and adolescent depressive symptoms was mostly based on latent variable modeling. This study, instead, applied item-level analysis to explore the cross-sectional relationship and longitudinal development between bullying victimization and adolescent depressive symptoms with network models.

METHODS

This study used Olweus Bully/Victim Questionnaire and Children's Depression Inventory to collect data. A total of 1911 middle school students (55.2 % female; Mage = 12.98 ± 0.60 at T1) completed measures on four occasions at 6-month intervals. Nine network analysis models were employed to better understand the relationship between variables.

RESULTS

(1) "Being threatened or intimidated" was the most influential bullying behavior within bullying victimization items; (2) "being excluded", "being spoken ill of" and "negative mood" were the bridge items between bullying victimization and adolescent depressive symptoms; (3) the most influential bullying victimization item on adolescent depressive symptoms was "being robbed or blackmailed" for short-term development and "being excluded" for long-term development. While the most affected depressive symptom by bullying victimization was "anhedonia" for short-term development and "negative mood" for long-term development.

LIMITATIONS

Self-report measure is adopted for all variables in the study, and there may be some deviation. Due to the questionnaires, the items of bullying behaviors and depressive symptoms included in the network analysis are limited.

CONCLUSIONS

From the item level, this study found more specific relationships between bullying victimization and adolescent depressive symptoms. These findings highlight depressed mood and anhedonia are depressive symptoms that should be more paid attention to in clinical intervention for bullying victims.

Identifying the Bridge Between Anxiety Response to COVID-19 and Depression Symptoms in General Population: A Network Analysis

BACKGROUND

Previous studies have suggested links between anxiety response to the novel coronavirus (COVID-19) and depression symptoms in general population. However, a symptom-level investigation has not been reported.

OBJECTIVE

This study aimed to use network analysis to identify central symptoms and bridge symptoms that link COVID-19 anxiety and depression.

METHODS

Data from 1788 participants were analyzed. Coronavirus anxiety and depression symptoms were measured using the Stress and Anxiety to Viral Epidemics-6 Items Scale and the Patient Health Questionnaire-9, respectively. Network analysis was performed using R.

RESULTS

The results revealed 'thoughts of suicide or self-harm' from Patient Health Questionnaire-9 and 'worry about others avoiding me' from Stress and Anxiety to Viral Epidemics-6 Items Scale as bridge symptoms. Findings suggest direct relationship between fear of social isolation and thoughts of suicide or self-harm. 'Feeling tired with little energy' and 'trouble concentrating' are strongly linked to 'thoughts of suicide or self-harm', suggesting these symptoms as risk factors for suicidal or self-injurious thoughts during the pandemic.

CONCLUSIONS

The findings suggest fear of social isolation as a risk factor for developing thoughts of suicide or self-harm. These results should be taken into account during evaluation of risk of suicide or mental health interventions for the pandemic.

Congruency of multimodal data-driven personalization with shared decision-making for StayFine: individualized app-based relapse prevention for anxiety and depression in young people

Tailoring interventions to the individual has been hypothesized to improve treatment efficacy. Personalization of target-specific underlying mechanisms might improve treatment effects as well as adherence. Data-driven personalization of treatment, however, is still in its infancy, especially concerning the integration of multiple sources of data-driven advice with shared decision-making. This study describes an innovative type of data-driven personalization in the context of StayFine, a guided app-based relapse prevention intervention for 13- to 21-year-olds in remission of anxiety or depressive disorders (n = 74). Participants receive six modules, of which three are chosen from five optional modules. Optional modules are Enhancing Positive Affect, Behavioral Activation, Exposure, Sleep, and Wellness. All participants receive Psycho-Education, Cognitive Restructuring, and a Relapse Prevention Plan. The personalization approach is based on four sources: (1) prior diagnoses (diagnostic interview), (2) transdiagnostic psychological factors (online self-report questionnaires), (3) individual symptom networks (ecological momentary assessment, based on a two-week diary with six time points per day), and subsequently, (4) patient preference based on shared decision-making with a trained expert by experience. This study details and evaluates this innovative type of personalization approach, comparing the congruency of advised modules between the data-driven sources (1-3) with one another and with the chosen modules during the shared decision-making process (4). The results show that sources of data-driven personalization provide complementary advice rather than a confirmatory one. The indications of the modules Exposure and Behavioral Activation were mostly based on the diagnostic interview, Sleep on the questionnaires, and Enhancing Positive Affect on the network model. Shared decision-making showed a preference for modules improving positive concepts rather than combating negative ones, as an addition to the data-driven advice. Future studies need to test whether treatment outcomes and dropout rates are improved through personalization.

Social and Medical Determinants of Diabetes: A Time-Constrained Multiple Mediator Analysis

Background A number of studies have shown an association between social determinants of health and the emergence of obesity and diabetes, but whether the relationship is causal is not clear. Objective To test whether social, environmental, and medical determinants directly or indirectly affect population-level diabetes prevalence after controlling for mediator-mediator interactions. Methods Data were obtained from the CDC and supplemented with nine other data sources for 3,109 US counties. The dependent variable was the prevalence of diabetes in 2017. Independent variables were a given county's 30 social, environmental, and medical characteristics in 2015 and 2016. A network multiple mediation analysis was conducted. First, we used Least Absolute Shrinkage and Selection Operator (LASSO) regression to relate the 2017 diabetes rate in each county to 30 predictors measured in 2016, identifying statistically significant and robust predictors as the mediators within the network model and as direct determinants of 2017 diabetes. Second, each of the direct causes of diabetes was taken as a new response variable and LASSO-regressed on the same 30 independent variables measured in 2015, identifying the indirect (mediated) causes of diabetes. Subsequently, these direct and indirect predictors were used to construct a network model. The completed network was then employed to estimate the direct and mediated impact of variables on diabetes. Results For 2017 diabetes rates, 63% of the variation was explained by five variables measured in 2016: the percentage of residents who were (1) obese, (2) African American, (3) physically inactive, (4) in poor health condition, and (5) had a history of diabetes. These five direct predictors, measured in 2016, mediated the effect of indirect variables measured in 2015, including the percentage of residents who were (1) Hispanic, (2) physically distressed, (3) smokers, (4) living with children in poverty, (5) experiencing limited access to healthy foods, and (6) had low income. Conclusion All of the direct predictors of diabetes prevalence, except the percentage of residents who were African American, were medical conditions potentially influenced by lifestyles. Counties characterized by higher levels of obesity, inactivity, and poor health conditions exhibited increased diabetes rates in the following year. The impact of social determinants of illness, such as low income, children in poverty, and limited access to healthy foods, had an indirect effect on the health of residents and, consequently, increased the prevalence of diabetes.

A network perspective on cognitive function and obsessive-compulsive related symptoms

BACKGROUND

The relationship between cognitive function and psychopathological symptoms has been an important research field in recent years. Previous studies have typically applied case-control designs to explore differences in certain cognitive variables. Multivariate analyses are needed to deepen our understanding of the intercorrelations among cognitive and symptom phenotypes in OCD.

METHODS

The present study used network analysis to construct networks of cognitive variables and OCD-related symptoms in patients with OCD and healthy controls (N = 226), aiming to explore the relationship among numerous cognitive function variables and OCD-related symptoms in detail and compare the network features between the two groups.

RESULTS

In the network of cognitive function and OCD-related symptoms, nodes representing IQ, letter/number span test, accuracy of task switching test and obsession were much important in the network in terms of their larger strengths and edges. By constructing the networks of these two groups respectively, there was a strong similarity except that the symptom's network in healthy group had a higher degree of overall connectivity.

LIMITATIONS

Due to the small sample size, the stability of the network cannot be guaranteed. Due to the cross-sectional nature of the data, we were unable to determine how the cognitive-symptom network would change with disease deterioration or treatment.

CONCLUSIONS

The present study highlights the important role of variables such as obsession and IQ from a network perspective. These results deepen our understanding of the multivariate relationship between cognitive dysfunction and OCD symptoms, and may promote the prediction and diagnosis of OCD.

The relationship between obsessive-compulsive symptoms and depressive symptoms in patients with obsessive-compulsive disorder: A network analysis

Aim

The network model suggests that the comorbidity of obsessive-compulsive disorder (OCD) and depression is due to direct interactions between OCD and depression symptoms. The study investigates the network structure of OCD and depressive symptoms in patients with OCD and explores the pathways that connect the OCD and depression symptoms.

Materials and Methods

The items of Yale-Brown Obsessive-Compulsive Symptom (Y-BOCS) Scale and the Depression Self-Rating Scale of 445 patients with OCD were analyzed by network model. Statistical analysis and visualization of the network were conducted using R software.

Results

Two bridge edges "uneasiness" and "time consumed by obsessions" and "low spirit" and "distress caused by obsessions" connected the OCD symptoms to depressive symptoms. Two closely related edges were between "interference due to obsessions" and "interference due to compulsions" and between "difficulty resisting obsessions" and "difficulty resisting compulsions." The symptoms "interference due to compulsions," "distress caused by obsessions," "time consumed by compulsions," and "uneasiness" had the highest expected influence centrality.

Conclusions

This study highlighted the relationship between "uneasiness" and "time consumed by obsessions" and between "low spirit" and "distress caused by obsessions." In addition, "interference due to compulsions" is found as the core symptom in the network. Targeting these symptoms may help prevent and treat the comorbidity of obsession-compulsion and depression in patients with OCD.

Moderating Role of Depression on the Association of Tic Severity With Functional Impairment in Children

BACKGROUND

Chronic tic disorders (CTDs) commonly co-occur with other psychiatric disorders. CTDs have been linked to functional impairment and reduction in quality of life. Insufficient research is available on depressive symptoms in patients with CTD, especially children and adolescents, yielding conflicting findings. To investigate the presence of depressive symptoms in a cohort of children and young adolescents with CTD and to test whether they moderate the link between tic severity and functional impairment.

METHODS

The sample consisted of 85 children and adolescents (six to 18 years) with a CTD who were treated in a large referral center. Participants were evaluated using gold-standard self- and clinician-reporting instruments to measure tic symptom severity and tic-related functional impairment (Yale Global Tic Severity Scale), depression (Child Depression Inventory), and obsessive-compulsive symptoms (Children Yale Brown Obsessive Compulsive Scale).

RESULTS

Depressive symptoms (mild to severe) were exhibited by 21% of our sample. Study participants with CTD and comorbid obsessive-compulsive disorder (OCD) and/or attention-deficit/hyperactivity disorder had higher rates of depressive symptoms compared with those without comorbidities. Significant correlations were found within and among all tic-related and OCD-related measures, yet depressive symptoms only correlated to tic-related functional impairment. Depression significantly and positively moderated the correlation between tic severity and tic-related functional impairment.

CONCLUSIONS

Findings suggest that depression plays an important part as a moderator in the link between tic severity and functional impairment in children and adolescents. Our study highlights the importance of screening for and treating depression in patients with CTD.

A Network Analysis of the Relationship Between Mental and Physical Health in Unsheltered Homeless Persons in Los Angeles County

Homelessness is a public health crisis both nationally, in the United States, and internationally. Nevertheless, due to the hidden vulnerabilities of persons who are without shelter, little is known about their experiences during periods of homelessness. The present research adopts a network approach that conceptualizes how the major risk factors of homelessness interact, namely substance abuse problems, poor mental health, disability, and exposure to physical or sexual violence by an intimate partner. Our analysis draws on a large demographic survey of over 5,000 unsheltered homeless persons conducted in 2017 by the Los Angeles Homeless Services Authority. We estimated a network structure for 12 survey items tapping individual risk using the graphical least absolute shrinkage and selection operator algorithm. We then examined network centrality metrics and implemented a community detection algorithm to detect communities in the network. Our results indicated that mental illness and intimate partner violence (IPV) are central measures that connect all other mental and physical health variables together and that post-traumatic stress disorder and IPV are both highly affected by changes in any part of the network and, in turn, affect changes in other parts of the network. A community detection analysis derived four communities characterized by disability, sexual victimization and health, substance use, and mental health issues. Finally, a directed acyclic graph revealed that drug abuse and physical disability were key drivers of the overall system. We conclude with a discussion of the major implications of our findings and suggest how our results might inform programs aimed at homelessness prevention and intervention.

A Pilot, Time-Series Investigation of Depression, Anxiety, and Eating Disorder Symptoms in Adults Experiencing Major Depressive Symptoms: The Need for Eating Disorder Assessment and Research in Depression

Major Depressive Disorder (MDD) is a prevalent psychiatric disorder impacting 10-16% of Americans in their lifetime. Approximately 60% of individuals with MDD have comorbid anxiety disorders. Additionally, although scarce research has examined eating disorders (EDs) in depression, a bidirectional association exists between ED and MDD symptoms. The current pilot study (N = 31 individuals with moderate to severe depression) modeled networks of depressive, anxiety, and ED symptoms using intensive time-series data. This study also tested if temporal central symptoms predicted six-month clinical outcomes. The most central symptoms were guilt, self-dislike, lack of energy, and difficulty concentrating. Several anxiety and ED symptoms were also central, including physical anxiety, social anxiety, body dissatisfaction, and desire for thinness. The central symptom crying predicted six-month depression with a medium effect size. These findings suggest anxiety and ED symptoms may influence the day-to-day course of depression in some individuals with comorbid diagnoses, but predictors of symptoms across hours may differ from predictors across longer time scales (i.e., months). Time scale should be considered when conducting and interpreting research on MDD. Research, assessment, and treatment for MDD should continue to explore transdiagnostic approaches including anxiety and ED symptoms to optimize care for individuals with complex presentations.

Network analysis of body-related complaints in patients with neurotic or personality disorders referred to psychotherapy

Background

Psychopathology theory and clinical practice require the most complex knowledge about patients' complaints. In patients seeking for psychotherapy, body-related symptoms often complicate treatment.

Aim

This study aimed at examining connections between body-related symptoms, and identification of symptoms which may be responsible for emergency and sustaining of anxiety, somatoform and personality disorders with the use of network analysis.

Methods

In our retrospective research we used data from a sample of 4616 patients of the Department of Psychotherapy, University Hospital in Cracow, diagnosed with anxiety, somatoform or personality disorders. We constructed the Triangulated Maximally Filtered Graph (TMFG) networks of 44 somatoform symptoms endorsed in the symptom checklist "O" (SCL-O) and identified the most central symptoms within the network for all patients and in subgroups of women vs. men, older vs. younger, and diagnosed in 1980-2000 vs. 2000-2015. We used bootstrap to determine the accuracy and stability of five networks' parameters: strength, expected influence, eigenvector, bridge strength and hybrid centrality.

Results

The most central symptoms within the overall network, and in six subnetworks were dyspnea and migratory pains. We identified some gender-related differences, but no differences were observed for the age and time of diagnosis.

Conclusions

Self-reported dyspnea and migratory pains are potential important targets for treatment procedures.

Comorbidity of posttraumatic stress disorder and depression among adolescents following an earthquake: A longitudinal study based on network analysis

BACKGROUND

High comorbidity between posttraumatic stress disorder (PTSD) and depression among adolescents often follows severe traumatic events. Models on the pathway to comorbidity dispute greatly and how PTSD and depression get comorbidity, remain unclear.

METHODS

A follow-up investigation was conducted of 424 adolescent survivors of the Jiuzhaigou earthquake at 12 months (T1) and 27 months (T2).

RESULTS

Contemporaneous network analysis and cross-lagged panel network analysis showed that PTSD and depression are two separate disorders with strong associations via links between dysphoric symptoms of PTSD and somatic or non-somatic symptoms of depression. However, the association weakened from T1 to T2, and internal connections between symptoms within each disorder became stronger.

LIMITATION

We only measured the comorbidity of PTSD and depression at two time points following the earthquake, which may limit the long-term applicability of our findings following trauma.

CONCLUSIONS

The findings also showed that the centrality in contemporaneous networks may indicate node connectivity rather than the influence or potential causality among nodes. These results help to elucidate the relationship between PTSD and depression and could contribute to the development of appropriate therapies.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

Associations between post-traumatic stress symptoms and quality of life among psychiatric healthcare personnel in China during the COVID-19 pandemic: A network approach

BACKGROUND

Post-traumatic stress symptoms (PTSS) are commonly reported by psychiatric healthcare personnel during the coronavirus disease 2019 (COVID-19) pandemic and negatively affect quality of life (QOL). However, associations between PTSS and QOL at symptom level are not clear. This study examined the network structure of PTSS and its connection with QOL in psychiatric healthcare personnel during the COVID-19 pandemic.

METHODS

This cross-sectional study was carried out between March 15 and March 20, 2020 based on convenience sampling. Self-report measures including the 17-item Post-Traumatic Stress Disorder Checklist - Civilian version (PCL-C) and World Health Organization Quality of Life Questionnaire - Brief Version (WHOQOL-BREF) were used to measure PTSS and global QOL, respectively. Network analysis was used to investigate the central symptoms of PTSS and pattern of connections between PTSS and QOL. An undirected network was constructed using an extended Bayesian Information Criterion (EBIC) model, while a directed network was established based on the Triangulated Maximally Filtered Graph (TMFG) method.

RESULTS

Altogether, 10,516 psychiatric healthcare personnel completed the assessment. "Avoidance of thoughts" (PTSS-6), "Avoidance of reminders" (PTSS-7), and "emotionally numb" (PTSS-11) were the most central symptoms in the PTSS community, all of which were in the Avoidance and Numbing domain. Key bridge symptoms connecting PTSS and QOL were "Sleep disturbances" (PTSS-13), "Irritability" (PTSS-14) and "Difficulty concentrating" (PTSS-15), all of which were within the Hyperarousal domain.

CONCLUSION

In this sample, the most prominent PTSS symptoms reflected avoidance while symptoms of hyper-arousal had the strongest links with QOL. As such, these symptom clusters are potentially useful targets for interventions to improve PTSS and QOL among healthcare personnel at work under pandemic conditions.

What Are the Differences in Psychological Outcomes Between People Who Posted COVID-19-Related Content on Social Media and Those Who Did Not?

OBJECTIVE

During the lockdown of cities and home quarantine, media became the only way for people to conveniently get coronavirus disease-2019 (COVID-19)-related information. And media engagement was closely related to psychological outcomes. But fewer researchers took COVID-19-related posting behaviors into consideration. Therefore, the present study aimed at examining the differences in psychological outcomes between people who posted COVID-19-related content on social media and those who did not.

METHODS

The present study included 917 participants (304 males, 613 females) who had answered the questionnaires of media engagement, positive affect, negative affect, depression, anxiety, stress, satisfaction with life, death anxiety, and meaning in life.

RESULTS

Results of t-tests showed that the Post group had lower levels of negative affect, anxiety, stress, and death anxiety than the Not Post (Npost) group. Network comparison tests indicated that the Npost group's network and the Post group's network differed in global strength, two edge-weights, and node centrality indices.

CONCLUSION

The results indicated that more attention should be paid to people who did not post any COVID-19-related content, especially when they have higher levels of stress and depression to prevent comorbidities. And for people who posted content, more attention should be paid when they have a higher level of negative affect.

Association between thyroid dysfunction, metabolic disturbances, and clinical symptoms in first-episode, untreated Chinese patients with major depressive disorder: Undirected and Bayesian network analyses

AIMS

Thyroid dysfunction and metabolic disturbances are common in major depressive disorder (MDD) patients. We aimed to assess the relationship between thyroid dysfunction, metabolic disturbances, and clinical symptoms in Chinese first-episode, drug-naïve (FEDN) MDD patients using undirected and Bayesian network methods.

METHODS

1718 FEDN MDD patients were recruited. Serum levels of free triiodothyronine (FT3), free thyroxine (FT4), thyroid stimulating hormone (TSH), anti-thyroglobulin (TgAb), thyroid peroxidases antibody (TPOAb), total cholesterol (TC), total triglycerides (TG), high-density lipoprotein (HDL-C), low-density lipoprotein (LDL-C), and glucose were assessed. Blood pressure and body mass index were measured. Hamilton Rating Scale for Depression (HAMD), Hamilton Rating Scale for Anxiety, and positive subscale of Positive And Negative Syndrome Scales were used to detect clinical symptoms. An undirected network with EBICglasso default and a directed acyclic graph (DAG) using the Bayesian network approach was conducted.

RESULTS

The prevalence rates of clinical symptoms, thyroid dysfunction, and metabolic dysfunction were as follows: anxiety (n=894, 52%), psychotic symptoms (171, 10%), subclinical hypothyroidism (SCH, n=1041, 61%), abnormal TgAb (n=297, 17%), abnormal TPOAb (n=438, 25%), hyperthyroidism (n=5, 0.3%), hypothyroidism (n=3, 0.2%), hyperglycemia (n=241, 14%), hypertriglyceridemia (n=668, 39%), low HDL-C (n=429, 25%), hypercholesterolemia (421, 25%), abnormal TC (357, 21%), abnormal LDL-C (185, 11%). overweight or obesity (n=1026, 60%), and hypertension (n=92, 5.4%). Both networks demonstrated serum TSH and TC levels and the severity of depression played an important role in the pathophysiology of MDD.

CONCLUSIONS

MDD patients may have thyroid and metabolic dysfunction in the early stage. Targeting hypercholesterolemia, depressive symptoms, and SCH in MDD patients may hold promise in reducing clinical symptoms, metabolic disturbances, and thyroid dysfunction.

What matters most? Network analysis of mental health, recovery experiences, sleep, and fatigue among career firefighters

Firefighters exhibit a higher prevalence of mental health issues than other occupations because they often directly or indirectly experience potentially traumatic events. Previous research has suggested that recovery experiences, sleep, and fatigue may be protective or risk factors for firefighters' mental health. This study conducted regularised partial correlation network analysis to investigate and visualise the complex relationships between these variables. We collected data through an online survey, and 3144 career firefighters from a large east-coast city in China, were included in the analyses. The results of the network analysis showed that: (1) only relaxation and mastery experiences directly correlated with one dimension of the mental health scale, namely, life/social functioning; (2) sleep quality had stronger correlations with mental health issues than sleep quantity; and (3) mental health issues were central nodes of the network. These results indicated that firefighters' mental health was close to other variables in the network and acted as a bridge linking other variables. Therefore, more attention should be paid to the mental health of firefighters to prevent comorbidities. The study's results also indicated that recovery experiences and sleep may be protective factors for firefighters' mental health.

The structure of eating disorder and somatic symptoms

Individuals with eating disorders (EDs) often present with somatic concerns in treatment, such as bloating, fullness, and feeling tight clothes on skin. However, most research generally focuses on general interoception (e.g., heartbeat) rather than sensations relevant to EDs (e.g., sensations related to the gastrointestinal system or body movement). In the current study (N = 181), we used network analysis to model the structure of ED symptoms and somatic concerns among individuals with anorexia nervosa, bulimia nervosa, and other specified feeding and eating disorder. Results showed that heightened sensitivity to somatic concerns had the highest strength centrality within a symptom network comprising ED and somatic symptoms. Exploratory graph analysis identified four symptom dimensions: cognitive-affective ED symptoms, behavioral ED symptoms, general interoception, and ED-specific proprioception. Findings suggest that heightened sensitivity to somatic concerns may maintain ED symptoms and mutually reinforce other somatic concerns. Implications concerning assessment and treatment of EDs are discussed.

Co-occurring depression and obsessive-compulsive disorder: A dimensional network approach

BACKGROUND

Depressive and obsessive-compulsive (OCD) symptoms often co-occur and a number of possible explanations for this co-occurrence have been explored, including shared biological and psychosocial risk factors. Network approaches have offered a novel hypothesis for the link between depression and OCD: functional inter-relationships across the symptoms of these conditions. The few network studies in this area have relied largely on item, rather than process-level constructs, and have not examined relationships dimensionally.

METHODS

Network analytic methods were applied to data from 463 treatment-seeking adults with OCD. Patients completed self-report measures of OCD and depression. Factor analysis was used to derive processes (i.e., nodes) to include in the network. Networks were computed, and centrality, bridge, and stability statistics examined.

RESULTS

Networks showed positive relations among specific OCD and depressive symptoms. Obsessions (particularly repugnant thoughts), negative affectivity, and cognitive-somatic changes (e.g., difficulty concentrating) were central to the network. Unique relations were observed between symmetry OCD symptoms and cognitive-somatic changes. No direct link between harm-related OCD symptoms and depression was observed.

CONCLUSIONS

Our results bring together prior findings, suggesting that both negative affective and psychomotor changes are important to consider in examining the relationship between OCD and depression. Increased consideration of heterogeneity in the content of OCD symptoms is key to improving clinical conceptualizations, particularly when considering the co-occurrence of OCD with other disorders.

Assessing the Effectiveness of Repetitive Transcranial Magnetic Stimulation in the Treatment of Obsessive- Compulsive Disorder

Objective. Obsessive-compulsive disorder (OCD) is a frequent and disabling neuropsychiatric disorder with a lifetime prevalence of 3%. About 40% to 60% of patients show no or just partial symptom improvement to treatment with a first-line drug and cognitive behavior therapy. Ten percent of patients remain treatment refractory despite several treatments. For these patients, repetitive Transcranial Magnetic Stimulation (rTMS) has been suggested as a treatment option. Method. We investigated the efficacy of rTMS on the Supplementary Motor Area (SMA) in 16 right handed pharmaco-resistant OCD patients in an outpatient setting. The patients have been diagnosed with OCD by two psychiatrists and referred for rTMS intervention. Patients received 16 sessions of low frequency (0.5 HZ) rTMS on SMA,100% motor threshold, 1200 stimuli/day for 40 minutes every other day. OCD, depression, and anxiety symptoms were measured at baseline, 2, 6, and 12 weeks by Yale-Brown Obsessive Compulsive Scale(Y-BOCS) and Hamilton Depressive and Anxiety rating scales (HAM-D and HAM-A). We assessed the side effects of rTMS by a self-administrative questionnaire. Results. Patients' scores in Y-BOCS, HAM-D, and HAM-A were significantly decreased following rTMS treatment. The baseline and 12 weeks scores of Y-BOCS were 28.94 and 18.31 (P-value < 0.01), HAM-D were 14.69 and 7.94 (P-value <0.01) and HAM-A were 16.38 and 6.94 (P- value < 0.01), respectively. The patients reported no serious side effects of rTMS except two case that reported light headach. Conclusion. This study showed that low-frequency rTMS on SMA improved OCD, anxiety, and depression symptoms after 16 sessions.

Testing intrusive thoughts as illness pathways between eating disorders and obsessive-compulsive disorder symptoms: a network analysis

Obsessive-compulsive disorder (OCD) and eating disorders (EDs) frequently co-occur. Intrusive thoughts are a mechanism that may maintain this comorbidity. This study used network analysis to identify central ED-related intrusive thoughts and tested which intrusive thoughts connected ED and OCD symptoms. Two cross-sectional graphical LASSO networks were computed using a sample of 353 non-clinical participants (mean age = 35.38, SD = 9.9, 40% female, 81.6% Caucasian) with elevated disordered eating symptoms. Model 1 included just ED-related intrusive thoughts, and Model 2 included ED-related intrusive thoughts, ED, and OCD symptoms. In Model 1, we found that thoughts about one's bodily appearance (i.e., looking horrible, getting fat, gaining weight) were most central. In Model 2, we found that desire to lose weight, eating in secret, and shape dissatisfaction were most central. We identified one illness pathway (i.e., difficulty concentrating due to thoughts of food/calories) connecting intrusive thoughts, ED symptoms, and OCD symptoms. However, intrusive thoughts did not bridge ED and OCD symptoms. Hence, we found some evidence that ED-related intrusive thoughts may contribute to ED and OCD symptoms based on thought content and frequency. However, other aspects of intrusive thoughts should be considered to ascertain whether they do in fact significantly contribute to ED and OCD comorbidity. Prevention efforts targeting ED-related intrusive thoughts may attenuate ED and OCD symptoms among subclinical individuals.

Interrelationships between childhood maltreatment, depressive symptoms, functional impairment, and quality of life in patients with major depressive disorder: A network analysis approach

BACKGROUND

Childhood maltreatment continues to pose a great challenge to psychiatry. Although there is growing evidence demonstrating that childhood maltreatment is an important risk factor for depressive disorders, it remains to be elucidated which specific symptoms occur after exposure to different kinds of childhood maltreatment, and whether certain pathways may account for these associations.

PARTICIPANTS AND SETTINGS

A total of 203 adult patients (18-53 years old) with MDD, diagnosed by Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria, were recruited from the outpatient clinic of Beijing Anding Hospital, Capital Medical University.

METHODS

Childhood maltreatment, depressive symptoms, functional impairment, and quality of life were evaluated by the Childhood Trauma Questionnaire - Short Form (CTQ-SF), 17-item Hamilton Depression Rating Scale (HAMD-17), Sheehan Disability Scale (SDS), and Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form (Q-LES-Q-SF). Undirected network analysis was used to explore the most relevant connections between them. Bayesian network analysis was used to estimate a directed acyclic graph (DAG) while investigating the most likely direction of the putative causal association.

RESULTS

In network analysis, the strongest edges were a positive correlation between emotional abuse and suicidal behavior as well as a negative association between emotional neglect and age of onset. In DAG analysis, emotional abuse emerged as the most pivotal network node, triggering both suicidal behaviors and depression symptoms.

CONCLUSIONS

Emotional abuse appears to be an extremely harmful form of childhood maltreatment in the clinical presentation of depression. This study has promise in informing the clinical intervention of depression.

Network analysis of comorbid depression and anxiety and their associations with quality of life among clinicians in public hospitals during the late stage of the COVID-19 pandemic in China

BACKGROUND

Mental health problems are common among clinicians working in public hospitals even in the late stage of the COVID-19 pandemic. Network analysis is a novel approach to explore interactions between mental health problems at the symptom level. This study examined the network structure of comorbid depression and anxiety and their associations with quality of life (QOL) among hospital clinicians in China during the late stage of the COVID-19 pandemic.

METHODS

A total of 4931 participants were recruited from October 13 to 22, 2020. The nine-item Patient Health Questionnaire (PHQ-9), seven-item Generalized Anxiety Disorder Scale (GAD-7), and the World Health Organization Quality of Life Questionnaire-Brief Version (WHOQOL-BREF) were used to measure depressive and anxiety symptoms, and QOL, respectively. Central and bridge symptoms were identified with centrality and bridge centrality indices, respectively. Network stability was examined using the case-dropping procedure.

RESULTS

The prevalence of depression (defined as PHQ-9 total score ≥ 5) was 35.1 % [95 % confidence interval (CI) = 33.73-36.41 %)], the prevalence of anxiety (GAD-7 total score ≥ 5) was 32.5 % (95 % CI = 31.20-33.84 %), while the prevalence of comorbid depression and anxiety was 26.9 % (95 % CI = 25.7-28.2 %). "Impaired motor skills", "Trouble relaxing" and "Uncontrollable worry" were the central symptoms in the whole depression-anxiety network. "Irritability", "Feeling afraid" and "Sad mood" were the most key bridge symptoms linking depression and anxiety. Three symptoms ("Fatigue", "Trouble relaxing" and "Nervousness") were the most strongly and negatively associated with QOL. Neither gender nor the experiences of caring for COVID-19 patients was associated with network global strength, distribution of edge weights or individual edge weights.

LIMITATIONS

The causality between variables could not be established. Depressive and anxiety symptoms were assessed by self-report measures, which may result in recall bias and limitations in capturing clinical phenomena.

CONCLUSIONS

Both the central (i.e., "Impaired motor skills", "Trouble relaxing" and "Uncontrollable worry") and bridge symptoms (i.e., "Irritability", "Feeling afraid" and "Sad mood") identified in this network analysis should be targeted in specific treatment and preventive measures for comorbid depressive and anxiety symptoms among clinicians in the late stage of the pandemic. Furthermore, "Fatigue", "Trouble relaxing" and "Nervousness" are key symptoms to address to improve clinicians' QOL.

Risk factors for depression in Pacific adolescents in New Zealand: A network analysis

BACKGROUND

Network analysis provides opportunities to gain a greater understanding of the complex interplay of risk factors for depression and heterogeneous symptom presentations. This study used network analysis to discover risk factors associated with both depression severity and depression symptoms amongst Pacific adolescents in New Zealand.

METHODS

Mixed graphical models with regularization were fitted to data from a community sample of New Zealand born, Pacific adolescents, (n = 561; 51% male; Mean age (SD) = 17 (0.35)) and associations between a wide range of potentially explanatory variables and depression severity and depression symptoms investigated. The associations identified were then tested for reliability, using resampling techniques and sensitivity analysis.

RESULTS

In the networks, the explanatory variables associated with both depression severity and depression symptoms were those related to quality of the relationships with mother or friends, school connectedness, and self-assessed weight, but the symptoms they were associated with varied substantially. In the depression severity networks, impulsivity appeared to be a bridging node connecting depression severity with delinquency and negative peer influence.

LIMITATIONS

The data were analysed cross-sectionally, so causal inferences about the directions of relationships could not be inferred and most of the data were self-reported.

CONCLUSIONS

The results illustrate the varied way that adolescent depression can manifest itself in terms of symptoms and suggest specific items on the depression inventory that might be suitable targets for prevention strategies and interventions, based on the risk factor - depression symptom profiles of individuals or groups.

The relations between mental well-being and burnout in medical staff during the COVID-19 pandemic: A network analysis

Background

Although poor mental well-being (MW) has been documented among individuals experiencing burnout during the coronavirus-19 (COVID-19) pandemic, little is known about the complex interrelationship between different components of MW and burnout. This study investigates this relationship among medical staff during the COVID-19 pandemic through network analysis.

Methods

A total of 420 medical staff were recruited for this study. Components of MW were measured by the 14-item Warwick-Edinburgh Mental Well-being Scale (WEMWBS), and components of burnout were measured by a 15-item Maslach Burnout Inventory-General Survey (MBI-GS) Questionnaire. Network structure was constructed via network analysis. Bridge variables were identified via the bridge centrality index.

Results

The edges across two communities (i.e., MW community and burnout community) are almost negative, such as edge MW2 ("Useful") - B14 ("Worthwhile") and edge MW1 ("Optimistic about future") - B13 ("Happy"). The edges within each community are nearly positive. In the MW community, components MW1 ("Optimistic about future") and MW6 ("Dealing with problems") have the lowest bridge centrality. And in the community of burnout, components B13 ("Happy") and B14 ("Worthwhile") have the lowest bridge expected influence.

Conclusion

We present the first study to apply the network approach to model the potential pathways between distinct components of MW and burnout. Our findings suggest that promoting optimistic attitudes and problem-solving skills may help reduce burnout among medical staff during the pandemic.

OCD and Comorbid Depression: Assessment, Conceptualization, and Cognitive Behavioral Treatment

Many individuals with obsessive-compulsive disorder (OCD) also meet criteria for additional diagnoses. Among the most common co-occurring diagnoses are mood disorders-especially depression. This article focuses on the comorbidity between OCD and major depression. After discussing nature of OCD and depression, the rates and clinical impact of depression on OCD, the conceptualization, assessment, and treatment of OCD when it appears along with depression is covered in detail. The derivation and implementation of a cognitive behavioral treatment program specifically for depressed OCD patients is described and illustrated using a case example.

Developing and Examining the Effectiveness of a Cognitive Behavioral Therapy-Based Psychoeducation Practice for Reducing Obsessive-Compulsive Symptoms in Adolescents: A Mixed-Methods Study With a Turkish Sample

This study developed a cognitive behavioral therapy (CBT)-based psychoeducation practice aimed at reducing obsessive-compulsive symptom levels in adolescents in Turkey and tested its effectiveness with a mixed-methods study. After the study was constructed as a pretest-posttest control group experimental application consisting of qualitative stages. The experimental application of the study was carried out with high school students in Turkey. In the sampling process, the schools, where the study will be carried out, were determined with the cluster sampling method. The experimental and control groups were formed with 30 students with high obsessive-compulsive disorder (OCD) symptoms from these schools, and the developed CBT-based psychoeducation practice was applied to the experimental group for 12 weeks. Quantitative data were collected through the "Child Version of the Obsessive-Compulsive Inventory," and normality analysis, t-test for dependent samples, and Single Factor Analysis of Covariance were used. Qualitative data of the study were collected through document review, session evaluation forms, and semi-structured interview protocol; content and descriptive analysis techniques were used in the analysis. It was concluded that the developed CBT-based psychoeducation application was an effective approach in reducing OCD symptoms in the Turkish adolescent sample, except for the neutralizing dimension. It was also determined that the findings obtained from the analysis during the application and the interviews after the application are parallel with the findings obtained by quantitative methods, and the qualitative and quantitative findings adequately explained the quantitative documents findings.

Examining the associations between PTSD symptoms and aspects of emotion dysregulation through network analysis

Despite the clearly established link between posttraumatic stress disorder (PTSD) and emotion dysregulation, little is known about how individual symptoms of PTSD and aspects of emotion dysregulation interrelate. The network approach to mental health disorders provides a novel framework for conceptualizing the association between PTSD and emotion dysregulation as a system of interacting nodes. In this study, we estimated the structural relations among PTSD symptoms and aspects of emotion dysregulation within a large sample of women who participated in a multi-site study of sexual revictimization (N = 463). We estimated expected influence to reveal differential associations among PTSD symptoms and aspects of emotion dysregulation. Further, we estimated bridge expected influence to identify influential nodes connecting PTSD symptoms and aspects of emotion dysregulation. Results highlighted the key role of concentration difficulties in expected influence and bridge expected influence. Findings highlight several PTSD symptoms and aspects of emotion dysregulation that may be targets for future intervention.

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.

A bibliometric analysis of 10 years of research on symptom networks in psychopathology and mental health

Psychopathology networks consist of aspects (e.g., symptoms) of mental disorders (nodes) and the connections between those aspects (edges). This article aims to analyze the research literature on network analysis in psychopathology and mental health for the last ten years. Statistical descriptive analysis was complemented with two bibliometric techniques: performance analysis and co-word analysis. There is an increase in publications that has passed from 1 article published in 2010 to 172 papers published in 2020. The 398 articles in the sample have 1,910 authors in total, being most of them occasional contributors. The Journal of Affective Disorders is the one with the highest number of publications on network analysis in psychopathology and mental health, followed by the Journal of Abnormal Psychology and Psychological Medicine stand out. The present study shows that this perspective in psychopathology and mental health is a recent field of study, but with solid advances in recent years from a wide variety of researchers, mainly from USA and Europe, who have extensively studied symptom networks in depression, anxiety, and post-traumatic stress disorders. However, gaps are identified in other psychological behaviors such as suicide, populations such as the elderly, and gender studies.

A Network Analysis Approach to Understanding the Relationship Between Childhood Trauma and Wellbeing Later in Life

Though childhood maltreatment negatively affects later in life functioning, current interventions do little to mitigate this impact. This ineffectiveness may be exacerbated by deficit-focused models which focus primarily on mental illness, ignoring other indicators of healthy functioning. This paper presents two studies that examine the relationships between childhood maltreatment and later in life functioning, including indicators of mental illness and mental health. In Study 1, network analysis was used as an exploratory tool to examine how childhood maltreatment relates to later in life wellbeing. Study 2 used a different sample of adults to provide a confirmatory test of the network obtained in Study 1 given remaining concerns about the replicability of networks from network analysis. Study 1 included a subset of participants from the Midlife Development in the United States Study 2 (MIDUS 2) Biomarker Project 4, 2004-2009. Study 2 included individuals from the MIDUS Refresher Biomarker Project 4, 2012-2016. Network comparison tests demonstrated that the networks generally replicated as they did not significantly vary in structure, global strength, or measures of strength centrality. In both studies, emotional forms of maltreatment (i.e., emotional abuse, emotional neglect) emerged as particularly influential in the networks. Childhood maltreatment impacts the ability to thrive in adulthood, beyond its impact on diagnosable mental illness, and also affects positive functioning. A stronger focus on emotional abuse and emotional neglect is warranted within maltreatment intervention and education initiatives, as is an emphasis on the impact of maltreatment on positive functioning in adulthood.