A Prospective Study on How Symptoms in a Network Predict the Onset of Depression

The role of borderline personality disorder traits in predicting longitudinal variability of major depressive symptoms among a sample of depressed adults

BACKGROUND

Major depressive disorder (MDD) and borderline personality disorder (BPD) often co-occur, with 20 % of adults with MDD meeting criteria for BPD. While MDD is typically diagnosed by symptoms persisting for several weeks, research suggests a dynamic pattern of symptom changes occurring over shorter durations. Given the diagnostic focus on affective states in MDD and BPD, with BPD characterized by instability, we expected heightened instability of MDD symptoms among depressed adults with BPD traits. The current study examined whether BPD symptoms predicted instability in depression symptoms, measured by ecological momentary assessments (EMAs).

METHODS

The sample included 207 adults with MDD (76 % White, 82 % women) recruited from across the United States. At the start of the study, participants completed a battery of mental health screens including BPD severity and neuroticism. Participants completed EMAs tracking their depression symptoms three times a day over a 90-day period.

RESULTS

Using self-report scores assessing borderline personality disorder (BPD) traits along with neuroticism scores and sociodemographic data, Bayesian and frequentist linear regression models consistently indicated that BPD severity was not associated with depression symptom change through time.

LIMITATIONS

Diagnostic sensitivity and specificity may be restricted by use of a self-report screening tool for capturing BPD severity. Additionally, this clinical sample of depressed adults lacks a comparison group to determine whether subclinical depressive symptoms present differently among individuals with BPD only.

CONCLUSIONS

The unexpected findings shed light on the interplay between these disorders, emphasizing the need for further research to understand their association.

Monozygotic twins discordant for depression: An extended network comparison of depressive symptoms, cognitive functions and daily activities

Monozygotic twins share the same genotype; however, they can be phenotypically discordant on various traits. Studying discordant monozygotic twins allows the investigation of differences in associations between symptoms and psychopathological risk factors, controlled for shared genetic liability. The network approach to psychopathology suggests that depressive symptoms, along with risk and protective factors (e.g., cognition, daily activities), form a complex system of mutually interacting components. We compared monozygotic twins discordant for lifetime depression on their respective extended networks of depressive symptoms, cognitive functions and daily activities (intellectual, physical, social), and evaluated if these networks differ in their associations between variables and in the role of each variable within the network. Regularized partial correlations investigated the networks' composition in 147 monozygotic twin pairs discordant for depression from the Danish Twin Registry. Affected twins had stronger overall associations within their network of depressive symptoms, cognitive functions and daily activities than their unaffected co-twins, while the importance of the network components' associations did not differ between the co-twins. In affected twins, decreased frequency in experiencing happiness had the strongest association with remaining variables (i.e., the most influence in activating other network elements). Also, variables from different groups were significantly associated (e.g., loneliness with delayed memory, pessimism with low social activities, verbal learning with intellectual activities). In unaffected twins, both mood symptoms and cognitive functions were important, but between-groups associations were quasi-absent. These results suggest that external events affecting the ability to feel happiness likely trigger the psychopathological process (depression network activation), independently from the genetic predisposition to depression.

Exploring core symptoms of alcohol withdrawal syndrome in alcohol use disorder patients: a network analysis approach

Background

Understanding the interplay between psychopathology of alcohol withdrawal syndrome (AWS) in alcohol use disorder (AUD) patients may improve the effectiveness of relapse interventions for AUD. Network theory of mental disorders assumes that mental disorders persist not of a common functional disorder, but from a sustained feedback loop between symptoms, thereby explaining the persistence of AWS and the high relapse rate of AUD. The current study aims to establish a network of AWS, identify its core symptoms and find the bridges between the symptoms which are intervention target to relieve the AWS and break the self-maintaining cycle of AUD.

Methods

Graphical lasso network were constructed using psychological symptoms of 553 AUD patients. Global network structure, centrality indices, cluster coefficient, and bridge symptom were used to identify the core symptoms of the AWS network and the transmission pathways between different symptom clusters.

Results

The results revealed that: (1) AWS constitutes a stable symptom network with a stability coefficient (CS) of 0.21-0.75. (2) Anger (Strength = 1.52) and hostility (Strength = 0.84) emerged as the core symptom in the AWS network with the highest centrality and low clustering coefficient. (3) Hostility mediates aggression and anxiety; anger mediates aggression and impulsivity in AWS network respectively.

Conclusions

Anger and hostility may be considered the best intervention targets for researching and treating AWS. Hostility and anxiety, anger and impulsiveness are independent but related dimensions, suggesting that different neurobiological bases may be involved in withdrawal symptoms, which play a similar role in withdrawal syndrome.

Identifying Person-Specific Drivers of Depression in Adolescents: Protocol for a Smartphone-Based Ecological Momentary Assessment and Passive Sensing Study

BACKGROUND

Adolescence is marked by an increasing risk of depression and is an optimal window for prevention and early intervention. Personalizing interventions may be one way to maximize therapeutic benefit, especially given the marked heterogeneity in depressive presentations. However, empirical evidence that can guide personalized intervention for youth is lacking. Identifying person-specific symptom drivers during adolescence could improve outcomes by accounting for both developmental and individual differences.

OBJECTIVE

This study leverages adolescents' everyday smartphone use to investigate person-specific drivers of depression and validate smartphone-based mobile sensing data against established ambulatory methods. We describe the methods of this study and provide an update on its status. After data collection is completed, we will address three specific aims: (1) identify idiographic drivers of dynamic variability in depressive symptoms, (2) test the validity of mobile sensing against ecological momentary assessment (EMA) and actigraphy for identifying these drivers, and (3) explore adolescent baseline characteristics as predictors of these drivers.

METHODS

A total of 50 adolescents with elevated symptoms of depression will participate in 28 days of (1) smartphone-based EMA assessing depressive symptoms, processes, affect, and sleep; (2) mobile sensing of mobility, physical activity, sleep, natural language use in typed interpersonal communication, screen-on time, and call frequency and duration using the Effortless Assessment of Risk States smartphone app; and (3) wrist actigraphy of physical activity and sleep. Adolescents and caregivers will complete developmental and clinical measures at baseline, as well as user feedback interviews at follow-up. Idiographic, within-subject networks of EMA symptoms will be modeled to identify each adolescent's person-specific drivers of depression. Correlations among EMA, mobile sensor, and actigraph measures of sleep, physical, and social activity will be used to assess the validity of mobile sensing for identifying person-specific drivers. Data-driven analyses of mobile sensor variables predicting core depressive symptoms (self-reported mood and anhedonia) will also be used to assess the validity of mobile sensing for identifying drivers. Finally, between-subject baseline characteristics will be explored as predictors of person-specific drivers.

RESULTS

As of October 2023, 84 families were screened as eligible, of whom 70% (n=59) provided informed consent and 46% (n=39) met all inclusion criteria after completing baseline assessment. Of the 39 included families, 85% (n=33) completed the 28-day smartphone and actigraph data collection period and follow-up study visit.

CONCLUSIONS

This study leverages depressed adolescents' everyday smartphone use to identify person-specific drivers of adolescent depression and to assess the validity of mobile sensing for identifying these drivers. The findings are expected to offer novel insights into the structure and dynamics of depressive symptomatology during a sensitive period of development and to inform future development of a scalable, low-burden smartphone-based tool that can guide personalized treatment decisions for depressed adolescents.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/43931.

Exploring the Association of Age with Depressive Symptomatology in Childhood and Adolescence: A Network Study

OBJECTIVE

This study aimed to explore the association of age with individual depression and anxiety symptoms and their connectivity (i.e., number/strength of connections with other symptoms) in girls and boys.

METHOD

Our study comprised cross-sectional data from 31,960 Dutch girls and 32,162 Dutch boys aged 8 to 18 and considered 11 depression symptoms and 14 anxiety symptoms measured by the Revised Child Anxiety and Depression Scale. Network estimations were used to examine whether age was associated with individual symptoms and, in a separate step, with the connectivity of depression symptoms with other depression symptoms and with the connectivity of depression symptoms with anxiety symptoms.

RESULTS

Age was, in general, positively associated with depression symptoms in girls, but not in boys, and with the connectivity of depression symptoms with other depression symptoms in both sexes. These findings were the most profound for energy-related symptoms in girls. Age was, in general, negatively associated with anxiety symptoms and not or negatively associated with the connectivity of depression symptoms with anxiety symptoms in girls and boys, respectively. Substantial differences across symptoms were found.

CONCLUSIONS

This study shows that it is important to focus on individual symptoms, for age is mainly associated with energy-related depression symptoms and their connectivity in girls. Future etiologic studies may examine the role of energy-related depression symptoms in the development of depressive symptomatology in girls as these symptoms seem potential targets for the prevention of depression in the female population.

Exploring the complex interrelation between depressive symptoms, risk, and protective factors: A comprehensive network approach

BACKGROUND

Depressive symptoms seem to be interrelated in a complex and self-reinforcing way. To gain a better understanding of this complexity, the inclusion of theoretically relevant constructs (such as risk and protective factors) offers a comprehensive view into the complex mechanisms underlying depression.

METHODS

Cross-sectional data from individuals diagnosed with a major depressive disorder (N = 986) and healthy controls (N = 1049) were analyzed. Participants self-reported their depressive symptoms, as well as several risk factors and protective factors. Regularized partial correlation networks were estimated for each group and compared using a network comparison test.

RESULTS

Symptoms of depression were more strongly connected in the network of depressed patients than in healthy controls. Among the risk factors, perceived stress, the experience of negative life events, emotional neglect, and emotional abuse were the most centrally embedded in both networks. However, the centrality of risk factors did not significantly differ between the two groups. Among the protective factors, social support, personal competence, and acceptance were the most central in both networks, where the latter was significantly more strongly associated with the symptom of self-hate in depressed patients.

CONCLUSION

The network analysis revealed that key symptoms of depression were more strongly connected for depressed patients than for healthy controls, and that risk and protective factors play an important role, particularly perceived stress in both groups and an accepting attitude for depressed patients. However, the purpose of this study is hypothesis generating and assisting in the potential selection of non-symptom nodes for future research.

Intrarelationships between suboptimal health status and anxiety symptoms: A network analysis

BACKGROUND

Suboptimal health status is a global public health concern of worldwide academic interest, which is an intermediate health status between health and illness. The purpose of the survey is to investigate the relationship between anxiety statuses and suboptimal health status and to identify the central symptoms and bridge symptoms.

METHODS

This study recruited 26,010 participants aged <60 from a cross-sectional study in China in 2022. General Anxiety Disorder-7 (GAD-7) and suboptimal health status short form (SHSQ-9) were used to quantify the levels of anxiety and suboptimal health symptoms, respectively. The network analysis method by the R program was used to judge the central and bridge symptoms. The Network Comparison Test (NCT) was used to investigate the network differences by gender, place of residence, and age in the population.

RESULTS

In this survey, the prevalence of anxiety symptoms, SHS, and comorbidities was 50.7 %, 54.8 %, and 38.5 %, respectively. "Decreased responsiveness", "Shortness of breath", "Uncontrollable worry" were the nodes with the highest expected influence. "Irritable", "Exhausted" were the two symptom nodes with the highest expected bridge influence in the network. There were significant differences in network structure among different subgroup networks.

LIMITATIONS

Unable to study the causal relationship and dynamic changes among variables. Anxiety and sub-health were self-rated and may be limited by memory bias.

CONCLUSIONS

Interventions targeting central symptoms and bridge nodes may be expected to improve suboptimal health status and anxiety in Chinese residents. Researchers can build symptom networks for different populations to capture symptom relationships.

A network analysis of exhaustion disorder symptoms throughout treatment

BACKGROUND

Stress-induced Exhaustion Disorder (ED) is associated with work absenteeism and adverse health outcomes. Currently, little is known regarding how the symptoms of ED are interrelated and whether the patterns of symptoms influence treatment outcomes. To this end, the current study applied network analyses on ED patients participating in a multimodal intervention.

METHODS

The first aim of the study was to explore the internal relationships between exhaustion symptoms and identify symptoms that were more closely related than others. A second aim was to examine whether the baseline symptom network of non-responders to treatment was more closely connected than the baseline symptom networks of responders, by comparing the sum of all absolute partial correlations in the respective groups' symptom network. This comparison was made based on the hypothesis that a more closely connected symptom network before treatment could indicate poorer treatment outcomes. Network models were constructed based on self-rated ED symptoms in a large sample of patients (n = 915) participating in a 24-week multimodal treatment program with a 12-month follow-up.

RESULTS

The internal relations between self-rated exhaustion symptoms were stable over time despite markedly decreased symptom levels throughout participation in treatment. Symptoms of limited mental stamina and negative emotional reactions to demands were consistently found to be the most closely related to other ED symptoms. Meanwhile, sleep quality and irritability were weakly related to other exhaustion symptoms. The symptom network for the full sample became significantly more closely connected from baseline to the end of treatment and 12-month follow-up. The symptom network of non-responders to treatment was not found to be more closely connected than the symptom network of responders at baseline.

CONCLUSIONS

The results of the current study suggest symptoms of limited mental stamina and negative emotional reactions to demands are central ED symptoms throughout treatment, while symptoms of irritability and sleep quality seem to have a weak relation to other symptoms of ED. The implications of these findings are discussed in relation to the conceptualization, assessment, and treatment of ED.

TRIAL REGISTRATION

The clinical trial was registered on Clinicaltrials.gov 2017-12-02 (Identifier: NCT03360136).

A novel machine learning-based prediction method for patients at risk of developing depressive symptoms using a small data

The prediction of depression is a crucial area of research which makes it one of the top priorities in mental health research as it enables early intervention and can lead to higher success rates in treatment. Self-reported feelings by patients represent a valuable biomarker for predicting depression as they can be expressed in a lower-dimensional network form, offering an advantage in visualizing the interactive characteristics of depression-related feelings. Furthermore, the network form of data expresses high-dimensional data in a compact form, making the data easy to use as input for the machine learning processes. In this study, we applied the graph convolutional network (GCN) algorithm, an effective machine learning tool for handling network data, to predict depression-prone patients using the network form of self-reported log data as the input. We took a data augmentation step to expand the initially small dataset and fed the resulting data into the GCN algorithm, which achieved a high level of accuracy from 86-97% and an F1 (harmonic mean of precision and recall) score of 0.83-0.94 through three experimental cases. In these cases, the ratio of depressive cases varied, and high accuracy and F1 scores were observed in all three cases. This study not only demonstrates the potential for predicting depression-prone patients using self-reported logs as a biomarker in advance, but also shows promise in handling small data sets in the prediction, which is critical given the challenge of obtaining large datasets for biomarker research. The combination of self-reported logs and the GCN algorithm is a promising approach for predicting depression and warrants further investigation.

Towards precision in the diagnostic profiling of patients: leveraging symptom dynamics as a clinical characterisation dimension in the assessment of major depressive disorder

BACKGROUND

International guidelines present overall symptom severity as the key dimension for clinical characterisation of major depressive disorder (MDD). However, differences may reside within severity levels related to how symptoms interact in an individual patient, called symptom dynamics.

AIMS

To investigate these individual differences by estimating the proportion of patients that display differences in their symptom dynamics while sharing the same overall symptom severity.

METHOD

Participants with MDD (n = 73; mean age 34.6 years, s.d. = 13.1; 56.2% female) rated their baseline symptom severity using the Inventory for Depressive Symptomatology Self-Report (IDS-SR). Momentary indicators for depressive symptoms were then collected through ecological momentary assessments five times per day for 28 days; 8395 observations were conducted (average per person: 115; s.d. = 16.8). Each participant's symptom dynamics were estimated using person-specific dynamic network models. Individual differences in these symptom relationship patterns in groups of participants sharing the same symptom severity levels were estimated using individual network invariance tests. Subsequently, the overall proportion of participants that displayed differential symptom dynamics while sharing the same symptom severity was calculated. A supplementary simulation study was conducted to investigate the accuracy of our methodology against false-positive results.

RESULTS

Differential symptom dynamics were identified across 63.0% (95% bootstrapped CI 41.0-82.1) of participants within the same severity group. The average false detection of individual differences was 2.2%.

CONCLUSIONS

The majority of participants within the same depressive symptom severity group displayed differential symptom dynamics. Examining symptom dynamics provides information about person-specific psychopathological expression beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may be a promising new dimension for clinical characterisation, warranting replication in independent samples. To inform personalised treatment planning, a next step concerns linking different symptom relationship patterns to treatment response and clinical course, including patterns related to spontaneous recovery and forms of disorder progression.

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

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

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

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

A Novel, Network-Based Approach to Assessing Romantic-Relationship Quality

How should romantic-relationship quality be approached psychometrically? This is a complicated theoretical and methodological challenge that we begin to address through three studies. In Study 1a, we identified 25 distinct romantic-relationship categories among 754 items from 26 romantic-relationship-quality instruments with a weak Jaccard index (0.38), indicating that the scales' item content was extremely heterogeneous. Study 1b then demonstrated limited structure validity evidence in 43 scale-development-validation articles of 23 of these 26 instruments. Finally, Study 2 surveyed 587 French-speaking participants in a romantic relationship on romantic-relationship quality. Applying a network-based model, we identified four dimensions, three of which are central to relationship quality. The inferences were mostly limited to French-speaking, monogamous, heterosexual women. To resolve challenges detected in the literature, we recommend a multicountry qualitative approach, more diverse sampling, better definitions of romantic-relationship quality, and a dynamic-systems approach to measuring romantic-relationship quality.

Violent behavior and the network properties of psychopathological symptoms and real-life functioning in patients with schizophrenia

Objective

To assess the interplay among psychopathological symptoms and real-life functioning, and to further detect their influence with violent behavior in patient with schizophrenia.

Methods

A sample of 1,664 patients with post-violence assessments and their propensity score-matched controls without violence from a disease registration report system of community mental health service in Guangdong, China, were studied by network analysis. Ising-Model was used to estimate networks of psychopathological symptoms and real-life functioning. Then, we tested whether network properties indicated the patterns of interaction were different between cases and controls, and calculated centrality indices of each node to identify the central nodes. Sensitivity analysis was conducted to examine the difference of interaction patterns between pre-violence and post-violence assessments in violence cases.

Results

Some nodes in the same domain were highly positive interrelations, while psychopathological symptoms were negatively related to real-life functioning in all networks. Many symptom-symptom connections and symptom-functioning connections were disconnected after the violence. The network density decreased from 23.53% to 12.42% without statistical significance (p = 0.338). The network structure, the global network strength, and the global clustering coefficient decreased significantly after the violence (p < 0.001, p = 0.019, and p = 0.045, respectively). Real-life functioning had a higher node strength. The strength of sleeping, lack of spontaneity and flow of conversation, and preoccupation were decreased in post-violence network of patients.

Conclusion

The decreasing connectivity may indicate an increased risk of violence and early warning for detecting violence. Interventions and improving health state based on nodes with high strength might prevent violence in schizophrenia patients.

Childhood Adversity and Emerging Psychotic Experiences: A Network Perspective

BACKGROUND AND HYPOTHESIS

Childhood adversity is associated with a myriad of psychiatric symptoms, including psychotic experiences (PEs), and with multiple psychological processes that may all mediate these associations.

STUDY DESIGN

Using a network approach, the present study examined the complex interactions between childhood adversity, PEs, other psychiatric symptoms, and multiple psychological mediators (ie, activity-related and social stress, negative affect, loneliness, threat anticipation, maladaptive cognitive emotion regulation, attachment insecurity) in a general population, adolescent sample (n = 865, age 12-20, 67% female).

STUDY RESULTS

Centrality analyses revealed a pivotal role of depression, anxiety, negative affect, and loneliness within the network and a bridging role of threat anticipation between childhood adversity and maladaptive cognitive emotion regulation. By constructing shortest path networks, we found multiple existing paths between different categories of childhood adversity and PEs, with symptoms of general psychopathology (ie, anxiety, hostility, and somatization) as the main connective component. Sensitivity analyses confirmed the robustness and stability of the networks. Longitudinal analysis in a subsample with Wave 2 data (n = 161) further found that variables with higher centrality (ie, depression, negative affect, and loneliness) better predicted follow-up PEs.

CONCLUSIONS

Pathways linking childhood adversity to PEs are complex, with multifaceted psychological and symptom-symptom interactions. They underscore the transdiagnostic, heterotypic nature of mental ill-health in young people experiencing PEs, in agreement with current clinical recommendations.

A network analysis of depressive symptoms and metabolomics

BACKGROUND

Depression is associated with metabolic alterations including lipid dysregulation, whereby associations may vary across individual symptoms. Evaluating these associations using a network perspective yields a more complete insight than single outcome-single predictor models.

METHODS

We used data from the Netherlands Study of Depression and Anxiety (N = 2498) and leveraged networks capturing associations between 30 depressive symptoms (Inventory of Depressive Symptomatology) and 46 metabolites. Analyses involved 4 steps: creating a network with Mixed Graphical Models; calculating centrality measures; bootstrapping for stability testing; validating central, stable associations by extra covariate-adjustment; and validation using another data wave collected 6 years later.

RESULTS

The network yielded 28 symptom-metabolite associations. There were 15 highly-central variables (8 symptoms, 7 metabolites), and 3 stable links involving the symptoms Low energy (fatigue), and Hypersomnia. Specifically, fatigue showed consistent associations with higher mean diameter for VLDL particles and lower estimated degree of (fatty acid) unsaturation. These remained present after adjustment for lifestyle and health-related factors and using another data wave.

CONCLUSIONS

The somatic symptoms Fatigue and Hypersomnia and cholesterol and fatty acid measures showed central, stable, and consistent relationships in our network. The present analyses showed how metabolic alterations are more consistently linked to specific symptom profiles.

Longitudinal network relations between symptoms of problematic internet game use and internalizing and externalizing problems among Chinese early adolescents

OBJECTIVE

There has been growing evidence of comorbidity between problematic internet game use and internalizing and externalizing problems in young people. However, little is known about the directionality and gender differences in these longitudinal relations at the symptoms level in the framework of network theory among youth. This study estimated the longitudinal relations between the symptoms of problematic internet game use, internalizing and externalizing problems, and the gender differences of these relations in Chinese youth using cross-lagged panel network modeling (CLPN).

METHODS

A sample of 1269 Chinese youth (M age = 10.35 years) participated in this study semi-annually at two time points. CLPN analysis was used to calculate the network model of problematic internet game use and internalizing and externalizing problems to explore bridge symptoms and find transmission pathways between problematic internet game use and internalizing and externalizing problems.

RESULTS

The CLPN revealed significant gender differences. For boys, depressed mood, which leads to relationships turning sour in order to play online games, bridges the relations between internalizing symptoms and problematic internet game use. For girls, irritability is the central predictive symptom, causing a range of problems related to problematic internet game use, which can, in turn, lead to fights or feelings of worthlessness. However, the effect sizes for the pathways between problematic internet game use and internalizing/externalizing problems were relatively weak, and the comorbidity between their relations should not be over-interpreted.

CONCLUSIONS

The current findings provide new evidence for understanding the directional relationship between the central characteristics of problematic internet game use and internalizing and externalizing problems in boys and girls. Gender-specific interventions targeting the central symptoms of internalizing and externalizing problems and problematic internet game use can help mitigate the vicious cycle of comorbidity among adolescents.

The bridge symptoms of childhood trauma, sleep disorder and depressive symptoms: a network analysis

BACKGROUND

This study aimed to elucidate the characteristics of symptom network of childhood trauma (CT) and sleep disorder (SD) in Chinese adolescents, with the influence of depressive symptoms taken into account.

METHOD

A total of 1301 adolescent students were included, and their CT, SD and depressive symptoms were measured using the Pittsburgh sleep quality index (PSQI), the Childhood Trauma Questionnaire-Short Form (CTQ-SF), and The Patient Health Questionnaire-9 (PHQ-9), respectively. Central symptoms and bridge symptoms were identified based on centrality indices and bridge centrality indices, respectively. Network stability was examined using the case-dropping procedure.

RESULTS

In CT and SD symptom network, emotional abuse and sleep quality symptoms had the highest centrality values, and two bridge symptoms, i.e., emotional abuse and sleep disturbance symptoms, were also identified. In symptom network for CT, SD, and depressive symptoms, sleeping difficulty symptoms, daily dysfunction symptoms, and emotional abuse appeared to be potential bridge symptoms. In symptom network of CT, SD, and depressive symptoms (excluding the symptom of sleeping difficulty), daily dysfunction symptoms, emotional abuse, and sleep disturbance symptoms appeared to be bridge symptoms.

CONCLUSIONS

In this study, emotional abuse and poor sleep quality were found to be central symptoms in the CT-SD network structure among Chinese adolescent students, with daytime dysfunction as the bridge symptom in the CT-SD-depression network structure. Systemic multi-level interventions targeting the central symptoms and bridge symptoms may be effective in alleviating the co-occurrence of CT, SD and depression in this population.

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

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

Toward a generalized developmental model of psychopathological liabilities and psychiatric disorders

BACKGROUND

Most psychiatric disorders are associated with several risk factors, but a few underlying psychopathological dimensions account for the common co-occurrence of disorders. If these underlying psychopathological dimensions mediate associations of the risk factors with psychiatric disorders, it would support a trans-diagnostic orientation to etiological research and treatment development.

METHOD

An analysis was performed of the 2012-2013 National Epidemiologic Survey on Alcohol and Related Conditions III (NESARC-III), a US nationally representative sample of non-institutionalized civilian adults, focusing on respondents who were aged ⩾21 (n = 34 712). Structural equation modeling was used to identify the psychopathological dimensions underlying psychiatric disorders; to examine associations between risk factors, psychopathological dimensions and individual disorders; and to test whether associations of risk factors occurring earlier in life were mediated by risk factors occurring later in life.

RESULTS

A bifactor model of 13 axis I disorders provided a good fit (CFI = 0.987, TLI = 0.982, and RMSEA = 0.011) including an overall psychopathology factor as measured by all 13 disorders and 2 specific factors, one for externalizing disorders and one for fear-related disorders. A substantial proportion of the total effects of the risk factors occurring early in life were indirectly mediated through factors occurring later in life. All risk factors showed a significant total effect on the general psychopathology, externalizing and fear-related factors. Only 23 of 325 direct associations of risk factors with psychiatric disorders achieved statistical significance.

CONCLUSION

Most risk factors for psychiatric disorders are mediated through broad psychopathological dimensions. The central role of these dimensions supports trans-diagnostic etiological and intervention research.

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

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

Perceived parenting styles and incidence of major depressive disorder: results from a 6985 freshmen cohort study

BACKGROUND

Even though a fair amount of studies focus on depression among college students, the effect of perceived parenting styles on the incidence of major depressive disorder (MDD) among representative freshmen in Chinese context is scarcely studied. The aim of this study is to investigate the effect of parenting styles on MDD in Chinese freshmen.

METHODS

A total of 9,928 Chinese freshmen were recruited in 2018. 6985 valid questionnaires were collected at one-year follow-up. Composite International Diagnostic Interview 3.0 (CIDI-3.0) was used for the diagnosis of MDD. Egna Minnen Beträffande Uppfostran (EMBU) questionnaire and Beck Depression Inventory-II (BDI-II) were used to assess parenting styles and baseline depressive symptoms, respectively. The associations between parenting styles and MDD incidence was analyzed with logistic regression.

RESULTS

The incidence of MDD in freshmen was 2.23% (95%CI: 1.91-2.60%). Maternal overprotection (OR = 1.03, 95%CI: 1.01-1.05) and disharmony relationship between parents (OR = 2.35, 95% CI: 1.42-3.89) increased the risk of new-onset MDD in freshmen, respectively. Mild depressive symptoms (OR = 2.06, 95%CI: 1.06-4.02), moderate (OR = 4.64, 95%CI: 2.55-8.44) and severe depressive symptoms (OR = 7.46, 95%CI: 2.71-20.52) at baseline increased the risk of new-onset MDD.

CONCLUSIONS

Maternal overprotection, disharmony relationship between parents and baseline depressive symptoms are risk factors for new-onset MDD in Chinese freshmen.

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

BACKGROUND

Facing multiple changes, adolescents are prone to have anxiety and depression concurrently, which would accompany a particularly high risk for suicide. However, most previous studies have ignored the heterogeneity of the components and used latent variable methods to explore the associations between these core variables, resulting in a lack of component-level discussions.

METHOD

Using a large sample of 9300 adolescents (Mean_age = 13.51; SD_age = 1.33; 49.82 % females), two network pathways of anxiety and depression and the associations between these variables and suicidal behavior were constructed. The central components and the stability of both networks were also identified.

RESULTS

Considering the network of anxiety and depression, there were two strong bridging symptoms of sleep problems and palpitation or tachycardia. The symptoms of depression showed a more vital centrality than anxiety, and the central symptoms were tachycardia, worthlessness, fatigue, and feeling of choking. For the network of suicidal behavior and symptoms of anxiety and depression, besides sleep problems, the edge linking lifetime suicide ideation and attempt and the frequency of suicide ideation in the past year was also a strong edge. Worthlessness connected symptoms of anxiety and depression with suicidal behavior. The central components were tachycardia, worthlessness, the frequency of suicidal ideation over the past year, and fatigue. Additionally, both networks had higher stability in terms of edge and centrality.

CONCLUSION

Based on the identified relevant strong bridging and central components, effective therapies would target these components first, which would lead to the alleviating effects on other components.

Depression networks: a systematic review of the network paradigm causal assumptions

The network paradigm for psychiatric disorder nosology was proposed based on the hypothesis that mental disorders are caused by networks of symptoms that are themselves causally related. Researchers have widely applied and integrated this paradigm to examine a variety of mental disorders, particularly depression. Existing studies generally focus on the correlation structure of symptoms, inferring causal relationships. Thus, presumption of causality may not be justified. The goal of this review was to examine the assumptions necessary for causal inference in network studies of depression. Specifically, we examined whether and how network studies address common violations of causal assumptions (i.e. no measurement error, exchangeability, and positivity). Of the 41 studies reviewed, five (12%) studies discussed sources of confounding unrelated to measurement error; none discussed positivity; and five conducted post-hoc analysis for measurement error. Depression network studies, in principle, are conducted under the assumption that symptom relationships are causal. Yet, in practice, studies seldomly discussed or adequately tested assumptions required to infer causality. Researchers continue to design studies that are unable to support the credibility of the network paradigm for the study of depression. There is a critical need to ensure scientific efforts cease to perpetuate problematic designs and findings to a potentially unsubstantiated paradigm.

Gut-Brain Axis Perspective on Negative Symptoms and Their Neighbors in Early Adolescence: Can We Move Care Upstream?

The current study investigated symptom network patterns in adolescents from a gut-brain-axis (GBA) biopsychosocial perspective. Our secondary analysis of data from the Adolescent Brain Cognitive Development Study assessed symptom relationships using network analysis to provide information about multivariate structural dependencies among 41 signs and symptoms. Cross-sectional EBICglasso symptom networks were evaluated to assess patterns associated with anhedonia and depressed mood. Significant differences were identified between symptom neighbors of anhedonia compared with depressed mood based on stratification by age. The GBA perspective revealed several symptom neighbors that could expand clinical assessment, diagnosing criteria, education, and interventions for adolescents at risk for, or with, anhedonia or depressed mood. Results speak to the unique impact of symptoms on health that are not interchangeable with other symptoms and do not have equal effects. Mental health nurses should consider a holistic and proactive precision health approach to improving health and well-being through evidence-based assessment of symptom associations. [Journal of Psychosocial Nursing and Mental Health Services, xx(xx), xx-xx.].

Predicting prognosis for adults with depression using individual symptom data: a comparison of modelling approaches

BACKGROUND

This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data.

METHODS

Individual patient data from all six eligible randomised controlled trials were used to develop (k = 3, n = 1722) and test (k = 3, n = 918) nine models. Predictors included depressive and anxiety symptoms, social support, life events and alcohol use. Weighted sum scores were developed using coefficient weights derived from network centrality statistics (models 1-3) and factor loadings from a confirmatory factor analysis (model 4). Unweighted sum score models were tested using elastic net regularised (ENR) and ordinary least squares (OLS) regression (models 5 and 6). Individual items were then included in ENR and OLS (models 7 and 8). All models were compared to one another and to a null model (mean post-baseline Beck Depression Inventory Second Edition (BDI-II) score in the training data: model 9). Primary outcome: BDI-II scores at 3-4 months.

RESULTS

Models 1-7 all outperformed the null model and model 8. Model performance was very similar across models 1-6, meaning that differential weights applied to the baseline sum scores had little impact.

CONCLUSIONS

Any of the modelling techniques (models 1-7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression.

Symptom Structure of Depression in Older Adults on the Qinghai-Tibet Plateau: A Network Analysis

Previous studies have confirmed that depression among residents in high-altitude areas is more severe, and that depression may be more persistent and disabling in older adults. This study aims to identify the symptom structure of depression among older adults on the Qinghai-Tibet Plateau (the highest plateau in the world) from a network perspective. This cross-sectional study enrolled 507 older adults (ages 60-80 years old) from the Yushu Prefecture, which is on the Qinghai-Tibet Plateau, China. Depressive symptoms were self-reported using the shortened Center for Epidemiological Studies-Depression Scale (CES-D-10). Then, a Gaussian graphical model (GGM) of depression was developed. Poor sleep, fear, and hopelessness about the future exhibited high centrality in the network. The strongest edge connections emerged between unhappiness and hopelessness about the future, followed by hopelessness about the future and fear; hopelessness about the future and poor sleep; fear and unhappiness; and then poor sleep and unhappiness in the network. The findings of this current study add to the small body of literature on the network structure and complex relationships between depressive symptoms in older adults in high-altitude areas.

A Network Approach to Assessing the Relationship between Discrimination and Daily Emotion Dynamics

Discrimination-health research has been critiqued for neglecting the endogeneity of reports of discrimination to negative affect and the multidimensionality of mental health. To address these challenges, we model discrimination’s relationship to multiple psychological variables without directional constraints. Using time-dense data to identify associational network structures allows for joint testing of the social stress hypothesis, prominent in discrimination-health literature, and the negativity bias hypothesis, an endogeneity critique rooted in social psychology. Our results show discrimination predicts negative emotions from day-to-day but not vice versa, indicating that racial discrimination is a risk factor and not symptom of negative emotion. Furthermore, we identify sadness, guilt, hostility, and fear as a locus of interrelated emotions sensitive to racism-related stressors that emerges over time. Thus, we find support for what race scholars have argued for 120+ years in a model without a priori directional restrictions and then build on this work by empirically identifying cascading mental health consequences of discrimination.

Network analysis to identify symptoms clusters and temporal interconnections in oncology patients

Oncology patients experience numerous co-occurring symptoms during their treatment. The identification of sentinel/core symptoms is a vital prerequisite for therapeutic interventions. In this study, using Network Analysis, we investigated the inter-relationships among 38 common symptoms over time (i.e., a total of six time points over two cycles of chemotherapy) in 987 oncology patients with four different types of cancer (i.e., breast, gastrointestinal, gynaecological, and lung). In addition, we evaluated the associations between and among symptoms and symptoms clusters and examined the strength of these interactions over time. Eight unique symptom clusters were identified within the networks. Findings from this research suggest that changes occur in the relationships and interconnections between and among co-occurring symptoms and symptoms clusters that depend on the time point in the chemotherapy cycle and the type of cancer. The evaluation of the centrality measures provides new insights into the relative importance of individual symptoms within various networks that can be considered as potential targets for symptom management interventions.

Neuropsychological networks in cognitively healthy older adults and dementia patients

Neuropsychological tests have commonly been used to determine the organization of cognitive functions by identifying latent variables. In contrast, an approach which has seldom been employed is network analysis. We characterize the network structure of a set of representative neuropsychological test scores in cognitively healthy older adults and MCI and dementia patients using network analysis. We employed the neuropsychological battery from the National Alzheimer's Coordinating Center which included healthy controls (n = 7623), mild cognitive impairment patients (n = 5981) and dementia patients (n = 2040), defined according to the Clinical Dementia Rating. The results showed that, according to several network analysis measures, the most central cognitive function is executive function followed by attention, language, and memory. At the test level, the most central test was the Trail Making Test B, which measures cognitive flexibility. Importantly, these results and most other network measures, such as the community organization and graph representation, were similar across the three diagnostic groups. Therefore, network analysis can help to establish a ranking of cognitive functions and tests based on network centrality and suggests that this organization is preserved in dementia. Central nodes might be particularly relevant both from a theoretical and clinical point of view, as they are more associated with other nodes, and their disruption is likely to have a larger effect on the overall network than peripheral nodes. The present analysis may provide a proof of principle for the application of network analysis to cognitive data.

Psychosocial work environment as a dynamic network: a multi-wave cohort study

While characteristics of psychosocial work environment have traditionally been studied separately, we propose an alternative approach that treats psychosocial factors as interacting elements in networks where they all potentially affect each other. In this network analysis, we used data from a prospective occupational cohort including 10,892 participants (85% women; mean age 47 years) and repeated measurements of seven psychosocial work characteristics (job demands, job control, job uncertainty, team climate, effort-reward imbalance, procedural justice and interactional justice) assessed in 2000, 2004, 2008 and 2012. Results from multilevel longitudinal vector autoregressive models indicated that job demands as well as interactional and procedural justice were most broadly associated with the subsequent perceptions of the work-related psychosocial factors (high out-Strength), suggesting these factors might be potentially efficient targets of workplace interventions. The results also suggest that modifying almost any of the studied psychosocial factors might be relevant to subsequent perceptions of effort-reward imbalance and interactional justice at the workplace.

A network approach can improve eating disorder conceptualization and treatment

Eating disorders are severe mental illnesses with the second highest mortality rate of all psychiatric illnesses. Eating disorders are exceedingly deadly because of their complexity. Specifically, eating disorders are highly comorbid with other psychiatric illnesses (up to 95% of individuals with an eating disorder have at least one additional psychiatric illness), have extremely heterogeneous presentations, and individuals often migrate from one specific eating disorder diagnosis to another. In this Perspective, we propose that understanding eating disorder comorbidity and heterogeneity via a network theory approach offers substantial benefits for both conceptualization and treatment. Such a conceptualization, strongly based on theory, can identify specific pathways that maintain psychiatric comorbidity, how diagnoses vary across individuals, and how specific symptoms and comorbidities maintain illness for one individual, thereby paving the way for personalized treatment.

The Mereology of Depression-Networks of Depressive Symptoms during the Course of Psychotherapy

(1) Background: Research has shown that it is important to examine depressive symptoms in the light of the mereology (the ratio between one symptom and the whole disorder). The goal of this study was to examine changes in the symptom interrelations of patients undergoing cognitive behavioral therapy treatment (CBT) via network analyses. (2) Method: Outpatients with depressive symptoms (N = 401) were assessed with the Beck Depression Inventory three times (pretreatment, after 12 sessions, and post-treatment) during CBT. Gaussian graphical models were used to estimate the relationships among symptoms. (3) Results: The severity of depressive symptoms significantly decreased over the course of therapy, but connectivity in the networks significantly increased. Communities of symptoms changed during treatment. The most central and predictable symptom was worthlessness at baseline and after 12 sessions, and loss of energy and self-dislike at post-treatment. (4) Conclusion: The results indicate that the severity of depressive symptoms decreased during cognitive behavior therapy, while network connectivity increased. Furthermore, the associations among symptoms and their centrality changed during the course of therapy. Future studies may investigate individual differences and their impact on the planning of psychotherapeutic treatment.

How is loneliness related to anxiety and depression: A population-based network analysis in the early lockdown period

High risk of mental health problems is associated with loneliness resulting from social distancing measures and “lockdowns” that have been imposed globally due to the COVID‐19 pandemic. This study explores the interconnectedness of loneliness, anxiety and depression on a symptom level using network analysis. A representative sample of participants (N = 1041), who were of at least 18 years of age, was recruited from the Republic of Ireland (ROI). Loneliness, anxiety and depression were assessed using validated instruments. Network analysis was used to identify the network structure of loneliness, anxiety and depression. Loneliness was found to be largely isolated from anxiety and depression nodes in the network. Anxiety and depression were largely interconnected. “Trouble relaxing,” “feeling bad about oneself” and “not being able to stop or control worrying” were suggested as the most influential nodes of the network. Despite the expectation that loneliness would be implicated more robustly in the anxiety and depression network of symptoms, the results suggest loneliness as a distinct construct that is not interwoven with anxiety and depression.

The Network Structure of Trauma Symptoms of Abuse-exposed Children and Adolescents in South Africa

Network theory promises new ways for conceptualizing, methods for investigating, and state-of-the-art lines of research that will improve our knowledge of mental health in high-risk children and adolescents. This study constructed a symptom network to examine associations between a wide range of trauma symptoms in a sample of children and adolescents (N = 270; M_age = 12.55 yrs, SD = 1.19; 67% = Female) who experienced different forms of abuse (i.e., sexual, physical, emotional and neglect). Symptom-pairs regularized partial correlations, with the Extended Bayesian Information Criterion Graphical Least Absolute Shrinkage and Selection Operator (EBICglasso), were computed to estimate the network structure and centrality measures of the TSCC-SF items. Results show sadness, dissociative amnesia, and sexual arousal to be the most central symptoms in the network, while suicidality was found to be the shortest pathway across all other symptoms (domains). By providing clinicians with specific symptoms to target in interventions, the network framework has the potential to guide and enhance the effectiveness of psychological therapies in high-risk populations.

The network structure of depressive symptomatology in Peruvian adults with arterial hypertension

Background: Globally, arterial hypertension (AH) has increased by 90% over the last four decades, and has increased by 1.6% in Peru over the previous four years. Scientific evidence indicates the prevalence of depressive symptoms in patients with AH and its importance in the comprehensive evaluation of the adult for adherence to clinical treatment. Previous studies carried out in the Peruvian population with AH mostly report the prevalence and associations, but do not indicate which depressive symptoms are more relevant in patients with AH. This study involved a network analysis of depressive symptomatology in Peruvian patients with AH using network estimation. Network analysis is used in this study for analysis, control, and monitoring purposes.

Method: A representative cross-sectional study at the national level, using secondary data from 2019 Demographic and Family Health Survey (ENDES) was performed. The sample used included men and women of age over 17 years diagnosed with AH and was able to respond to Patient Health Questionnaire-9 (PHQ-9).

Results: The symptoms of depressive mood (bridging force and centrality) and energy fatigue or loss (bridge centrality) play an essential role in the network structure, as does the feeling of uselessness in terms of closeness and intermediation.

Conclusion: The study highlighted the symptoms related to depressive mood and energy fatigue or loss as bridging symptoms, which could trigger a depressive episode in patients diagnosed with AH. The results will contribute to developing personalized treatments aimed at patients with specific depressive symptoms who have also been diagnosed with AH. The study analysis presents statistical coefficients of effect size (≤ 0,1 = small; > 0,1 to < 0,5 = moderate; ≥ 0,5 = large) to determine network connections.

Mapping Network Connectivity Among Symptoms of Depression and Pain in Wuhan Residents During the Late-Stage of the COVID-19 Pandemic

Background

Symptoms of depression and pain often overlap, and they negatively influence the prognosis and treatment outcome of both conditions. However, the comorbidity of depression and pain has not been examined using network analysis, especially in the context of a pandemic. Thus, we mapped out the network connectivity among the symptoms of depression and pain in Wuhan residents in China during the late stage of the COVID-19 pandemic.

Methods

This cross-sectional study was conducted from May 25, 2020 to June 18, 2020 in Wuhan, China. Participants' depressive and pain symptoms were assessed using the 9-item Patient Health Questionnaire (PHQ9) and a pain numeric rating scale (NRS), respectively. Network analyses were performed.

Results

In total, 2,598 participants completed all assessments. PHQ4 (fatigue) in the depression community showed the highest strength value, followed by PHQ6 (worthlessness) and PHQ2 (depressed or sad mood). PHQ4 (fatigue) was also the most key bridge symptom liking depression and pain, followed by PHQ3 (sleep difficulties). There were no significant differences in network global strength (females: 4.36 vs. males: 4.29; S = 0.075, P = 0.427), network structure-distribution of edge weights (M = 0.12, P = 0.541), and individual edge weights between male and female participants.

Conclusion

Depressive and pain symptoms showed strong cross-association with each other. "Fatigue" was the strongest central and bridge symptom in the network model, while "sleep difficulties" was the second strongest bridge symptom. Targeting treatment of both fatigue and sleep problems may help improve depressive and pain symptoms in those affected.

Network analysis of cognitive emotion regulation strategies and depressive symptoms in young adults after recent stressful events: The moderation of childhood maltreatment

BACKGROUND

The present study explored the interrelations between cognitive emotion regulation strategies (CERS) and depressive symptoms to better understand how young adults with childhood maltreatment (CM) are more prone to depression after recent stressful events.

METHODS

The sample consisted of 342 young adults (M = 19.51 years, range = 17-28 years, 64% female) who had experienced stressful events in the last six months. Network analysis was used to examine the interrelations between nine CERS and four depressive symptom clusters in exposed ('CM'; n = 182) and not exposed to CM groups ('non-CM'; n = 160).

RESULTS

We found that somatic symptoms, rumination, putting into perspective, and catastrophizing had high expected influence (EI) in the whole sample network. Global and local connectivity differed between the CM and non-CM groups. More specifically, the structure of the CM network had higher overall connectivity than the structure of the non-CM network. Considering local connectivity, depressed affect, rumination, positive refocusing, and putting into perspective had marginally significantly higher levels of EI in the CM network. We found some significant differences in partial correlations among CERS, such as stronger positive correlations between positive refocusing-catastrophizing, rumination-refocus on planning, and putting into perspective-blaming others in the CM group.

LIMITATIONS

This study was cross-sectional and limited by the use of retrospective self-report tools.

CONCLUSIONS

The findings shed light on the complex interrelations between CERS and depressive symptoms in the context of recent stressful events. Additionally, they highlight potential directions for population-based interventions.

Examining the relationships between emotional disorder symptoms in a mixed sample of community adults and patients: A network analysis perspective

According to the literature, comorbidity rates observed on emotional disorders are linked to how the main diagnostic classification systems have traditionally defined these disorders. This paper aims to analyze the structure of symptoms evaluated with the Inventory of Depression and Anxiety Symptoms-II (IDAS-II) with network analysis. A mixed sample (n = 2021) of 1692 community adults and 329 patients was used. 14.79% (n = 299) of the sample met the diagnostic criteria for at least one DSM-5 mental disorder and 5.29% (n = 107) had diagnostic comorbidity. The sample was randomly divided into two sub-samples: estimation sample (n = 1010) and replication sample (n  = 1011). The detection of community structures was carried out on estimation sample using the walktrap algorithm. Four local inference measures were estimated: Strength, one-step Expected Influence, two-step Expected Influence, and node predictability. Exploratory graphic analysis of modularity yielded an optimal solution of two communities on estimation sample: first linked to symptoms of depression and anxiety and second grouping symptoms of bipolar disorder and obsessive – compulsive disorder. Mania, Panic, Claustrophobia, and Low Well-Being Bridge emerged as bridge symptoms, connecting the two substructures. Networks estimated on replication subsamples did not differ significantly in structure. Dysphoria, Traumatic Intrusions and Checking and Ordering were the symptoms with greatest number of connections with rest of the network. Results sheds light on specific links between emotional disorder symptoms and provides useful information for the development of transdiagnostic interventions by identifying the influential symptoms within the internalizing spectrum.

A Systematic Review and Individual Patient Data Network Analysis of the Residual Symptom Structure Following Cognitive-Behavioral Therapy and Escitalopram, Mirtazapine and Venlafaxine for Depression

OBJECTIVE

Consistent evidence suggests residual depressive symptomology are the strongest predictors of depression relapse following cognitive-behavioral therapy (CBT) and antidepressant medications (ADM's). Psychometric network models help detecting and understanding central symptoms that remain post-treatment, along with their complex co-occurrences. However, individual psychometric network studies show inconsistent findings. This systematic review and IPD network analysis aimed to estimate and compare the symptom network structures of residual depressive symptoms following CBT, ADM's, and their combination.

METHODS

PsycINFO, PsycArticles, and PubMed were systematically searched through October 2020 for studies that have assessed individuals with major depression at post-treatment receiving either CBT and/or ADM's (venlafaxine, escitalopram, mirtazapine). IPD was requested from eligible samples to estimate and compare residual symptom psychometric network models post-CBT and post-ADM's.

RESULTS

In total, 25 from 663 eligible samples, including 1,389 patients qualified for the IPD. Depressed mood and anhedonia were consistently central residual symptoms post-CBT and post-ADM's. For CBT, fatigue-related and anxiety symptoms were also central post-treatment. A significant difference in network structure across treatments (CBT vs. ADM) was observed for samples measuring depression severity using the MADRS. Specifically, stronger symptom occurrences were present amongst lassitude-suicide post-CBT (vs. ADM's) and amongst lassitude-inability to feel post-ADM's (vs. CBT). No significant difference in global strength was observed across treatments.

CONCLUSIONS

Core major depression symptoms remain central across treatments, strategies to target these symptoms should be considered. Anxiety and fatigue related complaints also remain central post-CBT. Efforts must be made amongst researchers, institutions, and journals to permit sharing of IPD.Systematic Review Registration: A protocol was prospectively registered on PROSPERO (CRD42020141663; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=141663).

Modeling alcohol use disorder as a set of interconnected symptoms - Assessing differences between clinical and population samples and across external factors

Alcohol use disorder is argued to be a highly complex disorder influenced by a multitude of factors on different levels. Common research approaches fail to capture this breadth of interconnecting symptoms. To address this gap in theoretical assumptions and methodological approaches, we used a network analysis to assess the interplay of alcohol use disorder symptoms. We applied the exploratory analysis to two US-datasets, a population sample with 23,591 individuals and a clinical sample with 483 individuals seeking treatment for alcohol use disorder. Using a Bayesian framework, we first investigated differences between the clinical and population sample looking at the symptom interactions and underlying structure space. In the population sample the time spent drinking alcohol was most strongly connected, whereas in the clinical sample loss of control showed most connections. Furthermore, the clinical sample demonstrated less connections, however, estimates were too unstable to conclude the sparsity of the network. Second, for the population sample we assessed whether the network was measurement invariant across external factors like age, gender, ethnicity and income. The network differed across all factors, especially for age subgroups, indicating that subgroup specific networks should be considered when deriving implications for theory building or intervention planning. Our findings corroborate known theories of alcohol use disorder stating loss of control as a central symptom in alcohol dependent individuals.

Fear of Recurrence in Chinese Cancer Patients: Prevalence, Correlates, and Network Analysis

BACKGROUND

Fear of cancer recurrence (FCR) is a significant issue for most cancer patients. Until now, a detailed investigation of the structure of FCR and the interaction among its constituent elements is lacking. This study aims to investigate the phenomenon of FCR by means of network analysis in Chinese cancer patients.

METHODS

This is a multi-center, cross-sectional study that included 996 cancer patients from southern China. All participants were assessed by the 7-item Chinese version Fear of Cancer Recurrence Scale (FCR-7). Multivariate logistic regression, and network analyses were conducted. Central symptoms (nodes) in the FCR network were identified.

RESULTS

Among the 996 patients, 543 (54.52%) reported moderate FCR, and 137 (13.76%) reported high FCR. Chemotherapy (OR = 2.954, P = 0.016), and childhood severe illness experience (OR = 2.331, P = 0.016) were positively associated with high FCR, while higher monthly income (OR = 0.403, P = 0.046) was negative associated with high FCR. The node #FCR2 (Worried/anxious about recurrence) was the most central node within the FCR network (Strength = 1.190), while node #FCR6 (Examining for physical signs) was the least central node (Strength = 0.373). The edge FCR1-FCR2 ("Afraid"-"Worried/anxious") was the thickest and most saturated edge in the network. After controlling for age and gender, an almost identical network was obtained with respect to edges magnitude and strength.

CONCLUSION

Fear of recurrence is a frequently reported issue among Chinese cancer patients. Patients with chemotherapy and childhood severe illness experience were more vulnerable and should be particularly monitored. Compared to behavioral component (i.e., body checking, overscreening and overtreatment) and cognitive component (i.e., intrusions), emotional component (i.e., worry/anxious) is more central to identify FCR and might be potential targets for further interventions.

Structure of Hypomanic Symptoms in Adolescents With Bipolar Disorders: A Network Approach

BACKGROUND

Bipolar disorders (BD) are severe mental illnesses that are often misdiagnosed or under-diagnosed. The self-report 33-item Hypomania Checklist (HCL-33) and the 33-item Hypomania Checklist - external assessment (HCL-33-EA) are well-validated scales for BD symptom detection. This study compared the network structure, central symptoms, and network stability of hypomanic symptoms measured by the HCL-33 vs. the HCL-33-EA.

METHODS

This cross-sectional study was conducted from January to December 2019. Adolescents (aged between 12 and 18 years) with BD were recruited from the outpatient department of Child Psychiatry, First Affiliated Hospital of Zhengzhou University. All participants were asked to complete the HCL-33, and their caregivers completed the HCL-33-EA. Network analyses were conducted.

RESULTS

A total of 215 adolescents with BD and their family caregivers were recruited. Node HCL17 ("talk more," node strength = 4.044) was the most central symptom in the HCL-33 network, followed by node HCL2 ("more energetic," node strength = 3.822), and HCL18 ("think faster," node strength = 3.801). For the HCL-33-EA network model, node HCL27 ("more optimistic," node strength = 3.867) was the most central node, followed by node HCL18 ("think faster," node strength = 3.077), and HCL17 ("talk more," node strength = 2.998). In the network comparison test, there was no significant difference at the levels of network structure (M = 0.946, P = 0.931), global strength (S: 5.174, P = 0.274), or each specific edge (all P's > 0.05 after Holm-Bonferroni corrections) between HCL-33 and HCL-33-EA items. Network stabilities for both models were acceptable.

CONCLUSION

The nodes "talk more" and "think faster" acted as central symptoms in BD symptom network models based on the HCL-33 and HCL-33-EA. Although the most prominent central symptom differed between the two models ("talk more" in HCL-33 vs. "more optimistic" in HCL-33-EA model), networks based on each measure were highly similar and underscored similarities in BD symptom relations perceived by adolescents and their caregivers. This research provides foundations for future studies with larger sample sizes toward improving the accuracy and robustness of observed network structures.

Excessive Worrying as a Central Feature of Anxiety during the First COVID-19 Lockdown-Phase in Belgium: Insights from a Network Approach

Since the WHO declared the COVID-19 pandemic on March 11, 2020, the novel coronavirus, SARS-CoV-2, has profoundly impacted public health and the economy worldwide. But there are not the only ones to be hit. The COVID-19 pandemic has also substantially altered mental health, with anxiety symptoms being one of the most frequently reported problems. Especially, the number of people reporting anxiety symptoms increased significantly during the first lockdown-phase compared to similar data collected before the pandemic. Yet, most of these studies relied on a unitary approach to anxiety, wherein its different constitutive features (i.e., symptoms) were tallied into one sum-score, thus ignoring any possibility of interactions between them. Therefore, in this study, we seek to map the associations between the core features of anxiety during the first weeks of the first Belgian COVID-19 lockdown-phase (n = 2,829). To do so, we implemented, in a preregistered fashion, two distinct computational network approaches: a Gaussian graphical model and a Bayesian network modelling approach to estimate a directed acyclic graph. Despite their varying assumptions, constraints, and computational methods to determine nodes (i.e., the variables) and edges (i.e., the relations between them), both approaches pointed to excessive worrying as a node playing an especially influential role in the network system of the anxiety features. Altogether, our findings offer novel data-driven clues for the ongoing field's larger quest to examine, and eventually alleviate, the mental health consequences of the COVID-19 pandemic.

The Theoretical and Statistical Ising Model: A Practical Guide in R

The “Ising model” refers to both the statistical and the theoretical use of the same equation. In this article, we introduce both uses and contrast their differences. We accompany the conceptual introduction with a survey of Ising-related software packages in R. Since the model’s different uses are best understood through simulations, we make this process easily accessible with fully reproducible examples. Using simulations, we show how the theoretical Ising model captures local-alignment dynamics. Subsequently, we present it statistically as a likelihood function for estimating empirical network models from binary data. In this process, we give recommendations on when to use traditional frequentist estimators as well as novel Bayesian options.

The network structure of core depressive symptom-domains in major depressive disorder following antidepressant treatment: a randomized clinical trial

BACKGROUND

Network analysis (NA) conceptualizes psychiatric disorders as complex dynamic systems of mutually interacting symptoms. Major depressive disorder (MDD) is a heterogeneous clinical condition, and very few studies to date have assessed putative changes in its psychopathological network structure in response to antidepressant (AD) treatment.

METHODS

In this randomized trial with adult depressed outpatients (n = 151), we estimated Gaussian graphical models among nine core MDD symptom-domains before and after 8 weeks of treatment with either escitalopram or desvenlafaxine. Networks were examined with the measures of cross-sectional and longitudinal structure and connectivity, centrality and predictability as well as stability and accuracy.

RESULTS

At baseline, the most connected MDD symptom-domains were fatigue-cognitive disturbance, whereas at week 8 they were depressed mood-suicidality. Overall, the most central MDD symptom-domains at baseline and week 8 were, respectively, fatigue and depressed mood; in contrast, the most peripheral symptom-domain across both timepoints was appetite/weight disturbance. Furthermore, the psychopathological network at week 8 was significantly more interconnected than at baseline, and they were also structurally dissimilar.

CONCLUSION

Our findings highlight the utility of focusing on the dynamic interaction between depressive symptoms to better understand how the treatment with ADs unfolds over time. In addition, depressed mood, fatigue, and cognitive/psychomotor disturbance seem to be central MDD symptoms that may be viable targets for novel, focused therapeutic interventions.

Is symptom connectivity really the most important issue in depression? Depression as a dynamic system of interconnected symptoms revisited

According to the network theory strong associations between symptoms drive the disease process. We compared those with and without diagnosed depressive disorders (DD+/DD-) and analysed the effects of differences in (a) network connectivity, (b) symptom thresholds, and (c) autoregressive loops (i.e. how strongly specific symptoms predict themselves) on the potential activation of symptoms over time using simulations developed by Cramer and others (2016). The parameters for the simulation (symptom connectivity and symptom threshold) were obtained from Ising models and cross-lagged panel network analyses. Data were from the nationally representative samples (Health 2000-2011 Study) of 4190 participants measured in 2011 (cross-sectional analyses) and 3201 participants measured in 2000 and 2011 (longitudinal analyses). DD diagnosis was based on the Composite International Diagnostic Interview and depressive symptoms were self-reported using the 13-item version of the Beck Depression Inventory (BDI). Differences in symptom connectivity between participants with and without DD were not observed, but the mean probability (threshold) of symptom existence in the DD + group was higher than in the DD-group (0.41 vs. 0.12). Simulation showed that there are more active symptoms in the DD + group after 10 000 time points (means 1.2 vs. 4.6) than in the DD-group. This difference largely disappeared when we used longitudinal networks, including autoregressive loops, in the connectivity matrix. Our results suggest that the differences in symptom thresholds and autoregressive loops may be more important features than symptom connectivity in differentiating people with and without DD.

The Associations of Common Psychological Problems With Mental Disorders Among College Students

Psychological problems like procrastination, perfectionism, low self-esteem, test anxiety and stress are common among college students. There are evidence-based interventions available for these problems that not only have direct effects on these problems, but also indirect effects on mental disorders such as depression and anxiety disorders. Targeting these psychological problems may offer new opportunities to prevent and treat mental disorders in a way that is less stigmatizing to students. In this study we examined the association of five psychological problems with five common mental disorders (panic, generalized anxiety, bipolar, major depressive, and substance use disorder) in a sample of 2,449 students from two Dutch universities. Psychological problems were measured with one item for each problem and mental disorders were measured with the Composite International Diagnostic Interview Screening Scales. Associations were examined with Poisson regression models as relative risks (RR) of the disorders as a function of the psychological problems. The population attributable fraction (PAF) indicates by what percentage the prevalence of the mental disorder would be reduced if the psychological problem was addressed successfully by an intervention. Especially generalized anxiety disorder was strongly associated with psychological problems (strong associations with stress and low self-esteem and moderately with test anxiety). The group with three or more psychological problems had a strongly increased risk for generalized anxiety (RR = 11.25; 95% CI: 7.51-16.85), and a moderately increase risk for major depression (RR = 3.22; 95% CI: 2.63-3.95), panic disorder (RR = 3.19; 95% CI: 1.96-5.20) and bipolar disorder (RR = 3.66; 95% CI: 2.40-5.58). The PAFs for having any of the psychological problems (one or more) were considerable, especially for generalized anxiety (60.8%), but also for panic disorder (35.1%), bipolar disorder (30.6%) and major depression (34.0%). We conclude that common psychological problems are associated with mental disorders and with each other. After adjustment, psychological problems are associated with different patterns of mental disorders. If the impact of the psychological problems could be taken away, the prevalence of several mental disorders would be reduced considerably. The psychological problems may provide a promising target to indirectly prevent and intervene in psychopathology in hard to reach college students with mental disorders.

Can network analysis of self-reported psychopathology shed light on the core phenomenology of bipolar disorders in adolescents and young adults?

OBJECTIVES

Network analysis is increasingly applied to psychopathology research. We used it to examine the core phenomenology of emerging bipolar disorder (BD I and II) and 'at risk' presentations (major depression with a family history of BD).

METHODOLOGY

The study sample comprised a community cohort of 1867 twin and nontwin siblings (57% female; mean age ~26) who had completed self-report ratings of (i) depression-like, hypomanic-like and psychotic-like experiences; (ii) family history of BD; and (iii) were assessed for mood and psychotic syndromes using the Composite International Diagnostic Interview (CIDI). Symptom networks were compared for recent onset BD versus other cohort members and then for individuals at risk of BD (depression with/without a family history of BD).

RESULTS

The four key symptoms that differentiated recent onset BD from other cohort members were: anergia, psychomotor speed, hypersomnia and (less) loss of confidence. The four key symptoms that differentiated individuals at high risk of BD from unipolar depression were anergia, psychomotor speed, impaired concentration and hopelessness. However, the latter network was less stable and more error prone.

CONCLUSIONS

We are encouraged by the overlaps between our findings and those from two recent publications reporting network analyses of BD psychopathology, especially as the studies recruited from different populations and employed different network models. However, the advantages of applying network analysis to youth mental health cohorts (which include many individuals with multimorbidity) must be weighed against the disadvantages including basic issues such as judgements regarding the selection of items for inclusion in network models.

The exploratory value of cross-sectional partial correlation networks: Predicting relationships between change trajectories in borderline personality disorder

Objective

Within the network approach to psychopathology, cross-sectional partial correlation networks have frequently been used to estimate relationships between symptoms. The resulting relationships have been used to generate hypotheses about causal links between symptoms. In order to justify such exploratory use of partial correlation networks, one needs to assume that the between-subjects relationships in the network approximate systematic within-subjects relationships, which are in turn the results of some within-subjects causal mechanism. If this assumption holds, relationships in the network should be mirrored by relationships between symptom changes; if links in networks approximate systematic within-subject relationships, change in a symptom should relate to change in connected symptoms.

Method

To investigate this implication, we combined longitudinal data on the Borderline Personality Disorder Severity Index from four samples of borderline personality disorder patients (N = 683). We related parameters from baseline partial correlation networks of symptoms to relationships between change trajectories of these symptoms.

Results

Across multiple levels of analysis, our results showed that parameters from baseline partial correlation networks are strongly predictive of relationships between change trajectories.

Conclusions

By confirming its implication, our results support the idea that cross-sectional partial correlation networks hold a relevant amount of information about systematic within-subjects relationships and thereby have exploratory value to generate hypotheses about the causal dynamics between symptoms.