Application of network methods for understanding mental disorders: pitfalls and promise

Prioritization of Psychopathological Symptoms and Clinical Characterization in Psychiatric Diagnoses: A Narrative Review

Importance

Psychiatry mainly deals with conditions that are mediated by brain function but are not directly attributable to specific brain abnormalities. Given the lack of concrete biological markers, such as laboratory tests or imaging results, the development of diagnostic systems is difficult.

Observations

This narrative review evaluated 9 diagnostic approaches. The validity of the DSM and the International Classification of Disorders (ICD) is limited. The Research Domain Criteria is a research framework, not a diagnostic system. The clinical utility of the quantitatively derived, dimensional Hierarchical Taxonomy of Psychopathology is questionable. The Psychodynamic Diagnostic Manual Version 2 follows psychoanalytic theory and focuses on personality. Unlike the personality assessments in ICD-11 or DSM-5's alternative model, based on pathological extremes of the big 5 traits (extraversion, agreeableness, openness, conscientiousness, and neuroticism), it lacks foundation in empirical evidence. Network analytic approaches are intriguing, but their complexity makes them difficult to implement. Staging would be easier if individually predictive biological markers were available. The problem with all these new approaches is that they abstract patient experiences into higher-order constructs, potentially obscuring individual symptoms so much that they no longer reflect patients' actual problems.

Conclusions and Relevance

ICD and DSM diagnoses can be questioned, but the reality of psychopathological symptoms, such as hallucinations, depression, anxiety, compulsions, and the suffering stemming from them, cannot. Therefore, it may be advisable to primarily describe patients according to the psychopathological symptoms they present, and any resulting personal syndromes, embedded in a framework of contextual clinical characterization including personality assessment and staging. The DSM and ICD are necessary for reimbursement, but they should be simplified and merged. A primarily psychopathological symptoms-based, clinical characterization approach would be multidimensional and clinically useful, because it would lead to problem-oriented treatment and support transdiagnostic research. It should be based on a universally used instrument to assess psychopathology and structured clinical characterization.

Machine learning and Bayesian network analyses identifies associations with insomnia in a national sample of 31,285 treatment-seeking college students

BACKGROUND

A better understanding of the relationships between insomnia and anxiety, mood, eating, and alcohol-use disorders is needed given its prevalence among young adults. Supervised machine learning provides the ability to evaluate which mental disorder is most associated with heightened insomnia among U.S. college students. Combined with Bayesian network analysis, probable directional relationships between insomnia and interacting symptoms may be illuminated.

METHODS

The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. We used a 4-step statistical approach: (1) at the disorder level, an elastic net regularization model examined the relative importance of the association between insomnia and 7 mental disorders (major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder); (2) This model was evaluated within a hold-out sample. (3) at the symptom level, a completed partially directed acyclic graph (CPDAG) was computed via a Bayesian hill-climbing algorithm to estimate potential directionality among insomnia and its most associated disorder [based on SHAP (SHapley Additive exPlanations) values)]; (4) the CPDAG was then tested for generalizability by assessing (in)equality within a hold-out sample using structural hamming distance (SHD).

RESULTS

Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .44 (.01); RMSE = 5.00 (0.08)], with comparable performance in the hold-out sample (R2 = .33; RMSE = 5.47). SHAP values indicated that the presence of any mental disorder was associated with higher insomnia scores, with major depressive disorder as the most important disorder associated with heightened insomnia (mean |SHAP|= 3.18). The training CPDAG and hold-out CPDAG (SHD = 7) suggested depression symptoms presupposed insomnia with depressed mood, fatigue, and self-esteem as key parent nodes.

CONCLUSION

These findings provide insights into the associations between insomnia and mental disorders among college students and warrant further investigation into the potential direction of causality between insomnia and depression.

TRIAL REGISTRATION

Trial was registered on the National Institute of Health RePORTER website (R01MH115128 || 23/08/2018).

Characterizing the dynamics, reactivity and controllability of moods in depression with a Kalman filter

BACKGROUND

Mood disorders involve a complex interplay between multifaceted internal emotional states, and complex external inputs. Dynamical systems theory suggests that this interplay between aspects of moods and environmental stimuli may hence determine key psychopathological features of mood disorders, including the stability of mood states, the response to external inputs, how controllable mood states are, and what interventions are most likely to be effective. However, a comprehensive computational approach to all these aspects has not yet been undertaken.

METHODS

Here, we argue that the combination of ecological momentary assessments (EMA) with a well-established dynamical systems framework-the humble Kalman filter-enables a comprehensive account of all these aspects. We first introduce the key features of the Kalman filter and optimal control theory and their relationship to aspects of psychopathology. We then examine the psychometric and inferential properties of combining EMA data with Kalman filtering across realistic scenarios. Finally, we apply the Kalman filter to a series of EMA datasets comprising over 700 participants with and without symptoms of depression.

RESULTS

The results show a naive Kalman filter approach performs favourably compared to the standard vector autoregressive approach frequently employed, capturing key aspects of the data better. Furthermore, it suggests that the depressed state involves alterations to interactions between moods; alterations to how moods responds to external inputs; and as a result an alteration in how controllable mood states are. We replicate these findings qualitatively across datasets and explore an extension to optimal control theory to guide therapeutic interventions.

CONCLUSIONS

Mood dynamics are richly and profoundly altered in depressed states. The humble Kalman filter is a well-established, rich framework to characterise mood dynamics. Its application to EMA data is valid; straightforward; and likely to result in substantial novel insights both into mechanisms and treatments.

A network-based approach to explore comorbidity patterns among community-dwelling older adults living alone

The detailed comorbidity patterns of community-dwelling older adults have not yet been explored. This study employed a network-based approach to investigate the comorbidity patterns of community-dwelling older adults living alone. The sample comprised a cross-sectional cohort of adults 65 or older living alone in a Korean city (n = 1041; mean age = 77.7 years, 77.6% women). A comorbidity network analysis that estimates networks aggregated from measures of significant co-occurrence between pairs of diseases was employed to investigate comorbid associations between 31 chronic conditions. A cluster detection algorithm was employed to identify specific clusters of comorbidities. The association strength was expressed as the observed-to-expected ratio (OER). As a result, fifteen diseases were interconnected within the network (OER > 1, p-value < .05). While hypertension had a high prevalence, osteoporosis was the most central disease, co-occurring with numerous other diseases. The strongest associations among comorbidities were found between thyroid disease and urinary incontinence, chronic otitis media and osteoporosis, gastric duodenal ulcer/gastritis and anemia, and depression and gastric duodenal ulcer/gastritis (OER > 1.85). Three distinct clusters were identified as follows: (a) cataracts, osteoporosis, chronic otitis media, osteoarthritis/rheumatism, low back pain/sciatica, urinary incontinence, post-accident sequelae, and thyroid diseases; (b) hyperlipidemia, diabetes mellitus, and hypertension; and (c) depression, skin disease, gastric duodenal ulcer/gastritis, and anemia. The results may prove valuable in guiding the early diagnosis, management, and treatment of comorbidities in older adults living alone.

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

PURPOSE

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

METHOD

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

RESULTS

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

CONCLUSIONS

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

IMPLICATIONS FOR CANCER SURVIVORS

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

Emotion dysregulation links pathological eating styles and psychopathological traits in bariatric surgery candidates

Objectives

Approximately one-third of bariatric surgery patients experience weight regain or suboptimal weight loss within five years post-surgery. Pathological eating styles and psychopathological traits (e.g., emotion dysregulation) are recognized as potential hindrances to sustain weight loss efforts and are implicated in obesity development. A comprehensive understanding of these variables and their interplays is still lacking, despite their potential significance in developing more effective clinical interventions for bariatric patients. We investigate the prevalence of and interactions between pathological eating styles and psychopathological traits in this population.

Materials and methods

110 bariatric surgery candidates were characterized using the Binge Eating Scale (BES), Hamilton Depression/Anxiety Scales (HAM-D/A), Barratt Impulsiveness Scale (BIS-11), Experiences in Close Relationships (ECR), Difficulties in Emotion Regulation Scale (DERS). We analyzed these variables with multiple logistic regression analyses and network analysis.

Results

Patients with pathological eating styles showed more pronounced anxiety/depressive symptoms and emotion dysregulation. Network analysis revealed strong connections between BES and DERS, with DERS also displaying robust connections with HAM-A/D and ECR scales. DERS and attention impulsivity (BIS-11-A) emerged as the strongest nodes in the network.

Discussion

Our findings demonstrate the mediating role of emotion dysregulation between pathological eating styles and psychopathological traits, supporting existing literature on the association between psychopathological traits, insecure attachment styles, and pathological eating behaviors. This research emphasizes the significance of emotion regulation in the complex network of variables contributing to obesity, and its potential impact on bariatric surgery outcomes. Interventions focusing on emotion regulation may thus lead to improved clinical outcomes for bariatric patients.

Network analysis used to investigate the interplay among somatic and psychological symptoms in patients with cancer and cancer survivors: a scoping review

PURPOSE

Patients with cancer often experience multiple somatic and psychological symptoms. Somatic and psychological symptoms are thought to be connected and may reinforce each other. Network analysis allows examination of the interconnectedness of individual symptoms. The aim of this scoping review was to examine the current state of knowledge about the associations between somatic and psychological symptoms in patients with cancer and cancer survivors, based on network analysis.

METHODS

This scoping review followed the five-stage framework of Arksey and O'Malley. The literature search was conducted in May, 2023 in PubMed, APA PsycINFO, Embase Cochrane central, and CINAHL databases.

RESULTS

Thirty-two studies were included, with eleven using longitudinal data. Seventeen studies reported on the strength of the associations: somatic and psychological symptoms were associated, although associations among somatic as well as among psychological symptoms were stronger. Other findings were the association between somatic and psychological symptoms was stronger in patients experiencing more severe symptoms; associations between symptoms over time remained rather stable; and different symptoms were central in the networks, with fatigue being among the most central in half of the studies.

IMPLICATIONS FOR CANCER SURVIVORS

Although the associations among somatic symptoms and among psychological symptoms were stronger, somatic and psychological symptoms were associated, especially in patients experiencing more severe symptoms. Fatigue was among the most central symptoms, bridging the somatic and psychological domain. These findings as well as future research based on network analysis may help to untangle the complex interplay of somatic and psychological symptoms in patients with cancer.

Depression and anxiety symptom network structure among patients with coronary heart disease and association with quality of life: protocol for a multicentre cross-sectional and prospective longitudinal study

BACKGROUND

Anxiety and depression are critical mental health problems among persons with coronary heart disease (CHD). The range of symptoms is an important stressor for adverse cardiovascular events, and these symptoms can be involved in various ways during the course of CHD. However, the characteristics and mechanisms of comorbidity between the two mental states from the viewpoint of symptom interactions in patients with CHD remain unclear. Therefore, we aim to apply a symptom-oriented approach to identify core and bridge symptoms between anxiety and depression in a population with CHD and to identify differences in network structure over time and symptomatic link profiles.

METHODS AND ANALYSIS

We designed a multicentre, cross-sectional, longitudinal study of anxiety and depression symptoms among patients with CHD. We will evaluate degrees of symptoms using the Generalized Anxiety Disorder Scale, the Patient Health Questionnaire and the WHO Quality of Life-Brief version. Patients will be followed up for 1, 3 and 6 months after baseline measurements. We will analyse and interpret network structures using R software and its packages. The primary outcomes of interest will include centrality, bridge connections, estimates, differences in network structures and profiles of changes over time. The secondary outcome measures will be the stability and accuracy of the network. By combining cross-sectional and longitudinal analyses, this study should elucidate the central and potential causative pathways among anxiety and depression symptom networks as well as their temporal stability in patients with CHD.

ETHICS AND DISSEMINATION

The project conforms to the ethical principles enshrined in the Declaration of Helsinki (2013 amendment) and all local ethical guidelines. The ethics committee at the University of South China approved the study (Approval ID: 2023-USC-HL-414). The findings will be published and presented at conferences for widespread dissemination.

TRIAL REGISTRATION NUMBER

ChiCTR2300075813.

Transdiagnostic clustering and network analysis for questionnaire-based symptom profiling and drug recommendation in the UK Biobank and a Korean cohort

Clinical decision support systems (CDSSs) play a critical role in enhancing the efficiency of mental health care delivery and promoting patient engagement. Transdiagnostic approaches that utilize raw psychological and biological data enable personalized patient profiling and treatment. This study introduces a CDSS incorporating symptom profiling and drug recommendation for mental health care. Among the UK Biobank cohort, we analyzed 157,348 participants for symptom profiling and 14,358 participants with a drug prescription history for drug recommendation. Among the 1307 patients in the Samsung Medical Center cohort, 842 were eligible for analysis. Symptom profiling utilized demographic and questionnaire data, employing conventional clustering and community detection methods. Identified clusters were explored using diagnostic mapping, feature importance, and scoring. For drug recommendation, we employed cluster- and network-based approaches. The analysis identified nine clusters using k-means clustering and ten clusters with the Louvain method. Clusters were annotated for distinct features related to depression, anxiety, psychosis, drug addiction, and self-harm. For drug recommendation, drug prescription probabilities were retrieved for each cluster. A recommended list of drugs, including antidepressants, antipsychotics, mood stabilizers, and sedative-hypnotics, was provided to individual patients. This CDSS holds promise for efficient personalized mental health care and requires further validation and refinement with larger datasets, serving as a valuable tool for mental healthcare providers.

Machine Learning and Bayesian Network Analyses Identifies Psychiatric Disorders and Symptom Associations with Insomnia in a national sample of 31,285 Treatment-Seeking College Students

Background

A better understanding of the structure of relations among insomnia and anxiety, mood, eating, and alcohol-use disorders is needed, given its prevalence among young adults. Supervised machine learning provides the ability to evaluate the discriminative accuracy of psychiatric disorders associated with insomnia. Combined with Bayesian network analysis, the directionality between symptoms and their associations may be illuminated.

Methods

The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. Firstly, an elastic net regularization model was trained to predict, via repeated 10-fold cross-validation, which psychiatric disorders were associated with insomnia severity. Seven disorders were included: major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder. Secondly, using a Bayesian network approach, completed partially directed acyclic graphs (CPDAG) built on training and holdout samples were computed via a Bayesian hill-climbing algorithm to determine symptom-level interactions of disorders most associated with insomnia [based on SHAP (SHapley Additive exPlanations) values)] and were evaluated for stability across networks.

Results

Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .449 (.016); RMSE = 5.00 [.081]), with comparable performance in accounting for variance explained in the holdout sample [R2 = .33; RMSE = 5.47). SHAP indicated the presence of any psychiatric disorder was associated with higher insomnia severity, with major depressive disorder demonstrated to be the most associated disorder. CPDAGs showed excellent fit in the holdout sample and suggested that depressed mood, fatigue, and self-esteem were the most important depression symptoms that presupposed insomnia.

Conclusion

These findings offer insights into associations between psychiatric disorders and insomnia among college students and encourage future investigation into the potential direction of causality between insomnia and major depressive disorder.

Trial registration

Trial may be found on the National Institute of Health RePORTER website: Project Number: R01MH115128-05.

The importance of self-regulation and mental health for effective recovery after traffic injuries: A comprehensive network analysis approach

OBJECTIVE

Traffic injuries significantly impact people's psychological, physical and social wellbeing, and involve complex self-regulation responses. Psychological impacts are seldom recognized and addressed holistically. This study employs network analysis to investigate the interconnectedness between different dimensions that influence mental health vulnerability and recovery after traffic injuries.

METHODS

120 adults with mild-to-moderate traffic injuries and 112 non-injured controls were recruited. The network investigation employed two main approaches. Four cross-sectional networks examined the interrelationships between self-regulation responses (cognitive and autonomic) and various health dimensions (psychological, physical, social) over time (1, 3, 6, 12 months). Three predictive networks explored influences of acute self-regulation responses (1 month) on long-term outcomes. Network analyses focused on between-group differences in overall connectivity and centrality measures (nodal strength).

RESULTS

An overall measure of psychological wellbeing consistently emerged as the most central (strongest) node in both groups' networks. Injured individuals showed higher overall connectivity and differences in the centrality of self-regulation nodes compared to controls, at 1-month and 12-months post-injury. These patterns were similarly observed in the predictive networks, including differences in cognitive and autonomic self-regulation influences.

CONCLUSIONS

Network analyses highlighted the crucial role of psychological health and self-regulation, in promoting optimal wellbeing and effective recovery. Post-traffic injury, increased connectivity indicated prolonged vulnerability for at least a year, underscoring the need of ongoing support beyond the initial improvements. A comprehensive approach that prioritizes psychological health and self-regulation through psychologically informed services, early psychological screening, and interventions promoting cognitive and autonomic self-regulation is crucial for mitigating morbidity and facilitating recovery.

TRIAL REGISTRATION

IMPRINT study, ACTRN 12616001445460.

Suicidal behaviour in adolescents: A network analysis

OBJECTIVE

Suicidal behaviour has not yet been analysed from a network approach in adolescent samples. It is imperative to incorporate new psychological models to understand suicidal behaviour from a different perspective. The main objective of this work was twofold: (a) to examine suicidal behaviour through network analysis and (b) to estimate the psychological network between suicidal behaviour and protective and risk factors in school-age adolescents.

METHOD

Participants were 443 students (M=14.3 years; SD=0.53; 51.2% female) selected incidentally from different schools. Different instruments were administered to assess suicidal behaviour, emotional and behavioural difficulties, prosocial behaviour, subjective well-being, emotional intelligence, self-esteem, depressive symptomatology, empathy, positive and negative affect, and emotional regulation.

RESULTS

The resulting network of suicidal behaviour was strongly interconnected. The most central node in terms of strength and expected influence was "Consider taking your own life". In the estimated psychological network of suicidal behaviour and risk and protective factors, the nodes with the highest strength were depressive symptomatology, positive affect, and empathic concern. The most influential nodes were those related to emotional intelligence abilities. Suicidal behaviour was positively connected to depression symptoms and negative affect, and negatively connected to self-esteem and positive affect. The results of the stability analysis indicated that the networks were accurately estimated.

CONCLUSIONS

Suicidal behaviour can be conceptualized as a dynamic, complex system of cognitive, emotional, and affective characteristics. The new psychopathological and psychometric models allow us to analyse and understand human behaviour and mental health problems from a new perspective, suggesting new forms of conceptualization, evaluation, intervention, and prevention.

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 disordered eating symptoms, big-five personality traits, and psychological distress in Chinese adults

OBJECTIVE

Previous studies have revealed associations between disordered eating symptoms, big-five personality traits, and psychological distress. However, limited research has explored these relationships as a network, including their interconnections, and even less has done so in non-Western populations. We employed network analysis to investigate the co-occurrence of disordered eating symptoms, big-five personality traits, and psychological distress in Chinese adults.

METHOD

A sample of 500 Chinese adults (256 men) completed measures assessing big-five personality traits, psychological distress, and disordered eating symptoms. The network of personality traits, psychological distress, and disordered eating symptoms was estimated, including its central and bridge nodes.

RESULTS

The central nodes in the network were the facets of openness (like adventure), extraversion (like going to social and recreational parties), and disordered eating symptoms (dissatisfaction with body weight or shape). Moreover, certain facets of neuroticism (always worrying something bad will happen), psychological distress (feeling worthless), and an inverse facet of extraversion (bored by parties with lots of people) were identified as essential bridge nodes in maintaining the structure of the network.

CONCLUSION

Our findings suggest that personality traits (e.g., openness and extraversion) and body dissatisfaction are important in maintaining the network in a community sample of Chinese adults. While future replication is needed, findings from this study suggest that individuals with negative self-thinking, predisposed neuroticism, and extraversion may be at risk of developing disordered eating symptoms.

PUBLIC SIGNIFICANCE

The present study contributes to existing knowledge by employing a network perspective to examine the associations between disordered eating symptoms, big-five personality traits, and psychological distress in a Chinese adult community sample. The identified facets of neuroticism and extraversion and symptoms of psychological distress may be worthy of targeting in the prevention and treatment of disordered eating in the Chinese context.

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.

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.

Auditing the research practices and statistical analyses of the group-level temporal network approach to psychological constructs: A systematic scoping review

Network analyses have become increasingly common within the field of psychology, and temporal network analyses in particular are quickly gaining traction, with many of the initial articles earning substantial interest. However, substantial heterogeneity exists within the study designs and methodology, rendering it difficult to form a comprehensive view of its application in psychology research. Since the field is quickly growing and since there have been many study-to-study variations in terms of choices made by researchers when collecting, processing, and analyzing data, we saw the need to audit this field and formulate a comprehensive view of current temporal network analyses. To systematically chart researchers' practices when conducting temporal network analyses, we reviewed articles conducting temporal network analyses on psychological variables (published until March 2021) in the framework of a scoping review. We identified 43 articles and present the detailed results of how researchers are currently conducting temporal network analyses. A commonality across results concerns the wide variety of data collection and analytical practices, along with a lack of consistency between articles about what is reported. We use these results, along with relevant literature from the fields of ecological momentary assessment and network analysis, to formulate recommendations on what type of data is suited for temporal network analyses as well as optimal methods to preprocess and analyze data. As the field is new, we also discuss key future steps to help usher the field's progress forward and offer a reporting checklist to help researchers navigate conducting and reporting temporal network analyses.

C-Reactive Protein and Specific Depression Symptoms Among Older Adults: An Exploratory Investigation of Multi-Plane Networks Using Cross-Sectional Data From NHANES (2017-2020)

INTRODUCTION

Studies investigating the association between C-reactive protein (CRP) and depression among older adults have yielded inconsistent results. We suspect that this may be due to varying associations between CRP and particular depression symptom criteria, and we addressed this challenge using network analysis.

METHODS

We used cross-sectional data from prepandemic National Health and Nutrition Examination Survey questionnaires (2017-2020) and included a sample of 1698 adults aged 65 years or older. Depression symptoms were assessed using the Patient Health Questionnaire-9. Unregularized Mixed Graphical Models were estimated using the R package mgm before and after adjusting for relevant sociodemographic, clinical, and lifestyle covariates.

RESULTS

In the model with no covariates, the only symptom criterion associated with CRP was "appetite problems." This association remained robust after controlling for all covariates. Although not associated with CRP, other criteria such as "fatigue" and "concentration difficulty" showed associations with important covariates for older adults such as white blood cell count or hemoglobin, respectively.

DISCUSSION

The CRP-related variability in the depression symptom network that we have demonstrated may help explain the reported inconsistencies. The present study stands as exploratory, and future research should focus on applying longitudinal designs and including several other inflammatory proteins and covariates that were not measured in the current network model.

The relationship between cognitive functioning and psychopathology in patients with psychiatric disorders: a transdiagnostic network analysis

BACKGROUND

Patients with psychiatric disorders often experience cognitive dysfunction, but the precise relationship between cognitive deficits and psychopathology remains unclear. We investigated the relationships between domains of cognitive functioning and psychopathology in a transdiagnostic sample using a data-driven approach.

METHODS

Cross-sectional network analyses were conducted to investigate the relationships between domains of psychopathology and cognitive functioning and detect clusters in the network. This naturalistic transdiagnostic sample consists of 1016 psychiatric patients who have a variety of psychiatric diagnoses, such as depressive disorders, anxiety disorders, obsessive-compulsive and related disorders, and schizophrenia spectrum and other psychotic disorders. Psychopathology symptoms were assessed using various questionnaires. Core cognitive domains were assessed with a battery of automated tests.

RESULTS

Network analysis detected three clusters that we labelled: general psychopathology, substance use, and cognition. Depressive and anxiety symptoms, verbal memory, and visual attention were the most central nodes in the network. Most associations between cognitive functioning and symptoms were negative, i.e. increased symptom severity was associated with worse cognitive functioning. Cannabis use, (subclinical) psychotic experiences, and anhedonia had the strongest total negative relationships with cognitive variables.

CONCLUSIONS

Cognitive functioning and psychopathology are independent but related dimensions, which interact in a transdiagnostic manner. Depression, anxiety, verbal memory, and visual attention are especially relevant in this network and can be considered independent transdiagnostic targets for research and treatment in psychiatry. Moreover, future research on cognitive functioning in psychopathology should take a transdiagnostic approach, focusing on symptom-specific interactions with cognitive domains rather than investigating cognitive functioning within diagnostic categories.

In Vitro Cell Death Mechanisms Induced by Dicoma anomala Root Extract in Combination with ZnPcS4 Mediated-Photodynamic Therapy in A549 Lung Cancer Cells

Globally, lung cancer has remained the leading cause of morbidity and mortality in men and women. To enhance photodynamic therapeutic effects in vitro, the present study was designed to reduce dose-dependence in photodynamic therapy (PDT) and evaluate the anticancer effects of Dicoma anomala (D. anomala) root extracts (i.e., chloroform (Chl), ethyl acetate (EtOAc), and methanol (MeOH)) on A549 lung cancer cells. The most active extract of D. anomala (D.A) was used to establish the 50% inhibitory concentration (IC₅₀), which was further used to evaluate the anticancer efficacy of D.A in combination with ZnPcS₄-mediated PDT IC₅₀. The study further evaluated cell death mechanisms by cell viability/ cytotoxicity (LIVE/DEAD^TM assay), flow cytometry (Annexin V-fluorescein isothiocyanate (FITC)-propidium iodide (PI) staining), immunofluorescence (p38, p53, Bax, and caspase 3 expressions), and fluorometric multiplex assay (caspase 8 and 9) 24 h post-treatment with IC₅₀ concentrations of ZnPcS₄-mediated PDT and D.A MeOH root extract. Morphological changes were accompanied by a dose-dependent increase in cytotoxicity, decrease in viability, and proliferation in all experimental models. Apoptosis is the highly favored cell death mechanism observed in combination therapy groups. Apoptotic activities were supported by an increase in the number of dead cells in the LIVE/DEAD^TM assay, and the upregulation of p38, p53, Bax, caspase 3, 8, and 9 apoptotic proteins. In vitro experiments confirmed the cytotoxic and antiproliferative effects of D.A root extracts in monotherapy and in combination with ZnPcS₄-mediated PDT. Taken together, our findings demonstrated that D.A could be a promising therapeutic candidate worth exploring in different types of cancer.

A topography of 21 phobic fears: network analysis in an epidemiological sample of adult twins

BACKGROUND

Few factor analyses and no network analyses have examined the structure of DSM phobic fears or tested the specificity of the relationship between panic disorder and agoraphobic fears.

METHODS

Histories of 21 lifetime phobic fears, coded as four-level ordinal variables (no fear to fear with major interference) were assessed at personal interview in 7514 adults from the Virginia Twin Registry. We estimated Gaussian Graphical Models on individual phobic fears; compared network structures of women and men using the Network Comparison Test; used community detection to determine the number and nature of groups in which phobic fears hang together; and validated the anticipated specific relationship between panic disorder and agoraphobia.

RESULTS

All networks were densely and positively inter-connected; networks of women and men were structurally similar. Our most frequent and stable solution identified four phobic clusters: (i) blood-injection, (ii) social-agoraphobia, (iii) situational, and (iv) animal-disease. Fear of public restrooms and of diseases clustered with animal and not, respectively, social and blood-injury phobias. When added to the network, the three strongest connections with lifetime panic disorder were all agoraphobic fears: being in crowds, going out of the house alone, and being in open spaces.

CONCLUSIONS

Using network analyses applied to a large epidemiologic twin sample, we broadly validated the DSM-IV typography but did not entirely support the distinction of agoraphobic and social phobic fears or the DSM placements for fears of public restrooms and diseases. We found strong support for the specificity of the relationship between panic disorder and agoraphobic fears.

Sex Differences in Depressive Symptom Networks Among Community-Dwelling Older Adults

BACKGROUND

Compared to males, an increased prevalence of depression has been reported in older females consistently over time. Sex differences in depressive symptom networks may help explain the underlying causes of this increased vulnerability for females.

OBJECTIVE

This cross-sectional study investigated the sex differences in depressive symptom networks among community-dwelling older adults in South Korea.

METHODS

The analysis was based on the 2019 Korean Community Health Survey data targeting adults aged 65 years or older. Using network analysis, depressive symptom networks were constructed according to the items listed in the Patient Health Questionnaire-9 for propensity score-matched male and female groups. Strength centrality and network stability were tested. A network comparison test was performed to investigate the difference between the networks based on the invariance of global strength, network structure, edge strength, and specific centrality measures.

RESULTS

Symptoms central to the network were similar between sexes, which were suicidal ideation, hopelessness, and psychomotor retardation/agitation. However, the global structure and network structure differed between sexes. The female symptom network showed more strengthened edges. Notably, four edges-loss of interest-hopelessness, sleep disturbance-low energy/fatigue, loss of interest-concentration difficulty, and worthlessness-concentration difficulty-were more pronounced in the female network. Strength centrality did not differ between the two networks.

DISCUSSION

Our results may help guide future research and clinical interventions for female depression. In addition, educating health professionals on the differences in depressive symptom presentation will be crucial to ensuring that older female adults receive appropriate treatment.

The network analysis of depressive symptoms before and after two weeks of antidepressant treatment

BACKGROUND

A substantial proportion of patients do not benefit from selective serotonin reuptake inhibitors (SSRIs). We used network analysis to examine changes in symptom associations over time to identify SSRIs treatment targets for patients with major depressive disorder (MDD).

METHODS

This study was a post-hoc analysis of data originated from the 2-week open-label phase of a multicenter clinical trial. A total of 474 participants who completed 2-week paroxetine treatment and subsequent evaluation were included in this analysis. The sample was divided into early improvement (a reduction of the HAMD-17 total score ⩾20% at week 2) and not early improvement. The network analysis was performed to compare the pattern of relationships among depressive symptoms at baseline and endpoint. In addition, we compared the network structure of the participants who achieved early improvement with those without early improvement.

RESULTS

We found that the network structure and global strength increased significantly from baseline to endpoint (P<0.05). The baseline network of early improvers was more strongly connected than that of the participants who did not reach early improvement, and the global strength was significantly different (P = 0.049). Psychological anxiety and depressed mood were central symptoms of the early improvers, while somatic anxiety, insomnia, gastrointestinal symptoms and feelings of guilt were central in the network among the participants who did not show early improvement.

CONCLUSIONS

The connectivity of symptom network significantly increased with treatment. The baseline network connectivity of symptoms is tighter in early improvers than those without early improvement, and their central symptoms are different.

Dual case study of continued use vs cessation of cannabis in psychosis: a theoretically informed approach to a hard problem

Purpose

Cannabis use in the context of psychosis has been shown to have a negative impact on prognosis and yet it is difficult to treat. Recent randomized controlled trials all have negative findings and novel approaches is sought after. This paper aims to use an embodied cognition framework to add to the understanding of cannabis use in psychosis.

Design/methodology/approach

The paper presents longitudinal, qualitative data on two individuals diagnosed with schizophrenia and using cannabis at least twice weekly prior to inclusion in the study. Factors influencing cannabis use were mapped in dialogue with the participants. Each participant was interviewed six times over the course of a year. The analysis was informed theoretically to describe processes maintaining or ameliorating cannabis use over time.

Findings

This study shows that a systems approach for understanding changes in cannabis use is meaningful; the richness of observations add to the understanding of differences in outcomes. Findings suggest that reductions in cannabis use in psychosis could be dependent on synergistic effects between contextual conditions. Attending closer to the experience of patients may help inform future interventions. However, interventions focusing on single mechanisms may be futile, if an array of individual, formative experiences are a prerequisite for change. A systemic understanding of dual diagnosis calls for tailored, individualized interventions.

Originality/value

The research tests a novel systemic perspective on cannabis use in psychosis by applying it to qualitative longitudinal data. Adding a systemic perspective may help develop future interventions addressing cannabis use in psychosis, which has long been considered a “hard problem” in dual diagnosis treatment.

Risk and Protective Factors in Adolescent Suicidal Behaviour: A Network Analysis

Given that death by suicide continues to rank among the top three causes of death during adolescence, new psychological models may contribute critical insight towards understanding the complex interactions between risk and protective factors in suicidal behaviour. The main objective of this study was to analyse the psychological network structure of suicidal behaviour and putative risk and protective factors in school-aged adolescents. Methods: Stratified random cluster sampling was performed. The final sample comprised 1790 students (53.7% female, M = 15.7 years, SD = 1.26). Instruments were administered to assess suicidal behaviour, emotional and behavioural difficulties, prosocial behaviour, subjective well-being, self-esteem, depressive symptomatology, academic performance, socio-economic status, school engagement, bullying, and cyberbullying. Results: In the estimated psychological network, the node with the highest strength was depressive symptomatology, and that with the highest expected influence value was bullying. Suicidal behaviour was positively connected to symptoms of depression and behavioural problems. In addition, suicidal behaviour was negatively connected to self-esteem and personal well-being. The results of the stability analysis indicated that the network was accurately estimated. Conclusions: Suicidal behaviour can be conceptualised as a dynamic, complex system of cognitive, emotional, and affective characteristics. New psychological models allow us to analyse and understand human behaviour from a new perspective, suggesting new forms of conceptualisation, evaluation, intervention, and prevention.

Ten ignored questions for stress psychology research

Stress psychology is an interesting and important interdisciplinary research field. In this perspective article, we briefly discuss 10 challenges related to the conceptual definition, research methodology, and translation in the field of stress that do not receive sufficient attention or are ignored entirely. Future research should attempt to integrate a comprehensive stress conceptual framework into a multidimensional comprehensive stress model, incorporating subjective and objective indicators as comprehensive measures. The popularity of machine learning, cognitive neuroscience, and gene epigenetics is a promising approach that brings innovation to the field of stress psychology. The development of wearable devices that precisely record physiological signals to assess stress responses in naturalistic situations, standardize real-life stressors, and measure baselines presents challenges to address in the future. Conducting large individualized and digital intervention studies could be crucial steps in enhancing the translation of research.

The causal systems approach to prolonged grief: Recent developments and future directions

The network theory of prolonged grief posits that causal interactions among symptoms of prolonged grief play a significant role in their coherence and persistence as a syndrome. Drawing on recent developments in the broader network approach to psychopathology, we argue that advancing our understanding of the causal system that gives rise to prolonged grief will require that we (a) strengthen our assessment of each component of the grief syndrome, (b) investigate intra-individual relationships among grief components as they evolve over time within individuals, (c) incorporate biological and social components into network studies of grief, and (d) generate formal theories that posit precisely how these biological, psychological, and social components interact with one another to give rise to prolonged grief disorder.

Polygenic risk for immuno-metabolic markers and specific depressive symptoms: A multi-sample network analysis study

BACKGROUND

About every fourth patient with major depressive disorder (MDD) shows evidence of systemic inflammation. Previous studies have shown inflammation-depression associations of multiple serum inflammatory markers and multiple specific depressive symptoms. It remains unclear, however, if these associations extend to genetic/lifetime predisposition to higher inflammatory marker levels and what role metabolic factors such as Body Mass Index (BMI) play. It is also unclear whether inflammation-symptom associations reflect direct or indirect associations, which can be disentangled using network analysis.

METHODS

This study examined associations of polygenic risk scores (PRSs) for immuno-metabolic markers (C-reactive protein [CRP], interleukin [IL]-6, IL-10, tumour necrosis factor [TNF]-α, BMI) with seven depressive symptoms in one general population sample, the UK Biobank study (n = 110,010), and two patient samples, the Munich Antidepressant Response Signature (MARS, n = 1058) and Sequenced Treatment Alternatives to Relieve Depression (STAR*D, n = 1143) studies. Network analysis was applied jointly for these samples using fused graphical least absolute shrinkage and selection operator (FGL) estimation as primary analysis and, individually, using unregularized model search estimation. Stability of results was assessed using bootstrapping and three consistency criteria were defined to appraise robustness and replicability of results across estimation methods, network bootstrapping, and samples.

RESULTS

Network analysis results displayed to-be-expected PRS-PRS and symptom-symptom associations (termed edges), respectively, that were mostly positive. Using FGL estimation, results further suggested 28, 29, and six PRS-symptom edges in MARS, STAR*D, and UK Biobank samples, respectively. Unregularized model search estimation suggested three PRS-symptom edges in the UK Biobank sample. Applying our consistency criteria to these associations indicated that only the association of higher CRP PRS with greater changes in appetite fulfilled all three criteria. Four additional associations fulfilled at least two consistency criteria; specifically, higher CRP PRS was associated with greater fatigue and reduced anhedonia, higher TNF-α PRS was associated with greater fatigue, and higher BMI PRS with greater changes in appetite and anhedonia. Associations of the BMI PRS with anhedonia, however, showed an inconsistent valence across estimation methods.

CONCLUSIONS

Genetic predisposition to higher systemic inflammatory markers are primarily associated with somatic/neurovegetative symptoms of depression such as changes in appetite and fatigue, consistent with previous studies based on circulating levels of inflammatory markers. We extend these findings by providing evidence that associations are direct (using network analysis) and extend to genetic predisposition to immuno-metabolic markers (using PRSs). Our findings can inform selection of patients with inflammation-related symptoms into clinical trials of immune-modulating drugs for MDD.

Network structure of psychotic-like experiences in adolescents: Links with risk and protective factors

AIM

The main goal was to analyse the network structure of psychotic-like experiences (PLEs) in a large sample of adolescents. In addition, the network structure between PLEs and putative risk (mental health difficulties, suicidal behaviour, depression symptoms) and protective factors (prosocial behaviour, subjective well-being, self-esteem) for psychosis was analysed.

METHODS

The sample compromised a total of 1790 adolescents (M=15.7 years; SD=1.26), 816 men (45.6%), selected by stratified random cluster sampling. Various tools were used to measure PLEs, general psychopathology, suicide ideation and behaviour, depression symptoms, prosocial behaviour, subjective well-being, and self-esteem. The Gaussian graphical model for continuous variables and Ising model for binary variables were used for network estimation.

RESULTS

The PLEs estimated network was strongly interconnected. Unusual perceptual experiences were among the most central nodes. The average predictability of this network was 16.41%. The PLEs and risk and protective factors estimated network showed a high degree of interconnectedness between PLEs and psychopathology domains. PLEs, behavioural problems, and emotional symptoms were among the most central nodes. The mean predictability of this network was 43.46%. The results of the stability and accuracy analysis indicated that networks were accurately estimated.

CONCLUSIONS

At population level, extended psychosis phenotype can be conceptualized as a network of interacting cognitive, emotional, and behavioural features. The network model allows us to understand psychosis risk, at the same time opening new lines of study in the mental health arena.

A network analysis of culturally relevant anxiety sensitivity and posttraumatic stress disorder symptoms in Cambodians

The Anxiety Sensitivity Index (ASI) measures fears of anxiety-related symptoms based on respondent beliefs about their harmfulness. This is the first network analysis of anxiety sensitivity and PTSD, and the first to explore an addendum of culturally salient fears in such an analysis. The purpose of our study was to test whether relations among PTSD symptoms and facets of anxiety sensitivity, observed clinically, can be visualized by this approach. Using network analysis, we examined in a Cambodian population the relationship of PTSD symptoms to the standard Anxiety Sensitivity Index (ASI) and to an ASI Cambodian Addendum (ASICA) that taps culturally salient fears of somatic symptoms among Cambodians not assessed in the standard ASI. Computing relative importance networks, we found that the ASI subscales, ASICA, and PTSD subscales were strongly interconnected, with the ASICA having the strongest outstrength centrality. In the network analysis of the ASI subscales, disaggregated ASICA, and PTSD subscales, several of the ASICA items had very high outstrength. The results show that fear of mental and physical symptoms of anxiety should be a key part of the evaluation of trauma-related disorder, and that those fears should be targeted. It also suggests the need for ASI addenda to assess concerns about anxiety symptoms salient for certain cultures that are not assessed by the standard ASI: among Cambodian populations, fear of cold hands and feet, "out of energy in the arms and legs," neck soreness, tinnitus, and dizziness on standing.

The network structure of schizotypy in the general population

Schizotypal personality traits show similarity with schizophrenia at various levels of analysis. It is generally agreed that schizotypal personality is multidimensional; however, it is still debated whether impulsive nonconformity should be incorporated into theories and measurement of schizotypy. In addition, relatively little is known about the network structure of the four-dimensional model of schizotypal personality. To estimate the network structure of schizotypy, we used data from participants recruited from the community (N = 11,807) who completed the short version of the Oxford-Liverpool Inventory of Feelings and Experiences, a widespread self-report instrument that assesses the positive, negative, disorganised and impulsive domains of schizotypy. We performed community detection, then examined differences between communities in terms of centralities and compared the strength of edges within and between communities. We found communities that almost perfectly corresponded to the a priori-defined subscales (93% overlap, normalised mutual information = 0.74). Items in the disorganisation community had higher closeness centrality relative to items in the other communities (Cliff's Δs ranged from 0.55 to 0.83) and weights of edges within the disorganisation community were stronger as compared to the negative schizotypy and impulsive nonconformity communities (Cliff's Δs = 0.33). Our findings imply that the inclusion of impulsive nonconformity items does not dilute the classical three-factor structure of positive, negative and disorganised schizotypy. The high closeness centrality of disorganisation concurs with theories positing that cognitive slippage and associative loosening are core features of the schizophrenic phenotype.

Person-specific networks in psychopathology: Past, present, and future

In the psychological network approach, mental disorders such as major depressive disorder are conceptualized as networks. The network approach focuses on the symptom structure or the connections between symptoms instead of the severity (i.e., mean level) of a symptom. To infer a person-specific network for a patient, time-series data are needed. By far the most common model to statistically model the person-specific interactions between symptoms or momentary states has been the vector autoregressive (VAR) model. Although the VAR model helps to bring psychological network theory into clinical research and closer to clinical practice, several discrepancies arise when we map the psychological network theory onto the VAR-based network models. These challenges and possible solutions are discussed in this review.

Quantifying the Reliability and Replicability of Psychopathology Network Characteristics

Pairwise Markov random field networks-including Gaussian graphical models (GGMs) and Ising models-have become the "state-of-the-art" method for psychopathology network analyses. Recent research has focused on the reliability and replicability of these networks. In the present study, we compared the existing suite of methods for maximizing and quantifying the stability and consistency of PMRF networks (i.e., lasso regularization, plus the bootnet and NetworkComparisonTest packages in R) with a set of metrics for directly comparing the detailed network characteristics interpreted in the literature (e.g., the presence, absence, sign, and strength of each individual edge). We compared GGMs of depression and anxiety symptoms in two waves of data from an observational study (n = 403) and reanalyzed four posttraumatic stress disorder GGMs from a recent study of network replicability. Taken on face value, the existing suite of methods indicated that overall the network edges were stable, interpretable, and consistent between networks, but the direct metrics of replication indicated that this was not the case (e.g., 39-49% of the edges in each network were unreplicated across the pairwise comparisons). We discuss reasons for these apparently contradictory results (e.g., relying on global summary statistics versus examining the detailed characteristics interpreted in the literature) and conclude that the limited reliability of the detailed characteristics of networks observed here is likely to be common in practice, but overlooked by current methods. Poor replicability underpins our concern surrounding the use of these methods, given that generalizable conclusions are fundamental to the utility of their results.

The network structure of schema modes

A fundamental question in psychotherapy is whether interventions should target client problems (i.e., problem-focused approaches) or client strengths (i.e., strength-focused approaches). In this study, we first propose to address this question from a network perspective on schema modes (i.e., healthy or dysfunctional patterns of co-occurring emotions, cognitions, and behaviours). From this perspective, schema modes mutually influence each other (e.g., healthy modes reduce dysfunctional modes). Recent evidence suggests that changes in modes that are strongly associated to other modes (i.e., central modes) could be associated with greater treatment effects. We therefore suggest research should investigate the relative centrality of healthy and dysfunctional modes. To make an exploratory start, we investigated the cross-sectional network structure of schema modes in a clinical (comprising individuals diagnosed with paranoid, narcissistic, histrionic, and Cluster C personality disorders) and non-clinical sample. Results showed that, in both samples, the Healthy Adult was significantly less central than several dysfunctional modes (e.g., Undisciplined Child and Abandoned and Abused Child). Although our study cannot draw causal conclusions, this finding could suggest that weakening dysfunctional modes (compared to strengthening the Healthy Adult) might be more effective in decreasing other dysfunctional modes. Our study further indicates that several schema modes are negatively associated, which could suggest that decreasing one might increase another. Finally, the Healthy Adult was among the modes that most strongly discriminated between clinical and non-clinical individuals. Longitudinal and experimental research into the network structure of schema modes is required to further clarify the relative influence of schema modes.

Unique longitudinal relationships between symptoms of psychopathology in youth: A cross-lagged panel network analysis in the ABCD study

BACKGROUND

The network theory suggests that psychopathology may reflect causal relationships between individual symptoms. Several studies have examined cross-sectional relationships between individual symptoms in youth. However, these studies cannot address the directionality of the temporal relationships hypothesized by the network theory. Therefore, we estimated the longitudinal relationships between individual internalizing, externalizing, and attention symptoms in youth.

METHODS

Data from 4,093 youth participants in the Adolescent Brain Cognitive Development (ABCD) study were used. Symptoms were assessed using the Brief Problem Monitor, which was administered at three time points spaced six months apart. Unique longitudinal relationships between symptoms at T1 and T2 were estimated using cross-lagged panel network modeling. Network replicability was assessed by comparing this network to an identically estimated replication network of symptoms at T2 predicting symptoms at T3.

RESULTS

After controlling for all other symptoms and demographic covariates, depressed mood, inattention, and worry at T1 were most predictive of other symptoms at T2. In contrast, threats of violence and destructiveness at T2 were most prospectively predicted by other symptoms at T1. The reciprocal associations between depressed mood and worthlessness were among the strongest bivariate relationships in the network. Comparisons between the original network and the replication network (correlation between edge lists = .61; individual edge replicability = 64%-84%) suggested moderate replicability.

CONCLUSIONS

Although causal inferences are precluded by the observational design and methodological considerations, these findings demonstrate the directionality of relationships between individual symptoms in youth and highlight depressed mood, inattention, and worry as potential influencers of other symptoms.

Applying network analysis to investigate the links between dimensional schizotypy and cognitive and affective empathy

BACKGROUND

Although impairment in empathy has been reported in schizophrenia spectrum disorders, little is known about the relationship between empathy and schizotypal traits. This study examines this relationship by applying network analysis to a large sample collected at 18-months follow-up in a longitudinal dataset.

METHODS

One thousand four hundred and eighty-six college students were recruited and completed a set of self-reported questionnaires on empathy, schizotypy, depression, anxiety and stress. Networks were constructed by taking the subscale scores of these measures as nodes and partial correlations between each pair of nodes as edges. Network Comparison Tests were performed to investigate the differences between individuals with high and low schizotypy.

RESULTS

Cognitive and affective empathy were strongly connected with negative schizotypy in the network. Physical and social anhedonia showed high centrality measured by strength, closeness and betweenness while anxiety and stress showed high expected influence. Predictability ranged from 22.4% (personal distress) to 79.9% (anxiety) with an average of 54.4%. Compared with the low schizotypy group, the high schizotypy group showed higher global strength (S = 0.813, p < 0.05) and significant differences in network structure (M = 0.531, p < 0.001) and strength of edges connecting empathy with schizotypy (adjusted ps < 0.05).

LIMITATIONS

Only self-rating scales were used, and disorganized schizotypy was not included.

CONCLUSIONS

Our findings suggest that the cognitive and affective components of empathy and dimensions of schizotypy are closely related in the general population and their network interactions may play an important role in individuals with high schizotypy.

Using network analysis to examine links between individual depressive symptoms, inflammatory markers, and covariates

BACKGROUND

Studies investigating the link between depressive symptoms and inflammation have yielded inconsistent results, which may be due to two factors. First, studies differed regarding the specific inflammatory markers studied and covariates accounted for. Second, specific depressive symptoms may be differentially related to inflammation. We address both challenges using network psychometrics.

METHODS

We estimated seven regularized Mixed Graphical Models in the Netherlands Study of Depression and Anxiety (NESDA) data (N = 2321) to explore shared variances among (1) depression severity, modeled via depression sum-score, nine DSM-5 symptoms, or 28 individual depressive symptoms; (2) inflammatory markers C-reactive protein (CRP), interleukin 6 (IL-6), and tumor necrosis factor α (TNF-α); (3) before and after adjusting for sex, age, body mass index (BMI), exercise, smoking, alcohol, and chronic diseases.

RESULTS

The depression sum-score was related to both IL-6 and CRP before, and only to IL-6 after covariate adjustment. When modeling the DSM-5 symptoms and CRP in a conceptual replication of Jokela et al., CRP was associated with 'sleep problems', 'energy level', and 'weight/appetite changes'; only the first two links survived covariate adjustment. In a conservative model with all 38 variables, symptoms and markers were unrelated. Following recent psychometric work, we re-estimated the full model without regularization: the depressive symptoms 'insomnia', 'hypersomnia', and 'aches and pain' showed unique positive relations to all inflammatory markers.

CONCLUSIONS

We found evidence for differential relations between markers, depressive symptoms, and covariates. Associations between symptoms and markers were attenuated after covariate adjustment; BMI and sex consistently showed strong relations with inflammatory markers.

[Network model of mental disorders: Application and interest in post-stroke depression]

In contrast to the classic models in psychopathology, the network model considers that the temporal interactions between symptoms are the causes of their occurrence. This model could also be particularly suitable for understanding the processes involved in post-stroke depression. The aim of this paper is to perform a network analysis in order to describe the temporal dynamic of the links existing between depression symptoms during the acute phase after stroke. Twenty-five patients (64% male, mean age 58.1±14.9 years old) hospitalized for a minor stroke (no neurocognitive or motor impairment) were involved in an Ecological Momentary Assessment methodology-based study. They used a smartphone application in order to complete four brief questionnaires each day during the week after hospital discharge. The questionnaire included 7-point Likert scales to measure the severity of the following depressive symptoms: sadness, anhedonia, fatigue, diminished concentration ability, negative thoughts on oneself, pessimism. We used Multilevel Vector Autoregressive analysis to describe the temporal links between those symptoms. We used the software R 3.6.0 with the mlVAR package. The p-value was set at .05. The results show two independent symptoms networks. The first one involves the anhedonia, fatigue, negative thoughts on oneself and sadness. It shows that: anhedonia predicts the activation of later fatigue (β=0.135, P=0.037) and later negative thoughts (β=0.152, P=0.019); negative thoughts predict later negative thoughts (β=0.143, P=0.028) and later sadness (β=0.171, P=0.021); fatigue predicts later fatigue (β=0.261, P<0.000). Pessimism and diminished concentration ability compose the second network, and the results show that pessimism predicts later pessimism (β=0.215, P=0.012) and later diminished concentration ability (β=0.178, P=0.045). On the one hand, anhedonia thus plays an important role in the initial and progressive activation of the other symptoms of its network. On the other hand, the cognitive symptoms (negative thoughts and pessimism) cause the deterioration of the mood and the deficit of attentional abilities. Using behavioral and cognitive strategies to support patients after hospital discharge would reduce the risk of depressive complications after a stroke. This study provides convincing empirical elements for the interest of the network model for research in psychopathology and the clinical implications and perspectives allowed by network analysis.

Why we should move from reductionism and embrace a network approach to parental burnout

Network science has allowed varied scientific fields to investigate and visualize complex relations between many variables, and psychology research has begun to adopt a network perspective. In this paper, we consider how leaving behind reductionist approaches and instead embracing a network perspective can advance the field of parental burnout. Although research into parental burnout is in its early stages, we argue that a network approach to parental burnout could set the scene for radically new vistas in parental burnout research. We claim that such an approach can allow simultaneous investigations (and clear visualizations) of many variables related to parental burnout and their interactions, integrates smoothly with prior family systems theories, and prioritizes dynamic research questions. We likewise discuss potential future clinical applications, such as interventions targeting central nodes and treatment personalized to a specific family's network system. We also review practical considerations, limitations, and future directions for researchers interested in applying a network approach to parental burnout research.

Central and Peripheral Symptoms in Network Analysis are Differentially Heritable A Twin Study of Anxious Misery

BACKGROUND

Evidence suggests that depression and anxiety disorders are genetically based. Although symptoms of these internalizing disorders tend to correlate, the degree to which the related symptoms are heritable is unclear. This overlap has been conceptualized as Anxious Misery and existing research examining similar constructs of negative affect has revealed moderate heritability. However, it is unclear if some symptoms that characterize these constructs are more heritable than others. Modeling the symptom structure of Anxious Misery and examining which symptoms are most heritable may have implications for etiological models of internalizing disorders. Accordingly, the present study employed network analysis to explore the relationships across symptoms of Anxious Misery and to test if central symptoms in the network, compared to more peripheral symptoms, differ in their heritabilities.

METHODS

Twin pairs (N = 1,344 pairs) with a mean age of 39 years (SD = 16 years) completed measures of anxiety and neuroticism to represent the Anxious Misery network.

RESULTS

Panic-related symptoms were the most central in the network and were the most heritable, with genetic factors accounting for up to 59% of phenotypic variance. Peripheral symptoms were less heritable, accounting for as little as 21% of phenotypic variance. The degree of symptom heritability was strongly correlated with the degree of centrality of a symptom in the network (r = .53).

LIMITATIONS

Reliance on two self-report measures to represent Anxious Misery limits the generalizability of the findings.

CONCLUSIONS

Central and peripheral symptoms of an Anxious Misery network may differ in degree of heritability.

Exploring the interconnectedness of fatigue, depression, anxiety and potential risk and protective factors in cancer patients: a network approach

Researchers have extensively studied fatigue, depression and anxiety in cancer patients. Several risk and protective factors have been identified for these symptoms. As most studies address these constructs, independently from other symptoms and potential risk and protective factors, more insight into the complex relationships among these constructs is needed. This study used the multivariate network approach to gain a better understanding of how patients' symptoms and risk and protective factors (i.e. physical symptoms, social withdrawal, illness cognitions, goal adjustment and partner support) are interconnected. We used cross-sectional data from a sample of cancer patients seeking psychological care (n = 342). Using network modelling, the relationships among symptoms of fatigue, depression and anxiety, and potential risk and protective factors were explored. Additionally, centrality (i.e. the number and strength of connections of a construct) and stability of the network were explored. Among risk factors, the relationship of helplessness and physical symptoms with fatigue stood out as they were stronger than most other connections in the network. Among protective factors, illness acceptance was most centrally embedded within the network, indicating it had more and stronger connections than most other variables in the network. The network identified key connections with risk factors (helplessness, physical symptoms) and a key protective factor (acceptance) at the group level. Longitudinal studies should explore these risk and protective factors in individual dynamic networks to further investigate their causal role and the extent to which such networks can inform us on what treatment would be most suitable for the individual cancer patient.

Polygenic liability for schizophrenia and childhood adversity influences daily-life emotion dysregulation and psychosis proneness

OBJECTIVE

To test whether polygenic risk score for schizophrenia (PRS-S) interacts with childhood adversity and daily-life stressors to influence momentary mental state domains (negative affect, positive affect, and subtle psychosis expression) and stress-sensitivity measures.

METHODS

The data were retrieved from a general population twin cohort including 593 adolescents and young adults. Childhood adversity was assessed using the Childhood Trauma Questionnaire. Daily-life stressors and momentary mental state domains were measured using ecological momentary assessment. PRS-S was trained on the latest Psychiatric Genetics Consortium schizophrenia meta-analysis. The analyses were conducted using multilevel mixed-effects tobit regression models.

RESULTS

Both childhood adversity and daily-life stressors were associated with increased negative affect, decreased positive affect, and increased subtle psychosis expression, while PRS-S was only associated with increased positive affect. No gene-environment correlation was detected. There is novel evidence for interaction effects between PRS-S and childhood adversity to influence momentary mental states [negative affect (b = 0.07, P = 0.013), positive affect (b = -0.05, P = 0.043), and subtle psychosis expression (b = 0.11, P = 0.007)] and stress-sensitivity measures.

CONCLUSION

Exposure to childhood adversities, particularly in individuals with high PRS-S, is pleiotropically associated with emotion dysregulation and psychosis proneness.

The Symptom and Domain Structure of Psychotic Disorders: A Network Analysis Approach

Little is understood about the symptom network structure of psychotic disorders. In the current study, we aimed to examine the network structure of psychotic symptoms in a broad and transdiagnostic sample of subjects with psychotic disorders (n = 2240) and to determine whether network structure parameters vary across demographic, sampling method and clinical variables. Gaussian graphical models were estimated for 73 psychotic symptoms assessed using the Comprehensive Assessment of Symptoms and History. A 7-cluster solution (reality distortion, disorganization, catatonia, diminished expressivity, avolition/anhedonia, mania, and depression) best explained the underlying symptom structure of the network. Symptoms with the highest centrality estimates pertained to the disorganization and, to a lesser extent, negative domains. Most bridge symptoms pertained to the disorganization domain, which had a central position within the network and widespread connections with other psychopathological domains. A comparison of networks in subgroups of subjects defined by premorbid adjustment levels, treatment response, and course pattern significantly influenced both network global strength and network structure. The sampling method and diagnostic class influenced network structure but not network global strength. Subgroups of subjects with less densely connected networks had poorer outcomes or more illness severity than those with more densely connected networks. The network structure of psychotic features emphasizes the importance of disorganization symptoms as a central domain of psychopathology and raises the possibility that interventions that target these symptoms may prove of broad use across psychopathology. The network structure of psychotic disorders is dependent on the sampling method and important clinical variables.

Using Network Analysis for Examining Interpersonal Emotion Dynamics

Several contemporary models conceptualize emotion as inherently interpersonal. We demonstrate how network analysis, a class of statistical methods often used to examine intrapersonal dynamic processes, provides a potential avenue for parameterizing interpersonal emotion dynamics (and interpersonal dynamics in general). We claim that this method allows (a) observing interpersonal dynamics at various temporal levels; (b) examining interpersonal dynamics occurring through various emotional pathways; and (c) capturing variations in interpersonal networks, which can subsequently be used to predict changes in outcomes. To demonstrate the potential of this method, we used dyadic daily diary data on emotion dynamics from two samples; Sample 1 involved couples in their routine daily lives, whereas Sample 2 involved couples in their transition to parenthood. Graphical Multilevel-Vector-Autoregressive modeling was used to estimate partners' emotional networks, whereas in a second step, LASSO was used to test the predictive value of couple-level differences of the obtained networks. The analysis revealed several patterns. For example, the between-couple network of Sample 1 was more interpersonally dense, but couple-level differences in the networks' interpersonal associations were predictive of partners' relationship satisfaction over time only in Sample 2. We also include commented code implementing a new dyadmlvar R package developed for conducting this analysis.

The network approach to psychopathology: a review of the literature 2008-2018 and an agenda for future research

The network approach to psychopathology posits that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms. This approach, first posited in 2008, has grown substantially over the past decade and is now a full-fledged area of psychiatric research. In this article, we provide an overview and critical analysis of 363 articles produced in the first decade of this research program, with a focus on key theoretical, methodological, and empirical contributions. In addition, we turn our attention to the next decade of the network approach and propose critical avenues for future research in each of these domains. We argue that this program of research will be best served by working toward two overarching aims: (a) the identification of robust empirical phenomena and (b) the development of formal theories that can explain those phenomena. We recommend specific steps forward within this broad framework and argue that these steps are necessary if the network approach is to develop into a progressive program of research capable of producing a cumulative body of knowledge about how specific mental disorders operate as causal systems.

The replicability and generalizability of internalizing symptom networks across five samples

The popularity of network analysis in psychopathology research has increased exponentially in recent years. Yet, little research has examined the replicability of cross-sectional psychopathology network models, and those that have used single items for symptoms rather than multiitem scales. The present study therefore examined the replicability and generalizability of regularized partial correlation networks of internalizing symptoms within and across 5 samples (total N = 2,573) using the Inventory for Depression and Anxiety Symptoms, a factor analytically derived measure of individual internalizing symptoms. As different metrics may yield different conclusions about the replicability of network parameters, we examined both global and specific metrics of similarity between networks. Correlations within and between nonclinical samples suggested considerable global similarities in network structure (rss = .53-.87) and centrality strength (rss = .37-.86), but weaker similarities in network structure (rss = .36-.66) and centrality (rss = .04-.54) between clinical and nonclinical samples. Global strength (i.e., connectivity) did not significantly differ across all 5 networks and few edges (0-5.5%) significantly differed between networks. Specific metrics of similarity indicated that, on average, approximately 80% of edges were consistently estimated within and between all 5 samples. The most central symptom (i.e., dysphoria) was consistent within and across samples, but there were few other matches in centrality rank-order. In sum, there were considerable similarities in network structure, the presence and sign of individual edges, and the most central symptom within and across internalizing symptom networks estimated from nonclinical samples, but global metrics suggested network structure and symptom centrality had weak to moderate generalizability from nonclinical to clinical samples. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

A framework for the investigation of rare genetic disorders in neuropsychiatry

De novo and inherited rare genetic disorders (RGDs) are a major cause of human morbidity, frequently involving neuropsychiatric symptoms. Recent advances in genomic technologies and data sharing have revolutionized the identification and diagnosis of RGDs, presenting an opportunity to elucidate the mechanisms underlying neuropsychiatric disorders by investigating the pathophysiology of high-penetrance genetic risk factors. Here we seek out the best path forward for achieving these goals. We think future research will require consistent approaches across multiple RGDs and developmental stages, involving both the characterization of shared neuropsychiatric dimensions in humans and the identification of neurobiological commonalities in model systems. A coordinated and concerted effort across patients, families, researchers, clinicians and institutions, including rapid and broad sharing of data, is now needed to translate these discoveries into urgently needed therapies.

Applying a Dynamical Systems Model and Network Theory to Major Depressive Disorder

Mental disorders like major depressive disorder can be modeled as complex dynamical systems. In this study we investigate the dynamic behavior of individuals to see whether or not we can expect a transition to another mood state. We introduce a mean field model to a binomial process, where we reduce a dynamic multidimensional system (stochastic cellular automaton) to a one-dimensional system to analyse the dynamics. Using maximum likelihood estimation, we can estimate the parameter of interest which, in combination with a bifurcation diagram, reflects the expectancy that someone has to transition to another mood state. After numerically illustrating the proposed method with simulated data, we apply this method to two empirical examples, where we show its use in a clinical sample consisting of patients diagnosed with major depressive disorder, and a general population sample. Results showed that the majority of the clinical sample was categorized as having an expectancy for a transition, while the majority of the general population sample did not have this expectancy. We conclude that the mean field model has great potential in assessing the expectancy for a transition between mood states. With some extensions it could, in the future, aid clinical therapists in the treatment of depressed patients.