The pathoplasticity of dysphoric episodes: differential impact of stressful life events on the pattern of depressive symptom inter-correlations

Types of depression in patients with coronary heart disease: Results from the THORESCI study

BACKGROUND

Both coronary heart diseases (CHD) and depression are highly prevalent and bidirectionally related. The precise nature of this relationship remains unclear. Defining depressive subtypes could help unravel this relationship. Therefore, the aim of this study was to explore depressive subtypes in patients with CHD.

METHODS

1530 patients (21.3 % women, mean age: 64.7 years (SD = 10.1)) were included in latent class analysis with nine indicators derived from the PHQ-9 and BDI-II representing symptoms of depression as described in the DSM-5 criteria. The best-fitting latent class model was confirmed with double cross-validation. Classes were characterized using demographic, medical, psychiatric, and cardiovascular (risk) factors.

RESULTS

A 3-class model demonstrated the best fit to the data, resulting in a depressed (5.4 %), fatigued (13.5 %), and non-depressed class (81.1 %). Having medical comorbidities, a history of psychiatric problems, negative affectivity, and anxiety symptoms increased the odds of belonging to the depressed group (OR 3.02, 95%CI 1.19-7.68, OR 3.61, 95%CI 1.44-9.02, OR 1.16, 95%CI 1.04-1.30, and OR 1.89, 95%CI 1.66-2.15, respectively). Belonging to the fatigued group was associated with increased odds of having an elective PCI (OR 2.12, 95%CI 1.27-3.55), insufficient physical activity (OR 2.19, 95%CI 1.20-3.99), comorbid medical conditions (OR 2.15, 95%CI 1.21-3.81), a history of psychiatric problems (OR 2.25, 95%CI 1.25-4.05), and anxiety symptoms (OR 1.48, 95%CI 1.34-1.63) compared with the non-depressed group.

LIMITATIONS

Future studies should include more people with depressive symptoms.

CONCLUSIONS

Patients with CHD and medical or psychiatric risk factors should be offered support to decrease or prevent depressive or fatigue symptoms.

Multilayer depressive symptom networks in adults with bodily pain living in precarious housing or homelessness

Housing insecurity is associated with co-occurring depression and pain interfering with daily activities. Network analysis of depressive symptoms along with associated risk or protective exposures may identify potential targets for intervention in patients with co-occurring bodily pain. In a community-based sample of adults (n = 408) living in precarious housing or homelessness in Vancouver, Canada, depressive symptoms were measured by the Beck Depression Inventory; bodily pain and impact were assessed with the 36-item Short Form Health Survey. Network and bootstrap permutation analyses were used to compare depressive symptoms endorsed by Low versus Moderate-to-Severe (Mod + Pain) groups. Multilayer networks estimated the effects of risk and protective factors. The overall sample was comprised of 78% men, mean age 40.7 years, with 53% opioid use disorder and 14% major depressive disorder. The Mod + Pain group was characterized by multiple types of pain, more persistent pain, more severe depressive symptoms and a higher rate of suicidal ideation. Global network connectivity did not differ between the two pain groups. Suicidal ideation was a network hub only in the Mod + Pain group, with high centrality and a direct association with exposure to lifetime trauma. Antidepressant medications had limited impact on suicidal ideation. Guilt and increased feelings of failure represented symptoms from two other communities of network nodes, and completed the shortest pathway from trauma exposure through suicidal ideation, to the non-prescribed opioid exposure node. Interventions targeting these risk factors and symptoms could affect the progression of depression among precariously housed patients.

Stressful life events exhibit complex patterns of associations with depressive symptoms in two population-based samples using network analysis

BACKGROUND

Stressful life events (SLEs) constitute key risk factors for depression. However, previous studies examining associations between SLEs and depression have been limited by focusing on single events, combining events into broad categories, and/or ignoring interrelationships between events in statistical analyses. Network analysis comprises a set of statistical methods well-suited for assessing relationships between multiple variables and can help surpass several limitations of previous studies.

METHODS

We applied network analysis using mixed graphical models combining two large-scale population-based samples and >34,600 randomly sampled adults to investigate the associations between SLEs and current depressive symptoms in the general population.

RESULTS

Numerous SLEs were uniquely associated with specific symptoms. Strong pairwise links were observed between SLEs during the past year and individual symptoms, e.g., between having experienced illness or injury and sleeping problems, having been degraded or humiliated and feeling blue, and between financial problems and hopelessness and being worried and anxious. Several SLEs, such as financial problems, sexual abuse, and having been degraded or humiliated, were associated with symptoms across more than one timepoint. More recent SLEs were generally more strongly associated with depressive symptoms. Several life events were strongly interrelated, such as multiple forms of abuse, and financial problems, unemployment, divorce, and serious illness or injury.

LIMITATIONS

Limitations include a retrospective SLE measure, cross-sectional data, a brief self-report measure of depressive symptoms, and possible attrition bias in the sample.

CONCLUSIONS

Our findings may have implications for public health efforts seeking to improve population mental health.

Mediation of the Association Between Vascular Risk Factors and Depressive Symptoms by C-Reactive Protein

Background

This study examined whether C-reactive protein (CRP), a marker of low-grade systemic inflammation, mediates the association between vascular risk factor (VRF) burden and depressive symptoms.

Methods

We drew on the prospective design of the UK Biobank to include participants with longitudinal data on VRF burden, CRP, and depressive symptoms. Total, direct, and indirect effects were estimated using regression-based mediation models while controlling for confounding by sociodemographic factors, baseline CRP, and baseline depression. Sensitivity analyses probed the robustness of results to unmeasured confounding.

Results

We analyzed data from 10,470 participants from the UK Biobank (mean age = 56.75 years at baseline). Net of covariates, VRFs at baseline were associated with higher depressive symptoms at follow-up (total effect = 0.099; 95% CI, 0.002-0.163). CRP mediated this association (indirect effect = 0.010; 95% CI, 0.004-0.017), accounting for 10.0% (95% CI, 0.3%-30.0%) of the total effect of VRF burden on depressive symptoms. Exploratory analyses suggested that the total and indirect effects pertained to somatic depressive symptoms (tiredness and appetite).

Conclusions

These results suggest that inflammation-promoting effects of VRFs may contribute to depressive symptoms in mid- and later life. However, the mediating pathway via CRP explains only a small part of the association between VRFs and depression after accounting for important covariates and may pertain to specific depressive symptoms. Future studies leveraging similar longitudinal designs are needed to further disentangle the time-varying effects between VRFs, inflammation, and certain depressive symptoms while addressing important confounders.

Network modeling of major depressive disorder symptoms in adult women

BACKGROUND

Major depressive disorder (MDD) is one of the growing human mental health challenges facing the global health care system. In this study, the structural connectivity between symptoms of MDD is explored using two different network modeling approaches.

METHODS

Data are from 'the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders (VATSPSUD)'. A cohort of N = 2163 American Caucasian female-female twins was assessed as part of the VATSPSUD study. MDD symptoms were assessed using personal structured clinical interviews. Two network analyses were conducted. First, an undirected network model was estimated to explore the connectivity between the MDD symptoms. Then, using a Bayesian network, we computed a directed acyclic graph (DAG) to investigate possible directional relationships between symptoms.

RESULTS

Based on the results of the undirected network, the depressed mood symptom had the highest centrality value, indicating its importance in the overall network of MDD symptoms. Bayesian network analysis indicated that depressed mood emerged as a plausible driving symptom for activating other symptoms. These results are consistent with DSM-5 guidelines for MDD. Also, somatic weight and appetite symptoms appeared as the strongest connections in both networks.

CONCLUSIONS

We discuss how the findings of our study might help future research to detect clinically relevant symptoms and possible directional relationships between MDD symptoms defining major depression episodes, which would help identify potential tailored interventions. This is the first study to investigate the network structure of VATSPSUD data using both undirected and directed network models.

Impact of the COVID-19 Pandemic on Gender Differences in Depression Based on National Representative Data

BACKGROUND

Restrictions on daily life and changes in economic structure due to coronavirus disease 2019 (COVID-19) likely would have affected men and women differently. However, there is still a lack of research on the difference between men and women in the amount of change in depression during COVID-19 compared to before COVID-19. Therefore, the researchers investigated gender differences in the magnitude of increase in the prevalence of depression with its severity and individual symptoms during COVID-19 compared with pre-pandemic levels.

METHODS

The Korea National Health and Nutrition Examination Survey (KNHANES) 2016 and 2018 were used to assess depression levels pre-pandemic and the KNHANES 2020 for pandemic depression levels. Depression was evaluated using the Patient Health Questionnaire-9 (PHQ-9). To analyze the differences between men and women in the magnitude of the mental health impact of COVID-19, the researchers analyzed the weighted differences in depression prevalence, severity, and individual symptoms during the COVID-19 pandemic compared to before COVID-19 stratified by gender.

RESULTS

In men, there were significant increases in weighted prevalence for depression (1.2% percentage point; 95% confidence interval [CI], 0.0-2.3) and severe symptoms of depression (2.6-fold; 95% CI, 1.2-5.7). Among the individual symptoms of depression, significant increases during the pandemic compared to before were: little interest or pleasure in doing things, 1.26-fold; feeling tired or having little energy, 2.2-fold; and suicidal thoughts, 1.7-fold. However, there was no significant difference in prevalence, symptoms severity, and any symptom before and during COVID-19 in women.

CONCLUSIONS

Because the pandemic is likely to increase mental problems of the affected over time due to such problems as financial stress and joblessness or post-infection health issues, the researchers anticipate an increase in the prevalence of some mental illnesses. In particular, since the suicide rate of men is higher than that of women, from a public health perspective, active interventions are needed to prevent an increase in the suicide rate due to COVID-19. It is also necessary to establish national policies to overcome the psychological, social, and economic losses resulting from COVID-19.

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

BACKGROUND

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

PARTICIPANTS AND SETTINGS

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

An evolutionary investigation of depressed mood: The relationship between daily stressors and patterns of depressive symptoms

BACKGROUND AND OBJECTIVES

The situation-symptom congruence hypothesis (SSCH; (Keller & Nesse, 2006), grounded in evolutionary theory, argues that different types of adversity should lead to distinct patterns of depressive symptoms that help individuals deal with adaptive challenges. Situation-symptom congruence hypotheses were tested in this study using experience sampling methodology.

METHODS

Two hundred and sixty-five individuals, including 54% who scored at least 16 on the Center for Epidemiologic Studies Revised Depression Scale, responded to text prompts daily for up to 9 days, reporting depressive symptoms as well as the most stressful event or issue they had experienced or focused on within the past 24 h.

RESULTS

Multilevel modeling analyses indicated that the relationships between stressors and depressive symptom patterns were largely consistent with SSCH predictions. All stressors were significantly associated with symptoms hypothesized to be adaptive in response to those stressors. Moreover, in separate analyses, nine of the ten symptoms examined were either predicted by the stressors hypothesized to lead to that symptom or negatively related to stressors hypothesized to not elicit those symptoms.

LIMITATIONS

It is unclear whether the results generalize to those diagnosed with a major depressive disorder; the study did not assess actual life events.

CONCLUSIONS

Findings suggest that depressive symptoms may, in part, be adaptations that have evolved through natural selection to help individuals cope with adverse situations.

Six-year changes of posttraumatic stress symptoms and depressive symptoms among Chinese earthquake survivors: A network analysis

BACKGROUND

The risk factors of Posttraumatic Stress Symptoms (PTSS) and depressive symptoms have been well-established, but whether the network structure of their symptoms changes over time remains unclear. This study aims to examine the six-year changes of network structure of PTSS and depressive symptoms among Wenchuan earthquake adult survivors in China.

METHODS

In this pooled cross-sectional study, respondents were sampled from the same population at each of the five waves (N = 1343, 1205, 1177, 1376, and 1339). The network structure of them was investigated using network analyses.

RESULTS

The study shows network connections stable across waves were the positive connections between hyperarousal and intrusions, hyperarousal and somatic symptoms, and two dimensions of positive effects. Stable negative connections were those between depressed affects and positive effects, avoidance and depressed affects, avoidance with interpersonal symptoms and avoidance with somatic symptoms. Across waves, fearful emotion consistently was the strongest bridge symptom connecting with PTSS symptoms. However, for PTSS the strongest bridge symptom varied across time as it was avoidance in wave 1 but were hyperarousal in other four waves.

LIMITATIONS

This study was based on a pooled cross-sectional survey, which inhibits conclusions regarding causal influences between symptoms at the individual patient level.

CONCLUSIONS

The network structure of PTSS and depressive symptoms was partly stable, yet also varied across survey waves. Core symptom clusters for PTSS and depressive symptoms were avoidance and depressed affect, respectively. The central role of these clusters in PTSS and depressive symptoms has important implications to future psychiatric programs.

Revisiting the theoretical and methodological foundations of depression measurement

Depressive disorders are among the leading causes of global disease burden, but there has been limited progress in understanding the causes and treatments for these disorders. In this Perspective, we suggest that such progress crucially depends on our ability to measure depression. We review the many problems with depression measurement, including limited evidence of validity and reliability. These issues raise grave concerns about common uses of depression measures, such as diagnosis or tracking treatment progress. We argue that shortcomings arise because depression measurement rests on shaky methodological and theoretical foundations. Moving forward, we need to break with the field's tradition that has, for decades, divorced theories about depression from how we measure it. Instead, we suggest that epistemic iteration, an iterative exchange between theory and measurement, provides a crucial avenue for depression measurement to progress.

Apathy and Huntington's Disease: A Literature Review Based on PRISMA

OBJECTIVE

Although apathy is commonly reported among patients with Huntington's disease (HD), this psychiatric symptom has not yet been clearly defined or extensively studied in HD. Most researchers have adopted descriptive approaches, showing that apathy is a multidimensional entity but leaving the processes underlying its different dimensions relatively unexplored.

METHODS

A systematic review of the literature on apathy in HD, focusing on current approaches and measurement tools, was conducted.

RESULTS

Searches in PubMed and PubMed Central yielded 368 articles, 25 of which were included in the present review.

CONCLUSIONS

This systematic review suggests that more comprehensive research is needed to help shed light on apathy in HD, especially regarding its multidimensional aspect and underlying mechanisms.

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.

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

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

Which roads lead to depression in Latinas? A network analysis of prenatal depressive symptoms, discrimination, acculturative stress, and low birth weight

Although immigrant mothers from some Latinx subgroups initially achieve healthy birth outcomes despite lower socioeconomic status, this advantage deteriorates across generations in the United States. Interpersonal discrimination and acculturative stress may interact with economic hardship to predict an intergenerational cascade of emotional and biological vulnerabilities, particularly perinatal depression. Network analyses may elucidate not only how and which psychosocial experiences relate to depressive symptoms, but which symptom-to-symptom relationships emerge. This study aims to understand (1) how economic, acculturative, and discrimination stressors relate to prenatal depression and low birth weight and (2) how Latinas may respond to and cope with stressors by exploring symptom-symptom and symptom-experience relationships. A sample of 151 pregnant Latinas (predominantly foreign-born and Mexican and Central American descent) completed the EPDS and psychosocial questionnaires (discrimination, acculturation, acculturative stress, economic hardship) during pregnancy (24-32 weeks). Birth weights were recorded from postpartum medical records. We created network models using the Extended Bayesian Information Criterion Graphical Least Absolute Shrinkage and Selection Operator to estimate the relationship between variables. Discrimination exposure connected psychosocial stressors to depressive symptoms, particularly worry, crying, sadness, and self-blame. Discrimination also revealed a connection between acculturation and low birth weight. Furthermore, younger age of migration and greater acculturation levels were correlated to greater discrimination stress and low birth weights. Perinatal research in Latinas must account not only for measures of cultural adaptation but recognize how developmental exposures across the life span, including discrimination, may be associated with adverse health trajectories for a mother and her child.

Estimating the symptom structure of bipolar disorder via network analysis: Energy dysregulation as a central symptom

Using network analysis, we estimated the structure of relations among manic and depressive symptoms, respectively, in 486 patients (59% women; age: M = 37, SD = 12.1) with bipolar disorder prior to their entering a clinical trial. We computed three types of networks: (a) Gaussian graphical models (GGMs) depicting regularized partial correlations, (b) regression-based GGMs depicting nonregularized partial correlations, and (c) directed acyclic graphs (DAGs) via a Bayesian hill-climbing algorithm. Low energy and elevated energy were consistently identified as central nodes in the GGMs and as key parent nodes in the DAGs. Across analyses, pessimism about the future and depressed mood were the symptoms most strongly associated with suicidal thoughts and behavior. These exploratory analyses provide rich information about how bipolar disorder symptoms relate to one another, thereby furnishing a foundation for investigating how bipolar disorder symptoms may operate as a causal system. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Network analysis of anxiety and depressive symptoms among quarantined individuals: cross-sectional study

BACKGROUND

The mental health burden of COVID-19 has been examined in different settings. Existing research has relied on the latent variable model in assessing COVID-19-related distress. Network theory provides an alternative framework wherein symptoms are conceptualised as causal, interconnected constituents rather than outcomes of mental disorders.

AIMS

To assess networks of self-reported anxiety and depressive symptoms among quarantined individuals.

METHOD

Consenting individuals in different quarantine centres in Qatar completed the Patient Health Questionnaire Anxiety and Depression Scale. We used partial correlation network methods to illustrate interactions of self-reported psychopathology.

RESULTS

Participants with COVID-19 were significantly older and had a significantly higher proportion of males. The most central node was COVID-19, followed by thoughts of self-harm. COVID-19 status was strongly positively connected to thoughts of self-harm, which was positively connected to psychomotor changes, which were connected to decreased concentration. COVID-19 status was also positively connected to feeling anxious, which was strongly connected to inability to concentrate, which was connected to feeling afraid.

CONCLUSIONS

COVID-19 was the most influential factor, with the highest number and strength of connections to psychopathology in a network of anxiety and depressive symptoms in a quarantine setting. Beyond the resolution of the infection, therapeutic interventions targeting psychomotor changes might prove beneficial in reducing suicidality among quarantined individuals with COVID-19. Follow-up with mental health services after COVID-19 infection is needed to restore psychological well-being. Further research is needed to understand the short- and long-term psychological effects of COVID-19, and the outcomes of different therapeutic interventions.

Symptom networks in major depression do not diverge across sex, familial risk, and environmental risk

BACKGROUND

Major depression (MD) is a heterogeneous disorder in terms of its symptoms. Symptoms vary by presence of risk factors such as female sex, familial risk, and environmental adversity. However, it is unclear if these factors also influence interactions between symptoms. This study investigates if symptom networks diverge across sex, familial risk, and adversity.

METHODS

We included 9713 subjects from the general population who reported a lifetime episode of MD based on DSM-IV criteria. The survey assessed a wide set of symptoms, both from within the DSM criteria as well as other symptoms commonly experienced in MD. We compared symptom endorsement rates across sex, age at onset, family history and environmental adversity. We used the Network Comparison Test to test for symptom network differences across risk factors.

RESULTS

We found differences in symptom endorsement between groups. For instance, participants with an early onset of MD reported suicidal ideation nearly twice as often compared to participants with a later onset. We did not find any robust differences in symptom networks, which suggests that symptom networks do not diverge across sex, familial risk, and adversity.

LIMITATIONS

We estimated symptom networks of individuals during their worst lifetime episode of MD. Network differences might exist in a prodromal stage, while disappearing in full-blown MD (equifinality). Furthermore, as we used retrospective reports, results could be prone to recall bias.

CONCLUSIONS

Despite MD's heterogeneous symptomatology, interactions between symptoms are stable across risk factors and sex.

The association of phasic irritability (aggressive outbursts) and tonic irritability (irritable mood) to depression occurrences, symptoms, and subtypes

BACKGROUND

Research among adults has rarely differentiated between tonic irritability (i.e., irritable mood) and phasic irritability (i.e., aggressive outbursts) with respect to multiple dimensions of depression. The current study explored both tonic and phasic irritability in relation to depression severity, depression chronicity, age of depression onset, individual depressive symptom, and depression subtypes.

METHODS

The study included participants (N = 5692) from the National Comorbidity Survey - Replication (NCS-R) part two. The NCS-R used lay-administered, fully standardized diagnostic interviews. The current study implemented linear models, generalized linear models, Cox proportional hazard model, and latent class regression.

RESULTS

Both types of irritability were significantly associated with greater risk for MDD diagnosis, as well as risk for having at least one depressive symptom, early MDE onset, and MDE chronicity. Both phasic and tonic irritability were associated with greater odds of specific depressive symptoms and were differentially related to distinct depressive symptom constellations. Phasic irritability related only to severe depression. Lastly, both phasic and tonic irritability was associated with suicidal ideation, but only phasic irritability was associated with a suicide plan and attempt, above and beyond depression subtypes.

CONCLUSIONS

Both phasic and tonic irritability differentially related to almost all aspects of depression in adults. Specifically, tonic irritability showed overall stronger associations with various depressive features, whereas phasic irritability marked higher depressive severity.

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.

Network Analysis of Depressive Symptoms Among Residents of Wuhan in the Later Stage of the COVID-19 Pandemic

Background: Depression has been a common mental health problem during the COVID-19 epidemic. From a network perspective, depression can be conceptualized as the result of mutual interactions among individual symptoms, an approach that may elucidate the structure and mechanisms underlying this disorder. This study aimed to examine the structure of depression among residents in Wuhan, the epicenter of the COVID-19 outbreak in China, in the later stage of the COVID-19 pandemic. Methods: A total of 2,515 participants were recruited from the community via snowball sampling. The Patient Health Questionnaire was used to assess self-reported depressive symptoms with the QuestionnaireStar program. The network structure and relevant centrality indices of depression were examined in this sample. Results: Network analysis revealed Fatigue, Sad mood, Guilt and Motor disturbances as the most central symptoms, while Suicide and Sleep problems had the lowest centrality. No significant differences were found between women and men regarding network structure (maximum difference = 0.11, p = 0.44) and global strength (global strength difference = 0.04; female vs. male: 3.78 vs. 3.83, p = 0.51), a finding that suggests there are no gender differences in the structure or centrality of depressive symptoms. Limitations: Due to the cross-sectional study design, causal relationships between these depressive symptoms or dynamic changes in networks over time could not be established. Conclusions: Fatigue, Sad mood, Guilt, and Motor disturbances should be prioritized as targets in interventions and prevention efforts to reduce depression among residents in Wuhan, in the later stage of the COVID-19 pandemic.

Network analysis of depressive symptoms in Hong Kong residents during the COVID-19 pandemic

In network theory depression is conceptualized as a complex network of individual symptoms that influence each other, and central symptoms in the network have the greatest impact on other symptoms. Clinical features of depression are largely determined by sociocultural context. No previous study examined the network structure of depressive symptoms in Hong Kong residents. The aim of this study was to characterize the depressive symptom network structure in a community adult sample in Hong Kong during the COVID-19 pandemic. A total of 11,072 participants were recruited between 24 March and 20 April 2020. Depressive symptoms were measured using the Patient Health Questionnaire-9. The network structure of depressive symptoms was characterized, and indices of "strength", "betweenness", and "closeness" were used to identify symptoms central to the network. Network stability was examined using a case-dropping bootstrap procedure. Guilt, Sad Mood, and Energy symptoms had the highest centrality values. In contrast, Concentration, Suicide, and Sleep had lower centrality values. There were no significant differences in network global strength (p = 0.259), distribution of edge weights (p = 0.73) and individual edge weights (all p values > 0.05 after Holm-Bonferroni corrections) between males and females. Guilt, Sad Mood, and Energy symptoms were central in the depressive symptom network. These central symptoms may be targets for focused treatments and future psychological and neurobiological research to gain novel insight into depression.

Classifying patients with depressive and anxiety disorders according to symptom network structures: A Gaussian graphical mixture model-based clustering

Patients with mental disorders often suffer from comorbidity. Transdiagnostic understandings of mental disorders are expected to provide more accurate and detailed descriptions of psychopathology and be helpful in developing efficient treatments. Although conventional clustering techniques, such as latent profile analysis, are useful for the taxonomy of psychopathology, they provide little implications for targeting specific symptoms in each cluster. To overcome these limitations, we introduced Gaussian graphical mixture model (GGMM)-based clustering, a method developed in mathematical statistics to integrate clustering and network statistical approaches. To illustrate the technical details and clinical utility of the analysis, we applied GGMM-based clustering to a Japanese sample of 1,521 patients (Mage = 42.42 years), who had diagnostic labels of major depressive disorder (MDD; n = 406), panic disorder (PD; n = 198), social anxiety disorder (SAD; n = 116), obsessive-compulsive disorder (OCD; n = 66), comorbid MDD and any anxiety disorder (n = 636), or comorbid anxiety disorders (n = 99). As a result, we identified the following four transdiagnostic clusters characterized by i) strong OCD and PD symptoms, and moderate MDD and SAD symptoms; ii) moderate MDD, PD, and SAD symptoms, and weak OCD symptoms; iii) weak symptoms of all four disorders; and iv) strong symptoms of all four disorders. Simultaneously, a covariance symptom network within each cluster was visualized. The discussion highlighted that the GGMM-based clusters help us generate clinical hypotheses for transdiagnostic clusters by enabling further investigations of each symptom network, such as the calculation of centrality indexes.

Using clinical expertise and empirical data in constructing networks of trauma symptoms in refugee youth

Background: In recent years, many adolescents have fled their home countries due to war and human rights violations, consequently experiencing various traumatic events and putting them at risk of developing mental health problems. The symptomatology of refugee youth was shown to be multifaceted and often falling outside of traditional diagnoses. Objective: The present study aimed to investigate the symptomatology of this patient group by assessing the network structure of a wide range of symptoms. Further, we assessed clinicians' perceptions of symptoms relations in order to evaluate the clinical validity of the empirical network. Methods: Empirical data on Post-Traumatic Stress Disorder (PTSD), depression and other trauma symptoms from N = 366 refugee youth were collected during the routine diagnostic process of an outpatient centre for refugee youth in Germany. Additionally, four clinicians of this outpatient centre were asked how they perceive symptom relations in their patients using a newly developed tool. Separate networks were constructed based on 1) empirical symptom data and 2) clinicians' perceived symptom relations (PSR). Results: Both the network based on empirical data and the network based on clinicians' PSR showed that symptoms of PTSD and depression related most strongly within each respective cluster (connected mainly via sleeping problems), externalizing symptoms were somewhat related to PTSD symptoms and intrusions were central. Some differences were found within the clinicians' PSR as well as between the PSR and the empirical network. Still, the general PSR-network structure showed a moderate to good fit to the empirical data. Conclusion: Our results suggest that sleeping problems and intrusions play a central role in the symptomatology of refugee children, which has tentative implications for diagnostics and treatment. Further, externalizing symptoms might be an indicator for PTSD-symptoms. Finally, using clinicians' PSR for network construction offered a promising possibility to gain information on symptom networks and their clinical validity.

Network Analysis of Psychopathology: Controversies and Challenges

Empirical publications inspired by the network approach to psychopathology have increased exponentially in the twenty-first century. The central idea that an episode of mental disorder arises from causal interactions among its symptomatic elements has especially resonated with those clinical scientists whose disenchantment with traditional categorical and dimensional approaches to mental illness has become all too apparent. As the field has matured, conceptual and statistical concerns about the limitations of network approaches to psychopathology have emerged, inspiring the development of novel methods to address these concerns. Rather than reviewing the vast empirical literature, I focus instead on the issues and controversies regarding this approach and sketch directions where the field might go next.

Psychological State Among the General Chinese Population Before and During the COVID-19 Epidemic: A Network Analysis

Background: The infectious disease Coronavirus Disease 2019 (COVID-19) outbroke in 2019 spread to multiple countries. The quick spread of the virus and isolation strategies may trigger psychological problems. Our aim was to explore the dynamic network structure of the psychological state before and during the epidemic. Methods: A web-based survey was conducted in two stages: the T1 stage (1 January 2019 to 31 December 2019) and the T2 stage (1 February 2020 to 8 March 2020). In both stages, the Patient Health Questionnaire-9, General Anxiety Disorder-7, and Pittsburgh Sleep Quality Index were used to assess depression, anxiety, and sleep, respectively. Results: We matched the data based on IP addresses. We included 1,978, 1,547, and 2,061 individuals who completed the depression, anxiety, and sleep assessments, respectively, at both stages. During epidemics, psychomotor agitation/retardation, inability to relax, restless behavior, and the frequency of using medicine had high centrality. Meanwhile, the network structure of psychological symptoms becomes stronger than before the epidemic. Conclusion: Symptoms of psychomotor agitation/retardation, inability to relax, and restless behavior should be treated preferentially. It is necessary to provide mental health services, including timely and effective early psychological intervention. In addition, we should also pay attention to the way patients use medicines to promote sleep quality.

Network analysis of COVID-19-related PTSD symptoms in China: the similarities and differences between the general population and PTSD sub-population

BACKGROUND AND OBJECTIVES

Prevalent Post-traumatic Stress Disorder (PTSD) negatively affected individuals during the COVID-19 pandemic. Using network analyses, this study explored the construct of PTSD symptoms during the COVID-19 pandemic in China to identify similarities and differences in PTSD symptom network connectivity between the general Chinese population and individuals reporting PTSD.

METHODS

We conducted an online survey recruiting 2858 Chinese adults. PTSD symptoms were measured using the PCL-5 and PTSD was determined according to the DSM-5 criteria.

RESULTS

In the general population, self-destructive/reckless behaviours were on average the most strongly connected to other PTSD symptoms in the network. The five strongest positive connections were found between 1) avoidance of thoughts and avoidance of reminders, 2) concentration difficulties and sleep disturbance, 3) negative beliefs and negative trauma-related emotions, 4) irritability/anger and self-destructive/reckless behaviours, and 5) hypervigilance and exaggerated startle responses. Besides, negative connections were found between intrusive thoughts and trauma-related amnesia and between intrusive thoughts and self-destructive/reckless behaviours. Among individuals reporting PTSD, symptoms such as flashbacks and self-destructive/reckless behaviours were on average most strongly connected to other PTSD symptoms in the network. The five strongest positive connections were found between 1) concentration difficulty and sleep disturbance, 2) intrusive thoughts and emotional cue reactivity, 3) negative beliefs and negative trauma-related emotions, 4) irritability/anger and self-destructive/reckless behaviour, and 5) detachment and restricted affect. In addition, a negative connection was found between intrusive thoughts and self-destructive/reckless behaviours.

CONCLUSION

Our results indicate similarly positive connections between concentration difficulty and sleep disturbance, negative beliefs and negative trauma-related emotions, and irritability/anger and self-destructive/reckless behaviours in the general and PTSD-reported populations. We argue that self-destructive/reckless behaviours are a core symptom of COVID-19 related PTSD, worthy of more attention in future psychiatric programmers.

A Narrative Review of Network Studies in Depression: What Different Methodological Approaches Tell Us About Depression

The network theory of psychopathology proposes that mental disorders arise from direct interactions between symptoms. This theory provides a promising framework to understand the development and maintenance of mental disorders such as depression. In this narrative review, we summarize the literature on network studies in the field of depression. Four methodological network approaches are distinguished: (i) studies focusing on symptoms at the macro-level vs. (ii) on momentary states at the micro-level, and (iii) studies based on cross-sectional vs. (iv) time-series (dynamic) data. Fifty-six studies were identified. We found that different methodological approaches to network theory yielded largely inconsistent findings on depression. Centrality is a notable exception: the majority of studies identified either positive affect or anhedonia as central nodes. To aid future research in this field, we outline a novel complementary network theory, the momentary affect dynamics (MAD) network theory, to understand the development of depression. Furthermore, we provide directions for future research and discuss if and how networks might be used in clinical practice. We conclude that more empirical network studies are needed to determine whether the network theory of psychopathology can indeed enhance our understanding of the underlying structure of depression and advance clinical treatment.

Comorbid anxiety and depression psychopathology in university students: a network approach

Depression in university students is known to commonly co-occur with other mental disorders, especially anxiety. It is, however, not known how this comorbidity affects the psychopathology of depression in university students. Compared to commonly used methods, the clinical network approach provides a better framework for understanding comorbidity. Accordingly, regularized partial correlation network models were used in this study to (1) examine the severity structure of individual depressive symptoms by the level of comorbid anxiety, and (2) explore the gender differences among these symptoms in university students ( N = 919; Mage = 21 years., SD = 2.99; 72% = Female). Anhedonia, hopelessness, worthlessness, self-blame, and loneliness were the most central symptoms of depression in this study. The Network Comparison Test revealed no statistically significant global structure and strength of the depressive symptom network by comorbid anxiety level and gender. Implications of the results and network framework with regard to developing alternative treatment options, and the optimization of clinical care and assessment of depression are discussed.

Psychological adaptation and recovery in youth with sarcoma: a qualitative study with practical implications for clinical care and research

OBJECTIVES

This study explored factors that play a role in psychological adaptation and recovery of young people with sarcoma.

DESIGN

Qualitative study.

SETTING

National Health Service hospitals in the UK.

METHODS

Using purposive sampling, participants were recruited for semistructured interviews over the telephone or face to face in order to answer questions about how cancer impacted various domains of their life. Data were analysed using a framework approach.

RESULTS

Thirty participants, aged 15-39 years with primary sarcoma diagnosis provided in-depth accounts of their experience. Emerging themes from the interviews were grouped into two overarching themes that relate to one's adaptation to illness: individual level and environmental level. The qualitative nature of our study sheds light on meaningful connections between various factors and their role in one's psychological adaptation to sarcoma. We devised a visual matrix to illustrate how risk and protective factors in adaptation vary between and within individuals.

CONCLUSIONS

This study demonstrates that young people with sarcoma report an array of both positive and negative factors related to their illness experience. The route to recovery is a multifactorial process and a one-size-fits-all approach to psychosocial care proves inadequate. We propose that moving beyond the latent constructs of resilience and psychopathology towards a dynamic model of psychological adaptation and recovery in this population can result in optimisation of care. We offer some recommendations for professionals working with young people with sarcoma in clinic and research.

Network Structure of Physical, Cognitive, and Emotional Symptoms at Preseason Baseline in Student Athletes with Attention-Deficit/ Hyperactivity Disorder

OBJECTIVE

Preexisting attention-deficit/hyperactivity disorder (ADHD) may be a risk factor for worse outcome following sport-related concussion. We used a statistical and psychometric approach known as network analysis to examine the architecture of physical, cognitive, and emotional symptoms at preseason baseline among student athletes with ADHD.

METHOD

A cohort of 44,527 adolescent student athletes completed baseline preseason testing with ImPACT® between 2009 and 2015. A subsample of athletes reporting a diagnosis of ADHD and at least one symptom were included in this study (N = 3,074; 14-18 years old, 32.7% girls). All participants completed the 22-item Post-Concussion Symptom Scale at preseason baseline.

RESULTS

Student athletes reported high frequencies of difficulty concentrating (boys/girls = 50.7%/59.4%), emotional symptoms (nervousness: boys/girls = 30.2%/51.0%; irritability: boys/girls = 23.6%/34.8%; sadness: boys/girls = 21.4%/39.7%), sleep/arousal-related symptoms (trouble falling asleep: boys/girls = 39.5%/49.4%; sleeping less than usual: boys/girls = 36.2%/43.4%; and fatigue: boys/girls = 29.8%/36.4%), and headaches (boys/girls = 27.6%/39.0%) during preseason baseline testing. The most central symptoms included dizziness, which was related to multiple somatic symptoms, and increased emotionality, which was related to a cluster of emotional symptoms. Girls reported symptoms at a greater frequency than boys, and there was evidence for variance in the global strength of the symptom network across gender, but not specific intersymptom relationships.

CONCLUSION

In the absence of injury, symptoms that commonly occur after concussion interact and potentially reinforce each other among student athletes with ADHD at preseason. Symptoms common in ADHD (i.e., difficulty concentrating) are not necessarily the most central within the symptom network. These findings may inform more precise interventions for athletes with ADHD and prolonged recovery following concussion.

Time to get personal? The impact of researchers choices on the selection of treatment targets using the experience sampling methodology

OBJECTIVE

One of the promises of the experience sampling methodology (ESM) is that a statistical analysis of an individual's emotions, cognitions and behaviors in everyday-life could be used to identify relevant treatment targets. A requisite for clinical implementation is that outcomes of such person-specific time-series analyses are not wholly contingent on the researcher performing them.

METHODS

To evaluate this, we crowdsourced the analysis of one individual patient's ESM data to 12 prominent research teams, asking them what symptom(s) they would advise the treating clinician to target in subsequent treatment.

RESULTS

Variation was evident at different stages of the analysis, from preprocessing steps (e.g., variable selection, clustering, handling of missing data) to the type of statistics and rationale for selecting targets. Most teams did include a type of vector autoregressive model, examining relations between symptoms over time. Although most teams were confident their selected targets would provide useful information to the clinician, not one recommendation was similar: both the number (0-16) and nature of selected targets varied widely.

CONCLUSION

This study makes transparent that the selection of treatment targets based on personalized models using ESM data is currently highly conditional on subjective analytical choices and highlights key conceptual and methodological issues that need to be addressed in moving towards clinical implementation.

The symptom network structure of depressive symptoms in late-life: Results from a European population study

The network theory conceptualizes mental disorders as complex networks of symptoms influencing each other by creating feedback loops, leading to a self-sustained syndromic constellation. Symptoms central to the network have the greatest impact in sustaining the rest of symptoms. This analysis focused on the network structure of depressive symptoms in late-life because of their distinct etiologic factors, clinical presentation, and outcomes. We analyzed cross-sectional data from wave 2 of the 19 country Survey of Health, Ageing, and Retirement in Europe (SHARE) and included non-institutionalized adults aged 65 years or older (mean age 74 years, 59% females) endorsing at least one depressive symptom on the EURO-D scale for depression (N =8,557). We characterized the network structure of depressive symptoms in late-life and used indices of "strength", "betweenness", and "closeness" to identify symptoms central to the network. We used a case-dropping bootstrap procedure to assess network stability. Death wishes, depressed mood, loss of interest, and pessimism had the highest values of centrality. Insomnia, fatigue and appetite changes had lower centrality values. The identified network remained stable after dropping 74.5% of the sample. Sex or age did not significantly influence the network structure. In conclusion, death wishes, depressed mood, loss of interest, and pessimism constitute the "backbone" that sustains depressive symptoms in late-life. Symptoms central to the network of depressive symptoms may be used as targets for novel, focused interventions and in studies investigating neurobiological processes central to late-life depression.

Symptoms of mood disorders in family carers of older people with dementia who experience caregiver burden: a network approach

BACKGROUND

informal carers of people with dementia are at greater risk of anxiety and depressive disorders if they find caregiving to be a burden. The aim of this study was to use a network analysis of cross-sectional data to investigate the relationships between anxiety and depressive symptoms in family carers of older people with dementia who experience burden.

METHODS

sixty family carers exhibiting high levels of burden using the Zarit Burden Interview were included in the study. Participants completed the Hospital Anxiety and Depression Scale. The network analysis identified the depression and anxiety symptom network using features including a topological graph, network centrality metrics and community analysis. The network was estimated through the graphical LASSO technique in combination with a walktrap algorithm to obtain the clusters within the network and the connections between the nodes (symptoms). A directed acyclic graph was generated to model symptom interactions.

RESULTS

the resulting network architecture shows important bridges between depression and anxiety symptoms. Lack of pleasure and loss of enjoyment were identified as potential gateway symptoms to other anxiety and depression symptoms and represent possible therapeutic targets for psychosocial interventions. Fear and loss of optimism were highly central symptoms, indicating their importance as warning signs of more generalised anxiety and depression.

CONCLUSIONS

this network analysis of depressive and anxiety symptoms in overburdened family carers provides important insights as to what symptoms may be the most important targets for behavioural interventions.

Bereavement or breakup: Differences in networks of depression

BACKGROUND

Prior network analyses demonstrated that the death of a loved one potentially precedes specific depression symptoms, primarily loneliness, which in turn links to other depressive symptoms. In this study, we extend prior research by comparing depression symptom network structures following two types of marital disruption: bereavement versus separation.

METHODS

We fitted two Gaussian Graphical Models to cross-sectional data from a Swiss survey of older persons (145 bereaved, 217 separated, and 362 married controls), and compared symptom levels across bereaved and separated individuals.

RESULTS

Separated compared to widowed individuals were more likely to perceive an unfriendly environment and oneself as a failure. Both types of marital disruption were strongly linked to loneliness, from where different relations emerged to other depressive symptoms. Amongst others, loneliness had a stronger connection to perceiving oneself as a failure in separated compared to widowed individuals. Conversely, loneliness had a stronger connection to getting going in widowed individuals.

LIMITATIONS

Analyses are based on cross-sectional between-subjects data, and conclusions regarding dynamic processes on the within-subjects level remain putative. Further, some of the estimated parameters in the network exhibited overlapping confidence intervals and their order needs to be interpreted with care. Replications should thus aim for studies with multiple time points and larger samples.

CONCLUSIONS

The findings of this study add to a growing body of literature indicating that depressive symptom patterns depend on contextual factors. If replicated on the within-subjects level, such findings have implications for setting up patient-tailored treatment approaches in dependence of contextual factors.

Architecture of Physical, Cognitive, and Emotional Symptoms at Preseason Baseline in Adolescent Student Athletes With a History of Mental Health Problems

Objective: Pre-injury mental health problems are associated with greater symptom reporting following sport-related concussion. We applied a statistical and psychometric approach known as network analysis to examine the interrelationships among symptoms at baseline in adolescent student athletes with a history of mental health problems. Design: Cross-sectional study. Setting: High schools in Maine, USA. Participants: A cohort of 44,527 adolescent student athletes completed baseline preseason testing with ImPACT® between 2009 and 2015, and those with a history of mental health problems reporting at least one symptom were included (N = 2,412; 14-18 years-old, 60.1% girls). Independent Variables: Self-reported history of treatment for a psychiatric condition. Main Outcome Measures: Physical, cognitive, and emotional symptoms from the Post-Concussion Symptom Scale. Results: Student athletes reported high frequencies of emotional symptoms (nervousness: boys = 46.6%, girls = 58.3%; irritability: boys = 37.9%, girls = 46.9%; sadness: boys = 38.7%, girls = 53.2%), sleep/arousal-related symptoms (trouble falling asleep: boys = 50.4%, girls = 55.1%; sleeping less than usual: boys = 43.8%, girls = 45.2%; and fatigue: boys = 40.3%, girls = 45.2%), headaches (boys = 27.5%, girls = 41.8%), and inattention (boys = 47.8%, girls = 46.9%) before the start of the season. Although uncommonly endorsed, dizziness was the most central symptom (i.e., the symptom with the highest aggregate connectedness with different symptoms in the network), followed by feeling more emotional and feeling slowed down. Dizziness was related to physical and somatic symptoms (e.g., balance, headache, nausea, numbness/tingling) whereas increased emotionality was related to sadness, nervousness, and irritability. Feeling slowed down was connected to cognitive (e.g., fogginess, forgetfulness), and sensory symptoms (e.g., numbness/tingling, light sensitivity). There were no gender differences in the symptom network structure. Conclusions: We examined the interconnections between symptoms reported by student athletes with mental health problems at preseason baseline, identifying how physical, cognitive, and emotional symptoms interact and potentially reinforce each other in the absence of injury. These findings are a step toward informing more precise interventions for this subgroup of athletes if they are slow to recover following concussion.

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.

A Network Analysis of Post-traumatic Stress Disorder Symptoms and Correlates During the COVID-19 Pandemic

Background and Objective: The coronavirus disease 2019 (COVID-19) outbreak has been suggested as a collective trauma, which presents a continuing crisis. However, the specific post-traumatic implication of this crisis has not been adequately studied yet. The current study was aimed to ascertain the most central symptom and the strong connections between symptoms of post-traumatic stress disorder (PTSD). At the same time, exploring the relationship between covariates and the network of PTSD symptoms, by taking sex, anxiety, depression, suicidal ideation, quality of life, and social support as covariates, may help us to know the arise and maintenance of PTSD symptoms and give specified suggestions to people under the shadow of COVID-19. Method: The Post-traumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), was used to assess the PTSD symptoms extent of 338 healthy participants over the past month. Networks were analyzed using state-of-the-art regularized partial correlation models. In addition, the centrality of the symptoms and the robustness of the results were analyzed. Results: The network analysis revealed that the especially strong connections emerged between avoidance of thoughts and avoidance of reminders, hypervigilance and exaggerated startle response, intrusive thoughts and nightmares, flashbacks and emotional cue reactivity, and detachment and restricted affect. The most central symptoms were self-destructive/reckless behavior. Incorporation of covariates into the network revealed the strong connections path between self-destructive/reckless behavior and loss of interest and depression. Conclusion: Self-destructive/reckless behavior was the most central symptom in the network of PTSD symptoms related to the COVID-19 pandemic, which as an important target of interfere may have great benefits.

Diagnostic Issues of Depressive Disorders from Kraepelinian Dualism to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition

Because the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) was mainly influenced by the neo-Kraepelinian approach, its categorical approach to defining mental disorders has been criticized from the viewpoint of etiological neutrality. In the context of bridging the gap between "presumed etiologies-based symptomatology" and "identifiable pathophysiological etiologies," the content in 5th edition, the DSM-5, has been revised to incorporate a combination of categorical and dimensional approaches. The most remarkable change of note regarding the diagnostic classification of depressive disorders in the DSM-5 is the splitting of mood disorders into bipolar disorders and depressive disorders, which is in accordance with the deconstruction of the Kraepelinian dualism for psychoses. The transdiagnostic specifiers "with mixed features," "with psychotic features," and "with anxious distress" are introduced to describe the relationships of depressive disorders with bipolar disorders, schizophrenia, and generalized anxiety disorder, respectively, in a dimensional manner. The lowering of the diagnostic threshold for major depressive disorder (MDD) may be caused by the addition of "hopelessness" to the subjective descriptors of depressive mood and the elimination of "bereavement exclusion" from the definition of MDD. Since the heterogeneity of MDD is equivalent to the Wittgensteinian "games" analogy, the different types of MDD are related not by a single essential feature but rather by "family resemblance." Network analyses of MDD symptoms may therefore need further review to elucidate the connections among interrelated symptoms and other clinical elements.

The Study of Psychopathology from the Network Analysis Perspective: A Systematic Review

BACKGROUND

Network analysis (NA) is an analytical tool that allows one to explore the map of connections and eventual dynamic influences among symptoms and other elements of mental disorders. In recent years, the use of NA in psychopathology has rapidly grown, which calls for a systematic and critical analysis of its clinical utility.

METHODS

Following PRISMA guidelines, a systematic review of published empirical studies applying NA in psychopathology, between 2010 and 2017, was conducted. We included the literature published in PubMed and PsycINFO using as keywords any combination of "network analysis" with the terms "anxiety," "affective disorders," "depression," "schizophrenia," "psychosis," "personality disorders," "substance abuse" and "psychopathology."

RESULTS

The review showed that NA has been applied in a plethora of mental disorders in adults (i.e., 13 studies on anxiety disorders; 19 on mood disorders; 7 on psychosis; 1 on substance abuse; 1 on borderline personality disorder; 18 on the association of symptoms between disorders), and 6 on childhood and adolescence.

CONCLUSIONS

A critical examination of the results of each study suggests that NA helps to identify, in an innovative way, important aspects of psychopathology like the centrality of the symptoms in a given disorder as well as the mutual dynamics among symptoms. Yet, despite these promising results, the clinical utility of NA is still uncertain as there are important limitations on the analytic procedures (e.g., reliability of indices), the type of data included (e.g., typically restricted to secondary analysis of already published data), and ultimately, the psychometric and clinical validity of the results.

Network Analysis and Precision Rehabilitation for the Post-concussion Syndrome

Some people experience persistent symptoms following a mild traumatic brain injury (MTBI), and the etiology of those symptoms has been debated for generations. Post-concussion-like symptoms are caused by many factors both before and after MTBI, and this non-specificity is the bedrock of the conundrum regarding the existence of the post-concussion syndrome. A latent model or common cause theory for the syndrome is inconsistent with the prevailing biopsychosocial conceptualization. It is the thesis of this paper that adopting a network perspective for persistent symptoms following MTBI, including the post-concussion syndrome, could lead to new insights and targeted treatment and rehabilitation strategies. The network perspective posits that symptoms co-occur because they are strongly inter-related, activating, amplifying, and mutually reinforcing, not because they arise from a common latent disease entity. This approach requires a conceptual shift away from thinking that symptoms reflect an underlying disease or disorder toward viewing inter-related symptoms as constituting the syndrome or disorder. The symptoms do not arise from an underlying syndrome—the symptoms are the syndrome. A network analysis approach allows us to embrace heterogeneity and comorbidity, and it might lead to the identification of new approaches to sequenced care. The promise of precision rehabilitation requires us to better understand the interconnections among symptoms and problems so that we can produce more individualized and effective treatment and rehabilitation.

The network takeover reaches psychopathology

Borsboom et al. have written a trenchant critique of biological reductionism in psychopathology. After commenting on recent controversies concerning the network perspective, I discuss ways of integrating biology into the network enterprise.

Capturing the complexity of mental disorders in the medically ill: the network approach on behavioral medicine

Since Descartes introduced dualism, body and mind have been seen as separate entities. The latent disease model, the view that symptoms are caused by an underlying disease, was possible within the dualistic paradigm. This paradigm, although successful in some aspects of medicine, is also assumed to underlie psychiatric disorders. As an alternative to the latent disease model, the network approach conceptualizes disorders as complex networks of causally connected symptoms. It offers a new way of understanding psychiatric disorders by directing attention away from the underlying cause and towards the symptoms and their functional interconnectedness, making the distinction between mental and physical symptoms obsolete. This article discusses how the network perspective helps us to overcome some of the problems we have faced when diagnosing and treating psychopathology in the medically ill. Furthermore, we describe how the network perspective can stimulate new research to better understand psychopathology in medically ill patients and how it can help deliver the most suitable treatment to the individual patient.

A complex network approach to clinical science

Contemporary classification systems assume that psychiatric disorders are expressions of latent disease entities. However, some critics point to the comorbidity problem and other issues that question the validity of the latent disease model. An alternative to this traditional view is the complex network approach. This approach assumes that disorders exist as systems of inter-connected elements, without requiring that the elements are expressions of latent disease entities. Depending on the structure of the network, change can occur abruptly once the network reaches a tipping point. A dynamic complex network approach could be used to develop a functional analytic case conceptualization that may predict treatment change, relapse and recovery, thereby linking nosology and treatment. In conclusion, the complex network perspective offers an alternative and less restrictive approach to the latent disease model, while offering exciting new directions for future research in psychiatry.

New insights into the correlation structure of DSM-IV depression symptoms in the general population v. subsamples of depressed individuals

AIMS

Previous research failed to uncover a replicable dimensional structure underlying the symptoms of depression. We aimed to examine two neglected methodological issues in this research: (a) adjusting symptom correlations for overall depression severity; and (b) analysing general population samples v. subsamples of currently depressed individuals.

METHODS

Using population-based cross-sectional and longitudinal data from two nations (Switzerland, 5883 young men; USA, 2174 young men and 2244 young women) we assessed the dimensions of the nine DSM-IV depression symptoms in young adults. In each general-population sample and each subsample of currently depressed participants, we conducted a standardised process of three analytical steps, based on exploratory and confirmatory factor and bifactor analysis, to reveal any replicable dimensional structure underlying symptom correlations while controlling for overall depression severity.

RESULTS

We found no evidence of a replicable dimensional structure across samples when adjusting symptom correlations for overall depression severity. In the general-population samples, symptoms correlated strongly and a single dimension of depression severity was revealed. Among depressed participants, symptom correlations were surprisingly weak and no replicable dimensions were identified, regardless of severity-adjustment.

CONCLUSIONS

First, caution is warranted when considering studies assessing dimensions of depression because general population-based studies and studies of depressed individuals generate different data that can lead to different conclusions. This problem likely generalises to other models based on the symptoms' inter-relationships such as network models. Second, whereas the overall severity aligns individuals on a continuum of disorder intensity that allows non-affected individuals to be distinguished from affected individuals, the clinical evaluation and treatment of depressed individuals should focus directly on each individual's symptom profile.

Relating ASD symptoms to well-being: moving across different construct levels

BACKGROUND

Little is known about the specific factors that contribute to the well-being (WB) of individuals with autism spectrum disorder (ASD). A plausible hypothesis is that ASD symptomatology has a direct negative effect on WB. In the current study, the emerging tools of network analysis allow to explore the functional interdependencies between specific symptoms of ASD and domains of WB in a multivariate framework. We illustrate how studying both higher-order (total score) and lower-order (subscale) representations of ASD symptomatology can clarify the interrelations of factors relevant for domains of WB.

METHODS

We estimated network structures on three different construct levels for ASD symptomatology, as assessed with the Adult Social Behavior Questionnaire (item, subscale, total score), relating them to daily functioning (DF) and subjective WB in 323 adult individuals with clinically identified ASD (aged 17-70 years). For these networks, we assessed the importance of specific factors in the network structure.

RESULTS

When focusing on the highest representation level of ASD symptomatology (i.e. a total score), we found a negative connection between ASD symptom severity and domains of WB. However, zooming in on lower representation levels of ASD symptomatology revealed that this connection was mainly funnelled by ASD symptoms related to insistence on sameness and experiencing reduced contact and that those symptom scales, in turn, impact different domains of WB.

CONCLUSIONS

Zooming in across construct levels of ASD symptom severity into subscales of ASD symptoms can provide us with important insights into how specific domains of ASD symptoms relate to specific domains of DF and WB.