Robust symptom networks in recurrent major depression across different levels of genetic and environmental risk

Depressive symptoms, cognitive functions and daily activities: An extended network analysis in monozygotic and dizygotic twins

BACKGROUND

The network theory posits that depression emerges as the result of individual symptoms triggering each other. Risk factors for depression can impact these between-symptoms interactions through extended networks. The study aimed to model the extended network of depressive symptoms and known depression risk factors - objective cognitive function, intellectual, physical, and social daily activities, and then, compare the observed networks between monozygotic (MZ) and dizygotic (DZ) co-twins.

METHODS

Twin pairs, 722 MZ and 2200 DZ, aged 40-79, were selected from the Dansh Twin Registry for having complete measures of depressive symptoms (e.g., sadness), cognitive functions (e.g., verbal memory), physical (e.g., brisk walk), intellectual (e.g., reading newspapers) and social activities (e.g., phone calls). Gaussian graphical models were used to estimate and compare the networks first between co-twins and then, between MZ to DZ twin pairs separately.

RESULTS

Specific intellectual, physical and social activities were central in the extended networks of depressive symptoms and, with the exception of processing speed, more central than cognition. The extended networks' structure was more homogeneous between MZ co-twins relative to DZ co-twins. Cognitive nodes were more central in MZ than DZ co-twins.

LIMITATIONS

Cross-sectional design, participants were middle-aged or older, mostly affective (non-somatic) depressive symptoms.

CONCLUSIONS

In depression networks, core connecting elements were intellectual, physical and social activities. The interaction between cognition and daily activities seems critical for triggering depressive symptoms. Thus, clinical interventions aimed at preventing depression and associated cognitive deficits should focus on maintenance and/or engagement in stimulating daily activities.

Polygenic scores for psychiatric traits mediate the impact of multigenerational history for depression on offspring psychopathology

A family history of depression is a well-documented risk factor for offspring psychopathology. However, the genetic mechanisms underlying the intergenerational transmission of depression remain unclear. We used genetic, family history, and diagnostic data from 11,875 9-10 year-old children from the Adolescent Brain Cognitive Development study. We estimated and investigated the children's polygenic scores (PGSs) for 30 distinct traits and their association with a family history of depression (including grandparents and parents) and the children's overall psychopathology through logistic regression analyses. We assessed the role of polygenic risk for psychiatric disorders in mediating the transmission of depression from one generation to the next. Among 11,875 multi-ancestry children, 8,111 participants had matching phenotypic and genotypic data (3,832 female [47.2%]; mean (SD) age, 9.5 (0.5) years), including 6,151 [71.4%] of European ancestry). Greater PGSs for depression (estimate = 0.129, 95% CI = 0.070-0.187) and bipolar disorder (estimate = 0.109, 95% CI = 0.051-0.168) were significantly associated with higher family history of depression (Bonferroni-corrected P < .05). Depression PGS was the only PGS that significantly associated with both family risk and offspring's psychopathology, and robustly mediated the impact of family history of depression on several youth psychopathologies including anxiety disorders, suicidal ideation, and any psychiatric disorder (proportions mediated 1.39%-5.87% of the total effect on psychopathology; FDR-corrected P < .05). These findings suggest that increased polygenic risk for depression partially mediates the associations between family risk for depression and offspring psychopathology, showing a genetic basis for intergenerational transmission of depression. Future approaches that combine assessments of family risk with polygenic profiles may offer a more accurate method for identifying children at elevated risk.

Characterizing the hierarchical depression phenotype in sexually diverse individuals

INTRODUCTION

Sexual diverse individuals are at high risk for internalizing psychopathologies, such as depression. Understanding how symptom profiles of heterogeneous psychiatric disorders such as depression differ for sexually diverse vs. heterosexual individuals is thus critical to advance precision psychiatry and maximize our ability to effectively treat members of this population. Research has failed to consider the possibility of hierarchical phenotypes, wherein sexual orientation status may be uniquely and simultaneously associated with both depression broadly and with individual symptoms.

METHOD

To address these issues, we conducted a moderated nonlinear factor analysis in Wave IV of the Add Health study, using sexual diversity status as a predictor of (a) latent depression, (b) factor loadings, and (c) individual symptoms, with and without controlling for race.

RESULTS

Sexual diversity status was positively and simultaneously associated with latent depression, concentration difficulties, and happiness.

DISCUSSION

These findings suggest that sexually diverse populations not only face greater depression, broadly defined, but are disproportionately more likely to experience concentration difficulties and be happier compared to heterosexual counterparts. Methodologically, these models indicate that the CES-D is scalar noninvariant as a function of sexual diversity status (i.e., identical scores on the CES-D may represent different manifestations of depression for sexually diverse and heterosexual participants). Studies examining disparities in depression across heterosexual and sexually diverse samples should thus consider depression broadly as well as specific symptoms. Further, it is critical to examine whether these relations function via different mechanisms.

Hazardous drinking in young adults with co-occurring PTSD and psychosis symptoms: A network analysis

BACKGROUND

Existing literature suggests the co-occurrence of post-traumatic stress disorder (PTSD) and psychosis among young adults is related to hazardous drinking. However, the influencing mechanisms among these co-occurrences are inconclusive. Thus, this study aimed to investigate the symptomatic associations between PTSD, psychosis, and hazardous drinking.

METHODS

This study included 96,218 young Chinese adults, divided into three groups (PTSD, Psychosis, and co-occurring PTSD-Psychosis). PTSD, psychosis, and hazardous drinking were measured by the ten-item Trauma Screening Questionnaire, the seven-item Psychosis Screener Scale, and the four-item Alcohol Use Disorders Identification Test, respectively. Network analysis was utilized to explore and compare the symptomatic correlation between PTSD, psychosis, and hazardous drinking.

RESULTS

In this study, the most crucial symptom (both central and bridge) was "delusion of control" among the three networks. Hazardous drinking was another main bridge symptom. Compared to the Psychosis group and the co-occurring PTSD-Psychosis group, "Delusion of reference or persecution" to "Grandiose delusion" was the strongest edge in "the network structure of the PTSD group".

LIMITATIONS

The cross-sectional study cannot determine the causal relationship. Applying self-reporting questionnaires may cause inherent bias. Young adult participants limited the generalization of the results to other groups.

CONCLUSIONS

Among the three network structures, delusion of control was the most crucial symptom, and hazardous drinking was another bridge symptom; the edge of delusion of reference or persecution and grandiose delusion was strongest in the PTSD group's network. Efforts should be taken to develop diverse targeted interventions for these core symptoms to relieve PTSD, psychosis, and hazardous drinking in young adults.

Network Structure and Clustering Analysis Relating to Individual Symptoms of Problematic Internet Use in a Community Adolescent Population

INTRODUCTION

Problematic internet use (PIU) is a psychopathology that includes multiple symptoms and psychological constructs. Because no studies have considered both network structures and clusters among individual symptoms in the context of PIU in a Korean adolescent population, this study aimed to investigate network structures and clustering in relation to PIU symptoms in adolescents.

METHODS

Overall, 73,238 adolescents were included. PIU severity was assessed using a self-rating scale comprising 20 items and 6 subscales, namely, the Internet Addiction Proneness Scale for Youth-Short Form; KS scale. Network structures and clusters among symptoms were analyzed using a Gaussian graphical model and exploratory graph analysis, respectively. Centrality of strength, closeness, and betweenness scores was also calculated.

RESULTS

Our study identified four clusters: disturbance in adaptive functioning, virtual interpersonal relationships, withdrawal, and tolerance. The symptom of confidence served as a node bridging the cluster of virtual interpersonal relationships and other clusters of withdrawal and disturbances of adaptive function. The symptom of craving served as a bridge between the clusters of withdrawal and tolerance with high betweenness centrality.

CONCLUSION

This study identified network structures and clustering among PIU symptoms in adolescents and revealed that positive experiences derived from online interpersonal relationships were an important mechanism underlying PIU. These are novel insights concerning the interconnection among multiple symptoms and related clustering for the mechanism of adolescent PIU in terms of KS-scale PIU assessment.

Variation in global network properties across risk factors for adolescent internalizing symptoms: evidence of cumulative effects on structure and connectivity

BACKGROUND

Identifying adolescents at risk of internalizing problems is a key priority. However, studies have tended to consider such problems in simple ways using diagnoses, or item summaries. Network theory and methods instead allow for more complex interaction between symptoms. Two key hypotheses predict differences in global network properties for those at risk: altered structure and increased connectivity.

METHODS

The current study evaluated these hypotheses for nine risk factors (e.g. income deprivation and low parent/carer support) individually and cumulatively in a large sample of 12-15 year-olds (N = 34 564). Recursive partitioning and bootstrapped networks were used to evaluate structural and connectivity differences.

RESULTS

The pattern of network interactions was shown to be significantly different via recursive partitioning for all comparisons across risk-present/absent groups and levels of cumulative risk, except for income deprivation. However, the magnitude of differences appeared small. Most individual risk factors also showed relatively small effects for connectivity. Exceptions were noted for gender and sexual minority risk groups, as well as low parent/carer support, where larger effects were evident. A strong linear trend was observed between increasing cumulative risk exposure and connectivity.

CONCLUSIONS

A robust approach to considering the effect of risk exposure on global network properties was demonstrated. Results are consistent with the ideas that pathological states are associated with higher connectivity, and that the number of risks, regardless of their nature, is important. Gender/sexual minority status and low parent/carer support had the biggest individual impacts on connectivity, suggesting these are particularly important for identification and prevention.

Cognitive function and cardiovascular health in the elderly: network analysis based on hypertension, diabetes, cerebrovascular disease, and coronary heart disease

Introduction

Cognitive decline in the elderly population is a growing concern, and vascular factors, such as hypertension, diabetes, cerebrovascular disease, and coronary heart disease, have been associated with cognitive impairments. This study aims to provide deeper insights into the structure of cognitive function networks under these different vascular factors and explore their potential associations with specific cognitive domains.

Methods

Cognitive function was assessed using a modified Chinese version of the mini-mental state examination (MMSE) scale, and intensity centrality and side weights were estimated by network modeling. The network structure of cognitive function was compared across subgroups by including vascular factors as subgroup variables while controlling for comorbidities and confounders.

Results

The results revealed that cerebrovascular disease and coronary heart disease had a more significant impact on cognitive function. Cerebrovascular disease was associated with weaker centrality in memory and spatial orientation, and a sparser cognitive network structure. Coronary heart disease was associated with weaker centrality in memory, repetition, executive function, recall, attention, and calculation, as well as a sparser cognitive network structure. The NCT analyses further highlighted significant differences between the cerebrovascular disease and coronary heart disease groups compared to controls in terms of overall network structure and connection strength.

Conclusion

Our findings suggest that specific cognitive domains may be more vulnerable to impairments in patients with cerebrovascular disease and coronary heart disease. These insights could be used to improve the accuracy and sensitivity of cognitive screening in these patient populations, inform personalized cognitive intervention strategies, and provide a better understanding of the potential mechanisms underlying cognitive decline in patients with vascular diseases.

Comorbidity Between Internalising and Externalising Disorders Among Adolescents: Symptom Connectivity Features and Psychosocial Outcome

Internalising disorders are highly prevalent conditions in adolescence and tend to co-occur with externalising disorders. The present study used a symptom network approach to examine the interplay between symptoms of internalising disorders among adolescents with comorbid internalising and externalising disorders. Data comes from the National Comorbidity Survey-Adolescent Supplement, a nationally representative survey of adolescents aged 13 to 18 years. The most central symptoms across the disorders in the network were poor self-esteem and worry. The comorbidity between anxiety and depression increases the probability of having comorbid externalising disorders. Adolescents with both internalising and externalising disorders had the highest rate of health service utilisation. Comorbidity group, lifestyle factors, deficits in cognitive and academic competence and coping skills were significant covariates of the mental health outcomes. Understanding comorbidity profile of internalising and externalising disorders and central symptoms that bridge these disorders could have important clinical implications.

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

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

Role of polygenic and environmental factors in the co-occurrence of depression and psychosis symptoms: a network analysis

Depression and psychosis are often comorbid; they also have overlapping genetic and environmental risk factors, including trauma and area-level exposures. The present study aimed to advance understanding of this comorbidity via a network approach, by (1) identifying bridge nodes that connect clusters of lifetime depression and psychosis symptoms and (2) evaluating the influence of polygenic and environmental risk factors in these symptoms. This study included data from European ancestry participants in UK Biobank, a large population-based sample (N = 77,650). In Step 1, a network model identified bridge nodes between lifetime symptoms of depression and psychosis and functional impairment. In Step 2, genetic and environmental risk factors were incorporated to examine the degree to which symptoms associated with polygenic risk scores for depression and schizophrenia, lifetime exposure to trauma and area-level factors (including deprivation, air pollution and greenspace). Feelings of worthlessness, beliefs in unreal conspiracy against oneself, depression impairment and psychosis impairment emerged as bridges between depression and psychosis symptoms. Polygenic risk scores for depression and schizophrenia were predominantly linked with depression and psychosis impairment, respectively, rather than with specific symptoms. Cumulative trauma emerged as a bridge node associating deprivation with feelings of worthlessness and beliefs in unreal conspiracy, indicating that the experience of trauma is prominently linked with the co-occurrence of depression and psychosis symptoms related to negative views of oneself and others. These key symptoms and risk factors provide insights into the lifetime co-occurrence of depression and psychosis.

The structure of the symptoms of major depression: Factor analysis of a lifetime worst episode of depressive symptoms in a large general population sample

BACKGROUND

A range of depressive symptoms may occur during an episode of major depression (MD). Do these symptoms describe a single disorder liability or different symptom dimensions? This study investigates the structure and clinical relevance of an expanded set of depressive symptoms in a large general population sample.

METHODS

We studied 43,431 subjects from the Dutch Lifelines Cohort Study who participated in an online survey assessing the 9 symptom criteria of MD (DSM-IV-TR) and additional depressive symptoms during their worst lifetime episode of depressive symptoms lasting two weeks or more. Exploratory factor analyses were performed on expanded sets of 9, 14, and 24 depressive symptoms. The clinical relevance of the identified symptom dimensions was analyzed in confirmatory factor analyses including ten external validators.

RESULTS

A single dimension adequately accounted for the covariation among the 9 DSM-criteria, but multiple dimensions were needed to describe the 14 and 24 depressive symptoms. Five dimensions described the structure underlying the 24 depressive symptoms. Three cognitive affective symptom dimensions were mainly associated with risk factors for MD. Two somatic dimensions -appetite/weight problems and sleep problems-were mainly associated with BMI and age, respectively.

LIMITATIONS

Respondents of our online survey tended to be more often female, older, and more highly educated than non-respondents.

CONCLUSIONS

Different symptom dimensions described the structure of depressive symptoms during a lifetime worst episode in a general population sample. These symptom dimensions resembled those reported in a large clinical sample of Han-Chinese women with recurrent MD, suggesting robustness of the syndrome of MD.

Familial risk for depressive and anxiety disorders: associations with genetic, clinical, and psychosocial vulnerabilities

BACKGROUND

In research and clinical practice, familial risk for depression and anxiety is often constructed as a simple Yes/No dichotomous family history (FH) indicator. However, this measure may not fully capture the liability to these conditions. This study investigated whether a continuous familial loading score (FLS), incorporating family- and disorder-specific characteristics (e.g. family size, prevalence of depression/anxiety), (i) is associated with a polygenic risk score (PRS) for major depression and with clinical/psychosocial vulnerabilities and (ii) still captures variation in clinical/psychosocial vulnerabilities after information on FH has been taken into account.

METHODS

Data came from 1425 participants with lifetime depression and/or anxiety from the Netherlands Study of Depression and Anxiety. The Family Tree Inventory was used to determine FLS/FH indicators for depression and/or anxiety.

RESULTS

Persons with higher FLS had higher PRS for major depression, more severe depression and anxiety symptoms, higher disease burden, younger age of onset, and more neuroticism, rumination, and childhood trauma. Among these variables, FH was not associated with PRS, severity of symptoms, and neuroticism. After regression out the effect of FH from the FLS, the resulting residualized measure of FLS was still associated with severity of symptoms of depression and anxiety, rumination, and childhood trauma.

CONCLUSIONS

Familial risk for depression and anxiety deserves clinical attention due to its associated genetic vulnerability and more unfavorable disease profile, and seems to be better captured by a continuous score that incorporates family- and disorder-specific characteristics than by a dichotomous FH measure.

Unconsidered issues of measurement noninvariance in biological psychiatry: A focus on biological phenotypes of psychopathology

There is increasing appreciation that certain biological processes may not be equally related to all psychiatric symptoms in a given diagnostic category. Research on the biological phenotyping of psychopathology has begun examining the etiological and treatment implications of identified biotypes; however, little attention has been paid to a critical methodological implication of these results: measurement noninvariance. Measurement invariance is the ability of an instrument to measure the same construct, the same way, across different people, or across different time points for the same individual. If what a measure quantifies differs across different people (e.g., those with or without a particular biotype) or time points, then it is invalid to directly compare means on that measure. Using a running example of inflammatory phenotypes of depression, we first describe the biological phenotyping of psychopathology. Second, we discuss three types of measurement invariance. Third, we demonstrate how differential biology-symptom associations invariably creates measurement noninvariance using a theoretical example and simulated data (for which code is provided). We also show how this issue can lead to false conclusions about the broader diagnostic construct. Finally, we provide several suggestions for addressing these important issues to help advance the field of biological psychiatry.

Estimating group differences in network models using moderation analysis

Statistical network models such as the Gaussian Graphical Model and the Ising model have become popular tools to analyze multivariate psychological datasets. In many applications, the goal is to compare such network models across groups. In this paper, I introduce a method to estimate group differences in network models that is based on moderation analysis. This method is attractive because it allows one to make comparisons across more than two groups for all parameters within a single model and because it is implemented for all commonly used cross-sectional network models. Next to introducing the method, I evaluate the performance of the proposed method and existing approaches in a simulation study. Finally, I provide a fully reproducible tutorial on how to use the proposed method to compare a network model across three groups using the R-package mgm.

Estimating group differences in network models using moderation analysis

Statistical network models such as the Gaussian Graphical Model and the Ising model have become popular tools to analyze multivariate psychological datasets. In many applications, the goal is to compare such network models across groups. In this paper, I introduce a method to estimate group differences in network models that is based on moderation analysis. This method is attractive because it allows one to make comparisons across more than two groups for all parameters within a single model and because it is implemented for all commonly used cross-sectional network models. Next to introducing the method, I evaluate the performance of the proposed method and existing approaches in a simulation study. Finally, I provide a fully reproducible tutorial on how to use the proposed method to compare a network model across three groups using the R-package mgm.

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.

Symptom networks in patients with substance use disorders

BACKGROUND

Reciprocity between symptoms of psychiatric disorders is increasingly recognized to contribute to their chronicity. In substance use disorders (SUD) little is known on reciprocal interactions between symptoms. We applied network analyses to study these interactions.

METHODS

We analyzed 11 DSM-IV / DSM-5 criteria for SUD for the most prevalent substances in addiction care (alcohol, cannabis, cocaine, stimulants, and opioids) in a sample of 10,832 SUD patients in treatment. First, we estimated an overall symptom network. Second, we compared symptom networks between the different substances. Finally, we tested differences in symptom networks between DSM-IV and DSM-5.

RESULTS

In the overall symptom network for SUD patients the most central symptom was: "spending substantial amount of the day obtaining, using, or recovering from substance use". The symptoms "giving up or cutting back on important activities because of use" and "repeated usage causes or contributes to an inability to meet important obligations", were the symptoms that influenced each other the most. Networks differed between substances both in global strength and structure, especially regarding the position of "use despite health or interpersonal problems". Networks based on DSM-5 criteria differed moderately from DSM-IV, mainly because "craving" was more central in the DSM-5 network than "legal problems" in DSM-IV.

CONCLUSIONS

Network analyses can identify core symptoms of SUD that could maintain the disease processes in SUD. Future studies should address whether targeting these core symptoms with precedence, might help to break through the addictive process.

Comparison of depression and anxiety symptom networks in reporters and non-reporters of lifetime trauma in two samples of differing severity

BACKGROUND

Reported trauma is associated with differences in the course and outcomes of depression and anxiety. However, no research has explored the association between reported trauma and patterns of clinically relevant symptoms of both depression and anxiety.

METHODS

We used network analysis to investigate associations between reported trauma and depression and anxiety symptom interactions in affected individuals from the Genetic Links to Anxiety and Depression (GLAD) Study (n = 17720), and population volunteers from the UK Biobank (n = 11120). Participants with current moderate symptoms of depression or anxiety were grouped into reporters and non-reporters of lifetime trauma. Networks of 16 depression and anxiety symptoms in the two groups were compared using the network comparison test.

RESULTS

In the GLAD Study, networks of reporters and non-reporters of lifetime trauma did not differ on any metric. In the UK Biobank, the symptom network of reporters had significantly greater density (7.80) than the network of non-reporters (7.05).

LIMITATIONS

The data collected in the GLAD Study and the UK Biobank are self-reported with validated or semi-validated questionnaires.

CONCLUSIONS

Reported lifetime trauma was associated with stronger interactions between symptoms of depression and anxiety in population volunteers. Differences between reporters and non-reporters may not be observed in individuals with severe depression and/or anxiety due to limited variance in the presentation of disorder.

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.

Psychological distress associated with the COVID-19 lockdown: A two-wave network analysis

BACKGROUND

Although preliminary research has evidenced negative psychological consequences of the COVID-19 pandemic among the general population, little research has been carried out examining the interplay among the broader dimensions and correlates of individual distress. Via network analysis, the current study investigated the pathways that underlie some components of psychological distress and their changes over time (during and post COVID-19-related lockdown).

METHODS

1,129 adult participants (79.1% women) completed a two-wave online survey during and after the lockdown, and reported on variables such as depression, anxiety, stress, fear of COVID, intolerance of uncertainty, emotion regulation and social support. The networks were estimated via Gaussian Graphical Models and their temporal changes were compared through the centrality measures.

RESULTS

Depression, stress, anxiety and fear of COVID formed a spatially contiguous pattern, which remained unchanged in both the two waves. After the lockdown, the fear of COVID node reduced its strength in the network, whereas inhibitory intolerance of uncertainty and emotion suppression were associated with depression. Emotion regulation was connected to depression, but not to stress and anxiety during both waves. Perceived emotional support had few connections to the other nodes.

LIMITATIONS

Only 32.7% of participants provided complete responses for both waves.

CONCLUSION

The COVID-19 outbreak has had a significant psychosocial impact on adults. In the context of the network approach, depressive symptoms had the highest strength and their associations to other dimensions of individual distress may be key factors in understanding the influence of exposure to the COVID-19 outbreak on mental health.

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.

Sex differences in depressive symptoms and their networks in a treatment-seeking population - a cross-sectional study

BACKGROUND

The higher prevalence of major depressive disorder (MDD) in females relative to males is well-established. Some authors have posited this difference arises to divergent symptom profiles in females vs. males. However, empirical tests of this hypothesis have yielded equivocal results. Here, we investigate sex differences in MDD of individual symptoms and symptom networks in a treatment-seeking sample.

METHODS

We assessed depressive symptoms using Hamilton Depression Rating Scale (HDRS-17) in 590 treatment-seeking adults with MDD (300 females). We examined group differences in symptom endorsement. We investigated symptom networks and estimated Gaussian Graphical Models. Finally, we compared the female and male networks using the Network Comparison Test.

RESULTS

Females scored significantly higher in psychological anxiety (p <0.001; rB = -0.155), somatic anxiety (p = .001; rB = -0.150) and feelings of guilt (p = .002; rB = -0.139). Male and female patients did not differ in depression sum scores. There were no sex differences in network structure or global strength.

LIMITATIONS

Our study was sufficiently powered to detect only medium sized symptom differences. The generalizability of our study is limited to clinical samples and further studies are needed to investigate if findings also translate to outpatient samples.

CONCLUSION

Females reported elevated anxiety symptoms and guilt. Clinicians should assess these symptom differences and tailor treatment to individual symptom profiles. No differences between sexes emerged in MDD network structures, indicating that features may be more similar than previously assumed. Sex differences in psychopathological features of MDD are important for future research and personalized treatment.

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.

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.

Symptom networks in acute depression across bipolar and major depressive disorders: A network analysis on a large, international, observational study

Major Depressive Episode (MDE) is a transdiagnostic nosographic construct straddling Major Depressive (MDD) and Bipolar Disorder (BD). Prognostic and treatment implications warrant a differentiation between these two disorders. Network analysis is a novel approach that outlines symptoms interactions in psychopathological networks. We investigated the interplay among depressive and mixed symptoms in acutely depressed MDD/BD patients, using a data-driven approach. We analyzed 7 DSM-IV-TR criteria for MDE and 14 researched-based criteria for mixed features (RBDC) in 2758 acutely depressed MDD/BD patients from the BRIDGE-II-Mix study. The global network was described in terms of symptom thresholds and symptom centrality. Symptom endorsement rates were compared across diagnostic subgroups. Subsequently, MDD/BD differences in symptom-network structure were examined using permutation-based network comparison test. Mixed symptoms were the most central and highly interconnected nodes in the network, particularly agitation followed by irritability. Despite mixed symptoms, appetite gain and hypersomnia were significantly more endorsed in BD patients, associations between symptoms were highly correlated across MDD/BD (Spearman's r = 0.96, p<0.001). Network comparison tests showed no significant differences among MDD/BD in network strength, structure, or specific edges, with strong edges correlations (0.66-0.78). Upstream differences in MDD/BD may produce similar symptoms networks downstream during acute depression. Yet, mixed symptoms, appetite gain and hypersomnia are associated to BD rather than MDD. Symptoms during mixed-MDE might aggregate according to 2 different clusters, suggesting a possible stratification within mixed states. Future symptom-based studies should implement clinical, longitudinal, and biological factors, in order to establish tailored therapeutic strategies for acute depression.

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.

Network structure of depression symptomology in participants with and without depressive disorder: the population-based Health 2000-2011 study

PURPOSE

Putative causal relations among depressive symptoms in forms of network structures have been of recent interest, with prior studies suggesting that high connectivity of the symptom network may drive the disease process. We examined in detail the network structure of depressive symptoms among participants with and without depressive disorders (DD; consisting of major depressive disorder (MDD) and dysthymia) at two time points.

METHODS

Participants were from the nationally representative Health 2000 and Health 2011 surveys. In 2000 and 2011, there were 5998 healthy participants (DD-) and 595 participants with DD diagnosis (DD+). Depressive symptoms were measured using the 13-item version of the Beck Depression Inventory (BDI). Fused Graphical Lasso was used to estimate network structures, and mixed graphical models were used to assess network connectivity and symptom centrality. Network community structure was examined using the walktrap-algorithm and minimum spanning trees (MST). Symptom centrality was evaluated with expected influence and participation coefficients.

RESULTS

Overall connectivity did not differ between networks from participants with and without DD, but more simple community structure was observed among those with DD compared to those without DD. Exploratory analyses revealed small differences between the samples in the order of one centrality estimate participation coefficient.

CONCLUSIONS

Community structure, but not overall connectivity of the symptom network, may be different for people with DD compared to people without DD. This difference may be of importance when estimating the overall connectivity differences between groups with and without mental disorders.

Symptom network connectivity in adolescents with comorbid major depressive disorder and social phobia

PURPOSE

Major depressive disorder (MDD) and social phobia (SP) are both common and highly co-occurring psychiatric disorders. This study used symptom network analysis approach to examine comorbidity structure and the complex symptom dynamics which may play a role in the co-occurrence of MDD and SP.

METHOD

Data comes from the National Comorbidity Survey - Adolescent Supplement, a nationally representative survey of adolescents ages 13 to 18 years. This study examined data of adolescents with a lifetime diagnosis of MDD (n = 597), SP (n = 708), and adolescents with comorbid MDD and SP (n = 189). Networks were estimated by means of 26 symptoms from both disorders.

RESULTS

All MDD and SP symptoms were involved in the network of both pure disorders (MDD; SP) and comorbid condition (MDD + SP). Network structure was different between the pure disorders (p = 0.014), but not when comparing each of these disorders that have comorbid condition. Depressive symptoms of poor self-esteem and suicidal symptoms were central (i.e., showed a higher influence) in the symptom network for the pure disorders and for the comorbid condition. Other key symptoms in the comorbid condition network were two depressive symptoms: feelings of worthlessness and anhedonia. SP and MDD networks showed two common key SP symptoms: feeling uncomfortable when meeting new people and feeling uncomfortable talking to people do not know well.

CONCLUSION

The study of symptom dynamics will provide useful targets for preventing the development of comorbid disorders as well as new lines of intervention to deal with key symptoms of psychiatric disorders.

Exploring multicultural classroom dynamics: A network analysis

Students' relationships with peers and teachers strongly influence their motivation to engage in learning activities. Ethnic minority students, however, are often victimized in schools, and their educational achievement lags behind that of their majority group counterparts. The aim of the present study was to explore teachers' multicultural approach within their classrooms as a possible factor of influence over students' peer relationships and motivation. We utilized the novel methodology of estimating psychological networks in order to map out the interactions between these constructs within multicultural classrooms. Results indicate that a multicultural approach is directly connected to student motivation for both ethnic majority and minority students. Social integration within peer groups, however, seems to be a possible mediator of this relationship for the ethnic minority students. Due to the hypothesis generating nature of the psychological network approach, a more thorough investigation of this generated mediation hypothesis is called for.

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.

Mood rhythmicity is associated with depressive symptoms and caffeinated drinks consumption in South American young adults

Among the factors that contribute to the onset and maintenance of depressive disorders, rhythmicity of symptoms and consumption of caffeine have recently gained attention. The current study aimed to examine the differential rhythmicity of relevant variables in a sample of young participants, considering the presence of depressive symptomatology and the frequency of caffeinated drinks consumption. A significant 24-hour differential rhythmicity of mood, cognitive and physiological variables was found indicating an evening peak pattern in the participants with depressive symptoms. Interestingly, caffeinated drinks consumption was differentially associated with self-perceived peaks, according to the presence of depressive symptomatology. Our findings are among the first reports about the potential association of the 24-hours rhythmicity of relevant mood-related variables, depressive symptoms, and caffeine intake. These results support the view that the identification of risk factors for depression, and the application of novel measurements and analysis methods in the development of new preventive strategies should be a public health priority.

Network Analysis as an Alternative Approach to Conceptualizing Eating Disorders: Implications for Research and Treatment

PURPOSE OF REVIEW

Network analysis (NA) is an emerging methodology that allows for the characterization of maintaining symptoms and pathways among symptoms of mental disorders. The current paper provides background on NA and discusses the relevance of the network approach for the conceptualization of eating disorders (ED).

RECENT FINDINGS

We review the burgeoning literature conceptualizing ED from a network approach. Overall, these papers find that fear of weight gain and overvaluation of weight and shape are core symptoms in networks of ED pathology. We integrate literature on new advances in network methodology (e.g., within-person NA) and the clinical relevance of these approaches for the ED field (e.g., personalized ED treatment). We also provide several considerations (e.g., replicability, sample size, and node (item) selection) for researchers who are interested in using network science and recommend several emerging "best practices" for NA. Finally, we highlight novel applications of NA, specifically the ability to identify within-person maintaining symptoms, and the potential treatment implications for ED that network methods may hold. Overall, NA is a new methodology that holds significant promise for new treatment development in the ED field.