Problematic assumptions have slowed down depression research: why symptoms, not syndromes are the way forward

Predictors of substance use during treatment for addiction: A network analysis of ecological momentary assessment data

BACKGROUND AND AIMS

Ecological momentary assessment (EMA) studies have previously demonstrated a prospective influence of craving on substance use in the following hours. Conceptualizing substance use as a dynamic system of causal elements could provide valuable insights into the interaction of craving with other symptoms in the process of relapse. The aim of this study was to improve the understanding of these daily life dynamic inter-relationships by applying dynamic networks analyses to EMA data sets.

DESIGN, SETTING AND PARTICIPANTS

Secondary analyses were conducted on time-series data from two 2-week EMA studies. Data were collected in French outpatient addiction treatment centres. A total of 211 outpatients beginning treatment for alcohol, tobacco, cannabis, stimulants and opiate addiction took part.

MEASUREMENTS

Using mobile technologies, participants were questioned four times per day relative to substance use, craving, exposure to cues, mood, self-efficacy and pharmacological addiction treatment use. Multi-level vector auto-regression models were used to explore contemporaneous, temporal and between-subjects networks.

FINDINGS

Among the 8260 daily evaluations, the temporal network model, which depicts the lagged associations of symptoms within participants, demonstrated a unidirectional association between craving intensity at one time (T0) and primary substance use at the next assessment (T1, r = 0.1), after controlling for the effect of all other variables. A greater self-efficacy at T0 was associated with fewer cues (r = -0.04), less craving (r = -0.1) and less substance use at T1 (r = -0.07), and craving presented a negative feedback loop with self-efficacy (r = -0.09).

CONCLUSIONS

Dynamic network analyses showed that, among outpatients beginning treatment for addiction, high craving, together with low self-efficacy, appear to predict substance use more strongly than low mood or high exposure to cues.

Analysis of factors influencing medical students' learning engagement and its implications for teaching work-- a network analysis perspective

PURPOSE

Higher medical education has always been a major project in the fields of education and health, and therefore, the quality of education has received much attention. Learning engagement has emerged as a significant indicator of teaching quality, attracting considerable research attention. This study aims to explore the relationship between medical students' learning engagement and their sense of school belonging, professional identity, and academic self-efficacy.

METHODS

We conducted an online survey using convenience sampling method with 311 medical students. We employed Revised version of the Utrech Work Engagement Scale-Student (UWES-S), Chinese version of the Psychological Sense of School Membership (PSSM) scale, Academic Self-Efficacy Scale, and the questionnaire of college students' speciality identity for evaluation. Network analysis was used to analyze the relationships among these factors.

RESULTS

Medical students' overall performance in school showed a positive trend. However, there is still room for improvement. In the network structure of learning engagement and its influencing factors, the "emotional" aspect of professional identity (EI = 1.11) was considered to be an important node with strong centrality. And "academic competence self-efficacy" aspect of academic self-efficacy (BEI = 0.72) was considered an important node with strong transitivity.

CONCLUSION

Deepening medical students' emotional identification with their profession and enhancing their confidence in their academic abilities may improve their learning engagement and educational quality.

The association between online learning, perceived parental relationship, anxiety, and depression symptoms among secondary school students: insight from symptom network and cross-lagged panel network approach

INTRODUCTION

Anxiety and depression often co-occur in adolescents, with factors from family and school playing a significant role in the comorbidity. However, network analysis has not examined and clarified the detailed bridge and central symptoms of this comorbidity caused by online learning and perceived parental relationships across different COVID-19 times.

METHODS

Over four months, 2,356 secondary school students completed the Patient Health Questionnaire-9 and Generalized Anxiety Disorder Scale-7. Participants were divided into harmonious and disharmonious groups based on their answers to a question about parental conflicts.

RESULTS

The results indicated that adolescents perceiving more parental conflicts showed a denser comorbidity network after four months of online learning. Significant bridge symptoms decreased from three to two across two waves in the harmonious group, while in the disharmonious group, they increased from two to three. The number of central symptoms increased from one in wave 1 to three in wave 2 for the harmonious group, while four in wave 1 decreased to two in wave 2 for the disharmonious group. Furthermore, the CLPN analysis revealed that the strongest positive cross-lagged edge intensity between symptoms was anhedonia-energy in the harmonious group, with anhedonia being the most trigger symptom. In contrast, for the disharmonious group, guilt-suicide and trouble relaxing-excessive worry were the strongest cross-lagged edge, and trouble relaxing was the most trigger symptom.

CONCLUSION

These findings may have implications for interventions designed to promote adolescent mental health in the context of online learning and parental conflicts.

Practical Implications of Sum Scores Being Psychometrics' Greatest Accomplishment

This paper reflects on some practical implications of the excellent treatment of sum scoring and classical test theory (CTT) by Sijtsma et al. (Psychometrika 89(1):84-117, 2024). I have no major disagreements about the content they present and found it to be an informative clarification of the properties and possible extensions of CTT. In this paper, I focus on whether sum scores-despite their mathematical justification-are positioned to improve psychometric practice in empirical studies in psychology, education, and adjacent areas. First, I summarize recent reviews of psychometric practice in empirical studies, subsequent calls for greater psychometric transparency and validity, and how sum scores may or may not be positioned to adhere to such calls. Second, I consider limitations of sum scores for prediction, especially in the presence of common features like ordinal or Likert response scales, multidimensional constructs, and moderated or heterogeneous associations. Third, I review previous research outlining potential limitations of using sum scores as outcomes in subsequent analyses where rank ordering is not always sufficient to successfully characterize group differences or change over time. Fourth, I cover potential challenges for providing validity evidence for whether sum scores represent a single construct, particularly if one wishes to maintain minimal CTT assumptions. I conclude with thoughts about whether sum scores-even if mathematically justified-are positioned to improve psychometric practice in empirical studies.

Association of both depressive symptoms scores and specific depressive symptoms with all-cause and cardiovascular disease mortality

BACKGROUND

The presence of depression related to an increased risk of all-cause and cardiovascular disease (CVD) mortality has been reported. However, studies conducted on certain specific depressive symptoms are scarce. Our purpose was to assess the effect of both depressive symptoms scores and certain specific depressive symptoms on all-cause and CVD mortality.

METHODS

In the present cohort study, all participants, aged 18 years or older, were enrolled in the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2014. Depressive symptoms score was assessed using the validated 9-item Patient Health Questionnaire Depression Scale (PHQ-9), which ranges from 0 to 27, with a PHQ-9 score ≥ 10 diagnosed as depression. The outcome events were all-cause and CVD mortality, which were followed up from 2005 to 2014. The associations of both depressive symptoms score and certain specific depressive symptoms with all-cause and CVD mortality were examined by weighted multivariable proportional hazards models.

RESULTS

A total of 26,028 participants aged ≥ 18 years were included in the statistical analysis, including 12,813 (49.2%) males and 13,215 (50.8%) females, with a mean (SD) age of 47.34 (18.86) years. During the 9.32 (3.20) years of mean (SD) follow-up, 3261 deaths were recorded, of which 826 were cardiovascular deaths. All-cause mortality was 16.87/1000 person-years in subjects with depression. In terms of CVD mortality, these figures were 4.53/1000 person-years. In the full model (model 3), elevated depressive symptoms scores were independently associated with an increased risk of all-cause mortality (Highest depression symptom score group: adjusted hazard ratio, 1.63; 95% CI 1.44-1.85) and CVD mortality (Highest depression symptom score group: adjusted hazard ratio, 1.73; 95% CI 1.34-2.24). All 9 specific depressive symptoms that make up the PHQ-9 were related to an increased risk of all-cause mortality. However, only 3 symptoms, including trouble sleeping or sleeping too much, poor appetite or overeating, and suicidal ideation, were no significantly associated with an increased risk of CVD mortality.

CONCLUSIONS

The elevated depressive symptoms scores were strongly associated with an increased risk of all-cause and CVD mortality in US adults. Furthermore, all 9 specific depressive symptoms were associated with high all-cause mortality. However, trouble sleeping or sleeping too much, poor appetite or overeating, and suicidal ideation might not increase the risk of CVD mortality.

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

OBJECTIVE

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

METHOD

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

RESULTS

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

CONCLUSIONS

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

Associations between over-the-counter analgesics usage and symptoms of anxiety and depression in adolescents: a network analysis

BACKGROUND

The use of over-the-counter analgesics (OTCA) is common among adolescents and has been linked with increased symptoms of anxiety and depression. However, little is known about which specific symptoms are most strongly connected to OTCA usage. The current study assessed which anxiety and depression symptoms were most closely associated with OTCA usage in a large sample of adolescents and examined whether this differed across genders.

METHOD

The present study was based on data from 626,581 participants from the Ungdata survey in Norway. Associations between OTCA and anxiety and depression symptoms were examined using network analysis. Non-regularized partial-correlation networks were constructed to estimate the conditional dependent relations between the use of OTCA and symptoms while controlling for pain. Gender-specific networks were created for comparison.

RESULTS

OTCA usage was associated with most symptoms, even after controlling for pain, with the strongest associations with "sleep problems", "stiff or tense", "everything is a struggle" and "suddenly scared". There were some gender differences, showing that "sleep problems" and "hopeless" were more strongly related to OTCA usage in females, whereas "stiff or tense" was more strongly related to OTCA usage in males.

CONCLUSION

Overall, the somatic symptoms of anxiety and depression displayed the strongest associations with OTCA usage. When examining the gender-specific networks, both showed similar trends, although males exhibited slightly stronger associations between OTCA usage and somatic symptoms.

Post-stroke Depressive Symptoms and Cognitive Performances: A Network Analysis

OBJECTIVE

To examine the relationships between post-stroke depression and cognition using network analysis. In particular, we identified central depressive symptoms, central cognitive performances, and bridge components that connect these 2 constructs.

DESIGN

An observational study. We applied network analysis to analyze baseline data to visualize and quantify the relationships between depression and cognition.

SETTING

Home and Community.

PARTICIPANTS

202 participants with mild-to-moderate stroke (N=202; mean age: 59.7 years; 55% men; 55% Whites; 90% ischemic stroke).

INTERVENTION

Not applicable.

MAIN OUTCOME MEASURES

Patient Health Questionnaire (PHQ-8) for depressive symptoms and the NIH Toolbox Cognitive Battery for cognitive performances.

RESULTS

Depressive symptoms were positively intercorrelated with the network, with symptoms from similar domains clustered together. Mood (expected influence=1.58), concentration (expected influence=0.67), and guilt (expected influence=0.63) were the top 3 central depressive symptoms. Cognitive performances also showed similar network patterns, with executive function (expected influence=0.89), expressive language (expected influence=0.68), and processing speed (expected influence=0.48) identified as the top 3 central cognitive performances. Psychomotor functioning (bridge expected influence=2.49) and attention (bridge expected influence=1.10) were the components connecting depression and cognition.

CONCLUSIONS

The central and bridge components identified in this study might serve as targets for interventions against these deficits. Future trials are needed to compare the effectiveness of interventions targeting the central and bridge components vs general interventions treating depression and cognitive impairment as a homogenous clinical syndrome.

Risk and resilience factors for psychopathology during pregnancy: An application of the Hierarchical Taxonomy of Psychopathology (HiTOP)

Pregnancy is a time of increased vulnerability to psychopathology, yet limited work has investigated the extent to which variation in psychopathology during pregnancy is shared and unshared across syndromes and symptoms. Understanding the structure of psychopathology during pregnancy, including associations with childhood experiences, may elucidate risk and resilience factors that are transdiagnostic and/or specific to particular psychopathology phenotypes. Participants were 292 pregnant individuals assessed using multiple measures of psychopathology. Confirmatory factor analyses found evidence for a structure of psychopathology consistent with the Hierarchical Taxonomy of Psychopathology (HiTOP). A common transdiagnostic factor accounted for most variation in psychopathology, and both adverse and benevolent childhood experiences (ACEs and BCEs) were associated with this transdiagnostic factor. Furthermore, pregnancy-specific anxiety symptoms most closely reflected the dimension of Fear, which may suggest shared variation with manifestations of fear that are not pregnancy-specific. ACEs and BCEs also linked to specific prenatal psychopathology involving thought problems, detachment, and internalizing, externalizing, antagonistic, and antisocial behavior. These findings extend the dimensional and hierarchical HiTOP model to pregnant individuals and show how maternal childhood risk and resilience factors relate to common and specific forms of psychopathology during pregnancy as a period of enhanced vulnerability.

Common Cause Versus Dynamic Mutualism: An Empirical Comparison of Two Theories of Psychopathology in Two Large Longitudinal Cohorts

Mental disorders are among the leading causes of global disease burden. To respond effectively, a strong understanding of the structure of psychopathology is critical. We empirically compared two competing frameworks, dynamic-mutualism theory and common-cause theory, that vie to explain the development of psychopathology. We formalized these theories in statistical models and applied them to explain change in the general factor of psychopathology (p factor) from early to late adolescence (N = 1,482) and major depression in middle adulthood and old age (N = 6,443). Change in the p factor was better explained by mutualism according to model-fit indices. However, a core prediction of mutualism was not supported (i.e., predominantly positive causal interactions among distinct domains). The evidence for change in depression was more ambiguous. Our results support a multicausal approach to understanding psychopathology and showcase the value of translating theories into testable statistical models for understanding developmental processes in clinical sciences.

Coping, (mal)adaptive personality and identity in young adults: A network analysis

Coping, personality, and identity are three well-known constructs within the field of psychology. Yet, findings regarding how these constructs relate to each other have been inconsistent. The present study employs network analysis to investigate coping, adaptive and maladaptive personality, and identity and how they are related, using data from the Flemish Study on Parenting, Personality, and Development (FSPPD; Prinzie et al., 2003; 1999-current). Young adults (N = 457; 47% male), aged between 17-23 years old, completed a survey on coping, adaptive and maladaptive personality, and identity. Results indicate clear associations between coping and both adaptive and maladaptive personality within the network, suggesting coping and personality are distinct, yet highly related constructs whereas identity proved largely unrelated. Potential implications and suggestions for future research are discussed.

Time-Varying Network Models for the Temporal Dynamics of Depressive Symptomatology in Patients With Depressive Disorders: Secondary Analysis of Longitudinal Observational Data

BACKGROUND

As depression is highly heterogenous, an increasing number of studies investigate person-specific associations of depressive symptoms in longitudinal data. However, most studies in this area of research conceptualize symptom interrelations to be static and time invariant, which may lead to important temporal features of the disorder being missed.

OBJECTIVE

To reveal the dynamic nature of depression, we aimed to use a recently developed technique to investigate whether and how associations among depressive symptoms change over time.

METHODS

Using daily data (mean length 274, SD 82 d) of 20 participants with depression, we modeled idiographic associations among depressive symptoms, rumination, sleep, and quantity and quality of social contacts as dynamic networks using time-varying vector autoregressive models.

RESULTS

The resulting models showed marked interindividual and intraindividual differences. For some participants, associations among variables changed in the span of some weeks, whereas they stayed stable over months for others. Our results further indicated nonstationarity in all participants.

CONCLUSIONS

Idiographic symptom networks can provide insights into the temporal course of mental disorders and open new avenues of research for the study of the development and stability of psychopathological processes.

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.

Symptom clusters in adolescent depression and differential responses of clusters to pharmacologic treatment

OBJECTIVE

Symptoms of depression in adolescents are widely variable, but they are often interactive and clustered. The analysis of interactions and clusters among individual symptoms may help predict treatment outcomes. We aimed to determine clusters of individual symptoms in adolescent depression and their changes in the response to pharmacological treatment.

METHOD

A total of 95 adolescents, aged 12-17 years, with major depressive disorder were included. Participants were treated with escitalopram, and depressive symptoms were assessed at baseline (V1) and 1, 2, 4, 6, and 8 weeks (V6). The severity of depression was assessed using the Children's Depression Rating Scale-Revised. To construct network and clustering structures among symptoms, the Gaussian graphical model and Exploratory Graph Analysis with the tuning parameter to minimize the extended Bayesian information criterion were adopted.

RESULTS

Exploratory Graph Analysis revealed that symptoms of depression comprised four clusters: impaired activity, somatic concerns, subjective mood, and observed affect. The main effect of visit with decreased symptom severity was significant in all four clusters; however, the degree of symptom improvement differed among the four clusters. The effect size of score differences from V1 to V6 was the highest in the subjective mood (Cohen's d = 1.075), and lowest in impaired activity (d = 0.501) clusters.

CONCLUSION

The present study identified four symptom clusters associated with adolescent depression and their differential changes related to antidepressant treatment. This finding suggests that escitalopram was the most effective at improving subjective mood among different clusters. However, other therapeutic modalities may be needed to improve other clusters of symptoms, consequently leading to increased overall improvement of depression in adolescents.

Mapping the Pathways Between Posttraumatic Stress Disorder, Depression, and Alcohol and Cannabis Use: A Network Analysis

Objective: The present study examines the network structure and, using Bayesian network analysis, estimates the directional pathways among symptoms of posttraumatic stress disorder (PTSD), major depressive disorder (MDD), and levels of alcohol and cannabis use. Method: A sample of 1471 adults in the United States, who reported at least one potentially traumatic event, completed the PTSD Checklist (PCL-5), Patient Health Questionnaire (PHQ-9), and the Alcohol/Cannabis Use Disorders Identification Test (AUDIT/CUDIT). A regularized partial correlation network provided estimates of symptoms clusters and connections. Directional pathways within the network were then estimated using a directed acyclic graph (DAG). Results: Symptoms clustered in theoretically consistent ways. Risky behavior demonstrated the highest strength centrality and bridge strength. Neither alcohol nor cannabis use appeared central in the network, and DAG results suggested that MDD and PTSD symptoms are more likely to lead to substance use than the other way around. Conclusions: Results suggest that cannabis use is largely connected to alcohol use. Consistent with prior research, risky behavior appeared to be the primary bridge between substance use and PTSD. The direction of associations between substance use and psychological symptoms requires further attention.

Evaluating the sensitivity to threat and affiliative reward (STAR) model in relation to the development of conduct problems and callous-unemotional traits across early adolescence

BACKGROUND

The Sensitivity to Threat and Affiliative Reward (STAR) model proposes low threat sensitivity and low affiliation as risk factors for callous-unemotional (CU) traits. Preliminary evidence for the STAR model comes from work in early childhood. However, studies are needed that explore the STAR dimensions in late childhood and adolescence when severe conduct problems (CP) emerge. Moreover, it is unclear how variability across the full spectrum of threat sensitivity and affiliation gives rise to different forms of psychopathology beyond CU traits.

METHODS

The current study addressed these gaps using parent- and child-reported data from three waves and a sub-study of the Adolescent Brain Cognitive Development Study® of 11,878 youth (48% female; ages 9-12).

RESULTS

Consistent with the STAR model, low threat sensitivity and low affiliation were independently related to CU traits across informants and time. Moreover, there was significant interaction between the STAR dimensions, such that children with lower sensitivity to threat and lower affiliation had higher parent-reported CU traits. Unlike CU traits, children with higher threat sensitivity had higher parent-reported CP and anxiety. Finally, children with lower affiliation had higher parent-reported CP, anxiety, and depression. Results largely replicated across informants and time, and sensitivity analysis revealed similar findings in children with and without DSM-5 defined CP.

CONCLUSIONS

Results support the STAR model hypotheses as they pertain to CU traits and delineate threat sensitivity and affiliation as independent transdiagnostic risk factors for different types of psychopathology. Future research is needed to develop fuller and more reliable and valid measures of affiliation and threat sensitivity across multiple assessment modalities.

Parental Symptoms of Anhedonia, Parenting, and Youth Outcomes: A Multi-Method, Multi-Informant Investigation

Parental depression is a well-established risk factor for youth psychopathology; however, depression is highly heterogeneous, and different parental symptom profiles may be differentially associated with risk mechanisms and youth psychopathology outcomes. Thus, this study examined associations between parental anhedonic symptoms of depression, specifically, and (1) parenting and (2) youth outcomes using a multi-method, multi-informant approach. Participants included 595 parents (89% mothers) and youth (ages 8-16; M[SD] = 12.07[2.39]). Regression analyses indicated that parental self-reported anhedonic symptoms at baseline demonstrated relatively specific prospective associations with chronic parent-child stress assessed using contextual stress interview methods, as well as youth self-reported depressive symptoms at 18-month follow-up. Findings also indicated concurrent associations between parental anhedonic symptoms and observed parental criticism, conflict, and responsiveness in the context of a 5-min discussion task, as well as parent self-reported monitoring/supervision, although results were no longer significant after controlling for parental co-occurring non-anhedonic depressive symptoms. Findings suggest that parental anhedonic symptoms may contribute to relatively unique reductions in the quality of the parent-child relationship and may be a particularly salient risk factor for youth depression.

Impact of depressive symptoms on medication adherence in older adults with chronic neurological diseases

BACKGROUND

Nonadherence to medication contributes substantially to worse health outcomes. Especially among older adults with chronic illness, multimorbidity leads to complex medication regimes and high nonadherence rates. In previous research, depressive symptomology has been identified as a major contributor to nonadherence, and some authors hypothesize a link via motivational deficits and low self-efficacy. However, the exact mechanisms linking depressive symptomology and nonadherence are not yet understood. This is in part because the often-employed sum scores cannot do justice to the complexity of depressive symptomology; instead, it is recommended to assess the influence of individual symptoms.

METHODS

Following this symptom-based approach, we performed correlation, network and regression analysis using depressive symptoms as depicted by the items of the revised Beck Depression Inventory II (BDI) to assess their influence with nonadherence in N = 731 older adults with chronic neurological diseases. Nonadherence was measured with the self-report Stendal Adherence to Medication Score (SAMS).

RESULTS

Even when controlling for sociodemographic and health-related covariates, the BDI remained the most influential contributor to nonadherence. Across different methods, Loss of Interest and Difficulty with Concentration were identified as particularly influential for nonadherence, linking nonadherence with other affective or somatic BDI items, respectively. Additionally, Fatigue, Problems with Decision Making, Suicidal Thoughts, and Worthlessness contribute to nonadherence.

CONCLUSION

Using a symptom-driven approach, we aimed to understand which depressive symptoms contribute to higher levels of nonadherence. Our results refine previous hypotheses about motivation and control beliefs by suggesting that it is not merely a lack of beliefs in the efficacy of medication that connects depressive symptoms and nonadherence, but rather an overall lack of interest in improving one's health due to feelings of worthlessness and suicidal tendencies. This lack of interest is further substantiated by already sparse resources caused by changes in concentration and fatigue. In order to improve health outcomes and reduce nonadherence, these associations between depressive symptoms must be further understood and targeted in tailored interventions.

Depressive symptoms and loneliness among early adolescents: a psychometric network analysis approach

BACKGROUND

Previous studies demonstrate a high prevalence of depression and loneliness among adolescents. Although they often co-occur, the relationship between symptoms of depression and loneliness remains poorly understood. This study investigates: (a) the symptoms of depression that are connected to loneliness; (b) the role played by loneliness in the network of depression symptoms; and (c) whether the method used to measure loneliness (single-item direct or multi-item indirect) affects the relationship of loneliness with depressive symptoms.

METHODS

Participants were 496 Polish adolescents (50.8% girls) aged 11 to 13, who completed: (a) the 10-item Major Depressive Disorder subscale of the Revised Child Anxiety and Depression Scale; (b) the 11-item De Jong Gierveld Loneliness Scale (indirect loneliness), and (c) a single direct question evaluating loneliness: 'I'm lonely'. Networks were estimated using a Gaussian Graphical Model.

RESULTS

Loneliness shows a direct relationship with three affective symptoms of depression: sadness, worthlessness, and anhedonia, which mediate relationships with somatic symptoms. In contrast to previous studies, loneliness has the lowest level of centrality among all elements of the network. The method used to assess loneliness did not significantly affect the connections between loneliness and depressive symptoms.

CONCLUSIONS

Loneliness and depression overlap since they are formed by the same cognitive biases and deficits in emotion regulation but differ in the level of generality. In loneliness, they have an interpersonal context, while symptoms of depression can be intrapersonal. This helps us to understand why cognitive interventions, as compared to those which are social, are more effective in reducing loneliness.

Beyond the factor indeterminacy problem using genome-wide association data

Latent factors, such as general intelligence, depression and risk tolerance, are invoked in nearly all social science research where a construct is measured via aggregation of symptoms, question responses or other measurements. Because latent factors cannot be directly observed, they are inferred by fitting a specific model to empirical patterns of correlations among measured variables. A long-standing critique of latent factor theories is that the correlations used to infer latent factors can be produced by alternative data-generating mechanisms that do not include latent factors. This is referred to as the factor indeterminacy problem. Researchers have recently begun to overcome this problem by using information on the associations between individual genetic variants and measured variables. We review historical work on the factor indeterminacy problem and describe recent efforts in genomics to rigorously test the validity of latent factors, advancing the understanding of behavioural science constructs.

Dynamic time warp analysis of individual symptom trajectories in individuals with bipolar disorder

BACKGROUND

Manic and depressive mood states in bipolar disorder (BD) may emerge from the non-linear relations between constantly changing mood symptoms exhibited as a complex dynamic system. Dynamic Time Warp (DTW) is an algorithm that may capture symptom interactions from panel data with sparse observations over time.

METHODS

The Young Mania Rating Scale and Quick Inventory of Depressive Symptomatology were repeatedly assessed in 141 individuals with BD, with on average 5.5 assessments per subject every 3-6 months. Dynamic Time Warp calculated the distance between each of the 27 × 27 pairs of standardized symptom scores. The changing profile of standardized symptom scores of BD participants was analyzed in individual subjects, yielding symptom dimensions in aggregated group-level analyses. Using an asymmetric time-window, symptom changes that preceded other symptom changes (i.e., Granger causality) yielded a directed network.

RESULTS

The mean age of the BD participants was 40.1 (SD 13.5) years old, and 60% were female participants. Idiographic symptom networks were highly variable between subjects. Yet, nomothetic analyses showed five symptom dimensions: core (hypo)mania (6 items), dysphoric mania (5 items), lethargy (7 items), somatic/suicidality (6 items), and sleep (3 items). Symptoms of the "Lethargy" dimension showed the highest out-strength, and its changes preceded those of "somatic/suicidality," while changes in "core (hypo)mania" preceded those of "dysphoric mania."

CONCLUSION

Dynamic Time Warp may help to capture meaningful BD symptom interactions from panel data with sparse observations. It may increase insight into the temporal dynamics of symptoms, as those with high out-strength (rather than high in-strength) could be promising targets for intervention.

Network analysis of depressive and anxiety symptoms in older Chinese adults with diabetes mellitus

Background

The move away from investigating mental disorders as whole using sum scores to the analysis of symptom-level interactions using network analysis has provided new insights into comorbidities. The current study explored the dynamic interactions between depressive and anxiety symptoms in older Chinese adults with diabetes mellitus (DM) and identified central and bridge symptoms in the depression-anxiety network to provide potential targets for prevention and intervention for depression and anxiety.

Methods

This study used a cross-sectional design with data from the 2017-2018 wave of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). A regularized partial correlation network for depressive and anxiety symptoms was estimated based on self-reported scales completed by 1685 older adults with DM aged 65 years or older. Depressive and anxiety symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) and the Seven-Item Generalized Anxiety Disorder Scale (GAD-7), respectively. Expected influence (EI) and bridge expected influence (BEI) indices were calculated for each symptom.

Results

According to cutoff scores indicating the presence of depression and anxiety, the prevalences of depression and anxiety in our sample were 52.9% and 12.8%, respectively. The comorbidity rate of depression and anxiety was 11.5%. The six edges with the strongest regularized partial correlations were between symptoms from the same disorder. "Feeling blue/depressed", "Nervousness or anxiety", "Uncontrollable worry", "Trouble relaxing", and "Worry too much" had the highest EI values. "Nervousness or anxiety" and "Everything was an effort" exhibited the highest BEI values.

Conclusion

Central and bridge symptoms were highlighted in this study. Targeting these symptoms may be effective in preventing the comorbidity of depressive and anxiety symptoms and facilitate interventions in older Chinese adults with DM who are at risk for or currently have depressive and anxiety symptoms.

Baseline characteristics of a European patient population with difficult-to-treat depression (RESTORE-LIFE) treated with adjunctive vagus nerve stimulation

BACKGROUND

Major depressive disorder is a complex heterogeneous disorder. Treatment is especially challenging for patients with "difficult-to-treat depression" (DTD): a less stigmatizing and more clinically relevant framework defining depression that continues to cause significant burden despite usual treatment efforts.

METHODS

RESTORE-LIFE is a prospective, observational, multicenter, post-market study being conducted in Europe and is designed to reflect real-world clinical application of adjunctive Vagus Nerve Stimulation Therapy (VNS) for DTD. Baseline characteristics of RESTORE-LIFE patients were analyzed and compared to published treatment-resistant depression (TRD) trials.

RESULTS

This analysis includes the initial 98 RESTORE-LIFE patients who commenced treatment with VNS. Patients had a mean of 11.4 failed anti-depressant treatments, 1.1 suicide attempts, 87 % had prior electroconvulsive therapy, and 36 % had an endocrine/metabolic comorbidity. On average, disease severity was comparable to that in TRD trials (n = 15,463). However, RESTORE-LIFE patients appear to have been experiencing DTD for a longer duration and their DTD was characterized by a lack of positive mental health and meaningfulness of life, to a greater degree than by excess of negative mood. Despite high comorbidity rates in RESTORE-LIFE, VNS implantation was performed safely with no discontinuations due to surgical adverse events.

LIMITATIONS

RESTORE-LIFE enrolls any patient receiving adjunctive VNS for DTD. Prescription of VNS may be biased by differences in practices amongst sites and countries.

CONCLUSIONS

The present analysis offers insight into contemporary real-world use of VNS Therapy for DTD in Europe representing a comprehensive characterization of DTD and how this population may differ from those in the TRD literature.

Prevalence and network structure of post-traumatic stress symptoms and their association with suicidality among Chinese mental health professionals immediately following the end of China's Dynamic Zero-COVID Policy: a national survey

Studies on post-traumatic stress symptoms (PTSS) among mental health professionals (MHPs) are limited, particularly since restrictions due to coronavirus disease (COVID-19) have been lifted such as the recent termination of China's Dynamic Zero-COVID Policy. The current study filled this gap by exploring the prevalence, correlates, and network structure of PTSS as well as its association with suicidality from a network analysis perspective. A cross-sectional, national survey was conducted using a convenience sampling method on MHPs between January 22 and February 10, 2023. PTSS were assessed using the Post-Traumatic Stress Disorder Checklist-Civilian version, while suicidality was assessed using standardized questions related to ideation, plans, and attempts. Univariate and multivariate analyses examined correlates of PTSS. Network analysis explored the structure of PTSS and suicidality. The centrality index of "Expected influence" was used to identify the most central symptoms in the network, reflecting the relative importance of each node in the network. The "flow" function was adopted to identify specific symptoms that were directly associated with suicidality. A total of 10,647 MHPs were included. The overall rates of PTSS and suicidality were 6.7% (n = 715; 95% CI = 6.2-7.2%) and 7.7% (n = 821; 95% CI = 7.2-8.2%), respectively. Being married (OR = 1.523; P < 0.001), quarantine experience (OR = 1.288; P < 0.001), suicidality (OR = 3.750; P < 0.001) and more severe depressive symptoms (OR = 1.229; P < 0.001) were correlates of more PTSS. Additionally, higher economic status (e.g., good vs. poor: OR = 0.324; P = 0.001) and health status (e.g., good vs. poor: OR = 0.456; P < 0.001) were correlates of reduced PTSS. PCL6 ("Avoiding thoughts"; EI = 1.189), PCL7 ("Avoiding reminders"; EI = 1.157), and PCL11 ("Feeling emotionally numb"; EI = 1.074) had the highest centrality, while PCL12 ("Negative belief"), PCL 16 ("Hypervigilance") and PCL 14 ("Irritability") had the strongest direct, positive associations with suicidality. A high prevalence of lingering PTSS was found among MHPs immediately after China's "Dynamic Zero-COVID Policy" was terminated. Avoidance and hyper-arousal symptoms should be monitored among at-risk MHPs after the COVID-19 pandemic and serve as potential targets for the prevention and treatment of PTSS in this population.

Relationship of sleep-quality and social-anxiety in patients with breast cancer: a network analysis

BACKGROUND

There is a complex relationship between social anxiety and sleep quality. However, network analysis studies of associations between social anxiety and sleep quality are lacking, particularly among patients with breast cancer. The current study aimed to extend this research to a sample of patients with breast cancer and to examine symptom-level associations between social anxiety and sleep quality using network analysis.

METHODS

Network analysis was conducted to explore their associations and identify bridge items of social anxiety and sleep quality.

RESULTS

The network structure revealed 9 important edges between social anxiety and sleep quality. "Subjective sleep quality" had the highest EI value in the network. "Working difficulty under watching" and "Sleep disorders" had the highest BEI values in their own communities.

CONCLUSION

There are complex pathological correlation pathways between social anxiety and sleep quality in breast cancer patients. "Subjective sleep quality", "Working difficulty under watching" and "Sleep disorders" have the potential to be intervention targets for sleep disorder-social anxiety comorbidity. Medical staff can take corresponding interventions according to the the centrality indices and bridge centrality indicators identified in this study, which is likely to effectively reduce the comorbidity of sleep disorders and social anxiety.

Relations among symptoms of depression over time in at-risk youth

Depression consists of symptoms that may relate to each other in ways that go beyond simple co-occurrence. For example, some symptoms may precede and possibly contribute to the emergence of others. The present study examined several potential relations among the symptoms of depression. The overarching goals were to better understand how depression may unfold and to identify potential targets for intervention. The sample included 120 offspring of depressed parents. Youths' symptoms of depression were rated across 89 weeks. First, we investigated which symptoms preceded and potentially contributed to other symptoms 1 week later. This model revealed that sleep disturbance predicted the occurrence of other symptoms (e.g., sad mood, fatigue), and the occurrence of sad mood was predicted by other symptoms (e.g., worthlessness/guilt, psychomotor symptoms, sleep disturbance). Second, we investigated the within-person question of which symptoms tended to co-occur at the same time point. This model identified sad mood, irritability, and anhedonia as symptoms that tended to co-occur with each other and with many other depressive symptoms. Third, we investigated the between-person question of which symptoms tended to co-occur when averaged across time. This model identified worthlessness/guilt, fatigue, and anhedonia as symptoms strongly associated with other depressive symptoms across people irrespective of timing. Results indicate that the relations among the symptoms of depression vary, such that some symptoms preceded others by 1 week, some symptoms occurred at the same time, and other symptoms co-occurred in individuals. This more detailed view of the connections among depressive symptoms informs our understanding of depression as a dynamic set of unique indicators. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Network analysis of physical activity and depressive and affective symptoms during COVID-19 home confinement

INTRODUCTION

The aim of this study was to analyze the network structure of physical activity, frequency, depressive, and affective symptoms in people under home isolation due to COVID-19.

METHOD

A longitudinal study was conducted in two phases (beginning (March 19, 2020) and end of home confinement (April 8, 2020)). The sample consisted of 579 participants from Spain (250 men and 329 women) aged 16 to 92 years (overall sample: M = 47.06, SD = 14.52). A network analysis was performed.

RESULTS

Four clusters emerged (PA, depressive symptoms, positive affect, and negative affect). A higher frequency of physical activity was related to better-sustained attention, increased alertness, and enthusiasm. In addition, feelings of guilt and shame were mitigated, and confinement distress and irritability were reduced. Physical activity also mitigated fatigue in women, whereas feelings of unhappiness were reduced in men.

CONCLUSION

Physical activity seems to be an effective option for mitigating the negative effects of the COVID-19 pandemic. Public health policymakers should develop programs to promote physical activity in order to be able to cope with confinement or similar scenarios in the future.

Exploring Heterogeneity in perinatal depression: a comprehensive review

Perinatal depression (PND) is a significant contributor to maternal morbidity globally. Recognized as a major cause of poor infant development, epidemiological and interventional research on it has increased over the last decade. Recently, studies have pointed out that PND is a heterogeneous condition, with variability in its phenotypes, rather than a homogenous latent entity and a concrete diagnosis, as previously conceptualized in psychometric literature and diagnostic systems. Therefore, it is pertinent that researchers recognize this to progress in elucidating its aetiology and developing efficacious interventions.This systematic review is conducted in accordance with the Meta-analysis of observational studies in epidemiology (MOOSE). It aims to provide an updated and comprehensive account of research on heterogeneity in phenotypes of PND and its implications in research, public health, and clinical practice. It provides a synthesis and quality assessment of studies reporting heterogeneity in PND using cutting-edge statistical techniques and machine learning algorithms. After reporting the phenotypes of PND, based on heterogeneous trajectories and symptom profiles, it also elucidates the risk factors associated with severe forms of PND, followed by robust evidence for adverse child outcomes. Furthermore, recommendations are made to improve public health and clinical practice in screening, diagnosis, and treatment of PND.

Insights from personalized models of brain and behavior for identifying biomarkers in psychiatry

A main goal in translational neuroscience is to identify neural correlates of psychopathology ("biomarkers") that can be used to facilitate diagnosis, prognosis, and treatment. This goal has led to substantial research into how psychopathology symptoms relate to large-scale brain systems. However, these efforts have not yet resulted in practical biomarkers used in clinical practice. One reason for this underwhelming progress may be that many study designs focus on increasing sample size instead of collecting additional data within each individual. This focus limits the reliability and predictive validity of brain and behavioral measures in any one person. As biomarkers exist at the level of individuals, an increased focus on validating them within individuals is warranted. We argue that personalized models, estimated from extensive data collection within individuals, can address these concerns. We review evidence from two, thus far separate, lines of research on personalized models of (1) psychopathology symptoms and (2) fMRI measures of brain networks. We close by proposing approaches uniting personalized models across both domains to improve biomarker research.

The interplay of personality traits, anxiety, and depression in Chinese college students: a network analysis

Background

Anxiety and depression are among the greatest contributors to the global burden of diseases. The close associations of personality traits with anxiety and depression have been widely described. However, the common practice of sum scores in previous studies limits the understanding of the fine-grained connections between different personality traits and anxiety and depression symptoms and cannot explore and compare the risk or protective effects of personality traits on anxiety and depression symptoms.

Objective

We aimed to determine the fine-grained connections between different personality traits and anxiety and depression symptoms and identify the detrimental or protective effects of different personality traits on anxiety and depression symptoms.

Methods

A total of 536 college students from China were recruited online, and the average age was 19.98 ± 1.11. The Chinese version of the Ten-Item Personality Inventory, Generalized Anxiety Disorder-7, and Patient Health Questionnaire-9 was used to investigate the personality traits and symptoms of anxiety and depression of participants after they understood the purpose and filling method of the survey and signed the informed consent. The demographic characteristics were summarized, and the scale scores were calculated. The network model of personality traits and symptoms of anxiety and depression was constructed, and bridge expected influence (BEI) was measured to evaluate the effect of personality traits on anxiety and depression. The edge accuracy and BEI stability were estimated, and the BEI difference and the edge weight difference were tested.

Results

In the network, 29 edges (indicating partial correlations between variables) bridged the personality community and the anxiety and depression community, among which the strongest correlations were extraversion-fatigue, agreeableness-suicidal ideation, conscientiousness-uncontrollable worry, neuroticism-excessive worry, neuroticism-irritability, and openness-feelings of worthlessness. Neuroticism had the highest positive BEI value (0.32), agreeableness had the highest negative BEI value (-0.27), and the BEI values of neuroticism and agreeableness were significantly different from those of most other nodes (p < 0.05).

Conclusion

There are intricate correlations between personality traits and the symptoms of anxiety and depression in college students. Neuroticism was identified as the most crucial risk trait for depression and anxiety symptoms, while agreeableness was the most central protective trait.

The nature and neurobiology of fear and anxiety: State of the science and opportunities for accelerating discovery

Fear and anxiety play a central role in mammalian life, and there is considerable interest in clarifying their nature, identifying their biological underpinnings, and determining their consequences for health and disease. Here we provide a roundtable discussion on the nature and biological bases of fear- and anxiety-related states, traits, and disorders. The discussants include scientists familiar with a wide variety of populations and a broad spectrum of techniques. The goal of the roundtable was to take stock of the state of the science and provide a roadmap to the next generation of fear and anxiety research. Much of the discussion centered on the key challenges facing the field, the most fruitful avenues for future research, and emerging opportunities for accelerating discovery, with implications for scientists, funders, and other stakeholders. Understanding fear and anxiety is a matter of practical importance. Anxiety disorders are a leading burden on public health and existing treatments are far from curative, underscoring the urgency of developing a deeper understanding of the factors governing threat-related emotions.

Conceptualising social media addiction: a longitudinal network analysis of social media addiction symptoms and their relationships with psychological distress in a community sample of adults

BACKGROUND

Problematic social media use has been identified as negatively impacting psychological and everyday functioning and has been identified as a possible behavioural addiction (social media addiction; SMA). Whether SMA can be classified as a distinct behavioural addiction has been debated within the literature, with some regarding SMA as a premature pathologisation of ordinary social media use behaviour and suggesting there is little evidence for its use as a category of clinical concern. This study aimed to understand the relationship between proposed symptoms of SMA and psychological distress and examine these over time in a longitudinal network analysis, in order better understand whether SMA warrants classification as a unique pathology unique from general distress.

METHOD

N = 462 adults (Mage = 30.8, SDage = 9.23, 69.3% males, 29% females, 1.9% other sex or gender) completed measures of social media addiction (Bergen Social Media Addiction Scale), and psychological distress (DASS-21) at two time points, twelve months apart. Data were analysed using network analysis (NA) to explore SMA symptoms and psychological distress. Specifically, NA allows to assess the 'influence' and pathways of influence of each symptom in the network both cross-sectionally at each time point, as well as over time.

RESULTS

SMA symptoms were found to be stable cross-sectionally over time, and were associated with, yet distinct, from, depression, anxiety and stress. The most central symptoms within the network were tolerance and mood-modification in terms of expected influence and closeness respectively. Depression symptoms appeared to have less of a formative effect on SMA symptoms than anxiety and stress.

CONCLUSIONS

Our findings support the conceptualisation of SMA as a distinct construct occurring based on an underpinning network cluster of behaviours and a distinct association between SMA symptoms and distress. Further replications of these findings, however, are needed to strengthen the evidence for SMA as a unique behavioural addiction.

Intraindividual phenotyping of depression in high-risk youth: An application of a multilevel hidden Markov model

BACKGROUND

Traditionally, depression phenotypes have been defined based on interindividual differences that distinguish between subgroups of individuals expressing distinct depressive symptoms often from cross-sectional data. Alternatively, depression phenotypes can be defined based on intraindividual differences, differentiating between transitory states of distinct symptoms profiles that a person transitions into or out of over time. Such within-person phenotypic states are less examined, despite their potential significance for understanding and treating depression.

METHODS

The current study used intensive longitudinal data of youths (N = 120) at risk for depression. Clinical interviews (at baseline, 4, 10, 16, and 22 months) yielded 90 weekly assessments. We applied a multilevel hidden Markov model to identify intraindividual phenotypes of weekly depressive symptoms for at-risk youth.

RESULTS

Three intraindividual phenotypes emerged: a low-depression state, an elevated-depression state, and a cognitive-physical-symptom state. Youth had a high probability of remaining in the same state over time. Furthermore, probabilities of transitioning from one state to another did not differ by age or ethnoracial minority status; girls were more likely than boys to transition from a low-depression state to either the elevated-depression state or the cognitive-physical symptom state. Finally, these intraindividual phenotypes and their dynamics were associated with comorbid externalizing symptoms.

CONCLUSION

Identifying these states as well as the transitions between them characterizes how symptoms of depression change over time and provide potential directions for intervention efforts.

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

Aim

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

Materials and Methods

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

Results

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

Conclusions

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

An investigation of the relationships between suicidal ideation, psychache, and meaning in life using network analysis

BACKGROUND

Previous studies have investigated the relationships between psychache or meaning in life and suicidal ideation based on sum score of corresponding scale. However, this practice has hampered the fine-grained understanding of their relationships. This network analysis study aimed to conduct a dimension-level analysis of these constructs and the relationships among them in a joint framework, and identify potential intervention targets to address suicidal ideation.

METHODS

Suicidal ideation, psychache, and meaning in life were measured using self-rating scales among 738 adults. A network of suicidal ideation, psychache, and meaning in life was constructed to investigate the connections between dimensions and calculate the expected influence and bridge expected influence of each node.

RESULTS

"Psychache" was positively linked to "sleep" and "despair", while "presence of meaning in life" had negative associations with "psychache", "despair", and "pessimism". The most important central nodes were "sleep" and "despair", and the critical bridge nodes were "presence of meaning in life" and "psychache".

CONCLUSION

These preliminary findings uncover the pathological pathways underlying the relationships between psychache, meaning in life, and suicidal ideation. The central nodes and bridge nodes identified may be potential targets for effectively preventing and intervening against the development and maintenance of suicidal ideation.

Identifying Transdiagnostic Mechanisms in Mental Health Using Computational Factor Modeling

Most psychiatric disorders do not occur in isolation, and most psychiatric symptom dimensions are not uniquely expressed within a single diagnostic category. Current treatments fail to work for around 25% to 40% of individuals, perhaps due at least in part to an overreliance on diagnostic categories in treatment development and allocation. In this review, we describe ongoing efforts in the field to surmount these challenges and precisely characterize psychiatric symptom dimensions using large-scale studies of unselected samples via remote, online, and "citizen science" efforts that take a dimensional, mechanistic approach. We discuss the importance that efforts to identify meaningful psychiatric dimensions be coupled with careful computational modeling to formally specify, test, and potentially falsify candidate mechanisms that underlie transdiagnostic symptom dimensions. We refer to this approach, i.e., where symptom dimensions are identified and validated against computationally well-defined neurocognitive processes, as computational factor modeling. We describe in detail some recent applications of this method to understand transdiagnostic cognitive processes that include model-based planning, metacognition, appetitive processing, and uncertainty estimation. In this context, we highlight how computational factor modeling has been used to identify specific associations between cognition and symptom dimensions and reveal previously obscured relationships, how findings generalize to smaller in-person clinical and nonclinical samples, and how the method is being adapted and optimized beyond its original instantiation. Crucially, we discuss next steps for this area of research, highlighting the value of more direct investigations of treatment response that bridge the gap between basic research and the clinic.

Measurement Invariant but Non-Normal Treatment Responses in Guided Internet Psychotherapies for Depressive and Generalized Anxiety Disorders

Assessment of treatment response in psychotherapies can be undermined by lack of longitudinal measurement invariance (LMI) in symptom self-report inventories, by measurement error, and/or by wrong model assumptions. To understand and compare these threats to validity of outcome assessment in psychotherapy research, we studied LMI, sum scores, and Davidian Curve Item Response Theory models in a naturalistic guided internet psychotherapy treatment register of 2,218 generalized anxiety disorder (GAD) patients and 3,922 depressive disorder (DD) patients (aged ≥16 years). Symptoms were repeatedly assessed by Generalized Anxiety Disorder Assessment-7 (GAD-7) or Beck Depression Inventory. The symptom self-reports adhered to LMI under equivalence testing, suggesting sum scores are reasonable proxies for disorder status. However, the standard LMI assumption of normally distributed latent factors did not hold and inflated treatment response estimates by 0.2 to 0.3 standard deviation units compared with sum scores. Further methodological research on non-normally distributed latent constructs holds promise in advancing LMI and mental health assessment.

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

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

Network analysis of affect, emotion regulation, psychological capital, and resilience among Chinese males during the late stage of the COVID-19 pandemic

Background

Previous studies have confirmed that both affect and emotion regulation strategies are closely associated with psychological capital (PsyCap) and resilience. These factors are assumed to buffer the effect of the COVID-19 pandemic on mental health, especially among males. However, these interactions have not been closely examined to date. To fill this gap, this paper explores the dimension-level relationships of these psychological constructs among Chinese males during the late stage of the COVID-19 pandemic and identified critical bridge dimensions using network analysis.

Methods

A total of 1,490 Chinese males aged 21–51 years completed self-report scales assessing emotion regulation strategies, affect, PsyCap, and psychological resilience. Two regularized partial correlation networks, namely the affect and emotion regulation-PsyCap network and the affect and emotion regulation-psychological resilience network, were then constructed to examine links between the dimensions of these constructs. The bridge expected influence (BEI) index was also calculated for each node to identify important bridge nodes.

Results

Positive affect, negative affect, cognitive reappraisal, and expressive suppression showed distinct and complex links to various dimensions of PsyCap or psychological resilience. In both networks, positive affect, cognitive reappraisal, and negative affect were identified as critical bridge nodes, with the first two having positive BEI values and the third having a negative value.

Conclusion

The findings elucidate the specific role of the dimensions of emotion regulation or affect in relation to PsyCap and psychological resilience, which facilitates further understanding of the mechanisms underlying these interrelationships. These findings also provide implications for developing effective intervention strategies to increase PsyCap and psychological resilience.

Network analysis of somatic symptoms in Chinese patients with depressive disorder

Introduction

Network theory conceptualizes somatic symptoms as a network of individual symptoms that are interconnected and influenced by each other. In this conceptualization, the network's central symptoms have the strongest effect on other symptoms. Clinical symptoms of patients with depressive disorders are largely determined by their sociocultural context. To our knowledge, no previous study has investigated the network structure of somatic symptoms among Chinese patients with depressive disorders. The aim of this study was to characterize the somatic symptoms network structure in patients with depressive disorders in Shanghai, China.

Method

A total of 177 participants were recruited between October 2018 and June 2019. The Chinese version of the Patient Health Questionnaire-15 was used to assess somatic symptoms. In order to quantify the somatic symptom network structure, indicators of “closeness,” “strength,” and “betweenness” were employed as identifiers for network-central symptoms.

Result

The symptoms of “feeling your heart pound or race,” “shortness of breath,” and “back pain” had the highest centrality values, indicating that these symptoms were central to the somatic symptom networks. Feeling tired or mentally ill had the strongest positive correlation with insomnia or other sleep problems (r = 0.419), followed by chest pain and breathlessness (r = 0.334), back pain, and limb or joint pain (r = 0.318).

Discussion

Psychological and neurobiological research that offers insights into somatic symptoms may focus on these central symptoms as targets for treatment and future research.

Comparing the centrality symptoms of major depressive disorder samples across junior high school students, senior high school students, college students and elderly adults during city lockdown of COVID-19 pandemic-A network analysis

INTRODUCTION

Recently, in the view of network analysis, depression has been conceptualized as a complex and dynamic network model combining individual symptoms. To date, no studies have systematically examined and compared depressive symptom networks across different populations.

METHODS

A total of 36,105 participants were recruited and asked to complete the Patient Health Questionnaire-9 among junior high school students, senior high school students, college students, and elderly adults who were more susceptible to depression during the COVID-19 lockdown in China. In the analysis, we applied the optimal cutoff score ≥ 8 for students and a score ≥ 6 for elderly adults to identify 5830 participants who were likely to be depressed. The index of "strength" was used to identify central symptoms in the network structure.

RESULTS

The results showed that Sad Mood was the most central symptom among junior high school students, senior high school students, and college students, but the most central symptom in the elderly was Guilt. Among the top three central symptoms, Suicide Ideation was unique to senior high school students, while Anhedonia was most prevalent among college students. Guilt - Suicide Ideation, Anhedonia - Energy, Anhedonia - Sad Mood, and Sleep - Energy showed the strongest association among junior and senior high school students, college students, and elderly adults, respectively. NCT (i.e., Network Comparison Test) suggested that the network's global connectivity was ultimately inconsistent, but the network structure remained roughly intact.

CONCLUSION

In treatment, targeting central symptoms may be critical to alleviating depression.

Neurobiology of depression in Parkinson's disease: Insights into epidemiology, molecular mechanisms and treatment strategies

Parkinson's disease (PD) is characterized mainly by motor dysfunctions due to the progressive loss of dopaminergic neurons. However, PD patients experience a multitude of debilitating non-motor symptoms, including depression, which may have deleteriously detrimental effects on life. Depression is multifactorial and exhibits a bimodal progression in PD, but its underlying molecular mechanisms are poorly understood. Studies demonstrating the pathophysiology of depression in PD and the specific treatment strategies for depression-like symptoms in PD patients are largely lacking, often underrated, under-recognized and, consequently, inadequately/under-treated. Nevertheless, reports suggest that the incidence of depression is approximately 20-30% of PD patients and may precede the onset of motor symptoms. Diagnosing depression in PD becomes difficult due to the clinical overlap in symptomatology between the two diseases, and the nigrostriatal dysfunction alone is insufficient to explain depressive symptoms in PD. Therefore, the current study provides an overview of the molecular mechanisms underlying the development of depression in PD and new insights into developing current antidepressant strategies to treat depression in PD. This review will identify and understand the molecular pathological mechanisms of depression in PD that will fundamentally help tailoring therapeutic interventions for depressive symptoms in PD.

The Relationships Between Suicidal Ideation, Meaning in Life, and Affect: a Network Analysis

Transitioning from holistic analysis to a fine-grained level analysis may provide further understanding of psychopathology. This study aimed to explore dimension-level relationships between suicidal ideation, meaning in life, and affect in a joint framework using network analysis and to identify potential prevention and intervention targets to address suicidal ideation. A total of 852 healthy adults aged 18-35 years completed self-report scales to assess suicidal ideation, meaning in life, and affect. A regularized partial correlation network was then built to examine the links between these dimensions. Expected influence and bridge expected influence values were calculated for each node. The prevalence of suicidal ideation was 4.2%. The search for and presence of meaning in life and positive and negative affect exhibited distinct and complex links to the three dimensions of suicidal ideation (pessimism, sleep, and despair). The important central nodes were search for meaning in life, sleep, despair, and positive affect, while the critical bridge nodes were positive affect, negative affect, and presence of meaning in life. These findings provide further understanding of the specific roles of meaning in life and affect in suicidal ideation. The identified nodes may be promising targets for prevention and intervention for suicidal ideation.

Self-judgment dissected: A computational modeling analysis of self-referential processing and its relationship to trait mindfulness facets and depression symptoms

Cognitive theories of depression, and mindfulness theories of well-being, converge on the notion that self-judgment plays a critical role in mental health. However, these theories have rarely been tested via tasks and computational modeling analyses that can disentangle the information processes operative in self-judgments. We applied a drift-diffusion computational model to the self-referential encoding task (SRET) collected before and after an 8-week mindfulness intervention (n = 96). A drift-rate regression parameter representing positive-relative to negative-self-referential judgment strength positively related to mindful awareness and inversely related to depression, both at baseline and over time; however, this parameter did not significantly relate to the interaction between mindful awareness and nonjudgmentalness. At the level of individual depression symptoms, at baseline, a spectrum of symptoms (inversely) correlated with the drift-rate regression parameter, suggesting that many distinct depression symptoms relate to valenced self-judgment between subjects. By contrast, over the intervention, changes in only a smaller subset of anhedonia-related depression symptoms showed substantial relationships with this parameter. Both behavioral and model-derived measures showed modest split-half and test-retest correlations. Results support cognitive theories that implicate self-judgment in depression and mindfulness theories, which imply that mindful awareness should lead to more positive self-views.

Electrophysiological biomarkers and age characterize phenotypic heterogeneity among individuals with major depressive disorder

Introduction: Despite the high need for effective treatments for major depressive disorder (MDD), the development of novel medicines is hampered by clinical, genetic and biological heterogeneity, unclear links between symptoms and neural dysfunction, and tenuous biomarkers for clinical trial contexts of use. Methods: In this study, we examined the International Study to Predict Optimized Treatment in Depression (iSPOT-D) clinical trial database for new relationships between auditory event-related potential (ERP) responses, demographic features, and clinical symptoms and behavior, to inform strategies for biomarker-driven patient stratification that could be used to optimize future clinical trial design and drug development strategy in MDD. Results: We replicate findings from previous analyses of the classic auditory oddball task in the iSPOT-D sample showing smaller than typical N1 and P300 response amplitudes and longer P300 latencies for target and standard stimuli in patients with MDD, suggesting altered bottom-up sensory and top-down attentional processes. We further demonstrate that age is an important contributor to clinical group differences, affecting both topographic distribution of the clinically informative ERP responses and the types of the stimuli sensitive to group differences. In addition, the observed brain-behavior associations indicate that levels of anxiety and stress are major contributing factors to atypical sensory and attentional processing among patients with MDD, particularly in the older subgroups. Discussion: Our novel findings support the possibility of accelerated cognitive aging in patients with MDD and identify the frontal P300 latency as an additional candidate biomarker of MDD. These results from a large, well-phenotyped sample support the view that heterogeneity of the clinical population with MDD can be systematically characterized based on age and neural biomarkers of sensory and attentional processing, informing patient stratification strategies in the design of clinical trials.

Depression and quality of life among Macau residents in the 2022 COVID-19 pandemic wave from the perspective of network analysis

Background

In the summer of 2022, Macau experienced a surge of COVID-19 infections (the 618 COVID-19 wave), which had serious effects on mental health and quality of life (QoL). However, there is scant research on mental health problems and QoL among Macau residents during the 618 COVID-19 wave. This study examined the network structure of depressive symptoms (hereafter depression), and the interconnection between different depressive symptoms and QoL among Macau residents during this period.

Method

A cross-sectional study was conducted between 26th July and 9th September 2022. Depressive symptoms were measured with the 9-item Patient Health Questionnaire (PHQ-9), while the global QoL was measured with the two items of the World Health Organization Quality of Life-brief version (WHOQOL-BREF). Correlates of depression were explored using univariate and multivariate analyses. The association between depression and QoL was investigated using analysis of covariance (ANCOVA). Network analysis was used to evaluate the structure of depression. The centrality index "Expected Influence" (EI) was used to identify the most central symptoms and the flow function was used to identify depressive symptoms that had a direct bearing on QoL.

Results

A total 1,008 participants were included in this study. The overall prevalence of depression was 62.5% (n = 630; 95% CI = 60.00-65.00%). Having depression was significantly associated with younger age (OR = 0.970; p < 0.001), anxiety (OR = 1.515; p < 0.001), fatigue (OR = 1.338; p < 0.001), and economic loss (OR = 1.933; p = 0.026). Participants with depression had lower QoL F (1, 1,008) =5.538, p = 0.019). The most central symptoms included PHQ2 ("Sad Mood") (EI: 1.044), PHQ4 ("Fatigue") (EI: 1.016), and PHQ6 ("Guilt") (EI: 0.975) in the depression network model, while PHQ4 ("Fatigue"), PHQ9 ("Suicide"), and PHQ6 ("Guilt") had strong negative associations with QoL.

Conclusion

Depression was common among Macao residents during the 618 COVID-19 wave. Given the negative impact of depression on QoL, interventions targeting central symptoms identified in the network model (e.g., cognitive behavioral therapy) should be developed and implemented for Macau residents with depression.

Transdiagnostic symptom networks in relation to mental health service use in community youth

OBJECTIVE

The objective of this study is to scrutinize whether psychopathology symptom networks differ between those with and without lifetime: treatment seeking, treatment and treatment of longer duration.

METHODS

We created non-exclusive groups of subjects with versus without lifetime treatment seeking, treatment and treatment of mid-long-term duration. We estimated Ising models and carried out network comparison tests (NCTs) to compare (a) overall connectivity and (b) network structure. Furthermore, we examined node strength. We used propensity score matching (PSM) to minimize potential confounding by indication for service use.

RESULTS

Based on data from 9,172 participants, there were no statistically significant differences in overall connectivity and network structure in those with versus without lifetime: treatment seeking (p = .75 and p = .82, respectively), treatment (p = .63 and p = .49, respectively) and treatment of mid-longterm duration (p = .15 and p = .62, respectively). Notably, comparing networks with versus without service use consistently revealed higher node strength in 'obsessions' and 'aggression' and lower node strength in 'elevated mood' in all networks with service use.

CONCLUSIONS

Findings suggest that after adjusting for potential confounding by indication for service use, there was no indication of an association in overall connectivity or network structure for lifetime treatment seeking, treatment and treatment of longer duration. However, selected structurally important symptoms differed consistently in all three comparisons. Our findings highlight the potential of network analysis methods to examine treatment mechanisms and outcomes. Specifically, more granular network characteristics on the node level may complement and enrich traditional outcomes in clinical research.

Longitudinal Trajectories of Post-Stroke Depression Symptom Subgroups

BACKGROUND

Post-stroke depressive symptoms are prevalent and impairing, and elucidating their course and risk factors is critical for reducing their public health burden. Previous studies have examined the course of post-stroke depression, but distinct depressive symptom dimensions (eg, somatic symptoms, negative affect [eg, sadness], anhedonia [eg, loss of interest]) may vary differently over time.

OBJECTIVE

The present study examined within-person and between-person associations between depressive symptom dimensions across 3 timepoints in the year following discharge from in-patient rehabilitation hospitals, as well as the impact of multiple clinical variables (eg, aphasia).

METHODS

Stroke survivors completed the Center for Epidemiologic Depression Scale (CES-D) at discharge from post-stroke rehabilitation ("T1") and at 3-month ("T2") and 12-month ("T3") follow-ups. Scores on previously identified CES-D subscales (somatic symptoms, anhedonia, and negative affect) were calculated at each timepoint. Random intercept cross-lagged panel model analysis examined associations between symptom dimensions while disaggregating within-person and between-person effects.

RESULTS

There were reciprocal, within-person associations between somatic symptoms and anhedonia from T1 to T2 and from T2 to T3. Neither dimension was predictive of, or predicted by negative affect.

CONCLUSIONS

The reciprocal associations between somatic symptoms and anhedonia may reflect a "vicious cycle," and suggest these 2 symptom dimensions may be useful indicators of risk and/or intervention targets. Regularly assessing depression symptoms starting during inpatient rehabilitation may help identify stroke survivors at risk for depression symptoms and facilitate early intervention.

Network analysis of the relationships between problematic smartphone use and anxiety, and depression in a sample of Chinese college students

Background

Problematic smartphone use (PSU) is associated with both anxiety and depression. However, the relationships between components of PSU and symptoms of anxiety or depression have not been investigated. Hence, the aim of this study was to closely examine the relationships between PSU and anxiety and depression to identify the pathological mechanisms underpinning those relationships. A second aim was to identify important bridge nodes to identify potential targets for intervention.

Methods

Symptom-level network structures of PSU and anxiety, and PSU and depression were constructed to investigate the connections between the variables and evaluate the bridge expected influence (BEI) of each node. Network analysis using data from 325 Chinese healthy college students was performed.

Results

Five strongest edges appeared within the communities in both the PSU-anxiety and PSU-depression networks. The "Withdrawal" component had more connections with symptoms of anxiety or depression than any other PSU node. In particular, the edges between "Withdrawal" and "Restlessness" and between "Withdrawal" and "Concentration difficulties" were the strongest cross-community edges in the PSU-anxiety network and PSU-depression network, respectively. Furthermore, "Withdrawal" had the highest BEI in the PSU community in both networks.

Conclusions

These findings provide preliminary evidence of the pathological pathways linking PSU with anxiety and depression, with "Withdrawal" linking PSU with both anxiety and depression. Hence, "Withdrawal" may be a potential target for preventing and intervening in cases of anxiety or depression.

The harmonium model and its unified system view of psychopathology: a validation study by means of a convolutional neural network

The harmonium model (HM) is a recent conceptualization of the unifying view of psychopathology, namely the idea of a general mechanism underpinning all mental disorders (the p factor). According to HM, psychopathology consists of a low dimensional Phase Space of Meaning (PSM), where each dimension of meaning maps a component of the environmental variability. Accordingly, the lower thenumber of independent dimensions in the PSM, and hence its intrinsic complexity, the more limited the way of interpreting the environment. The current simulation study, based on a Convolutional Neural Network (CNN) framework, aims at validating the HM low-dimensionality hypothesis. CNN-based classifiers were employed to simulate normotypical and pathological cognitive processes. Results revealed that normotypical and pathological CNNs were different in terms of both classification performance and layer activation patterns. Using Principal Component Analysis to characterize the PSM associated with the two algorithms, we found that the performance of the normotypical CNN relies on a larger and more evenly distributed number of components, compared with the pathological one. This finding might be indicative of the fact that psychopathology can be modelled as a low-dimensional, poorly modulable PSM, which means the environment is detected through few components of meaning, preventing complex information patterns from being taken into account.