Psychopathological networks: Theory, methods and practice

Unraveling job demand-control-support patterns and job stressors as predictors: Cross-sectional latent profile and network analysis among Italian hospital workers

The Job Demand-Control-Support (JDCS) model postulates that patterns of job demand, job control, and social support lead to eight job types that are associated with well-being and health. This study employed latent profile analysis (LPA) to identify JDCS profiles among Italian hospital workers (n = 1464) and examined the predictive roles of role clarity and negative relationships at work on profile membership. Furthermore, adopting a network perspective, this study explored the interrelationships among JDCS factors within each identified profile. The LPA results revealed four profiles: isolated prisoner, moderate strain, low strain, and participatory leader. In addition, role clarity increased the likelihood of being included in the low-strain, moderate-strain, and participatory leader profiles. In contrast, negative relationships at work increased the risk of being included in the isolated prisoner profile. Finally, the results of network analysis revealed that networks differed across profiles in terms of density (interconnections between nodes) and edge strength (magnitude of relationships between nodes). Our study extends previous JDCS research by highlighting that researchers should consider empirically identified profiles rather than theoretically defined subgroups. The implications for stress theory, future research, and practice are also discussed.

A Transdiagnostic Network Analysis of Childhood Trauma and Psychopathology

BACKGROUND AND HYPOTHESIS

Psychiatric comorbidities suggest that symptoms overlap across different diagnoses; the transdiagnostic network approach is valuable for studying psychopathology. Childhood trauma is a common transdiagnostic risk factor for psychiatric disorders, but the complex relationship between childhood trauma and psychopathology has seldom been investigated using a large cross-sectional transdiagnostic sample.

STUDY DESIGN

This study recruited 869 patients with different diagnoses, including 418 schizophrenia, 215 bipolar disorder, and 236 major depressive disorder. Participants completed psychiatric interviews and self-report questionnaires. We constructed dimension- and item-level Least Absolute Shrinkage and Selection Operator-based (LASSO) networks to explore the relationship between childhood trauma, psychopathology, and duration of illness. Moreover, we constructed directed acyclic graphs (DAGs) to tentatively clarify the potential directions of associations among these variables. Network Comparison Tests (NCTs) were conducted for different diagnostic groups and gender-stratified groups.

STUDY RESULTS

The transdiagnostic LASSO networks showed that different types of childhood trauma exerted distinct impacts on various psychopathological dimensions. Emotional abuse was linked to depressive symptoms, physical abuse to excited symptoms, sexual abuse to positive and disorganized symptoms, emotional neglect to depressive symptoms and motivation and pleasure (MAP) deficits factor of negative symptoms, and physical neglect to MAP factor. The DAG findings generally concurred with the LASSO network. The NCT showed comparable networks.

CONCLUSIONS

Our findings suggest that childhood trauma is significantly associated with the development of psychopathology across different diagnostic groups. The affective pathway model suggests that early identification and tailored interventions would be needed for people with a history of childhood trauma.

Subclinical Psychiatric Symptomatology and a Proposed Diagnostic Criterion Separating Psychopathological Procrastinators From Trait Procrastinators

Procrastination trait describes irrational delays of scheduled tasks despite clear awareness of the adverse consequences of doing so. Although procrastination is well-known to be linked to psychiatric or pathological processes, the criterion for "psychopathological procrastination" distinguishing from the procrastination trait is understudied. This is a 5-year longitudinal observational study. Participants (N = 464) completed measures of trait procrastination in 2018, with a follow-up conducted in 2023 (N = 267) collecting subclinical symptomatology. A constrained multivariate direct gradient model (cmDGM) was employed to prospectively predict subclinical psychiatric symptomatology formulated by the DSM-5 framework. The two-stage psychopathological connectome model was then constructed to constitute a "diagnostic criterion" reflecting "psychopathological procrastination." Procrastination prospectively predicted subclinical psychopathological symptoms and unhealthy lifestyles. Subclinical bridge hubs of "failure to self-regulate delays," "failure to control adverse consequences," "useless to self-change," "out-of-control irruptions," "poor sleep quality," and "negative emotional reactions" were identified in the two-stage psychopathological network. These hubs constituted the 9-item pathological procrastination diagnostic criterion (3PDC) with good diagnostic performance (AUC = 0.82, p < 0.01). The present study revealed the predictive role of procrastination for subclinical psychiatric symptomatology and further established the subclinical 3PDC to lay the foundation for the "diagnostics of psychopathological procrastinators."

Readdressing the Ongoing Challenge of Missing Data in Youth Ecological Momentary Assessment Studies: Meta-Analysis Update

BACKGROUND

Ecological momentary assessment (EMA) is pivotal in longitudinal health research in youth, but potential bias associated with nonparticipation, omitted reports, or dropout threatens its clinical validity. Previous meta-analytic evidence is inconsistent regarding specific determinants of missing data.

OBJECTIVE

This meta-analysis aimed to update and expand upon previous research by examining key participation metrics-acceptance, compliance, and retention-in youth EMA studies. In addition, it sought to identify potential moderators among sample and design characteristics, with the goal of better understanding and mitigating the impact of missing data.

METHODS

We used a bibliographic database search to identify EMA studies involving children and adolescents published from 2001 to November 2023. Eligible studies used mobile-delivered EMA protocols in samples with an average age up to 18 years. We conducted separate meta-analyses for acceptance, compliance, and retention rates, and performed meta-regressions to address sample and design characteristics. Furthermore, we extracted and pooled sample-level effect sizes related to correlates of response compliance. Risk of publication bias was assessed using funnel plots, regression tests, and sensitivity analyses targeting inflated compliance rates.

RESULTS

We identified 285 samples, including 17,441 participants aged 5 to 17.96 years (mean age 14.22, SD 2.24 years; mean percentage of female participants 55.7%). Pooled estimates were 67.27% (k=88, 95% CI 62.39-71.96) for acceptance, 71.97% (k=216, 95% CI 69.83-74.11) for compliance, and 96.57% (k=169, 95% CI 95.42-97.56) for retention. Despite overall poor moderation of participation metrics, acceptance rates decreased as the number of EMA items increased (log-transformed b=-0.115, SE 0.036; 95% CI -0.185 to -0.045; P=.001; R2=19.98), compliance rates declined by 0.8% per year of publication (SE 0.25, 95% CI -1.3 to -0.3; P=.002; R2=4.17), and retention rates dropped with increasing study duration (log-transformed b=-0.061, SE 0.015; 95% CI -0.091 to 0.032; P<.001; R2=10.06). The benefits of monetary incentives on response compliance diminished as the proportion of female participants increased (b=-0.002, SE 0.001; 95% CI -0.003 to -0.001; P=.003; R2=9.47). Within-sample analyses showed a small but significant effect indicating higher compliance in girls compared to boys (k=25; g=0.18; 95% CI 0.06-0.31; P=.003), but no significant age-related effects were found (k=14; z score=0.05; 95% CI -0.01 to 0.16).

CONCLUSIONS

Despite a 5-fold increase in included effect sizes compared to the initial review, the variability in rates of missing data that one can expect based on specific sample and design characteristics remains substantial. The inconsistency in identifying robust moderators highlights the need for greater attention to missing data and its impact on study results. To eradicate any health-related bias in EMA studies, researchers should collectively increase transparent reporting practices, intensify primary methodological research, and involve participants' perspectives on missing data.

TRIAL REGISTRATION

PROSPERO CRD42022376948; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022376948.

From the lab to the clinic: Advancing pain exposure using principles of functional analysis

In this article, we use the example of pain exposure therapy to illustrate how behavioral pain treatments can be systematically personalized following the principles of functional analysis. Based on the fear-avoidance model, pain exposure therapy has evolved as a mechanistically-based treatment to modify the mechanism of avoidance learning with the aim to reduce disability levels. We first present experimental evidence on avoidance learning from a general psychological perspective. We then illustrate how functional analysis can help therapists understand and address individual drivers of pain avoidance behavior. Finally, we explore how the network theory can translate the principles of functional analysis into statistical parameters using intensive longitudinal data, potentially making exposure experiments more relevant to the daily lives of individuals.

Ecological Momentary Assessment in psychotherapy research: A systematic review

Ecological Momentary Assessment (EMA) stands as a valuable method to capture real-time data on individuals' daily experiences and behaviors. In recent years, the utilization of EMA as a measurement method has substantially increased with the majority of studies emphasizing its clinical utility. However, a comprehensive overview of its use in psychotherapy research is lacking. This study addresses that gap by systematically reviewing EMA's application in psychotherapy research. In total, 168 studies met the inclusion criteria and were classified according to clinical utilization. Six areas of clinical EMA application were identified: prediction of therapy outcome (n = 8), prediction of psychopathology (n = 40), prediction of biopsychosocial states (n = 44), evaluation of therapy outcome (n = 21), acquisition of further clinical insights into specific disorders (n = 68) and adaptation of treatment processes (n = 18). Despite studies consistently highlighting EMA's potential in tailoring psychotherapeutic treatments, its limited use in this area warrants further research. Drawing from our findings, we discuss future research directions for the direct application of EMA in psychotherapeutic settings.

Enhancing psychological assessment and treatment of chronic pain: A research agenda for personalized and process-based approaches

The heterogeneity of chronic pain and stagnating improvements in treatment effectiveness have prompted calls for a shift toward personalized and process-based approaches to the assessment and treatment of chronic pain. As this opens a new line of research, several fundamental questions arise. We begin by defining key terms and reviewing attempts to personalize treatment to date. Despite progress in personalization, long-term effects remain unclear. Existing studies are limited by group-based approaches that overlook individual variability. Future research should use idiographic methods and process-based therapy to tailor interventions to individual needs. A person- and process-oriented research agenda is needed that combines ambulatory assessment, network modeling, and single-case designs to advance personalized treatments for chronic pain and improve clinical decision-making.

Network analysis of peer attachment and internet addiction among chinese adolescents

Previous studies have found a correlation between peer attachment and Internet addiction. The three dimensions (peer trust, peer communication, and peer alienation) of peer attachment reflect different needs in peer relationships. This study used network analysis to construct a network model of the three dimensions of peer attachment and Internet addiction. The primary aim was to identify which peer relationship needs are most significantly associated with Internet addiction in adolescents. A total of 782 adolescents (413 girls and 369 boys, Mean age = 13.52, SD age = 1.17) from school participated in this study. Basic demographic information was obtained through a questionnaire. Inventory of Parent and Peer Attachment and Young Internet Addiction Test were used to measure peer attachment and Internet addiction in adolescents. Internet addiction was negatively correlated with the three dimensions of peer attachment: peer trust (r = -0.22), peer communication (r = -0.17), and peer alienation (r = -0.47). Peer trust was the central factor in the network model. Prominent symptoms in the network model included IA2 ("How often do you neglect household chores to spend more time online?") and IA12 ("How often do you fear that life without the Internet would be boring, empty, and joyless?"). Peer communication acted as a bridge between peer attachment and Internet addiction in the network model. Less trust in peers is associated with a higher risk of becoming addicted to the Internet. Fostering peer trust may encourage adolescents to engage in real-life social activities, thus reducing their reliance on the Internet for social fulfillment.

Sex and age differences in depression and anxiety networks among adolescents with idiopathic scoliosis: A network analysis

BACKGROUND

Depression and anxiety are prevalent psychological challenges among patients with adolescent idiopathic scoliosis (AIS), affecting individuals across both sex and age groups.

AIM

To explore the network structure of depression and anxiety symptoms, with a focus on identifying differences at the symptom level between sex and age subgroups.

METHODS

A total of 1955 participants diagnosed with AIS aged 10-18 years were assessed using the Patient Health Questionnaire Depression Scale (PHO-9) and the Generalized Anxiety Disorder Scale (GAD-7), and 765 patients exhibiting PHQ-9 or GAD-7 scores ≥ 5 were enrolled in our study. Network analysis and network comparison tests were utilized to construct and compare the depression-anxiety symptoms networks among sex and age subgroups.

RESULTS

The results revealed GAD3 "Excessive worry" and PHQ2 "Sad mood" were the most significant central symptoms in all subgroups, while "Sad mood" had higher strength than "Excessive worry" in the lower age group. In the network comparisons, the female network exhibited tighter connectivity, especially on GAD6 "Irritability" and GAD2 "Uncontrollable worry", while only PHQ3 "Sleep" and PHQ9 "Suicidal ideation" had differences at the local level in the lower age group.

CONCLUSION

Several interventions targeting excessive worry and sad mood could reduce the risk of depression and anxiety symptoms in the AIS population. Furthermore, specific anxiety symptoms in females, along with sleep disturbances and suicidal ideation in the lower age group, should be addressed at an early stage to prevent significant disruptions in mental health trajectories.

Revised network loadings

Psychometric assessment is the foundation of psychological research, where the accuracy of outcomes and their interpretations depend on measurement. Due to the widespread application of factor models, factor loadings are fundamental to modern psychometric assessment. Recent advances in network psychometrics introduced network loadings which aim to provide network models with a metric similar to factor loadings to assess measurement quality when the data are generated from a factor model. Our study revisits and refines the original network loadings to account for properties of (regularized) partial correlation networks, such as the reduction of partial correlation size as the number of variables increase, that were not considered previously. Using a simulation study, the revised network loadings demonstrated greater congruence with the simulated factor loadings across conditions relative to the original formulation. The simulation also evaluated how well correlations between factors can be captured by scores estimated with network loadings. The results show that not only can these network scores adequately estimate the simulated correlations between factors, they can do so without the need for rotation, a standard requirement for factor loadings. The consequence is that researchers do not need to choose a rotation with the revised network loadings, reducing the analytic degrees of freedom and eliminating this common source of variability in factor analysis. We discuss the interpretation of network loadings when data are believed to be generated from a network model and how they may fit into a network theory of measurement.

Schizotypal Traits, Psychopathology, and Reflective Functioning Impairments During Adolescence: A Bayesian Network Approach

BACKGROUND

New theoretical and measurement models related to Bayesian networks can usefully be implemented to enrich our understanding of psychosis risk. The present study aims to explore, using a directed acyclic graph (DAG), the putative causal relationship within schizotypal facets, as well as between schizotypal dimensions, psychopathology, and reflective functioning (RF) impairments, in a representative sample of non-clinical adolescents.

STUDY DESIGN

A sample of 1476 adolescents from the general population participated in a cross-sectional survey. The Oviedo Schizotypy Assessment Questionnaire-Revised, the Strengths and Difficulties Questionnaire, and the Reflective Functioning Questionnaire (RFQ) were used.

STUDY RESULTS

Schizotypal traits were positively associated with psychopathology and hypomentalizing. Putative causal relationships are presented between Reality distortion, Social disorganization, and Anhedonia. In addition, estimated DAG suggests that schizotypal dimensions influence psychopathology and RF impairments.

CONCLUSIONS

The findings suggest different pathways connecting schizotypal traits, mental health problems, and RF impairments during adolescence. The use of probabilistic DAG may allow us to make more robust conclusions about the direction of causation and to unravel potentially complex causal chains in the study of psychosis risk.

Relationship Between Schizotypal Traits, Emotion Regulation, and Negative Affect in Children: A Network Analysis

BACKGROUND AND HYPOTHESIS

Evidence suggests that emotion regulation is related to schizotypal traits and negative affect in adults. Few studies examined the interplay among these constructs in school-aged children. We examined the complex relationship between schizotypal traits, emotion regulation, and negative affect in children aged 9-12 years.

STUDY DESIGN

One-thousand-and-nineteen children completed the Schizotypal Personality Questionnaire-children (SPQ-C), the Depression Anxiety Stress Scales (DASS-21), and the Emotion Regulation Questionnaire for Children and Adolescence (ERQ-CA). Using subscales of these measures as nodes, we estimated a partial correlation network. We estimated a Directed Acyclic Graph to explore the putative directional relationship between schizotypal traits, emotion regulation, and negative affect. Node and bridge centrality indices were estimated.

RESULTS

We found positive correlations between schizotypal dimensions and negative affect (depressed mood, anxiety, and stress) in the network. Emotion suppression was positively correlated with interpersonal and disorganized schizotypal dimensions, and negative affect. Emotion reappraisal was positively correlated with the cognitive-perceptual dimension and negatively correlated with interpersonal schizotypal traits, depressed mood, and stress. Stress showed higher strength than all nodes except depressed mood, and stress showed the highest expected influence (EI). The Bayesian network revealed that schizotypal traits appeared to be driven by stress. Network comparisons preliminarily showed higher EI for emotion reappraisal in girls' than boys' networks, and significant impacts of age and schizotypy levels on network patterns.

CONCLUSION

Children with higher levels of schizotypal traits may have more negative affect and suppression. Stress appears to drive schizotypal traits.

A network approach exploring the effects of cognitive remediation on cognition, symptoms, and functioning in early psychosis

BACKGROUND

Although cognitive remediation (CR) improves cognition and functioning, the key features that promote or inhibit its effectiveness, especially between cognitive domains, remain unknown. Discovering these key features will help to develop CR for more impact.

AIM

To identify interrelations between cognition, symptoms, and functioning, using a novel network analysis approach and how CR affects these recovery outcomes.

METHODS

A secondary analysis of randomized controlled trial data (N = 165) of CR in early psychosis. Regularized partial correlation networks were estimated, including symptoms, cognition, and functioning, for pre-, post-treatment, and change over time. Pre- and post-CR networks were compared on global strength, structure, edge invariance, and centrality invariance.

RESULTS

Cognition, negative, and positive symptoms were separable constructs, with symptoms showing independent relationships with cognition. Negative symptoms were central to the CR networks and most strongly associated with change in functioning. Verbal and visual learning improvement showed independent relationships to improved social functioning and negative symptoms. Only visual learning improvement was positively associated with personal goal achievement. Pre- and post-CR networks did not differ in structure (M = 0.20, p = 0.45) but differed in global strength, reflecting greater overall connectivity in the post-CR network (S = 0.91, p = 0.03).

CONCLUSIONS

Negative symptoms influenced network changes following therapy, and their reduction was linked to improvement in verbal and visual learning following CR. Independent relationships between visual and verbal learning and functioning suggest that they may be key intervention targets to enhance social and occupational functioning.

A Gentle Introduction and Application of Feature-Based Clustering with Psychological Time Series

Psychological researchers and practitioners collect increasingly complex time series data aimed at identifying differences between the developments of participants or patients. Past research has proposed a number of dynamic measures that describe meaningful developmental patterns for psychological data (e.g., instability, inertia, linear trend). Yet, commonly used clustering approaches are often not able to include these meaningful measures (e.g., due to model assumptions). We propose feature-based time series clustering as a flexible, transparent, and well-grounded approach that clusters participants based on the dynamic measures directly using common clustering algorithms. We introduce the approach and illustrate the utility of the method with real-world empirical data that highlight common ESM challenges of multivariate conceptualizations, structural missingness, and non-stationary trends. We use the data to showcase the main steps of input selection, feature extraction, feature reduction, feature clustering, and cluster evaluation. We also provide practical algorithm overviews and readily available code for data preparation, analysis, and interpretation.

Embracing complexity in psychiatry-from reductionistic to systems approaches

The understanding and treatment of psychiatric disorders present unique challenges due to these conditions' multifaceted nature, comprising dynamic interactions between biological, psychological, social, and environmental factors. Traditional reductionistic approaches often simplify these conditions into linear cause-and-effect relationships, overlooking the complexity and interconnectedness inherent in psychiatric disorders. Advances in complex systems approaches provide a comprehensive framework to capture and quantify the non-linear and emergent properties of psychiatric disorders. This Personal View emphasises the importance of identifying rules for generative models that govern brain and behaviour over time, which might contribute to personalised assessments and interventions for psychiatric disorders. For instance, mood fluctuations in bipolar disorder can be understood through dynamical systems modelling, which identifies modifiable parameters, such as circadian disruption, that can be addressed through targeted therapies such as light therapy. Similarly, recognition of depression as an emergent property arising from complex interactions highlights the need for integrated treatment strategies that enhance adaptive reactions in the individual. A framework for quantifying multilevel interactions and network dynamics can help researchers and clinicians to understand the interplay between neural circuits, behaviours, and social contexts. Probabilistic models and self-organisation concepts contribute to building concrete dynamical systems models of mental disorders, facilitating early identification of risk states and promoting resilience through adaptive interventions delivered with optimal timing. Embracing these complex systems approaches in psychiatry could capture the true nature of psychiatric disorders as properties of a dynamic complex system and not the manifestation of any lesion or insult. This line of thinking might improve diagnosis and treatment, offering new hope for individuals affected by psychiatric conditions and paving the way for more effective, personalised mental health care.

New diagnosis in psychiatry: beyond heuristics

BACKGROUND

Diagnosis in psychiatry faces familiar challenges. Validity and utility remain elusive, and confusion regarding the fluid and arbitrary border between mental health and illness is increasing. The mainstream strategy has been conservative and iterative, retaining current nosology until something better emerges. However, this has led to stagnation. New conceptual frameworks are urgently required to catalyze a genuine paradigm shift.

METHODS

We outline candidate strategies that could pave the way for such a paradigm shift. These include the Research Domain Criteria (RDoC), the Hierarchical Taxonomy of Psychopathology (HiTOP), and Clinical Staging, which all promote a blend of dimensional and categorical approaches.

RESULTS

These alternative still heuristic transdiagnostic models provide varying levels of clinical and research utility. RDoC was intended to provide a framework to reorient research beyond the constraints of DSM. HiTOP began as a nosology derived from statistical methods and is now pursuing clinical utility. Clinical Staging aims to both expand the scope and refine the utility of diagnosis by the inclusion of the dimension of timing. None is yet fit for purpose. Yet they are relatively complementary, and it may be possible for them to operate as an ecosystem. Time will tell whether they have the capacity singly or jointly to deliver a paradigm shift.

CONCLUSIONS

Several heuristic models have been developed that separately or synergistically build infrastructure to enable new transdiagnostic research to define the structure, development, and mechanisms of mental disorders, to guide treatment and better meet the needs of patients, policymakers, and society.

Network analysis of interactions of rumination and anxiety on smartphone dependence symptoms

Objective

Rumination and anxiety have been posited as correlates of smartphone dependence (SPD). However, little is known regarding how the components of both affect SPD symptoms at subtle levels. Therefore, we used the network analysis approach to identify the connections at a micro level to provide possible interventions for reducing SPD symptoms.

Methods

Using symptom-level network analysis, we used the ruminative response scale-10, the generalized anxiety disorder scale-7, and the mobile phone addiction index scale-17 to investigate Chinese preservice teachers (M age = 21.1, N = 1160). Subsequently, we estimated a graphical lasso correlation network for these teachers, which encompassed rumination components, anxiety components, and SPD symptoms. Specifically, the central and bridge centralities within the network structure were examined for the impacts of rumination and anxiety on SPD symptoms.

Results

The three intracluster connections of rumination, anxiety, and SPD were tighter than the intercluster, with structural connections in rumination and anxiety networks closer than the triggered SPD symptoms cluster. Importantly, reflection reactions towards "write down what you are thinking and analyze it" (a component of rumination) were identified as a central and bridging node that might be a target for intervention for SPD symptoms.

Conclusion

We identify potential edge-bridging rumination and anxiety on SPD and locate highly central components within each cluster via network analysis.

Network analysis of meaning in life, perceived social support, and depressive symptoms among vocational undergraduate students

Background

Depression poses a considerable personal and public health problem, particularly in the post-epidemic era. The present study aimed to investigate the association between meaning in life (MIL) and perceived social support (PSS) with depressive symptoms among vocational undergraduate students, employing a network analysis approach to gain a deeper understanding of the underlying pathways and to prevent the progression of depressive symptoms into disorders.

Methods

A total of 1367 Chinese vocational undergraduates (M age = 20.1, SD = 1.6; 44.7% female) were recruited and were asked to complete a series of questionnaires, including the meaning in life questionnaire, perceived social support scale, and patient health questionnaire. The regularized partial correlation network was estimated. The partial correlations between nodes were calculated as edges. Moreover, network comparison tests were conducted to compare three subnetworks based on different levels of depression (minimal, subthreshold, and moderate/severe).

Results

The top strength nodes within each network were identified as sleep and motor in minimal group, anhedonia and concentration in subthreshold group, and anhedonia and sleep in moderate/severe group. Additionally, the bridge strength nodes were determined as MIL-3, MIL-4, sleep, guilt, and school in minimal group; MIL-4, anhedonia, suicide, and friend in subthreshold group; MIL-9, MIL-7, anhedonia, sleep, and family in moderate/severe group. Furthermore, network comparison tests showed significant differences in centrality (all p < 0.05), while network invariance remained constant across groups. Notably, the accuracy and stability coefficients for all network structures were greater than 0.5, indicating stable and reliable results.

Conclusion

These findings elucidate specific pathways and potential central nodes for interactions of MIL or PSS with depressive symptoms at different levels of depression, providing valuable insights for targeted prevention and intervention strategies.

Utilizing network analysis to identify core items of quality of life for children with autism spectrum disorder

The purpose of this study was to develop and validate an ultra-short scale called the Quality of Life for Children with Autism Spectrum Disorder 3 (QOLASD-C3) from the full 16-item QOLASD-C scale. We first used network analysis to identify three core items to be retained on the QOLASD-C3 scale. Second, we used Cronbach's alpha and Pearson Product Moment correlations to determine the reliability and validity of the scale. Third, an optimal cut-off score of 6 was identified for the using receiver operating characteristic curve analysis. Finally, we used logistic regression to examine the similarities in the classification status based on demographic characteristics between the quality of life (QOL) status using the QOLASD-C and the QOLASD-C3 scales. Results were similar across the two versions and suggested variations in QOL status based on race/ethnicity, autism spectrum disorder (ASD) severity, and parents' socio-economic status. Implications for research and practice are discussed.

Mapping the maze: A network analysis of social-emotional skills among children and adolescents with social-emotional difficulties

Developing social-emotional skills is crucial for all children and adolescents, particularly those experiencing social and emotional difficulties. This study used network analysis to identify the central skills and network association of different social-emotional skills and investigated how these networks differ between childhood and adolescence. Data were obtained from the 2019 Survey on Social and Emotional Skills by the Organization for Economic Co-operation and Development. Our study focused on the bottom quartile of participants aged 10 and 15 years, including 7737 and 7439 individuals from each age group. Optimism and cooperation consistently emerged as the central skills of social-emotional competence across both age groups. When comparing network structures, there was a significant difference between children and adolescents. The connectivity of social-emotional networks was stronger among adolescents, indicating closer skill associations. Understanding these developmental differences is important for educators and practitioners to more effectively support the social-emotional development of children and adolescents experiencing social-emotional difficulties.

WEB OF TRANSFORMATIONS: impact of multicomponent intervention on the relationship between sociodemographic indicators, changes in body composition, cardiorespiratory fitness and biochemical markers in adolescents with overweight and obesity

The study aims to analyze the relationships between changes after multicomponent intervention in sociodemographic indicators, body composition, cardiorespiratory fitness and biochemical markers in overweight/obese adolescents. Quasi-experimental study with 33 overweight/obese adolescents (17 in the intervention group (IG) and 16 in the control group (16)), in which the GI participated in the multicomponent intervention for 24 weeks. Sociodemographic indicators, body composition, cardiorespiratory fitness and biochemical markers were evaluated. Network analysis was performed using JASP software. In GI, the reduction in %BF proved to be the variable with greater connectivity and strength in the network compared to the control network. Changes in %BF were related to changes in ACR, BMI and leptin. It is concluded that the reduction in %BF is the most important variable in network relationships after the intervention, suggesting that the greater the reduction in %BF, the greater the effect on variables such as BMI, ACR and leptina.

Network Structure of Transdiagnostic Dimensions of Emotional Disorders in Adolescents With Subthreshold Anxiety and Depression: Links With Psychopathology and Socio-Emotional Adjustment

BACKGROUND

The transdiagnostic and network model approaches to psychopathology have emerged as an alternative to taxonomic systems. The main goal was to examine the network structure of the transdiagnostic dimensions of emotional disorders in adolescents with subclinical emotional symptoms. In addition, cross-sectional network analyses were conducted to investigate the relationships between transdiagnostic dimensions, psychopathology, and socio-emotional adjustment.

METHOD

The sample consisted of a total of 476 adolescents from 85 schools from different provinces in Spain with subthreshold anxiety and depression. The mean age was 13.77 years (SD = 1.43, range: 10-18 years), 73.9% were female. Several questionnaires assessing transdiagnostic dimensions of emotional disorders, mental health difficulties, self-esteem, perceived social support, sense of belonging at school, and prosocial behaviour were used.

RESULTS

A strong interrelation was found between the transdiagnostic variables, except with the positive temperament dimension. The dimensions with the greatest weight were those of traumatic re-experiencing, intrusive cognitions, avoidance, neuroticism, and depressed mood. Negative relationships were found between the transdiagnostic dimensions of emotional disorders and the protective factors included in the study. Stability analyses indicated that the networks were accurately estimated.

CONCLUSION

The present paper points to the value of conceptualising emotional disorders from a transdiagnostic and network model perspective. In addition, the work provides new insights into the nature of the relationships between transdiagnostic dimensions of emotional disorders, and the role played by risk and protective factors.

Exercise moderates longitudinal group psychopathology networks in individuals with eating disorders

Individuals with eating disorders (EDs) often engage in exercise no matter potential negative long-term outcomes (e.g., weight loss, injury). Yet exercising may temporarily attenuate ED symptoms, but whether exercise also affects network structure and pairwise associations of ED symptoms remained unclear. We used a novel approach called Moderated Multilevel Graphical Vector Autoregression to estimate changes in psychopathology networks from before to after exercising in ecological momentary assessment data from 102 individuals with EDs across multiple days (M = 22.14, SD = 5.40; range: 6-22 days) at 4 times daily. Between-person and within-person temporal networks were computed, obtaining stable centrality coefficients for temporal networks only. In those, autoregressive effects of several symptoms, including binge-eating, overeating, or weighing oneself, were attenuated when participants previously exercised. Exercise mostly downregulated temporal effects of ED symptoms on other symptoms, including effects of binge eating and other compensatory behaviors on feeling guilty after the most recent meal, vomiting on weighing oneself, and overeating on fear of weight gain. Our study highlights the complex dynamic effects of exercise on ED symptoms in daily life and calls for novel studies investigating mechanisms of exercise to inform treatments targeting detrimental long-term effects of exercise in EDs.

Ecological momentary assessment in prolonged grief research: Feasibility, acceptability, and measurement reactivity

Ecological momentary assessment (EMA) is a method of data collection that entails prompting individuals to report their experiences (e.g., thoughts, feelings, and behaviors) in real time over the course of their day-to-day lives. By providing rich information about how these experiences unfold over time within an individual, EMA has the potential to substantially advance our understanding of grief. However, there is uncertainty about how bereaved adults will respond to EMA, especially among those with high prolonged grief symptom severity. Accordingly, we evaluated the feasibility and acceptability of an EMA protocol in bereaved adults with low and high prolonged grief severity. Participants completed six 12-item EMA surveys per day on their smartphones for 17 days. Adherence was high (mean survey completion = 90%, median = 96%), and only 6% of participants withdrew. Adherence remained high in those with high prolonged grief symptom severity (mean = 86%; median = 96%). On average, participants reported agreement that survey frequency and length were acceptable. There was no evidence for systematic worsening of symptoms during EMA data collection. Together, these findings suggest that EMA is feasible, acceptable, and safe for bereaved adults, including those with high prolonged grief symptom severity.

Network analysis: An overview for mental health research

Network approaches to psychopathology have become increasingly common in mental health research, with many theoretical and methodological developments quickly gaining traction. This article illustrates contemporary practices in applying network analytical tools, bridging the gap between network concepts and their empirical applications. We explain how we can use graphs to construct networks representing complex associations among observable psychological variables. We then discuss key network models, including dynamic networks, time-varying networks, network models derived from panel data, network intervention analysis, latent networks, and moderated models. In addition, we discuss Bayesian networks and their role in causal inference with a focus on cross-sectional data. After presenting the different methods, we discuss how network models and psychopathology theories can meaningfully inform each other. We conclude with a discussion that summarizes the insights each technique can provide in mental health research.

[Competence-Orientated and Transtheoretical? Ideas and Barriers for Psychotherapeutic Training]

BACKGROUND

The education and training of psychotherapists is usually based on a procedure-specific theoretical orientation. However, this can have disadvantages as it hinders the utilisation of the possibilities and experiences offered by the overall field of psychotherapy. It could therefore be useful to combine different theoretical approaches and to orientate psychotherapeutic education and training in a transtheoretical and cross-procedural way.

METHOD

The article describes the extent to which competence-orientated psychotherapeutic training can provide a framework for interdisciplinary exchange within psychotherapy. It presents a practical example of a university-based training programme in basic psychotherapeutic skills that integrates different theoretical approaches. Transtheoretical training first requires an overarching model for case conceptualisation and a transtheoretical definition of intervention goals.

RESULTS

Treatment methods should be classified according to their transtheoretical goals in order to better compare and combine them. Dynamic network models offer a perspective for modern transtheoretical case formulations. We present a competence-oriented approach for psychotherapeutic training in a university context and give an outlook on the barriers of competence-oriented training.

CONCLUSION

A competence-orientated approach in the education and training of prospective psychotherapists will help to bridge the gap between different psychotherapeutic traditions. It should be seen as a complement to existing methods, not as competition. Overall, however, there are still some barriers to overcome.

Dynamic time warp of emotions in patients with cutaneous T-cell lymphoma treated with corticosteroids

Background

A substantial number of patients treated systemically with synthetic glucocorticoids undergo emotional disturbances during treatment. Patients with cutaneous T-cell lymphoma frequently experience skin inflammation and itching and often require glucocorticoid treatment.

Objective

This case-series study aimed to examine how emotional and skin-related symptoms interact throughout glucocorticoid treatment.

Methods

Five cutaneous T-cell lymphoma patients undergoing systemic glucocorticoid treatment completed daily ecological momentary assessments for on average 30 assessments. Fluctuations in their emotions and symptoms were analyzed using undirected and directed dynamic time warp analyses, and were visualized in symptom networks.

Results

Toward the end of the glucocorticoid treatment, a decline was found in positive psychological symptoms. Idiographic dynamic time warp analyses revealed highly variable symptom networks. Directed time-lag group-level analyses revealed irritability, enthusiastic, and excited as variables with highest outstrength, in which mainly decreasing levels of positive emotions were associated with a higher likelihood of experiencing increases in itchy skin and skin problems the next day.

Conclusion

The end of glucocorticoid treatment, potentially via the induction of hypocortisolism, seems to coincide with decreased energy, motivation, and enthusiasm. Itch and skin problems could be a consequence of low-positive emotions the day before.

A control theoretic approach to evaluate and inform ecological momentary interventions

OBJECTIVES

Ecological momentary interventions (EMI) are digital mobile health interventions administered in an individual's daily life to improve mental health by tailoring intervention components to person and context. Experience sampling via ecological momentary assessments (EMA) furthermore provides dynamic contextual information on an individual's mental health state. We propose a personalized data-driven generic framework to select and evaluate EMI based on EMA.

METHODS

We analyze EMA/EMI time-series from 10 individuals, published in a previous study. The EMA consist of multivariate psychological Likert scales. The EMI are mental health trainings presented on a smartphone. We model EMA as linear dynamical systems (DS) and EMI as perturbations. Using concepts from network control theory, we propose and evaluate three personalized data-driven intervention delivery strategies. Moreover, we study putative change mechanisms in response to interventions.

RESULTS

We identify promising intervention delivery strategies that outperform empirical strategies in simulation. We pinpoint interventions with a high positive impact on the network, at low energetic costs. Although mechanisms differ between individuals - demanding personalized solutions - the proposed strategies are generic and applicable to various real-world settings.

CONCLUSIONS

Combined with knowledge from mental health experts, DS and control algorithms may provide powerful data-driven and personalized intervention delivery and evaluation strategies.

Understanding the transdiagnostic mechanisms underlying emerging psychopathology in adolescence: study protocol of a 1-year prospective epidemiological (EMERGE) study

INTRODUCTION

Adolescent mental health is a global public health challenge as most cases remain undetected and untreated, and consequently, have a high likelihood of persistence or recurrence. It is critical to improve early detection of mental disorders and to target individuals experiencing subclinical symptoms. However, most indicated prevention approaches have been developed for risk syndromes of specific mental disorders. This contradicts the increasing recognition of emerging psychopathology as a complex system characterised by rapid shifts in subclinical symptoms, cutting across diagnostic categories and interacting with each other over time. Therefore, this study aims to examine the dynamic course, pattern and network of subclinical symptoms and transdiagnostic mechanisms over time.

METHOD AND ANALYSIS

The EMERGE-study is a prospective, naturalistic, 1-year follow-up study. A general population sample of 1196 adolescents will be recruited. Inclusion criteria are age between 11 and 17 years, German language skills, main residency in Switzerland and access to internet. Individuals will be excluded if they have a current or lifetime axis I mental disorder. Assessments of subclinical symptoms of several mental disorders and potential transdiagnostic mechanisms will be conducted at baseline and at 3-month, 6-month, 9-month and 12-month follow-up. Structural equation modelling will be used to estimate the homotypic and heterotypic patterns of subclinical symptoms and the associations with transdiagnostic mechanisms. Latent growth mixture modelling and growth mixture survival analysis will be carried out to identify subclasses of individuals with different trajectories of subclinical symptoms that may be predictive of an onset of a mental disorder. Network analysis will be applied to assess the centrality of subclinical symptoms and how networks of emerging psychopathology change over time.

ETHICS AND DISSEMINATION

Ethical approval was obtained from the Bern Cantonal Ethics Committee (ID 2020-02108). All findings will be disseminated by publication in peer-reviewed scientific journals and by presentation of the results to conferences and stakeholder organisation events.

Understanding internal dynamics of state anxiety during COVID-19 pandemic: Seven-wave longitudinal findings via panel network analysis

Research on state anxiety has long been dominated by the traditional psychometric approach that assumes anxiety symptoms have a common cause. Yet state anxiety can be conceptualized as a network system. In this study, we utilized data from the COVID-Dynamic dataset from waves 7 to 13, collected at three-week intervals from June 6, 2020, to October 13, 2020, and included 1,042 valid participants to characterize the internal dynamics of state anxiety. Using the Gaussian graphical model along with strength centrality, we estimated three network models of state anxiety. The between-subjects and contemporaneous network showed numerous positive relations between items and some unexpected negative relations. Three communities were identified in the between-subjects network, and two communities were identified in the contemporaneous network. The temporal network showed the coexistence of positive and negative predictions between items after three weeks. Several items exhibited significant positive autocorrelations after three weeks. These findings have implications for anxiety theory and clinical interventions at between-subjects and within-subjects levels.

The psychological side of menopause: evidence from the comorbidity network of menopausal, anxiety, and depressive symptoms

OBJECTIVE

Numerous studies have uncovered a correlation between menopausal, anxiety, and depressive symptoms. How these symptoms interrelate and influence each other, however, remains unclear. This study aimed to identify the associations between menopausal, anxiety, and depressive symptoms using network analysis.

METHODS

The participants comprised 423 women ( Mage = 49.21 ± 4.01 y; range, 40-60 y) recruited from a menopause clinic at a tertiary hospital in Beijing, China. Demographic characteristics and menopausal, anxiety, and depressive symptoms were obtained through self-report questionnaires. Two networks were established: a partial correlation network and a Bayesian network.

RESULTS

The menopausal symptom of nervousness exhibited a robust association with anxiety symptoms in both networks. Within the partial correlation network, the depressive symptom of tiredness emerged as a pivotal symptom, facilitating the co-occurrence of menopausal and depressive symptoms. Bayesian network analysis exhibited that the depressive symptom of a loss of interest was related to certain menopausal symptoms through depressive symptoms of tiredness and motor problems, both serving as critical links between menopausal symptoms and depression. Notably, four menopausal symptoms-arthralgia/myalgia, formication, sexual complaints, and urinary tract infection-appeared independent of other menopausal, anxiety, and depressive symptoms.

CONCLUSIONS

Both psychological (eg, fatigue) and somatic (eg, hot flashes, headaches, and dizziness) menopausal symptoms demonstrate strong associations with depression. In providing optimal support for women's health during menopause, psychological interventions aimed at depression, particularly among those experiencing a loss of interest or pleasure in activities, should complement conventional therapies.

Precision Mental Health and Data-Informed Decision Support in Psychological Therapy: An Example

Outcome measurement including data-informed decision support for therapists in psychological therapy has developed impressively over the past two decades. New technological developments such as computerized data assessment, and feedback tools have facilitated advanced implementation in several seetings. Recent developments try to improve the clinical decision-making process by connecting clinical practice better with empirical data. For example, psychometric data can be used by clinicians to personalize the selection of therapeutic programs, strategies or modules and to monitor a patient's response to therapy in real time. Furthermore, clinical support tools can be used to improve the treatment for patients at risk for a negative outcome. Therefore, measurement-based care can be seen as an important and integral part of clinical competence, practice, and training. This is comparable to many other areas in the healthcare system, where continuous monitoring of health indicators is common in day-to-day clinical practice (e.g., fever, blood pressure). In this paper, we present the basic concepts of a data-informed decision support system for tailoring individual psychological interventions to specific patient needs, and discuss the implications for implementing this form of precision mental health in clinical practice.

Early warning signals observed in motor activity preceding mood state change in bipolar disorder

INTRODUCTION

Alterations in motor activity are well-established symptoms of bipolar disorder, and time series of motor activity can be considered complex dynamical systems. In such systems, early warning signals (EWS) occur in a critical transition period preceding a sudden shift (tipping point) in the system. EWS are statistical observations occurring due to a system's declining ability to maintain homeostasis when approaching a tipping point. The aim was to identify critical transition periods preceding bipolar mood state changes.

METHODS

Participants with a validated bipolar diagnosis were included to a one-year follow-up study, with repeated assessments of the participants' mood. Motor activity was recorded continuously by a wrist-worn actigraph. Participants assessed to have relapsed during follow-up were analyzed. Recognized EWS features were extracted from the motor activity data and analyzed by an unsupervised change point detection algorithm, capable of processing multi-dimensional data and developed to identify when the statistical property of a time series changes.

RESULTS

Of 49 participants, four depressive and four hypomanic/manic relapses among six individuals occurred, recording actigraphy for 23.8 ± 0.2 h/day, for 39.8 ± 4.6 days. The algorithm detected change points in the time series and identified critical transition periods spanning 13.5 ± 7.2 days. For depressions 11.4 ± 1.8, and hypomania/mania 15.6 ± 10.2 days.

CONCLUSION

The change point detection algorithm seems capable of recognizing impending mood episodes in continuous flowing data streams. Hence, we present an innovative method for forecasting approaching relapses to improve the clinical management of bipolar disorder.

Causal inference on human behaviour

Making causal inferences regarding human behaviour is difficult given the complex interplay between countless contributors to behaviour, including factors in the external world and our internal states. We provide a non-technical conceptual overview of challenges and opportunities for causal inference on human behaviour. The challenges include our ambiguous causal language and thinking, statistical under- or over-control, effect heterogeneity, interference, timescales of effects and complex treatments. We explain how methods optimized for addressing one of these challenges frequently exacerbate other problems. We thus argue that clearly specified research questions are key to improving causal inference from data. We suggest a triangulation approach that compares causal estimates from (quasi-)experimental research with causal estimates generated from observational data and theoretical assumptions. This approach allows a systematic investigation of theoretical and methodological factors that might lead estimates to converge or diverge across studies.

Network analyses of ecological momentary emotion and avoidance assessments before and after cognitive behavioral therapy for anxiety disorders

Negative emotions and associated avoidance behaviors are core symptoms of anxiety. Current treatments aim to resolve dysfunctional coupling between them. However, precise interactions between emotions and avoidance in patients' everyday lives and changes from pre- to post-treatment remain unclear. We analyzed data from a randomized controlled trial where patients with anxiety disorders underwent 16 sessions of cognitive behavioral therapy (CBT). Fifty-six patients (68 % female, age: M = 33.31, SD = 12.45) completed ecological momentary assessments five times a day on 14 consecutive days before and after treatment, rating negative emotions and avoidance behaviors experienced within the past 30 min. We computed multilevel vector autoregressive models to investigate contemporaneous and time-lagged associations between anxiety, depression, anger, and avoidance behaviors within patients, separately at pre- and post-treatment. We examined pre-post changes in network density and avoidance centrality, and related these metrics to changes in symptom severity. Network density significantly decreased from pre- to post-treatment, indicating that after therapy, mutual interactions between negative emotions and avoidance were attenuated. Specifically, contemporaneous associations between anxiety and avoidance observed before CBT were no longer significant at post-treatment. Effects of negative emotions on avoidance assessed at a later time point (avoidance instrength) decreased, but not significantly. Reduction in avoidance instrength positively correlated with reduction in depressive symptom severity, meaning that as patients improved, they were less likely to avoid situations after experiencing negative emotions. Our results elucidate mechanisms of successful CBT observed in patients' daily lives and may help improve and personalize CBT to increase its effectiveness.

Is depression a global brain disorder with topographic dynamic reorganization?

Major depressive disorder (MDD) is characterized by a multitude of psychopathological symptoms including affective, cognitive, perceptual, sensorimotor, and social. The neuronal mechanisms underlying such co-occurrence of psychopathological symptoms remain yet unclear. Rather than linking and localizing single psychopathological symptoms to specific regions or networks, this perspective proposes a more global and dynamic topographic approach. We first review recent findings on global brain activity changes during both rest and task states in MDD showing topographic reorganization with a shift from unimodal to transmodal regions. Next, we single out two candidate mechanisms that may underlie and mediate such abnormal uni-/transmodal topography, namely dynamic shifts from shorter to longer timescales and abnormalities in the excitation-inhibition balance. Finally, we show how such topographic shift from unimodal to transmodal regions relates to the various psychopathological symptoms in MDD including their co-occurrence. This amounts to what we describe as 'Topographic dynamic reorganization' which extends our earlier 'Resting state hypothesis of depression' and complements other models of MDD.

Methodological and Statistical Practices of Using Symptom Networks to Evaluate Mental Health Interventions: A Review and Reflections

The network approach to psychopathology, which assesses associations between individual symptoms, has recently been applied to evaluate treatments for mental disorders. While various options for conducting network analyses in intervention research exist, an overview and an evaluation of the various approaches are currently missing. Therefore, we conducted a review on network analyses in intervention research. Studies were included if they constructed a symptom network, analyzed data that were collected before, during or after treatment of a mental disorder, and yielded information about the treatment effect. The 56 included studies were reviewed regarding their methodological and analytic strategies. About half of the studies based on data from randomized trials conducted a network intervention analysis, while the other half compared networks between treatment groups. The majority of studies estimated cross-sectional networks, even when repeated measures were available. All but five studies investigated networks on the group level. This review highlights that current methodological practices limit the information that can be gained through network analyses in intervention research. We discuss the strength and limitations of certain methodological and analytic strategies and propose that further work is needed to use the full potential of the network approach in intervention research.

Symptoms of a feather flock together? An exploratory secondary dynamic time warp analysis of 11 single case time series of suicidal ideation and related symptoms

Suicidal ideation fluctuates over time, as does its related risk factors. Little is known about the difference or similarities of the temporal patterns. The current exploratory secondary analysis examines which risk symptoms have similar time dynamics using a mathematical algorithm called dynamic time warping (DTW). Ecological momentary assessment data was used of 11 depressed psychiatric outpatients with suicidal ideation who answered three daytime surveys at semi-random sampling points for a period of three to six months. Patients with 45 assessments or more were included. Results revealed significant inter-individual variability in symptom dynamics and clustering, with certain symptoms often clustering due to similar temporal patterns, notably feeling sad, hopelessness, feeling stuck, and worrying. The directed network analyses shed light on the temporal order, highlighting entrapment and worrying as symptoms strongly related to suicide ideation. Still, all patients also showed unique directed networks. While for some patients changes in entrapment directly preceded change in suicide ideation, the reverse temporal ordering was also found. Relatedly, within some patients, perceived burdensomeness played a pivotal role, whereas in others it was unconnected to other symptoms. The study underscores the individualized nature of symptom dynamics and challenges linear models of progression, advocating for personalized treatment strategies.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Exploring networks of complex developmental trauma symptomatology among children and adolescents involved in child welfare

Background

Clinical presentations of child and adolescent psychopathology can vary systematically for boys and girls. While network analysis is increasingly being applied to explore psychopathology in adults, there is a dearth of network studies considering differences in symptoms for boys and girls, particularly in developmental trauma-related symptomatology.

Methods

This study involves rural children (n = 375, 39.47% girls) and adolescents (n = 291, 51.20% girls) involved with child protection services in Ontario, Canada. Caregivers completed the Assessment Checklist for Children or Adolescents within the first 6 months of care. Psychometric network analyses were conducted using subscales for boys and girls. Differences were examined via network comparison permutation tests, moderated network models, and independent t-tests.

Results

Attachment-related interpersonal difficulties were the most central nodes in the child and adolescent networks for both boys and girls. Emotional dysregulation also had high strength centrality for adolescents. While network comparison tests found the overall network structures and global network strength to be invariant between boys and girls for children and adolescents, moderated network models and independent t-tests revealed several differences with regards to the expression of specific symptoms. Among children, girls exhibited more indiscriminate and pseudomature interpersonal behaviors, whereas boys expressed significantly more non-reciprocal interpersonal behaviors and self-injury. Adolescent girls exhibited more behavioral dysregulation and suicide discourse in the moderated network model; t-tests also indicated higher levels of emotional dysregulation, negative self-image, and other items considered clinically important complex trauma symptoms (e.g., distrust of adults, confused belonging).

Discussion

This study supports evidence of differences in the expression of complex trauma symptomatology for boys and girls. Additionally, girls exhibit more symptoms, in general. Consistent with the transdiagnostic conceptualization of the consequences of developmental trauma, findings demonstrate the primacy of attachment-specific difficulties and emotion dysregulation.

The future of psychological treatments: The Marburg Declaration

Although psychological treatments are broadly recognized as evidence-based interventions for various mental disorders, challenges remain. For example, a substantial proportion of patients receiving such treatments do not fully recover, and many obstacles hinder the dissemination, implementation, and training of psychological treatments. These problems require those in our field to rethink some of our basic models of mental disorders and their treatments, and question how research and practice in clinical psychology should progress. To answer these questions, a group of experts of clinical psychology convened at a Think-Tank in Marburg, Germany, in August 2022 to review the evidence and analyze barriers for current and future developments. After this event, an overview of the current state-of-the-art was drafted and suggestions for improvements and specific recommendations for research and practice were integrated. Recommendations arising from our meeting cover further improving psychological interventions through translational approaches, improving clinical research methodology, bridging the gap between more nomothetic (group-oriented) studies and idiographic (person-centered) decisions, using network approaches in addition to selecting single mechanisms to embrace the complexity of clinical reality, making use of scalable digital options for assessments and interventions, improving the training and education of future psychotherapists, and accepting the societal responsibilities that clinical psychology has in improving national and global health care. The objective of the Marburg Declaration is to stimulate a significant change regarding our understanding of mental disorders and their treatments, with the aim to trigger a new era of evidence-based psychological interventions.

A network analysis of exhaustion disorder symptoms throughout treatment

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

TRIAL REGISTRATION

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

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.

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

PURPOSE

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

METHOD

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

RESULTS

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

CONCLUSIONS

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

IMPLICATIONS FOR CANCER SURVIVORS

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

Psychotic-like experiences predict the perceived intent to seek treatment: A network perspective

We aimed to investigate as to whether psychotic-like experiences (PLEs) predict the perceived intent to seek treatment. Our secondary aim was to explore which PLEs predict the perceived need to seek treatment using a network analysis. The study was based on a community sample of individuals with a negative history of psychiatric treatment. At baseline, they completed questionnaires recording the presence of PLEs, depressive, and anxiety symptoms. After 6-7 months, they were reassessed with respect to the perceived intent to seek treatment. A total of 1100 individuals were assessed at baseline (aged 27.1 ± 5.1 years, 48.6 % males). The follow-up assessment was completed by 581 individuals (52.8 %). Higher baseline levels of PLEs were associated with a greater intent to seek treatment at the follow-up before (Beta = 0.289, p < 0.001) and after adjustment for sociodemographic characteristics, depressive and anxiety symptoms (Beta = 0.128, p = 0.004). A network analysis demonstrated that the intent to seek treatment was connected to five nodes of PLEs including "déjà vu experiences" (weight = 0.046), "problems in differentiating reality and imagination" (weight = 0.103), "a lack of control over own ideas or thoughts" (weight = 0.077), "being distracted by distant sounds" (weight = 0.105), and "paranoid thoughts" (weight = 0.145). Findings from the present study indicate that PLEs might contribute to help-seeking behaviors regardless of co-occurring depressive and anxiety symptoms. However, specific PLEs may differ with respect to their effects on the perceived intent to seek treatment.

Emotion dynamics of clients with test anxiety before and after an imagery-based treatment

BACKGROUND AND OBJECTIVES

Imagery-based techniques have become a promising means in the treatment of test anxiety (TA). Although previous studies have demonstrated the effectiveness of imagery-based treatment, not all clients seem to benefit from it. The present study compares clients' pre- as well as post-treatment emotion dynamics between responders and non-responders. Furthermore, it examines treatment-related changes in emotion dynamics in both subgroups.

METHODS

The results are based on 44 clients suffering from TA who underwent a six-session imagery-based treatment and include Ecological Momentary Assessment (EMA). Emotions were assessed with the Profile of Mood States four times a day over the course of two weeks before and after the treatment. Temporal networks were computed to index emotion dynamics.

RESULTS

Pre-treatment emotion dynamics differed between responders and non-responders. Similarly, post-treatment emotion dynamics differed as well between both groups. Some changes were also observed between pre-treatment and post-treatment networks: for responders, fatigue no longer predicted anger, and depression predicted itself; for non-responders, calmness predicted fatigue, anger, depression, contentment, and anxiety. In addition, fatigue no longer predicted itself and anxiety predicted vigor.

LIMITATIONS

The investigation is marked by several limitations: a liberal inclusion threshold of at least a 50% response to EMA prompts, and a relatively homogenous sample.

CONCLUSION

These results provide first evidence for the idea that emotion dynamics may be associated with response to treatment for TA. Furthermore, effective imagery-based treatments may be tied to changes within these dynamics.

Self-regulatory control processes in youths: A temporal network analysis approach

Objective

This study aimed to better understand the temporal interrelationships among self-control, response inhibition, and anger (i.e., momentary state and rumination) on both the within- and between-person levels in male adolescents.

Method

We applied temporal network analyses among 62 male adolescents with a wide range of behavioral difficulties. Self-control, momentary anger, and anger rumination were mapped by self-report measures, whereas we measured response inhibition through an ambulatory Go/No-go task (two measures a day-morning and afternoon-over a 9-day period).

Results

Temporal network analysis, at the within-person level, revealed that morning measures of response inhibition, anger rumination, and self-control were related to the corresponding measure in the afternoon. More efficient response inhibition in the morning was associated with higher self-control in the afternoon. Higher anger rumination in the morning led to higher momentary anger in the afternoon. In a concurrent within-person network, higher momentary anger was reciprocally associated with lower self-control. At the between-person level, higher momentary anger was correlated to higher anger rumination, lower response inhibition, and lower self-control.

Discussion

This study provides insight into the dynamic interactions among self-control, response inhibition, and anger (momentary state and rumination) in male adolescents, advancing the understanding of self-regulatory control functioning.

Skewness and Staging: Does the Floor Effect Induce Bias in Multilevel AR(1) Models?

Multilevel autoregressive models are popular choices for the analysis of intensive longitudinal data in psychology. Empirical studies have found a positive correlation between autoregressive parameters of affective time series and the between-person measures of psychopathology, a phenomenon known as the staging effect. However, it has been argued that such findings may represent a statistical artifact: Although common models assume normal error distributions, empirical data (for instance, measurements of negative affect among healthy individuals) often exhibit the floor effect, that is response distributions with high skewness, low mean, and low variability. In this paper, we investigated whether-and to what extent-the floor effect leads to erroneous conclusions by means of a simulation study. We describe three dynamic models which have meaningful substantive interpretations and can produce floor-effect data. We simulate multilevel data from these models, varying skewness independent of individuals' autoregressive parameters, while also varying the number of time points and cases. Analyzing these data with the standard multilevel AR(1) model we found that positive bias only occurs when modeling with random residual variance, whereas modeling with fixed residual variance leads to negative bias. We discuss the implications of our study for data collection and modeling choices.