Psychometric network models from time-series and panel data

At-home, telehealth-supported ketamine treatment for depression: Findings from longitudinal, machine learning and symptom network analysis of real-world data

BACKGROUND

Improving safe and effective access to ketamine therapy is of high priority given the growing burden of mental illness. Telehealth-supported administration of sublingual ketamine is being explored toward this goal.

METHODS

In this longitudinal study, moderately-to-severely depressed patients received four doses of ketamine at home over four weeks within a supportive digital health context. Treatment was structured to resemble methods of therapeutic psychedelic trials. Patients receiving a second course of treatment were also examined. Symptoms were assessed using the Patient Health Questionnaire (PHQ-9) for depression. We conducted preregistered machine learning and symptom network analyses to investigate outcomes (osf.io/v2rpx).

RESULTS

A sample of 11,441 patients was analyzed, demonstrating a modal antidepressant response from both non-severe (n = 6384, 55.8 %) and severe (n = 2070, 18.1 %) baseline depression levels. Adverse events were detected in 3.0-4.8 % of participants and predominantly neurologic or psychiatric in nature. A second course of treatment helped extend improvements in patients who responded favorably to initial treatment. Improvement was most strongly predicted by lower depression scores and age at baseline. Symptoms of Depressed mood and Anhedonia sustained depression despite ongoing treatment.

LIMITATIONS

This study was limited by the absence of comparison or control groups and lack of a fixed-dose procedure for ketamine administration.

CONCLUSIONS

At-home, telehealth-supported ketamine administration was largely safe, well-tolerated, and associated with improvement in patients with depression. Strategies for combining psychedelic-oriented therapies with rigorous telehealth models, as explored here, may uniquely address barriers to mental health treatment.

The cross-sectional and longitudinal interconnectedness of physical, psychological and role functioning following physical trauma: A network analysis

OBJECTIVE

Surviving physical trauma can have a large impact on one's daily life. Patients are at increased risk for poor physical health, psychological complaints, and problems in role functioning - which is often experienced simultaneously. The present study explores the interconnectedness of physical, psychological, and role functioning during the first two years post-injury, both cross-sectionally and longitudinally from a network perspective.

METHODS

3785 trauma patients (Mage = 64.2 years, SDage = 18.9 years, 50.5% female) completed questionnaires on physical, psychological, and role functioning across six measurement occasions during the first two years post-injury. The Injury Severity Score (ISS) was retrieved from the local trauma registry. Mixed graphical network models and cross-lagged network models were estimated to examine which items of recovery played a central role and were mostly related to other items in cross-sectional and longitudinal networks respectively.

RESULTS

The cross-sectional networks showed especially strong interconnections between impairments of physical and role functioning and also within post-traumatic stress symptoms. The longitudinal networks extended these results by showing that pain, impaired mobility, limitations in self-care, anxiety/depressive symptoms, and several post-traumatic stress symptoms were strong predictors for impairments in functioning at later stages of recovery.

CONCLUSION

Our findings showed that impairments in physical, psychological, and role functioning experienced by trauma patients are largely intertwined across the two years following injury. Monitoring physical impairments and psychological complaints early in recovery might help to more promptly provide the best fitting aftercare for trauma patients, which can improve recovery on the long-term.

Role of interoceptive fear and maladaptive attention and behaviors in the escalation of psychopathology-a network analysis

The complex interplay of fear, attention, and behavior toward bodily sensations with psychopathological symptoms and how they mutually influence and potentially reinforce one another remains to be fully elucidated. In this study, we used a network analytical approach to unravel these complex interactions. Specifically, we aimed to identify central symptoms and etiologically relevant factors that might be associated with anxiety and depressive core symptoms. To this end, the following clusters were assessed in 791 adults: interoceptive fear, interoceptive attention, maladaptive behaviors related to bodily sensations, and core symptoms of anxiety and depression. This network was modeled using a Gaussian Graphical Model. Central variables (nodes) were identified using centrality indices and bridge analysis. Self-examination and attention to bodily sensations emerged as central nodes. Moreover, time spent paying attention to bodily sensations, fear of anxiety-related sensations, and self-examination were identified as central bridge nodes, that is, central nodes connecting psychopathologically relevant symptom clusters. The present study indicates that fear of bodily sensations, the amount of attention and time spent focusing on somatic sensations, and self-examination are central factors. The findings suggest potential targets for future longitudinal studies on the impact of these factors for the escalation of anxiety and depressive symptoms.

Modelling the longitudinal dynamics of paranoia in psychosis: A temporal network analysis over 20 years

BACKGROUND

Paranoia is a key feature of psychosis that can be highly debilitating. Theories of paranoia mostly interface with short-scale or cross-sectional data models, leaving the longitudinal course of paranoia underspecified.

METHODS

We develop an empirical characterisation of two aspects of paranoia - persecutory and referential delusions - in individuals with psychosis over 20 years. We examine delusional dynamics by applying a Graphical Vector Autoregression Model to data collected from the Chicago Follow-up Study (n = 135 with a range of psychosis-spectrum diagnoses). We adjusted for age, sex, IQ, and antipsychotic use.

RESULTS

We found that referential and persecutory delusions are central themes, supported by other primary delusions, and are strongly autoregressive - the presence of referential and persecutory delusions is predictive of their future occurrence. In a second analysis we demonstrate that social factors influence the severity of referential, but not persecutory, delusions.

IMPLICATIONS

We suggest that persecutory delusions represent central, resistant states in the cognitive landscape, whereas referential beliefs are more flexible, offering an important window of opportunity for intervention. Our data models can be collated with prior biological, computational, and social work to contribute toward a more complete theory of paranoia and provide more time-dependent evidence for optimal treatment targets.

A Network Approach to Understanding the Role of Executive Functioning and Alpha Oscillations in Inattention and Hyperactivity-Impulsivity Symptoms of ADHD

OBJECTIVE

ADHD is a prevalent neurodevelopmental disorder characterized by symptoms of inattention and hyperactivity-impulsivity. Impairments in executive functioning (EF) are central to models of ADHD, while alpha-band spectral power event-related decreases (ERD) have emerged as a putative electroencephalography (EEG) biomarker of EF in ADHD. Little is known about the roles of EF and alpha ERD and their interactions with symptoms of ADHD.

METHOD

We estimated network models of ADHD symptoms and integrated alpha ERD measures into the symptom network.

RESULTS

EF emerges as a bridge network node connecting alpha ERD and the hyperactivity/impulsivity and inattention symptoms. We found that EF most closely relates to a subset of symptoms, namely the motoric symptoms, "seat" (difficulty staying seated), and "runs" (running or climbing excessively).

CONCLUSIONS

EF functions as a bridge node connecting alpha ERD and the ADHD symptom network. Motoric-type symptoms and EF deficits may constitute important nodes in the interplay between behavior/symptoms, cognition, and neurophysiological markers of ADHD.

Path and Direction Discovery in Individual Dynamic Factor Models: A Regularized Hybrid Unified Structural Equation Modeling with Latent Variable

There has been an increasing call to model multivariate time series data with measurement error. The combination of latent factors with a vector autoregressive (VAR) model leads to the dynamic factor model (DFM), in which dynamic relations are derived within factor series, among factors and observed time series, or both. However, a few limitations exist in the current DFM representatives and estimation: (1) the dynamic component contains either directed or undirected contemporaneous relations, but not both, (2) selecting the optimal model in exploratory DFM is a challenge, (3) the consequences of structural misspecifications from model selection is barely studied. Our paper serves to advance DFM with a hybrid VAR representations and the utilization of LASSO regularization to select dynamic implied instrumental variable, two-stage least squares (MIIV-2SLS) estimation. Our proposed method highlights the flexibility in modeling the directions of dynamic relations with a robust estimation. We aim to offer researchers guidance on model selection and estimation in person-centered dynamic assessments.

Dynamics of postnatal depressive symptoms in early parenthood

BACKGROUND

New mothers and fathers are at risk of developing postnatal depressive problems. To understand how postnatal depressive symptoms unfold over time, analyses at the within-person level are necessary. Inspecting postnatal depressive problems at the symptom level provides a novel perspective, ultimately offering insight into which symptoms contribute to the elevation of other symptoms over time.

METHODS

Panel graphical vector-autoregression (GVAR) models were applied to analyze the within-person temporal and contemporaneous relations between depressive symptoms across the postnatal period in new mothers and fathers (at T1; Nmothers = 869, Nfathers = 579). Depressive symptoms were assessed at 6-, 12-, and 18-months postpartum, using the Edinburgh Postnatal Depression Scale.

RESULTS

The results revealed that for mothers, sadness was a key symptom predicting symptom increases in multiple other depressive symptoms and itself (autoregressive effect) over time. Furthermore, anxiousness and feeling scared predicted each other across the postnatal period in mothers. For fathers, the most central predicting symptom in the overall network of symptoms was being anxious, while self-blame and being overwhelmed had strong self-maintaining roles in the fathers' symptomatology, indicating that these could be key features in fathers experiencing postnatal depressive problems. The pattern of symptoms that mothers and fathers experienced within the same time window (contemporaneous associations), shared many of the same characteristics compared to the temporal structure.

CONCLUSIONS

This study suggests that across the postnatal period, from 6- to 18-months postpartum, depressive symptoms in mothers and fathers contribute differently to the pattern of depressive problems, highlighting sadness as a key feature in maternal symptomatology and anxiousness components in paternal symptomatology.

Estimation of Personal Symptom Networks Using the Ising Model for Adult Survivors of Childhood Cancer: A Simulation Study with Real-World Data Application

Purpose

Childhood cancer survivors experience interconnected symptoms, patterns of which can be elucidated by network analysis. However, current symptom networks are constructed based on the average survivors without considering individual heterogeneities. We propose to evaluate personal symptom network estimation using the Ising model with covariates through simulations and estimate personal symptom network for adult childhood cancer survivors.

Patients and Methods

We adopted the Ising model with covariates to construct networks by employing logistic regressions for estimating associations between binary symptoms. Simulation experiments assessed the robustness of this method in constructing personal symptom network. Real-world data illustration included 1708 adult childhood cancer survivors from the St. Jude Lifetime Cohort Study (SJLIFE), a retrospective cohort study with prospective follow-up to characterize the etiology and late effects for childhood cancer survivors. Patients' baseline symptoms in 10 domains (cardiac, pulmonary, sensation, nausea, movement, pain, memory, fatigue, anxiety, depression) and individual characteristics (age, sex, race/ethnicity, attained education, personal income, and marital status) were self-reported using survey. Treatment variables (any chemo or radiation therapy) were obtained from medical records. Personal symptom network of 10 domains was estimated using the Ising model, incorporating individual characteristics and treatment data.

Results

Simulations confirmed the robustness of the Ising model with covariates in constructing personal symptom networks. Real-world data analysis identified age, sex, race/ethnicity, education, marital status, and treatment (any chemo and radiation therapy) as major factors influencing symptom co-occurrence. Older childhood cancer survivors showed stronger cardiac-fatigue associations. Survivors of racial/ethnic minorities had stronger pain-fatigue associations. Female survivors with above-college education demonstrated stronger pain-anxiety associations. Unmarried survivors who received radiation had stronger association between movement and memory problems.

Conclusion

The Ising model with covariates accurately estimates personal symptom networks. Individual heterogeneities exist in symptom co-occurrence patterns for childhood cancer survivors. The estimated personal symptom network offers insights into interconnected symptom experiences.

The hospital emigration to another region in the light of the environmental, social and governance model in Italy during the period 2004-2021

The following article presents an analysis of the impact of the Environmental, Social and Governance-ESG determinants on Hospital Emigration to Another Region-HEAR in the Italian regions in the period 2004-2021. The data are analysed using Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled Ordinary Least Squares-OLS, Weighted Least Squares-WLS, and Dynamic Panel at 1 Stage. Furthermore, to control endogeneity we also created instrumental variable models for each component of the ESG model. Results show that HEAR is negatively associated to the E, S and G component within the ESG model. The data were subjected to clustering with a k-Means algorithm optimized with the Silhouette coefficient. The optimal clustering with k=2 is compared to the sub-optimal cluster with k=3. The results suggest a negative relationship between the resident population and hospital emigration at regional level. Finally, a prediction is proposed with machine learning algorithms classified based on statistical performance. The results show that the Artificial Neural Network-ANN algorithm is the best predictor. The ANN predictions are critically analyzed in light of health economic policy directions.

Using Conditional Entropy Networks of Ordinal Measures to Examine Changes in Self-Worth Among Adolescent Students in High School

We implement an analytic approach for ordinal measures and we use it to investigate the structure and the changes over time of self-worth in a sample of adolescents students in high school. We represent the variations in self-worth and its various sub-domains using entropy-based measures that capture the observed uncertainty. We then study the evolution of the entropy across four time points throughout a semester of high school. Our analytic approach yields information about the configuration of the various dimensions of the self together with time-related changes and associations among these dimensions. We represent the results using a network that depicts self-worth changes over time. This approach also identifies groups of adolescent students who show different patterns of associations, thus emphasizing the need to consider heterogeneity in the data.

Unveiling temporal dynamics of PTSD and its functional impairments: A longitudinal study in UK healthcare workers

PTSD has been associated with negative long-term consequences, including social and occupational impairments. Yet, a nuanced understanding of the interplay between PTSD symptoms and distinct domains of impairments on a short-term basis (weeks/ months) at the within-person level remains underexplored. In a large sample (nwave 1 = 1096, nwave 7 = 304) of UK healthcare workers assessed across seven assessment waves during the COVID-19 pandemic (spaced 6 weeks apart), we employed exploratory graphical vector autoregression models (GVAR) models to discern within-person temporal (across time) and contemporaneous (within same time window) dynamics between PTSD symptoms and functional impairment domains. The contemporaneous network highlighted strong co-occurrences between different symptoms and impairments. The temporal network revealed a mutually reinforcing cycle between intrusion and avoidance symptoms. Intrusion symptoms showed the highest out-strength (i.e., most predictive symptom), predicting avoidance symptoms, elevated sense of current threat, and various functional impairments. Avoidance symptoms, elevated after increased levels of intrusions, predicted work impairments that in turn were associated with difficulties in fulfilling other obligations. Our findings underscore the dynamics between perceived threat and intrusions, and the role intrusions may play in predicting a cascade of adverse effects. Targeted interventions aimed at mitigating intrusions may disrupt this negative cycle.

A Note on Ising Network Analysis with Missing Data

The Ising model has become a popular psychometric model for analyzing item response data. The statistical inference of the Ising model is typically carried out via a pseudo-likelihood, as the standard likelihood approach suffers from a high computational cost when there are many variables (i.e., items). Unfortunately, the presence of missing values can hinder the use of pseudo-likelihood, and a listwise deletion approach for missing data treatment may introduce a substantial bias into the estimation and sometimes yield misleading interpretations. This paper proposes a conditional Bayesian framework for Ising network analysis with missing data, which integrates a pseudo-likelihood approach with iterative data imputation. An asymptotic theory is established for the method. Furthermore, a computationally efficient Pólya-Gamma data augmentation procedure is proposed to streamline the sampling of model parameters. The method's performance is shown through simulations and a real-world application to data on major depressive and generalized anxiety disorders from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC).

Dynamics of Perceived Social Isolation, Secondary Conditions, and Daily Activity Patterns Among Individuals With Stroke: A Network Analysis of Ecological Momentary Assessment Data

OBJECTIVE

To assess contemporaneous and temporal dynamics of perceived social isolation (PSI), secondary conditions, and daily activity patterns in individuals post-stroke.

DESIGN

Longitudinal observational study using ecological momentary assessment (EMA) as a real-time assessment of an individual's lived experiences. We conducted dynamic network analyses to examine longitudinal associations among EMA variables.

SETTINGS

Home and Community.

PARTICIPANTS

202 individuals with mild-to-moderate chronic stroke (median age=60 years; 45% women; 44% black; 90% ischemic stroke; median NIHSS score=2; N=202).

INTERVENTION

Not applicable.

MAIN OUTCOME MEASURES

EMA questions measured PSI, secondary conditions (pain, tiredness, stress, anxiety, worthlessness, difficulty concentrating, and cheerfulness), and daily activity patterns (being at home, being alone, and participating in productive activities).

RESULTS

The median EMA response rate was 84%. The contemporaneous model showed that PSI was associated with being home, alone, and all symptoms except pain. The temporal model revealed a pathway indicating that feelings of worthlessness predicted PSI (regression coefficient=0.06, P=.019), followed by stress (regression coefficient=0.06, P=.024), and then by being not at home (regression coefficient=-0.04, P=.013).

CONCLUSION

Implementing dynamic network analyses on EMA data can uncover dynamic connections among PSI, secondary conditions, and daily activity patterns after stroke. This study found a significant temporal association between PSI and negative emotions. Feeling isolated was followed by feeling stressed, which was followed by a tendency to be out of home, indicating adaptive behaviors in individuals with stroke. These findings highlight the importance of engaging in out-of-home or outdoor activities to mitigate PSI and negative emotions.

Exploring mental health dynamics during eating disorder treatment: A psychometric network study with panel data

To explore mental health associations during eating disorder (ED) treatment. Based on the dual-continua model of mental health, general and ED-specific psychopathology, as well as emotional, psychological, and social well-being were considered as mental health domains.

Network analyses with panel data were applied to explore within- (temporal and contemporaneous networks) and between-person effects in a sample of 1250 female ED patients during 12 months of outpatient treatment. The associations between the domains and their centrality were examined. Autoregressive and cross-lagged effects were also estimated.

ED psychopathology was the most central domain in the temporal network. ED psychopathology changes predicted further ED psychopathology changes and small changes in the other domains. Weak bi-directional associations were found between changes in the well-being domains and general psychopathology. In contrast to the temporal network, ED psychopathology was the least central and psychological well-being the most central domain in the contemporaneous and between-subjects networks. This suggests a central role of psychological well-being for experiencing mental health within time points.

ED psychopathology may change relatively independent from other mental health domains. Well-being domains may be considered as more stable aspects of mental health.

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.

Polygenic Scores and Networks of Psychopathology Symptoms

Importance

Studies on polygenic risk for psychiatric traits commonly use a disorder-level approach to phenotyping, implicitly considering disorders as homogeneous constructs; however, symptom heterogeneity is ubiquitous, with many possible combinations of symptoms falling under the same disorder umbrella. Focusing on individual symptoms may shed light on the role of polygenic risk in psychopathology.

Objective

To determine whether polygenic scores are associated with all symptoms of psychiatric disorders or with a subset of indicators and whether polygenic scores are associated with comorbid phenotypes via specific sets of relevant symptoms.

Design, Setting, and Participants

Data from 2 population-based cohort studies were used in this cross-sectional study. Data from children in the Avon Longitudinal Study of Parents and Children (ALSPAC) were included in the primary analysis, and data from children in the Twins Early Development Study (TEDS) were included in confirmatory analyses. Data analysis was conducted from October 2021 to January 2024. Pregnant women based in the Southwest of England due to deliver in 1991 to 1992 were recruited in ALSPAC. Twins born in 1994 to 1996 were recruited in TEDS from population-based records. Participants with available genetic data and whose mothers completed the Short Mood and Feelings Questionnaire and the Strength and Difficulties Questionnaire when children were 11 years of age were included.

Main Outcomes and Measures

Psychopathology relevant symptoms, such as hyperactivity, prosociality, depression, anxiety, and peer and conduct problems at age 11 years. Psychological networks were constructed including individual symptoms and polygenic scores for depression, anxiety, attention-deficit/hyperactivity disorder (ADHD), body mass index (BMI), and educational attainment in ALSPAC. Following a preregistered confirmatory analysis, network models were cross-validated in TEDS.

Results

Included were 5521 participants from ALSPAC (mean [SD] age, 11.8 [0.14] years; 2777 [50.3%] female) and 4625 participants from TEDS (mean [SD] age, 11.27 [0.69] years; 2460 [53.2%] female). Polygenic scores were preferentially associated with restricted subsets of core symptoms and indirectly associated with other, more distal symptoms of psychopathology (network edges ranged between r = -0.074 and r = 0.073). Psychiatric polygenic scores were associated with specific cross-disorder symptoms, and nonpsychiatric polygenic scores were associated with a variety of indicators across disorders, suggesting a potential contribution of nonpsychiatric traits to comorbidity. For example, the polygenic score for ADHD was associated with a core ADHD symptom, being easily distracted (r = 0.07), and the polygenic score for BMI was associated with symptoms across disorders, including being bullied (r = 0.053) and not thinking things out (r = 0.041).

Conclusions and Relevance

Genetic associations observed at the disorder level may hide symptom-level heterogeneity. A symptom-level approach may enable a better understanding of the role of polygenic risk in shaping psychopathology and comorbidity.

A Network Analysis of the Fear Avoidance Model of Genital Pain

Using a novel data-driven network approach, this study aimed to examine the interconnection between the key elements of the Fear-Avoidance Model of female genital pain - sexual arousal, fear-avoidant cognitions, and motivational coping - and its associated factors to predict the intensity and frequency of genital pain across women over time. Network modeling allowed for a comprehensive evaluation of the Fear-Avoidance model while capturing the dynamic features of genital pain. We estimated a cross-sectional and a temporal, contemporaneous, and between-persons network model on convenience-based data of 543 female students (mean age = 23.7 years, SD = 3.6) collected at three time points. Results showed that lubrication, pain catastrophizing, pain avoidance, fear-avoidance beliefs, sexual satisfaction, anxiety, and frequency of coital and non-coital sex predicted pain, with lubrication being the most consistent predictor across estimations. The network of women with recurrent genital pain showed a similar pattern as the network of the total sample, except that pain avoidance and fear-avoidance beliefs rather than pain catastrophizing predicted pain directly, and frequency of coital and non-coital sexual activities played a more prominent role. These results suggest that the main problem of genital pain centers around women not being sufficiently aroused during intercourse and inadequate ways of pain coping, which are critical targets of cognitive-behavioral therapy treatment and should be developed further.

Contemporaneous and temporal network analysis of complex Posttraumatic stress disorder among Chinese college students with Childhood adversity: A longitudinal study

OBJECTIVE

To enhance understanding of the longitudinal progression of complex posttraumatic stress disorder (CPTSD) symptoms, this longitudinal study examined how CPTSD symptoms interact over time in Chinese college students with childhood trauma.

METHODS

From 18,933 college students who took part in two surveys 12 months apart, 4,006 participants who reported adverse childhood experiences were screened. Cross-sectional network comparisons and cross-lagged panel network (CLPN) analysis characterized interactions among CPTSD symptoms.

RESULTS

In the cross-sectional networks, feeling like a failure and avoid activities reminiscent of the trauma were the central symptoms. Takes long time to calm down and exaggerated startle are important bridge symptoms in the two networks respectively. The comparison of cross-sectional networks indicates that the global network strength was stable. The findings of the CLPN model reveal that feel worthless and feel like a failure had the highest "out" expected influence; exaggerated startle and avoid thoughts and feelings about the trauma had the highest "in" expected influence.

CONCLUSIONS

By conducting cross-sectional network analyses, the study illuminated the attributes of CPTSD networks across various time points. Additionally, the CLPN analysis uncovered the longitudinal patterns of CPTSD symptoms.

Peer functioning difficulties may exacerbate symptoms of attention-deficit/hyperactivity disorder and irritability over time: a temporal network analysis

BACKGROUND

Children with attention-deficit/hyperactivity disorder (ADHD) have been consistently found to experience impairments in peer functioning. Irritability is highly prevalent in children with ADHD and may worsen social impairments given the frequent temper outbursts and low frustration tolerance characterizing irritability. However, it is still unclear how ADHD and irritability symptoms interact with peer functioning difficulties over time. Assessing these temporal dynamics using a novel longitudinal approach (i.e., temporal network analysis) may reveal precise targets for intervention.

METHODS

This study investigates the dynamic associations between ADHD symptoms, irritability, and peer functioning in a community sample of 739 children (ages 8-11 years, Mage = 10.06 [SD = 0.59], 47.77% females) assessed at three timepoints, 6 months apart, in a school-based study. Parents reported their child's ADHD symptoms using the Swanson, Nolan, and Pelham Rating Scale (SNAP-IV), and irritability symptoms using the Child Behavior Checklist (CBCL) irritability items. Children's peer functioning (i.e., peer acceptance, peer rejection, number of friendships, and victimization) was measured via peer nomination. To estimate the longitudinal associations between the variables, we built a graphical vector autoregression model for panel data.

RESULTS

The longitudinal network highlighted that poor peer functioning contributed to increases in symptoms over time. Specifically, (1) physical victimization predicted increases in inattention, hyperactivity, and irritability; (2) peer rejection predicted increases in inattention, which in turn predicted increases in irritability; (3) peer acceptance predicted decreases in inattention and irritability; and (4) higher numbers of mutual friendships increased inattention.

CONCLUSIONS

These results suggest that a negative social environment involving physical bullying and rejection may aggravate ADHD and irritability symptoms. Conversely, positive social interactions, such as being liked by peers, may improve inattention and irritability symptoms. Fostering social-emotional skills and positive social interactions and environments in children with ADHD and irritability may be a promising target for future interventions to reduce symptoms.

The Impact of Music Therapy in a Pediatric Oncology Setting: An Italian Observational Network Study

BACKGROUND

Music Therapy (MT) is a non-pharmacological, art-based intervention that employs music experiences within a therapeutic alliance to attend to clients' physical, emotional, cognitive, and social requirements. This is the first study aiming at investigating the impact of MT on the psychological facets of children suffering from cancer.

METHODS

The study, combining the AQR and m-YPAS assessment tools, evaluated behavioral, sound-musical, and interactive parameters in pediatric oncology patients undergoing MT sessions during hospitalization. Fifty patients admitted to the Paediatric Oncology and Haematology Unit at Policlinico S. Orsola Hospital in Bologna, Italy, were enrolled, irrespective of their treatment regimen. Data collection occurred on the first day of the MT session between 3 p.m. and 5 p.m., with observations conducted by independent observers. In addition to traditional statistical analysis, network analysis was used to explore the combined interactions of all parameters, effectively discerning the distinctive roles played by each one during therapy sessions and their influence on all others.

RESULTS

Network analysis highlighted distinct patterns of interactions among parameters during the various sessions, emphasizing the role of positive emotions and a calm setting, the child's ability to take the initiative in sessions, their sense of agency, and the parent's role in guiding them. Significant differences were recorded at each time point between all variables considered.

CONCLUSIONS

The results of this innovative study may pave the way for future multicenter studies aimed at further exploring the role of MT in children undergoing both curative and palliative treatments for cancer.

Towards precision in the diagnostic profiling of patients: leveraging symptom dynamics as a clinical characterisation dimension in the assessment of major depressive disorder

BACKGROUND

International guidelines present overall symptom severity as the key dimension for clinical characterisation of major depressive disorder (MDD). However, differences may reside within severity levels related to how symptoms interact in an individual patient, called symptom dynamics.

AIMS

To investigate these individual differences by estimating the proportion of patients that display differences in their symptom dynamics while sharing the same overall symptom severity.

METHOD

Participants with MDD (n = 73; mean age 34.6 years, s.d. = 13.1; 56.2% female) rated their baseline symptom severity using the Inventory for Depressive Symptomatology Self-Report (IDS-SR). Momentary indicators for depressive symptoms were then collected through ecological momentary assessments five times per day for 28 days; 8395 observations were conducted (average per person: 115; s.d. = 16.8). Each participant's symptom dynamics were estimated using person-specific dynamic network models. Individual differences in these symptom relationship patterns in groups of participants sharing the same symptom severity levels were estimated using individual network invariance tests. Subsequently, the overall proportion of participants that displayed differential symptom dynamics while sharing the same symptom severity was calculated. A supplementary simulation study was conducted to investigate the accuracy of our methodology against false-positive results.

RESULTS

Differential symptom dynamics were identified across 63.0% (95% bootstrapped CI 41.0-82.1) of participants within the same severity group. The average false detection of individual differences was 2.2%.

CONCLUSIONS

The majority of participants within the same depressive symptom severity group displayed differential symptom dynamics. Examining symptom dynamics provides information about person-specific psychopathological expression beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may be a promising new dimension for clinical characterisation, warranting replication in independent samples. To inform personalised treatment planning, a next step concerns linking different symptom relationship patterns to treatment response and clinical course, including patterns related to spontaneous recovery and forms of disorder progression.

Network Structure of Depressive Symptomatology in Elderly with Cognitive Impairment

Objective and objectives: Patients with cognitive disorders such as Alzheimer's disease (AD) and mild cognitive impairment (MCI) frequently exhibit depressive symptoms. Depressive symptoms can be evaluated with various measures and questionnaires. The geriatric depression scale (GDS) is a scale that can be used to measure symptoms in geriatric age. Many questionnaires sum up symptom scales. However, core symptoms of depression in these patients and connections between these symptoms have not been fully explored yet. Thus, the objectives of this study were (1) to determine core symptoms of two cognitive disorders, Alzheimer's disease and mild cognitive impairment, and (2) to investigate the network structure of depressive symptomatology in individuals with cognitive impairment in comparison with those with Alzheimer's disease. Materials and Methods: This study encompassed 5354 patients with cognitive impairments such as Alzheimer's disease (n 1889) and mild cognitive impairment (n = 3464). The geriatric depression scale, a self-administered questionnaire, was employed to assess depressive symptomatology. Using exploratory graph analysis (EGA), a network analysis was conducted, and the network structure was evaluated through regularized partial correlation models. To determine the centrality of depressive symptoms within each cohort, network parameters such as strength, betweenness, and closeness were examined. Additionally, to explore differences in the network structure between Alzheimer's disease and mild cognitive impairment groups, a network comparison test was performed. Results: In the analysis of centrality indices, "worthlessness" was identified as the most central symptom in the geriatric depression scale among patients with Alzheimer's disease, whereas "emptiness" was found to be the most central symptom in patients with mild cognitive impairment. Despite these differences in central symptoms, the comparative analysis showed no statistical difference in the overall network structure between Alzheimer's disease and mild cognitive impairment groups. Conclusions: Findings of this study could contribute to a better understanding of the manifestation of depressive symptoms in patients with cognitive impairment. These results are expected to aid in identifying and prioritizing core symptoms in these patients. Further research should be conducted to explore potential interventions tailored to these core symptoms in patients with Alzheimer's disease and mild cognitive impairment. Establishing core symptoms in those groups might have clinical importance in that appropriate treatment for neuropsychiatric symptoms in patients with cognitive impairment could help preclude progression to further impairment.

Intraindividual time-varying dynamic network of affects: linear autoregressive mixed-effects models for ecological momentary assessment

An interesting recent development in emotion research and clinical psychology is the discovery that affective states can be modeled as a network of temporally interacting moods or emotions. Additionally, external factors like stressors or treatments can influence the mood network by amplifying or dampening the activation of specific moods. Researchers have turned to multilevel autoregressive models to fit these affective networks using intensive longitudinal data gathered through ecological momentary assessment. Nonetheless, a more comprehensive examination of the performance of such models is warranted. In our study, we focus on simple directed intraindividual networks consisting of two interconnected mood nodes that mutually enhance or dampen each other. We also introduce a node representing external factors that affect both mood nodes unidirectionally. Importantly, we disregard the potential effects of a current mood/emotion on the perception of external factors. We then formalize the mathematical representation of such networks by exogenous linear autoregressive mixed-effects models. In this representation, the autoregressive coefficients signify the interactions between moods, while external factors are incorporated as exogenous covariates. We let the autoregressive and exogenous coefficients in the model have fixed and random components. Depending on the analysis, this leads to networks with variable structures over reasonable time units, such as days or weeks, which are captured by the variability of random effects. Furthermore, the fixed-effects parameters encapsulate a subject-specific network structure. Leveraging the well-established theoretical and computational foundation of linear mixed-effects models, we transform the autoregressive formulation to a classical one and utilize the existing methods and tools. To validate our approach, we perform simulations assuming our model as the true data-generating process. By manipulating a predefined set of parameters, we investigate the reliability and feasibility of our approach across varying numbers of observations, levels of noise intensity, compliance rates, and scalability to higher dimensions. Our findings underscore the challenges associated with estimating individualized parameters in the context of common longitudinal designs, where the required number of observations may often be unattainable. Moreover, our study highlights the sensitivity of autoregressive mixed-effect models to noise levels and the difficulty of scaling due to the substantial number of parameters.

Network analysis of maternal parenting practices and adolescent mental health problems: a longitudinal study

BACKGROUND

An extensive literature has shown a strong connection between maternal parenting practices and adolescent mental health problems. However, it has been difficult for previous research to map a dynamic concurrent and prospective relationships within and between types of parenting practices and adolescent mental health problems. The present study addressed these issues using a network analysis approach and a longitudinal design.

METHODS

This study involved 591 Chinese adolescents (249 males; mean age at T1 = 13.53) and their mothers (mean age at T1 = 39.71) at two time points (T1 and T2) with eighteen months apart. Mothers reported their parenting practices including warmth, monitoring, inductive reasoning, hostility, and harshness, while adolescents reported their mental health problems including anxiety, depression, aggression, and conduct problems. Network analysis was conducted for contemporaneous networks at T1 and T2 and temporal networks from T1 to T2.

RESULTS

The contemporaneous networks revealed the negative association between monitoring and conduct problems served as the main pathway through which parenting practices and adolescent mental health mutually influenced each other, and further, warmth was the most influential parenting practice on adolescent mental health. The temporal network revealed that maternal hostility exerted the most influence on adolescent mental health problems, whereas adolescents' depression was most influenced by maternal parenting practices. Moreover, maternal hostility was most predicted by maternal harshness.

CONCLUSIONS

This study presents a novel perspective to gain a better understanding of the dynamics between and within maternal parenting practices and adolescent mental health problems. Findings highlight maternal harshness and warmth as potential prevention and intervention targets for adolescent mental health problems.

A Longitudinal Network of Borderline-Related Trait Vulnerabilities from Childhood to Adolescence

Early maladaptive traits are predictive of later borderline personality pathology (BPP), but little is known about their dynamic interplay over time. This is an important issue to address, however, as significant differences in the 'clinical weight' of various traits constituting the early BPP trait phenotype may inform the field on important target constructs from an early intervention perspective. Therefore, the current study aims to uncover the complex dependencies between BPP traits across the crucial developmental period of childhood and adolescence, by using longitudinal network analysis. Both between- and within-person networks were constructed to identify how early mother-reported borderline-related traits are connected across a timespan of six years (ntime 1 = 718, Mtime1 = 10.73 years, SDtime1 = 1.39, 55.1% girls). Overall, the temporal network suggested various trait interdependencies, with internalizing traits being particularly influential in the development of the BPP trait network structure. At the same time, externalizing traits likely inhibit the negative effects of these core traits. In addition, results also revealed that internalizing and externalizing clusters of early borderline-related traits are linked through emotional lability. Implications of these findings are discussed in view of the change mechanisms at play and potential targets for early intervention.

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

BACKGROUND

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

METHODS AND ANALYSIS

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

ETHICS AND DISSEMINATION

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

TRIAL REGISTRATION NUMBER

ChiCTR2300075813.

The Role of Ageist Sexual Stereotypes in the Network of Sexual Difficulties, Sex and Relationship Satisfaction Among Adults Aged 50

As people age, their sexual activity, including sexual and relationship satisfaction, may change due to the onset of health and sexual difficulties and the activation of internalized ageist sexual stereotypes. We studied whether ageist sexual stereotypes were relevant for the importance of sex for people aged 50+ and how sexual and relationship satisfaction are interlinked with health and sexual difficulties when ageist sexual stereotypes are considered. Data for the network analysis were collected from a sample of 897 Czechs and Slovaks aged 50-96 (52% men, mean age = 62.01). Two models were analyzed - with and without the inclusion of ageist sexual stereotypes. The findings indicated that ageist sexual stereotypes play an important role in the sexuality of women and men aged 50 + .The fewer ageist sexual stereotypes people had, the less sexual difficulties they reported and the more they perceived sex as important. The inclusion of stereotypes into the models, however, weakened the negative link between sexual difficulties and the importance of sex for both women and men. These findings show that the effect of ageist sexual stereotypes are likely complex in the sexual lives of people aged 50 +.

The network dynamics of self-compassion components and psychological symptoms during an intervention

The change process of psychological interventions is complex and should be understood with a systems perspective. This study sought to examine the network dynamics of self-compassion components and psychological symptoms during an intervention. A total of 139 participants completed daily assessments during a 28-day intervention. Utilizing multilevel vector autoregressive (VAR) model, temporal and contemporaneous networks were generated and group differences in network dynamics were evaluated through descriptive assessment and permutation tests. The intervention group displayed a significant increase in self-compassion and decrease in psychological symptoms, with self-compassion mediating the intervention effects on symptoms. Network analysis revealed some network dynamics that might be relevant to desirable therapeutic changes in the intervention group. The intervention group demonstrated a significantly less connected contemporaneous depression network, indicating a decreased vulnerability to symptom activation. Additionally, the intervention group showed significantly more temporal connections from self-compassion to anxiety, indicating an increased influence of self-compassion on anxiety. These findings suggest that the intervention may have reshaped the interconnection pattern of symptoms and that between self-compassion components and symptoms.

Dynamic Individual Risk Networks: Personalized Network Modelling Based on Experience Sampling Data

Following a network perspective, risk of sexual reoffending can be understood as a construct that emerges from the interactions between risk factors. If these interrelationships are validly mapped out, this leads to an increased understanding of the risk and thus may contribute to more effective and/or more efficient interventions. This paper reports on personalized network modeling mapping the interrelationships of dynamic risk factors for an individual convicted of sexual offenses, using experience sampling (ESM) based on Stable-2007 items. The longitudinal character of ESM enables both the assessment of interrelations between risk factors within a timeframe and the relationships between risk factors over time. Networks are calculated and compared to the clinical assessment of interrelationships between the risk factors.

Longitudinal Association Between Posttraumatic Stress Symptoms and Depression Symptoms Among Chinese Adolescents During COVID-19: Evidence from Network Perspective

Although there are an increasing number of studies that have explored the mental health consequences of COVID-19 focusing on revealing risk factors, the longitudinal research examining the potential mechanism of the co-occurrence of posttraumatic stress symptoms (PTSS) and depression symptoms among adolescents were scarce. The present study identified the important comorbidity symptoms and explored longitudinal relationship of PTSS and depression symptoms from the network perspective. A two-wave investigation (4 months interval; T1 and T2) was conducted with a sample of 1225 Chinese adolescents. Cross-sectional network and cross-lagged panel network (CLPN) analyses were adopted. Results showed that comorbidity symptoms consisted of both overlapping and non-overlapping symptoms, including "Future foreshortening" at T1 and T2 from PTSS, and "Hard to get started" at T1 and "Not sleep well" at T2 from depression symptoms. Strong longitudinal pathways appeared from all PTSS to depression symptoms, among which the pathway from "Difficulty concentrating" to "Hard to get started" was the strongest. These findings suggest that the possible comorbidity between PTSS and depression symptoms is caused by their independent and related structures, and their longitudinal association. Clinical intervention for these symptoms may alleviate adolescents' psychological problems in the aftermath of traumatic events.

Are central eating disorder network symptoms sensitive to item selection and sample? Implications for conceptualization of eating disorder psychopathology from a network perspective

Item selection is a critical decision in modeling psychological networks. The current preregistered two-study research used random selections of 1,000 symptom networks to examine which eating disorder (ED) and co-occurring symptoms are most central in longitudinal networks among individuals with EDs (N = 71, total observations = 6,060) and tested whether centrality changed based on which items were included in the network. Participants completed 2 weeks of ecological momentary assessment (five surveys/day). In Study 1, we obtained initial strength centrality values by estimating an a priori network using eight items with the highest means. We then estimated 1,000 networks and their centrality from a random selection of unique eight-item symptom combinations. We compared the strength centrality from the a priori network to the distribution of strength centrality estimates from the random-item networks. In Study 2, we repeated this procedure in an independent longitudinal dataset (N = 41, total observations = 4,575) to determine if our results generalized across samples. Shame, guilt, worry, and fear of losing control were consistently central across networks, regardless of items included in the network or sample. Results suggest that these symptoms may be important to the structure of ED psychopathology and have implications for how we understand the structure of ED psychopathology. Existing methods for item inclusion in psychological networks may distort the structure of ED symptom networks by either under- or overestimating strength centrality, or by omitting consistently central symptoms that are nontraditional ED symptoms. Future research should consider including these symptoms in models of ED psychopathology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A network approach to understanding social distancing behaviour during the first UK lockdown of the COVID-19 pandemic

OBJECTIVE

Given the highly infectious nature of COVID-19, social distancing practices are key in stemming the spread of the virus. We aimed to assess the complex interplay among psychological factors, socio-demographic characteristics and social distancing behaviours within the framework of the widely used Capability, Opportunity, Motivation-Behaviour (COM-B) model.

DESIGN

The present research employed network psychometrics on data collected during the first UK lockdown in April 2020 as part of the COVID-19 Psychological Research Consortium (C19PRC) Study. Using a network approach, we examined the predictions of psychological and demographic variables onto social distancing practices at two levels of analysis: macro and micro.

RESULTS

Our findings revealed several factors that influenced social distancing behaviour during the first UK lockdown. The COM-B model was successful in predicting particular aspects of social-distancing via the influence of psychological capability and motivation at the macro-and micro-levels, respectively. Notably, demographic variables, such as education, income, and age, were directly and uniquely predictive of certain social distancing behaviours.

CONCLUSION

Our findings reveal psychological factors that are key predictors of social distancing behaviour and also illustrate how demographic variables directly influence such behaviour. Our research has implications for the design of empirically-driven interventions to promote adherence to social distancing practices in this and future pandemics.

Supplemental data for this article is available online at.

The Predictive Role of Tolerance and Health Problems in Problem Gambling: A Cross-Sectional and Cross-Lagged Network Analyses

The existing symptomatic networks of problem gambling are all based on cross-sectional data. Thus, there is a need to explore longitudinal symptom networks of problem gambling. Moreover, the replicability of cross-sectional symptom networks can be limited; therefore, further research should assess the convergence between cross-sectional networks of problem gambling symptoms. The present study aimed (i) to examine cross-sectional networks of problem gambling symptoms and evaluate their replicability and (ii) to examine a longitudinal cross-lagged network of problem gambling symptoms. The study included a representative sample of young adult gamblers (born between 1984 and 2000) from the first two waves of the Budapest Longitudinal Study (original sample: N = 2777; final sample: N = 335). The Problem Gambling Severity Index was used to assess symptoms of problem gambling. Cross-sectional symptom networks showed differences in the centrality of nodes. Correlations between the two cross-sectional networks were low in the presence vs. absence of edges, rank order of edge weights, and centrality estimates. However, network invariance tests indicated non-significant differences between them. The cross-lagged network revealed that the symptoms of tolerance and health problems could predict the subsequent presence of multiple problem gambling symptoms. Overall, limited evidence demonstrated the replicability of cross-sectional symptom networks of problem gambling. Future research needs to explore the utility of cross-sectional networks of problem gambling and assess more precisely causal relationships between problem gambling symptoms by distinguishing within- and between-subject effects.

Associations of screen use with physical activity and social capital amid the COVID-19 pandemic: A network analysis of youths in China

Inconsistent correlations of screen use with physical activity (PA) and social capital (SC) in youths have been observed in existing cross-sectional studies. This study aimed to elucidate associations among variables in screen use, PA, and SC domains during COVID-19, to improve the prediction and prevention of suboptimal health status in youths. An online survey based on the nationwide COVID-19 Impact on Lifestyle Change Survey (COINLICS) was conducted in China, and 10,082 youths reported their screen use, PA, and SC in the months immediately before, during, and after the COVID-19 lockdown. Cross-sectional and longitudinal network models were used to identify associations of variables in domains of screen use with PA and SC. Effect modifications of bridges and predictors in the associations were examined. The network models suggested that individual SC was a bridge that strongly connected other types of SC, and domains of screen use and PA before lockdown, while phone use became such a bridge during and after lockdown. More PC/TV use before lockdown predicted less household-related PA during lockdown (β = -0.142); more phone use during lockdown was a predictor for higher levels of household-related PA (β = 0.106), active transport (β = 0.096), and individual SC (β = 0.072) after lockdown. Phone use was negatively associated with PA through PC/TV use in the more phone use subgroup. Relationships among screen use, PA, and SC dynamically changed during COVID-19, and phone use that was identified as a bridge and a predictor may be the potential action point for health intervention in youths during lockdown.

Self-management of geriatric syndromes - an observational study

BACKGROUND

Self-management of health refers to various actions and decisions that impact health outcomes. To improve health, independence, and quality of life (QoL) while reducing healthcare utilization and costs, patients' self-management abilities can be enhanced. However, disease-specific self-management interventions may not be applicable for older adults with multiple illnesses. Instead, focusing on prevalent geriatric syndromes, such as frailty, cognitive decline, pain, incontinence, or impaired mobility, may be more beneficial. To achieve this, a detailed understanding of the specific needs of the older population is crucial.

METHODS

Patients who are 70 years old or older will be chosen from four geriatric hospitals, which include both inpatient and outpatient facilities. At baseline, each participant will undergo a comprehensive geriatric evaluation and answer various questionnaires that focus on their current self-management abilities, self-efficacy, anxiety, aging perception, and QoL. Moreover, extensive data on the presence and impact of geriatric syndromes will be gathered. Three and six months after the initial evaluation, follow-up assessments will be conducted to identify any changes in participants' health, independence in daily activities, geriatric syndromes, cognition and mood, QoL, and self-management.

DISCUSSION

The present investigation aims to assess the factors that may facilitate or impede self-management in older adults afflicted with geriatric syndromes. Instead of concentrating on particular diseases, this study will analyze the association between self-management and geriatric syndromes. The information obtained will contribute to clinical expertise on the self-management habits of older adults and their effects on their well-being, autonomy, and overall QoL, as well as provide insights into geriatric syndromes. This valuable knowledge will be crucial for developing personalised programs to enhance self-management among older adults.

TRIAL REGISTRATION

German Trial Register (Deutsches Register Klinischer Studien) DRKS00031016.

Symptom Network Analysis Tools for Applied Researchers With Cross-Sectional and Panel Data - A Brief Overview and Multiverse Analysis

In recent years, there has been a growing interest in utilizing symptom-network models to study psychopathology and relevant risk factors, such as cognitive and physical health. Various methodological approaches can be employed by researchers analyzing cross-sectional and panel data (i.e., several time points over an extended period). This paper provides an overview of some commonly used analytical tools, including moderated network models, network comparison tests, cross-lagged network analysis, and panel graphical vector-autoregression (VAR) models. Using an easily accessible dataset (easySHARE), this study demonstrates the use of different analytical approaches when investigating (a) the association between mental health and cognitive functioning, and (b) the role of chronic disease in mediating or moderating this association. This multiverse analysis showcases both converging and diverging evidence from different analytical avenues. These findings underscore the importance of multiverse investigations to increase transparency and communicate the extent to which conclusions depend on analytical choices.

Network Structure of Autism Spectrum Disorder Behaviors and Its Evolution in Preschool Children: Insights from a New Longitudinal Network Analysis Method

Network modeling of the social, communication and restrictive/repetitive behaviors (RRBs) included in the definition of Autism Spectrum Disorder was performed. The Autism Diagnostic Interview-Revised (ADI-R) assessed behaviors in 139 pre-school cases at two cross-sections that averaged 34.8 months apart. Cross-sectional networks were based on the correlation matrix of the ADI-R behavioral items and the "bootCross" method was developed and enabled the estimation of a longitudinal network. At both stages, RRB items/nodes formed a consistent peripheral cluster, while social and communication nodes formed a core cluster that diverged with time. These differences in the nature and evolution of the RRB and socio-communicative dimensions indicate that their inter-behavior dynamics are very different. The most central behaviors across stages are proposed as prime targets for efficient therapeutic intervention.

Developmental Trajectories of Internalizing and Externalizing Symptoms in Youth and Associated Gender Differences: A Directed Network Perspective

Psychopathology in youth is highly prevalent and associated with psychopathology in adulthood. However, the developmental trajectories of psychopathology symptoms, including potential gender differences, are markedly underspecified. The present study employed a directed network approach to investigate longitudinal relationships and gender differences among eight transdiagnostic symptom domains across three years, in a homogenous age sample of youth participants (n = 6,414; mean baseline age = 10.0 years; 78.6% White; Adolescent Brain Cognitive Development study). Anxious/depressed problems and aggressive behaviors were central symptoms and most predictive of increases in other symptom clusters at later timepoints. Rule-breaking behaviors, aggressive behaviors, and withdrawn/depressed problems emerged as bridge symptoms between externalizing and internalizing problems. Results supported cascade models in which externalizing problems predicted future internalizing problems, but internalizing problems also significantly predicted future externalizing problems, which is contrary to cascade models. Network structure, symptom centrality, and patterns of bridge symptoms differed between female and male participants, suggesting gender differences in the developmental trajectories of youth psychopathology. Results provide new insights into symptom trajectories and associated gender differences that may provide promising pathways for understanding disorder (dis)continuity and co-occurrence. The central and bridge symptoms identified here may have important implications for screening and early intervention for youth psychopathology.

Identifying the bridge between depression and mania: A machine learning and network approach to bipolar disorder

OBJECTIVES

Although the cyclic nature of bipolarity is almost by definition a network system, no research to date has attempted to scrutinize the relationship of the two bipolar poles using network psychometrics. We used state-of-the-art network and machine learning methodologies to identify symptoms, as well as relations thereof, that bridge depression and mania.

METHODS

Observational study that used mental health data (12 symptoms for depression and 12 for mania) from a large, representative Canadian sample (the Canadian Community Health Survey of 2002). Complete data (N = 36,557; 54.6% female) were analysed using network psychometrics, in conjunction with a random forest algorithm, to examine the bidirectional interplay of depressive and manic symptoms.

RESULTS

Centrality analyses pointed to symptoms relating to emotionality and hyperactivity as being the most central aspects of depression and mania, respectively. The two syndromes were spatially segregated in the bipolar model and four symptoms appeared crucial in bridging them: sleep disturbances (insomnia and hypersomnia), anhedonia, suicidal ideation, and impulsivity. Our machine learning algorithm validated the clinical utility of central and bridge symptoms (in the prediction of lifetime episodes of mania and depression), and suggested that centrality, but not bridge, metrics map almost perfectly onto a data-driven measure of diagnostic utility.

CONCLUSIONS

Our results replicate key findings from past network studies on bipolar disorder, but also extend them by highlighting symptoms that bridge the two bipolar poles, while also demonstrating their clinical utility. If replicated, these endophenotypes could prove fruitful targets for prevention/intervention strategies for bipolar disorders.

Drinking motives, personality traits and life stressors-identifying pathways to harmful alcohol use in adolescence using a panel network approach

BACKGROUND AND AIMS

Models of alcohol use risk suggest that drinking motives represent the most proximal risk factors on which more distal factors converge. However, little is known about how distinct risk factors influence each other and alcohol use on different temporal scales (within a given moment versus over time). We aimed to estimate the dynamic associations of distal (personality and life stressors) and proximal (drinking motives) risk factors, and their relationship to alcohol use in adolescence and early adulthood using a novel graphical vector autoregressive (GVAR) panel network approach.

DESIGN, SETTING AND CASES

We estimated panel networks on data from the IMAGEN study, a longitudinal European cohort study following adolescents across three waves (aged 16, 19 and 22 years). Our sample consisted of 1829 adolescents (51% females) who reported alcohol use on at least one assessment wave.

MEASUREMENTS

Risk factors included personality traits (NEO-FFI: neuroticism, extraversion, openness, agreeableness and conscientiousness; SURPS: impulsivity and sensation-seeking), stressful life events (LEQ: sum scores of stressful life events), and drinking motives [drinking motives questionnaire (DMQ): social, enhancement, conformity, coping anxiety and coping depression]. We assessed alcohol use [alcohol use disorders identification test (AUDIT): quantity and frequency] and alcohol-related problems (AUDIT: related problems).

FINDINGS

Within a given moment, social [partial correlation (pcor) = 0.17] and enhancement motives (pcor = 0.15) co-occurred most strongly with drinking quantity and frequency, while coping depression motives (pcor = 0.13), openness (pcor = 0.05) and impulsivity (pcor = 0.09) were related to alcohol-related problems. The temporal network showed no predictive associations between distal risk factors and drinking motives. Social motives (beta = 0.21), previous alcohol use (beta = 0.11) and openness (beta = 0.10) predicted alcohol-related problems over time (all P < 0.01).

CONCLUSIONS

Heavy and frequent alcohol use, along with social drinking motives, appear to be key targets for preventing the development of alcohol-related problems throughout late adolescence. We found no evidence for personality traits and life stressors predisposing towards distinct drinking motives over time.

Treatment-associated network dynamics in patients with globus sensations: a proof-of-concept study

In this proof-of-concept study, we used a systems perspective to conceptualize and investigate treatment-related dynamics (temporal and cross-sectional associations) of symptoms and elements related to the manifestation of a common functional somatic syndrome (FSS), Globus Sensations (GS). We analyzed data from 100 patients (M = 47.1 years, SD = 14.4 years; 64% female) with GS who received eight sessions of group psychotherapy in the context of a randomized controlled trial (RCT). Symptoms and elements were assessed after each treatment session. We applied a multilevel graphical vector-autoregression (ml GVAR) model approach resulting in three separate, complementary networks (temporal, contemporaneous, and between-subject) for an affective, cognitive, and behavioral dimension, respectively. GS were not temporally associated with any affective, cognitive, and behavioral elements. Temporally, catastrophizing cognitions predicted bodily weakness (r = 0.14, p < 0.01, 95% confidence interval (CI) [0.04-0.23]) and GS predicted somatic distress (r = 0.18, p < 0.05, 95% CI [0.04-0.33]). Potential causal pathways between catastrophizing cognitions and bodily weakness as well as GS and somatic distress may reflect treatment-related temporal change processes in patients with GS. Our study illustrates how dynamic NA can be used in the context of outcome research.

Network modeling of major depressive disorder symptoms in adult women

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

The Ripple Effect: Unveiling the Bidirectional Relationship Between Negative Life Events and Depressive Symptoms in Medical Cadets

Background

Previous studies have explored the relationship between negative life events and depression, but little is known about the bidirectional relationship between negative life events and depression, particularly in specific groups of medical cadets.

Purpose

This study aimed to explore the relationship between negative life events and depressive symptoms among medical cadets during their four years of college.

Methods

An analysis of 4-wave longitudinal data collected from 2015-2018 was conducted using a cross-lagged panel network (CLPN) model to explore the complex causal relationship between negative life events and depressive symptoms in medical cadets (N=433).

Results

We found differences in negative life events and depressive symptoms among medical cadets across four network models over four years of university. Nodes A-21, A-20, A-23 and A-24, and depressive symptoms D-6 showed greater lagged effect values.

Conclusion

Our findings suggest that there is a lagged and mutually causal interaction between negative life events and depressive symptoms in medical cadets over 4 years of college, but that the predictability of negative life events is more important. However, more attention needs to be paid to the predictive role of depressive symptoms, especially those in early life which are often overlooked. Our study provides new insights into the relationship between negative life events and depressive symptoms in university students and helps to refine strategies for prevention and intervention of depression.

Prospective network analysis of proinflammatory proteins, lipid markers, and depression components in midlife community women

BACKGROUND

Vulnerability theories propose that suboptimal levels of lipid markers and proinflammatory proteins predict future heightened depression. Scar models posit the reverse association. However, most studies that tested relationships between non-specific immune/endocrine markers and depression did not separate temporal inferences between people and within-person and how different immunometabolism markers related to unique depression symptoms. We thus used cross-lagged prospective network analyses (CLPN) to investigate this topic.

METHODS

Community midlife women (n = 2224) completed the Center for Epidemiologic Studies-Depression scale and provided biomarker samples across five time-points spanning 9 years. CLPN identified significant relations (edges) among components (nodes) of depression (depressed mood, somatic symptoms, interpersonal issues), lipid markers [insulin, fasting glucose, triglycerides, low-density lipoprotein-cholesterol (LDL), high-density lipoprotein-cholesterol (HDL)], and proinflammatory proteins [C-reactive protein (CRP), fibrinogen], within and across time-points. All models adjusted for age, estradiol, follicle-stimulating hormone, and menopausal status.

RESULTS

In within-person temporal networks, higher CRP and HDL predicted all three depression components (d = 0.131-2.112). Increased LDL preceded higher depressed mood and interpersonal issues (v. somatic symptoms) (d = 0.251-0.327). Elevated triglycerides predicted more somatic symptoms (v. depressed mood and interpersonal problems) (d = 0.131). More interpersonal problems forecasted elevated fibrinogen and LDL levels (d = 0.129-0.331), and stronger somatic symptoms preceded higher fibrinogen levels (d = 0.188).

CONCLUSIONS

Results supported both vulnerability and scar models. Long-term dysregulated immunometabolism systems, social disengagement, and related patterns are possible mechanistic accounts. Cognitive-behavioral therapies that optimize nutrition and physical activity may effectively target depression.

Network analysis of ecological momentary assessment identifies frustration as a central node in irritability

BACKGROUND

Irritability presents transdiagnostically, commonly occurring with anxiety and other mood symptoms. However, little is known about the temporal and dynamic interplay among irritability-related clinical phenomena. Using a novel network analytic approach with smartphone-based ecological momentary assessment (EMA), we examined how irritability and other anxiety and mood symptoms were connected.

METHODS

Sample included 152 youth ages 8-18 years (M ± SD = 12.28 ± 2.53; 69.74% male; 65.79% White) across several diagnostic groups enriched for irritability including disruptive mood dysregulation disorder (n = 34), oppositional defiant disorder (n = 9), attention-deficit/hyperactivity disorder (n = 47), anxiety disorder (n = 29), and healthy comparisons (n = 33). Participants completed EMA on irritability-related constructs and other mood and anxiety symptoms three times a day for 7 days. EMA probed symptoms on two timescales: "since the last prompt" (between-prompt) versus "at the time of the prompt" (momentary). Irritability was also assessed using parent-, child- and clinician-reports (Affective Reactivity Index; ARI), following EMA. Multilevel vector autoregressive (mlVAR) models estimated a temporal, a contemporaneous within-subject and a between-subject network of symptoms, separately for between-prompt and momentary symptoms.

RESULTS

For between-prompt symptoms, frustration emerged as the most central node in both within- and between-subject networks and predicted more mood changes at the next timepoint in the temporal network. For momentary symptoms, sadness and anger emerged as the most central node in the within- and between-subject network, respectively. While anger was positively related to sadness within individuals and measurement occasions, anger was more broadly positively related to sadness, mood lability, and worry between/across individuals. Finally, mean levels, not variability, of EMA-indexed irritability were strongly related to ARI scores.

CONCLUSIONS

This study advances current understanding of symptom-level and temporal dynamics of irritability. Results suggest frustration as a potential clinically relevant treatment target. Future experimental work and clinical trials that systematically manipulate irritability-related features (e.g. frustration, unfairness) will elucidate the causal relations among clinical variables.

The associations between interoceptive awareness, emotion regulation, acceptance, and well-being in patients receiving multicomponent treatment: a dynamic panel network model

Mechanisms of change represent the cornerstone of the therapeutic process. This study aimed to investigate how network models could be used to test mechanisms of change at a group level. A secondary aim was to investigate which of the several hypothesized mechanisms (emotion regulation, interoceptive awareness, and acceptance) are related to changes in psychological well-being. The sample comprised adult patients suffering from psychological disorders (N=444; 70% women) from 7 clinical sites in the Czech Republic who were undergoing groupbased multicomponent treatment composed mainly of psychodynamic psychotherapy (lasting from 4 to 12 weeks depending on the clinical site). Data were collected weekly using the multidimensional assessment of interoceptive awareness, emotion regulation skills questionnaire, chronic pain acceptance questionnaire-symptoms and outcome rating scale. A lag-1 longitudinal network model was employed for exploratory analysis of the panel data. The pruned final model demonstrated a satisfactory fit. Three networks were computed, i.e., temporal, contemporaneous, and between-person networks. The most central node was the modification of negative emotions. Mechanisms that were positively associated with well-being included modification, readiness to confront negative emotions, activity engagement, and trust in bodily signals. Acceptance of negative emotions showed a negative association with well-being. Moreover, noticing bodily sensations, not worrying, and self-regulation contributed indirectly to changes in well-being. In conclusion, the use of network methodology to model panel data helped generate novel hypotheses for future research and practice; for instance, well-being could be actively contributing to other mechanisms, not just a passive outcome.

Heterogeneity in Individual Network Analysis: Reality or Illusion?

The use of idiographic research techniques has gained popularity within psychological research and network analysis in particular. Idiographic research has been proposed as a promising avenue for future research, with differences between idiographic results highlighting evidence for radical heterogeneity. However, in the quest to address the individual in psychology, some classic statistical problems, such as those arising from sampling variation and power limitations, should not be overlooked. This article aims to determine to what extent current tools to compare idiographic networks are suited to disentangle true from illusory heterogeneity in the presence of sampling error. To this end, we investigate the performance of tools to inspect heterogeneity (visual inspection, comparison of centrality measures, investigating standard deviations of random effects, and GIMME) through simulations. Results show that power limitations hamper the validity of conclusions regarding heterogeneity and that the power required to assess heterogeneity adequately is often not realized in current research practice. Of the tools investigated, inspecting standard deviations of random effects and GIMME proved the most suited. However, all tools evaluated leave the door wide open to misinterpret all observed variability in terms of individual differences. Hence, the current paper calls for caution in the use and interpretation of new time-series techniques when it comes to heterogeneity.

A cross-lagged prospective network analysis of depression and anxiety and cognitive functioning components in midlife community adult women

BACKGROUND

Scar theory proposes that heightened depression and anxiety precede and predict worse cognitive functioning outcomes, whereas the vulnerability theory posits the opposite pathway. However, most investigations on this topic have been cross-sectional, precluding causal inferences. Thus, we used cross-lagged prospective network analyses to facilitate causal inferences in understanding the relations between psychopathology and cognitive functioning components.

METHODS

Racially-diverse midlife women (n = 1816) participated in the Study of Women's Health Across the Nation at two time-points, spanning one year apart. Five psychopathology (anxiety severity, depressed mood, somatic symptoms, positive affect, interpersonal problems) and four cognitive functioning nodes (working memory (WM), processing speed (PS), facial recognition (FCR), and verbal memory (VRM)) were assessed. All analyses adjusted for age, menopausal status, estradiol, and follicle-stimulating hormones.

RESULTS

Contemporaneous networks yielded notable inverse between-node relations (edges) between interpersonal problems and reduced FCR and PS, and between depressed mood and lower FCR, VRM, or PS. Nodes that had the highest likelihood to bridge other constructs were positive affect, anxiety severity, WM, and VRM. Temporal networks produced edges consistent with the scar (v. vulnerability) hypotheses. Higher somatic symptoms were related to reduced PS and WM, and greater depressed mood was correlated with lower future PS and WM. Also, higher anxiety severity coincided with decreased future PS and WM. Greater positive affect was associated with stronger future PS, FCR, and WM. Also, positive affect had the strongest relations with other nodes.

CONCLUSIONS

Findings suggest the importance of targeting symptoms and cognitive functioning simultaneously.

The Longitudinal Features of Depressive Symptoms During the COVID-19 Pandemic Among Chinese College Students: A Network Perspective

There is substantial evidence that the Corona Virus Disease 2019 (COVID-19) pandemic increased the risk of depressive symptoms among college students, but the long-term features of depressive symptoms on a symptom level have been poorly described. The current study investigated interaction patterns between depressive symptoms via network analysis. In this longitudinal study, participants included 860 Chinese college students (65.8% female; M_age = 20.6, SD_age = 1.8, range: 17-27) who completed a questionnaire at three-time points three months apart. Results demonstrated that fatigue was the most influential symptom, and the occurrence of fatigue could give rise to other depressive symptoms. In addition to predicting other symptoms, fatigue could be predicted by other symptoms in the measurement. The network structures were similar across time, suggesting that the overall interaction pattern of depressive symptoms was stable over the longitudinal course. These findings suggest that depressive symptoms during the COVID-19 period are associated with the presence of fatigue.