Association of Symptom Network Structure With the Course of [corrected] Depression

Network analysis of fatigue symptoms in Chinese patients with advanced cancer

Objective

This study was aimed at investigating the network structures of fatigue symptoms in patients with advanced cancer, with a focus on identifying the central symptom-an aspect crucial for targeted and effective fatigue symptom management.

Methods

In this cross-sectional study, patients with advanced cancer were recruited from the cancer treatment center of a tertiary hospital in China between January and December of 2022. Symptom occurrence and severity were assessed with the Cancer Fatigue Scale. Network analysis was conducted to explore the network structure and identify the core fatigue symptoms.

Results

The study included 416 patients with advanced cancer. Lack of energy (2.25 ​± ​1.24), lack of interest in anything (2.20 ​± ​1.22), and lack of self-encouragement (2.03 ​± ​1.25) were the most severe fatigue symptoms and belonged to the affective fatigue dimension. In the overall network, reluctance (r s  ​= ​5.622), a heavy and tired body (r s  ​= ​5.424), and tiring easily (r s  ​= ​5.319) had the highest strength values. All these core symptoms were classified within the physical fatigue dimension and remained stable before and after adjustment for covariates.

Conclusions

This study identified reluctance, a heavy and tired body, and tiring easily as the core fatigue symptoms in patients with advanced cancer, thus providing valuable insight to help clinical nurses formulate more effective symptom management strategies. Future interventions could assess the efficacy of targeting the central symptom cluster in alleviating other symptoms and patient burden.

The differential impact of processing speed and cognitive flexibility on cognitive emotion regulation strategies and depression

INTRODUCTION

Existing cognitive models for depression propose interactions between cognitive emotion regulation strategies (CERS) and cognitive processes. However, often oversimplify these interactions and do not accommodate the complexity of interactions. We aimed to construct and explore the micro-level properties of a network model that can integrate this complexity of the interactions and explore through computational simulations the differential impact of improvements/deterioration in cognitive flexibility and processing speed in the network.

METHODS

We used the Leipzig Study for Mind-Body-Emotion Interactions dataset (N = 227). The Trail Making Test assessed processing speed and cognitive flexibility, while depression and CERS were measured using the Hamilton Depression Rating Scale and the Cognitive Emotional Regulation Questionnaire. The network was estimated using the Mixed Graphical Model. Expected influence, bridge expected influence and predictability were explored as micro-level properties. Simulation procedures were done by estimating the expected network activity and comparing it with the baseline network activity.

RESULTS

CERS mediated impact of cognitive processes on depression. Processing speed emerges as a mediator with a bridging role, while cognitive flexibility seems to have a more substantial impact in overall connectivity. Rumination, exhibit high centrality, suggesting a pivotal role in the network.

LIMITATIONS

Use of cross-sectional data, the assessment of depression with HDRS sum-score, as well as the low number of depressed individuals in the sample.

CONCLUSIONS

The different constituents of the network seem to have different roles in the network. This might have important implications in the future for personalized and preventive interventions.

Age-related changes in depression symptom networks in children in China with parental absence: A comparative analysis of youth aged nine to 18

BACKGROUND

The network perspective on psychopathology views depression as a system of interacting symptoms. Research shows that mental health problems change with age. Children with parental absence are at risk for depression, but it's unclear how their depressive symptom networks evolve across developmental stages.

METHOD

Network analysis was conducted on data from 179,519 children with parental absence who completed the depression scale CESD. The Graphical LASSO algorithm was used to construct depressive symptom networks for each age group. Global network metrics and centrality measures were then calculated and compared across age groups.

RESULTS

Depressive symptoms increased with age, with mean CES-D scores rising from 3.44 at age nine to 10.8 at age 17. Network density showed a general increase from age nine (0.045) to age 17 (0.047), while average path length decreased from age nine (18.380) to age 18 (15.338) and clustering coefficient decreased from age 9 (0.879) to age 18 (0.706). Closeness centrality demonstrated the most substantial age-related effect (F = 1445.111, p < 0.001, η2 = 0.225), with significant increases from early to late adolescence. Core emotional symptoms remained central across ages, while loneliness and feelings of failure became more central with age.

CONCLUSION

As children with parental absence age, their depressive symptom networks become more severe, interconnected, and efficiently structured. This suggests a need for age-specific interventions addressing both core symptoms and emerging adolescent self-evaluative concerns, advancing our understanding of developmental psychopathology in this vulnerable group.

Gender-specific network analysis of parenting styles, depressive symptoms, and anxiety symptoms among 5157 Chinese adolescents

BACKGROUND

In this study, we aimed to use network analyses to explore the gender-specific interactions between individual items of depressive symptoms, anxiety symptoms, and parenting styles in Chinese adolescents.

METHODS

We conducted a cross-sectional study with a total of 5157 adolescents in 5 primary schools, 8 junior high schools, and 2 senior high schools in Shandong province, China. Male and female adolescents' networks were assessed in all grades using the Egna Minnen Betraffande Uppfostran (EMBU), the 9-item Patient Health Questionnaire (PHQ-9), and the 7-item Generalized Anxiety Disorder Assessment (GAD-7). Network differences by gender and educational stages were further assessed stratified. Network analysis was used to identify central items and edges with important associations between depressive symptoms, anxiety symptoms, and parenting styles. All analyses in this study were performed using the R program (version 4.3.2).

RESULTS

Parenting styles can influence the overall depressive network by acting on PHQ9 (Suicidal thoughts) through EMBU1 (Punishment denial) and EMBU2 (Emotional warmth) in adolescents. Parenting styles had displayed gender-specific emotional impacts on adolescents. EMBU1 (Punishment denial) affected the anxiety network in senior high school male adolescents by increasing GAD6 (Easily annoyed/Irritable) and impacting their depression network by reducing the PHQ6 (Failure). However, none of these effects were found in senior high school female adolescents.

CONCLUSIONS

The mood disorder and anxiety in male students were susceptible to the influence of the manner of upbringing. Depressive and anxiety symptom-specific issues related to parenting styles may be important in the development and maintenance of mood disorders in adolescents. Punishment rejection and emotional warmth might be the strong influencing factors.

A network analysis of alcohol-related harms: An exploratory study in United Kingdom adolescents

BACKGROUND

This study applied a network analysis approach to the study of individual self-reported alcohol-related harms (ARHs) across four waves of data.

METHODS

Data were from a large clustered randomised control trial (N = 12,738) involving 105 schools. Data were collected at 4 time points over 4 academic years (mean age 12.5 [Time 0], 13.5 [T1], 14.5 [T2], and 15.3 years [Time 3]). Data were gathered on the experience of 16 separate ARHs experienced during the previous six months, and these were dichotomised (yes/no). We estimated cross-lagged panel networks for the 16 ARHs, capturing both the auto-regressive relationships (a harm predicting itself at follow up) and the cross-lagged relationships (a harm predicting another harm at follow-up) across the study (T0 → T1; T1 → T2; T2 →T3).

RESULTS

Exposure to all ARHs increased with age. However, the most serious ARHs (e.g., getting in trouble with the police because of your drinking) remained relatively rare, even at age 15. Actively planning to get drunk, coupled with an inability to control levels of intoxication (drinking more than planned) appeared central to each network, facilitating the emergence of all other ARHs. While the prevalence of ARHs increased with age, network complexity declined, and networks becoming more stable.

CONCLUSIONS

Interventions aimed at improving the capacity to self-regulate alcohol consumption, and actively challenging the planning of drunken episodes, may be pivotal in reducing the emergence of both acute and chronic ARHs in adolescence.

Effects of ketamine on individual symptoms and symptom networks of depression in a randomised controlled trial of ketamine for treatment-resistant depression

BACKGROUND

Understanding the effects of ketamine on depressive symptoms could help identify which patients might benefit and clarify its mechanism of action in both the early (≤1 day post-infusion) and late (e.g. 2-30 days post-infusion) post-infusion periods. Symptom network analyses could provide complementary information regarding relationships between symptoms.

AIMS

To identify the effects of ketamine on symptom-level changes in depression across both the early and late post-infusion periods and on depressive symptom network changes.

METHODS

In this secondary analysis of 152 adults with treatment-resistant depression (with 38.8% reporting suicidal ideation at baseline), we compared symptom changes in the early and late post-infusion periods between individuals randomised to a single 40 min infusion of intravenous ketamine 0.5 mg/kg (n = 103) or saline (n = 49) and identified changes in symptom networks between pre- and post-ketamine treatment using network analyses.

RESULTS

In the early post-infusion period, the greatest improvement (comparing ketamine with saline) was in depressive symptoms related to sadness. In network analyses, symptom network connectivity increased following ketamine infusion. Symptoms of sadness and lassitude showed persistent improvement in the first week post-infusion, whereas improvements in suicidal thoughts first emerged 3-4 weeks post-infusion.

CONCLUSION

Ketamine improved all symptoms but showed the greatest effect on symptoms of sadness, both immediately and in the initial week after treatment. Ketamine also rapidly altered the topology of symptom networks, strengthening interrelationships between residual symptoms. The efficacy of ketamine (compared with saline) regarding suicidal symptoms emerged later. Our findings suggest potentially divergent efficacy, time courses and mechanisms for different symptoms of depression.

Network analysis of depression emotion suppression digital burnout and protective psychological factors

This study employed network analysis to investigate the complex relationship between emotion regulation strategies and depression, with particular focus on digital burnout as a contemporary stressor and the moderating role of various psychological protective factors. Based on a large sample of 9400 Chinese participants, we constructed a psychological network model incorporating depression, digital burnout, psychological resilience, self-compassion, emotion suppression, mindfulness, and sleep quality using EBIC-GLASSO regularization technique. Results revealed emotion suppression as the most central node in the network, demonstrating the highest betweenness (2.268), closeness (1.302), and strength (1.157) centrality. The network exhibited significant positive connections between emotion suppression and depression (0.890), as well as between emotion suppression and digital burnout (0.848). Notable negative associations were observed between sleep quality and depression (- 0.780), and between resilience and digital burnout (- 0.665). Network stability analysis yielded CS-coefficients exceeding 0.75 for all centrality measures, substantially above the recommended threshold of 0.5, confirming the reliability of our findings. Community detection analysis identified two distinct clusters: a Risk Factor Community (depression, digital burnout, emotion suppression) and a Protective Factor Community (resilience, self-compassion, mindfulness). The average predictability of nodes was 39.5%, ranging from 23.8% for cognitive reappraisal to 74.4% for depression. The innovation of this research lies in being the first to integrate digital burnout into a depression network, revealing its significant role as a connecting variable. Our findings suggest that interventions targeting emotion regulation may be particularly effective; digital wellness initiatives might produce cascading benefits for mental health; and comprehensive interventions simultaneously addressing resilience, self-compassion, and mindfulness may be more effective than those focusing on single protective factors. These findings provide novel insights into understanding depression in the digital age and offer important implications for both clinical practice and public health policy.

Anxiety, depressive and insomnia symptoms among patients with depression: a network perspective

BACKGROUND

The aim of this study was to utilize network analysis to explore the interconnections among anxiety, depressive, and insomnia symptoms in depressed patients in China.

METHODS

The study included two surveys, the baseline survey was conducted from May 18, 2020 to June 18, 2020, and the follow-up survey was conducted 5 months later. A total of 4476 patients completed the baseline survey, and 1877 of them completed the follow-up survey. Depression symptoms were evaluated using the 9-item Patient Health Questionnaire-9 (PHQ-9), anxiety symptoms were evaluated using the 7-item Generalized Anxiety Disorder (GAD-7), and insomnia symptoms were evaluated using the 7-item Insomnia Severity Index (ISI). The centrality indices are utilized in the network analysis, and using Network Comparison Test (NCT) to evaluate the differences between the network structures at two different time points.

RESULTS

Network analysis revealed that the central symptom value was ISI5 ("Interfere with your daily functioning") in the baseline networks and ISI4 ("Worried/distressed") in the follow-up networks, the symptom with the bridge symptom value in both networks was PHQ9-3 ("Sleep"). The NCT results revealed no significant differences in edge weights and global strength among participants who completed both baseline and follow-up surveys.

CONCLUSIONS

Our results suggest that central symptom (e.g., "Interfere with your daily functioning","Worried/distressed") and bridge symptom PHQ9-3 ("Sleep") can be prioritized as a target for intervention and treatment in patients with depression.

Uncovering the complex interactions of mental health symptoms in Chinese college students: insights from network analysis

Mental health problems are prevalent among Chinese college students, with gender differences in symptom presentation. Network analysis provides a novel approach to investigate the complex interactions between symptoms and identify gender differences in the structure and dynamics of mental health problems. Psychological assessment data were collected from 18,629 freshmen at a university in Chengdu, China, between 2020 and 2023. Gaussian Graphical Models and centrality indices were used to estimate and visualize symptom networks. Network comparison tests, accuracy and stability tests, and community detection were performed using R packages to examine gender differences. Mental health symptom networks differed across psychological distress levels. In the severe distress group, male and female students' networks exhibited significant differences in 10 edges and overall strength. Inferiority, depression, and anxiety emerged as central symptoms, and revealed by community detection. The single-university setting may limit the generalizability of the findings to other populations or cultural contexts. The cross-sectional design precludes causal inferences about symptom relationships. Network analysis offers valuable insights into the complex interactions of mental health symptoms among Chinese college students, highlighting gender differences in the severe distress group. The findings reveal central symptoms and distinct symptom clusters, underscoring the importance of developing targeted, personalized interventions that address these specific patterns of psychological distress. By illuminating the intricate structure of mental health networks, this research provides a foundation for more effective, tailored approaches to support student well-being in higher education settings.

Understanding the impact of COVID-19 on comorbid depression, anxiety and eating disorders in adolescent psychiatric inpatients: a network analysis

BACKGROUND

Many studies have aimed to understand the impact of the COVID-19 pandemic on mental health. However, less research has focused on the changes in symptom clusters of comorbid disorders. To understand the impact of the COVID-19 pandemic it is necessary to evaluate the relationships between symptoms of comorbid disorders. This was the first study to compare comorbidity networks of depression, anxiety and eating disorder (ED) symptoms to investigate the overall connectivity of symptoms before and during the onset of the pandemic.

METHODS

Self-report questionnaire data from 1361 adolescent psychiatric inpatients (Mage = 15.32, SD = 1.47) were used for this study. A network analysis was conducted including 52 questionnaire items of depression, anxiety and eating disorder to identify and compare core symptoms and bridge symptoms in a pre and a peri pandemic sample.

RESULTS

A significantly higher network density and overall connectivity were found in the peri pandemic sample. Links between feelings of failure in the depression cluster and worry what other people think in the anxiety cluster as well as between difficulties getting rid of bad/ silly thoughts in the anxiety cluster and suicidal thoughts in the depression cluster emerged as the strongest pathways in both networks. Body image disturbance emerged as the strongest bridge symptom for eating disorders in both networks. There were no significant differences in the most prominent core and bridge symptoms between the networks, indicating a high stability of core symptoms and pathways across circumstances.

CONCLUSIONS

Our findings suggest a multidimensional relationship between symptoms of depression, anxiety, and eating disorders. The persistence of symptom pathways after the onset of the pandemic implies that these pathways may be responsible for the occurrence of comorbidity and should be primary targets of psychotherapy for affected patients. Addressing core and bridge symptoms in the therapy of comorbid disorders should be a priority and may be more effective than conventional treatment strategies.

Social and obstetric risk factors of antenatal depression: A cross-sectional study in China

BACKGROUND

Antenatal depression is a disabling mental disorder among pregnant women and may cause adverse outcomes for both the mother and the offspring. Early identification and intervention of antenatal depression can help to prevent adverse outcomes. However, there have been few population-based studies focusing on the association of social and obstetric risk factors with antenatal depression in China.

AIM

To assess the sociodemographic and obstetric factors of antenatal depression and compare the network structure of depressive symptoms across different risk levels based on a large Chinese population.

METHODS

The cross-sectional survey was conducted in Shenzhen, China from 2020 to 2024. Antenatal depression was assessed using the Chinese version of the Edinburgh Postnatal Depression Scale (EPDS), with a score of ≥ 13 indicating the presence of probable antenatal depression. The χ 2 test and binary logistic regression were used to identify the factors associated with antenatal depression. Network analyses were conducted to investigate the structure of depressive symptoms across groups with different risk levels.

RESULTS

Among the 44220 pregnant women, the prevalence of probable antenatal depression was 4.4%. An age ≤ 24 years, a lower level of education (≤ 12 years), low or moderate economic status, having a history of mental disorders, being in the first trimester, being a primipara, unplanned pregnancy, and pregnancy without pre-pregnancy screening were found to be associated with antenatal depression (all P < 0.05). Depressive symptom networks across groups with different risk levels revealed robust interconnections between symptoms. EPDS8 ("sad or miserable") and EPDS4 ("anxious or worried") showed the highest nodal strength across groups with different risk levels.

CONCLUSION

This study suggested that the prevalence of antenatal depression was 4.4%. Several social and obstetric factors were identified as risk factors for antenatal depression. EPDS8 ("sad or miserable") and EPDS4 ("anxious or worried") are pivotal targets for clinical intervention to alleviate the burden of antenatal depression. Early identification of high-risk groups is crucial for the development and implementation of intervention strategies to improve the overall quality of life for pregnant women.

Central symptoms of depression and their associations with health-related quality of life among migrant older with children: A network analysis

BACKGROUND

Depression has emerged as a significant concern in migrant older with children (MOC).

OBJECTIVE

This study aimed to identify the central symptoms of depression among MOC and explore the relationship between depressive symptoms and health-related quality of life (HRQOL).

METHODS

A total of 3016 MOC were included in this study. Depressive symptoms were measured using the Geriatric Depression Scale-15. HRQOL was assessed using the Short Form 12 Health Survey. Network analyses were performed using the bootnet R-package to identify central symptoms of depression.

RESULTS

"Unhappy," "Bored," "Worthless," and "Lack of Energy" were central symptoms of depression in this study. We also found "Lack of Energy," "Afraid" and "Unhappy" were most negatively associated with HRQOL among the MOC.

CONCLUSIONS

In the future, it will be necessary to develop interdisciplinary cross-departmental collaborations to formulate mental care programs tailored to the MOC to relieve depressive symptoms.

Changes in network centrality of anxiety and depression symptoms associated with childhood trauma among Chinese college students

BACKGROUND

Childhood trauma is strongly linked to anxiety and depression, significantly increasing the risk of negative outcomes in adulthood. This study employed network analysis to investigate the complex interplay of anxiety and depression symptoms among Chinese college students, focusing on identifying the core symptoms most directly affected by childhood trauma and those exerting the greatest influence on others.

METHODS

Data were collected from December 2020 to January 2021 from 2,266 college students at 16 institutions in southwestern and eastern coastal China. Depression, anxiety, and childhood trauma were assessed using the Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and Childhood Trauma Questionnaire-28, respectively. Separate symptom networks were constructed for participants with and without childhood trauma experiences. Central indices were employed to identify the central symptom within each network. The accuracy and stability of the networks were then evaluated. Finally, a network comparison test was used to analyze differences in network properties between the trauma and non-trauma groups.

RESULTS

Loss of Energy and Worry too much were the central symptoms in the non-trauma group, while anhedonia and nervousness were the central symptoms in the trauma group. There was a significant difference in the global strength of the network between the trauma group and the non-trauma group (pFDR< 0.01), but no significant difference in the distribution of edge weights between the two networks (pFDR =0.14). Anhedonia, Suicide ideation and Feeling afraid in the trauma group showed increased network centrality compared with the non-trauma group.

CONCLUSIONS

This study demonstrates the profound impact of childhood trauma on the central symptoms of anxiety and depression in college students. Further research is warranted to investigate the specific pathways through which these symptoms develop, with the goal of developing targeted interventions for this vulnerable population.

What time-varying network models based on functional analysis tell us about the course of a patient's problem

Background: Relations among psychological variables are assumed to be complex and to vary over time. Personalized networks can model multivariate complex interactions. The development of time-varying networks allows to model the variation of parameters over time. Objectives: We aimed to determine the value of time-varying networks for clinical practice. Methods: We applied time-varying mixed graphical models (TV-MGM) and time-varying vector autoregressive models (TV-VAR) to intensive longitudinal data of nine participants with depressive symptoms (n = 6) or anxiety (n = 3). Results: Most of the participants showed temporal changes in network topology within the assessment period of 30 days. Time-varying networks of participants with small, medium, and large time variability in edge parameters clearly show the different temporal evolvements of dynamic interactions between variables. The case example indicates clinical utility but also limitations to the application of time-varying networks in clinical practice. Conclusion: Time-varying network models provide a data-driven and exploratory approach that could complement current diagnostic standards by reflecting interacting, often mutually reinforcing processes of mental health problems and by accounting for variation over time. They can be used to generate hypotheses for further confirmatory and clinical testing.

Mental Health Symptoms Between Developed and Developing Regions for People Living With HIV in China: A Network Analysis of 40 Psychological Symptom Scales

BACKGROUND

People living with HIV (PLWH) frequently encounter mental health symptoms. Yet, a notable gap exists regarding the divergence in core mental health symptoms among PLWH across developed and developing regions. This study aims to explore the differences in mental health symptom networks among PLWH in both developed and developing regions.

METHODS

A multicenter cross-sectional study was conducted in China from April 2022 to April 2023. Six designated HIV hospitals enrolled 2436 participants, including 1430 PLWH from developed regions and 1006 PLWH from developing regions. The study assessed 40 mental health symptoms across six dimensions: somatization symptoms, negative affect, cognitive processes, cognitive function, interpersonal communication, and social adaptation among PLWH.

RESULTS

The diverse developed regions exhibited varying mental health symptoms among PLWH, particularly concerning their core symptoms. In the developed regions of China, PLWH predominantly experience core symptoms centered around "Sadness," "Anger," and "Distress." In contrast, PLWH from developing regions tends to manifest core symptoms such as "Inability to integrate into society," "Difficulty in managing daily work and study," and "Hostility."

CONCLUSIONS

The regional variation in mental health symptoms among PLWH underscores the disparities in their circumstances. This insight is crucial for crafting tailored intervention strategies for urban PLWH. In developed regions, psychological interventions such as catharsis and empathy are integral to clinical practice, while in less developed regions, family support interventions are paramount, given the limited social interactions available to PLWH.

REPORTING METHOD

This study was reported according to the STROBE checklist.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Network analysis of loneliness, mental, and physical health in Czech adolescents

BACKGROUND

The increasing urgency to address rising loneliness among adolescents has become a critical issue, underscoring the need for further studies on its association with mental and physical health. The objective was to examine the changes in loneliness and its relation to mental and physical health issues in three adolescent age groups.

METHODS

A total sample of 14,588 Czech pupils (50.7% boys, mean age 13.6 ± 1.7 years) in grades 5, 7 and 9 was used from a representative dataset of the Health Behaviour in School-aged Children (HBSC) study. The network analysis based on undirected graphical models was used as an exploratory technique to assess and test the structure of the data.

RESULTS

The association between loneliness and health decreased with age. There was a significant positive association between loneliness, feeling low, and irritability. No significant direct association between loneliness and physical health complaints was found.

CONCLUSION

Further studies, preferably of longitudinal character, are needed to confirm the changes in associations between loneliness and mental and physical health outcomes.

Network analysis of central symptoms in Chinese young adults with subthreshold depression

Subthreshold depression (SD) is a prevalent condition among young adults, significantly increasing the risk of developing major depressive disorder (MDD). While the symptoms of MDD are well-documented, the network structure and key symptoms of SD, which forms a complex, interdependent system, have not been fully elucidated. This study sought to identify the central symptoms and their interconnections within the depressive symptom network in young adults with SD. A total of 834 Chinese young adults with SD completed the 21-item Beck Depression Inventory 2nd version (BDI-II) and were included in this study. Network analysis was employed to identify central symptoms (nodes) and associations between symptoms (edges) as assessed by the BDI-II. Additionally, centrality indicators for network robustness underwent assessment through stability and accuracy tests. The analysis revealed that Loss of interest was the most central node in the SD symptom network, with Tiredness/fatigue and Agitation following closely. Significant associations were observed between Loss of energy and Concentration difficulties, Agitation and Irritability, Guilty feelings and Self-dislike, as well as Tiredness and Loss of pleasure. The network demonstrated robustness across stability and accuracy assessments. Loss of interest, Tiredness/fatigue, and Agitation were pivotal symptoms within the depressive symptom network of SD in young adults. These symptoms may serve as critical targets for therapeutic interventions and should be prioritized in future psychological and neurobiological research to advance our understanding of SD.

The Bidirectional Relationship Between Subjective Well-Being and Depression: A Cross-Sectional and Cross-Lagged Network Analysis

Purpose

Network modeling has been suggested as an effective approach to uncover intricate relationships among emotional states and their underlying symptoms. This study aimed to explore the dynamic interactions between subjective well-being (SWB) and depressive symptoms over time, using cross-sectional and cross-lagged network analysis.

Methods

Data were drawn from three waves (2016, 2018, and 2020) of the China Family Panel Studies (CFPS), including 13,409 participants aged 16 and above. SWB was measured through indicators like life satisfaction and future confidence, while depressive symptoms were assessed using the CES-D8 scale. Symptom-level interactions were analyzed via cross-sectional network analysis at each wave, and cross-lagged panel network analysis was employed to examine the temporal dynamics and bidirectional relationships between SWB and depressive symptoms.

Results

The cross-sectional symptom network analysis showed that the number of non-zero edges at T1, T2, and T3 were 50, 44, and 49, respectively, with network densities of 0.90, 0.80, and 0.89. The core symptom "feeling sad" (D7) consistently had a significantly higher strength than other symptoms. The negative correlation between "life satisfaction" (Z2) and depressive symptoms was particularly evident at T3. The cross-lagged symptom network analysis revealed the key roles of "feeling lonely" (D5) and "feeling sad" (D7), as well as "feeling unhappy" (D4) and "not enjoying life" (D6) across different time periods, which may form a negative feedback loop. "Life satisfaction" (Z2) and "confidence in the future" (Z3) exhibited significant protective effects, forming a positive feedback loop that suppresses negative emotions through mutual reinforcement. Stability analysis showed that the network structure was stable, with a centrality stability coefficient of 0.75.

Conclusion

The study reveals a dynamic, bidirectional relationship between SWB and depressive symptoms. These results offer valuable insights for targeted interventions and public health initiatives aimed at improving mental well-being.

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.

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 network approach to understanding bullying victimization and its co-occurrence with depressive symptoms among Chinese students in different developmental periods

BACKGROUND

Bullying victimization and its co-occurrence with depressive symptoms have previously been explored. However, the pattern of bullying victimization development and the detailed associations between victimization and depressive symptoms remain unknown.

OBJECTIVE

This study aimed to explore bullying victimization and its comorbidity with depressive symptoms across different developmental periods via network analysis.

PARTICIPANTS AND SETTING

Data from the National Children's Study of China (NCSC) were analysed, including a nationally representative sample (N = 23,917, 46.6 % girls, grades 4 to 9) covering 31 provinces in China.

METHODS

Network analysis was applied to identify core nodes and edges of bullying victimization as well as bridge nodes and bridge edges connecting the victimization community and the depressive symptom community across late childhood, early adolescence, and middle adolescence. The network of bullying victimization and the bridge network of victimization and depressive symptoms in these three periods were compared.

RESULTS

The results revealed the following developmental inconsistencies: 1) "being spoken ill of", "being hit, kicked, pushed, or shoved", and "being threatened or intimidated" were the core nodes of victimization in late childhood, early adolescence, and middle adolescence, respectively, and 2) "being hit, kicked, pushed, or shoved" was more likely to co-occur with "being spoken ill of" in late childhood and early adolescence than in middle adolescence. The analysis also revealed the following consistencies: 1) the consistent bridge nodes were "being spoken ill of" for bullying victimization and "lack of friendship" for depressive symptoms, and 2) the consistent bridge edges were the connections between "being spoken ill of"/"being excluded" and "loneliness"/"lack of friendship".

CONCLUSIONS

The findings highlighted the stable critical connection between relational victimization and loneliness/the absence of friendships across the three developmental stages, which might be the basis for the co-occurrence of bullying victimization and depressive symptoms. Joint efforts should focus on identifying and addressing bullying (especially relational bullying) to reduce the risk of depressive symptoms for victims.

Unraveling depressive symptom networks: A three-year longitudinal study among Chinese junior high school adolescents

Adolescence is the peak period for the occurrence of mental health issues, particularly in the stage of junior high school. Depressive symptoms are among the most frequently experienced psychological problems. However, little is known about the symptom-level interaction features of depressive symptoms and the roles of different symptoms across the junior high school stage. To address these gaps, this study conducted a three-year longitudinal study that recruited 1301 Chinese junior high school adolescents (48.2% females; mean age = 12.46 ± 0.62, ranging from 11 to 14 in the first year). The Center for Epidemiological Studies Depression Scale was used to assess depressive symptoms. The regularized partial correlation network and the cross-lagged panel network models were used to explore the symptom-level interaction pattern. In regularized partial correlation networks, "I felt depressed" was a stable central symptom throughout the junior high school stage. Besides, "I felt lonely" and "I felt that people disliked me" were the other central symptoms in grade 7 and grade 8, and "I felt everything I did was an effort" played a central role in grade 9. Within cross-lagged panel networks, "I felt that people disliked me" and "I felt hopeless about the future" had important effects on predicting other depressive symptoms from grade 7 to 8 and from grade 8 to 9. By investigating the longitudinal interaction patterns of depressive symptoms among junior high school adolescents, the current study identifies core symptoms that could be potential prevention or intervention targets and provides a novel insight for understanding depressive symptoms during adolescence in depth.

Temporal Dynamics of Depressive Symptoms, Apathy, Daily Activities, and Cognitive Decline in Older People From the General Population: A Network Analysis

BACKGROUND

The prevalence of depressive symptoms, apathy, and cognitive decline increases with age. Understanding the temporal dynamics of these symptoms could provide valuable insights into the early stages of cognitive decline, allowing for more timely and effective treatment and management.

METHODS

Participants from the Prevention of Dementia by Intensive Vascular Care (preDIVA) trial cohort with baseline and ≥3 follow-up measurements were included, with a median of 7.8 (0.68) years of follow-up. Dynamic Time Warping (DTW) analysis was used to model temporal dynamics of cognition using the Mini Mental State Exam (MMSE), activities of daily living (ADL) using the Amsterdam Linear Disability Scale (ALDS), and apathy and depressive symptoms using the 15-item Geriatric Depression Scale (GDS-15) at the individual and group level.

RESULTS

The 1,537 participants were aged 74 (2.0) years at baseline, 56.5% were female, and 19.9% had finished higher education. A decline in ADL and increase in apathy tended to precede most indicators of cognitive decline, with all apathy items (i.e. being 'dropped activities/interests', 'not feeling energetic' and 'not doing new things') and ADL showing significant outstrength (all p's < 0.001). Many mood-related symptoms other than apathy, and the MMSE items 'immediate memory', 'verbal comprehension' and 'naming objects' tended to be the last to deteriorate, showing significant instrength (all p's < 0.001).

CONCLUSION

An increase apathy and a decline in ADL tended to precede mood-related symptoms and cognitive impairment in older adults from the general population. These changes may thus serve as potential early warning signs of both depression and dementia, and may allow for timely intervention.

A systematic review of studies using network analysis to assess dynamics of psychotic-like experiences in community samples

Several studies have used a network analysis to recognize the dynamics and determinants of psychotic-like experiences (PLEs) in community samples. Their synthesis has not been provided so far. A systematic review of studies using a network analysis to assess the dynamics of PLEs in community samples was performed. Altogether, 27 studies were included. The overall percentage ranks of centrality metrics for PLEs were 23.5% for strength (20 studies), 26.0% for betweenness (5 studies), 29.7% for closeness (6 studies), 26.9% for expected influence (7 studies), and 29.1% for bridge expected influence (3 studies). Included studies covered three topics: phenomenology of PLEs and associated symptom domains (14 studies), exposure to stress and PLEs (7 studies), and PLEs with respect to suicide-related outcomes (6 studies). Several other symptom domains were directly connected to PLEs. A total of 6 studies investigated PLEs with respect to childhood trauma (CT) history. These studies demonstrated that PLEs are directly connected to CT history (4 studies) or a cumulative measure of environmental exposures (1 study). Moreover, CT was found to moderate the association of PLEs with other symptom domains (1 study). Two studies that revealed direct connections of CT with PLEs also found potential mediating effects of cognitive biases and general psychopathology. PLEs were also directly connected to suicide-related outcomes across all studies included within this topic. The findings imply that PLEs are transdiagnostic phenomena that do not represent the most central domain of psychopathology in community samples. Their occurrence might be associated with CT and suicide risk.

Network analysis of clinical features in patients with treatment-resistant schizophrenia

Objective

This study compares the clinical features of Treatment-Resistant Schizophrenia (TRS) and Non-Treatment-Resistant Schizophrenia (NTRS) using network analysis.

Methods

We recruited 511 patients, dividing them into TRS (N = 269) and NTRS (N = 242) groups. Eight scales were used: Positive and Negative Syndrome Scale (PANSS), Positive Symptom Assessment Scale (SAPS), Scale for Assessment of Negative Symptoms (SANS), Simpson-Angus Scale (SAS), Abnormal Involuntary Movements Scale (AIMS), Barnes Akathisia Rating Scale (BARS), Calgary Schizophrenia Depression Scale (CDSS), and Global Assessment of Functioning Scale (GAF). Demographic and clinical data were analyzed using T-tests and Chi-square tests. Network analysis was then applied to compare clinical features.

Results

Significant differences were found in the overall architectures (S = 1.396, p < 0.002) and edge weights (M = 0.289, p < 0.009) of TRS and NTRS networks. Nine edges (p < 0.05) and five nodes (p < 0.01) differed, indicating a correlation between clinical symptoms of the two groups. TRS core symptoms were linked to social functions through both positive (SAPS) and negative symptoms (SANS), while NTRS core symptoms were related to general psychopathological symptoms (PANSS-G).

Conclusion

For TRS, it is essential to address both negative and positive symptoms, focusing on the impact of negative symptoms on functioning. Additionally, managing medication side effects is crucial to avoid worsening negative symptoms.

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.

Network models of late life depression symptoms and cognitive impairments across time

OBJECTIVES

Prevalence of late life depression symptoms is estimated to be up to 29%. For older adults, depression symptoms can constitute both a risk and outcome for cognitive impairment. Understanding how specific depression symptoms and cognitive impairment domains interact over time in older adults is key for prevention, early identification, and treatment.

METHOD

Using cross-sectional psychometric network models, this study aimed to explore interactions between late-life depression symptoms and cognitive impairment domains across different time points using data from waves 7-9 of the English Longitudinal Study of Ageing (ELSA).

RESULTS

Across 3544 participants, ≥65 years of age, with no diagnosed dementia-related disorders, the depression symptom everything was an effort showed high expected influence across all time points. Across two time points, object naming and verbal fluency also showed high expected influence. Self-reported memory demonstrated high bridge centrality connecting depression symptom and cognitive impairment domains. Network centralities differed significantly across time points.

CONCLUSION

For older adults, fatigue appears a key depression symptom. Cognitive impairment domains become more influential over time, and perceived memory loss links cognitive impairment to depression symptoms. Practical implications are discussed in relation to targeting depression symptoms and cognitive impairment domains.

Exploring delusional themes and other symptoms in first episode psychosis: A network analysis over two timepoints

Delusions are a defining feature of psychosis and play an important role in the conceptualization and diagnosis of psychotic disorders; however, the particular role that different delusions play in the prognosis of these disorders is not well understood. This study explored relationships between delusions and other symptoms in 674 first episode psychosis (FEP) individuals by comparing symptom networks between baseline and 12 months after intake to an early intervention service. Specifically, we (1) estimated regularized partial correlation networks at baseline and month 12, (2) identified the most central symptoms in each network, (3) identified clusters of highly connected symptoms, and (4) compared networks to examine changes in structure and connectivity. At baseline, the most central symptoms were depression, delusions of mind reading, and delusions of thought insertion. At month 12, they were hallucinations, persecutory delusions, and delusions of thought insertion. A symptom cluster was identified at both timepoints comprising of five delusions corresponding to passivity experiences. While network structures did not differ significantly, the month 12 network was significantly more highly connected. Our study captures a shift in illness trajectory over time, wherein transdiagnostic symptomatology at baseline becomes more consolidated around psychotic symptoms by month 12.

Exploring the relationships between complex post-traumatic stress disorder and depression symptoms in the context of childhood maltreatment through network analysis

BACKGROUND

Individuals with a history of childhood maltreatment commonly experience the co-occurrence of complex post-traumatic stress disorder (CPTSD) and depression, but the underlying mechanisms of their comorbidities remain unclear.

METHODS

We recruited 2740 college students, including 1366 who experienced childhood maltreatment to assess the co-occurrence network of CPTSD and depression symptoms. We constructed a Gaussian graphical model to visualize the associations between symptoms and a directed acyclic graph to explore inferred relationships among symptoms.

RESULTS

(1) We identified the following five subnetworks within the co-occurring network of CPTSD and depression symptoms: post-traumatic stress disorder (PTSD), disturbance in self-organization (DSO), depression with vegetative symptoms, depression with interpersonal problems, and lack of positive affect subnetworks. (2) Core symptoms, identified by their high expected influence, such as sadness, low spirits, and not feeling loved have the highest EI in the depression subnetwork, whereas failure, distant, avoiding clues, and avoiding thoughts have the highest EI in the DSO and PTSD subnetworks. Bridging symptoms in the childhood maltreatment network included failure, self-denial, startlement, and hyperactivity. (3) The inferred mechanism identified includes PTSD activating DSO, which subsequently triggers depression in the childhood maltreatment network.

LIMITATIONS

This study involved a non-clinical sample.

CONCLUSION

Our study contributes to a deeper understanding of the mechanisms of CPTSD and depression co-occurrence at a transdiagnostic level and has implications for better clinical interventions targeting influential symptoms.

Depressive, anxiety, and sleep disturbance symptoms in patients with obstructive sleep apnea: a network analysis perspective

BACKGROUND

Patients with obstructive sleep apnea (OSA) frequently experience sleep disturbance and psychological distress, such as depression and anxiety, which may have a negative impact on their health status and functional abilities. To gain a more comprehensive understanding of the symptoms of depression, anxiety, and sleep disturbance in patients with OSA, the current study utilized network analysis to examine the interconnections among these symptoms.

METHODS

Depressive and anxiety symptoms were evaluated using the Hospital Anxiety and Depression Scale (HADS), and sleep disturbance symptoms were evaluated using the Pittsburgh Sleep Quality Index (PSQI). A total of 621 patients with OSA completed the questionnaires. The indices 'Expected influence' and 'Bridge expected influence' were used as centrality measures in the symptom network. The Least Absolute Shrinkage and Selection Operator (LASSO) technique and the Extended Bayesian Information Criterion (EBIC) were utilized to estimate the network structure of depressive, anxiety, and sleep disturbance symptoms. A Network Comparison Test (NCT) was performed to evaluate the differences between the mild to moderate OSA and severe OSA networks.

RESULTS

Network analysis revealed that A6 ("Getting sudden feelings of panic") had the highest expected influence value and D6 ("Feeling being slowed down") had the highest bridge expected influence values in the networks. The NCT results revealed that the edge weights significantly differed between patients with mild to moderate OSA and those with severe OSA (M = 0.263, p = 0.008). There was no significant difference in global strength variation between the two networks (S = 0.185, p = 0.773).

CONCLUSIONS

Our results suggest that the highest expected influence value and bridge symptoms (e.g., A6 and D6) can be prioritized as potential targets for intervention and treatment in patients with OSA.

Research on the network structure and gender/age differences of psychological safety among urban residents: network analysis based on a large sample

BACKGROUND

Psychological safety as the key to mental health, not only affects individual happiness and quality of life but also relates to social stability and harmony. However, psychological safety is complex and multidimensional, with unclear internal structures and influencing factors and insufficient research on gender and age differences. Urban residents are living in an environment characterized by fast-paced, high-pressure, multicultural integration, and complex social relationships. Therefore, in-depth exploration of its core dimensions and network structure is crucial for formulating effective mental health strategies and enhancing residents' sense of psychological safety.

METHODS

A survey was conducted on 9,282 urban residents using the Psychological Safety Scale. Using R version 4.3.2 for network estimation, centrality estimation, accuracy and stability estimation, and network comparison.

RESULTS

The results found that the strength centrality index of the general sense of safety dimension is always the highest in the total network and networks of different genders and ages. The network comparison results show that there are significant gender and age differences in the dimensions/item networks of psychological safety. There are connections between trust and relaxation, excitement, and calmness in the dimension network of male samples, while there are no such connections in the dimension network of female samples. The general sense of safety and relaxation connection strength on the male dimension network is significantly stronger than that on the female dimension. In the dimension network of the youth sample, the strength of the connection between calmness and relaxation, trust and relaxation were significantly stronger than those of the middle-age sample, while the strength of the connection between relaxation and excitement was significantly weaker than that of the middle-age sample.

CONCLUSION

Researchers should fully consider gender and age factors and adopt more personalized and differentiated strategies for promoting individual psychological safety.

A network-based analysis anticipates time to recovery from major depression revealing a plasticity by context interplay

Predicting disease trajectories in patients with major depressive disorder (MDD) can allow designing personalized therapeutic strategies. In this study, we aimed to show that measuring patients' plasticity - that is the susceptibility to modify the mental state - identifies at baseline who will recover, anticipating the time to transition to wellbeing. We conducted a secondary analysis in two randomized clinical trials, STAR*D and CO-MED. Symptom severity was assessed using the Quick Inventory of Depressive Symptomatology while the context was measured at enrollment with the Quality-of-Life Enjoyment and Satisfaction Questionnaire. Patients were retrospectively grouped based on both their time to response or remission and their plasticity levels at baseline assessed through a network-based mathematical approach that operationalizes plasticity as the inverse of the symptom network connectivity strength. The results show that plasticity levels at baseline anticipate time to response and time to remission. Connectivity strength among symptoms is significantly lower - and thus plasticity higher - in patients experiencing a fast recovery. When the interplay between plasticity and context is considered, plasticity levels are predictive of disease trajectories only in subjects experiencing a favorable context, confirming that plasticity magnifies the influence of the context on mood. In conclusion, the assessment of plasticity levels at baseline holds promise for predicting MDD trajectories, potentially informing the design of personalized treatments and interventions. The combination of high plasticity and the experience of a favorable context emerges as critical to achieve recovery.

Longitudinal evolution of the transdiagnostic prodrome to severe mental disorders: a dynamic temporal network analysis informed by natural language processing and electronic health records

Modelling the prodrome to severe mental disorders (SMD), including unipolar mood disorders (UMD), bipolar mood disorders (BMD) and psychotic disorders (PSY), should consider both the evolution and interactions of symptoms and substance use (prodromal features) over time. Temporal network analysis can detect causal dependence between and within prodromal features by representing prodromal features as nodes, with their connections (edges) indicating the likelihood of one feature preceding the other. In SMD, node centrality could reveal insights into important prodromal features and potential intervention targets. Community analysis can identify commonly occurring feature groups to define SMD at-risk states. This retrospective (2-year) cohort study aimed to develop a global transdiagnostic SMD network of the temporal relationships between prodromal features and to examine within-group differences with sub-networks specific to UMD, BMD and PSY. Electronic health records (EHRs) from South London and Maudsley (SLaM) NHS Foundation Trust were included from 6462 individuals with SMD diagnoses (UMD:2066; BMD:740; PSY:3656). Validated natural language processing algorithms extracted the occurrence of 61 prodromal features every three months from two years to six months before SMD onset. Temporal networks of prodromal features were constructed using generalised vector autoregression panel analysis, adjusting for covariates. Edge weights (partial directed correlation coefficients, z) were reported in autocorrelative, unidirectional and bidirectional relationships. Centrality was calculated as the sum of (non-autoregressive) connections leaving (out-centrality, cout) or entering (in-centrality, cin) a node. The three sub-networks (UMD, BMD, PSY) were compared using permutation analysis, and community analysis was performed using Spinglass. The SMD network revealed strong autocorrelations (0.04 ≤ z ≤ 0.10), predominantly positive connections, and identified aggression (cout = 0.103) and tearfulness (cin = 0.134) as the most central features. Sub-networks for UMD, BMD, and PSY showed minimal differences, with 3.5% of edges differing between UMD and PSY, 0.8% between UMD and BMD, and 0.4% between BMD and PSY. Community analysis identified one positive psychotic community (delusional thinking-hallucinations-paranoia) and two behavioural communities (aggression-cannabis use-cocaine use-hostility, aggression-agitation-hostility) as the most common. This study represents the most extensive temporal network analysis conducted on the longitudinal interplay of SMD prodromal features. The findings provide further evidence to support transdiagnostic early detection services across SMD, refine assessments to detect individuals at risk and identify central features as potential intervention targets.

Examining the effects of school-vacation transitions on depression and anxiety in adolescents: network analysis

BACKGROUND

The school-vacation cycle may have impacts on the psychological states of adolescents. However, little evidence illustrates how transition from school to vacation impacts students' psychological states (e.g. depression and anxiety).

AIMS

To explore the changing patterns of depression and anxiety symptoms among adolescent students within a school-vacation transition and to provide insights for prevention or intervention targets.

METHOD

Social demographic data and depression and anxiety symptoms were measured from 1380 adolescent students during the school year (age: 13.8 ± 0.88) and 1100 students during the summer vacation (age: 14.2 ± 0.93) in China. Multilevel mixed-effect models were used to examine the changes in depression and anxiety levels and the associated influencing factors. Network analysis was used to explore the symptom network structures of depression and anxiety during school and vacation.

RESULTS

Depression and anxiety symptoms significantly decreased during the vacation compared to the school period. Being female, higher age and with lower mother's educational level were identified as longitudinal risk factors. Interaction effects were found between group (school versus vacation) and the father's educational level as well as grade. Network analyses demonstrated that the anxiety symptoms, including 'Nervous', 'Control worry' and 'Relax' were the most central symptoms at both times. Psychomotor disturbance, including 'Restless', 'Nervous' and 'Motor', bridged depression and anxiety symptoms. The central and bridge symptoms showed variation across the school vacation.

CONCLUSIONS

The school-vacation transition had an impact on students' depression and anxiety symptoms. Prevention and intervention strategies for adolescents' depression and anxiety during school and vacation periods should be differentially developed.

Gender differences in behavioral and emotional problems among school children and adolescents in China: National survey findings from a comparative network perspective

BACKGROUND

Behavioral and emotional problems are common and often co-occur during childhood and adolescence. The aim of this study was to assess gender differences in the network structures of behavioral and emotional problems of children and adolescents in China based on a national survey.

METHODS

The Parent version of Achenbach' s Child Behavior Checklist (CBCL) was used to assess behavioral and emotional problems. To account for potential confounding factors in comparisons between boys and girls, propensity score matching was utilized. Network model differences were assessed using Network Comparison Test (NCT).

RESULTS

Data from 60,715 children and adolescents were included for analyses. Boys exhibited more severe total behavioral and emotional problems compared to girls. While several edges showed significant differences between boys and girls, the strongest association was consistently found between "Attention problems" (CBCL6) and "Aggressive behavior"(CBCL8) in both boys and girls, regardless of age. Network centrality was higher among adolescents compared to children. The most central problems commonly found across different genders and age groups were "Aggressive behavior" (CBCL8) (centrality values were 1.142 for boys aged 6-11 years, 1.051 for boys aged 12-16 years, 1.148 for girls aged 6-11 years, and 1.028 for girls aged 12-16 years), "Anxious/depressed" (CBCL1) (centrality values of 0.892 for boys aged between 6 and 11 years, 1.031 for boys aged 12-16 years, 0.951 for girls aged 6-11 years, and 1.099 for girls aged 12-16 years) and "Social problems" (CBCL4) (centrality values of 1.080 for boys aged 6-11 years, 0.978 for boys aged 12-16 years, 1.086 for girls aged between 6 and 11 years, and 0.929 for girls aged 12-16 years).

CONCLUSION

Testing effective interventions that address aggressive behavior, anxiety/depression, and social problems may be beneficial for reducing risk of psychopathology among children and adolescents.

Echoes of Strain: A Two-Year Longitudinal Study on the Impact of China's Zero-COVID Policy on College Students' Insomnia and Depressive Symptoms

Purpose

In China, stringent and long-lasting infection control measures, which were called "dynamic zero-COVID policy", have significantly affected the mental health of college students, particularly concerning depressive and insomnia symptoms. This study aims to investigate how depressive and insomnia symptoms evolved among Chinese college students throughout the pandemic, including the beginning and end of the dynamic zero-COVID policy period.

Patients and Methods

We conducted a 2-years longitudinal survey involving 1102 college students, collecting data at three key time points. Depressive symptoms were assessed using the Patient Health Questionnaire-9, and insomnia symptoms were measured with the Youth Self-rating Insomnia Scale-8. Three contemporaneous symptom networks and two cross-lagged panel networks were constructed.

Results

In the current sample, the prevalence of clinically significant depressive symptoms was 6.1%, 8.9%, and 7.7% during the first, second, and third waves, respectively. The prevalence of clinically significant insomnia symptoms was 8.1%, 13.0%, and 14.1%. Over time, the severity of depressive and insomnia symptoms and network density increased, persisting at least one year after the pandemic and control measures ended. "Difficulty initiating sleep" bridged the two disorders, while "anhedonia" played a pivotal role in triggering and sustaining other symptoms.

Conclusion

This study underscores the lasting impact of the evolving zero-COVID policy on depressive and insomnia symptoms among college students, elucidating the underlying interaction mechanisms. There is a pressing need for a more comprehensive evaluation of the implementation of restrictive public policies, taking into account their potential long-term consequences.

Similarities and differences in core symptoms of problematic smartphone use among Chinese students enrolled in grades 4 to 9: A large national cross-sectional study

Children and adolescents are highly susceptible to problematic smartphone usage. We employed network analysis to explore the similarities and differences in the core symptoms of problematic smartphone use across grades 4-9, using a large nationwide sample. This study included 8552 children and adolescents (Mage = 12.98, SD=1.51) who met the critical value for problematic smartphone use. The results showed that the core symptoms of problematic smartphone use exhibit both similarities and differences between grades 4 and 9. 'Withdrawal symptoms' and 'preoccupation symptoms' were the stable core symptoms of problematic smartphone use across grades 4 to 9, suggesting that problematic smartphone use begin to appear from earlier grades, such as grade 4. 'Feel impatient and fretful', 'never give up' and 'always thinking about' were the core symptoms in grades 4 and 5. 'Longer than I had intended' and 'hard to concentrate' emerged as additional core symptoms in grade 6, with the intensity indicators peaking in grades 8 and 9, suggesting that the issue of problematic smartphone use among Chinese children and adolescents has become intensified and intricate. Symptoms of problematic smartphone use vary across grades and exhibit both continuity and stage specificity. Consequently, to address this issue, the formulation of intervention measures should comprehensively consider both the grade levels and symptoms.

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSIONS

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

Predicting Antidepressant Treatment Response From Cortical Structure on MRI: A Mega-Analysis From the ENIGMA-MDD Working Group

Accurately predicting individual antidepressant treatment response could expedite the lengthy trial-and-error process of finding an effective treatment for major depressive disorder (MDD). We tested and compared machine learning-based methods that predict individual-level pharmacotherapeutic treatment response using cortical morphometry from multisite longitudinal cohorts. We conducted an international analysis of pooled data from six sites of the ENIGMA-MDD consortium (n = 262 MDD patients; age = 36.5 ± 15.3 years; 154 (59%) female; mean response rate = 57%). Treatment response was defined as a ≥ 50% reduction in symptom severity score after 4-12 weeks post-initiation of antidepressant treatment. Structural MRI was acquired before, or < 14 days after, treatment initiation. The cortex was parcellated using FreeSurfer, from which cortical thickness and surface area were measured. We tested several machine learning pipeline configurations, which varied in (i) the way we presented the cortical data (i.e., average values per region of interest, as a vector containing voxel-wise cortical thickness and surface area measures, and as cortical thickness and surface area projections), (ii) whether we included clinical data, and the (iii) machine learning model (i.e., gradient boosting, support vector machine, and neural network classifiers) and (iv) cross-validation methods (i.e., k-fold and leave-one-site-out) we used. First, we tested if the overall predictive performance of the pipelines was better than chance, with a corrected 10-fold cross-validation permutation test. Second, we compared if some machine learning pipeline configurations outperformed others. In an exploratory analysis, we repeated our first analysis in three subpopulations, namely patients (i) from a single site, (ii) with comparable response rates, and (iii) showing the least (first quartile) and the most (fourth quartile) treatment response, which we call the extreme (non-)responders subpopulation. Finally, we explored the effect of including subcortical volumetric data on model performance. Overall, performance predicting antidepressant treatment response was not significantly better than chance (balanced accuracy = 50.5%; p = 0.66) and did not vary with alternative pipeline configurations. Exploratory analyses revealed that performance across models was only significantly better than chance in the extreme (non-)responders subpopulation (balanced accuracy = 63.9%, p = 0.001). Including subcortical data did not alter the observed model performance. Cortical structural MRI alone could not reliably predict individual pharmacotherapeutic treatment response in MDD. None of the used machine learning pipeline configurations outperformed the others. In exploratory analyses, we found that predicting response in the extreme (non-)responders subpopulation was feasible on both cortical data alone and combined with subcortical data, which suggests that specific MDD subpopulations may exhibit response-related patterns in structural data. Future work may use multimodal data to predict treatment response in MDD.

The prospective associations between problematic gaming and phubbing among Chinese adolescents: Insights from a cross-lagged panel network model

BACKGROUND AND AIMS

Previous studies are limited in addressing the directionality of temporal relationships between problematic gaming and phubbing symptoms by exploring cross-sectional studies. Therefore, we estimated the longitudinal relationships between individual behavioral addictive symptoms including problematic gaming and phubbing in adolescence, and explored potential sex differences in these relationships.

METHODS

This study included 3296 participants in Shandong Province, China. Data were collected from November 2021 (mean [SD] age: 15.17 [1.44] years) to May 2023 (mean [SD] age: 17.50 [1.18] years), with females comprising 54.5 % of the sample. Problematic gaming and phubbing were assessed using validated scales at each wave. We construct cross-sectional networks and cross-lagged panel networks (CLPN) to explore the contemptuous and temporal relationships between problematic gaming and phubbing.

RESULTS

Contemporaneous networks revealed significant differences in problematic gaming and phubbing networks between males and females. Additionally, temporal network analyses indicated that among male adolescents, feeling anxious when unable to play games was the most influential predictor of subsequent behavioral addictive symptoms. For female adolescents, fantasizing about gaming had the most significant associations with future addictive behaviors. The strongest bridge symptom linking problematic gaming and phubbing for both sexes was focusing on phones rather than engaging in conversation.

DISCUSSION AND CONCLUSIONS

The study applied network modeling to panel data from a large, population-based cohort of adolescents, identifying unique longitudinal relationships between problematic gaming and phubbing across symptom domains. It provides valuable insights into the characterization of behavioral addictive symptoms among adolescents and the potential predictive relationships among these symptoms among different sexes, guiding sex-specific targeted interventions for adolescents.

The network analysis of anxiety, depression and academic burnout symptom relationships throughout early, middle, and late adolescence

BACKGROUND

Previous research has explored the associations between anxiety, depression, and academic burnout primarily from a variable-level perspective. However, there is limited understanding of which symptoms might play a significant role in anxiety, depression, and academic burnout among adolescents at different stages.

METHODS

This study included 7,286 adolescents aged 10 to 18. Questionnaires assessed participants' anxiety, depression, and academic burnout. Network analysis was conducted on the overall sample and segmented by early, middle, and late adolescence to explore relationships between symptoms and variations in symptom expression across these stages, aiming to propose effective interventions targeting anxiety, depression, and academic burnout symptoms in early, middle, and late adolescence.

RESULTS

The study found that "feeling that studying is meaningless" emerged as a core symptom in the overall sample. Additionally, "acting or speaking slowly" emerged as a core symptom in early adolescence, while "the thought of dying or hurting" and "feeling bad about yourself, letting your family down" were prominent in middle adolescence, and "easily annoyed or irritable" and "feeling tired" may be prioritized in late adolescence. The varying central symptoms across different adolescent stages suggest the need for targeted interventions.

CONCLUSION

These findings underscore the importance of interventions tailored to specific symptoms to meet the unique needs of adolescents at different developmental stages.

Coexisting patterns of posttraumatic stress disorder and depression symptoms in college students who experienced childhood maltreatment: Different types of maltreatment exposure

BACKGROUND

Childhood maltreatment is often associated with comorbid posttraumatic stress disorder (PTSD) and depression, but the impact of different types of maltreatment on this comorbidity is not well understood.

METHODS

Using network analysis, we examined differences in comorbidity patterns of PTSD and depression symptoms among college students who experienced different forms of childhood maltreatment. We selected a subsample of 2968 students (Mage = 19.38, SD = ±1.45) who reported exposure to childhood maltreatment from a larger sample of 5231 students.

RESULTS

This study showed that symptoms of negative emotions and cognitive change, intrusive symptoms, and increased alertness might play a significant role in the diagnosis and prognosis of comorbid PTSD and depression. The most central nodes in the network of physical maltreatment were flashbacks, and irritability, whereas the most central nodes in the network of emotional and compound trauma, were low mood and sadness. Moreover, network structure and strength differed significantly between maltreatment types, and differences in specific symptom associations were also observed.

CONCLUSION

Network analysis provides insights into which symptoms contribute to the development of comorbidities in individuals with different childhood maltreatment types, as well as how specific symptoms are interconnected in the network. This information can aid in developing targeted and effective interventions for different maltreatment forms.

A network comparison analysis of socio-ecological protective and risk factors of depression between Chinese urban and rural adolescents

BACKGROUND

Anchoring in the socio-ecological framework and the differential impact theory, the present study pioneered to explore the differential network structures of multilevel risk and protective factors that influence depression among Chinese urban and rural adolescents.

METHOD

A sample of 684 urban adolescents and 1123 rural adolescents completed a battery of self-report questionnaires measuring their depressive symptoms, as well as risk and protective factors at intrapersonal (psychological flexibility, emotion regulation), interpersonal (social support, parental control), and social levels (social capital, stressful life events).

RESULTS

Central risk and protective factors in both groups included psychological flexibility, which bridged intrapersonal, interpersonal and social resources, along with social support, social capital, rumination, catastrophizing, and self-blame. Network comparison tests revealed significant differences in the global strength and network structures between the two groups. Rural adolescents showed denser connections between positive refocusing - rumination, positive refocusing - other-blame, refocusing on planning - self-blame, and family support - self-blame, while urban adolescents showed a stronger relationship between rumination - blaming others - depression. Rural adolescents uniquely benefited from a protective loop of reappraisal - social satisfaction - depression.

CONCLUSION

The findings suggest both beneficial and trade-off effects of a denser psychosocial network in adolescents growing up in a high-risk environment. Such results imply that only increasing the number of protective factors (e.g., social resources) may not be sufficient; instead, practical strategies that can neutralize the drawbacks of protective mechanisms may serve as critical strategies in promoting the socio-ecological well-being of adolescents in China and elsewhere.

Network analysis of depressive symptoms and C-reactive protein levels in major depressive disorder

BACKGROUND

C-reactive protein (CRP) levels have been implicated in the severity and symptomatology of major depressive disorder (MDD). The aim of this study was to explore the structure of depressive symptoms in patients with MDD according to different groups of CRP levels using network analysis.

METHODS

The study included 864 individuals (mean age = 54.05, 67.48 % male) diagnosed with MDD from the 2015-2020 National Health and Nutrition Examination Survey (NHANES). Analyses examined how depressive symptoms and CRP level were related to each other, and how the network structure of depressive symptoms differed across groups with different CRP levels.

RESULTS

A direct positive correlation was observed between CRP levels and specific depressive symptoms (e.g., appetite change, energy loss, and feelings of worthlessness). Moreover, there was a stronger correlation between depressive symptoms in the medium CRP and high CRP groups compared to the low CRP group. Furthermore, it was observed that there were notable structural differences between the high-CRP and moderate-CRP groups.

LIMITATIONS

The study is based on cross-sectional data, which precludes the drawing of causal conclusions. Furthermore, it does not take into account confounding factors such as body mass index (BMI) and lifestyle.

CONCLUSIONS

The findings underscore the pivotal role of CRP as a marker of the severity of depressive symptoms. Routine CRP level testing and anti-inflammatory therapies may be beneficial for depressed patients with elevated CRP levels in clinical practice.

Variations in Some Features of Oral Health by Personality Traits, Gender, and Age: Key Factors for Health Promotion

Background: Oral diseases remain among the most common non-communicable diseases worldwide, affecting almost half of the world's population. This is partly because there has been a separation of the mouth from the rest of the body and human health, and psychological aspects such as personality, thoughts, and emotions are not taken into account in the dental office. The objective was to analyze the relationship between oral health conditions and personality traits in adult patients who underwent dental treatment at the Center for Health Studies and Services. Methods: This was a descriptive, observational, and correlational study, carried out at the Center for Health Studies and Services. A total of 184 patients who attended the dentistry area in the period from October 2022 to May 2023 participated in the study, of which 59.78% were women and 40.21% men. The age of the population was 18 to 79 years, with the age range of 21-40 years being the most prevalent (48.37%). Results: The results show that although the hygiene of the population treated was good (0.77 ± 0.79) and that the perception of oral health was positive (14.34 ± 9.43), the means and percentages of oral pathologies and parafunctional habits were high (i.e., DMFT: 9.98 ± 5.40; attrition: 87.50%; onychophagia: 45.10%). It is noteworthy that both the correlation, network, multiple line regression, and logistic regression analyses showed associations of the personality, gender, and age variables with a history of caries and oral hygiene as well as with parafunctional habits. Conclusions: Therefore, variations in both the personality and the age and gender of the patients treated have repercussions on oral health conditions, which can be used in the prevention of oral diseases and in health promotion.

Topological properties of psychopathological networks of healthy and disordered individuals across mental disorders

The identification of psychopathological markers has been the focus of several scientific fields. The results were inconsistent due to lack of a clear nosology. Network analysis, focusing on the interactions between symptoms, provided important insights into the nosology of mental disorders. These interactions originate several topological properties that could constitute markers of psychopathology. One of these properties is network connectivity, which has been explored in recent years. However, the results have been inconsistent, and the topological properties of psychopathological networks remain largely unexplored and unknown. We compared several topological properties (i.e., connectivity, average path length, assortativity, average degree, modularity, global clustering) of psychopathological networks of healthy and disordered participants across depression (N = 2830), generalized anxiety (N = 13,463), social anxiety (N = 12,814), and obsessive-compulsive disorder (N = 16,426). Networks were estimated using Bayesian Gaussian Graphical Models. The Janson-Shannon measure of divergence was used to identify differences between the network properties. Network connectivity distinguished healthy and disordered participants' networks in all disorders. However, in depression and generalized anxiety, network connectivity was higher in healthy participants. The presence and number of motifs also distinguished the networks of healthy and disordered participants. Other topological properties (i.e., modularity, clustering, average path length and average degree) seem to be disorder-specific. The psychopathological significance of network connectivity must be clarified. Some topological properties of psychopathological networks are promising markers of psychopathology and may contribute to clarifying the nosology of mental disorders.

Cognitive reorganization in patients with Parkinson's Disease and Mild Cognitive Impairment: a neuropsychological network approach

Parkinson's Disease (PD) exhibits heterogeneous cognitive deficits that may represent different cognitive phenotypes. While previous studies have described them in a "macro" manner, only one study has applied Network Analysis (NA) in PD. NA represents a model to explore relationships between cognitive abilities, aiding in understanding cognitive phenotypes. This study aims to verify whether the cognitive system undergoes reorganization in PD with Mild Cognitive Impairment (PD-MCI) patients. To explore this, a Level II cognitive assessment was administered to 275 PD patients, who were classified into two diagnostic categories: PD-Cognitive Unimpaired (CU) (n = 171) and PD-MCI (n = 104). NA was applied to construct Gaussian Graphical Models for each diagnostic group, where nodes represent cognitive tests and demographic factors, and edges represent their interconnections. The NA revealed substantial differences between the cognitive networks of PD-CU and PD-MCI patients. Specifically, the network of PD-MCI patients appears less sparse, with some weakened relationships between nodes. Overall, the results support the presence of a cognitive reorganization in PD-MCI patients, potentially indicating a functional compensation mechanism. In conclusion, this study enhances the understanding of the cognitive mechanisms underlying cognitive decline in patients with PD.

Associations between personality and problematic internet use among Chinese adolescents and young adults: A network analysis

BACKGROUND

With the number of internet users booming, problematic internet use (PIU) has become a public health threat. This study aims to figure out the inter-relationships between PIU symptoms and personality traits with network-based analysis among young people and to discuss the gender difference in the above networks.

METHODS

Based on a national cross-sectional study in 2022, 4655 Chinese adolescents and young adults aged 14 to 25 were included. We adopted the 6-item Short-Form Problematic Internet Use Questionnaire (PIUQ-SF-6) and the 10-item version of the Big Five Inventory (BFI-10) to measure PIU and personality traits, respectively. Network analysis was used to identify influential nodes and edges and compare the network models between male and female participants.

RESULTS

The mean age of 4655 participants was 19.84, and 52.1 % (2424) of them were females. There are differences in age and gender between participants with and without PIU (P < 0.05). The network of personality and PIU showed that 22 out of the 28 edges were estimated to be nonzero, and "obsession-neuroticism" was the strongest positive edge between the two communities. Central symptoms (i.e., "obsession" and "control disorder") and bridge symptoms (i.e., "obsession" and "neuroticism") have been identified. Gender differences existed in network global strength: female = 3.71, male = 3.18 (p < 0.001).

LIMITATIONS

The cross-sectional study needs more evidence to build causal inference.

CONCLUSIONS

The results of PIU-personality networks may contribute to the personalized prevention and treatment of PIU. The gender difference in PIU-personality networks also requires more attention and discussion.

Unraveling the relationships among pandemic fear, cyberchondria, and alexithymia after China's exit from the zero-COVID policy: insights from a multi-center network analysis

Objective

China's abrupt exit from the zero-COVID policy in late 2022 led to a rapid surge in infections, overwhelming healthcare systems and exposing healthcare providers to intensified psychological pressures. This sudden shift exacerbated pandemic-related psychological issues, including fear, health anxiety, and emotional processing difficulties. This study aimed to unravel the relationships among pandemic fear, cyberchondria, and alexithymia following China's exit from the zero-COVID policy.

Methods

A multi-center cross-sectional survey was conducted among 4088 nurses from 43 public hospitals in China. The web-based survey comprised the Fear of COVID-19 Scale, Cyberchondria Severity Scale, and Toronto Alexithymia Scale. Network analysis was employed to explore the interconnections and identify central components within these psychological and behavioral constructs.

Results

The analysis revealed a dense network with predominantly positive connections. Specific aspects of cyberchondria and pandemic fear exhibited the highest strength centrality, indicating their critical influence. The externally oriented thinking dimension of alexithymia emerged as a crucial bridge node, linking pandemic fear and cyberchondria. The network structure demonstrated consistency across diverse educational backgrounds and career stages.

Conclusion

These findings highlight the need for targeted interventions focusing on key network components, particularly externally oriented thinking, to disrupt the detrimental cycle of pandemic fear and cyberchondria. Healthcare organizations should promote balanced objective fact-focused and problem-solving approaches while also fostering skills in emotional awareness and expression, thereby mitigating the risk of maladaptive pandemic fear responses and dysfunctional online health information-seeking behaviors.

Patterns and relationships of prolonged grief, post-traumatic stress, and depressive symptoms in Chinese shidu parents: Latent profile and network analyses

Parents who experience the trauma of losing an only child are called "shidu" parents in China. There are individual differences in post-loss outcomes.1,061 Chinese shidu parents were asked to complete questionnaires assessing prolonged grief, post-traumatic stress, and depressive symptoms. The mean age of the sample was 59.68 (SD = 7.52), with the average time since the loss was 9.46 years (SD = 7.05). Most participants were female (62.3%). The main cause of the loss was an unnatural case (52.7%). Latent profile analysis was used to identify similar symptom patterns. Network analysis was used to explore the relationships among symptoms within different subgroups. A two-profile model based on symptom severity identified a "low symptom severity" subgroup (n = 419) and a "high symptom severity" subgroup (n = 642). In the low symptom severity subgroup network, the most central symptoms were loss of interest, feeling numb, and meaninglessness. In the high symptom severity subgroup network, the most central symptoms were physiological cue reactivity, emotional pain, and feeling easily startled. Individual differences in the post-loss outcomes of Chinese shidu parents are reflected not only in symptom patterns but also in the relationships among symptoms.