Symptom patterns in the co-occurrence of depressive and generalized anxiety symptoms: A network analysis of a Canadian nationally representative sample

BACKGROUND

This study aimed to investigate the symptom patterns of major depressive disorder (MDD) and generalized anxiety disorder (GAD) in a matched nationally representative sample of the Canadian population. We also tested whether childhood maltreatment (CM) exposures and sex would be linked with different symptom patterns.

METHODS

A total of 3296 participants from the Canadian Community Health Survey-Mental Health with complete information on MDD and GAD symptoms and being matched on the studied sociodemographic characteristics were included in the current study. Network analysis was performed to examine the MDD-GAD symptom network, network stability and centrality indices were also estimated. Finally, network comparison in connectivity patterns was conducted to explore the impact of maltreatment experience and sex differences in the MDD-GAD symptom networks.

RESULTS

The CM group had stronger network connections and showed differences in the network structures from the non-CM group. In the CM group, depressed mood and diminished interest were central symptoms and strongly connected with other symptoms. Additionally, females had stronger connections in the MDD-GAD symptom network than males, and sleep disturbance was a central symptom for females, alongside depressed mood and diminished interest.

LIMITATIONS

The cross-sectional design restricts our capacity to establish longitudinal or causal connections between symptoms.

CONCLUSIONS

Depressed mood was the most central node that was strongly connected with other symptoms in the network. Distinct MDD-GAD symptom networks were discovered in the CM and the female group when compared to their counterparts. Noteworthy, individuals with CM had a stronger correlation between worry and suicidal ideation. Clinical management and intervention efforts should pay close attention to these core symptoms to yield optimal treatment effects, particularly for females and individuals with CM.

Mapping network connection and direction among symptoms of depression and anxiety in patients with chronic gastritis

Regarding neurophysiological and developmental findings, anxiety and depression are usual comorbidities of gastritis patients. However, research related to anxiety and depression among chronic gastritis patients was conducted on the disease level while ignoring symptoms. Hence, we rendered the network approach to reveal the symptoms of anxiety and depression among chronic gastritis patients. Three hundred and sixty-nine chronic gastritis patients (female = 139, Mage = 55.87 years) were asked to complete the Self-Rating Anxiety Scale and Self-Rating Depression Scale. Three symptom networks and one directed acyclic graph (DAG) network were formed. First, in the anxiety network of chronic gastritis patients, dizziness was the most influential symptom. In the depression network of chronic gastritis patients, depressed affect and psychomotor retardation were the influential symptoms. Second, panic, easy fatiguability, weakness, palpitation, depressed affect, tachycardia, fatigue, and psychomotor agitation bridged the anxiety-depression network of chronic gastritis patients. Third, DAG networks showed that anxiousness and hopelessness could trigger other symptoms in the anxiety-depression networks of chronic gastritis patients. The current study provided insightful information on patients with chronic gastritis by examining the structures of symptoms.

Investigating change in network structure of eating disorder symptoms after delivery of a smartphone app-based intervention

BACKGROUND

Eating disorder (ED) research has embraced a network perspective of psychopathology, which proposes that psychiatric disorders can be conceptualized as a complex system of interacting symptoms. However, existing intervention studies using the network perspective have failed to find that symptom reductions coincide with reductions in strength of associations among these symptoms. We propose that this may reflect failure of alignment between network theory and study design and analysis. We offer hypotheses for specific symptom associations expected to be disrupted by an app-based intervention, and test sensitivity of a range of statistical metrics for identifying this intervention-induced disruption.

METHODS

Data were analyzed from individuals with recurrent binge eating who participated in a randomized controlled trial of a cognitive-behavioral smartphone application. Participants were categorized into one of three groups: waitlist (n = 155), intervention responder (n = 49), and intervention non-responder (n = 77). Several statistical tests (bivariate associations, network-derived strength statistics, network invariance tests) were compared in ability to identify change in network structure.

RESULTS

Hypothesized disruption to specific symptom associations was observed through change in bivariate correlations from baseline to post-intervention among the responder group but were not evident from symptom and whole-of-network based network analysis statistics. Effects were masked when the intervention group was assessed together, ignoring heterogeneity in treatment responsiveness.

CONCLUSION

Findings are consistent with our contention that study design and analytic approach influence the ability to test network theory predictions with fidelity. We conclude by offering key recommendations for future network theory-driven interventional studies.

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.

Psychological flexibility and cognitive-affective processes in young adults' daily lives

Psychological flexibility plays a crucial role in how young adults adapt to their evolving cognitive and emotional landscapes. Our study investigated a core aspect of psychological flexibility in young adults: adaptive variability and maladaptive rigidity in the capacity for behavior change. We examined the interplay of these elements with cognitive-affective processes within a dynamic network, uncovering their manifestation in everyday life. Through an Ecological Momentary Assessment design, we collected intensive longitudinal data over 3 weeks from 114 young adults ages 19 to 32. Using a dynamic network approach, we assessed the temporal dynamics and individual variability in flexibility in relation to cognitive-affective processes in this sample. Rigidity exhibited the strongest directed association with other variables in the temporal network as well as highest strength centrality, demonstrating particularly strong associations to other variables in the contemporaneous network. In conclusion, the results of this study suggest that rigidity in young adults is associated with negative affect and cognitions at the same time point and the immediate future.

Relationship between depression and burnout among nurses in Intensive Care units at the late stage of COVID-19: a network analysis

BACKGROUND

Mental health problems are critical and common in medical staff working in Intensive Care Units (ICU) even at the late stage of COVID-19, particularly for nurses. There is little research to explore the inner relationships between common syndromes, such as depression and burnout. Network analysis (NA) was a novel approach to quantified the correlations between mental variables from the perspective of mathematics. This study was to investigate the interactions between burnout and depression symptoms through NA among ICU nurses.

METHOD

A cross-sectional study with a total of 616 Chinese nurses in ICU were carried out by convenience sampling from December 19, 2022 to January19, 2023 via online survey. Burnout symptoms were measured by Maslach Burnout Inventory-General Survey (MBI-GS) (Chinese version), and depressive symptoms were assessed by the 9-item Patient Health Questionnaire (PHQ-9). NA was applied to build interactions between burnout and depression symptoms. We identified central and bridge symptoms by R package qgraph in the network model. R package bootnet was used to examined the stability of network structure.

RESULTS

The prevalence of burnout and depressive symptoms were 48.2% and 64.1%, respectively. Within depression-burnout network, PHQ4(Fatigue)-MBI2(Used up) and PHQ4(Fatigue)-MBI5(Breakdown) showed stronger associations. MBI2(Used up) had the strongest expected influence central symptoms, followed by MBI4(Stressed) and MBI7 (Less enthusiastic). For bridge symptoms. PHQ4(Fatigue), MBI5(Breakdown) and MBI2(Used up) weighed highest. Both correlation stability coefficients of central and bridge symptoms in the network structure were 0.68, showing a high excellent level of stability.

CONCLUSION

The symptom of PHQ4(Fatigue) was the bridge to connect the emotion exhaustion and depression. Targeting this symptom will be effective to detect mental disorders and relieve mental syndromes of ICU nurses at the late stage of COVID-19 pandemic.

Unveiling the relationship between social anxiety, loneliness, motivations, and problematic smartphone use: A network approach

BACKGROUND

Previous studies suggested social anxiety as an essential risk factor for problematic smartphone use, but the complex interactions and the most influential components affecting this relationship remain unclear. This study capitalizes on network analysis to identify the central factors and possible mediating paths among social anxiety, loneliness, five types of motivation, and problematic smartphone use.

MATERIAL AND METHODS

Employing 549 emerging adults, we obtained a stable network of the above variables. The central components and the stability of this network were also identified.

RESULTS

Within this network, the edge linking withdrawal behavior and use of application (APP) exhibits the most robust edge intensity. The central components include social comfort, use of APP, withdrawal behavior, and companionship while the bridge central nodes include social anxiety and escapism motivation. The direct link between social anxiety and PSU revealed only fragile edges with both withdrawal behavior and use of APP. Considering the possible mediating pathways, three pathways were observed in our network. Loneliness and escapism mediated the relationship between social anxiety and social comfort. Moreover, another mediating way was from social anxiety, loneliness, social interaction motivation, and escapism motivation to social comfort.

DISCUSSION

Based on the above identification of related components and pathways, future researchers could intervene against problematic smartphone usage in this socially anxious population.

Centrality statistics of symptom networks of schizophrenia: a systematic review

The network theory of psychological disorders posits that systems of symptoms cause, or are associated with, the expression of other symptoms. Substantial literature on symptom networks has been published to date, although no systematic review has been conducted exclusively on symptom networks of schizophrenia, schizoaffective disorder, and schizophreniform (people diagnosed with schizophrenia; PDS). This study aims to compare statistics of the symptom network publications on PDS in the last 21 years and identify congruences and discrepancies in the literature. More specifically, we will focus on centrality statistics. Thirty-two studies met the inclusion criteria. The results suggest that cognition, and social, and occupational functioning are central to the network of symptoms. Positive symptoms, particularly delusions were central among participants in many studies that did not include cognitive assessment. Nodes representing cognition were most central in those studies that did. Nodes representing negative symptoms were not as central as items measuring positive symptoms. Some studies that included measures of mood and affect found items or subscales measuring depression were central nodes in the networks. Cognition, and social, and occupational functioning appear to be core symptoms of schizophrenia as they are more central in the networks, compared to variables assessing positive symptoms. This seems consistent despite heterogeneity in the design of the studies.

Dynamic symptom associations in posttraumatic stress disorder: a network approach

Background and objective: The current study aimed to investigate the within-day symptom dynamics in PTSD patients, specifically focusing on symptoms that most predict changes in other symptoms. The study included a baseline diagnostic assessment, followed by an assessment using the experience sampling method (ESM) via a smartphone.Method: Participants answered questions related to their PTSD symptoms four times per day for 15 consecutive days (compliance rate 75%). The clinical sample consisted of 48 treatment-seeking individuals: 44 with PTSD as a primary diagnosis, and four patients with subsyndromal PTSD, all of whom had not yet begun trauma-focused treatment. The ESM assessment included the 20 items from the PTSD Checklist for DSM-5, five items from the International Trauma Questionnaire (ITQ) assessing disturbances in relationships and functional impairment, and two items from the Clinician-Administered PTSD Scale for DSM-5 assessing symptoms of depersonalization and derealization.Results: Temporal networks (prospective associations between symptoms) showed that changes in hypervigilance predicted changes in the greatest number of symptoms at the next time point. Furthermore, hypervigilance showed temporal connections with at least one additional symptom from each of the DSM-5 PTSD symptom clusters.Conclusions: Results show that the contemporaneous network (representing the relationship between given symptoms within the same assessment occasion) and the temporal network (representing prospective associations between symptoms) differ and that it is important to estimate both. Some findings from earlier research are replicated, but heterogeneity across studies remains. Future studies should include potential moderators.

A network analysis of ICD-11 Complex PTSD, emotional processing, and dissociative experiences in the context of psychological trauma at different developmental stages

Objective

Traumatic experiences are a significant risk factor for psychological disturbances, including disorders such as complex posttraumatic stress disorder, emotion-processing problems, and trauma-related dissociative experiences. The present investigation examined the coexistence of these symptoms using a network analysis model.

Method

This study included a sample of 406 people referred to comprehensive health centers in Tehran from September to December 2023 with psychopathological syndromes. Variables were assessed using The International Trauma Questionnaire, International Measurement of Exposure to Traumatic Event checklist, Baker Emotional Processing Questionnaire, and Dissociative Experiences. A regularized partial correlation network and Glasso algorithm, in combination with Extended Bayesian information criteria, were applied to estimate the network structure.

Results

Signs of unprocessed emotions and disturbance in self-organization symptoms were the most important symptoms in the symptom network, forming strong connections with other nodes. Thereby, these two symptoms can be regarded as the most important clinical manifestations in the symptom network following traumatic experiences. Three distinct symptom communities were identified: the community of traumatic experiences (childhood, adolescence, adulthood), the community of dissociative experiences (amnesia, depersonalization/derealization, and absorption), and the community of emotional processing (suppression, unpleasant emotional experience, Signs of unprocessed emotions, avoidance, and emotional control, posttraumatic stress disorder symptoms and disturbance in self-organization symptoms). The strongest edges observed were between childhood trauma-adolescence trauma (0.473) in the community of traumatic experiences, between amnesia and depersonalization/derealization (0.644) in the community of dissociative experiences, and between disturbance in self-organization symptoms and unprocessed emotions (0.324) in the community of emotional processing, indicating the recurrent occurrence of these symptoms.

Conclusion

In this study, disturbance in self-organization symptoms was identified as the central psychopathologic symptom in individuals experiencing traumas at different developmental stages. It seems that adolescent trauma and not childhood trauma plays a more decisive role in the symptoms that a person manifests after traumatic experiences. Also, posttraumatic stress disorder symptoms and disturbance in self-organization symptoms were recognized in the cluster of emotional processing symptoms and can have substantial roles in prioritizing therapeutic measures.

Fear of disease in patients with epilepsy - a network analysis

Background

Disease-related fear among patients with epilepsy has significantly impacted their quality of life. The Disease-Related Fear Scale (D-RFS), comprising three dimensions, serves as a relatively well-established tool for assessing fear in these patients. However, certain problems potentially exist within the D-RFS's attribution of items, and its internal structure is still unclear. To establish an appropriate dimensional structure and gain deeper comprehension of its internal structure-particularly its core variables-is vital for developing more effective interventions aimed at alleviating disease-related fear among patients with epilepsy.

Methods

This study employed a cross-sectional survey involving 609 patients with epilepsy. All participants underwent assessment using the Chinese version of the D-RFS. We used exploratory network analysis to discover a new structure and network analysis to investigate the interrelationships among fear symptom domains. In addition to the regularized partial correlation network, we also estimated the node and bridge centrality index to identify the importance of each item within the network. Finally, it was applied to analyze the differences in network analysis outcomes among epilepsy patients with different seizure frequencies.

Results

The research findings indicate that nodes within the network of disease-related fear symptoms are interconnected, and there are no isolated nodes. Nodes within groups 3 and 4 present the strongest centrality. Additionally, a tight interconnection exists among fear symptoms within each group. Moreover, the frequency of epileptic episodes does not significantly impact the network structure.

Conclusion

In this study, a new 5-dimension structure was constructed for D-RFS, and the fear of disease in patients with epilepsy has been conceptualized through a network perspective. The goal is to identify potential targets for relevant interventions and gain insights for future research.

eHealth tools use and mental health: a cross-sectional network analysis in a representative sample

eHealth tools usage is vital for health care systems and increased significantly after the COVID-19 pandemic, which aggravated mental health issues. This cross-sectional study explored whether sociodemographic characteristics and mental health indices (stress and symptoms of anxiety and depression) were linked to the behavioral intention to use eHealth tools and eHealth tools usage in a representative sample from Poland using a network approach. Measurements were conducted in March 2023 among 1000 participants with a mean age of 42.98 (18-87) years, with 51.50% women. The measures included the behavioral intention to use eHealth tools (BI) based on the UTUAT2; eHealth tool use frequency (use behavior) including ePrescription, eSick leave, eReferral, electronic medical documentation (EMD), Internet Patient Account (IKP), telephone consultation, video consultation, mobile health applications, and private and public health care use; and the PSS-4, GAD-2, and PHQ-2. Furthermore, sociodemographic factors (sex, age, children, relationship status, education, and employment) were included in the research model. Network analysis revealed that mental health indices were weakly related to eHealth tools use. Higher stress was positively linked with mobile health application use but negatively linked to video consultation use. Use of various eHealth tools was intercorrelated. Sociodemographic factors were differentially related to the use of the eight specific eHealth tools. Although mental health indices did not have strong associations in the eHealth tools use network, attention should be given to anxiety levels as the factor with the high expected influence.

Network Analysis on Family Resilience, Fear of Progression, and Quality of Life in Chinese Patients with Pulmonary Hypertension

BACKGROUND

The long-term quality of life of Chinese patients with pulmonary hypertension has been seriously compromised. Interventions to enhance patient quality of life of are urgently required.

OBJECTIVE

To investigate the relationship between quality of life and its influencing factors in patients through network analysis.

METHOD

247 patients with pulmonary hypertension in 3 hospitals in Jinan, Shandong Province, self-reported their immediate family resilience, fear of progression, and quality of life via questionnaires. A mixed graphical model was constructed to investigate the relationship among multidimensional structures of variables.

RESULTS

A total of 247 patients (173 female and 74 male) were included (age 18-34, n=115; age 35-49, n=99; age 50-64, n=23; age > 65 years, n=10). "Family communication and problem solving" was at the center of the network, with serving as a bridge node. Within communities, the strongest edge was "Family communication and problem solving-maintaining a positive outlook." Across communities, the strongest edge between family resilience and quality of life was "Family communication and problem solving-environmental domain quality of life," and the strongest edge across the fear of progression and quality of life was "physiological health fear-psychological domain quality of life."

CONCLUSIONS

Family resilience and fear of progression may affect the quality of life in patients with pulmonary hypertension. Developing a program with "family communication and problem solving" as a primary target may reduce patient fear of progression, enhance family resilience, and improve patient quality of life.

A network model of depressive and anxiety symptoms: a statistical evaluation

BACKGROUND

Although network analysis studies of psychiatric syndromes have increased in recent years, most have emphasized centrality symptoms and robust edges. Broadening the focus to include bridge symptoms within a systematic review could help to elucidate symptoms having the strongest links in network models of psychiatric syndromes. We conducted this systematic review and statistical evaluation of network analyses on depressive and anxiety symptoms to identify the most central symptoms and bridge symptoms, as well as the most robust edge indices of networks.

METHODS

A systematic literature search was performed in PubMed, PsycINFO, Web of Science, and EMBASE databases from their inception to May 25, 2022. To determine the most influential symptoms and connections, we analyzed centrality and bridge centrality rankings and aggregated the most robust symptom connections into a summary network. After determining the most central symptoms and bridge symptoms across network models, heterogeneity across studies was examined using linear logistic regression.

RESULTS

Thirty-three studies with 78,721 participants were included in this systematic review. Seventeen studies with 23 cross-sectional networks based on the Patient Health Questionnaire (PHQ) and Generalized Anxiety Disorder (GAD-7) assessments of clinical and community samples were examined using centrality scores. Twelve cross-sectional networks based on the PHQ and GAD-7 assessments were examined using bridge centrality scores. We found substantial variability between study samples and network features. 'Sad mood', 'Uncontrollable worry', and 'Worrying too much' were the most central symptoms, while 'Sad mood', 'Restlessness', and 'Motor disturbance' were the most frequent bridge centrality symptoms. In addition, the connection between 'Sleep' and 'Fatigue' was the most frequent edge for the depressive and anxiety symptoms network model.

CONCLUSION

Central symptoms, bridge symptoms and robust edges identified in this systematic review can be viewed as potential intervention targets. We also identified gaps in the literature and future directions for network analysis of comorbid depression and anxiety.

Identifying the core residual symptom in patients with major depressive disorder using network analysis and illustrating its association with prognosis: A study based on the national cohorts in China

OBJECTIVE

To identify the core residual symptom of MDD and assess its relationship with patients' long-term outcomes.

METHOD

All patients were administered antidepressants during the acute phase and treated continuously. The 521 patients remitted at month 6 of a multicenter prospective project were included. Remission was defined as a Quick Inventory of Depressive Symptoms-Self-Report total score of ≤5. Functional impairments were measured with the Sheehan Disability Scale, quality of life with the Quality of Life Enjoyment and Satisfaction Questionnaire - short form, and family burden with the Family Burden Scale of Disease. Visits were scheduled at baseline, weeks 2, 8, 12, and month 6.

RESULTS

Difficulty with concentration/decision making was the core residual symptom of MDD, determined with the centrality measure of network analysis. It was positively associated with functional impairments and family burden (r = 0.35, P < 0.01 and r = 0.31, P < 0.01, respectively) and negatively associated with life satisfaction (r = -0.29, P < 0.01). The exhibition of this residual symptom was associated with a family history of psychiatric disorders (OR = 2.610 [1.242-5.485]).

CONCLUSIONS

The core residual symptom of MDD, difficulty with concentration/decision making, is associated with poorer social functioning, heavier family burden, and lower life satisfaction. Early detection and intervention of this symptom may be beneficial.

CLINICAL TRIALS REGISTRATION NUMBER

(Chinese Clinical Trials.gov identifier) ChiCTR-OOC-17012566 and ChiCTR-INR-17012574.

The association between team cohesion and performance: A network analysis of nurses

Team cohesion, as a necessary condition for the cooperation and development of a team, has been shown to have a strong association with team performance. However, the mechanism of this internal correlation is unclear and more in-depth studies are lacking. The study aimed to explore the complex links between the dimensions of team cohesion and performance in nurses. A total of 1639 practice nurses from 118 nursing teams were included in this cross-sectional study. Data were collected using the Team Cohesion Scale (including consistency of affection, behavior, and cognition) and the Team Effectiveness Scale (including cooperation satisfaction, and task performance). Using network analysis, the team cohesion and performance network was constructed, and the strength and bridge strength of nodes were calculated. The results showed that the edges between team cohesion and performance dimensions were all positively correlated. Cooperation satisfaction and consistency of affection are the core variables in the network. Interventions targeting cooperation satisfaction and consistency of affection need to be developed at the team level to maximize team cohesion and performance among nurses.

Uncovering the symptom relationship between anxiety, depression, and internet addiction among left-behind children: A large-scale purposive sampling network analysis

Facing long-term separation from their parents, left-behind children are at risk of the co-occurrence of internalizing and externalizing problems. Although previous research has gained substantial information examining the relationship between anxiety, depression, and internet addiction at the aggregate level of variables, little is known about the heterogeneity and interactions between these components at the symptom level with a large-scale purposive sample. Adopting the network approach, two network pathways, depression and anxiety, and associations between these variables and internet addiction were constructed. Our sample included 5367 left-behind children (Mage = 13.57; SDage = 1.37; 50.07% females). Relevant bridging, central symptoms, and network stability were identified. Two relatively stable networks were obtained. For the network of anxiety and depression, sleep problems and tachycardia were vital bridging symptoms. Central symptoms, including tachycardia, restlessness, fatigue, and emptiness, were symptoms of depression. For the network of symptoms of anxiety, depression, and internet addiction, the bridging symptoms remained the same, and the central symptoms included tachycardia, restlessness, loss of control, and emptiness. By identifying relevant bridging and central symptoms, those with higher levels of these symptoms could be regarded as intervention targets, providing a reference for the current issue of valuing diagnosis over prevention in left-behind children.

Pain trajectories and their associations with cognition among older adults: a 10-year cohort study from network perspective

BACKGROUND

Few studies have examined the associations between pain trajectories and cognitive function in older adults. This study explored the associations between pain trajectories and different cognitive domains in older adults from a network perspective.

METHODS

Data on pain trajectories were derived from the Health and Retirement Study between 2010 and 2020 using latent class growth analyses. Measurements of key cognition domains, including memory, attention, calculation, orientation and language, were included. Linear regression and network analysis were performed to evaluate the associations between different pain trajectories and cognition.

RESULTS

A total of 9,551 older adults were included in this study and three trajectories of pain were identified. After controlling for the covariates, persistent severe pain trajectory was associated with poorer overall cognition, memory and calculation ability when compared to mild or non-persistent pain trajectory. In the pain and cognition network model, memory (expected influence (EI) = 0.62), language (EI = 0.58) and calculation (EI = 0.41) were the most central domains.

CONCLUSIONS

Pain trajectories appeared stable over time among older adults in this study. Severity of persistent pain was an important risk factor for poor cognition, especially in relation to memory and calculation domains. Interventions targeting memory, language and calculation domains might be useful in addressing cognitive decline in older adults with persistent pain.

The influence of working memory and processing speed on other aspects of cognitive functioning in de novo Parkinson's disease: Initial findings from network modelling and graph theory

Deficits in working memory (WM) and processing speed (PS) are thought to undermine other cognitive functions in de novo Parkinson's disease (dnPD). However, these interrelationships are only partially understood. This study investigated whether there are stronger relationships between verbal WM and verbal episodic memory encoding and retrieval, whether verbal WM and PS have a greater influence on other aspects of cognitive functioning, and whether the overall strength of interrelationships among several cognitive functions differs in dnPD compared to health. Data for 198 healthy controls (HCs) and 293 dnPD patients were analysed. Participants completed a neuropsychological battery probing verbal WM, PS, verbal episodic memory, semantic memory, language and visuospatial functioning. Deficit analysis, network modelling and graph theory were combined to compare the groups. Results suggested that verbal WM performance, while slightly impaired, was more strongly associated with measures of verbal episodic memory encoding and retrieval, as well as other measured cognitive functions in the dnPD network model compared to the HC network model. PS task performance was impaired and more strongly associated with other neuropsychological task scores in the dnPD model. Associations among task scores were stronger overall in the dnPD model. Together, these results provide further evidence that WM and PS are important influences on the other aspects of cognitive functioning measured in this study in dnPD. Moreover, they provide novel evidence that verbal WM and PS might bear greater influence on the other measured cognitive functions and that these functions are more strongly intertwined in dnPD compared to health.

Individual-Specific Animated Profiles of Mental Health

How important is the timing of the pretreatment evaluation? If we consider mental health to be a relatively fixed condition, the specific timing (e.g., day, hour) of the evaluation is immaterial and often determined on the basis of technical considerations. Indeed, the fundamental assumption underlying the vast majority of psychotherapy research and practice is that mental health is a state that can be captured in a one-dimensional snapshot. If this fundamental assumption, underlying 80 years of empirical research and practice, is incorrect, it may help explain why for decades psychotherapy failed to rise above the 50% efficacy rate in the treatment of mental-health disorders, especially depression, a heterogeneous disorder and the leading cause of disability worldwide. Based on recent studies suggesting within-individual dynamics, this article proposes that mental health and its underlying therapeutic mechanisms have underlying intrinsic dynamics that manifest across dimensions. Computational psychotherapy is needed to develop individual-specific pretreatment animated profiles of mental health. Such individual-specific animated profiles are expected to improve the ability to select the optimal treatment for each patient, devise adequate treatment plans, and adjust them on the basis of ongoing evaluations of mental-health dynamics, creating a new understanding of therapeutic change as a transition toward a more adaptive animated profile.

Revisiting functioning recovery in persons with spinal cord injury undergoing first rehabilitation: Trajectory and network analysis of a Swiss cohort study

Information about an individual's functioning and its longitudinal development is key to informing clinical rehabilitation. However, the description and understanding of the detailed longitudinal course of functioning, i.e., functioning trajectories, is rare in the current SCI literature. The aim of this study was to re-estimate previously identified functioning trajectories of individuals with spinal cord injury (SCI) undergoing initial rehabilitation in Switzerland using trajectory analysis, and to identify highly influential functioning domains that could become trajectory-specific targets for clinical interventions using network analysis. The study was based on data from the Swiss SCI Cohort Study and included individuals with SCI (N = 1099) who completed their rehabilitation in one of four collaborating centers between May 2013 and March 2022. For the trajectory analysis, functioning was operationalized using the total sum score of the Spinal Cord Independence Measure version III (SICM III), which was assessed at up to four time points (T1-T4) during rehabilitation. For the network analysis, individual SCIM III items were used to operationalize relevant functioning problems at T1 (admission) and T4 (discharge). The re-estimation of trajectory analysis confirmed the previously identified mean functioning trajectory classes of stable high functioning (N = 239; 21.75%), early (N = 33; 3.00%), moderate (N = 753; 68.52%), and slow (N = 74; 6.73%) functioning improvement. The network analysis revealed highly connected functioning problems at T1 for the moderate functioning improvement class, including "Feeding", "Dressing upper body", and "Dressing lower body", "Mobility in bed", and "Use of toilet". These functioning domains might indicate potential trajectory-specific targets for clinical interventions. This study has increased our knowledge about functioning trajectories of individuals with SCI undergoing initial rehabilitation in Switzerland and its findings may inform discussions about the application and use of functioning trajectories in clinical practice. Due to the exploratory nature of this study, further research is needed to confirm the findings presented.

Core and bridge symptoms of demoralization in Chinese female cancer patients: a network analysis

Objective

In this study, we explore the core and bridge symptoms of demoralization in female cancer patients in China, and provide a basis for precise psychological intervention among female cancer patients.

Methods

This study used a cross-sectional survey. Participants were recruited from three third-class hospitals in Jiangsu Province from June 2022 to June 2023 using the convenience sampling method. The severity of each symptom of demoralization was investigated in female cancer patients using the Demoralization Scale (DS). Network analysis was performed using the R language to identify core and bridge symptoms in the network and further explore some characteristic edge connections in the network.

Results

The network structure model of demoralization had strong accuracy and stability. In the network, the symptoms with the highest strength centrality were "Discouragement" (C3, strength=2.19), "No self-worth" (A3, strength=1.21), "Don't want to live" (A5, strength=1.20), "Hopeless" (D4, strength=0.81), and "Vulnerability" (B3, strength=0.74), respectively. The bridge strength analysis identified "Hopeless" (D4, bridge strength=0.92), "Discouragement" (C3, bridge strength=0.85), "No self-worth" (A3, bridge strength=0.75), "Poor spirits" (E2, bridge strength=0.71), and "Vulnerability" (B3, bridge strength=0.69) as the bridge symptoms. The strongest edge connections of all dimensions were "No self-worth" and "Worthless" (A3-E6, edge weighting=0.27), "Poor spirits" and "Loss of emotional control" (E2-D1, edge weighting=0.22), "Discouragement" and "Vulnerability" (C3-B3, edge weighting=0.14), and "Hopeless" and "No meaning of survival" (D4-A4, edge weighting=0.12).

Conclusion

"Discouragement (C3)", "No self-worth (A3)", "Hopeless (D4)", and "Vulnerability (B3)" are both core symptoms and bridge symptoms. These symptoms can not only trigger a patient's demoralization but also stimulate more severe symptom clusters through interactions. The early recognition of and intervention regarding these symptoms could be important for the prevention and treatment of demoralization among female cancer patients.

Addressing the Complex Links between Psychopathy and Childhood Maltreatment, Emotion Regulation, and Aggression-A Network Analysis in Adults

Childhood maltreatment is the strongest predictor of psychopathology and personality disorders across the lifespan and is strongly associated with a variety of psychological problems, namely, mood and anxiety disorders, behavioral and personality disorders, substance abuse, aggression, and self-harm. In this study, we aim to provide a comprehensive picture of the interplay between different traits of psychopathy and distinct dimensions of childhood maltreatment, emotion regulation, and aggression. Using a cross-sectional design, we employed correlational network analysis to explore the nomological network of psychopathy and provide a sample-based estimate of the population parameters reflecting the direction, strength, and patterns of relationships between variables. The sample consisted of 846 adults (71% females) who completed questionnaires measuring psychopathy, childhood maltreatment, emotion regulation, and aggression. The results highlight that disinhibition traits of psychopathy are the closest attributes of early experiences of abuse (but not neglect) in childhood and correlate with all dimensions of emotion regulation difficulties, being specifically associated with reactive aggression. Neglect was a unique attribute in the nomological network of meanness, with widespread correlations with emotion regulation difficulties but also an increased ability to engage in goal-directed behavior. Physical abuse was the only dimension of childhood adversity that was found to be intercorrelated with boldness and increased emotional regulation was found in this psychopathic trait. No significant associations were found between boldness, meanness, and aggression once shared variance with disinhibition was controlled. These results are discussed in terms of their implication for research and clinical practice.

Relationships between psychopathological symptoms, pandemic-related stress, perceived social support, and COVID-19 infection history: a network analysis in Chinese college students

Introduction

Previous coronavirus, 2019 (COVID-19) research has applied network analysis to examine relationships between psychopathological symptoms but rarely extended to potential risk and protective factors or the influence of COVID-19 infection history. This study examined complex inter-relationships between psychopathological symptoms, COVID-19-related stressors, perceived social support, and COVID-19 infection history among Chinese university/college students during the peak of fifth pandemic wave using a network analysis approach.

Methods

A Least Absolute Shrinkage and Selection Operator-regularized partial correlation network using Gaussian graphical model was constructed in 1,395 Chinese university/college students in Hong Kong who completed a survey between 15 March and 3 April, 2022. Depressive, anxiety, and acute/traumatic stress symptoms were measured by Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and Impact of Event Scale-6, respectively. COVID-19-related stressors and perceived social support were measured. Network differences by COVID-19 infection history (COVID-network vs. no_COVID-network) and network communities were examined.

Results

Our results showed that the most influential nodes were depressed mood, uncontrollable worries, and uncontrollable thoughts about COVID-19. The main bridging symptoms were concentration problems and psychomotor problems. The COVID-network, comprising participants with a history of COVID-19 infection only, was significantly stronger than the no_COVID-network. Perceived social support and stress from conflicts with family/friends formed a unique community with negative cognition and suicidal idea in the COVID-network only.

Conclusion

Our findings indicate that specific interventions targeting interpersonal conflicts and concentration problems as well as facilitating stress buffering effects of social support may represent effective strategies to reduce psychological distress in university/college students during COVID-19 and should be considered for future pandemic preparedness.

The inner workings of anger: A network analysis of anger and emotion regulation

OBJECTIVE

This study aimed to investigate the interrelations between emotion regulation strategies and different types of anger using network analysis.

METHOD

Data were drawn from a cross-sectional sample of 538 adults (55% females; mean age = 39.8 years, SD = 12.3) seeking treatment for anger. Data were collected between March and November 2019 in Sweden. Participants completed measures of anger problems (anger expression, anger suppression, angry reactions, anger rumination, trait anger, hostility, physical aggression, and verbal aggression) and emotion regulation (cognitive reappraisal, expressive suppression, anger relaxation, and five mindfulness strategies). To determine whether distinct clusters of anger nodes would emerge, exploratory graph analysis was employed. Based on clustering of nodes, we estimated separate networks including all measures of emotion regulation.

RESULTS

Two clusters emerged: one consisting primarily of cognitive components of anger, and another of behavioral. Across networks, anger nodes were strongly interconnected, and anger rumination and anger suppression were especially influential. Several direct links were found between specific emotion regulation strategies and cognitive components of anger, whereas most strategies were only indirectly related to angry behavior. Cognitive reappraisal showed no direct link with any of the anger nodes.

CONCLUSIONS

Our findings reveal potential pathways by which different emotion regulation strategies may influence different types of anger, which could serve as therapeutic targets.

Social-ecological perspective on the suicidal behaviour factors of early adolescents in China: a network analysis

Background

In early adolescence, youth are highly prone to suicidal behaviours. Identifying modifiable risk factors during this critical phase is a priority to inform effective suicide prevention strategies.

Aims

To explore the risk and protective factors of suicidal behaviours (ie, suicidal ideation, plans and attempts) in early adolescence in China using a social-ecological perspective.

Methods

Using data from the cross-sectional project 'Healthy and Risky Behaviours Among Middle School Students in Anhui Province, China', stratified random cluster sampling was used to select 5724 middle school students who had completed self-report questionnaires in November 2020. Network analysis was employed to examine the correlates of suicidal ideation, plans and attempts at four levels, namely individual (sex, academic performance, serious physical illness/disability, history of self-harm, depression, impulsivity, sleep problems, resilience), family (family economic status, relationship with mother, relationship with father, family violence, childhood abuse, parental mental illness), school (relationship with teachers, relationship with classmates, school-bullying victimisation and perpetration) and social (social support, satisfaction with society).

Results

In total, 37.9%, 19.0% and 5.5% of the students reported suicidal ideation, plans and attempts in the past 6 months, respectively. The estimated network revealed that suicidal ideation, plans and attempts were collectively associated with a history of self-harm, sleep problems, childhood abuse, school bullying and victimisation. Centrality analysis indicated that the most influential nodes in the network were history of self-harm and childhood abuse. Notably, the network also showed unique correlates of suicidal ideation (sex, weight=0.60; impulsivity, weight=0.24; family violence, weight=0.17; relationship with teachers, weight=-0.03; school-bullying perpetration, weight=0.22), suicidal plans (social support, weight=-0.15) and suicidal attempts (relationship with mother, weight=-0.10; parental mental illness, weight=0.61).

Conclusions

This study identified the correlates of suicidal ideation, plans and attempts, and provided practical implications for suicide prevention for young adolescents in China. Firstly, this study highlighted the importance of joint interventions across multiple departments. Secondly, the common risk factors of suicidal ideation, plans and attempts were elucidated. Thirdly, this study proposed target interventions to address the unique influencing factors of suicidal ideation, plans and attempts.

Symptom structure of posttraumatic stress disorder in workplace trauma: A "distraction-avoidance" pattern

BACKGROUND

Individuals who experience a workplace trauma are at a high risk of developing posttraumatic stress disorder (PTSD). Most of these people return to work soon after the trauma (i.e., experience early-stage natural exposure). This study aims to explore the response pattern of workers who have experienced a workplace trauma and early-stage natural exposure through network analysis and provide insights into the potential impact of early-stage natural exposure.

METHODS

Our study included 278 workers directly experiencing the workplace explosion in the Tianjin Economic and Technological Development Zone. A partial correlation network analysis was used to explore the PTSD symptoms relationship and identify central symptoms.

RESULTS

The results suggest that emotional numbness, difficulty in concentration, re-experiencing symptoms, and avoidance of thoughts are the most central symptoms, reflecting a "distraction-avoidance" pattern.

CONCLUSIONS

The current study found that workers who experienced workplace trauma exhibited a "distraction-avoidance" pattern, which helps deepen our understanding of the PTSD network and leads to some suggestions on intervention measures.

Using a longitudinal network structure to subgroup depressive symptoms among adolescents

BACKGROUND

Network modeling has been proposed as an effective approach to examine complex associations among antecedents, mediators and symptoms. This study aimed to investigate whether the severity of depressive symptoms affects the multivariate relationships among symptoms and mediating factors over a 2-year longitudinal follow-up.

METHODS

We recruited a school-based cohort of 1480 primary and secondary school students over four semesters from January 2020 to December 2021. The participants (n = 1145) were assessed at four time points (ages 10-13 years old at baseline). Based on a cut-off score of 5 on the 9-item Patient Health Questionnaire at each time point, the participants were categorized into the non-depressive symptom (NDS) and depressive symptom (DS) groups. We conducted network analysis to investigate the symptom-to-symptom influences in these two groups over time.

RESULTS

The global network metrics did not differ statistically between the NDS and DS groups at four time points. However, network connection strength varied with symptom severity. The edge weights between learning anxiety and social anxiety were prominently in the NDS group over time. The central factors for NDS and DS were oversensitivity and impulsivity (3 out of 4 time points), respectively. Moreover, both node strength and closeness were stable over time in both groups.

CONCLUSIONS

Our study suggests that interrelationships among symptoms and contributing factors are generally stable in adolescents, but a higher severity of depressive symptoms may lead to increased stability in these relationships.

Dyslexia, the Amsterdam Way

The current aim is to illustrate our research on dyslexia conducted at the Developmental Psychology section of the Department of Psychology, University of Amsterdam, in collaboration with the nationwide IWAL institute for learning disabilities (now RID). The collaborative efforts are institutionalized in the Rudolf Berlin Center. The first series of studies aimed at furthering the understanding of dyslexia using a gamified tool based on an artificial script. Behavioral measures were augmented with diffusion modeling in one study, and indices derived from the electroencephalogram were used in others. Next, we illustrated a series of studies aiming to assess individuals who struggle with reading and spelling using similar research strategies. In one study, we used methodology derived from the machine learning literature. The third series of studies involved intervention targeting the phonics of language. These studies included a network analysis that is now rapidly gaining prominence in the psychopathology literature. Collectively, the studies demonstrate the importance of letter-speech sound mapping and word decoding in the acquisition of reading. It was demonstrated that focusing on these abilities may inform the prediction, classification, and intervention of reading difficulties and their neural underpinnings. A final section examined dyslexia, conceived as a neurobiological disorder. This analysis converged on the conclusion that recent developments in the psychopathology literature inspired by the focus on research domain criteria and network analysis might further the field by staying away from longstanding debates in the dyslexia literature (single vs. a multiple deficit, category vs. dimension, disorder vs. lack of skill).

Violent behavior and the network properties of psychopathological symptoms and real-life functioning in patients with schizophrenia

Objective

To assess the interplay among psychopathological symptoms and real-life functioning, and to further detect their influence with violent behavior in patient with schizophrenia.

Methods

A sample of 1,664 patients with post-violence assessments and their propensity score-matched controls without violence from a disease registration report system of community mental health service in Guangdong, China, were studied by network analysis. Ising-Model was used to estimate networks of psychopathological symptoms and real-life functioning. Then, we tested whether network properties indicated the patterns of interaction were different between cases and controls, and calculated centrality indices of each node to identify the central nodes. Sensitivity analysis was conducted to examine the difference of interaction patterns between pre-violence and post-violence assessments in violence cases.

Results

Some nodes in the same domain were highly positive interrelations, while psychopathological symptoms were negatively related to real-life functioning in all networks. Many symptom-symptom connections and symptom-functioning connections were disconnected after the violence. The network density decreased from 23.53% to 12.42% without statistical significance (p = 0.338). The network structure, the global network strength, and the global clustering coefficient decreased significantly after the violence (p < 0.001, p = 0.019, and p = 0.045, respectively). Real-life functioning had a higher node strength. The strength of sleeping, lack of spontaneity and flow of conversation, and preoccupation were decreased in post-violence network of patients.

Conclusion

The decreasing connectivity may indicate an increased risk of violence and early warning for detecting violence. Interventions and improving health state based on nodes with high strength might prevent violence in schizophrenia patients.

APOE ε4 positivity predicts centrality of episodic memory nodes in patients with mild cognitive impairment: A cohort-based, graph theory-informed study of cognitive networks

As network neuroscience can capture the systemic impact of APOE variability at a neuroimaging level, this study investigated the network-based cognitive endophenotypes of ε4-carriers and non-carriers across the continuum between normal ageing and Alzheimer's dementia (AD). We hypothesised that the impact of APOE-ε4 on cognitive functioning can be reliably captured by the measurement of graph-theory centrality. Cognitive networks were calculated in 8118 controls, 3482 MCI patients and 4573 AD patients, recruited in the National Alzheimer's Coordinating Center (NACC) database. Nodal centrality was selected as the neurofunctional readout of interest. ε4-carrier-vs.-non-carrier differences were tested in two independent NACC sub-cohorts assessed with either Version 1 or Version 2 of the Uniform Data Set neuropsychological battery. A significant APOE-dependent effect emerged from the analysis of the Logical-Memory nodes in MCI patients in both sub-cohorts. While non-carriers showed equal centrality in immediate and delayed recall, the latter was significantly less central among carriers (v1: bootstrapped confidence interval 0.107-0.667, p < 0.001; v2: bootstrapped confidence interval 0.018-0.432, p < 0.001). This indicates that, in carriers, delayed recall was, overall, significantly more weakly correlated with the other cognitive scores. These findings were replicated in the sub-groups of sole amnestic-MCI patients (n = 2971), were independent of differences in network communities, clinical severity or other demographic factors. No effects were found in the other two diagnostic groups. APOE-ε4 influences nodal properties of cognitive networks when patients are clinically classified as MCI. This highlights the importance of characterising the impact of risk factors on the wider cognitive network via network-neuroscience methodologies.

Depression and anxiety among caregivers of psychiatric patients during the late stage of the COVID-19 pandemic: A perspective from network analysis

BACKGROUND

Depressive and anxiety symptoms (depression and anxiety hereafter) are common among psychiatric patients and their caregivers during the COVID-19 pandemic. Network analysis is a novel method to assess the associations between psychiatric syndromes/disorders at the symptom level. This study examined depression and anxiety among caregivers of psychiatric inpatients during the late stage of the COVID-19 pandemic from the perspective of network analysis.

METHODS

A total of 1101 caregivers of psychiatric inpatients were included in this study. The severity of depression was assessed using the nine-item Patient Health Questionnaire (PHQ-9), while anxiety was assessed with the seven-item Generalized Anxiety Disorder Scale (GAD-7). The expected index (EI) and bridge EI index were used to identify the central and bridge symptoms, respectively. The stability of the network was evaluated via a case-dropping bootstrap procedure.

RESULTS

The prevalence of depression and anxiety were 32.4 % (95%CI: 29.7 %-35.3 %) and 28.0 % (95%CI: 25.4 %-30.7 %), respectively while the prevalence of comorbid depression and anxiety was 24.9 % (95%CI: 22.4 %-27.6 %). The most central symptom was "Fatigue", followed by "Trouble Relaxing" and "Restlessness". The highest bridge symptom was "Restlessness", followed by "Uncontrollable worry" and "Suicide ideation". The bootstrap test indicated that the whole network model was stable, and no network difference was detected between genders and between different education levels.

CONCLUSIONS

Depression, anxiety, and comorbid depression and anxiety were common among caregivers of psychiatric inpatients during the late stage of the COVID-19 pandemic. Central and bridge symptoms identified in this network analysis should be considered key target symptoms to address in caregivers of patients.

Investigating the Theory of Clinical Perfectionism in a Transdiagnostic Eating Disorder Sample Using Network Analysis

Eating disorders are deadly psychiatric illnesses, with treatments working for less than half of individuals who seek treatment. The transdiagnostic theory of eating disorders proposes that eating disorders share similar maintaining symptoms, such as what this theory calls clinical perfectionism (i.e., high levels of concern over mistakes and personal standards). However, it has been difficult to examine the interrelationship of specific aspects of perfectionism, beyond assessing moderation effects, which have generally not found support for the theory of clinical perfectionism in eating disorders. Thus, we used network analysis to test the theory of perfectionism by testing the interrelationships between maladaptive perfectionism facets (concern over mistakes, personal standards, parental criticism, parental expectations, and personal standards) and eating disorder symptoms in 397 individuals diagnosed with an eating disorder. Concern over mistakes was a central symptom and demonstrated the strongest interrelationships with eating disorder symptoms compared to the other aspects of perfectionism, connecting to eating concerns and cognitive restraint. Objective binge eating had a strong negative connection to personal standards. We identified specific central symptoms and illness pathways of perfectionism, which partially supports the theory of clinical perfectionism. Results, if replicated, may suggest that concern over mistakes might be best reconceptualized as part of eating disorder pathology and be targeted to improve treatment outcomes for eating disorders.

The interrelatedness of cognitive abilities in very preterm and full-term born children at 5.5 years of age: a psychometric network analysis approach

BACKGROUND

Very preterm (VP) birth is associated with a considerable risk for cognitive impairment, putting children at a disadvantage in academic and everyday life. Despite lower cognitive ability on the group level, there are large individual differences among VP born children. Contemporary theories define intelligence as a network of reciprocally connected cognitive abilities. Therefore, intelligence was studied as a network of interrelated abilities to provide insight into interindividual differences. We described and compared the network of cognitive abilities, including strength of interrelations between and the relative importance of abilities, of VP and full-term (FT) born children and VP children with below-average and average-high intelligence at 5.5 years.

METHODS

A total of 2,253 VP children from the EPIPAGE-2 cohort and 578 FT controls who participated in the 5.5-year-follow-up were eligible for inclusion. The WPPSI-IV was used to measure verbal comprehension, visuospatial abilities, fluid reasoning, working memory, and processing speed. Psychometric network analysis was applied to analyse the data.

RESULTS

Cognitive abilities were densely and positively interconnected in all networks, but the strength of connections differed between networks. The cognitive network of VP children was more strongly interconnected than that of FT children. Furthermore, VP children with below average IQ had a more strongly connected network than VP children with average-high IQ. Contrary to our expectations, working memory had the least central role in all networks.

CONCLUSIONS

In line with the ability differentiation hypothesis, children with higher levels of cognitive ability had a less interconnected and more specialised cognitive structure. Composite intelligence scores may therefore mask domain-specific deficits, particularly in children at risk for cognitive impairments (e.g., VP born children), even when general intelligence is unimpaired. In children with strongly and densely connected networks, domain-specific deficits may have a larger overall impact, resulting in lower intelligence levels.

Life meaning and feelings of ineffectiveness as transdiagnostic factors in eating disorder and comorbid internalizing symptomatology - A combined undirected and causal network approach

The field of eating disorders is facing problems ranging from a suboptimal classification system to low long-term success rates of treatments. There is evidence supporting a transdiagnostic approach to explain the development and maintenance of eating disorders. Meaning in life has been proposed as a promising key transdiagnostic factor that could potentially not only bridge between the different eating disorder subtypes but also explain frequent co-occurrence with symptoms of comorbid psychopathology, such as anxiety and depression. The present study used self-report data from 501 participants to construct networks of eating disorder and comorbid internalizing symptomatology, including factors related to meaning in life, i.e., presence of life meaning, perceived ineffectiveness, and satisfaction with basic psychological needs. In an undirected network model, it was found that ineffectiveness is a central node, also bridging between eating disorder and other psychological symptoms. A directed network model displayed evidence for a causal effect of presence of life meaning both on the core symptomatology of eating disorders and depressive symptoms via ineffectiveness. These results support the notion of meaning in life and feelings of ineffectiveness as transdiagnostic factors within eating disorder symptomatology in the general population.

Mapping network connection and direction between anxiety and depression symptoms across the early, middle, and late adolescents: Insights from a large Chinese sample

Depression and anxiety are two mental disorders prevailing among adolescents. However, issues regarding the trajectory of depression and anxiety are still controversial on both the disease and symptom dimensions. The novel method of network analysis was used to provide insight into the symptom dimension. 20,544 adolescents (female = 10,743, 52.3%) aged between 14 and 24 years (age mean ± sd = 16.9 ± 2.94) were divided into three subgroups according to age so that the course of depression and anxiety could be traced. Network analysis and the Bayesian network model were used in the current study. The results indicated that uncontrollable worry - excessive worry was the most significant edge for all adolescents, whereas concentration - motor had the highest edge weights for early adolescents, and anhedonia - energy was the most critical pairwise symptom for middle and late adolescents. Irritability can bridge anxiety and depression in the early and middle stages of adolescence, while suicide plays a bridging role in the early and late stages of adolescence. Restlessness and guilt can bridge anxiety and depression in middle- and late-stage adolescents, and feeling afraid plays a unique role in middle-stage adolescents. Except for sad mood, which can trigger middle adolescents' anxiety and depression, the other three subgroups were mainly triggered by nervousness. In addition, all results in our current study were shown to be stable and accurate. In treatment, targeting central and triggering symptoms at different stages of adolescence may be critical to alleviating the comorbidity of anxiety and depression.

Childhood Adversity and Emerging Psychotic Experiences: A Network Perspective

BACKGROUND AND HYPOTHESIS

Childhood adversity is associated with a myriad of psychiatric symptoms, including psychotic experiences (PEs), and with multiple psychological processes that may all mediate these associations.

STUDY DESIGN

Using a network approach, the present study examined the complex interactions between childhood adversity, PEs, other psychiatric symptoms, and multiple psychological mediators (ie, activity-related and social stress, negative affect, loneliness, threat anticipation, maladaptive cognitive emotion regulation, attachment insecurity) in a general population, adolescent sample (n = 865, age 12-20, 67% female).

STUDY RESULTS

Centrality analyses revealed a pivotal role of depression, anxiety, negative affect, and loneliness within the network and a bridging role of threat anticipation between childhood adversity and maladaptive cognitive emotion regulation. By constructing shortest path networks, we found multiple existing paths between different categories of childhood adversity and PEs, with symptoms of general psychopathology (ie, anxiety, hostility, and somatization) as the main connective component. Sensitivity analyses confirmed the robustness and stability of the networks. Longitudinal analysis in a subsample with Wave 2 data (n = 161) further found that variables with higher centrality (ie, depression, negative affect, and loneliness) better predicted follow-up PEs.

CONCLUSIONS

Pathways linking childhood adversity to PEs are complex, with multifaceted psychological and symptom-symptom interactions. They underscore the transdiagnostic, heterotypic nature of mental ill-health in young people experiencing PEs, in agreement with current clinical recommendations.

A comparison of psychiatric symptoms between mental health professionals with and without post-infection sequelae of COVID-19

Post-infection sequelae of COVID-19 (PISC) have raised public health concerns. However, it is not clear whether infected mental health professionals (MHPs) with PISC have experienced more psychiatric symptoms than MHPs without PISC do. This study examined differences in the prevalence of self-reported depression, anxiety, insomnia and suicidality as well as the network structures of these symptoms between these two groups. Participants completed questionnaire measures of psychiatric symptoms and demographics. Expected influence was used to measure centrality of symptoms and network comparison tests were adopted to compare differences in the two network models. The sample comprised 2,596 participants without PISC and 2,573 matched participants with PISC. MHPs with PISC had comparatively higher symptom levels related to depression (55.2% vs. 23.5 %), anxiety (32.0% vs. 14.9 %), insomnia (43.3% vs. 17.3 %), and suicidality (9.6% vs. 5.3 %). PHQ4 ("Fatigue"), PHQ6 ("Guilt"), and GAD2 ("Uncontrollable Worrying") were the most central symptoms in the "without PISC" network model. Conversely, GAD3 ("Worry too much"), GAD5 ("Restlessness"), and GAD4 ("Trouble relaxing") were more central in the "with PISC" network model. In sum, MHPs with PISC experienced comparatively more psychiatric symptoms and related disturbances. Network results provide foundations for the expectation that MHPs with PISC may benefit from interventions that address anxiety-related symptoms, while those without PISC may benefit from interventions targeting depression-related symptoms.

Eating disorders symptoms and depressive symptoms in Chinese Tibetan University students: a network analysis

BACKGROUND

Depression is being increasingly acknowledged as a global public health concern, and following this trend, attention towards eating disorders (EDs) has surged within China's national consciousness. EDs symptoms frequently coexist with various mental health conditions, including depression. However, research focusing on EDs symptoms and depressive symptoms among Tibetan students in China remains scant. This study employs network analysis to estimate the relational network between EDs and depressive symptoms.

METHODS

Tibetan (n = 2,582) and Han (n = 1,743) students from two universities in the Xizang Autonomous Region, China, completed the Eating Attitude Test-26 (EAT-26) and the Patient Health Questionnaire-9 (PHQ-9). We estimated the network structure of EDs symptoms and depressive symptoms, identified central and bridge symptoms, and examined whether network characteristics differed by gender and ethnic.

RESULTS

The core symptoms identified within this study were Calorie_awareness, Desire_to_thin and Fatigue. Conversely, bridge symptoms included Appetite, Suicide, Anhedonia, Guilty, Body_fat_awareness, and Food_preoccupation. The study also revealed no significant gender differences within the network model. However, disparities among ethnic groups were observed within the network structure.

CONCLUSIONS

Our study examined the correlation between EDs symptoms and depressive symptoms in Tibetan college students. Focusing on the individual's quest for the perfect body shape and some Tibetan students' appetite problems - potentially stemming from transitioning to a new university environment, adapting to the school canteen's diet, or being away from their hometown - could aid in the prevention and management of EDs and depression symptoms. It could reduce the incidence of complications by helping students maintain good physical and mental health. Concurrently, our research provides insights into the relatively higher levels of depression triggered by the unique plateau environment.

Outlining a novel psychometric model of mental flexibility and affect dynamics

Theoretically, affective states have always been conceived as complex phenomena enabling individuals to respond flexibly and dynamically to environmental demands. Methodologically, the novel field of Affect Dynamics has started to analyze affective states as inherently dynamic and interdependent phenomena by focusing on how and why they fluctuate over time. Fluctuations of affective states can also be conceived as a function of individuals' ability to flexibly modulate their responses according to environmental demands. However, this ability has been sparsely investigated in different disciplines and domains, thus, engendering a plethora of terms and models. In this conceptual analysis, we first aimed to disentangle the puzzle of flexibility by outlining the distinctive cross-domain features of this concept, thus providing a novel comprehensive operationalization. We termed this novel unitary concept "mental flexibility," the general ability to variably adapt to environmental demands. Then, we outlined the interplay between individuals' mental flexibility and affect dynamics by proposing a novel psychometric model of affect dynamics, using Markovian chain.

Exploring symptom-level associations between anxiety and depression across developmental stages of adolescence: a network analysis approach

BACKGROUND

Anxiety and depression often co-occur during adolescence, but the associations between symptoms of these two disorders in this developmental period are not yet fully understood. Network analysis provides a valuable approach to uncover meaningful associations among symptoms and offers insights for prevention and intervention strategies. This study aimed to investigate symptom-level associations between anxiety and depression using network analysis and to identify core symptoms, bridge symptoms, and differences in network structure across different stages of adolescence.

METHODS

The cross-sectional study was conducted in March 2022 in Shenzhen, China. Participants completed the Generalized Anxiety Disorder Scale-7 and Patient Health Questionnaire Depression Scale, along with demographic questionnaires assessing age and gender. Chinese adolescents aged 10 to 17 who were in Grades 5 or 6 of elementary school, Grades 1 or 2 of middle school, or Grades 1 or 2 of high school, and who could comprehensively understand and read Chinese were recruited as participants. Students in Grade 3 of middle and high schools were excluded due to their upcoming high school or college entrance examinations. Based on age, participants were categorized into early, middle, and late developmental stages of adolescence.

RESULTS

"Loss of control" was among the most central symptoms in the comorbidity network throughout all three developmental stages; "excessive worry" and "anhedonia" emerged as the core symptoms in early adolescence, and "restlessness" as the core symptom in late adolescence. "Anhedonia," "sad mood," and "fatigue" were identified as bridge symptoms between anxiety and depression across all three developmental stages of adolescence. The global strength of the network in middle adolescence was significantly higher compared to the other two stages.

CONCLUSION

These findings highlight the core and bridge symptoms that require special attention and intervention at each stage of adolescence. Moreover, significantly higher network connectivity in middle adolescence suggests this is a critical period for intervention to prevent the development of comorbid mental disorders.

Symptom structure of complex posttraumatic stress disorder among Chinese young adults with childhood trauma: a network analysis

BACKGROUND

The 11th revision of the World Health Organization's International Classification of Diseases (ICD-11) includes a new disorder, complex posttraumatic stress disorder (CPTSD), the diagnostic applicability of which has not been discussed sufficiently in Chinese culture. The network approach to psychopathology enables investigation of the structure of disorders at the symptom level, which allows for analysis of direct symptom interactions. The main objectives of the present study were to explore CPTSD symptom structure and identify key symptoms in CPTSD among young adults in China.

METHODS

The present study collected a large, stratified sample of Beijing university students (1368), ranging from 18 to 25 years old, the majority of whom (65.4%) were female. CPTSD symptoms were assessed using the International Trauma Questionnaire (ITQ). A regularized partial correlation network and Bayesian network were applied to estimate the network structure and the upstream symptoms of CPTSD, respectively.

RESULTS

The regularized partial correlation network showed that the high central symptoms were feelings of failure and hypervigilance, while the bridge symptom between posttraumatic stress disorder (PTSD) and disturbance in self-organization (DSO) domains was long-term upset. The Bayesian network showed that external avoidance and hypervigilance symptoms were upstream in CPTSD symptoms.

CONCLUSIONS

Hypervigilance is a central symptom that can be predictive of other symptoms of CPTSD. While feeling of failure is also a highly central symptom, it may be influenced by other symptoms. In the diagnosis and intervention of CPTSD, more attention should be given to hypervigilance symptoms.

Factor and network analysis of psychosis-like experiences and depressive symptoms in a sample of Finnish adolescents entering psychiatric services

AIM

Psychosis-Like Experiences (PLEs) and depressive symptoms are correlated in clinical adolescent populations. However, symptom-level associations between the two phenomena are not clear.

METHODS

We analysed factor structures of a PLE instrument, the Prodromal Questionnaire-Brief (PQ-B), based on dimensions of positive symptoms of schizophrenia, and a depression measurement instrument, the Beck Depression Inventory (BDI-21A) and performed a network analysis of item-level associations between the two measures. The sample consisted of 417 adolescents (range 13-18 years of age, mean 14.9 years, 72.4% females) entering secondary psychiatric services at Helsinki metropolitan area, Finland.

RESULTS

Confirmatory factor analysis resulted in adequately fitting 2-factor solution, one for PQ-B and one for BDI-21, with a strong correlation coefficient of 0.605 between the two factors. In the network analysis, PQ-B and BDI-21 both formed their own clusters, and two significant pathways were estimated between PQ-B and BDI-21 clusters: 1. the association between paranoid thinking and distorted body image, and 2. the association between somatic preoccupation and worry about problems of one's mind.

CONCLUSIONS

Even though on a general, factor level, PLEs and depressive symptoms were strongly correlated, unique associations between symptoms of the two constructs were sparse. These findings should be considered in the psychiatric assessment and in the care of adolescents.

Network analysis of frailty indicators in hospitalized elderly patients: unveiling the role of depression and hemoglobin as core factors

BACKGROUND

Frailty is a significant concern among hospitalized older adults, influenced by multiple factors. Understanding the complex interactions between these variables can be facilitated through a network perspective.

AIM

This study aimed to identify the core factor and physiological indicator of frailty in hospitalized elderly patients and visualize their interactions within the network structure.

METHODS

Frailty was assessed using the Tilburg Frailty Indicators, with a score of 5 or higher indicating frailty. Additional variables related to sociodemographic, physical and clinical, psychological and cognitive aspects, as well as physiological indicators, were extracted from electronic health records. A partial correlation network analysis was conducted using an adaptive LASSO algorithm, based on univariate correlation and logistic regression, to examine the network structure and identify influential nodes.

RESULTS

The average age of participants was 70.74 ± 7.52 years, with 24.27% classified as frail. Frailty was associated with 38 of 145 initially included variables (P < 0.05). The network analysis revealed depression as the most central node, followed by drugs used, sleep disorders, loneliness, masticatory obstacles, drinking, and number of teeth missing. Hemoglobin emerged as the most central biochemical indicator in the network, based on network center index analysis (Strength = 4.858, Betweenness = 223, Closeness = 0.034).

CONCLUSIONS

Frailty in hospitalized older adults is influenced by various social, physical, and psychological factors, with depression as the core factor of utmost importance. Changes in hemoglobin levels could serve as an essential indicator. This innovative network approach provides insights into the multidimensional structure and relationships in real-world settings.

Mapping PTSD, depression, and anxiety: A network analysis of co-occurring symptoms in treatment-seeking first responders

First responders are at high risk for a range of co-occurring mental health conditions due to their repeated exposure to traumatic events. When first responders present for treatment, their complex presentation of symptoms including posttraumatic stress disorder (PTSD), depression, and generalized anxiety disorder (GAD) can prove challenging to differentiate for clinical purposes. Network analysis provides a means to identify the nuanced associations between the symptoms of these conditions and to identify groups of related symptoms. In this study, a treatment-seeking sample of first responders (N = 432) completed self-report measures of PTSD, depression, and GAD. Network analysis was used to identify symptom clusters within the sample. Our cross-sectional data yielded six empirically distinct communities: depression symptoms, GAD symptoms, and four communities comprising PTSD symptoms - intrusion and avoidance; irritability and aggression; negative affect; and arousal and sleep. Network associations underscore the heterogeneity of PTSD and also highlight overlapping and diverging symptoms of depression and GAD. These findings are discussed within the context of existing research on first responders, and recommendations for further study and treatment interventions are provided.

Exploring gender differences in the relationships among childhood maltreatment, PTSD, and depression in young adults through network analysis

BACKGROUND AND OBJECTIVES

College students who have experienced childhood maltreatment commonly exhibit adverse psychological consequences such as post-traumatic stress disorder (PTSD) and depression. The aim of this study is to use network analysis to investigate the gender differences in complex connections among various types of childhood maltreatment, PTSD, and depression.

METHODS

This study involved 481 participants (M = 19.25 years, 54.5 % female) who were selected from a larger sample of 5231 college students. These participants had experienced childhood maltreatment and exhibited significant clinical symptoms of PTSD and depression. The participants completed validated measures that assessed childhood trauma, PTSD, and depression.

RESULTS

The network analysis performed on the entire sample revealed robust connections among various types of childhood maltreatment, symptom clusters of PTSD, and depression. The most central symptom that emerged was negative alterations in cognitions and mood (NACM), with emotional abuse displaying the highest centrality measure among the various types of childhood maltreatment. Upon comparing the subnetworks, the centrality analysis identified significant gender differences in nodes such as sexual abuse, physical neglect, emotional neglect, and avoidance.

CONCLUSIONS

The study's findings confirm that various forms of childhood abuse have intricate and multifaceted connections with depression and PTSD symptoms in adulthood. The study suggests that NACM could be the most significant symptom, and emotional abuse may play a vital role in adverse psychological outcomes. Furthermore, notable gender discrepancies were identified in the relationship between various forms of maltreatment and psychopathological symptoms.

Using network analysis to identify leverage points based on causal loop diagrams leads to false inference

Network analysis is gaining momentum as an accepted practice to identify which factors in causal loop diagrams (CLDs)-mental models that graphically represent causal relationships between a system's factors-are most likely to shift system-level behaviour, known as leverage points. This application of network analysis, employed to quantitatively identify leverage points without having to use computational modelling approaches that translate CLDs into sets of mathematical equations, has however not been duly reflected upon. We evaluate whether using commonly applied network analysis metrics to identify leverage points is justified, focusing on betweenness- and closeness centrality. First, we assess whether the metrics identify the same leverage points based on CLDs that represent the same system but differ in inferred causal structure-finding that they provide unreliable results. Second, we consider conflicts between assumptions underlying the metrics and CLDs. We recognise six conflicts suggesting that the metrics are not equipped to take key information captured in CLDs into account. In conclusion, using betweenness- and closeness centrality to identify leverage points based on CLDs is at best premature and at worst incorrect-possibly causing erroneous identification of leverage points. This is problematic as, in current practice, the results can inform policy recommendations. Other quantitative or qualitative approaches that better correspond with the system dynamics perspective must be explored.

The unique role of smartphone addiction and related factors among university students: a model based on cross-sectional and cross-lagged network analyses

Smartphone addiction is a global problem affecting university students. Previous studies have explored smartphone addiction and related factors using latent variables. In contrast, this study examines the role of smartphone addiction and related factors among university students using a cross-sectional and cross-lagged panel network analysis model at the level of manifest variables. A questionnaire method was used to investigate smartphone addiction and related factors twice with nearly six-month intervals among 1564 first-year university students (M = 19.14, SD = 0.66). The study found that procrastination behavior, academic burnout, self-control, fear of missing out, social anxiety, and self-esteem directly influenced smartphone addiction. Additionally, smartphone addiction predicted the level of self-control, academic burnout, social anxiety, and perceived social support among university students. Self-control exhibited the strongest predictive relationship with smartphone addiction. Overall, self-control, self-esteem, perceived social support, and academic burnout were identified as key factors influencing smartphone addiction among university students. Developing prevention and intervention programs that target these core influencing factors would be more cost-effective.

Bridge symptoms between parenting styles and proximal psychological risk factors associated with adolescent suicidal thoughts: a network analysis

BACKGROUND

Parenting styles and the associated proximal psychological factors are suggested to increase suicidal risks in adolescents. However, how the two factors interact and confer risks on the emergence of adolescent suicidal thoughts remains unclear. Herein, we used a network approach to investigate their interrelationship and explore whether the network properties predict adolescent suicidal thoughts.

METHODS

Self-report questionnaires were completed by 1171 students aged 12-16. Network analyses were performed by Gaussian graphical models estimating the adolescent psychosocial network structure of parenting styles and psychological variables including depression, anxiety, affective lability, rumination, and resilience. Furthermore, we re-examined the network by adding a variable measuring active suicidal thoughts. Moreover, we conducted linear regressions to examine the predictive utility of bridge symptoms for adolescent suicidal thoughts.

RESULTS

Resilience, Afraid, Rumination, Concentration, and affective lability (Anger) had the highest bridge strengths in the adolescent psychosocial network. Among the identified bridge symptoms, Resilience was negatively correlated with active suicidal thoughts (regularized edge weights = -0.181, bootstrapped 95% CIs: [-0.043, -0.155]), whereas affective lability (from Anxiety to Depression, Anger), Rumination, and Afraid were positively correlated with active suicidal thoughts, with edge weights (bootstrapped 95% CIs) ranging from 0.057 (0.001, 0.112) to 0.081(0.026, 0.136). Regression analysis showed that bridge strength was significantly correlated with active suicidal thoughts (R2 = 0.432, P = 0.001).

CONCLUSION

Negative parenting styles may drive and maintain suicidal thoughts by modifying the key proximal psychological variables. Our findings highlight the important role of bridge symptoms, which may serve as vital targets for triggering adolescent suicide.

Understanding suicidal ideation-a network analysis of the Interpersonal Needs Questionnaire

INTRODUCTION

Identifying various interacting risk factors for suicidality is important to develop preventive measures. The Interpersonal-Psychological Theory of Suicidal Behavior (IPTS) postulates suicidal ideation resulting from the occurrence of Perceived Burdensomeness (PB) and Thwarted Belongingness (TB). Suicidal behavior ultimately occurs if people have a Capability for Suicide. In past studies, the validity of TB was often not empirically confirmed, questioning which of the aspects of TB are central and related to suicidal ideation and whether applied measurement methods adequately capture the construct.

METHOD

Using a sample of 3,404 individuals from different clinical and nonclinical settings, 30% (1,023) of whom reported suicidal ideation, two network analyses were conducted on the Interpersonal Needs Questionnaire (INQ) and a variable mapping suicidal ideation.

RESULTS

Analyses revealed that some items of the INQ were not related to suicidal ideation and the most central items did not have the strongest associations to suicidal ideation.

CONCLUSION

Based on these results, a shortened version of the INQ with the four items that showed the strongest associations with suicidal ideation in the network analyses was suggested.