Network analysis of cognitive emotion regulation strategies and depressive symptoms in young adults after recent stressful events: The moderation of childhood maltreatment

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

INTRODUCTION

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSIONS

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

Adapting a Strategy-Based Measure of Behavioral Emotion Regulation: An Exploration of Links with Psychopathology from a Network Perspective

Within the strategy-based approach, research has extensively explored cognitive emotion regulation strategies and their association with psychopathology. The present study aims to focus on behavioral strategies by adapting the Behavioral Emotion Regulation Questionnaire (BERQ). Additionally, to explore the interplay between multiple types of strategies and their interaction with psychopathology, we assessed the links between both adaptive and maladaptive cognitive and behavioral strategies with symptoms of depression, anxiety, and stress, utilizing a network perspective. Using a sample of 518 native Spanish speakers, the results corroborated the factor structure of both the BERQ-SP and the reduced version, demonstrating comparable reliability and validity. In terms of associations with psychopathology, maladaptive strategies exhibited stronger links with symptoms, particularly cognitive ones, which appeared more closely connected to the symptoms within the network. Considering bridge centrality, adaptive strategies such as actively approaching and seeking distraction, and maladaptive strategies such as self-blame and rumination, exhibited a central role in the network, connecting different types of strategies and symptoms. Notably, rumination and seeking distraction showed positive links with both adaptive and maladaptive strategies. These findings highlight the complexity of the interactions between strategies and symptoms, suggesting the need to consider these connections in the context of therapeutic interventions.

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

This study involved a non-clinical sample.

CONCLUSION

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

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

BACKGROUND

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

METHOD

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

RESULTS

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

CONCLUSION

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

The Relationship Between Cognitive Emotion Regulation Strategy and Mental Health Among University Students During Public Health Emergency: A Network Analysis

Background

Public health emergencies pose threats to mental health, and cognitive emotional regulation can be a crucial coping strategy. This study explored the relationship between cognitive emotion regulation strategies and mental health among university students during the COVID-19 pandemic using network analysis.

Methods

1100 university students completed questionnaires assessing depression, anxiety, somatization, and cognitive emotion regulation strategies. Network analysis was conducted to identify network structures and bridge symptoms.

Results

(1) In the depression network, the strongest edge is D1 (Little interest)-D2 (Feeling down), while D2 emerged as the node with the highest centrality. C1 (Self-blame), C8 (Catastrophizing), D6 (Feeling bad), and D9 (Suicide) are bridge symptoms. (2) In the anxiety network, A2 (Uncontrollable worrying)-A3 (Worrying too much) were identified as the strongest edge, and A2 exhibiting the highest centrality. C1 (Self-blame), C8 (Catastrophizing), and A6 (Easy annoyance) are bridge symptoms. (3) In the somatization network, the strongest edge is S14 (Fatigue)-S15 (Sleep disturbances) and S9 (Palpitations) exhibited the highest centrality. C1 (Self-blame), C3 (Rumination), C8 (Catastrophizing), S9 (Palpitations), and S14 (Fatigue) are bridge symptoms.

Conclusion

Self-blame and catastrophizing are important bridge symptoms for cognitive emotion regulation strategies and mental health networks, so cognitive behavioral therapy, focusing on self-blame and catastrophizing as intervention targets, could most effectively improve mental health during public health emergencies.

Differences between networks of cognitive emotion regulation strategies in medical students with and without a history of non-suicidal self-injury

BACKGROUND

Non-suicidal self-injury (NSSI) is deliberate self-harm without the intent to die. NSSI is associated with mental health issues and has a higher prevalence among medical students. Understanding the cognitive emotion regulation strategies in NSSI is important for intervention and prevention. Network analysis provides a holistic perspective on these strategies.

MATERIALS AND METHODS

Participants were 405 individuals (68.6% female) with a mean age of 22.7 years (standard deviation: 3.97, range: 17-55 years), recruited from Tehran, Iran. Upon conducting an initial screening, it was found that 68 individuals from our sample acknowledged a prior history of NSSI.

RESULTS

A network analysis showed that that students with a history of NSSI perceived themselves to have difficulties in putting into perspective, positive reappraisal, and self-blame. Compared to students without NSSI, those with NSSI exhibited negative associations between putting into perspective and rumination (PIP__RUM: -0.240) and positive associations between positive refocusing and catastrophizing (PR__Catast: 0.61).

CONCLUSIONS

This study discovered notable distinctions in the cognitive processing and coping strategies of medical students with and without a history of NSSI. These findings contribute to the existing literature on the link between cognitive-emotional processes and NSSI, informing the development of interventions to decrease such behaviors.

A network approach to emotion regulation and symptom activation in depression and anxiety

Background

Emotions can be regulated through several regulatory strategies that are involved in the development of psychopathological symptoms. Despite the well-established association between psychopathology and emotion dysregulation, little is known about the relationship between individual symptoms of depression and anxiety and emotion regulation strategies (ERS), as well as between ERS themselves.

Method

We conducted a cross-sectional study and examined the interactions between six ERS (reappraisal, engagement, rumination, suppression, arousal control, and distraction) and assessed their distinctive association with the activation of specific symptoms of depression and anxiety in a community sample of 376 adults (80.4% female; Mage  = 32.70; SDage  = 11.80). The Regulation Emotion Systems Survey (RESS) was used to measure ERS. The Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder (GAD-7) were used to assess psychological symptoms. An exploratory graph analysis was performed to examine the structural properties of the network of interactions between these behaviors. Additionally, to test the association of ERS with the activation of the depression symptoms network, an expected symptoms activity (ESA) was conducted.

Results

Six communities were found that correspond to the six ERS. Rumination and suppression have a significant association with symptom activation (particularly low self-esteem), whereas reappraisal reduces symptomatic activation. The effect of arousal control, engagement, and distraction appears to depend on the remaining ERS rather than having much influence on their own.

Conclusion

This study provides insight into how ERS interact with each other and with individual symptoms of depression and anxiety. Understanding the effects of these interactions on symptom activation and comorbidity can improve our understanding of psychopathology.

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.

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.

The relations between different components of intolerance of uncertainty and symptoms of depression during the COVID-19 pandemic: A network analysis

BACKGROUND

The relations between depression and intolerance of uncertainty (IU) have been extensively investigated during the COVID-19 pandemic. However, there is a lack of understanding on how each component of IU may differentially affect depression symptoms and vice versa. The current study used a network approach to reveal the component-to-symptom interplay between IU and depression and identify intervention targets for depression during the COVID-19 pandemic.

METHODS

A total of 624 college students participated in the current study. An IU-Depression network was estimated using items from the 12-item Intolerance of Uncertainty Scale and the Patient Health Questionnaire-9. We examined the network structure, node centrality, and node bridge centrality to identify component-to-symptom pathways, central nodes, and bridge nodes within the IU-Depression network.

RESULTS

Several distinct pathways (e.g., "Frustration when facing uncertainty" and "Feelings of worthlessness") emerged between IU and Depression. "Fatigue" and "Frustration when facing uncertainty" were identified as the central nodes in the estimated network. "Frustration when facing uncertainty," "Psychomotor agitation/retardation," and "Depressed or sad mood" were identified as bridging nodes between the IU and Depression communities.

CONCLUSION

By delineating specific pathways between IU and depression and highlighting the influential role of "Frustration when facing uncertainty" in maintaining the IU-Depression co-occurrence, current findings may inform targeted prevention and interventions for depression during the COVID-19 pandemic.