Network intervention analysis of anxiety-related outcomes and processes of acceptance and commitment therapy (ACT) for anxious cancer survivors

Network analysis: An overview for mental health research

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

Network analysis of resilience, anxiety and depression in clinical nurses

BACKGROUND

Resilience is a protective feature against anxiety and depression disorders. However, the precise relationship and structure of resilience and anxiety and depression remain poorly understood. This study sought to investigate the link among resilience' components and anxiety as well as depression.

METHODS

1,279 clinical nurses were recruited. 10-item Connor-Davidson Resilience Scale, Generalized Anxiety Disorder 7, and Patient Health Questionnaire 9 were employed to evaluate resilience, anxiety, and depression, respectively. The regularized partial-correlation network was generated utilizing data from cross-sectional survey and the bridge expected influence index was utilized to quantify bridge components.

RESULTS

The rates of anxiety and depression within clinical nurses were 67.3% and 67.2%, accordingly. Four strongest bridge edges appeared in the resilience-anxiety network, like "Adapt to change"- "Fear that something might happen", and "Stay focused under pressure"- "Uncontrollable worry". Two strongest bridge edges appeared in the resilience-depression network, like "Adapt to change"- "Concentration difficulties" and "Stay focused under pressure"- "Fatigue". "Adapt to change" was recognized as bridging nodes in both the resilience-anxiety network and the resilience-depression network.

CONCLUSIONS

Interventions targeting the bridge component "Adapt to change" within resilience, may mitigate the intensity of anxiety and depression symptoms among clinical nurses.

Centrality measures in psychological networks: A simulation study on identifying effective treatment targets

The network theory of psychopathology suggests that symptoms in a disorder form a network and that identifying central symptoms within this network might be important for an effective and personalized treatment. However, recent evidence has been inconclusive. We analyzed contemporaneous idiographic networks of depression and anxiety symptoms. Two approaches were compared: a cascade-based attack where symptoms were deactivated in decreasing centrality order, and a normal attack where symptoms were deactivated based on original centrality estimates. Results showed that centrality measures significantly affected the attack's magnitude, particularly the number of components and average path length in both normal and cascade attacks. Degree centrality consistently had the highest impact on the network properties. This study emphasizes the importance of considering centrality measures when identifying treatment targets in psychological networks. Further research is needed to better understand the causal relationships and predictive capabilities of centrality measures in personalized treatments for mental disorders.

The dynamic interaction between symptoms and pharmacological treatment in patients with major depressive disorder: the role of network intervention analysis

INTRODUCTION

The Major Depressive Disorder (MDD) is a mental health disorder that affects millions of people worldwide. It is characterized by persistent feelings of sadness, hopelessness, and a loss of interest in activities that were once enjoyable. MDD is a major public health concern and is the leading cause of disability, morbidity, institutionalization, and excess mortality, conferring high suicide risk. Pharmacological treatment with Selective Serotonin Reuptake Inhibitors (SSRIs) and Serotonin Noradrenaline Reuptake Inhibitors (SNRIs) is often the first choice for their efficacy and tolerability profile. However, a significant percentage of depressive individuals do not achieve remission even after an adequate trial of pharmacotherapy, a condition known as treatment-resistant depression (TRD).

METHODS

To better understand the complexity of clinical phenotypes in MDD we propose Network Intervention Analysis (NIA) that can help health psychology in the detection of risky behaviors, in the primary and/or secondary prevention, as well as to monitor the treatment and verify its effectiveness. The paper aims to identify the interaction and changes in network nodes and connections of 14 continuous variables with nodes identified as "Treatment" in a cohort of MDD patients recruited for their recent history of partial response to antidepressant drugs. The study analyzed the network of MDD patients at baseline and after 12 weeks of drug treatment.

RESULTS

At baseline, the network showed separate dimensions for cognitive and psychosocial-affective symptoms, with cognitive symptoms strongly affecting psychosocial functioning. The MoCA tool was identified as a potential psychometric tool for evaluating cognitive deficits and monitoring treatment response. After drug treatment, the network showed less interconnection between nodes, indicating greater stability, with antidepressants taking a central role in driving the network. Affective symptoms improved at follow-up, with the highest predictability for HDRS and BDI-II nodes being connected to the Antidepressants node.

CONCLUSION

NIA allows us to understand not only what symptoms enhance after pharmacological treatment, but especially the role it plays within the network and with which nodes it has stronger connections.

Measuring the active elements of cognitive-behavioral therapies

Understanding how and for whom cognitive-behavioral therapies work is central to the development and improvement of mental health interventions. Suboptimal quantification of the active elements of cognitive-behavioral therapies has hampered progress in elucidating mechanisms of change. To advance process research on cognitive-behavioral therapies, we describe a theoretical measurement framework that focuses on the delivery, receipt, and application of the active elements of these interventions. We then provide recommendations for measuring the active elements of cognitive-behavioral therapies aligned with this framework. Finally, to support measurement harmonization and improve study comparability, we propose the development of a publicly available repository of assessment tools: the Active Elements of Cognitive-Behavioral Therapies Measurement Kit.