Waters for sharing with us their data. Additionally, we would like to thank Fabio A. Melis for raising discussion points that were of inspiration for this work during his master thesis. Part of the current work was presented during the Psychological Networks Amsterdam Summer School 2020, and will be presented in a poster at the 54 th Annual Convention of the Association for Behavioral and Cognitive Therapies.
Post-traumatic stress disorder (PTSD), and a novel related condition, complex PTSD, remain a growing public health challenge across the globe and are associated with negative and persistent long-term consequences. The last decades of research revealed different processes and mechanisms associated with the development and persistence of PTSD and complex PTSD symptoms, including different maladaptive coping strategies, cognitive and experiential avoidance, positive, and negative metacognitions. Despite these advances, little is known about how these different processes interact with specific (C)PTSD symptoms, and how they influence each other over time at the within-person level. Leveraging a large (N > 1,800) longitudinal dataset representative of the Norwegian population during the COVID-19 pandemic, this pre-registered study investigated these symptom-process interactions over an eight-month period. Our panel graphical vector autoregressive (GVAR) network model revealed the dominating role of substance use to cope in predicting higher levels of all different PTSD symptoms over time and being associated with increases in CPTSD symptomatology within more proximal time-windows (i.e., within six weeks). Threat monitoring was associated with increased suicidal ideation, while threat monitoring itself was increasing upon decreased avoidance behavior, greater presence of negative metacognitions, and higher use of substances to cope. In addition, our analyses revealed several, particularly strongly co-occurring PTSD symptoms and processes within the same time window (six weeks, contemporaneous associations). Our findings speak to the importance of attending to different coping strategies, particularly the use of substances as a coping behavior in efforts to prevent PTSD chronicity upon symptom onset. We outline future directions for research efforts to better understand the complex interactions and temporal pathways leading up to the development and maintenance of (C)PTSD symptomatology.
Importance. While cannabis use in women is increasing worldwide, research into gender differences in cannabis use disorder (CUD) symptomology is lacking. Objective. In response to limited effectiveness of addiction treatment, research focus has been shifting from clinical diagnoses towards interactions between symptoms, as patterns of symptoms and their interactions could be crucial in understanding etiological mechanisms in addiction. The aim of the current study was to evaluate the CUD symptom network and assess whether there are gender differences therein. Design. Cross-sectional. Setting. Online self-report study. Participants. A convenience sample of 1257 weekly cannabis users, including 745 men and 512 women. Main outcome and measure. Participants completed questionnaires assessing DSM-5 CUD symptoms and additional items on plans to quit or reduce use, cigarette use, and the presence of psychological diagnoses. Gender differences were assessed for all variables and an Ising model estimation method was used to estimate CUD symptom networks in men and women using network comparison tests to assess differences. Results. The estimated networks were dense with all symptoms except for tolerance and risky use being highly central to the network. There were gender differences in the prevalence of 6 of the 11 symptoms, but symptom networks did not differ between men and women. Cigarette use appeared to only be connected to the network through withdrawal, indicating a potential role of cigarette smoking in enhancing cannabis withdrawal symptoms. Furthermore, there were gender differences in the network associations of mood and anxiety disorders with CUD symptoms. Conclusion and relevance. While men and women differ in symptom prevalence, the pattern and weights of the associations between symptoms were found to be very similar. However, gender differences in the role of comorbidities in the network and the relation between smoking and withdrawal highlight the importance of gender differences in understanding CUD, which may have implications for treatment.
Behavioral control, the ability to manage one’s exposure to a given stressor, influences the impacts of both the present and future stressors. Behavioral control over a stressor may decrease stress caused by the stressor, and promote resilience towards future stressors. A lack of behavioral control may exacerbate the stress response and lead to learned helplessness, a generalized view that one cannot control other, unrelated stressors in their environment. The ventromedial prefrontal cortex (vmPFC) may detect the presence of behavioral control over a stressor and communicate this to subcortical regions involved in stress responses, such as the nucleus accumbens (NAc). Building on previous research in animals and humans, piloted a paradigm to investigate how behavioral control over a physical threat (electric shocks), generalizes to responses for a subsequent social stressor (anticipation of public speaking). Our manipulation of behavioral control over a physical threat effected perceived control but not stress, and no effects generalized to the subsequent social stressor in behavioral, physiological, or neural responses. We discuss refinements to the paradigm to strengthen the effect of the manipulation, the potential impacts of statistical power on the present results, and metrics to measure the generalization of behavioral control in addition to vmPFC-subcortical connectivity.
Within-person network dynamics on a monthly or yearly level have been difficult to study due to the lack of suitable analytic methods. In this study, a new method for estimating networks from panel data was used to investigate how symptoms of depression, mechanisms proposed in the meta-cognitive therapy (MCT) model, and loneliness interact across a nine-month period. Four data waves were delivered by a representative population sample of 4,361 participants during the COVID-19 pandemic in Norway. Networks were estimated using the panel graphical vector-autoregression (panelgvar) method. In the temporal network, use of substance to cope with negative feelings positively predicted threat monitoring and depressed mood. In turn, threat monitoring positively predicted suicidal ideation. Meta-cognitive beliefs that thoughts and feelings are dangerous positively predicted anhedonia. Suicidal ideation positively predicted sleep problems and worthlessness. Loneliness was positively predicted by depressed mood. In turn, more loneliness predicted more control of emotions. In the contemporaneous network, worry/rumination was positively related to depressed mood, worthlessness, and sleep problems. In conclusion, the findings indicate the importance of the theory-derived variables threat monitoring, beliefs that thoughts and feelings are dangerous, and substance use as potential targets for intervention to alleviate long-term depressive symptoms. Suicidal ideation may have a detrimental long-term effect on worthlessness. The results also suggest that depressed mood leads to loneliness in the long run but does not support that loneliness leads to depressive symptoms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.