Use of substances to cope predicts posttraumatic stress disorder symptom persistence: Investigating patterns of interactions between symptoms and its maintaining mechanisms.

OBJECTIVE: Posttraumatic stress disorder (PTSD) remains a growing public health challenge across the globe and is associated with negative and persistent long-term consequences. The last decades of research have identified different mechanisms associated with the development and persistence of PTSD, including maladaptive coping strategies, cognitive and experiential avoidance, and positive and negative metacognitions. Despite these advances, little is known about how these different processes interact with specific PTSD symptoms, and how they influence each other over time at the within-person level. METHOD: Leveraging a large (N > 1,800) longitudinal data set representative of the Norwegian population during the COVID-19 pandemic, this preregistered study investigated these symptom-process interactions over four assessment waves spanning an 8-month period. RESULTS: Our panel graphical vector autoregressive network model revealed the dominating role of substance use to cope in predicting higher levels of PTSD symptoms over time and increases in PTSD symptomatology within more proximal time windows (i.e., within 6 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. CONCLUSIONS: 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 PTSD symptomatology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Heart Rate Variability as a Translational Dynamic Biomarker of Altered Autonomic Function in Health and Psychiatric Disease.

The autonomic nervous system (ANS) is responsible for the precise regulation of tissue functions and organs and, thus, is crucial for optimal stress reactivity, adaptive responses and health in basic and challenged states (survival). The fine-tuning of central ANS activity relies on the internal central autonomic regulation system of the central autonomic network (CAN), while the peripheral activity relies mainly on the two main and interdependent peripheral ANS tracts, the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS). In disease, autonomic imbalance is associated with decreased dynamic adaptability and increased morbidity and mortality. Acute or prolonged autonomic dysregulation, as observed in stress-related disorders, affects CAN core centers, thereby altering downstream peripheral ANS function. One of the best established and most widely used non-invasive methods for the quantitative assessment of ANS activity is the computerized analysis of heart rate variability (HRV). HRV, which is determined by different methods from those used to determine the fluctuation of instantaneous heart rate (HR), has been used in many studies as a powerful index of autonomic (re)activity and an indicator of cardiac risk and ageing. Psychiatric patients regularly show altered autonomic function with increased HR, reduced HRV and blunted diurnal/circadian changes compared to the healthy state. The aim of this article is to provide basic knowledge on ANS function and (re)activity assessment and, thus, to support a much broader use of HRV as a valid, transdiagnostic and fully translational dynamic biomarker of stress system sensitivity and vulnerability to stress-related disorders in neuroscience research and clinical psychiatric practice. In particular, we review the functional levels of central and peripheral ANS control, the main neurobiophysiologic theoretical models (e.g., polyvagal theory, neurovisceral integration model), the precise autonomic influence on cardiac function and the definition and main aspects of HRV and its different measures (i.e., time, frequency and nonlinear domains). We also provide recommendations for the proper use of electrocardiogram recordings for HRV assessment in clinical and research settings and highlight pathophysiological, clinical and research implications for a better functional understanding of the neural and molecular mechanisms underlying healthy and malfunctioning brain-heart interactions in individual stress reactivity and psychiatric disorders.

The generalization of behavioral control over physical threats to social stressors in humans: A pilot fMRI study.

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, we piloted a paradigm to investigate how behavioral control over a physical threat (electric shocks), generalized to responses for a subsequent social stressor (anticipation of public speaking). Our manipulation of behavioral control effected perceived control between groups, increased stress across but not between groups, and no effects generalized to the subsequent social stressor in behavioral, physiological, or neural responses. We discuss refinements to the paradigm to strengthen 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.

Gender differences in cannabis use disorder symptoms: A network analysis.

BACKGROUND: While cannabis use in women is increasing worldwide, research into gender differences in cannabis use disorder (CUD) symptomology is lacking. 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 this study was to evaluate the CUD symptom network and assess whether there are gender differences therein. METHODS: A total of 1257 Dutch individuals reporting weekly cannabis use, including 745 men and 512 women, completed online 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: 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: The association between smoking and withdrawal as well as gender differences in the role of comorbidities in the CUD network highlight the value of using network models to understand CUD and how symptom interactions might affect treatment.

Investigating the feasibility of idiographic network models.

Recent times have seen a call for personalized psychotherapy and tailored communication during treatment, leading to the necessity to model the complex dynamics of mental disorders in a single subject. To this aim, time-series data in one patient can be collected through ecological momentary assessment and analyzed with the graphical vector autoregressive model, estimating temporal and contemporaneous idiographic networks. Idiographic networks graph interindividual processes that may be potentially used to tailor psychotherapy and provide personalized feedback to clients and are regarded as a promising tool for clinical practice. However, the question whether we can reliably estimate them in clinical settings remains unanswered. We conducted a large-scale simulation study in the context of psychopathology, testing the performance of personalized networks with different numbers of time points, percentages of missing data, and estimation methods. Results indicate that sensitivity is low with sample sizes feasible for clinical practice (75 and 100 time points). It seems possible to retrieve the global network structure but not to recover the full network. Estimating temporal networks appears particularly challenging; thus, with 75 and 100 observations, it is advisable to reduce the number of nodes to around six variables. With regard to missing data, full information maximum likelihood and the Kalman filter are effective in addressing random item-level missing data; consequently, planned missingness is a valid method to deal with missing data. We discuss possible methodological and clinical solutions to the challenges raised in this work. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

COVID-19 and mental health among at-risk university students: A prospective study into risk and protective factors.

OBJECTIVE: The COVID-19 pandemic has confronted young adults with an unprecedented mental health challenge. Yet, prospective studies examining protective factors are limited. METHODS: In the present study, we focused on changes in mental health in a large sample (N = 685) of at-risk university students, which were measured before and during the pandemic. Network modeling was applied to 20 measured variables to explore intercorrelations between mental health factors, and to identify risk and protective factors. Latent change score modeling was used on a subset of variables. RESULTS: The main findings indicate that (1) mental health problems increased at group level, especially depression-anxiety and loneliness; (2) emotional support during the COVID pandemic was associated with smaller increases in loneliness and depression-anxiety; (3) COVID-related stress predicted increases in depression-anxiety; (4) loneliness acted as a bridge construct between emotional support and changes in mental health. CONCLUSION: To mitigate the impact of the COVID-19 pandemic on the mental health of young adults, is it recommended to focus on interventions that strengthen internal resources (stress-regulating abilities) and reduce loneliness.