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) 2022 APA, all rights reserved).

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.

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.

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. 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. 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. 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.

The previous chapter introduced time into the sampling design of studies. With the addition of time, longitudinal analysis of multiple measures per person becomes possible. As the previous chapter discussed, this step is vital in separating within-from between-person relationships. Time adds a new level of complexity to the modeling frameworks we have used before: temporal dependencies between observations of the same person over time. This complicates the models—as many more parameters need to be estimated—but also comes with the substantial benefit of allowing researchers to study temporal effects— often termed ‘dynamical relationships.’ With the advent of modern data collection methodologies, such as electronic diaries or wearables, time series data have become a new data source to estimate networks. For example, in experience sampling method and ecological momentary assessment, a smartphone can be used to query a subject multiple times per day (also termed ‘beeps’) on a set of questions. Through these advances, the 170use of dynamical network models has grown prominent. This chapter introduces the estimation of dynamical networks from longitudinal data. The chapter begins with a summary of the main modeling framework used, followed by explanations on how to estimate these models from N = 1 and N > 1 data sets. The chapter will then conclude with an overview of practical and methodological challenges in dynamical network analysis.

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 6 variables. With regard to missing data, FIML 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.