In the present study we examined loneliness among individuals with and without social anxiety disorder (SAD), contexts that may moderate the experience of loneliness, and the temporal relationship between loneliness and social anxiety. We examined 88 individuals (44 with SAD and 44 without SAD) and used experience sampling (ES) methods, comprising of real-time measurement of naturally occurring events and participants' emotional reactions to them during participants' daily lives over the course of 21 days. Hierarchical linear models indicated that individuals with SAD reported significantly more loneliness compared to individuals without SAD. We also found that characteristics of social situations (negativity, positivity, and meaningfulness) were all positively and significantly associated with loneliness among individuals with SAD. Thus, social situations that were experienced as more negative, more positive, and more meaningful all resulted in increased loneliness for individuals with SAD. We also found a significant Positivity × Meaningfulness interaction that indicated that the effect of positivity was attenuated when meaningfulness was high. Notably, none of these effects were found for individuals without SAD. Finally, we found that for individuals with SAD, both anxiety and loneliness predicted changes in each other and combined to form a deleterious cycle. However, evidence for such a cycle was not found for individuals without SAD. The role of loneliness in the psychopathology of SAD and its maintenance, as well as clinical implications are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Elevated neuroticism may confer vulnerability to the depressogenic effects of stressful life events (SLEs). However, the mechanisms underlying this susceptibility remain poorly understood. Accumulating evidence suggests that stress-related disruptions in neural reward processing might undergird links between stress and depression. Using data from the Saint Louis Personality and Aging Network (SPAN) study and Duke Neurogenetics Study (DNS), we examined whether neuroticism moderates links between stressful life events (SLE) and depression as well as SLEs and ventral striatum (VS) response to reward. In the longitudinal SPAN sample (n = 971 older adults), SLEs prospectively predicted future depressive symptoms, especially among those reporting elevated neuroticism, even after accounting for prior depressive symptoms and previous SLE exposure (NxSLE interaction: p = .016, ΔR² = 0.003). Cross-sectional analyses of the DNS, a young adult college sample with neuroimaging data, replicated this interaction (n = 1,343: NxSLE interaction: p = .019, ΔR² = 0.003) and provided evidence that neuroticism moderates the association between SLEs and reward-related VS response (n = 1,195, NxSLE: p = .017, ΔR² = 0.0048). Blunted left VS response to reward was associated with a lifetime depression diagnosis, r = -0.07, p = .02, but not current depressive symptoms, r = -0.003, p = .93. These data suggest that neuroticism may promote vulnerability to stress-related depression and that sensitivity to stress-related reductions in VS response may be a potential neural mechanism underlying vulnerability to clinically significant depression. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Over the past 2 decades Bayesian methods have been gaining popularity in many scientific disciplines. However, to this date, they are rarely part of formal graduate statistical training in clinical science. Although Bayesian methods can be an attractive alternative to classical methods for answering certain research questions, they involve a heavy "overhead" (e.g., advanced mathematical methods, complex computations), which pose significant barriers to researchers interested in adding Bayesian methods to their statistical toolbox. To increase the accessibility of Bayesian methods for psychopathology researchers, this article presents a gentle introduction of the Bayesian inference framework and a tutorial on implementation. We first provide a primer on the key concepts of Bayesian inference and major implementation considerations related to Bayesian estimation. We then demonstrate how to apply hierarchical Bayesian modeling (HBM) to experimental psychopathology data. Using a real dataset collected from two clinical groups (schizophrenia and bipolar disorder) and a healthy comparison sample on a psychophysical gaze perception task, we illustrate how to model individual responses and group differences with probability functions respectful of the presumed underlying data-generating process and the hierarchical nature of the data. We provide the code with explanations and the data used to generate and visualize the results to facilitate learning. Finally, we discuss interpretation of the results in terms of posterior probabilities and compare the results with those obtained using a traditional method. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
