PROJECT ABSTRACT Anxiety disorders are some of the most common and debilitating psychiatric diagnoses, affecting ~1 in 3 people worldwide. Despite the enormous impact on global well-being, key concepts that are critical to understanding these disorders remain poorly defined in the extant literature. Although heightened sensitivity to "uncertain threat anticipation" is regarded as a core contributor to anxiety and anxiety disorders and Threat Uncertainty is a core construct that cuts across models of anxiety in the NIMH RDoC framework, there is no ground-truth definition of "uncertainty". Previous studies have found that anticipation of temporally “uncertain” threats, as compared to temporally “certain” threats, are associated with increases self-reported anxiety, startle-responses, and brain activation in anxiety-related brain regions. In this proposal, we will test an alternate explanation for these results using a computational, statistical-learning approach. In particular, we suggest that lab-based assessments of uncertain anticipation have confounded “uncertainty” with alterations in the probability of a threat given that it has not already occurred, i.e. the hazard-rate. Here, building on our team’s expertise in anxiety and decision-making, we will test the hypothesis that it hazard-rate, and not uncertainty per se, accounts for increases in self-reported anxiety and persistence (Aim 1), as well as alterations in anxiety-related BOLD responses in anxiety-relevant brain regions (Aim 2). To this end, we have developed a novel paradigm that allows for a 2x2 design where we manipulate uncertainty (high/low) and hazard-rate (high-low). In contrast to existing theories, which predict a main effect of uncertainty, we predict a main effect of hazard-rate on all relevant measures. Relevant measures include self-reported anxiety, persistence in a threatening environment, and fMRI measures of brain activation. We will contrast this paradigm with previous paradigms that have confounded uncertainty and hazard-rate to provide a more precise interpretation of extant findings. This theory-driven computational approach to understanding uncertain anticipation has the potential to provide a “course correct” for the field, refine our understanding of anxiety, and inform the development of new treatments.

PROJECT NARRATIVE Although anxiety disorders are among the most common psychiatric illnesses, existing treatments are incomplete with many patients continuing to suffer. To understand how excessive uncertainty about an upcoming threat can elicit and exacerbate anxiety, we will conduct a series of experiments, while measuring participants' anxious feelings, behaviors, and brain function. Advancing our understanding of how uncertain and challenging environments give rise to anxiety promises to shed new light on the biological processes underlying anxiety disorders, ultimately leading to new treatment approaches.