Despite growing concerns about validity, the NIMH Research Domain Criteria (RDoC) framework plays a key role in organizing basic, translational, and clinical research. RDoC’s approach to fear and anxiety is categorical: threat is either acute or potential; engages either the Amygdala or the bed nucleus of the stria terminalis (BST); and elicits either fear or anxiety. Recent work casts doubt on this binary perspective, spurring the development of alternative approaches. Dimensional models posit that threat responses vary along a smooth continuum of perceived danger—from absolutely safety to on-going attack. Danger perceptions are thought to emerge from parametric estimates of threat proximity, probability, and certainty, which are computed in weakly segregated cortico-subcortical circuits. To date, there have been no systematic, well-powered efforts to computationally implement these competing models and compare their validity. Furthermore, while both models highlight the importance of threat uncertainty, they do not specify which kind. Computational psychiatry recognizes 2 mathematically distinct kinds of uncertainty: Risk and Ambiguity. Which of these is more relevant to threat reactivity and how they map onto the underlying neurobiology is unknown. To address these fundamental questions, we will recruit a racially diverse community sample enriched for elevated fear/anxiety symptoms. Two parametric threat-anticipation paradigms will allow us to simultaneously probe circuits sensitive to categorical (RDoC) and dimensional variation in threat for the first time. Smartphone phenotyping will assess real-world threat exposure, uncertainty, and distress. A1. We will test a series of competing predictions about the architecture of threat-sensitive brain circuits. We will use theory-driven computational modeling to go beyond binary threat categories; identify regions sensitive to risk, ambiguity, and other dimensional facets of threat; and explore trial-by-trial relations with signs and symptoms of fear and anxiety. A2. RDoC implies that Acute and Potential Threat are represented in different patterns of brain activity; indeed, this was the major rationale for creating separate RDoC constructs. Dimensional models predict substantial similarities. Multivoxel machine- learning approaches provide a rigorous means of adjudicating these claims and clarifying the importance of the Amygdala, BST, and other regions. A3. Fusing the fMRI and smartphone data-streams will enable us to establish the relevance of specific facets of threat and specific brain regions to real-world distress. We will also explore relations between neuroimaging metrics and fear- and anxiety-related diagnoses, symptoms, and traits. Significance. Extreme fear and anxiety are leading causes of human misery and morbidity. This project will provide a potentially transformative opportunity to develop the first computationally grounded model of fear and anxiety. It will help adjudicate on-going theoretical debates, validate a new conceptual approach for use with other read-outs and species, set the stage for new kinds of translational models and clinical studies, prioritize new targets for neuromodulation and other therapeutics development, and guide the development of RDoC 2.0.

PUBLIC HEALTH SIGNIFICANCE Fear and anxiety disorders imposes a staggering burden on public health and the global economy and existing treatments are far from curative, underscoring the urgency of developing a deeper understanding of the underlying neurobiology. This study will harness an innovative combination of approaches—including brain imaging, state-of-the-art computational techniques, and smartphone digital phenotyping—to understand the architecture of the brain circuits underlying fear and anxiety and determine their relevance to real-world distress, setting the stage for developing more effective brain-based intervention strategies.