Using theory- and data-driven neurocomputational approaches and digital phenotyping to understand RDoC Acute and Potential Threat

Background. Fear and anxiety are leading causes of human misery and morbidity. Recognizing the barriers that traditional diagnoses pose for scientific discovery and therapeutics development, NIMH launched RDoC, an alternative framework, defined by expert consensus and centered on a matrix of cross-cutting biobehavioral systems. Despite mounting validity concerns, RDoC has come to play a central role in psychopathology research. RDoC organizes fear and anxiety into 2 strictly segregated systems. The Acute Threat system is centered on the Amygdala, is sensitive to dangers that are imminent and certain, and promotes fear. In contrast, the Potential Threat system is centered on the bed nucleus of the stria terminalis (BST); is sensitive to dangers that are distal, low probability, or uncertain; and promotes anxiety. RDoC embodies an ‘either/or’ perspective: Threat is either certain or uncertain; engages either the Amygdala or the BST; and elicits either fear or anxiety. Yet recent work by our team and many others casts doubt on this framework. These observations have fueled the development of alternative models, which re-cast the categorical features of Acute and Potential Threat as dimensions. In dimensional models, threat responses vary along a gradient of perceived danger—from absolute safety to on-going attack. Danger perceptions are hypothesized to reflect parametric estimates of threat proximity, probability, and certainty computed in weakly segregated cortico-subcortical circuits.

Key Gaps. (1) To date, there have been no systematic, well-powered efforts to computationally implement these competing models and test their validity. (2) Both models underscore the importance of threat uncertainty, but they do not specify which kind. In fact, computational psychiatry recognizes several mathematically distinct kinds of uncertainty, including Risk (known probabilistic outcomes) and Ambiguity (partially unknowable probabilistic outcomes). Which is more relevant to fear and anxiety and how they are implemented in the brain remains unexplored and unknown.

Approach. This project will use a novel combination of cutting-edge computational tools and smartphone digital phenotyping to test and extend competing models of fear and anxiety, to understand the functional organization of the underlying neural systems, and to assess their real-world relevance in a clinically relevant sample. Leveraging our team’s multidisciplinary expertise, we will use stratified sampling to recruit a racially diverse community/campus sample that encompasses a broad spectrum of fear/anxiety, over-sampling individuals with elevated symptoms (N=240; 60% high; >50% BIPOC). Diagnoses, symptoms, and traits will be assessed. Two parametric threat-anticipation fMRI paradigms will allow us to probe circuits sensitive to Categorical Threat (certain safety, certain threat, uncertain threat) and DimensionalThreat (threat proximity, probability, risk, and ambiguity) for the first time. Smartphone ecological momentary assessment (EMA) will quantify real-world threat exposure, threat uncertainty, and distress.

Specific Aims. 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.

Overall 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.