The lab was fortunate enough to receive a very generous new NIMH award focused on understanding the computational and neural architecture of fear and anxiety.

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 by our team, Dean Mobbs, and other investigators 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. Using techniques adapted from neuroeconomics, a parametric threat-anticipation paradigm 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.

Extreme fear and anxiety are leading causes of human misery and morbidity. This project will provide an exciting opportunity to develop one of the first computationally grounded models of fear and anxiety in a relatively large and diverse “DMV” (DC, MD, & VA) sample. 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 set the stage for the development of RDoC 2.0.

This project represents a team-science collaboration between the University of Maryland (Drs. Alex Shackman & Jason Smith) and the University of California-Davis (Drs. Andrew Fox & Erie Boorman)

You can read more about the project at Maryland Today.