Quantifying neural signatures of multi-step avoidance behavior in anxiety

Anxiety and related disorders are common and impairing; in particular, avoidance is a highly detrimental component of anxiety that is poorly understood. To better understand and treat avoidance, a theory of avoidance that can account for experimental findings and translate across species is needed. This application proposes to use an interdisciplinary approach to combine neural and behavioral data to validate a novel computational model of avoidance, to test differences in avoidance behavior in people with clinically impairing anxiety and avoidance using this model, and to test whether model-predicted behavior is correlated with and has a causal influence on real-world avoidance behavior. The expected outcome of the proposed research is a novel approach to modify avoidance via a computational model-based operationalization of normative and maladaptive avoidance, opening avenues for translational human and non-human studies and treatment development. This expected outcome consistent with Goals 1 and 3 of NIMH’s Strategic Plan (specifically: 1.1.B: “Applying novel behavioral assays of [cognitive, affective, and social] domains that are causally linked to specific mechanisms at multiple units of analysis [e.g., genetic, molecular, cellular, circuit, physiological, behavioral, systems].”; 1.1.C: “Advancing novel assays to develop biomarkers of disease and for therapeutic discovery.”; 3.1.A: “Developing promising preventive and treatment intervention strategies that target specific molecular, cellular, neural circuit, or psychological mechanisms driving core domains of cognitive, behavioral, and affective function that are disrupted in mental illnesses, including those that cut across diagnostic categories.”; and 3.1.B: “Developing and validating quantitative behavioral and neurophysiological measures of target engagement in humans and animals as translational assays linked to functional domains disrupted in and across mental illnesses.”).