PROJECT SUMMARY
Anorexia nervosa (AN) is a devastating disorder with a mortality rate among the highest of any psychiatric
illness. Current treatments are often inadequate, and no pharmacologic agents have proven effective. A better
understanding of the pathophysiology of AN is greatly needed. Avoidance of high-fat food is a central
behavioral disturbance contributing to morbidity and mortality of AN, yet little is known about the underlying
cognitive and neural mechanisms. Anxiety and fear of fat are thought to play important roles: individuals with
AN are highly anxious in general, with the primary focus of this anxiety being food or eating related. Extreme
food avoidance and anxiety surrounding food are well documented in AN, yet a mechanistic framework for
understanding these phenomena and their relationship has not been rigorously studied. The proposed
research tests a novel mechanism potentially underlying the persistent avoidance of high-calorie, high-fat
food—excessive generalization of food avoidance—and leverages recent advances in computational and
cognitive neuroscience to elucidate this mechanism. With the use of a novel paradigm, we can operationalize
avoidance behavior and quantify learning and generalization of threat and safety signals. Our specific aim is to
examine the neural and cognitive mechanisms supporting generalization of avoidance behavior in AN. We will
use functional magnetic resonance imaging (fMRI) and computational modeling to characterize, in AN and
healthy comparison participants (HC), how active avoidance behavior is generalized both when the feared
outcome is electric shock (aversive to AN and HC) and high-fat food (aversive to AN). Our central prediction is
that individuals with AN will generalize more broadly when avoiding food than when avoiding shock, and that
this will be related to 1) activity in brain circuits involving the insula, striatum, and amygdala and 2) real-world
avoidance of high-calorie, high-fat food. The study proposes a novel mechanism to account for the severely
maladaptive eating behavior in AN, which has proven very difficult to treat. The application of computational
neuroscience tools will allow for more precise specification of mechanisms at both the cognitive and neural
levels, which is essential for identifying novel treatment targets, characterizing biomarkers of illness
vulnerability and trajectory, and translational research more broadly. This study will create a foundation for a
large-scale R01 to investigate these mechanisms developmentally and development and testing of
mechanism-based treatments in clinical trials.