Predicting maladaptive aversive learning via computational modeling of insular single cell ensemble activity patterns

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Project Summary Anxiety disorders such as panic disorders, generalized anxiety disorder, and post-traumatic stress disorder (PTSD) affect approximately 18% of the American population with a health care cost of more than $42 billion a year, a significant burden to the US economy. Development and maintenance of anxiety disorders have been attributed to persistent fear memories, inadequate fear extinction, and maladaptive avoidance behavior. Thus, it is imperative to understand the neural mechanisms underlying aversive learning in order to be able to develop efficacious treatments for these disorders. In this project, we will focus on understanding the involvement of the insula, a brain region heavily involved not only in aversive learning in general but also processes determining approach/avoidance behaviors. Specifically, using in-vivo single cell calcium imaging via miniscopes, we will record activity patterns of insular single cell ensembles during fear learning when the aversive outcome (footshock) is inescapable as well as when the aversive outcome is omitted (fear extinction; Aim1a) and when it becomes escapable (avoidance learning; Aim1b). Finally, using a novel theoretical-computational approach to functionally cluster fear learning single cell ensembles in the insula, we will predict whether mice will develop extinction resistant fear or impaired avoidance learning (Aim2). Thus, in this proposal, we aim to investigate the involvement of the insular single cell ensembles in aversive learning and develop a novel computational tool to predict future maladaptive aversive learning phenotypes based on the neural signaling in the insula.