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

项目摘要 焦虑症，如恐慌症、广泛性焦虑症和创伤后应激障碍 (创伤后应激障碍)影响大约18%的美国人口，每年的医疗费用超过420亿美元 去年，这对美国经济是一个沉重的负担。焦虑症的发展和维持一直是 归因于持续的恐惧记忆、不充分的恐惧消退和不适应的回避行为。因此， 了解厌恶学习背后的神经机制是非常必要的，这样才能发展 对这些疾病的有效治疗。在这个项目中，我们将重点了解 脑岛是一个大脑区域，它不仅广泛参与厌恶学习，而且还参与决定 接近/回避行为。具体地说，通过微型望远镜进行体内单细胞钙成像，我们将 当厌恶结果出现时，记录恐惧学习期间岛单细胞群的活动模式 (足部电击)是不可避免的，以及当厌恶的结果被省略时(恐惧消退；Aim1a) 它变得可以逃避(回避学习；Aim1b)。最后，使用一种新的理论-计算方法 为了从功能上将恐惧学习的单细胞集合聚集在脑岛上，我们将预测小鼠是否会发展 抗消退恐惧或回避学习受损(AIM2)。因此，在这项建议中，我们的目标是调查 岛状单细胞集合参与厌恶学习，并开发一种新的计算工具来 根据脑岛的神经信号预测未来不良适应厌恶学习的表型。

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.