Acquisition and Expression of Avoidance: Computational Modeling and Human Studies

描述（由申请人提供）： 项目摘要 虽然军事人员可能具有与非军事人口类似的焦虑症的脆弱性和易感性，但部署和战时服务的极端压力源增加了发展焦虑症如创伤后应激障碍（PTSD）的可能性。然而，只有一个子集的个人暴露于这种压力源发展创伤后应激障碍，导致预先存在的脆弱性因素的概念，这样的个人与一个或多个这些脆弱性因素，谁是然后暴露于极端的压力源，更有可能发展创伤后应激障碍比个人谁接受同等的暴露，但没有（或更少）的脆弱性因素。 回避是PTSD的核心症状;回避症状在疾病过程中倾向于增加而不是减少，并且可能特别预测哪些创伤暴露个体可能发展为PTSD。因此，创伤后应激障碍的脆弱性可能部分反映了个体先前存在的获得、表达或保持回避行为倾向的个体差异。了解这些脆弱性转化为退伍军人精神病理学的机制是更好地理解PTSD的关键，也是设计针对这些机制的有效治疗干预措施的关键。 研究动物的回避学习有着丰富的传统，包括我们实验室和其他人的工作，但将动物数据与人类回避直接联系起来的工作已经落后了。在这里，我们将采用一个并行程序的计算建模和人类回避的研究，开始弥合差距之间的人类和动物的研究。首先，我们将开发一个回避学习的计算模型来处理来自动物的数据，包括来自易受创伤后应激障碍和焦虑影响的大鼠模型的数据，该模型显示了促进学习和回避学习的持续性。其次，在建模工作的同时，我们将对具有不同程度PTSD回避症状的退伍军人进行回避学习研究，以确定具有严重回避症状的退伍军人是否在实验室任务中表现出与大鼠中观察到的行为相似的回避行为。这些研究将为退伍军人的回避学习提供有价值的经验数据，此外还将直接测试大鼠行为与人类行为的相似程度。第三，我们将把计算模型应用于老兵回避学习数据，以确定足以解释大鼠数据的相同计算机制是否也可以解释人类数据。如果是这样，这将增加我们对大鼠模型翻译潜力的信心;如果不是，这将确定在未来的动物和人类工作中可以研究的重要限制。无论哪种情况，计算模型的结果将开始弥合我们对大鼠和有或没有PTSD回避症状的退伍军人中驱动回避学习的机制的理解上的差距。 这项工作计划的长期目标是使用计算建模来更好地理解回避的脆弱性转化为焦虑症（包括PTSD）的精神病理学的过程。这些过程及其潜在机制的知识可能会指导未来靶向治疗的发展，调节这些机制，影响异常回避的发展和维持，提供策略，治疗或预防PTSD回避症状的发展。

DESCRIPTION (provided by applicant): Project Summary Although military personnel may have vulnerabilities and susceptibilities for anxiety disorders similar to the non-military population, the extreme stressors of deployment and war time service enhance the likelihood of developing anxiety disorders such as post-traumatic stress disorder (PTSD). However, only a subset of individuals exposed to such stressors develops PTSD, leading to the concept of pre-existing vulnerability factors such that individuals with one or more of these vulnerability factors, who are then exposed to extreme stressors, are more likely to develop PTSD than individuals who receive equivalent exposure but have no (or fewer) vulnerability factors. Avoidance is a core symptom of PTSD; avoidance symptoms tend to increase rather than decrease over the course of the disorder, and may be particularly predictive of which trauma-exposed individuals are likely to develop PTSD. Vulnerability to PTSD may therefore partially reflect individual differences in an individual's pre-existing tendency to acquire, express, or maintain avoidant behaviors. Understanding the mechanisms by which such vulnerabilities translate into psychopathology in veterans is key for a better understanding of PTSD and also for designing effective therapeutic interventions that target these mechanisms. There is a rich tradition of studying avoidance learning in animals, including work from our lab and others, but work directly linking animal data to human avoidance has lagged behind. Here, we will employ a parallel program of computational modeling and human avoidance studies to begin to bridge the gap between human and animal studies. First, we will develop a computational model of avoidance learning to address data from animals, including data from a rat model of vulnerability to PTSD and anxiety that shows facilitated learning and persistence of avoidance learning. Second, in parallel with the modeling work, we will conduct studies of avoidance learning in veterans with varying degrees of PTSD avoidance symptoms, to determine whether veterans with severe avoidance symptoms show avoidance behavior in laboratory tasks that parallels the behaviors observed in the rats. These studies will provide valuable empirical data on avoidance learning in veterans, in addition to directly testing how closely the rat behavior mimics that observed in the humans. Third, we will apply the computational model to the veteran avoidance learning data, to determine whether the same computational mechanisms that sufficed to account for the rat data can also account for the human data. If so, this will increase our confidence in the translational potential of the rat model; if not, this will identify important limitations that can be studied in future animal and human work. In either case, the results from the computational modeling will begin to bridge the gap in our understanding of the mechanisms driving avoidance learning in rats and in veterans with and without PTSD avoidance symptoms. The long term goal of this program of work is to use computational modeling to better understand the processes by which vulnerability to avoidance translates into psychopathology in anxiety disorders including PTSD. Knowledge of these processes and their underlying mechanisms may guide future development of targeted therapies that modulate these mechanisms to affect the development and maintenance of aberrant avoidance, providing strategies to treat or prevent development of avoidance symptoms in PTSD.