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

通过岛叶单细胞整体活动模式的计算模型来预测适应不良的厌恶学习

基本信息

批准号：
10575313
负责人：
Munir Gunes Kutlu
金额：
$ 1.52万
依托单位：
VANDERBILT UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-03-08 至 2023-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10575313
关键词：
Adaptive Behaviors Affect Algorithms American Animal Behavior Animals Anterior Anxiety Anxiety Disorders Association Learning Auditory Aversive Stimulus Avoidance Learning Behavior Behavioral Brain Brain region Calcium Cells Complex Computer Models Cues Data Decision Making Development Disease Exhibits Extinction Freezing Fright Future Generalized Anxiety Disorder Goals Health Care Costs Homeostasis Human Image Impairment Individual Insula of Reil Intervention Learning Maintenance Mediating Mediator Memory Mental Depression Mental disorders Modeling Mus Neural Network Simulation Neurons Nose Outcome Pain Panic Disorder Pattern Phenotype Phobias Photons Population Positioning Attribute Post-Traumatic Stress Disorders Pre-Clinical Model Process Psychopathology Punishment Regulation Resistance Rodent Safety Sensory Shock Signal Transduction Social Anxiety Disorder Stress Structure Symptoms Taste Perception Testing Thalamic structure Theoretical model Training avoidance behavior behavioral outcome behavioral phenotyping computerized tools conditioned fear coping cost efficacious treatment fear memory feeding functional adaptation in vivo maladaptive behavior neural neural circuit neurobiological mechanism neuromechanism neurotransmission novel novel strategies response stress disorder theories treatment strategy

项目摘要

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.

项目摘要焦虑症，例如恐慌症，普遍焦虑症和创伤后应激障碍（PTSD）影响约18％的美国人口，医疗保健成本超过420亿美元一年，这给美国经济带来了重大负担。焦虑症的发展和维持已经归因于持续的恐惧记忆，不足的恐惧灭绝和适应不良的回避行为。因此，必须了解厌恶学习的神经机制，以便能够发展这些疾病有效治疗。在这个项目中，我们将专注于理解岛上的大脑区域不仅在整体上厌恶学习，而且还可以处理确定的厌恶学习方法/回避行为。具体而言，使用Miniscopes使用体内单细胞钙成像，我们将记录恐惧学习期间孤立的单细胞集合的活动模式时厌恶结果（脚印）是不可避免的，以及省略厌恶结果的何时（恐惧灭绝； aim1a）以及何时它变得可以逃脱（回避学习； AIM1B）。最后，使用一种新颖的理论计算方法为了在功能上群体恐惧学习岛上的单细胞集合，我们将预测小鼠是否会发展抗灭绝的恐惧或回避学习受损（AIM2）。因此，在此提案中，我们旨在调查隔离单细胞组合参与厌恶性学习，并开发出一种新颖的计算工具基于绝缘体中神经信号传导预测未来的不良适应性厌恶学习表型。