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.

项目概要焦虑症，例如恐慌症、广泛性焦虑症和创伤后应激障碍 (PTSD) 影响着约 18% 的美国人口，每年造成的医疗保健费用超过 420 亿美元今年，给美国经济带来了沉重负担。焦虑症的发展和维持归因于持续的恐惧记忆、恐惧消退不足和适应不良的回避行为。因此，为了能够发展厌恶学习的神经机制，必须了解厌恶学习背后的神经机制。对这些疾病的有效治疗。在这个项目中，我们将重点了解岛叶，一个大脑区域，不仅与一般的厌恶学习密切相关，而且还参与决定性学习的过程接近/回避行为。具体来说，通过微型显微镜使用体内单细胞钙成像，我们将当厌恶结果出现时，记录恐惧学习期间岛状单细胞群的活动模式（脚震）是不可避免的，以及当厌恶结果被忽略时（恐惧灭绝；目标1a）以及当它变得可以逃避（回避学习；目标1b）。最后，使用一种新颖的理论计算方法为了对岛叶中的恐惧学习单细胞群进行功能聚类，我们将预测小鼠是否会发育抗灭绝恐惧或回避学习受损（Aim2）。因此，在本提案中，我们旨在调查岛单细胞群参与厌恶学习并开发一种新颖的计算工具根据岛叶的神经信号预测未来的适应不良、厌恶学习表型。