Implementation of dynamic threat belief updating within cerebro-spinal systems and opioidergic pathways

在脑脊髓系统和阿片途径中实施动态威胁信念更新

• 批准号: 521832690
• 负责人: Professor Dr. Christian Büchel
• 金额: --
• 依托单位: Institut für Systemische Neurowissenschaften
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/521832690?language=en
• 关键词: Implementation; dynamic; threat; belief; updating

Background: Learning to predict threats builds on integration of aversive, nociceptive, outcomes to form a threat belief. Yet, threat learning also captures dynamic updating of established threat beliefs, when the environment changes. To characterize both, formation and dynamic belief updating (DynBU), a neurocomputational model for statistical learning has been established. Yet, a statistical learning framework to delineate the neurobiological mechanisms of dynamic threat learning is missing. Aims: The overarching aim is to delineate the neural basis of dynamic updating of threat beliefs by deciphering the interplay between nociceptive outcome processing in the spinal cord, brain-stem mediated arousal, together with encoding of threat beliefs in forebrain circuits. This project further aims to delineate a neuropharmacological, opioidergic mechanism of dynamic threat belief updating.Hypotheses: We hypothesize (1) an interaction between encoding of nociceptive, aversive outcomes (i.e., spinal cord) and brainstem-mediated arousal (e.g., locus coeruleus, LC), which gates updating of threat beliefs via established brain circuits for threat processing (e.g., amygdala and dorsal anterior cingulate cortex, dACC). In particular, we hypothesize (2) that orchestration of defensive threat responses that are either proximal or distal requires a functional connection between brainstem mediated arousal (e.g., LC) and brain regions that encode proximity of threats (periaqueductal gray, PAG). We expect (3) that opioid-receptor blockade enhances aversive outcome integration, which can be simulated in learning models. Finally, we hypothesize (4) that this enhancement of aversive outcome integration by opioid-receptors blockade will be implemented in interactions between the PAG, amygdala and LC. Planned methods: Participants will a undergo a change-point task that requires faster and slower defense against proximal vs. distal predators (i.e., “predator task”, harmonized with project 8), while acquiring cerebro-spinal fMRI. Opioid-receptor effects on the predator task will first be simulated and tested in a second sample that undergoes brain fMRI. Expected impact: This project will provide psychological, neural, and computational mechanisms underlying DynBU of threats and thereby make a significant contribution to the major aims of this RU. Specifically, we will advance the proposed computational and neurobiological model of statistical learning by mechanism of threat learning. This project will further provide a unique contribution by identification of a neuropsychopharmacological mechanism that controls updating of threat beliefs in humans. Together with project 8, we will further be able to translate the results in healthy volunteers to the anxiety phenotype. Furthermore, distinct links with other projects allow to bridge between statistical learning of threats and the effects of stress (project 3) or statistical learning from rewarding outcomes (project 4).

背景：学习预测威胁是建立在整合厌恶性、伤害性结果以形成威胁信念的基础上的。然而，威胁学习也捕捉动态更新已建立的威胁信念，当环境发生变化。为了表征形成和动态信念更新（DynBU），已经建立了一个用于统计学习的神经计算模型。然而，一个统计学习框架来描绘动态威胁学习的神经生物学机制是缺失的。 目的：总体目标是通过破译脊髓中的伤害性结果处理、脑干介导的唤醒以及前脑回路中的威胁信念编码之间的相互作用来描绘威胁信念动态更新的神经基础。该项目进一步旨在描绘动态威胁信念更新的神经药理学阿片能机制。假设：我们假设（1）伤害性、厌恶性结果（即，脊髓）和脑干介导的觉醒（例如，蓝斑（locus coeruleus），LC），其通过用于威胁处理的已建立的脑回路来门控威胁信念的更新（例如，杏仁核和背侧前扣带皮层，dACC）。特别是，我们假设（2）近端或远端防御性威胁反应的协调需要脑干介导的唤醒（例如，LC）和编码邻近威胁的大脑区域（导水管周围灰质，PAG）。我们预期（3）阿片受体阻断剂可以增强厌恶性结果整合，这可以在学习模型中模拟。最后，我们假设（4）阿片受体阻滞剂对厌恶性结果整合的增强作用将在PAG、杏仁核和LC之间的相互作用中实现。计划方法：参与者将经历一个变化点任务，需要更快和更慢地防御近端与远端捕食者（即，“捕食者任务”，与项目8协调），同时获得脊髓功能磁共振成像。阿片受体对捕食者任务的影响将首先在第二个接受大脑功能磁共振成像的样本中进行模拟和测试。预期影响：该项目将提供DynBU威胁的心理，神经和计算机制，从而为RU的主要目标做出重大贡献。具体来说，我们将提出的计算和神经生物学模型的统计学习的威胁学习的机制。该项目将进一步通过识别控制人类威胁信念更新的神经精神药理学机制来提供独特的贡献。与项目8一起，我们将进一步能够将健康志愿者的结果转化为焦虑表型。此外，与其他项目的明显联系使威胁和压力影响的统计学习（项目3）或奖励结果的统计学习（项目4）之间架起了桥梁。