Network Dynamics of Negative and Positive Valence Systems in Decision Making

决策中负价和正价系统的网络动力学

• 批准号: 10382218
• 负责人: Dalton N. Hughes
• 金额: $ 4.21万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2022-10-17
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10382218
• 关键词: Affect; Amygdaloid structure; Anatomy; Animals; Anxiety; Appetitive Behavior; Area; Behavior; Behavioral; Biological Assay; Brain; Brain region; Chronic; Cues; Data; Decision Making; Development; Diagnosis; Disease; Electrophysiology (science); Equilibrium; Fright; Goals; Health; Hippocampus (Brain); Impairment; Individual; Lesion; Machine Learning; Major Depressive Disorder; Mental disorders; Modeling; Monitor; Motivation; Mus; Negative Valence; Nucleus Accumbens; Outcome; Pattern; Persons; Phenotype; Play; Positive Valence; Prefrontal Cortex; Psychiatric therapeutic procedure; Punishment; Research; Rewards; Risk; Role; Signal Transduction; Site; Stimulus; Stress; Structure; Sucrose; Symptoms; System; Task Performances; Techniques; Testing; Thalamic structure; Training; Ventral Tegmental Area; Work; avoidance behavior; base; chronic depression; clinically relevant; cohort; depression model; design; disability; experience; goal oriented behavior; in vivo; insight; light intensity; maladaptive behavior; millisecond; motivated behavior; mouse model; neural model; optogenetics; preference; relating to nervous system; response; segregation; social defeat; spatiotemporal

ABSTRACT Evaluating risk and reward potential in the execution of motivated behaviors is important in decision-making. Positive valence systems in the brain encode positive stimuli and play a key role in motivation, reward expectance, and appetitive behavior. Negative valence systems, on the other hand, encode negative stimuli such as fear and anxiety, and drive avoidance. Critically, an imbalance in these valence systems is thought to underlie many core symptoms in Major Depressive Disorder (MDD). Recent studies have shown that the brain regions responsible for encoding these divergent valence systems have anatomical and functional overlap. This raises the hypothesis that differences in network-level activity involving these overlapping areas may discriminate information of positive and negative valence. Here, I propose to employ in vivo recordings of electrical activity across multiple brain regions concurrently as mice perform a behavioral task designed to probe both reward and aversion. This task, modeled after the classic elevated plus maze and sucrose preference tasks, will directly quantify the impact of anxiogenic stimuli on reward-motivated behavior. Using machine-learning techniques, I will then generate neural models that reflect the network-level activity engaged during the performance of this task. I anticipate that this strategy with discover an independent network that corresponds with the positive valence system, and another independent network that corresponds with the negative valence system. I also anticipate that I will discover a network that directly integrates network-level activity in these two systems to drive decisions making. Lastly, the structure of these networks will be validated in a cohort of mice that will be subjected to chronic social defeat stress. A validated model of MDD, chronic social defeat stress induces increased anxiety-like phenotypes and decreased reward drive in a subset of mice (stress-susceptible mice) while only increasing anxiety-like phenotypes in other animals (stress-resilient mice). Thus, successful completion of the proposed work will lead to a network-level understanding of positive and negative valence systems. Furthermore, the framework discovered through this study has the potential to facilitate the development of new revolutionary approaches for diagnosis and treatment of MDD.

摘要 评估执行动机行为的风险和回报潜力在决策中很重要。 大脑中的正价系统编码积极的刺激，并在动机，奖励和情感方面发挥关键作用。 期望和食欲行为。另一方面，负价系统编码负刺激 例如恐惧和焦虑，以及驾驶回避。重要的是，这些价体系的不平衡被认为是 是重度抑郁症（MDD）许多核心症状的基础。最近的研究表明大脑 负责编码这些不同价系统的区域具有解剖学和功能重叠。 这就提出了一个假设，即涉及这些重叠区域的网络水平活动的差异可能 正、负效价的判别信息。在这里，我建议采用活体记录， 当老鼠执行行为任务时，多个大脑区域同时发生电活动， 探索奖赏和厌恶。这项任务，仿照经典的高架十字迷宫和蔗糖 偏好任务，将直接量化奖励动机行为的焦虑刺激的影响。使用 机器学习技术，然后我将生成反映参与的网络级活动的神经模型 在执行这项任务时。我预计，这一战略与发现一个独立的网络， 对应于正价系统，另一个独立的网络对应于 负价体系我还预计，我将发现一个网络，直接集成网络级 这两个系统中的活动来推动决策。最后，对这些网络的结构进行验证 在一组将经受长期社交失败压力的小鼠中。MDD的经验证模型，慢性 社交失败压力诱导小鼠亚组中焦虑样表型增加和奖励驱动减少 （应激敏感小鼠），而仅增加其他动物（应激恢复小鼠）中的焦虑样表型。 因此，成功完成拟议的工作将导致网络一级对积极和 负价体系此外，通过这项研究发现的框架有可能 促进MDD诊断和治疗的革命性新方法的发展。