Network Dynamics of Negative and Positive Valence Systems in Decision Making

摘要 评估动机行为执行中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正向刺激，并在激励、奖励等方面发挥关键作用 期望值和食欲。另一方面，负价系统编码负刺激。 例如恐惧和焦虑，以及驱使回避。关键的是，这些价格体系中的不平衡被认为是 许多核心症状是严重抑郁障碍(MDD)的基础。最近的研究表明，大脑 负责编码这些不同价态系统的区域在解剖和功能上存在重叠。 这提出了一种假设，即涉及这些重叠区域的网络水平活动的差异可能 正价信息与负价信息的辨析。在这里，我建议使用活体录音 当小鼠执行一项旨在 同时探究奖励和厌恶。这项任务是模仿经典的高架加迷宫和蔗糖 偏好任务，将直接量化焦虑刺激对奖励动机行为的影响。vbl.使用 机器学习技术，然后我将生成反映网络层面活动的神经模型 在执行这项任务期间。我预计这一战略将发现一个独立的网络， 对应于正价系统，而另一个独立网络对应于 负价体系。我还预计，我将发现一个直接集成网络级别的网络 这两个系统中推动决策制定的活动。最后，对这些网络结构进行了验证 在一群将受到长期社会失败压力的小鼠中。一种经过验证的MDD模型，慢性 社交失败应激导致部分小鼠焦虑样表型增加和奖励驱动减少 而在其他动物(压力恢复能力强的小鼠)中，焦虑样表型只会增加。 因此，拟议工作的圆满完成将导致在网络一级理解积极和 负价系统。此外，通过这项研究发现的框架有可能 促进开发诊断和治疗MDD的新的革命性方法。

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.