CRCNS: Decision dynamics in cortico-basal ganglia-thalamic networks

CRCNS：皮质-基底节-丘脑网络的决策动态

• 批准号: 10830650
• 负责人: Jonathan E. Rubin
• 金额: $ 34.21万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10830650
• 关键词: Addictive Behavior; Behavior; Behavioral; Brain; Cells; Computer Models; Coupled; Data; Decision Making; Development; Diffusion; Dimensions; Electrophysiology (science); Encapsulated; Entropy; Environment; Environmental Risk Factor; Feedback; Functional disorder; Future; Ganglia; Individual; Investigation; Learning; Link; Maps; Measures; Mediating; Modeling; Mus; Obesity; Opiate Addiction; Pathway interactions; Pattern; Policies; Population; Process; Property; Public Health; Research; Rewards; Rodent; Series; Side; Signal Transduction; Stream; Structure; System; Testing; Thalamic structure; Update; Work; addiction; analytical method; arm; cognitive process; experimental study; flexibility; in silico; in vivo; insight; motor control; network models; neural; neural circuit; novel; optogenetics; programs; reward processing; spatiotemporal; theories; two-dimensional

The mammalian brain is particularly well suited for managing streams of (often noisy) evidence, both internally and externally generated, to converge to a decision. This evidence accumulation process can adapt to changing environments and reward opportunities, mediated by cortico-basal-ganglia-thalamic (CBGT) circuits that both contribute to action selection and use feedback signals to modify future decisions. Dysfunction in how these pathways use feedback to guide future decisions is a primary mechanism for many addictive behaviors (e.g., opioid addiction, obesity). Our prior work has identified subsystems, which we call control ensembles, within the CBGT pathways that regulate dimensions of the evidence accumulation process, leading to various neural states with differing receptivity to the evidence streams that drive decisions, encapsulated in a particular decision policy. We propose a series of empirical and theoretical investigations that bridge across levels of analysis to understand the flow of information through CBGT circuits during the decision-making process. On the theory side we will use our models to understand the low-dimensional representational space of CBGT circuits throughout the decision-making process, using energy landscape models coupled with dimensionality reduction. Using computational models we will model decision trajectories through CBGT networks by applying entropy based analyses to the network behavior and building predictions of observed dynamics in both discrete and continuous actions (Specific Aim 1). Empirically, we will test predictions emerging from our network model and provide new observations to support model refinement using experiments in rodents (optogenetics, electrophysiology) as they perform both tasks with dynamic reward contingencies featuring either discrete choices or continuous motor control (Specific Aim 2). Our theoretical and empirical work will evolve in a mutual-development cycle, with theoretical experiments being used to derive novel behavioral and neural predictions and empirical experimental results being used to revise and update the generative model properties that lead to subsequent predictions.

哺乳动物的大脑特别适合处理证据流(通常是嘈杂的)，两者都 内部和外部生成的，以汇聚为决策。这一证据积累过程可以 适应不断变化的环境和奖励机会，由皮质-基底节-丘脑调节 (CBGT)电路，既有助于动作选择，又使用反馈信号修改未来 决定。这些通路如何使用反馈来指导未来决策的功能障碍是主要原因 许多成瘾行为(如阿片成瘾、肥胖)的机制。我们之前的工作已经确定 子系统，我们称之为控制集合，在CBGT通路内调节 证据积累过程，导致不同的神经状态对证据的接受性不同 驱动决策的流，封装在特定的决策策略中。 我们提出了一系列的实证和理论研究，这些研究跨越了不同的分析层次 了解决策过程中通过CBGT电路的信息流。论 在理论方面，我们将使用我们的模型来理解CBGT的低维表示空间 在整个决策过程中使用能源景观模型，并结合 降维。使用计算模型，我们将通过CBGT对决策轨迹进行建模 通过将基于熵的分析应用于网络行为并建立观察到的预测来构建网络 离散和连续行动的动力学(具体目标1)。从经验上讲，我们将检验预测 从我们的网络模型中脱颖而出，并提供新的观察以支持使用 在啮齿动物身上进行实验(光遗传学、电生理学)，因为它们在动态奖励下完成这两项任务 以离散选择或连续电机控制为特征的意外情况(具体目标2)。我们的理论 实证工作将在一个共同发展的周期中发展，理论实验将用于 得出新的行为和神经预测以及经验实验结果，用于修订和 更新导致后续预测的生成模型属性。