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管理（通常是嘈杂的）证据，两者都 内部和外部产生，以融合决定。这个证据积累过程可以 适应不断变化的环境和奖励机会，由Cortico-Basal-Ganglia-Thalamic介导 （CBGT）既有助于行动选择并使用反馈信号来修改未来的电路 决定。这些途径如何使用反馈指导未来决策的功能障碍是主要的 许多成瘾行为的机制（例如阿片类药物成瘾，肥胖）。我们先前的工作已经确定 在调节尺寸的CBGT途径内，我们称之为控制集合的子系统 证据积累过程，导致各种神经状态，对证据的接受程度不同 驱动决策的流媒体，封装在特定决策政策中。 我们提出了一系列经验和理论研究，这些研究跨越了分析水平 在决策过程中，通过CBGT电路了解信息流。在 理论方面，我们将使用我们的模型来了解CBGT的低维代表空间 在整个决策过程中的电路，使用能量景观模型与 减少维度。使用计算模型，我们将通过CBGT建模决策轨迹 网络通过将基于熵的分析应用于网络行为并构建观察到的预测 离散和连续作用的动力学（特定目标1）。从经验上，我们将测试预测 从我们的网络模型中出现，并提供新的观察结果，以支持模型改进 在啮齿动物（光遗传学，电生理学）中进行的实验，他们都以动态奖励执行这两个任务 具有离散选择或连续电动机控制的意外事件（特定目标2）。我们的理论 经验工作将在相互发展的周期中发展，理论实验被用来 得出新颖的行为和神经预测以及用于修改和的经验实验结果 更新导致后续预测的生成模型属性。