State-dependent Decision-making in Brainwide Neural Circuits

摘要/摘要 动物不断地做出决定，比如如何评估潜在的威胁或在哪里寻找食物。然而， 同样的动物在相同的环境中可以在不同的场合做出不同的决定，因为它的 内部状态与外部输入之间存在强大的交互作用，从而决定行为。这项提案的首要目标是 是了解内部状态如何影响决策，并确定潜在的神经机制。在一个 在鼠标决策任务中，这些实验将考察三种类型的内部状态变化的影响： 在一项任务中因投入和脱离而自发产生的，因改变而产生的 在任务过程中的期望，以及在几天内和跨天学习所产生的期望。要确定内部 状态影响大脑活动和行为，该团队将在全脑范围内应用尖端技术进步 规模，包括从行为推断内部状态的统计工具；从大的 在行为和光遗传扰动期间许多区域的神经元种群；分析 从功能和分子上定义的特定细胞类型的跨区域连接性地图；以及计算 模拟跨区域神经通讯如何依赖内部状态的方法。 这些雄心勃勃的目标超出了单个实验室的能力，非常适合已经- 生产性财团。这个团队是国际大脑实验室的一部分，该实验室已经开发出一种 标准化的小鼠决策任务和训练、神经测量和数据的标准化方法 分析，以及用于共享数据的工作的、可扩展的基础设施。拟议的研究充分利用了这一点。 现有的基础设施，并将其带向一个新的方向。项目1-5将同时审查记录 人群活动，评估因果关系，研究正常和自闭症患者学习期间的神经活动和行为 模型小鼠，通过测量神经元活性、基因表达和轴突投射模式来确定细胞类型 在相同的神经元群体中，并建立所有这些实验的综合计算模型 结果。核心A-D将支持大型数据集的收集、复制、管理和分析 通过对神经回路的这种全脑检查而产生。 综上所述，这项拟议的研究将严格定义多种内部状态和 评估它们对大脑中与决策相关的信息流的影响。结果将极大地 通过对内部状态如何反映产生全面、机械性的理解来推进该领域 以及这些状态如何与外部输入相互作用来指导决策。此外，该团队将 生产和传播开源工具和协议，使其他实验室能够收集和 管理通过全脑测量产生的大规模数据集。

Summary/Abstract Animals constantly make decisions, such as how to evaluate a potential threat or where to look for food. Yet the same animal in the same environment can produce different decisions on different occasions, because its internal state interacts powerfully with external inputs to determine behavior. This proposal’s overarching goal is to understand how internal states influence decisions and to identify the underlying neural mechanisms. In a mouse decision-making task, these experiments will examine the effects of three types of internal state changes: those arising spontaneously with engagement and disengagement in a task, those resulting from changing expectations during the task, and those resulting from learning within and across days. To determine how internal states affect brain activity and behavior, the team will apply cutting-edge technical advances on a brainwide scale, including statistical tools to infer internal states from behavior; simultaneous recordings from large populations of neurons across many regions during behavior and during optogenetic perturbations; assays that map functionally and molecularly defined cell-type-specific, cross-region connectivity; and computational approaches to model how cross-region neural communication depends on internal states. These ambitious goals go beyond the capabilities of an individual laboratory and are ideally suited for an already- productive consortium. This team is part of the International Brain Laboratory, which has already developed a standardized mouse decision-making task and standardized methods for training, neural measurement, and data analysis, along with a working, scalable infrastructure for sharing data. The proposed research leverages this existing infrastructure and takes it in a new direction. Projects 1-5 will examine simultaneously recorded population activity, evaluate causality, study neural activity and behavior during learning in normal and autism model mice, identify cell types by measuring neuronal activity, gene expression, and axonal projection patterns in the same populations of neurons, and build a comprehensive computational model of all these experimental results. Cores A-D will support the collection, replicability, management, and analysis of the large datasets produced by this brainwide examination of neural circuits. Taken together, the proposed research will rigorously define the neural basis of multiple internal states and evaluate their impact on the flow of decision-relevant information through the brain. The results will greatly advance the field by generating a comprehensive, mechanistic understanding of how internal states are reflected in the brain, and how these states interact with external inputs to guide decisions. Moreover, the team will produce and disseminate open-source tools and protocols that will enable other laboratories to collect and manage large-scale datasets produced through brainwide measurements.