State-dependent Decision-making in Brainwide Neural Circuits

全脑神经回路中的状态相关决策

• 批准号: 10461991
• 负责人: ANNE KATHRYN CHURCHLAND
• 金额: $ 365.01万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10461991
• 关键词: Affect; Anatomy; Animals; Area; Axon; Bar Codes; Behavior; Behavioral; Biological Assay; Brain; Brain region; Collaborations; Collection; Communication; Computer Models; Computing Methodologies; Data Analyses; Data Collection; Data Science Core; Data Set; Decision Making; Development; Disease; Environment; Etiology; Food; Gene Expression; Genetic; Goals; Histology; Impairment; Individual; Infrastructure; International; Laboratories; Learning; Maps; Measurement; Measures; Methods; Modeling; Molecular; Mus; Mutation; Neurons; Pattern; Population; Protocols documentation; Reproducibility; Research; Research Personnel; Rewards; Schizophrenia; Standardization; Structure; Testing; Training; Ultrasonography; Wild Type Mouse; Work; addiction; autism spectrum disorder; cell type; data infrastructure; data sharing; deep learning; expectation; experimental study; flexibility; integrated circuit; large datasets; large scale data; mouse model; neural circuit; neuromechanism; next generation; novel; open source tool; optogenetics; relating to nervous system; theories; tool

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.

总结/摘要 动物不断地做出决定，例如如何评估潜在的威胁或在哪里寻找食物。然而 同样的动物在同样的环境中，在不同的场合会产生不同的决定，因为它 内部状态与外部输入有力地相互作用以决定行为。该提案的总体目标是 是了解内部状态如何影响决策，并确定潜在的神经机制。中 小鼠决策任务，这些实验将检查三种类型的内部状态变化的影响： 那些在任务中参与和脱离时自发产生的，那些由于变化而产生的， 任务期间的期望，以及那些在几天内和几天内学习产生的期望。为了确定内部 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，包括统计工具，以推断内部状态的行为;同时记录，从大 在行为期间和在光遗传学扰动期间跨越许多区域的神经元群体; 映射功能和分子定义的细胞类型特异性，跨区域连接性;和计算 方法来模拟跨区域神经通信如何依赖于内部状态。 这些雄心勃勃的目标超越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，该实验室已经开发了一种 标准化的小鼠决策任务和标准化的训练方法，神经测量和数据 沿着一个有效的、可扩展的数据共享基础设施。拟议的研究利用了这一点 现有的基础设施，并把它带到一个新的方向。项目1-5将同时检查记录 群体活动，评估因果关系，研究正常人和自闭症患者学习过程中的神经活动和行为 模型小鼠，通过测量神经元活动、基因表达和轴突投射模式来识别细胞类型 在相同的神经元群体中，并建立一个全面的计算模型， 结果核心A-D将支持大型数据集的收集、复制、管理和分析 是由全脑范围的神经回路检查产生的。 总之，拟议的研究将严格定义多种内部状态的神经基础， 评估它们对大脑中决策相关信息流的影响。结果将大大 通过对内部状态如何反映的全面、机械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以指导决策。此外，该团队将 制作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。