Cracking Genetically Defined Neocortical Circuits across Learning and Behavior

破解学习和行为中基因定义的新皮质回路

• 批准号: 10561327
• 负责人: Jerry L Chen
• 金额: $ 6.27万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10561327
• 关键词: Address; Affect; Anatomy; Behavior; Cells; Cognition; Cognitive; Decision Making; Disease; Functional Imaging; Gene Expression; Gene Expression Profiling; Genes; Genome; Human; Immediate-Early Genes; Individual; Knowledge; Learning; Maps; Measures; Memory; Methodology; Molecular; Mus; Neocortex; Neurodevelopmental Disorder; Neurons; Pattern; Population; Sensory; Surveys; Vibrissae; Work; behavior measurement; cell type; cognitive capacity; design; innovation; learned behavior; neocortical; neural circuit; novel; psychologic; relating to nervous system; sensorimotor system; targeted treatment

PROJECT SUMMARY How does our genome instruct the circuit-level neural computations that give rise to cognition? This is a fundamental question that aims to explain our cognitive capacity as humans. It requires a deep understanding for how gene expression in individual neurons relates to activity patterns across the population during behavior. Comprehensively integrating molecular, anatomical, functional, and behavioral measurements is the main technical challenge in achieving such an understanding. Here, I propose to determine how circuit-level implementations of gene expression define specific neural computations and learning rules in the neocortex. In the course of my work I will seek to address the following questions: 1) How pervasive are genetically defined circuit motifs in the neocortex and what do they compute? 2) How are activity-regulated genes induced and expressed across neocortical circuits during learning? Addressing this requires innovative approaches to characterize circuit components and their functional interactions. To this end, I will combine large-scale single-cell functional imaging and transcriptional profiling into an integrated methodological platform called CRACK (Comprehensive Readout of neuronal Activity and Cell type marKers). I will use this platform to “crack” genetically defined neural circuits underlying sensorimotor integration, higher-level sensory processing, and decision making in the mouse whisker sensorimotor system. I will first apply CRACK in a forward screen to identify novel circuit motifs by characterizing unique functional relationships between known cell types and validating their connections with subsequent anatomical measures. I will then apply this platform to survey the relationship for how learning drives plasticity in cortical circuits by generating intersectional circuit maps of activity patterns and immediate early gene expression in defined cell types. Through these projects, I aim to accelerate the discovery of core circuits underlying learning and behavior in the mammalian neocortex.

项目概要 我们的基因组如何指导产生认知的电路级神经计算？ 旨在解释我们作为人类的认知能力的基本问题需要深入的理解。 了解单个神经元中的基因表达如何与行为过程中整个群体的活动模式相关。 全面整合分子、解剖、功能和行为测量是主要的 在这里，我建议如何确定电路级的技术挑战。 基因表达的实现定义了新皮质中特定的神经计算和学习规则。 在我的工作过程中，我将寻求解决以下问题： 1）新皮质中基因定义的电路基序有多普遍？它们计算什么？ 2）学习过程中活动调节基因如何在新皮质回路中诱导和表达？ 解决这个问题需要创新的方法来表征电路元件及其功能 为此，我将结合大规模单细胞功能成像和转录分析。 进入一个名为 CRACK（神经活动和细胞综合读出）的综合方法平台 我将使用这个平台来“破解”感觉运动背后的基因定义的神经回路。 小鼠胡须感觉运动系统 I 中的集成、高级感觉处理和决策。 将首先在正向屏幕中应用 CRACK，通过表征独特的功能来识别新颖的电路图案 已知细胞类型之间的关系，并通过后续的解剖测量验证它们的联系。 然后，我将应用这个平台来调查学习如何驱动皮质回路可塑性的关系： 生成特定细胞中活动模式和早期基因表达的交叉回路图 通过这些项目，我的目标是加速发现学习和行为背后的核心回路。 在哺乳动物的新皮质中。