CRCNS: Dissecting Directed Interactions Amongst Multiple Neuronal Populations

CRCNS：剖析多个神经元群之间的定向相互作用

• 批准号: 10830525
• 负责人: ADAM KOHN
• 金额: $ 34.05万
• 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: 2023-09-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10830525
• 关键词: Address; Anatomy; Area; Brain; Calcium; Collaborations; Communication; Data; Dimensions; Disease; Electrodes; Feedback; Floods; Functional Magnetic Resonance Imaging; Goals; Image; Macaca; Mental disorders; Methods; Neurons; Pathway interactions; Pattern; Population; Poverty; Process; Property; Role; Sampling; Schizophrenia; Signal Transduction; Statistical Methods; System; Techniques; Technology; Time; Utah; Visual Cortex; Visual System; Work; analytical tool; area V1; area striata; autism spectrum disorder; insight; neural; nonhuman primate; response; retinotopic; tool; transmission process

Nearly all brain functions involve activity that is distributed across multiple areas. To understand these functions, it is critical to understand the flow of signals across this distributed network. To date approaches to understanding inter-areal signaling have been limited in several critical ways. First, they often focus on single neurons or a few voxels to summarize an area’s activity—an impoverished sample of the intricate neuronal population activity patterns that are known to represent and transmit information. Second, prior approaches often consider only pairwise inter-areal interactions, though the relevant network of areas is often much larger. Third, they rarely consider the concurrent flow of signals both from and to any given node in the network. In this project, we aim to overcome all three of these limitations. In Aim 1, we will develop and validate statistical methods that allow us to assess the directed, multi-dimensional flow of neuronal population signals among multiple (more than two) brain areas. We will identify directed interactions based on if the activation of a population activity pattern in one brain area tends to reliably precede the activation of a population activity pattern in another brain area with a consistent time delay. In Aim 2, we will refine and deploy the methods we develop to assess signal flow across multiple stages of the macaque visual system, an ideal testbed given a great deal of prior work on the anatomical and functional properties of the sampled areas. Specifically, we will record hundreds of neurons distributed across different layers of primary visual cortex (V1), V2, and V3. We will determine how columnar interactions within each area interact with feedforward and feedback processes, at a laminar level. Our project aims to provide insights that will strongly advance understanding of fundamental aspects of cortical function—how neuronal populations communicate with each other and how that communication relates to cortical processing. We expect the understanding we gain, and the analytic and conceptual tools we develop, will be broadly applicable across different brain systems. Our ambitious goals will be accomplished by pooling complementary expertise of three PIs, building on a successful collaboration that has extended over many years.

几乎所有大脑功能都涉及分布在多个区域的活动。要了解这些 功能，了解跨此分布式网络的信号流至关重要。迄今为止接近 理解区域间信号传输在几个关键方面受到限制。首先，他们常常关注 单个神经元或几个体素来总结一个区域的活动——复杂的样本 已知代表和传递信息的神经元群体活动模式。二、事前 方法通常只考虑成对的区域间相互作用，尽管相关的区域网络是 通常要大得多。第三，他们很少考虑来自和流向任何给定信号的并发流。 网络中的节点。在这个项目中，我们的目标是克服所有这三个限制。在目标 1 中，我们将 开发和验证统计方法，使我们能够评估定向的、多维的流 多个（两个以上）大脑区域之间的神经元群体信号。我们将确定定向 基于一个大脑区域的群体活动模式的激活是否趋于可靠的相互作用 以一致的时间延迟先于另一个大脑区域的群体活动模式的激活。在 目标 2，我们将完善和部署我们开发的方法来评估跨多个阶段的信号流 猕猴视觉系统是一个理想的测试平台，在解剖学和视觉系统方面进行了大量的前期工作 采样区域的功能属性。具体来说，我们将记录数百个分布的神经元 跨越初级视觉皮层 (V1)、V2 和 V3 的不同层。我们将确定柱状 每个区域内的相互作用在层流水平上与前馈和反馈过程相互作用。我们的 该项目旨在提供深刻见解，从而有力地促进对皮质基本方面的理解 功能——神经元群体如何相互沟通以及这种沟通如何与 皮质处理。我们期望获得理解，以及我们获得的分析和概念工具 开发，将广泛适用于不同的大脑系统。我们的宏伟目标将是 通过汇集三位 PI 的互补专业知识，在成功合作的基础上完成 已延续多年。