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.

几乎所有的大脑功能都涉及到分布在多个区域的活动。要理解这些 功能，了解该分布式网络中的信号流动是至关重要的。到目前为止的方法 对区域间信号的理解在几个关键方面受到了限制。首先，他们经常把重点放在 单个神经元或几个体素来总结一个区域的活动--一个复杂的 已知的代表和传递信息的神经元群体活动模式。第二，优先 方法通常只考虑区域间的成对互动，尽管相关的区域网络是 通常要大得多。第三，它们很少考虑来自和去往任何给定的信号的并发流 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 多个(两个以上)脑区之间的神经元群体信号。我们将确定定向 基于群体活动模式在一个大脑区域的激活是否趋于可靠的交互作用 在激活另一个大脑区域的群体活动模式之前，具有一致的时间延迟。在……里面 目标2，我们将改进和部署我们开发的方法，以评估跨多个阶段的信号流 猕猴视觉系统，一个理想的试验台，提供了大量的先前在解剖学和 采样区域的功能属性。具体地说，我们将记录数百个神经元的分布 横跨初级视觉皮质(V1)、V2和V3的不同层。我们将确定栏目如何 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 该项目旨在提供洞察力，将有力地促进对皮质的基本方面的理解 功能-神经元群体如何相互通信，以及这种通信如何与 皮质处理。我们期待我们获得的理解，以及我们的分析和概念工具 开发，将广泛适用于不同的大脑系统。我们雄心勃勃的目标将是 通过汇集三个PI的互补专业知识，在以下成功协作的基础上实现 已经延续了很多年。