P4: Multiregion interactions

P4：多区域交互

• 批准号: 10705966
• 负责人: Ilana Witten
• 金额: $ 75.23万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-08 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10705966
• 关键词: Address; Affect; Area; BRAIN initiative; Basal Ganglia; Behavior; Brain; Brain region; Cerebellum; Chronic; Cognition; Cognitive; Communication; Computing Methodologies; Contralateral; Corpus striatum structure; Cues; Data; Data Science; Data Set; Decision Making; Electrophysiology (science); Etiology; Foundations; Geometry; Goals; Image; Individual; Ipsilateral; Lead; Left; Measures; Methods; Modeling; Motor; Motor output; Nature; Neuroanatomy; Neurons; Optics; Pathway interactions; Population; Probability; Prosencephalon; Regulation; Resolution; Role; Sensory; Short-Term Memory; Side; Structure; Surveys; System; Technology; Testing; Thalamic structure; Time; Variant; Work; biophysical model; cell type; cognitive process; design; experimental study; imaging modality; insight; neural; neural circuit; neuromechanism; next generation; optogenetics; programs; theories; transmission process; virtual reality

Project Summary/Abstract: Project 4, Multi-Region Interactions So far, our U19 program has largely focused on how individual brain regions support working memory and decision-making. While this work has identified a large network of regions that contribute to decision-making, we now need to understand how these regions interact with each other. What information is sent between which regions, and when? How does such interaction depend on task demands? This project’s goal is to address these gaps by combining cutting-edge recording methods, recordings during cell-type- specific perturbations, new computational methods, and a panel of decision-making tasks. We propose to collect two datasets of multi-region, large-scale, cellular-resolution recordings (Aim 1). In the first, we will record simultaneously from a large network of regions, using next-generation Neuropixels NXT technology. In the second, we will image simultaneously from three regions with our mesoscope while obtaining cell-type-specific information through Core 4, Neuroanatomy. For both datasets, we will apply new methods that we have developed to estimate time-dependent information flow across regions. In addition to this broad, large-scale survey, we will perform two more targeted experiments. In the basal ganglia, we will address the puzzle of how perturbation of neural activity that is not choice selective can nevertheless lead to choice biases, by combining cell-type-specific perturbations with downstream recordings (Aim 2). We will also address the broad and recently prominent question of the cerebellum’s role in cognition (Van Overwalle 2020; Wagner 2020; Stoodley 2021). By manipulating cerebellar populations while recording in thalamus and cortex, we will test the hypothesis that, by analogy to the cerebellum’s role in refining and stabilizing motor trajectories, this region also serves to refine and stabilize neural trajectories in frontal association areas (Aim 3). Taken together, we expect that these experimental and computational approaches will provide the foundation for a multi-region description of how decisions are formed, which will provide important constraints for our multi-region mechanistic model in Project 5. This work will substantially advance three priority areas of the BRAIN Initiative: the brain in action, demonstrating causality, and identifying fundamental principles.

项目总结/摘要：项目4，多区域互动 到目前为止，我们的 U19 项目主要关注各个大脑区域如何支持工作记忆 虽然这项工作已经确定了一个大型的区域网络，可以为这些区域做出贡献。 为了做出决策，我们现在需要了解这些区域如何相互交互，信息是什么。 这种交互如何取决于该项目的任务需求？ 目标是通过结合最先进的记录方法、细胞类型过程中的记录来解决这些差距 具体的扰动、新的计算方法和一组决策任务。 我们建议收集两个多区域、大规模、细胞分辨率记录的数据集（目标 1）。 首先，我们将使用下一代 Neuropixels 从大型区域网络同时记录 在第二个技术中，我们将使用介观镜同时从三个区域进行成像。 通过核心 4，神经解剖学获取细胞类型特异性信息 对于这两个数据集，我们将应用新的。 我们开发的用于估计跨区域的时间相关信息流的方法。 除了这项广泛、大规模的调查之外，我们还将进行两项更有针对性的实验。 基底神经节，我们将解决非选择选择性的神经活动扰动如何影响这一难题 然而，通过将细胞类型特异性扰动与下游记录相结合，会导致选择偏差 （目标 2）我们还将解决小脑在认知中的作用这一广泛且最近突出的问题。 （Van Overwalle 2020；Wagner 2020；Stoodley 2021）通过在记录时操纵小脑群体。 丘脑和皮质，我们将通过类比小脑在精炼和大脑皮层中的作用来检验这一假设： 除了稳定运动轨迹外，该区域还可以细化和稳定额叶的神经轨迹 关联领域（目标 3）。 总而言之，我们期望这些实验和计算方法将提供 为决策如何形成的多区域描述奠定了基础，这将提供重要的 项目 5 中我们的多区域机械模型的约束。这项工作将大大推进三个 BRAIN Initiative 的优先领域：大脑在行动、展示因果关系并确定基本原理 原则。