Circuit architecture and dynamics of the insular cortex underlying motivational behaviors

动机行为背后的岛叶皮层的电路结构和动力学

• 批准号: 10729654
• 负责人: Tianyi Mao
• 金额: $ 268.05万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10729654
• 关键词: Algorithms; Anatomy; Animal Behavior; Animals; Anterior; Architecture; Behavior; Brain; Calcium; Cells; Classification; Cognition; Corpus striatum structure; Cyclic AMP; Cyclic AMP-Dependent Protein Kinases; Discipline; Dopamine; Electrophysiology (science); Emotions; Event; Functional disorder; Genetic; Genetic Markers; Goals; Image; Individual; Interoception; Learning; Link; Logic; Machine Learning; Maintenance; Maps; Measures; Mediating; Mediator; Metabolic; Mood Disorders; Morphology; Motivation; Neuromodulator; Neurons; Nucleus solitarius; Play; Property; Punishment; Pyramidal Tracts; Resolution; Rewards; Role; Shapes; Signal Pathway; Signal Transduction; Stains; Technology; Testing; Visualization; addiction; cell cortex; cell type; extracellular; hippocampal pyramidal neuron; in vivo calcium imaging; indexing; infancy; lens; machine learning algorithm; motivated behavior; multimodality; neuronal circuitry; neuropsychiatric disorder; neuroregulation; optogenetics; pharmacologic; reconstruction; response; two-photon; Algorithms; Anatomy; Animal Behavior; Animals; Anterior; Architecture; Behavior; Brain; Calcium; Cells; Classification; Cognition; Corpus striatum structure; Cyclic AMP; Cyclic AMP-Dependent Protein Kinases; Discipline; Dopamine; Electrophysiology (science); Emotions; Event; Functional disorder; Genetic; Genetic Markers; Goals; Image; Individual; Interoception; Learning; Link; Logic; Machine Learning; Maintenance; Maps; Measures; Mediating; Mediator; Metabolic; Mood Disorders; Morphology; Motivation; Neuromodulator; Neurons; Nucleus solitarius; Play; Property; Punishment; Pyramidal Tracts; Resolution; Rewards; Role; Shapes; Signal Pathway; Signal Transduction; Stains; Technology; Testing; Visualization; addiction; cell cortex; cell type; extracellular; hippocampal pyramidal neuron; in vivo calcium imaging; indexing; infancy; lens; machine learning algorithm; motivated behavior; multimodality; neuronal circuitry; neuropsychiatric disorder; neuroregulation; optogenetics; pharmacologic; reconstruction; response; two-photon

PROJECT SUMMARY The insular cortex (IC) is a multimodal hub that integrates interoceptive and exteroceptive information to control diverse aspects of animal behaviors related to cognition, emotion, and motivation. Among other functions, the IC receives information regarding an animal’s metabolic states and drives motivation and valence-specific behaviors. However, our understanding of the neuronal substrates and circuit principles underlying IC function is still in its infancy. An important step forward is to determine the activities of individual neurons within discrete IC circuits before, during, and after an animal behavior. To achieve this goal, a prerequisite is to delineate the events in individual IC neuronal types that give rise to the diverse functions in motivated behaviors. However, two major challenges exist. First, neuronal circuits are organized around subregions and neuronal types. It is increasingly clear that traditional classifications of IC subregions and cell types are insufficient to explain the functional diversity of the IC. Precise classification of subregions, neuronal types, and neuron-specific connectivity is needed. Second, an animal’s internal state is in part encoded by neuromodulators, such as dopamine, which dynamically modulate the functions of individual IC circuits. Despite recent progress in measuring extracellular dopamine and other neuromodulators, they trigger intracellular signaling events in a cell type-specific manner. Herein, we propose to overcome these barriers by integrating the latest complementary technological advances from the three PIs. First, we will use machine learning-based algorithms to comprehensively identify functional subdivisions, neuronal types, and cell-specific connectivity in the IC. Second, we will link the activities of individual IC cell types and subregions to animal vigor or valence using two-photon calcium imaging through a gradient-index (GRIN) lens. Third, we will simultaneously image the dynamics of cAMP/protein kinase A (PKA), a key intracellular signaling pathway mediating neuromodulation. Using these approaches, we aim to gain an unparalleled understanding of the activities and neuromodulations of discrete IC circuits that underlie vigor and valence processing, two essential and distinct aspects of motivated behaviors, at cellular resolution. We will test the hypothesis that different IC pyramidal neuronal types form distinct local and long-range circuits, which differentially yet cooperatively drive vigor and valence for motivated behaviors in a manner depending on neuromodulation.

项目摘要 Insular Cortex（IC）是一个多模式集线器，可以整合互感和扩展信息 控制与认知，情感和动机有关的动物行为的潜水方面。之中 其他功能，IC接收有关动物代谢状态和驱动器的信息 动机和价值的行为。但是，我们对神经元底物的理解 IC功能基础的电路原理仍处于起步阶段。向前迈出的重要一步是 确定在离散IC电路内，期间和之后的单个神经元的活动 动物行为。为了实现这一目标，先决条件是描述单个IC中的事件 神经元类型引起了动机行为的潜水功能。但是，两个主要 存在挑战。首先，神经元电路围绕子区域和神经元类型进行组织。这是 越来越清楚的是，IC子区域和细胞类型的传统分类不足 解释IC的功能多样性。子区域，神经元类型和 需要神经特异性连通性。其次，动物的内部状态部分由 神经调节剂，例如多巴胺，它动态调节单个IC的功能 尽管最近在测量细胞外多巴胺和其他神经调节剂方面取得了进展，但 它们以细胞类型特异性的方式触发细胞内信号事件。在此，我们建议 通过整合最新的完成者技术进步来克服这些障碍 三个pis。首先，我们将使用基于机器学习的算法来全面识别功能 IC中的细分，神经元类型和细胞特异性连通性。第二，我们将链接活动 使用两光子钙成像的单个IC细胞类型和分区的动物活力或价 通过梯度指数（微笑）镜头。第三，我们将简单地映像的动态 CAMP/蛋白激酶A（PKA），一种介导神经调节的关键细胞内信号通路。使用 这些方法，我们旨在获得对活动和 基于活力和价值处理的离散IC电路的神经调节，两个必不可少的 在细胞分辨率下，成熟行为的不同方面。我们将检验以下假设 IC锥体神经元类型形成不同的局部和远距离电路，它们差异化 以某种方式合作推动积极行为的活力和价 神经调节。