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

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 岛叶皮层是整合内、外感受信息的多通道中枢 控制动物行为的各个方面，包括认知、情感和动机。之间 在其他功能中，IC接收关于动物代谢状态的信息，并驱动 动机和特定效价的行为。然而，我们对神经元基质的理解 和集成电路功能的电路原理仍处于起步阶段。向前迈出的重要一步是 确定离散IC电路内的单个神经元在一次运动之前、期间和之后的活动。 动物行为要达到这一目标，一个先决条件是在单个IC中描述事件 在动机行为中产生不同功能的神经元类型。然而，两大 挑战是存在的。首先，神经元回路围绕子区域和神经元类型组织。是 越来越清楚的是，IC亚区和细胞类型的传统分类不足以 解释IC的功能多样性。精确的亚区域分类，神经元类型， 需要神经元特异性连接。其次，动物的内部状态部分是由 神经调节剂，如多巴胺，动态调节个体IC的功能 电路.尽管最近在测量细胞外多巴胺和其他神经调质方面取得了进展， 它们以细胞类型特异性方式触发细胞内信号传导事件。在此，我们建议 克服这些障碍，整合最新的互补技术进步， 三个私家侦探首先，我们将使用基于机器学习的算法来全面识别功能 细分，神经元类型和IC中的细胞特异性连接。第二，我们将活动联系起来 单个IC细胞类型和亚区的动物活力或效价使用双光子钙成像 通过梯度折射率（GRIN）透镜。第三，我们将同时对 cAMP/蛋白激酶A（PKA）是介导神经调节的关键细胞内信号传导途径。使用 这些方法，我们的目标是获得一个无与伦比的了解的活动， 神经调制的离散IC电路的基础活力和价处理，两个基本的， 动机行为的不同方面，在细胞分辨率。我们将测试不同的假设， IC锥体神经元类型形成不同的局部和长程回路， 合作驱动活力和效价的激励行为的方式取决于 神经调节