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.

项目概要 岛叶皮层（IC）是一个多模式枢纽，整合内感受和外感受信息 控制与认知、情感和动机相关的动物行为的各个方面。 其他功能，IC 接收有关动物代谢状态和驱动力的信息 然而，我们对神经基质的理解。 IC功能的基础和电路原理仍处于起步阶段，一个重要的进步是。 确定分立 IC 电路内的单个神经元的活动之前、期间和之后 为了实现这一目标，先决条件是描述个体 IC 中的事件。 然而，两种主要的神经类型在动机行为中产生不同的功能。 首先，神经回路是围绕子区域和神经类型组织的。 越来越明显的是，传统的IC分区和细胞类型分类不足以 解释 IC 的精确分类、神经类型和功能。 其次，动物的内部状态部分由神经元编码。 神经调节剂，例如多巴胺，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， 它们以细胞类型特异性的方式触发细胞内信号传导事件。 通过整合最新的互补技术进步来克服这些障碍 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、神经类型和细胞特异性连接 其次，我们将链接活动。 使用双光子钙成像将单个 IC 细胞类型和亚区域与动物活力或价态联系起来 第三，我们将同时对梯度折射率（GRIN）透镜进行动态成像。 cAMP/蛋白激酶 A (PKA)，介导神经调节的关键细胞内信号通路。 这些方法，我们的目标是获得对活动和活动的无与伦比的理解 分立 IC 电路的神经调节是活力和价处理的基础，这两个基本和 我们将在细胞分辨率上检验动机行为的不同方面。 IC 锥体神经类型形成不同的局部和远程回路，它们之间存在差异 以取决于的方式合作地驱动动机行为的活力和效价 神经调节。