Functional Analysis of Whole-Brain Dynamics in Learning

学习中全脑动态的功能分析

• 批准号: 10527358
• 负责人: Hang Lu
• 金额: $ 47.49万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10527358
• 关键词: Acute; Address; Affect; Anatomy; Animal Model; Animals; Area; Behavior; Behavioral; Biochemical; Brain; Brain imaging; Brain region; Caenorhabditis elegans; Cells; Characteristics; Communication; Complex; Data; Defect; Development; Dimensions; Engineering; Exhibits; Food; Foundations; Genetic; Genetic Identity; Glean; Goals; Head; Human; Image; Impairment; Individual; Interneurons; Intervention; Invertebrates; Learning; Learning Module; Maps; Measures; Methods; Mission; Modeling; Molecular; Molecular Genetics; Nematoda; Nervous System; Nervous System Physiology; Neurons; Neurosciences; Olfactory Learning; Optics; Organism; Outcome; Pattern; Play; Process; Property; Psychophysics; Regulation; Research; Resolution; Role; Sensory; Sensory Process; Smell Perception; Structure; System; Systems Analysis; Taste Perception; Techniques; Testing; Therapeutic; Time; Training; Work; behavior test; connectome; design; experience; genetic makeup; in vivo calcium imaging; innovation; insight; learned behavior; learning ability; learning engagement; nervous system disorder; neural; neuron component; pathogenic bacteria; prevent; sensory input; spatiotemporal; temporal measurement; therapy design; tool

PROJECT SUMMARY Learning is a complex process, and likely involves many areas of the brain that detect and process sensory inputs, integrate experience, and display behavior. Consistently, various neurological diseases that impair different brain areas are associated with profound defects in learning. Thus, bridging different spatial scales and understanding the dynamics of different brain regions are essential to understanding how learning occurs and potentially designing strategies to mitigate learning deficiency. However, it is currently not possible to achieve these goals in most experimental systems, and our understanding of learning is limited by the technical approaches by which either local circuit and cellular properties or coarse psychophysical parameters underlying learning are measured. Here, we propose to address these fundamental questions in a reduced system – the nervous system of the nematode C. elegans. The rationale is that the wiring and genetic make-up of this network are well known, probing whole-brain dynamics with single-cell resolution with exquisite temporal resolution is technically ready for C. elegans, and the fundamental principles for the development and the function of the nervous system are well conserved between C. elegans and more complex animal models. Further, C. elegans exhibits many forms of learning, similar to those displayed by higher organisms in behavioral characteristics and molecular cellular underpinnings. Particularly, we will use an olfactory learning paradigm whereby C. elegans learns to avoid the odorants of pathogenic bacteria, a type of learning similar to the Garcia effect through which many animals, including humans, learn to avoid the smell and/or taste of a food that makes them ill. Our long-term goal is to understand how learning is encoded and executed by the function of the whole brain, and to inform the design of potential therapeutic strategies. The central hypothesis of this project is that learning engages global activity and the learned information is encoded in distinct functional modules. Specifically, we will test whether learned information is encoded in the learning-dependent changes in the activity patterns of individual functional modules and/or the interactions among the modules. To this end, we aim to image and analyze multi-cell and whole-brain dynamics under naive and learned conditions to characterize how learning alters the structure of the brain activities; further, we will introduce perturbations to the whole-brain dynamics and examine the consequences for learning. This work is innovative because (1) it brings a conceptual advance to understanding learning across scales, (2) it introduces technical advancement in whole-brain imaging and analyses, and (3) it demonstrates perturbation strategies for altering whole-brain dynamics that have behavioral consequences. It is significant, because it tests several highly plausible and likely conserved cellular and whole-brain dynamic models for learning and examine their behavioral consequences, it informs and facilitates learning studies in other systems, and it paves the way for designing interventions.

项目总结 学习是一个复杂的过程，可能涉及大脑中探测和处理感觉的许多区域 输入、集成体验和显示行为。一直以来，各种神经疾病都会损害 不同的大脑区域与学习中的严重缺陷有关。因此，将不同的空间尺度连接起来 了解大脑不同区域的动态对于理解学习是如何发生的是必不可少的 以及潜在地设计策略来缓解学习缺陷。然而，目前还不可能 在大多数实验系统中实现这些目标，我们对学习的理解受到 局部电路和细胞特性或粗略心理物理的技术方法 测量了作为学习基础的参数。在这里，我们建议在以下方面解决这些基本问题 减少的系统-线虫的神经系统。其基本原理是布线和 这个网络的基因构成是众所周知的，用单细胞分辨率探测全脑动力学 具有精致的时间分辨率，在技术上已经为线虫做好了准备，而 在线虫和其他动物之间，神经系统的发育和功能被很好地保存下来 复杂的动物模型。此外，线虫展示了许多形式的学习，类似于 具有行为特征和分子细胞基础的高等生物。特别是，我们将使用 一种嗅觉学习范例，线虫借此学习避免病原菌的气味，一种 一种类似于加西亚效应的学习类型，许多动物，包括人类，都通过这种学习来学习避免 使他们生病的食物的气味和/或味道。我们的长期目标是理解学习是如何 编码和执行由整个大脑的功能，并通知潜在的治疗设计 战略。这个项目的中心假设是，学习涉及全球活动，而被学习的人 信息被编码在不同的功能模块中。具体地说，我们将测试学习到的信息 编码在各个功能模块和/或活动模式的依赖于学习的变化中 模块之间的交互。为此，我们的目标是对多细胞和全脑进行成像和分析 在幼稚和习得的条件下描述学习如何改变大脑结构的动力学 活动；此外，我们将在全脑动力学中引入扰动并检查其后果。 为了学习。这项工作的创新之处在于：(1)它为理解学习带来了概念上的进步 跨尺度，(2)介绍了全脑成像和分析的技术进步，(3)它 演示了改变具有行为后果的全脑动力学的扰动策略。 这是有意义的，因为它测试了几个高度可信和可能保守的细胞和全脑 学习的动态模型并检查其行为后果，它提供信息并促进学习 在其他系统中进行研究，并为设计干预措施铺平道路。