Functional analysis of whole-brain dynamics in learning

学习中全脑动态的功能分析

• 批准号: 10063920
• 负责人: Hang Lu
• 金额: $ 46.18万
• 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/10063920
• 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 Physiology; Nervous system structure; 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; nervous system disorder; pathogenic bacteria; prevent; relating to nervous system; 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.

项目概要 学习是一个复杂的过程，可能涉及大脑中检测和处理感觉的许多区域 输入、整合经验并一致地表现出损害各种神经系统疾病。 不同的大脑区域与学习的严重缺陷相关，因此，弥合了不同的空间尺度。 了解不同大脑区域的动态对于理解学习如何发生至关重要 并可能设计缓解学习缺陷的策略但是，目前不可能。 在大多数实验系统中实现这些目标，而我们对学习的理解受到以下因素的限制： 技术方法，通过局部电路和细胞特性或粗略的心理物理学 在这里，我们建议解决这些基本问题。 一个简化的系统——线虫的神经系统。基本原理是布线和 该网络的遗传组成是众所周知的，以单细胞分辨率探测全脑动力学 具有精致的时间分辨率，在技术上已经为秀丽隐杆线虫做好了准备，并且基本原理 线虫和其他动物之间神经系统的发育和功能都得到了很好的保存 此外，线虫表现出多种形式的学习，与线虫所表现出的相似。 特别是，我们将使用高等生物的行为特征和分子细胞基础。 一种嗅觉学习范式，秀丽隐杆线虫学会避开病原菌的气味， 类似于加西亚效应的学习类型，包括人类在内的许多动物通过这种学习方式学会避免 使他们生病的食物的气味和/或味道我们的长期目标是了解学习是如何发生的。 由整个大脑的功能编码和执行，并为潜在治疗的设计提供信息 该项目的中心假设是学习涉及全球活动和学习者。 具体来说，我们将测试是否学习到信息。 被编码在各个功能模块的活动模式中依赖于学习的变化和/或 为此，我们的目标是对多细胞和全脑进行成像和分析。 幼稚和学习条件下的动力学来表征学习如何改变大脑的结构 活动；此外，我们将对全脑动力学引入扰动并检查其后果 这项工作具有创新性，因为（1）它为理解学习带来了概念上的进步。 跨尺度，（2）它引入了全脑成像和分析方面的技术进步，（3）它 展示了改变具有行为后果的全脑动态的扰动策略。 它很重要，因为它测试了几种高度可信且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，它为学习提供信息并促进学习 其他系统的研究，并为设计干预措施铺平了道路。