NCS-FO: Collaborative Research: Ground-Truth Analysis and Modeling of Entire Individual C. elegans Nervous Systems

NCS-FO：协作研究：整个线虫个体神经系统的地面实况分析和建模

• 批准号: 1734870
• 负责人: Edward Boyden
• 金额: $ 70.73万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734870&HistoricalAwards=false
• 关键词: NCS; FO; Collaborative; Research; Ground

How does the brain compute? Understanding this process could lead to many advances in science and technology. The Boyden, Flavell, Barabasi, and Tegmark groups propose to examine how the cells within the brain of a simple animal work together to generate the computations that underlie behavior. The teams will study C. elegans, a small worm with just a few hundred neurons, yet capable of learning and adaptive behavior in complex real-world environments.  The teams will apply new technologies to measure and control the neural circuits of C. elegans, in order to investigate how they works. The project will also generate new mathematical tools to analyze the data that is collected - tools that could help analyze how the brain goes wrong in disorders such as Parkinson's or Alzheimer's. Using the data acquired, the project will reveal how brain circuits compute, which could inspire new algorithms for machine learning and computer information processing. These in turn could have broad impact on economic prosperity as well as in advancing human quality of life. The Boyden, Flavell, Barabasi, and Tegmark groups will launch a novel integrative endeavor to reveal how entire nervous systems - from sensory input neurons, to motor output neurons, and including the networks that underlie learning, decision making, and other processes - work together as emergent wholes to generate the computations that underlie behavior. They will utilize C. elegans, with just 302 neurons, yet capable of learning and adaptive behavior in complex real-world environments.  They will optimize and deploy novel technologies, including a new fluorescent voltage indicator for C. elegans, and a method for 3-D visualization of entire nervous systems with molecular information via physical expansion by up to 10,000 fold in volume. They will record neural and behavioral dynamics, imaging the activity of neurons throughout entire brains and even entire nervous systems of freely moving as well as fictively behaving C. elegans engaged in complex decision-making tasks, or forming new memories. They will then use expansion microscopy to map the structure and molecular profiles of entire individual nervous systems. They will analyze the resultant network structures to determine how individual variation in these features connect to details of an individual's behavior, and make mathematical models of the relevant neural circuits capable of predicting how the nervous system would respond in complex contexts. The outcome of their work will yield radical new theories of how nervous systems operate, as well as a diversity of tools for the neuroscience and computational communities.This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NSF-NCS), a multidisciplinary program jointly supported by the Directorates for Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE).

大脑是如何计算的？了解这一过程可能会导致科学和技术的许多进步。Boyden、Flavell、Barabasi和Tegmark的研究小组提议研究简单动物大脑中的细胞如何协同工作，以产生行为背后的计算。他们将研究C。elegans，一种只有几百个神经元的小蠕虫，但能够在复杂的现实世界环境中学习和适应行为。 研究小组将应用新技术来测量和控制C。为了研究它们是如何工作的 该项目还将产生新的数学工具来分析收集的数据-这些工具可以帮助分析大脑如何在帕金森氏症或阿尔茨海默氏症等疾病中出错。利用获得的数据，该项目将揭示大脑电路如何计算，这可能会激发机器学习和计算机信息处理的新算法。 这些反过来又会对经济繁荣和提高人类生活质量产生广泛影响。 Boyden、Flavell、Barabasi和Tegmark团队将开展一项新的综合奋进，揭示整个神经系统--从感觉输入神经元到运动输出神经元，包括构成学习、决策和其他过程基础的网络--如何作为涌现的整体协同工作，产生构成行为基础的计算。他们将使用C。线虫，只有302个神经元，但能够在复杂的现实世界环境中学习和适应行为。 他们将优化和部署新技术，包括一种新的荧光电压指示器。elegans，以及一种通过物理膨胀高达10，000倍体积的分子信息对整个神经系统进行三维可视化的方法。他们将记录神经和行为动力学，对整个大脑甚至整个神经系统的神经元活动进行成像，这些神经系统自由运动以及虚构的行为C。优雅的人从事复杂的决策任务，或形成新的记忆。然后，他们将使用扩展显微镜来绘制整个个体神经系统的结构和分子图谱。 他们将分析由此产生的网络结构，以确定这些特征中的个体差异如何与个体行为的细节联系起来，并建立相关神经回路的数学模型，以预测神经系统在复杂环境中的反应。他们的工作成果将产生神经系统如何运作的激进的新理论，以及神经科学和计算社区的工具多样性。该项目由理解神经和认知系统的综合策略（NSF-NCS）资助，这是一个由计算机和信息科学与工程（CISE），教育和人力资源（EHR），工程学（ENG）以及社会、行为和经济科学（SBE）。

项目成果

Caenorhabditis elegans and the network control framework—FAQs

• DOI: 10.1098/rstb.2017.0372
• 发表时间: 2018-05
• 期刊: Philosophical Transactions of the Royal Society B: Biological Sciences
• 作者: Emma K. Towlson;P. Vértes;Gang Yan;Yee Lian Chew;Denise S Walker;W. Schafer;A. Barabási

Expansion microscopy: principles and uses in biological research

• DOI: 10.1038/s41592-018-0219-4
• 发表时间: 2019-01-01
• 期刊: NATURE METHODS
• 影响因子: 48
• 作者: Wassie, Asmamaw T.;Zhao, Yongxin;Boyden, Edward S.
• 通讯作者: Boyden, Edward S.

Population imaging of neural activity in awake behaving mice

• DOI: 10.1038/s41586-019-1641-1
• 发表时间: 2019-10-17
• 期刊: NATURE
• 影响因子: 64.8
• 作者: Piatkevich, Kiryl D.;Bensussen, Seth;Han, Xue
• 通讯作者: Han, Xue

The final frontier in connectomics: Forward engineering brain networks

连接组学的最后前沿：正向工程大脑网络

• DOI: 10.1016/j.plrev.2019.11.004
• 发表时间: 2019
• 期刊: Physics of Life Reviews
• 影响因子: 11.7
• 作者: Towlson, Emma K.