NeuroNex Technology Hub: Integrated Circuit Cracking (ICC) with Linked Tools for Diverse Systems

NeuroNex 技术中心：集成电路破解 (ICC) 以及适用于不同系统的链接工具

• 批准号: 1707261
• 负责人: Karl Deisseroth
• 金额: $ 368万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-12-01 至 2022-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1707261&HistoricalAwards=false
• 关键词: NeuroNex; Technology; Hub; Integrated; Circuit

This NeuroNex Technology Hub, based at Stanford and the Salk Institute in California, will profoundly advance the understanding of the brain by developing technologies to study the brain's structure and function. The investigators will develop new approaches to understand how the individual components that make up the nervous system operate during behavior, and indeed cause behavior. The team will merge principles from genetics, physics, optics, engineering, and biology, to build and disseminate methodology, instrumentation, and analytics that enable targeting and control of individual kinds of brain cells, and the technology developed will be taught via hands-on training available to the scientific community. The outcome will be a broadly-applicable platform for discovering how neural circuit activity gives rise to complex cognitions and behaviors in the brain, which is essential to understand how the nervous stem fails to operate well in neurological and psychiatric diseases. The structure of the NeuroNex Training Core is designed to drive the participation of investigators across the spectrum of background and demography, including junior investigators and students as well as women and other underrepresented groups in STEM. Current understanding of brain function at the cellular network level is limited by the lack of integrative tools that (in the same individual organism) can be used for molecularly defining neural circuit components, for tracing local and global wiring of those same circuit components, and for observing and controlling activity in those same circuit components during precisely controlled and quantified behaviors. This integration, or "datastream linking", will fundamentally advance knowledge but is an enormous practical and intellectual challenge. This NeuroNex Technology Hub will 1) address this challenge with molecular, genetic and optical tools while also developing computational methods to discover the underlying natural and causal structural and dynamical motifs; 2) do so in a vertically-linked fashion so that all technologies built are mutually compatible in the same nervous system at the same time; 3) do so in a horizontally-linked fashion, so that the technologies built are suitable for primate rat, mouse, fly, and diverse fish species; 4) engage in outreach, training, and dissemination, open for broadest impact to the entire NSF community throughout the program. This teaching will leverage our current state-of-the-art methods and educational infrastructure, and will advance alongside the technology development and integration.

这个设在斯坦福大学和加州索尔克研究所的NeuroNex技术中心将通过开发研究大脑结构和功能的技术来深刻地促进对大脑的理解。研究人员将开发新的方法，以了解组成神经系统的各个组件在行为过程中是如何运作的，并确实导致行为。该团队将融合遗传学、物理学、光学、工程学和生物学的原理，建立和传播方法学、仪器和分析，以实现对各种脑细胞的靶向和控制，开发的技术将通过科学界可用的实践培训来教授。这一成果将成为一个广泛适用的平台，用于发现神经回路活动如何导致大脑中复杂的认知和行为，这对于理解神经干如何在神经和精神疾病中无法良好运行至关重要。NeuroNex培训核心的结构旨在推动调查人员参与各种背景和人口学领域的调查，包括初级调查人员和学生以及妇女和其他在STEM中代表性不足的群体。目前在细胞网络水平上对大脑功能的理解受到缺乏综合工具的限制，这些工具可以(在同一个体生物体中)用于从分子上定义神经电路组件，用于跟踪这些相同电路组件的局部和全局连接，以及用于在精确控制和量化行为期间观察和控制这些相同电路组件中的活动。这种集成，或“数据流链接”，将从根本上促进知识进步，但也是一个巨大的实践和智力挑战。该NeuroNex技术中心将1)通过分子、遗传和光学工具应对这一挑战，同时还将开发发现潜在的自然和因果结构及动态基序的计算方法；2)以垂直链接的方式开展工作，使所构建的所有技术在同一神经系统中同时相互兼容；3)以水平链接的方式开展工作，使所构建的技术适用于灵长类动物、老鼠、苍蝇和各种鱼类；4)开展外联、培训和传播，对整个项目的整个NSF社区产生最广泛的影响。这项教学将利用我们目前最先进的方法和教育基础设施，并将与技术的发展和整合一起推进。

项目成果

Complementary Genetic Targeting and Monosynaptic Input Mapping Reveal Recruitment and Refinement of Distributed Corticostriatal Ensembles by Cocaine

互补遗传靶向和单突触输入映射揭示了可卡因对分布式皮质纹状体集合的招募和细化

• DOI: 10.1016/j.neuron.2019.10.032
• 发表时间: 2019
• 期刊: Neuron
• 影响因子: 16.2
• 作者: Wall, Nicholas R.;Neumann, Peter A.;Beier, Kevin T.;Mokhtari, Ava K.;Luo, Liqun;Malenka, Robert C.
• 通讯作者: Malenka, Robert C.

Fast, in vivo voltage imaging using a red fluorescent indicator.

• DOI: 10.1038/s41592-018-0188-7
• 发表时间: 2018-12
• 期刊: Nature methods
• 影响因子: 48
• 作者: Kannan M;Vasan G;Huang C;Haziza S;Li JZ;Inan H;Schnitzer MJ;Pieribone VA
• 通讯作者: Pieribone VA

Kilohertz two-photon brain imaging in awake mice.

• DOI: 10.1038/s41592-019-0597-2
• 发表时间: 2019-11
• 期刊: NATURE METHODS
• 影响因子: 48
• 作者: Zhang, Tong;Hernandez, Oscar;Chrapkiewicz, Radoslaw;Shai, Adam;Wagner, Mark J.;Zhang, Yanping;Wu, Cheng-Hsun;Li, Jin Zhong;Inoue, Masatoshi;Gong, Yiyang;Ahanonu, Biafra;Zeng, Hongkui;Bito, Haruhiko;Schnitzer, Mark J.
• 通讯作者: Schnitzer, Mark J.

Functional circuit architecture underlying parental behaviour.

• DOI: 10.1038/s41586-018-0027-0
• 发表时间: 2018-04
• 期刊: Nature
• 影响因子: 64.8
• 作者: Kohl J;Babayan BM;Rubinstein ND;Autry AE;Marin-Rodriguez B;Kapoor V;Miyamishi K;Zweifel LS;Luo L;Uchida N;Dulac C
• 通讯作者: Dulac C

Genetic tagging of active neurons in auditory cortex reveals maternal plasticity of coding ultrasonic vocalizations.

• DOI: 10.1038/s41467-018-03183-2
• 发表时间: 2018-02-28
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Tasaka GI;Guenthner CJ;Shalev A;Gilday O;Luo L;Mizrahi A
• 通讯作者: Mizrahi A