NeuroNex: Enabling Identification and Impact of Synaptic Weight in Functional Networks

数万亿个突触连接着神经回路中的数十亿个神经元，这些神经元允许感觉、思想、行动、学习和记忆。该 NeuroNex 网络涉及开发新方法来确定大脑中神经元之间的连接强度（突触权重）。了解突触权重至关重要，但尽管经过一个多世纪的探索，即使是一个明确的定义仍然难以捉摸。这个 NeuroNex 网络聚集了世界各地的专家来研究从分子到行为的突触，以回答这个基本且雄心勃勃的问题：突触重量的构成是什么，以及它在塑造神经回路中发挥什么作用？假设突触重量涉及关键蛋白质和亚细胞资源的差异组成和共现。多学科方法用于评估涉及多种细胞类型、大脑区域和不同行为的神经回路明确状态下的这些特征。突触状态的一致预测因子被映射到神经连接体上，以增强对突触权重如何影响回路组织和功能的理解。该项目开发和使用的新电子显微镜技术弥补了图像尺寸和分辨率方面的差距，以更深入地了解从纳米级到电路级别的大脑功能和调节。持久、深远的影响涉及利用 NeuroNex 网络与其他 BRAIN Initiative 项目的工作来实现新知识的获取和共享。这里开发的知识和工具的未来应用，甚至超越大脑，将产生解决复杂自组织系统的基本和新颖原理的数据。 NeuroNex 网络还涉及训练下一代，包括通过实验室间和同行交流。什么构成了突触权重，它在塑造神经回路中发挥什么作用，以及它在生长和可塑性过程中如何变化？答案需要转变，不再将突触视为孤立的实体。突触不仅仅存在于一位机器上，也存在于一位机器之外。相反，作为权重的代表，突触大小中存储的信息内容要高得多。突触权重在广泛的时间和空间尺度上受神经活动动态调节。新的证据表明，亚细胞资源（内质网、线粒体、内体、核糖体）是驱动突触功效和可塑性的媒介。该项目旨在了解突触组成和结构如何在揭示亚细胞水平生物学机制的规模上预测突触重量和功能。一种新的 3D 电子显微镜 (EM) 方法是在透射模式下运行的扫描 EM (tomoSEM) 上使用锥形倾斜断层扫描开发的。 TomoSEM 填补了目前结构生物学方法（高分辨率、小体积）和连接组学方法（分辨率相对较低、体积较大）之间的分辨率与体积差距。 TomoSEM 消除了其他 EM 方法的主要伪影，同时减少了人力和成本。研究人员由蛋白质化学、细胞生物学、连接组学和行为学领域的世界专家组成。 EM 专家实施、验证和部署 tomoSEM。图像分析、几何、统计、机器学习和多级建模方面的专家创建了搜索数据隐藏顺序的平台。这些策略共享国际资源，以克服一次在本地积累一个突触数据的限制。该项目由生物科学理事会新兴前沿项目共同资助。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Trillions of synapses connect billions of neurons in neural circuits that allow sensation, thought, action, learning, and memory. This NeuroNex Network involves the development of new approaches to determine the strength of connections between neurons—synaptic weight--in the brain. Understanding synaptic weight is crucial, yet even a clear definition remains elusive, despite more than a century of searching. This NeuroNex Network assembles world experts to study synapses from molecules to behavior, to answer this fundamental and ambitious question: What constitutes synaptic weight, and what role does it play in shaping neural circuits? Synaptic weight is hypothesized to involve the differential composition and co-occurrence of key proteins and subcellular resources. Multidisciplinary approaches are used to assess these features in well-defined states of neural circuits involving multiple cell types, brain regions, and diverse behaviors. Consistent predictors of synaptic state are mapped onto neural connectomes to enhance understanding of how synaptic weight influences circuit organization and function. New electron microscopy technologies developed and used in this project bridge gaps in image size and resolution needed to achieve deeper understanding of brain function and regulation from nanoscale to circuit levels. A long-lasting, far-reaching impact involves leveraging work from this NeuroNex Network with other BRAIN Initiative projects to enable acquisition and sharing of the new knowledge. Future applications, even beyond the brain, of the knowledge and tools developed here will give rise to data that address fundamental and novel principles of complex self-organizing systems. The NeuroNex Network also involves training the next generation, including through inter-laboratory and fellow exchanges.What constitutes synaptic weight, what role does it play in shaping neural circuits, and how does it change during growth and plasticity? Answers require a shift away from thinking about synapses as isolated entities. Synapses are not simply on or off one-bit machines; instead the information content stored in synapse size, as a proxy for weight, is much higher. Synaptic weight is controlled over broad temporal and spatial scales dynamically regulated by neural activity. New evidence points to subcellular resources (endoplasmic reticulum, mitochondria, endosomes, ribosomes) as brokers that drive synaptic efficacy and plasticity. This project seeks to understand how synapse composition and structure predict synaptic weight and function at a scale that reveals biological mechanisms at the subcellular level. A new 3D electron microscopy (EM) approach is developed using conical tilt tomography on the scanning EM operating in the transmission mode (tomoSEM). TomoSEM fills the current resolution-to-volume gap between methods of structural biology (high resolution, small volumes) and connectomics (relatively low resolution, larger volumes). TomoSEM eliminates major artifacts of other EM methods while reducing human effort and cost. The investigators comprise world experts in protein chemistry, cell biology, connectomics, and behavior. Experts in EM implement, validate, and deploy tomoSEM. Experts in image analysis, geometry, statistics, machine learning, and multilevel modeling create platforms to search data for hidden order. These strategies share international resources to overcome limits of accumulating data locally one synapse at a time. This project is co-funded by Emerging Frontiers in the Directorate for Biological Sciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.