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，这些神经回路允许感觉、思维、行动、学习和记忆。这个NeuroNex网络涉及开发新的方法来确定大脑中神经元之间的连接强度--突触重量。了解突触的重量至关重要，然而，尽管进行了一个多世纪的研究，但即使是一个明确的定义也仍然难以捉摸。这个NeuroNex网络汇集了世界各地的专家，研究从分子到行为的突触，以回答这个基本而雄心勃勃的问题：突触重量是什么构成的，它在塑造神经回路方面扮演着什么角色？突触重量被认为涉及关键蛋白质和亚细胞资源的不同组成和共存。多学科方法被用来在涉及多种细胞类型、大脑区域和不同行为的神经回路的明确定义的状态下评估这些特征。突触状态的一致预测因子被映射到神经连接体上，以增强对突触重量如何影响电路组织和功能的理解。在该项目中开发和使用的新的电子显微镜技术弥补了图像大小和分辨率方面的差距，需要对从纳米级到电路级的大脑功能和调节进行更深入的了解。一个持久而深远的影响包括利用该NeuroNex网络与其他Brain Initiative项目的工作，使新知识的获取和共享成为可能。未来，甚至在大脑之外，对这里开发的知识和工具的应用将产生解决复杂自组织系统的基本和新原理的数据。NeuroNex网络还包括训练下一代，包括通过实验室间和同事交换。突触重量是什么组成的，它在塑造神经回路中扮演什么角色，以及它在生长和可塑性过程中如何变化？答案需要改变，不再把突触视为孤立的实体。突触并不是简单地打开或关闭一位机器；相反，存储在突触大小中的信息内容，作为重量的代理，要高得多。突触的重量在广阔的时间和空间尺度上受到神经活动的动态调节。新的证据表明，亚细胞资源(内质网、线粒体、内糖体、核糖体)是驱动突触效能和可塑性的中介。这个项目试图了解突触的组成和结构如何在揭示亚细胞水平的生物学机制的范围内预测突触的重量和功能。提出了一种利用锥形倾斜层析技术对透射式扫描电子显微镜(TomoSEM)进行三维成像的方法。TomoSEM填补了目前结构生物学方法(高分辨率、小体积)和连接学方法(相对低分辨率、大体积)之间的分辨率与体积的差距。TomoSEM消除了其他EM方法的主要伪影，同时减少了人力和成本。研究人员包括蛋白质化学、细胞生物学、连接学和行为学方面的世界专家。EM方面的专家实施、验证和部署TomoSEM。图像分析、几何学、统计学、机器学习和多层次建模方面的专家创建了搜索数据以寻找隐藏秩序的平台。这些战略共享国际资源，以克服一次在一个突触本地积累数据的限制。这个项目是由生物科学局的新兴前沿共同资助的。这个奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.