NCS-FO: Analyzing Synapses, Motifs and Neural Networks for Large-Scale Connectomics

对大脑连通性的高分辨率分析，揭示了连接大脑神经细胞的实际接线图，提供了任何其他方式无法获得的对健康大脑工作方式的见解，以及疾病和神经系统失调的问题。这种方法的主要挑战是，目前还没有可靠的、强大的、强大的基于计算机的技术来分析极其庞大和极其复杂的脑细胞网络，以检测其高度分支和连接结构中的连接基序。也没有可视化工具允许神经科学家有效地探索大脑网络模式。这项工作将从电子显微镜数据集分析年轻和老年哺乳动物大脑样本的大型大脑网络。这些数据集每个都包含数十万个神经细胞和数十亿个相互连接的突触。该提案旨在开发新的方法和工具来分析这些庞大的大脑网络在突触，基序和网络水平。如果成功，该项目将为大脑如何工作的新理论的发展提供数据和分析工具。使用多束连续切片电子显微镜（sSEM）和自动分割方法的图像采集的最新进展使各种动物的大型组织样本的数据收集成为可能。这些数据将用于管理大规模的数据集，其中包含一百万个带有突触间隙位置的标记突触，突触前和突触后极性预测，以及兴奋和抑制类型预测。由于数据量巨大，这在以前是无法实现的。目的是通过将复杂的神经网络细分为可量化和有意义的子图来发现突触基序。通过开发一种高效的以神经突为中心的接线图重建方法和子图检测算法来寻找共同模式，将自动生成候选基序。这些数据将用于量化和比较来自不同时空尺度的不同标本的重建神经网络，并建立一个可视化平台，以帮助神经科学家分析这些网络，因为他们试图提出和回答与大脑神经回路相关的基本问题。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

High-resolution analysis of the brain's connectivity, which reveals the actual wiring diagram connecting nerve cells of the brain, provides insights unattainable any other way into the way the healthy brain works and what goes awry in diseases and disorders of the nervous system. The primary challenge of this approach is that at present there are no reliable, robust and powerful computer-based techniques to analyze the extraordinarily large and vastly complicated networks of brain cells to detect connectional motifs in their highly branching and connected structure. Nor are there visualization tools that allow neuroscientists to explore the brain network patterns effectively. This work will analyze large brain networks from electron microscopy datasets in young and old mammalian brain samples. These data sets each contains hundreds of thousands of nerve cells and billions of synapses that interconnect them. The proposal aims to develop new methods and tools to analyze these vast brain networks at the synapse, motif, and network levels. If successful, the project will provide data and analysis tools for the development of new theories of how the brain works.Recent advances in image acquisition using multi-beam serial-section electron microscopy (sSEM) and automated segmentation methods have enabled data collection for large tissue samples in a variety of animals. These data will be used to curate large-scale datasets with one million labeled synapses with synaptic cleft locations, pre- and postsynaptic polarity predictions, and excitatory and inhibitory type predictions. This has not been accomplished previously given the enormous amount of data. The aim is to discover synaptic motifs by subdividing complex neural networks into quantifiable and meaningful subgraphs. Automatic generation of candidates for motifs will be created by developing an efficient neurite-centric wiring-diagram reconstruction method and subgraph detection algorithm to find common patterns. These data will be used to quantify and compare reconstructed neural networks from different specimens at different spatial and temporal scales and build a visualization platform to assist neuroscientists to analyze these networks as they seek to ask and answer fundamental questions related to neural circuits in the brain.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.