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

对大脑连接性的高分辨率分析揭示了连接大脑神经细胞的实际线路图，为了解健康大脑的工作方式以及神经系统疾病和障碍中的问题提供了其他任何方式都无法获得的见解。这种方法的主要挑战是，目前还没有可靠、健壮和强大的基于计算机的技术来分析非常庞大和极其复杂的脑细胞网络，以检测其高度分枝和连接结构中的连接模体。也没有可视化工具可以让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的大脑样本中的大型大脑网络。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，在突触、基元和网络层面分析这些庞大的大脑网络。如果成功，该项目将为开发大脑工作原理的新理论提供数据和分析工具。使用多束连续切片电子显微镜(SSEM)和自动分割方法获取图像的最新进展使各种动物的大组织样本数据收集成为可能。这些数据将用于整理具有100万个标记突触的大规模数据集，这些突触具有突触裂隙位置、突触前和突触后的极性预测以及兴奋性和抑制性预测。考虑到海量的数据，这是以前没有做到的。其目的是通过将复杂的神经网络细分为可量化的有意义的子图来发现突触基元。通过开发一种高效的以轴突为中心的接线图重建方法和寻找常见模式的子图检测算法，将创建候选图案的自动生成。这些数据将被用来量化和比较不同空间和时间尺度上不同样本的重建神经网络，并建立一个可视化平台来帮助神经科学家分析这些网络，因为他们寻求提出和回答与大脑中神经回路相关的基本问题。这一奖项反映了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。

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.