NCS-FO: Empowering Data-Driven Hypothesis Generation for Scalable Connectomics Analysis

连接学的fiLD旨在以纳米分辨率重建神经元和突触的接线图，以使人们能够对大脑的工作原理有新的见解。在图像获取和机器学习方法方面的最新进展已经产生了大组织样本的神经连接的完全重建。研究人员有一个这样的数据集，来自人类脑组织，由来自电子显微镜的两拍字节的原始图像数据组成。在过去的两年里，他们与谷歌合作，重建了大约5万个细胞的完整3D形状，其中包括1.8万个神经元，并识别了大约1.33亿个突触。这些数据将使他们能够检查各种神经元形状的原型，这些神经元类型与其内部结构之间的相关性，以及它们是如何相互联系的。这将在一个比以前的大脑样本大几个数量级的数据集中完成。这些密集的大脑重建结果具有复杂的空间结构和网络结构，这给希望探索和分析此类数据的科学家提出了新的挑战。该计划将开发一个可扩展的视觉分析系统，允许研究人员从PB级的连接学数据中生成新的数据驱动假设。这个为期三年的项目旨在建立新的视觉分析工具和有效的fi有效的深度学习方法，以推进连接学的fi领域。项目成果将使神经科学家能够在一立方毫米的体积内分析包含数万个神经元和数亿个突触连接的大型大脑网络。该项目旨在从神经元和网络层面分析大脑。它将研究可扩展的视觉分析方法，用于比较形态特征和分析细胞细胞器的空间分布和邻近程度。将支持网络级别的分析，从局部突触网络主题到不同皮质层的更大规模的连接模式。将开发一个紧密结合的有针对性的校对/分析循环，利用机器学习的技术自动提出错误建议和指导校对过程，以最大限度地减少用户互动，获得高质量的数据。为了支持基于数据驱动的可视化分析的直观假设生成，将设计一个直观的领域特定fic查询框架，并研究自动用户指导和假设建议的方法。最终，这个项目将提供数据和分析工具来开发大脑如何工作的新理论。该项目由了解神经和认知系统的综合策略(NCS)资助，这是一个多学科计划，由生物学(BIO)、计算机和信息科学与工程(CEISE)、教育和人力资源(EHR)、工程(ENG)以及社会、行为和经济科学(SBE)的主管联合支持。该奖项反映了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。

The field of connectomics aims to reconstruct the wiring diagram of neurons and synapses at nanometer resolution to enable new insights into the workings of the brain. Recent advances in image acquisition and machine learning methods have yielded complete reconstructions of neural connectivity of large tissue samples. The investigators have one such dataset from a human brain tissue consisting of two petabytes of raw image data from electron microscopy. In collaboration with Google, they have spent the past two years reconstructing the complete 3D shape of about 50,000 cells, including 18,000 neurons, and identifying about 133 million synapses. This data will enable them to examine the prototypes of various neuron shapes, the correlations between these neuron types and their internal structures, and how they are connected to each other. This will be done in a dataset that is orders of magnitude larger than previous brain samples. These dense brain reconstruction results come with complex spatial and network structures, posing new challenges for scientists who wish to explore and analyze such data. The proposed program will develop a scalable visual analytics system that allows researchers to generate novel data-driven hypotheses from the petabyte-scale connectomics data.This three-year project aims to build novel visual analytics tools and efficient deep learning methods to advance the field of connectomics. Project deliverables will empower neuroscientists to analyze large brain networks in a one cubic millimeter volume containing tens of thousands of neurons and hundreds of millions of synaptic connections. The project aims to analyze the brain at the neuron level and network level. It will investigate scalable visual analytics methods for the comparison of morphological features and analysis of spatial distributions and proximity of cell organelles. The network-level analysis will be supported, from local synaptic network motifs to larger-scale connectivity patterns of different cortical layers. A tightly integrated targeted proofreading/analysis loop will be developed, using techniques from machine learning for automatic error suggestion and guidance of the proofreading process to obtain high-quality data with minimal user interaction. To support intuitive hypothesis generation based on the data-driven visual analysis, an intuitive domain-specific query framework and investigate methods for automatic user guidance and hypothesis suggestion will be designed. Ultimately, this project will provide data and analysis tools to develop new theories of how the brain works.This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NCS), a multidisciplinary program jointly supported by the Directorates for Biology (BIO), Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE).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.