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

NCS-FO：为可扩展的连接组学分析提供数据驱动的假设生成

• 批准号: 2124179
• 负责人: Hanspeter Pfister
• 金额: $ 100万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2124179&HistoricalAwards=false
• 关键词: NCS; FO; Empowering; Data; Driven

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.

连接组学领域旨在以纳米分辨率重建神经元和突触的接线图，从而对大脑的运作产生新的见解。图像采集和机器学习方法的最新进展已经产生了大型组织样本的神经连接的完整重建。研究人员从人类脑组织中获得了一个这样的数据集，其中包括来自电子显微镜的2拍字节的原始图像数据。在与谷歌的合作中，他们花了两年的时间重建了大约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，以及它们如何相互连接。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伴随着复杂的空间和网络结构，给希望探索和分析这些数据的科学家带来了新的挑战。该计划将开发一个可扩展的视觉分析系统，使研究人员能够从pb级的连接组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颖的视觉分析工具和高效的深度学习方法，以推进连接组学领域的发展。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水平和网络水平分析大脑。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空间分布和细胞器的接近性。网络层面的分析将得到支持，从局部突触网络基序到不同皮质层的更大规模连接模式。将开发一个紧密集成的有针对性的校对/分析循环，使用机器学习技术自动提示错误并指导校对过程，以最少的用户交互获得高质量的数据。为了支持基于数据驱动的可视化分析的直观假设生成，设计了面向用户自动引导和假设建议的直观领域查询框架和调查方法。最终，该项目将提供数据和分析工具，以发展大脑如何工作的新理论。该项目由理解神经和认知系统的综合策略（NCS）资助，这是一个由生物学（BIO）、计算机与信息科学与工程（CISE）、教育与人力资源（EHR）、工程学（ENG）和社会、行为和经济科学（SBE）联合支持的多学科项目。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。

项目成果

3D Domain Adaptive Instance Segmentation via Cyclic Segmentation GANs.

• DOI: 10.1109/jbhi.2023.3281332
• 发表时间: 2023-08
• 期刊: IEEE journal of biomedical and health informatics
• 影响因子: 7.7

VIMO - Visual Analysis of Neuronal Connectivity Motifs

• DOI: 10.1109/tvcg.2023.3327388
• 发表时间: 2024-01-01
• 期刊: IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
• 影响因子: 5.2
• 作者: Troidl，Jakob;Warchol，Simon;Beyer，Johanna
• 通讯作者: Beyer，Johanna

Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data

Visinity：多重组织成像数据的视觉空间邻域分析

• DOI: 10.1101/2022.05.09.490039v5
• 发表时间: 2022
• 期刊: IEEE transactions on visualization and computer graphics
• 影响因子: 5.2
• 作者: Warchol S.;Krueger R.;Nirmal A.J.;Gaglia G.;Jessup J.;Ritch C.C.;Hoffer J.;Muhlich J.;Burger M.L.;Jacks T.
• 通讯作者: Jacks T.

Barrio: Customizable Spatial Neighborhood Analysis and Comparison for Nanoscale Brain Structures

Barrio：纳米级大脑结构的可定制空间邻域分析和比较

• 发表时间: 2022
• 期刊: Proceedings Eurographics/IEEE Symposium on Visualization (EuroVis
• 作者: Troidl, J.;Cali, C.;Gröller, E.;Pfister, H.;Hadwiger, M.;Beyer, J.
• 通讯作者: Beyer, J.