Mozak: Creating an Expert Community to accelerate neuronal reconstruction at scale

Mozak：创建专家社区以加速大规模神经元重建

• 批准号: 10442510
• 负责人: Zoran Popovic
• 金额: $ 58.01万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10442510
• 关键词: 3-Dimensional; Adopted; Algorithms; Area; Axon; Brain; Characteristics; Classification; Communities; Complex; Computers; Computing Methodologies; Data; Data Analyses; Data Set; Data Sources; Databases; Dendritic Spines; Disease; Ensure; Future; Generations; Goals; Gold; Guidelines; Human; Image; Imaging technology; Infrastructure; Institutes; International; Laboratories; Machine Learning; Manuals; Maps; Methods; Modality; Molecular; Morphology; Neurons; Neurosciences; Neurosciences Research; Outcome; Output; Process; Research; Science; Semantics; Slice; Source; Standardization; Structure; Techniques; Testing; Three-dimensional analysis; Time; Training; Update; Variant; Varicosity; Work; automated algorithm; base; citizen science; cloud based; crowdsourcing; data repository; experience; improved; large scale data; member; neuronal cell body; novel; online community; petabyte; programs; reconstruction; repository; tool; two-dimensional; web portal

Project Summary This project aims to leverage the best of both computational and human expertise in neuronal reconstruction towards the goal of accelerating global neuroscience discovery from internationally-sourced imaging data. We propose to create a cloud-based unified platform for converging 3-dimensional images of neurons onto a single analysis platform to (1) train and grow a new expert community of global reconstructors to work across the data from these groups, to (2) generate a community-sourced neuronal reconstruction database of open imaging data that can be incorporated into a 3-dimensional map of neuronal interconnectivity - onto which (3) novel annotations and more complex functional and molecular data can be overlaid. Our approach will evolve with the growing needs of the neuroscience community over time. To do this, in Aim One (Neuronal Reconstruction at Scale), we will test if the newly developed crowd-sourced game-based platform Mozak can develop a collective of new human experts at scale, capable of accelerating the rate of current reconstruction by at least an order of magnitude, at the same time as increasing the robustness, quality and unbiasedness of the final reconstructions. In Aim Two (Robust Multi-Purpose Annotation), we will enhance basic neuronal reconstruction by adding specific semantic annotation— including soma volume and morphological quantification, volumetric analysis, and ongoing features (e.g. dendritic spines, axonal varicosities) requested from the neuroscience community. Experienced and high-ranking members will be given the opportunity to advance through increasingly complex neurons into full arbor brain-wide neuronal projections and multiple clustered groups of neurons in localized circuits. Finally, in Aim 3 (Creation of a Research-Adaptive Data Repository), we aim to develop a database of neuronal images reconstructed using the Mozak interface that will directly serve the general and specific needs of different research groups. Our goal is to make this database dynamically adaptive — as new research questions will invariably bring new needs for additional annotations and cross-referencing with other data modalities. This highquality unbiased processing repository will also be perfectly suited for training sets for automated algorithms, and the generation of a 3-dimensional maps such as Allen Institute for Brain Science (AIBS) common coordinate framework. We expect that the computational reconstruction methods will further improve with the new large corpus of “gold standard” reconstructions. Collectively, the completion of these three aims will create an analysis suite as well as an online community of experts capable of performing in depth analysis of large-scale datasets that will significantly accelerate neuroscience research, enhance machine learning for reconstruction analysis, and create a common platform of baseline neuronal morphology data against which aberrantly functioning neurons can be analyzed.

项目概要 该项目旨在充分利用神经重建方面的计算和人类专业知识 我们的目标是利用国际来源的成像数据加速全球神经科学发现。 建议创建一个基于云的统一平台，将神经元的 3 维图像汇聚到单个 分析平台，以 (1) 培训和发展全球重建者的新专家社区，以跨领域开展工作 来自这些组的数据，以（2）生成一个来自社区的开放神经重建数据库 可以纳入神经互连 3 维图的成像数据 - 其上 (3) 新颖的注释以及更复杂的功能和分子数据可以被覆盖，我们的方法将会不断发展。 随着时间的推移，神经科学界的需求不断增长，为了做到这一点，在《Aim One》（神经元）中。 Reconstruction at Scale），我们将测试新开发的众包游戏平台 Mozak 是否可以 大规模培养一批新的人类专家，能够加快当前重建的速度 至少提高一个数量级，同时提高稳健性、质量和公正性 在最终的重建中，我们将增强基本的神经网络。 通过添加特定的语义注释来重建——包括体体体积和形态学 要求定量、体积分析和持续特征（例如树突棘、轴突静脉曲张） 来自神经科学界的经验丰富的高级成员将有机会 通过日益复杂的神经元进入完整的全脑神经投射和多个 最后，目标 3（创建研究自适应数据）。 Repository），我们的目标是开发一个使用 Mozak 接口重建的神经图像数据库 将直接服务于不同研究小组的一般和特定需求，我们的目标是做到这一点。 数据库动态自适应——因为新的研究问题总是会带来新的额外需求 注释和与其他数据模式的交叉引用。 也将非常适合自动化算法的训练集以及 3 维 我们期望艾伦脑科学研究所（AIBS）通用坐标框架等地图。 随着新的“金标准”大型语料库的出现，计算重建方法将进一步改进 总的来说，这三个目标的完成将创建一个分析套件以及一个分析套件。 能够对大规模数据集进行深入分析的专家在线社区 显着加速神经科学研究，增强用于重建分析的机器学习，以及 创建一个基线神经元形态数据的通用平台，针对功能异常的神经元 可以分析。