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: 10005472
• 负责人: Zoran Popovic
• 金额: $ 62.84万
• 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 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10005472
• 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; Gap Junctions; 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 warehouse; 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.

项目摘要 该项目旨在利用神经元重建中计算和人类专业知识的最佳 实现从国际化成像数据中加速全球神经科学发现的目标。我们 提出创建基于云的统一平台，以将神经元的3维图像收敛到一个 分析平台（1）培训和发展一个新的全球重建者专家社区 这些组的数据，到（2）生成一个社区产生的神经元重建数据库 成像数据可以纳入神经元互连的3维图 - （3） 新颖的注释和更复杂的功能和分子数据可以覆盖。我们的方法会发展 随着神经科学社区的不断增长，随着时间的流逝。为此，在AIM ONE（神经元）中 大规模重建），我们将测试新开发的基于众群游戏的平台Mozak是否可以 根据大规模开发一群新的人类专家，能够加速当前重建的速度 至少有一个数量级，同时增加了鲁棒性，质量和无偏见 最终重建。在目标两个（强大的多用途注释）中，我们将增强基本神经元 通过添加特定的语义注释来重建 - 包括soma卷和形态学 要求的数量，体积分析和持续的特征（例如树突状刺，轴突静脉曲张） 来自神经科学社区。经验丰富且高级成员将有机会 通过越来越复杂的神经元进入全乔木的全脑神经元投影和多个 局部电路中的神经元组聚类。最后，在AIM 3中（创建研究自适应数据 存储库），我们旨在开发一个使用Mozak接口重建的神经元图像的数据库 将直接满足不同研究小组的一般和特定需求。我们的目标是 数据库动态适应性 - 因为新的研究问题将不断带来新的需求 注释和交叉引用与其他数据模式。这个高质量的无偏处理存储库 还将非常适合用于自动化算法的训练集，并产生3维 艾伦脑科学研究所（AIBS）诸如公共坐标框架等地图。我们期望 通过新的“黄金标准”大型语料库，计算重建方法将进一步改善 重建。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 在线专家社区，能够对大型数据集进行深入分析 显着加速神经科学研究，增强机器学习以进行重建分析，并 创建一个基线神经元形态数据的通用平台 可以分析。