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

项目摘要 该项目旨在利用最好的计算和人类的专业知识在神经元重建 致力于从国际来源的成像数据中加速全球神经科学发现的目标。我们 我建议创建一个基于云的统一平台，将神经元的三维图像汇聚到一个单一的 分析平台，以（1）培训和发展一个新的全球重建专家社区， 数据，以（2）生成一个社区来源的神经元重建数据库， 成像数据，可以纳入神经元互连的三维图-在其上（3） 可以覆盖新的注释和更复杂的功能和分子数据。我们的方法将不断发展 随着神经科学界的需求不断增长为此，在Aim One（神经元 Reconstruction at Scale），我们将测试新开发的基于众包游戏的平台Mozak是否能够 培养一批新的人类专家，能够加快当前的重建速度 至少提高一个数量级，同时提高 最后的重建。在目标二（鲁棒多用途注释）中，我们将增强基本神经元 通过添加特定语义注释进行重建-包括索马体积和形态 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。经验丰富的高级成员将有机会 通过越来越复杂的神经元进入全脑神经元投射和多个神经元投射， 在局部回路中聚集的神经元群。最后，在目标3（创建研究自适应数据 Repository），我们的目标是开发一个使用Mozak接口重建的神经元图像数据库， 将直接服务于不同研究小组的一般和具体需要。我们的目标是 数据库动态自适应-因为新的研究问题总是会带来新的需求， 注释和与其他数据模态的交叉引用。这个高质量的无偏见的处理库 也将非常适合于自动算法的训练集，以及生成三维 地图，如艾伦脑科学研究所（AIBS）的共同坐标框架。我们预计 随着新的大型语料库的“金标准”， 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，能够对大规模数据集进行深入分析， 显著加速神经科学研究，增强机器学习用于重建分析， 创建一个共同的基线神经元形态学数据平台， 可以分析。

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.