Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
Computer Science Department, Princeton University, Princeton, NJ, USA.
Nat Methods. 2022 Jan;19(1):119-128. doi: 10.1038/s41592-021-01330-0. Epub 2021 Dec 23.
Due to advances in automated image acquisition and analysis, whole-brain connectomes with 100,000 or more neurons are on the horizon. Proofreading of whole-brain automated reconstructions will require many person-years of effort, due to the huge volumes of data involved. Here we present FlyWire, an online community for proofreading neural circuits in a Drosophila melanogaster brain and explain how its computational and social structures are organized to scale up to whole-brain connectomics. Browser-based three-dimensional interactive segmentation by collaborative editing of a spatially chunked supervoxel graph makes it possible to distribute proofreading to individuals located virtually anywhere in the world. Information in the edit history is programmatically accessible for a variety of uses such as estimating proofreading accuracy or building incentive systems. An open community accelerates proofreading by recruiting more participants and accelerates scientific discovery by requiring information sharing. We demonstrate how FlyWire enables circuit analysis by reconstructing and analyzing the connectome of mechanosensory neurons.
由于自动化图像采集和分析技术的进步，拥有 10 万个或更多神经元的全脑连接组即将面世。由于涉及的数据量巨大，全脑自动重建的校对工作需要耗费多年的人力。在这里，我们介绍 FlyWire，这是一个用于校对果蝇大脑神经回路的在线社区，并解释其计算和社交结构是如何组织起来以适应全脑连接组学的。基于浏览器的三维交互式分割，通过协作编辑空间分块的超体素图，可以将校对工作分配给位于世界任何地方的个人。编辑历史中的信息可用于各种用途，例如估计校对准确性或构建激励系统。开放社区通过招募更多参与者来加速校对，并通过要求信息共享来加速科学发现。我们通过重建和分析机械感觉神经元的连接组来展示 FlyWire 如何实现电路分析。
