中国科学院脑科学与智能技术卓越创新中心严军团队近期取得重要工作进展，他们研究开发了基于Gapr的大规模协同单神经元重建方案。相关研究成果2024年7月3日在线发表于《自然—方法学》杂志上。

据介绍，针对单个神经元形态的全脑分析对于揭示大脑的复杂结构至关重要。然而，从最先进的光学显微镜生成的哺乳动物大脑的TB甚至PB数据中大规模重建神经元是一项艰巨的任务。

研究人员开发了“Gapr”（Gapr加速项目组重建），它简化了基于深度学习的自动重建，“自动校对”减少了高可信度站点的人工工作量，以及通过互联网由群组用户进行的高通量协作校对。

此外，Gapr提供了一个无缝的用户界面，确保每个注释器的高校对速度、用于处理大型数据集的按需转换、针对不同数据集定制的灵活工作流程以及用于质量控制的严格错误跟踪。最后，研究人员通过重建小鼠大脑中的4000多个神经元，揭示了皮层中间神经元和下丘脑神经元的形态多样性，证明了Gapr的功效。

总之，研究人员开发了Gapr工具，作为大规模单神经元重建项目的解决方案。

附：英文原文

Title: Gapr for large-scale collaborative single-neuron reconstruction

Author: Gou, Lingfeng, Wang, Yanzhi, Gao, Le, Zhong, Yiting, Xie, Lucheng, Wang, Haifang, Zha, Xi, Shao, Yinqi, Xu, Huatai, Xu, Xiaohong, Yan, Jun

Issue&Volume: 2024-07-03

Abstract: Whole-brain analysis of single-neuron morphology is crucial for unraveling the complex structure of the brain. However, large-scale neuron reconstruction from terabyte and even petabyte data of mammalian brains generated by state-of-the-art light microscopy is a daunting task. Here, we developed ‘Gapr’ (Gapr accelerates projectome reconstruction) that streamlines deep learning-based automatic reconstruction, ‘automatic proofreading’ that reduces human workloads at high-confidence sites, and high-throughput collaborative proofreading by crowd users through the Internet. Furthermore, Gapr offers a seamless user interface that ensures high proofreading speed per annotator, on-demand conversion for handling large datasets, flexible workflows tailored to diverse datasets and rigorous error tracking for quality control. Finally, we demonstrated Gapr’s efficacy by reconstructing over 4,000 neurons in mouse brains, revealing the morphological diversity in cortical interneurons and hypothalamic neurons. Here, we present Gapr as a solution for large-scale single-neuron reconstruction projects.

DOI: 10.1038/s41592-024-02345-z

Source: https://www.nature.com/articles/s41592-024-02345-z