BRAIN EAGER: Building reliable high-throughput consensus for neuronal morphologies

EAGER项目将为所有神经科学家和计算机科学家提供急需的可靠、可重复、高通量、定量数据，以开始拼凑神经结构-活动-功能关系的复杂谜题。最近在遗传标记和显微成像方面的突破使研究界对收集大量数据的能力充满了前所未有的乐观，这些数据是量化多个物种，发育阶段和条件下的神经元统计代表性样本所必需的，在整个神经系统中的细胞类型种类繁多。然而，由于轴突和树突乔木的绝对范围和分支复杂性，在这奋进的努力进展的瓶颈不再是原始数据采集，而是相应形态的数字重建。BigNeuron计划（bigneuron.org）承诺巩固并进一步推进自动跟踪方面的成果，而多种算法的持续开发为鲁棒性提供了强有力的保证。现在，从这些不同的结果中形成共识对于防止分散的碎片化并将该领域推向新的发现时代至关重要。BigNeuron正在移植所有可用的算法，以便在统一的开源框架下自动重建神经元形态。多个BigNeuron算法中的每一个都将从每个神经元图像堆栈中创建不同的数字跟踪。剩下的一个未解决的步骤是将这些多个变体转变为一个单一的最佳共识重建，这将成为事实上的社区标准。虽然人类专业知识目前是黄金标准（并且可能不知道基本事实），但即使是由两名训练有素的人类操作员重建完全相同的神经元也不会相同，需要协调。因此，为了确保可扩展到全脑吞吐量，需要一种自动化方法来将不相同的跟踪版本的集合转换为一致性重建，理想地具有与每个分支相关联的置信度（或方差）。该项目的具体目标是设计，实施，测试，改进和部署一种方法，以从每个可用算法产生的多个数字描记中生成共识神经元重建。具体来说，该团队将首先通过协同结合两种最近引入的互补方法来创建一个工作算法草案。由此产生的初步程序形态共识生产将作为稻草人社区讨论在几次会议和研讨会。在专家反馈和新想法被纳入后，共识生成过程将最终确定，以纳入BigNeuron管道。该项目的结果将通过NeuroMorpho.org网站提供给研究人员和科学教育用户。

This EAGER project will provide all neuroscientists and computer scientists with much needed reliable, repeatable, high-throughput, quantitative data to begin piecing together the complex puzzle of the neural structure-activity-function relationship. Recent breakthroughs in genetic labeling and microscopic imaging have energized the research community with unprecedented optimism in the ability to collect the enormous amount of data that is necessary to quantify statistically representative samples of neurons in multiple species, developmental stages, and conditions, across the overwhelming variety of cell types throughout the nervous system. Due to the sheer extent and branching complexity of axonal and dendritic arbors, however, the bottleneck in the advancement of progress in this endeavor is no longer raw data acquisition, but the digital reconstruction of the corresponding morphology. The BigNeuron initiative (bigneuron.org) promises to consolidate and further advance the gains in automated tracing, and the ongoing development of multiple algorithms provides a strong insurance of robustness. Now, formulating a consensus from these alternative results is critical to prevent dispersive fragmentation and thrust the field into a new era of discovery. BigNeuron is porting all available algorithms for automated reconstruction of neuronal morphology under a unified open source framework. Each of the multiple BigNeuron algorithms will create non-identical digital tracings from every neuronal image stack. A remaining unsolved step is to morph these multiple variants into a single optimal consensus reconstruction that would de facto become a community standard. While human expertise is currently the gold standard (and the ground truth may not be known), even the reconstructions of the exact same neuron by two trained human operators will not be identical and need to be reconciled. Thus, to ensure scalable to whole-brain throughput, an automated method is needed to transform a collection of non-identical tracing versions into a consensus reconstruction, ideally with a confidence (or variance) associated with each branch. The specific aims of this project are to design, implement, test, refine, and deploy a method to generate a consensus neuronal reconstruction from the multiple digital tracings produced by each of the available algorithms. Specifically, the team will first create a draft working algorithm by synergistically combining two recently introduced complementary approaches. The resulting initial procedure for morphological consensus production will serve as straw man for community discussion in several meetings and workshops. After expert feedback and new ideas have been incorporated, the consensus generation process will be finalized for incorporation into the BigNeuron pipeline. Results from this project will be available to researchers and science educational users through the NeuroMorpho.Org website.