Reilly Elizabeth P, Garretson Jeffrey S, Gray Roncal William R, Kleissas Dean M, Wester Brock A, Chevillet Mark A, Roos Matthew J
Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States.
Front Neuroinform. 2018 Nov 5;12:74. doi: 10.3389/fninf.2018.00074. eCollection 2018.
Neuroscientists are actively pursuing high-precision maps, or consisting of networks of neurons and connecting synapses in mammalian and non-mammalian brains. Such graphs, when coupled with physiological and behavioral data, are likely to facilitate greater understanding of how circuits in these networks give rise to complex information processing capabilities. Given that the automated or semi-automated methods required to achieve the acquisition of these graphs are still evolving, we developed a metric for measuring the performance of such methods by comparing their output with those generated by human annotators ("ground truth" data). Whereas classic metrics for comparing annotated neural tissue reconstructions generally do so at the voxel level, the metric proposed here measures the "integrity" of neurons based on the degree to which a collection of synaptic terminals belonging to a single neuron of the reconstruction can be matched to those of a single neuron in the ground truth data. The metric is largely insensitive to small errors in segmentation and more directly measures accuracy of the generated brain graph. It is our hope that use of the metric will facilitate the broader community's efforts to improve upon existing methods for acquiring brain graphs. Herein we describe the metric in detail, provide demonstrative examples of the intuitive scores it generates, and apply it to a synthesized neural network with simulated reconstruction errors. Demonstration code is available.
神经科学家们正在积极探索高精度图谱，即哺乳动物和非哺乳动物大脑中由神经元网络和连接突触组成的图谱。当这些图谱与生理和行为数据相结合时，可能会有助于更深入地理解这些网络中的回路是如何产生复杂信息处理能力的。鉴于获取这些图谱所需的自动化或半自动化方法仍在不断发展，我们开发了一种指标，通过将这些方法的输出与人类注释者生成的输出（“真实数据”）进行比较，来衡量这些方法的性能。虽然比较注释神经组织重建的经典指标通常在体素水平上进行，但这里提出的指标基于重建中属于单个神经元的一组突触终端与真实数据中单个神经元的突触终端的匹配程度来衡量神经元的“完整性”。该指标在很大程度上对分割中的小误差不敏感，并且更直接地测量生成的脑图谱的准确性。我们希望该指标的使用将有助于更广泛的社区努力改进现有的获取脑图谱的方法。在此，我们详细描述该指标，提供它生成的直观分数的示例，并将其应用于具有模拟重建误差的合成神经网络。演示代码可供使用。
