一种新的统计方法用于定量周围神经轴突的空间排列
A novel statistical methodology for quantifying the spatial arrangements of axons in peripheral nerves
摘要：
为了更好地了解周围神经的功能以及神经调节工具和策略的发展，需要对周围神经的神经解剖学有全面的了解。在生物物理建模中，通常假设周围神经中有髓和无髓轴突的复杂空间排列是随机的，然而，实际上轴突组织是不均匀和各向异性的。目前的定量神经解剖学方法分析周围神经的轴突的数量和轴突的形态特征，如面积和直径。在这项研究中，我们采用空间统计和点过程模型来描述轴突和Sinkhorn距离的空间排列，以计算这些安排之间的相似性（在一阶和二阶统计）在各种迷走神经和盆神经横截面。我们利用了高分辨率TEM图像，这些图像已经使用基于高度修改的U-Net架构的定制高通量深度学习系统进行了分割。我们的研究结果显示了一种新颖的和创新的方法来量化空间点模式之间的相似性，使用来自最佳运输问题的解决方案的度量。我们还提出了一个可推广的管道周围神经结构的定量分析。我们的数据表明男性和女性起源的样本之间的差异和骨盆和腹部迷走神经之间的相似性。
英文摘要：
A thorough understanding of the neuroanatomy of peripheral nerves is required for a better insight into their function and the development of neuromodulation tools and strategies. In biophysical modeling, it is commonly assumed that the complex spatial arrangement of myelinated and unmyelinated axons in peripheral nerves is random, however, in reality the axonal organization is inhomogeneous and anisotropic. Present quantitative neuroanatomy methods analyze peripheral nerves in terms of the number of axons and the morphometric characteristics of the axons, such as area and diameter. In this study, we employed spatial statistics and point process models to describe the spatial arrangement of axons and Sinkhorn distances to compute the similarities between these arrangements (in terms of first- and second-order statistics) in various vagus and pelvic nerve cross-sections. We utilized high-resolution TEM images that have been segmented using a custom-built high-throughput deep learning system based on a highly modified U-Net architecture. Our findings show a novel and innovative approach to quantifying similarities between spatial point patterns using metrics derived from the solution to the optimal transport problem. We also present a generalizable pipeline for quantitative analysis of peripheral nerve architecture. Our data demonstrate differences between male- and female-originating samples and similarities between the pelvic and abdominal vagus nerves.
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