Center for Neural Informatics, Structures, & Plasticity and Bioengineering Department, George Mason University, Mail Stop 2A1, 4400 University Dr, Fairfax, VA, United States of America.
Center for Neural Informatics, Structures, & Plasticity and Bioengineering Department, George Mason University, Mail Stop 2A1, 4400 University Dr, Fairfax, VA, United States of America.
Neurosci Res. 2022 Aug;181:39-45. doi: 10.1016/j.neures.2022.05.004. Epub 2022 May 14.
Most functions of the nervous system depend on neuronal and glial morphology. Continuous advances in microscopic imaging and tracing software have provided an increasingly abundant availability of 3D reconstructions of arborizing dendrites, axons, and processes, allowing their detailed study. However, efficient, large-scale methods to rank neural morphologies by similarity to an archetype are still lacking. Using the NeuroMorpho.Org database, we present a similarity search software enabling fast morphological comparison of hundreds of thousands of neural reconstructions from any species, brain regions, cell types, and preparation protocols. We compared the performance of different morphological measurements: 1) summary morphometrics calculated by L-Measure, 2) persistence vectors, a vectorized descriptor of branching structure, 3) the combination of the two. In all cases, we also investigated the impact of applying dimensionality reduction using principal component analysis (PCA). We assessed qualitative performance by gauging the ability to rank neurons in order of visual similarity. Moreover, we quantified information content by examining explained variance and benchmarked the ability to identify occasional duplicate reconstructions of the same specimen. We also compared two different methods for selecting the number of principal components using this benchmark. The results indicate that combining summary morphometrics and persistence vectors with applied PCA using maximum likelihood based automatic dimensionality selection provides an information rich characterization that enables efficient and precise comparison of neural morphology. We have deployed the similarity search as open-source online software both through a user-friendly graphical interface and as an API for programmatic access.
神经系统的大多数功能都依赖于神经元和神经胶质的形态。显微镜成像和追踪软件的不断进步，为树突、轴突和突起的分支的 3D 重建提供了越来越丰富的资源，从而可以对其进行详细研究。然而，仍然缺乏有效、大规模的方法来根据相似性对神经形态进行排序。使用 NeuroMorpho.Org 数据库，我们提出了一种相似性搜索软件，能够快速比较来自任何物种、脑区、细胞类型和制备方案的数十万神经重建。我们比较了不同形态测量的性能：1）由 L-Measure 计算的总结形态计量学，2）持久向量，分支结构的向量化描述符，3）两者的组合。在所有情况下，我们还研究了应用主成分分析（PCA）进行降维的影响。我们通过衡量按视觉相似性对神经元进行排序的能力来评估定性性能。此外，我们通过检查解释方差来量化信息量，并对识别同一标本的偶尔重复重建的能力进行基准测试。我们还比较了使用此基准测试选择主成分数量的两种不同方法。结果表明，将总结形态计量学和持久向量与应用 PCA 相结合，使用基于最大似然的自动维度选择提供了丰富的信息特征，能够有效地、精确地比较神经形态。我们已经通过用户友好的图形界面和用于编程访问的 API 将相似性搜索部署为开源在线软件。
