Department of Molecular Neuroscience, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University Fairfax, VA, USA.
Front Neuroanat. 2014 Dec 4;8:138. doi: 10.3389/fnana.2014.00138. eCollection 2014.
Neuronal morphology is diverse among animal species, developmental stages, brain regions, and cell types. The geometry of individual neurons also varies substantially even within the same cell class. Moreover, specific histological, imaging, and reconstruction methodologies can differentially affect morphometric measures. The quantitative characterization of neuronal arbors is necessary for in-depth understanding of the structure-function relationship in nervous systems. The large collection of community-contributed digitally reconstructed neurons available at NeuroMorpho.Org constitutes a "big data" research opportunity for neuroscience discovery beyond the approaches typically pursued in single laboratories. To illustrate these potential and related challenges, we present a database-wide statistical analysis of dendritic arbors enabling the quantification of major morphological similarities and differences across broadly adopted metadata categories. Furthermore, we adopt a complementary unsupervised approach based on clustering and dimensionality reduction to identify the main morphological parameters leading to the most statistically informative structural classification. We find that specific combinations of measures related to branching density, overall size, tortuosity, bifurcation angles, arbor flatness, and topological asymmetry can capture anatomically and functionally relevant features of dendritic trees. The reported results only represent a small fraction of the relationships available for data exploration and hypothesis testing enabled by sharing of digital morphological reconstructions.
动物物种、发育阶段、脑区和细胞类型之间的神经元形态差异很大。即使在同一细胞类型中，单个神经元的几何形状也有很大差异。此外，特定的组织学、成像和重建方法可能会对形态计量学测量产生不同的影响。神经元树突的定量特征对于深入了解神经系统的结构-功能关系是必要的。NeuroMorpho.Org 上提供的大量社区贡献的数字化重建神经元构成了神经科学发现的“大数据”研究机会，超出了单个实验室通常采用的方法。为了说明这些潜力和相关挑战，我们对树突进行了数据库范围的统计分析，能够量化广泛采用的元数据类别之间的主要形态相似性和差异。此外，我们采用基于聚类和降维的补充无监督方法来识别导致最具统计信息量的结构分类的主要形态参数。我们发现，与分支密度、整体大小、扭曲度、分支角度、树突平整度和拓扑不对称性相关的特定测量组合可以捕获树突的解剖学和功能相关特征。报告的结果仅代表可用于数据探索和假设检验的关系的一小部分，这些关系可以通过共享数字化形态重建来实现。
