Department of Electrical and Computer Engineering, University of California Santa Barbara, Santa Barbara, CA, USA.
BMC Bioinformatics. 2013 Oct 2;14:287. doi: 10.1186/1471-2105-14-287.
Dendritic spines serve as key computational structures in brain plasticity. Much remains to be learned about their spatial and temporal distribution among neurons. Our aim in this study was to perform exploratory analyses based on the population distributions of dendritic spines with regard to their morphological characteristics and period of growth in dissociated hippocampal neurons. We fit a log-linear model to the contingency table of spine features such as spine type and distance from the soma to first determine which features were important in modeling the spines, as well as the relationships between such features. A multinomial logistic regression was then used to predict the spine types using the features suggested by the log-linear model, along with neighboring spine information. Finally, an important variant of Ripley's K-function applicable to linear networks was used to study the spatial distribution of spines along dendrites.
Our study indicated that in the culture system, (i) dendritic spine densities were "completely spatially random", (ii) spine type and distance from the soma were independent quantities, and most importantly, (iii) spines had a tendency to cluster with other spines of the same type.
Although these results may vary with other systems, our primary contribution is the set of statistical tools for morphological modeling of spines which can be used to assess neuronal cultures following gene manipulation such as RNAi, and to study induced pluripotent stem cells differentiated to neurons.
树突棘作为大脑可塑性的关键计算结构。关于神经元之间的空间和时间分布，仍有许多需要了解。我们在这项研究中的目的是基于树突棘的群体分布进行探索性分析，研究其形态特征和在分离的海马神经元中的生长时期。我们拟合了一个对数线性模型到树突棘特征的列联表中，如棘类型和与胞体的距离，以首先确定哪些特征对建模树突棘很重要，以及这些特征之间的关系。然后，使用对数线性模型建议的特征以及邻近的棘信息，使用多项逻辑回归来预测棘类型。最后，使用适用于线性网络的 Ripley K 函数的一个重要变体来研究沿树突的棘的空间分布。
我们的研究表明，在培养系统中，（i）树突棘密度是“完全空间随机的”，（ii）棘类型和与胞体的距离是独立的数量，最重要的是，（iii）棘有与同类型的其他棘聚类的趋势。
尽管这些结果可能因其他系统而异，但我们的主要贡献是一套用于棘形态建模的统计工具，可用于评估基因操作（如 RNAi）后的神经元培养物，并研究诱导多能干细胞分化为神经元。
