Department of Biology II, Ludwig-Maximilians-Universität München, Grosshadernerstr, 2, Planegg-Martinsried, 82152, Germany.
Department of Earth System Science, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka-shi, Fukuoka, 814-0180, Japan.
BMC Bioinformatics. 2018 Apr 18;19(1):143. doi: 10.1186/s12859-018-2136-z.
Morphological features are widely used in the study of neuronal function and pathology. Invertebrate neurons are often structurally stereotypical, showing little variance in gross spatial features but larger variance in their fine features. Such variability can be quantified using detailed spatial analysis, which however requires the morphologies to be registered to a common frame of reference.
We outline here new algorithms - Reg-MaxS and Reg-MaxS-N - for co-registering pairs and groups of morphologies, respectively. Reg-MaxS applies a sequence of translation, rotation and scaling transformations, estimating at each step the transformation parameters that maximize spatial overlap between the volumes occupied by the morphologies. We test this algorithm with synthetic morphologies, showing that it can account for a wide range of transformation differences and is robust to noise. Reg-MaxS-N co-registers groups of more than two morphologies by iteratively calculating an average volume and registering all morphologies to this average using Reg-MaxS. We test Reg-MaxS-N using five groups of morphologies from the Droshophila melanogaster brain and identify the cases for which it outperforms existing algorithms and produce morphologies very similar to those obtained from registration to a standard brain atlas.
We have described and tested algorithms for co-registering pairs and groups of neuron morphologies. We have demonstrated their application to spatial comparison of stereotypic morphologies and calculation of dendritic density profiles, showing how our algorithms for registering neuron morphologies can enable new approaches in comparative morphological analyses and visualization.
形态特征广泛应用于神经元功能和病理学研究。无脊椎动物神经元在结构上通常是刻板的，在大体空间特征上变化很小，但在精细特征上变化较大。这种可变性可以通过详细的空间分析来量化，然而，这需要将形态学注册到共同的参考框架中。
我们在这里概述了新的算法——Reg-MaxS 和 Reg-MaxS-N——分别用于配准对和组形态。Reg-MaxS 应用一系列平移、旋转和缩放变换，在每个步骤中估计变换参数，使形态占据的体积之间的空间重叠最大化。我们用合成形态学测试了这个算法，表明它可以解释广泛的变换差异，并且对噪声具有鲁棒性。Reg-MaxS-N 通过迭代计算平均体积，并使用 Reg-MaxS 将所有形态注册到这个平均体积上来配准超过两个形态的组。我们使用来自果蝇大脑的五个形态组测试了 Reg-MaxS-N，并确定了它优于现有算法的情况，并生成了与从注册到标准脑图谱获得的形态非常相似的形态。
我们已经描述和测试了用于配准对和组神经元形态的算法。我们已经证明了它们在对刻板形态进行空间比较和计算树突密度分布方面的应用，展示了我们用于注册神经元形态的算法如何能够为比较形态分析和可视化提供新的方法。
