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Quantitative classification of cerebellar foliation in cartilaginous fishes (Class: Chondrichthyes) using 3D shape analysis and its implications for evolutionary biology

机译:利用3D形状分析对软骨鱼类小脑叶片的定量分类(类:软骨鱼类)及其对进化生物学的意义

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摘要

A true cerebellum appeared at the onset of the chondrichthyan radiation and is known to be essential for executing fast, accurate, and efficient movement. In addition to a high degree of variation in size, the corpus cerebellum in this group has a high degree of variation in convolution (or foliation) and symmetry, which ranges from a smooth cerebellar surface to deep, branched convexities and folds, although the functional significance of this trait is unclear. As variation in the degree of foliation similarly exists throughout vertebrate evolution, it becomes critical to understand this evolutionary process in a wide variety of species. However, current methods are either qualitative and lack numerical rigor or are restricted to two dimensions. In this paper, a recently developed method for the characterization of shapes embedded within noisy, three-dimensional (3D) data called the spherical wave decomposition (SWD) is applied to the problem of characterizing cerebellar foliation in cartilaginous fishes. The SWD method provides a quantitative characterization of shapes in terms of well-defined mathematical functions. An additional feature of the SWD method is the construction of a statistical criterion for the optimal fit, which represents the most parsimonious choice of parameters that fits to the data, without overfitting to background noise. We propose that this optimal fit can replace a previously described qualitative visual foliation index (VFI) in cartilaginous fishes with a quantitative analogue, the cerebellar foliation index (CFI). The capability of the SWD method is demonstrated on a series of volumetric images of brains from different chondrichthyan species that span the range of foliation gradings currently described for this group. The CFI is consistent with the qualitative grading provided by the VFI, delivers a robust measure of cerebellar foliation, and can provide a quantitative basis for brain shape characterization across taxa.
机译:真正的小脑出现在软骨软骨放射开始时,并且众所周知对于执行快速,准确和有效的运动至关重要。除了大小上的高度变化外,该组中的小脑在卷积(或叶面)和对称性上也具有高度变化,其范围从光滑的小脑表面到深,分支的凸起和褶皱,尽管功能此特征的意义尚不清楚。由于整个脊椎动物进化过程中相似地存在着叶化程度的变化,因此了解多种物种的进化过程变得至关重要。然而,当前的方法要么是定性的,而且缺乏数值上的严格性,要么仅限于二维。在本文中,最近开发的一种用于表征包含在嘈杂的三维(3D)数据中的形状的方法(称为球面波分解(SWD))被应用于表征软骨鱼类小脑叶片的问题。 SWD方法根据定义明确的数学函数对形状进行定量表征。 SWD方法的另一个功能是构建用于最佳拟合的统计标准,该标准表示适合数据的最简约的参数选择,而不会过度拟合背景噪声。我们建议,这种最佳拟合可以用定量类似物小脑叶片指数(CFI)代替软骨鱼中先前描述的定性视觉叶片指数(VFI)。 SWD方法的功能在一系列来自不同软骨鱼类物种的大脑的容积图像中得到了证明,这些图像跨越了目前针对该组描述的叶片等级的范围。 CFI与VFI提供的定性分级一致,可提供小脑叶的可靠测量,并可以为整个分类群的大脑形状表征提供定量基础。

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