Shape analysis provides a unique insight into biological processes. This paper evaluates the properties, performance, and utility of elliptical Fourier (eFourier) analysis to operationalise global shape, focussing on the human corpus callosum. 8000 simulated corpus callosum contours were generated, systematically varying in terms of global shape (midbody arch, splenium size), local complexity (surface smoothness), and nonshape characteristics (e.g., rotation). 2088 real corpus callosum contours were manually traced from the PATH study. Performance of eFourier was benchmarked in terms of its capacity to capture and then reconstruct shape and systematically operationalise that shape via principal components analysis. We also compared the predictive performance of corpus callosum volume, position in Procrustes-aligned Landmark tangent space, and position in eFourier n-dimensional shape space in relation to the Symbol Digit Modalities Test. Jaccard index for original vs. reconstructed from eFourier shapes was excellent (M=0.98). The combination of eFourier and PCA performed particularly well in reconstructing known n-dimensional shape space but was disrupted by the inclusion of local shape manipulations. For the case study, volume, eFourier, and landmark measures were all correlated. Mixed effect model results indicated all methods detected similar features, but eFourier estimates were most predictive, and of the two shape operationalization techniques had the least error and better model fit. Elliptical Fourier analysis, particularly in combination with principal component analysis, is a powerful, assumption-free and intuitive method of quantifying global shape of the corpus callosum and shows great promise for shape analysis in neuroimaging more broadly.

中文翻译：

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Ass体形状的无假设评估：基准测试和应用

形状分析提供了对生物学过程的独特见解。本文评估了椭圆傅立叶（eFourier）分析的性质，性能和效用，以使整体形状可操作，重点是人类体。生成了8000个模拟的call体轮廓，系统地改变了整体形状（中弓，脾脏大小），局部复杂性（表面光滑度）和非形状特征（例如旋转）。从PATH研究中手动跟踪了2088个真实的call体轮廓。eFourier的性能以其捕获，然后重建形状以及通过主成分分析系统地操作形状的能力为基准。我们还比较了call体体积，Procrustes对齐的Landmark切线空间中位置的预测性能，符号数字模态测试在eFourier n维形状空间中的位置和位置。从eFourier形状重构后的Jaccard指数非常出色（M＝ 0.98）。eFourier和PCA的组合在重建已知的n维形状空间方面表现特别出色，但由于包含局部形状操作而被破坏。对于案例研究，体积，eFourier和标志性度量均相关。混合效果模型结果表明，所有方法都检测到相似的特征，但是eFourier估计最具预测性，并且两种形状运算技术中的误差最小且模型拟合更好。椭圆傅立叶分析，特别是与主成分分析相结合，是一种强大的，无需假设的直观方法，可以对call体的整体形状进行量化，并且为神经成像中的形状分析提供了广阔的前景。