Zhu Hongtu, Styner Martin, Li Yimei, Kong Linglong, Shi Yundi, Lin Weili, Coe Christopher, Gilmore John H
Department of Biostatistics, Radiology, Psychiatry and Computer Science, and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, USA.
Med Image Comput Comput Assist Interv. 2010;13(Pt 1):690-7. doi: 10.1007/978-3-642-15705-9_84.
Diffusion tensor imaging (DTI) is important for characterizing the structure of white matter fiber bundles as well as detailed tissue properties along these fiber bundles in vivo. There has been extensive interest in the analysis of diffusion properties measured along fiber tracts as a function of age, diagnostic status, and gender, while controlling for other clinical variables. However, the existing methods have several limitations including the independent analysis of diffusion properties, a lack of method for accounting for multiple covariates, and a lack of formal statistical inference, such as estimation theory and hypothesis testing. This paper presents a statistical framework, called VCMTS, to specifically address these limitations. The VCMTS framework consists of four integrated components: a varying coefficient model for characterizing the association between fiber bundle diffusion properties and a set of covariates, the local polynomial kernel method for estimating smoothed multiple diffusion properties along individual fiber bundles, global and local test statistics for testing hypotheses of interest along fiber tracts, and a resampling method for approximating the p-value of the global test statistic. The proposed methodology is applied to characterizing the development of four diffusion properties along the splenium and genu of the corpus callosum tract in a study of neurodevelopment in healthy rhesus monkeys. Significant time effects on the four diffusion properties were found.
扩散张量成像（DTI）对于在体内表征白质纤维束的结构以及沿这些纤维束的详细组织特性非常重要。在控制其他临床变量的同时，人们对分析沿纤维束测量的扩散特性作为年龄、诊断状态和性别的函数产生了广泛兴趣。然而，现有方法存在几个局限性，包括对扩散特性的独立分析、缺乏考虑多个协变量的方法以及缺乏正式的统计推断，如估计理论和假设检验。本文提出了一个名为VCMTS的统计框架，以专门解决这些局限性。VCMTS框架由四个集成组件组成：用于表征纤维束扩散特性与一组协变量之间关联的变系数模型、用于估计沿单个纤维束的平滑多个扩散特性的局部多项式核方法、用于检验沿纤维束感兴趣假设的全局和局部检验统计量，以及用于近似全局检验统计量p值的重采样方法。在一项健康恒河猴神经发育研究中，所提出的方法被应用于表征沿胼胝体束的压部和膝部的四种扩散特性的发展。发现了对这四种扩散特性的显著时间效应。
