PDF(Pubmed)
Abstract：
Morphometric (i.e., shape and size) differences in the anatomy of cortical structures are associated with neurodevelopmental and neuropsychiatric disorders. Such differences can be quantized and detected by a powerful tool called Labeled Cortical Distance Map (LCDM). The LCDM method provides distances of labeled gray matter (GM) voxels from the GM/white matter (WM) surface for specific cortical structures (or tissues). Here we describe a method to analyze morphometric variability in the particular tissue using LCDM distances. To extract more of the information provided by LCDM distances, we perform pooling and censoring of LCDM distances. In particular, we employ Brown-Forsythe (BF) test of homogeneity of variance (HOV) on the LCDM distances. HOV analysis of pooled distances provides an overall analysis of morphometric variability of the LCDMs due to the disease in question, while the HOV analysis of censored distances suggests the location(s) of significant variation in these differences (i.e., at which distance from the GM/WM surface the morphometric variability starts to be significant). We also check for the influence of assumption violations on the HOV analysis of LCDM distances. In particular, we demonstrate that BF HOV test is robust to assumption violations such as the non-normality and within sample dependence of the residuals from the median for pooled and censored distances and are robust to data aggregation which occurs in analysis of censored distances. We recommend HOV analysis as a complementary tool to the analysis of distribution/location differences. We also apply the methodology on simulated normal and exponential data sets and assess the performance of the methods when more of the underlying assumptions are satisfied. We illustrate the methodology on a real data example, namely, LCDM distances of GM voxels in ventral medial prefrontal cortices (VMPFCs) to see the effects of depression or being of high risk to depression on the morphometry of VMPFCs. The methodology used here is also valid for morphometric analysis of other cortical structures.
摘要：
形态测量(即，形状和大小）皮质结构解剖结构的差异与神经发育和神经精神疾病有关。这种差异可以通过称为标记皮质距离图（LCDM）的强大工具进行量化和检测。LCDM方法为特定皮质结构(或组织)提供标记的灰质(GM)体素与GM/白质(WM)表面的距离。在这里，我们描述了一种使用LCDM距离分析特定组织中形态测量变异性的方法。为了提取LCDM距离提供的更多信息，我们执行LCDM距离的汇集和审查。特别是,我们在LCDM距离上采用了Brown-Forsythe(BF)方差齐性(HOV)检验。混合距离的HOV分析提供了由于所讨论的疾病引起的LCDM的形态测量变异性的总体分析。而审查距离的HOV分析表明这些差异的显著变化的位置(即，在距GM/WM表面的距离上，形态测量变异性开始显着)。我们还检查假设违规对LCDM距离的HOV分析的影响。特别是,我们证明了BFHOV检验对于假设违规是稳健的,例如对于合并和删失距离,残差与中位数的非正态性和样本内依赖性,并且对于在删失距离分析中发生的数据聚集是稳健的.我们建议将HOV分析作为分析分布/位置差异的补充工具。我们还将该方法应用于模拟的正常和指数数据集，并在满足更多基本假设时评估方法的性能。我们在一个真实的数据例子中说明了方法，即,腹侧内侧前额叶皮质（VMPFCs）中GM体素的LCDM距离，以观察抑郁症或抑郁症高风险对VMPFCs形态计量学的影响。此处使用的方法也适用于其他皮质结构的形态计量学分析。
