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Analysis of cortical morphometric variability using labeled cortical distance maps

机译:使用标记的皮质距离图分析皮质形态计量学变异性

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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.
机译:皮质结构的解剖学上的形态学差异(即形状和大小)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签皮层距离图(LCDM)的强大工具进行量化和检测。 LCDM方法为特定的皮层结构(或组织)提供了标记的灰质(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距离分析特定组织的形态变异性的方法。为了提取LCDM距离提供的更多信息,我们执行LCDM距离的合并和检查。特别是,我们在LCDM距离上采用Brown-Forsythe(BF)检验方差同质性(HOV)。合并距离的HOV分析提供了有关疾病引起的LCDM形态变化的整体分析,而经过审查距离的HOV分析则表明这些差异存在显着变化的位置(即距GM的距离) / WM表面的形态变异性开始变得很明显。我们还检查假设违规对LCDM距离的HOV分析的影响。尤其是,我们证明了BF HOV检验对于合并违约距离和删失距离中位数的残差的非正态性和样本相关性之类的假设违例具有鲁棒性,并且对受删失距离分析中发生的数据聚合也具有鲁棒性。我们建议HOV分析作为分布/位置差异分析的补充工具。我们还将方法应用于模拟的正态和指数数据集,并在满足更多基本假设的情况下评估方法的性能。我们在一个真实的数据示例上说明了该方法,即,在腹内侧前额叶皮层(VMPFCs)中GM体素的LCDM距离,以查看抑郁症或抑郁症的高风险对VMPFCs形态的影响。这里使用的方法对于其他皮质结构的形态计量学分析也是有效的。

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