Neuropsychiatric disorders have been demonstrated to manifest shape differences in cortical structures. Labeled Cortical Distance Mapping (LCDM) is a powerful tool in quantifying such morphometric differences and characterizes the morphometry of the laminar cortical mantle of cortical structures. Specifically, LCDM data are distances of labeled gray matter (GM) voxels with respect to the gray/white matter cortical surface. Volumes and descriptive measures (such as means and variances for each subject) based on LCDM distances provide descriptive summary information on some of the shape characteristics. However, additional morphometrics are contained in the data and their analysis may provide additional clues to underlying differences in cortical characteristics. To use more of this information, we pool (merge) LCDM distances from subjects in the same group. These pooled distances can help detect morphometric differences between groups, but do not provide information about the locations of such differences in the tissue in question. In this article, we check for the influence of the assumption violations on the analysis of pooled LCDM distances. We demonstrate that the classical parametric tests are robust to the non-normality and within sample dependence of LCDM distances and nonparametric tests are robust to within sample dependence of LCDM distances. We specify the types of alternatives for which the tests are more sensitive. We also show that the pooled LCDM distances provide powerful results for group differences in distribution of LCDM distances. As an illustrative example, we use GM in the ventral medial prefrontal cortex (VMPFC) in subjects with major depressive disorder (MDD), subjects at high risk (HR) of MDD, and healthy subjects. Significant morphometric differences were found in VMPFC due to MDD or being at HR. In particular, the analysis indicated that distances in left and right VMPFCs tend to decrease due to MDD or being at HR, possibly as a result of thinning. The methodology can also be applied to other cortical structures.

中文翻译：

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使用基于标记的皮质距离图的汇总距离对皮质形态学进行统计分析。

神经精神疾病已被证明表现出皮质结构的形状差异。标记皮质距离映射 (LCDM) 是量化这种形态学差异的强大工具，并表征皮质结构的层状皮质地幔的形态学。具体而言，LCDM 数据是标记的灰质 (GM) 体素相对于灰/白质皮质表面的距离。基于 LCDM 距离的体积和描述性度量（例如每个对象的均值和方差）提供了一些形状特征的描述性摘要信息。然而，数据中包含额外的形态测量学，它们的分析可能为皮层特征的潜在差异提供额外的线索。要使用更多此类信息，我们汇集（合并）来自同一组受试者的 LCDM 距离。这些合并距离可以帮助检测组之间的形态测量差异，但不提供有关此类差异在相关组织中的位置的信息。在本文中，我们检查假设违反对池化 LCDM 距离分析的影响。我们证明了经典的参数检验对非正态性和 LCDM 距离的样本依赖性是稳健的，非参数检验对 LCDM 距离的样本依赖性具有鲁棒性。我们指定了测试更敏感的替代品类型。我们还表明，合并的 LCDM 距离为 LCDM 距离分布的组差异提供了强有力的结果。作为一个说明性的例子，我们在重度抑郁症 (MDD) 受试者、MDD 高风险 (HR) 受试者和健康受试者的腹侧内侧前额叶皮层 (VMPFC) 中使用 GM。由于 MDD 或处于 HR，在 VMPFC 中发现了显着的形态测量差异。特别是，分析表明，由于 MDD 或处于 HR 状态，左右 VMPFC 的距离趋于减小，这可能是变薄的结果。该方法也可以应用于其他皮质结构。