Structural covariance analysis is a widely used structural MRI analysis method which characterises the co-relations of morphology between brain regions over a group of subjects. To our knowledge, little has been investigated in terms of the comparability of results between different data sets of healthy human subjects, as well as the reliability of results over the same subjects in different rescan sessions, image resolutions, or FreeSurfer versions. In terms of comparability, our results show substantial differences in the structural covariance matrix between data sets of age- and sex-matched healthy human adults. These differences persist after univariate site correction, they are exacerbated by low sample sizes, and they are most pronounced when using average cortical thickness as a morphological measure. Down-stream graph theoretic analyses further show statistically significant differences. In terms of reliability, substantial differences were also found when comparing repeated scan sessions of the same subjects, image resolutions, and even FreeSurfer versions of the same image. We could further estimate the relative measurement error and showed that it is largest when using cortical thickness as a morphological measure. Using simulated data, we argue that cortical thickness is least reliable because of larger relative measurement errors. Practically, we make the following recommendations (1) combining subjects across sites into one group should be avoided, particularly if sites differ in image resolutions, subject demographics, or preprocessing steps; (2) surface area and volume should be preferred as morphological measures over cortical thickness; (3) a large number of subjects (n≫30 for the Desikan-Killiany parcellation) should be used to estimate structural covariance; (4) measurement error should be assessed where repeated measurements are available; (5) if combining sites is critical, univariate (per ROI) site-correction is insufficient, but error covariance (between ROIs) should be explicitly measured and modelled.

中文翻译：

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人脑结构协方差网络的可靠性和可比性

结构协方差分析是一种广泛使用的结构 MRI 分析方法，它表征一组受试者大脑区域之间形态学的相关关系。据我们所知，关于健康人类受试者不同数据集之间结果的可比性，以及相同受试者在不同重新扫描会话、图像分辨率或 FreeSurfer 版本中结果的可靠性方面的研究很少。在可比性方面，我们的结果显示年龄和性别匹配的健康成人数据集之间的结构协方差矩阵存在显着差异。这些差异在单变量位点校正后仍然存在，它们因样本量低而加剧，并且在使用平均皮质厚度作为形态学测量时最为明显。下游图论分析进一步显示了统计学上的显着差异。在可靠性方面，在比较相同对象的重复扫描会话、图像分辨率甚至同一图像的 FreeSurfer 版本时，也发现了显着差异。我们可以进一步估计相对测量误差，并表明当使用皮质厚度作为形态学测量时它是最大的。使用模拟数据，我们认为皮质厚度最不可靠，因为相对测量误差较大。实际上，我们提出以下建议 (1) 应避免将跨站点的主题合并为一组，特别是如果站点的图像分辨率、主题人口统计或预处理步骤不同；(2) 表面积和体积应优先作为形态学测量而不是皮质厚度；(3) 应该使用大量的主题（对于 Desikan-Killiany 分割为 n≫30）来估计结构协方差；(4) 在可以重复测量的情况下，应评估测量误差；(5) 如果组合站点很重要，单变量（每个 ROI）站点校正是不够的，但应该明确测量和建模误差协方差（ROI 之间）。