Institute of Cognitive Science, Osnabrueck University, Osnabrueck, Germany.
Humboldt University Berlin, Berlin, Germany.
Neuroimage. 2020 Oct 15;220:117104. doi: 10.1016/j.neuroimage.2020.117104. Epub 2020 Jul 2.
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.
结构协方差分析是一种广泛使用的结构 MRI 分析方法，用于描述一组被试的脑区形态之间的相关性。据我们所知，对于健康人类被试的不同数据集之间的结果可比性，以及同一被试在不同重扫会话、图像分辨率或 FreeSurfer 版本中的结果可靠性，还很少有研究。就可比性而言，我们的研究结果表明，年龄和性别匹配的健康成年人的数据集之间的结构协方差矩阵存在实质性差异。这些差异在进行单变量站点校正后仍然存在，在样本量较小的情况下会加剧，并且当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著差异。就可靠性而言，当比较同一被试的重复扫描会话、图像分辨率，甚至是同一图像的 FreeSurfer 版本时，也发现了实质性的差异。我们还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，相对测量误差最大。使用模拟数据，我们认为皮质厚度的可靠性最低，因为其相对测量误差较大。实际上，我们提出以下建议：(1) 应避免将来自不同站点的被试组合成一个组，特别是如果站点在图像分辨率、被试人口统计学特征或预处理步骤方面存在差异；(2) 应优先选择表面积和体积作为形态学测量，而不是皮质厚度；(3) 应使用大量的被试（对于 Desikan-Killiany 分割，n≫30）来估计结构协方差；(4) 在有重复测量的情况下，应评估测量误差；(5) 如果合并站点是关键的，单变量（每个 ROI）站点校正是不够的，但应明确测量和建模误差协方差（ROI 之间）。
