Department of Mathematics and Statistics, Colby College, Waterville, ME 04901, USA.
Psychiatry Res. 2011 Aug 30;193(2):113-22. doi: 10.1016/j.pscychresns.2011.01.007.
Volumetric magnetic resonance imaging (MRI) brain data provide a valuable tool for detecting structural differences associated with various neurological and psychiatric disorders. Analysis of such data, however, is not always straightforward, and complications can arise when trying to determine which brain structures are "smaller" or "larger" in light of the high degree of individual variability across the population. Several statistical methods for adjusting for individual differences in overall cranial or brain size have been used in the literature, but critical differences exist between them. Using agreement among those methods as an indication of stronger support of a hypothesis is dangerous given that each requires a different set of assumptions be met. Here we examine the theoretical underpinnings of three of these adjustment methods (proportion, residual, and analysis of covariance) and apply them to a volumetric MRI data set. These three methods used for adjusting for brain size are specific cases of a generalized approach which we propose as a recommended modeling strategy. We assess the level of agreement among methods and provide graphical tools to assist researchers in determining how they differ in the types of relationships they can unmask, and provide a useful method by which researchers may tease out important relationships in volumetric MRI data. We conclude with the recommended procedure involving the use of graphical analyses to help uncover potential relationships the ROI volumes may have with head size and give a generalized modeling strategy by which researchers can make such adjustments that include as special cases the three commonly employed methods mentioned above.
容积磁共振成像（MRI）脑数据为检测与各种神经和精神障碍相关的结构差异提供了有价值的工具。然而，此类数据的分析并不总是简单直接的，并且在试图确定哪些脑结构在考虑到人群中高度的个体变异性时“更小”或“更大”时，可能会出现并发症。文献中已经使用了几种用于调整总体颅或脑大小个体差异的统计方法，但它们之间存在关键差异。鉴于每种方法都需要满足不同的假设，因此使用这些方法之间的一致性作为对假设更强有力支持的指标是危险的。在这里，我们检查了三种调整方法（比例、残差和协方差分析）的理论基础，并将它们应用于容积 MRI 数据集。这三种用于调整脑大小的方法是我们提出的一种广义方法的特例，我们将其作为推荐的建模策略。我们评估了方法之间的一致性，并提供图形工具来帮助研究人员确定它们在可以揭示的关系类型方面的差异，并提供了一种有用的方法，研究人员可以通过该方法在容积 MRI 数据中梳理出重要的关系。最后，我们提出了一个建议的程序，涉及使用图形分析来帮助揭示 ROI 体积与头部大小之间可能存在的潜在关系，并提供了一种广义的建模策略，研究人员可以通过该策略进行此类调整，包括上述三种常用方法作为特例。
