Statistical adjustments for brain size in volumetric neuroimaging studies: some practical implications in methods.
Statistical adjustments for brain size in volumetric neuroimaging studies: some practical implications in methods.
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DOI：
10.1016/j.pscychresns.2011.01.007
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发表时间：
2011-08-30
影响因子：
11.3
通讯作者：
Locascio JJ
中科院分区：
文献类型：
--
作者：
O'Brien LM;Ziegler DA;Deutsch CK;Frazier JA;Herbert MR;Locascio JJ
体积磁共振成像（MRI）脑数据为检测与各种神经和精神疾病相关的结构差异提供了有价值的工具。然而，对这些数据的分析并不总是直截了当的，当试图根据人群中高度的个体差异确定哪个大脑结构“更小”或“更大”时，可能会出现并发症。文献中已经使用了几种统计方法来调整总体颅骨或大脑大小的个体差异，但它们之间存在着重大差异。使用这些方法之间的一致性作为对假设的更强支持的指示是危险的，因为每种方法都需要满足不同的假设集。在这里，我们研究了其中三种调整方法（比例、残差和协方差分析）的理论基础，并将它们应用于体积MRI数据集。这三种用于调整大脑大小的方法是我们建议作为推荐建模策略的广义方法的具体案例。我们评估了方法之间的一致程度，并提供了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上的差异，并提供了一种有用的方法，研究人员可以通过该方法梳理出体积MRI数据中的重要关系。我们总结了推荐的程序，包括使用图形分析来帮助揭示ROI量可能与头部大小的潜在关系，并给出了一个通用的建模策略，研究人员可以通过该策略进行调整，其中包括作为特殊情况的三种常用方法。
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.
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