Spence Jeffrey S, Carmack Patrick S, Gunst Richard F, Schucany William R, Woodward Wayne A, Haley Robert W
Epidemiology Division, Department of Internal Medicine, University of Texas Southwestern Medical Center at Dallas, 5323 Harry Hines Blvd., Dallas, TX 75390, USA.
Neuroimage. 2006 Aug 1;32(1):49-53. doi: 10.1016/j.neuroimage.2006.03.025. Epub 2006 May 2.
Proportional scaling models are often used in functional imaging studies to remove confounding of local signals by global effects. It is generally assumed that global effects are uncorrelated with experimental conditions. However, when the global effect is estimated by the global signal, defined as the intracerebral average, incorrect inference may result from the dependency of the global signal on preexisting conditions or experimental manipulations. In this paper, we propose a simple alternative method of estimating the global effect to be used in a proportional scaling model. Specifically, by defining the global signal with reference strictly to a white matter region within the centrum semiovale, the dependency is removed in experiments where white matter is unaffected by the disease effect or experimental treatments. The increase in the ability to detect changes in regional blood flow is demonstrated in a SPECT study of healthy and ill Gulf War veterans in whom it is suspected that brain abnormalities influence the traditional calculation of the global signal. Controlling for the global effect, ill veterans have significantly lower intracerebral averages than healthy controls (P = 0.0038), evidence that choice of global signal has an impact on inference. Scaling by the modified global signal proposed here results in an increase in sensitivity leading to the identification of several regions in the insula and frontal cortex where ill veterans have significantly lower SPECT emissions. Scaling by the traditional global signal results in the loss of sensitivity to detect these regional differences. Advantages of this alternative method are its computational simplicity and its ability to be easily integrated into existing analysis frameworks such as SPM.
比例缩放模型常用于功能成像研究，以消除全局效应引起的局部信号混淆。通常认为全局效应与实验条件无关。然而，当通过将全局信号定义为脑内平均值来估计全局效应时，由于全局信号对先前存在的条件或实验操作的依赖性，可能会导致错误的推断。在本文中，我们提出了一种简单的替代方法来估计比例缩放模型中使用的全局效应。具体而言，通过严格参照半卵圆中心内的一个白质区域来定义全局信号，在白质不受疾病效应或实验处理影响的实验中消除了这种依赖性。在一项针对健康和患病海湾战争退伍军人的单光子发射计算机断层扫描（SPECT）研究中，证明了检测局部血流变化能力的提高，在该研究中怀疑脑异常会影响全局信号的传统计算。在控制全局效应的情况下，患病退伍军人的脑内平均值显著低于健康对照组（P = 0.0038），这证明全局信号的选择对推断有影响。用本文提出的修正全局信号进行缩放会导致灵敏度提高，从而识别出岛叶和额叶皮质中的几个区域，患病退伍军人在这些区域的SPECT发射显著较低。用传统全局信号进行缩放会导致检测这些区域差异的灵敏度丧失。这种替代方法的优点是计算简单，并且能够轻松地集成到现有的分析框架（如统计参数映射软件包（SPM））中。
