Genovese C R, Noll D C, Eddy W F
Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Magn Reson Med. 1997 Sep;38(3):497-507. doi: 10.1002/mrm.1910380319.
A common problem in the analysis of functional magnetic resonance imaging (fMRI) data is quantifying the statistical reliability of an estimated activation map. While visual comparison of the classified active regions across replications of an experiment can sometimes by informative, it is typically difficult to draw firm conclusions by inspection; noise and complex patterns in the estimated map make it easy to be misled. Here, several statistical models, of increasing complexity, are developed, under which "test-retest" reliability can be meaningfully defined and quantified. The method yields global measures of reliability that apply uniformly to a specified set of brain voxels. The estimates of these reliability measures and their associated uncertainties under these models can be used to compare statistical methods, to set thresholds for detecting activation, and to optimize the number of images that need to be acquired during an experiment.
功能磁共振成像（fMRI）数据分析中的一个常见问题是量化估计激活图的统计可靠性。虽然在检查实验中对分类激活区域的视觉比较有时能提供信息，但通过检查通常很难得出明确结论；估计图中的噪声和复杂模式很容易使人产生误解。在此，我们开发了几种复杂度不断增加的统计模型，在这些模型下，可以有意义地定义和量化“重测”可靠性。该方法产生适用于指定脑体素集的全局可靠性度量。这些可靠性度量的估计值及其在这些模型下的相关不确定性可用于比较统计方法、设置检测激活的阈值以及优化实验期间需要采集的图像数量。
