Logan Brent R, Geliazkova Maya P, Rowe Daniel B
Division of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, USA.
Hum Brain Mapp. 2008 Dec;29(12):1379-89. doi: 10.1002/hbm.20471.
Many fMRI experiments have a common objective of identifying active voxels in a neuroimaging dataset. This is done in single subject experiments, for example, by performing individual voxel-wise tests of the null hypothesis that the observed time course is not significantly related to an assigned reference function. A voxel activation map is then constructed by applying a thresholding rule to the resulting statistics (e.g., t-statistics). Typically the task-related activation is expected to occur in clusters of voxels rather than in isolated single voxels. A variety of spatial thresholding techniques have been proposed to reflect this belief, including smoothing the raw t-statistics, cluster size inference, and spatial mixture modeling. We study two aspects of these spatial thresholding procedures applied to single subject fMRI analysis through simulation. First, we examine the performance of these procedures in terms of sensitivity to detect voxel activation, using receiver operating characteristic curves. Second, we consider the accuracy of these spatial thresholding procedures in estimation of the size of the activation region, both in terms of bias and variance. The findings indicate that smoothing has the highest sensitivity to modest magnitude signals, but tend to overestimate the size of the activation region. Spatial mixture models estimate the size of a spatially distributed activation region well, but may be less sensitive to modest magnitude signals, indicating that additional research into more sensitive spatial mixture models is needed. Finally, the methods are illustrated with a real bilateral finger-tapping fMRI experiment.
许多功能磁共振成像（fMRI）实验都有一个共同目标，即识别神经成像数据集中的活跃体素。例如，在单受试者实验中，通过对零假设进行逐个体素的检验来实现这一目标，该零假设为观察到的时间进程与指定的参考函数没有显著关系。然后，通过对所得统计量（例如t统计量）应用阈值规则来构建体素激活图。通常，与任务相关的激活预计会出现在体素簇中，而不是孤立的单个体素中。已经提出了各种空间阈值技术来反映这一观点，包括对原始t统计量进行平滑处理、簇大小推断和空间混合建模。我们通过模拟研究了应用于单受试者fMRI分析的这些空间阈值程序的两个方面。首先，我们使用接收器操作特征曲线，从检测体素激活的敏感性方面检查这些程序的性能。其次，我们从偏差和方差两方面考虑这些空间阈值程序在估计激活区域大小时的准确性。研究结果表明，平滑处理对中等强度信号具有最高的敏感性，但往往会高估激活区域的大小。空间混合模型能很好地估计空间分布激活区域的大小，但可能对中等强度信号不太敏感，这表明需要对更敏感的空间混合模型进行更多研究。最后，用一个实际的双侧手指敲击fMRI实验对这些方法进行了说明。
