Skudlarski P, Constable R T, Gore J C
Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, Connecticut 06520-8042, USA.
Neuroimage. 1999 Mar;9(3):311-29. doi: 10.1006/nimg.1999.0402.
The complicated structure of fMRI signals and associated noise sources make it difficult to assess the validity of various steps involved in the statistical analysis of brain activation. Most methods used for fMRI analysis assume that observations are independent and that the noise can be treated as white gaussian noise. These assumptions are usually not true but it is difficult to assess how severely these assumptions are violated and what are their practical consequences. In this study a direct comparison is made between the power of various analytical methods used to detect activations, without reference to estimates of statistical significance. The statistics used in fMRI are treated as metrics designed to detect activations and are not interpreted probabilistically. The receiver operator characteristic (ROC) method is used to compare the efficacy of various steps in calculating an activation map in the study of a single subject based on optimizing the ratio of the number of detected activations to the number of false-positive findings. The main findings are as follows: Preprocessing. The removal of intensity drifts and high-pass filtering applied on the voxel time-course level is beneficial to the efficacy of analysis. Temporal normalization of the global image intensity, smoothing in the temporal domain, and low-pass filtering do not improve power of analysis. Choices of statistics. the cross-correlation coefficient and t-statistic, as well as nonparametric Mann-Whitney statistics, prove to be the most effective and are similar in performance, by our criterion. Task design. the proper design of task protocols is shown to be crucial. In an alternating block design the optimal block length is be approximately 18 s. Spatial clustering. an initial spatial smoothing of images is more efficient than cluster filtering of the statistical parametric activation maps.
功能磁共振成像（fMRI）信号的复杂结构以及相关的噪声源，使得评估大脑激活统计分析中各个步骤的有效性变得困难。大多数用于fMRI分析的方法都假定观测值是独立的，并且噪声可被视为白高斯噪声。这些假设通常并不成立，但很难评估这些假设被违背的严重程度以及它们的实际后果。在本研究中，对用于检测激活的各种分析方法的效能进行了直接比较，而不涉及统计显著性估计。fMRI中使用的统计量被视为旨在检测激活的指标，而非概率性解释。基于优化检测到的激活数量与假阳性结果数量的比率，采用接收者操作特征（ROC）方法比较了在单个受试者研究中计算激活图的各个步骤的效能。主要发现如下：预处理。在体素时间进程水平上去除强度漂移和应用高通滤波有利于分析效能。全局图像强度的时间归一化、时域平滑和低通滤波并不能提高分析效能。统计量选择。根据我们的标准，互相关系数、t统计量以及非参数曼-惠特尼统计量被证明是最有效的，并且在性能上相似。任务设计。任务协议的合理设计被证明至关重要。在交替块设计中，最佳块长度约为18秒。空间聚类。图像的初始空间平滑比统计参数激活图的聚类滤波更有效。
