Smith Andrew T, Singh Krishna D, Balsters Joshua H
CUBIC and Department of Psychology, Royal Holloway, University of London, Egham, TW20 0EX, UK.
Neuroimage. 2007 Jun;36(2):282-8. doi: 10.1016/j.neuroimage.2006.09.044. Epub 2006 Nov 13.
Analysis of fMRI time-series data is usually performed within the general linear model (GLM), and conclusions are based on the probability (p) of a false-positive voxel. In its simplest form, this process assumes that temporal noise in the time-series is random (white); if it is not, then an error in the estimation of p will occur. Various methods for correcting the problem have been developed, but they are not always used. Analysis packages vary in their ability to deal with this problem, and there is a lack of consistent advice to non-experts concerning whether this issue is serious or is small enough to be ignored. The variability of expert opinion partly reflects the fact that the magnitude of the error that occurs when estimating p in fMRI data is largely undocumented. In this discussion, aimed more at "neuroscience users" than methods experts, we try to clarify the position by documenting the scale of the error in a simple block-design experiment. Based on a fixed-effects group analysis of null datasets (8 participants scanned while at rest), we show that the magnitude of the problem can be very substantial. Without correction, it was necessary to set a nominal probability threshold of about p=0.000001 in order to achieve an actual probability of a false positive of 0.001. This means that if task-related activations were present in such a dataset, only if they reached the former, very low nominal p value could they be relied upon. With the use of standard correction methods, the error was largely removed. Our results suggest that some form of pre-whitening or correction for effects of non-white noise is essential in single-subject GLM analyses and fixed-effects group analyses of fMRI data. It is less important but probably prudent to apply a correction at the first level of random-effects group analyses. We also evaluate the effect of high-pass filtering. We find that the number of false positives in a fixed-effects analysis increases markedly as the filter cut-off frequency is increased. The sensitivity benefits derived from high-pass filtering may thus be partially offset by a significant increase in the number of false positives.
功能磁共振成像（fMRI）时间序列数据的分析通常在一般线性模型（GLM）中进行，结论基于假阳性体素的概率（p）。在其最简单的形式中，这个过程假设时间序列中的时间噪声是随机的（白色的）；如果不是，那么p值的估计就会出现误差。已经开发了各种方法来纠正这个问题，但它们并不总是被使用。分析软件包处理这个问题的能力各不相同，并且对于非专家来说，关于这个问题是严重还是小到可以忽略，缺乏一致的建议。专家意见的差异部分反映了这样一个事实，即fMRI数据中估计p值时出现的误差大小在很大程度上没有记录。在本次讨论中，我们更多地针对“神经科学用户”而非方法专家，试图通过在一个简单的组块设计实验中记录误差规模来阐明立场。基于对空数据集（8名参与者在静息状态下扫描）的固定效应组分析，我们表明问题的严重程度可能非常大。如果不进行校正，为了使假阳性的实际概率达到0.001，需要设置约p = 0.000001的名义概率阈值。这意味着，如果在这样的数据集中存在与任务相关的激活，只有当它们达到前者非常低的名义p值时，才可以被信赖。使用标准校正方法后，误差在很大程度上被消除。我们的结果表明，在fMRI数据的单受试者GLM分析和固定效应组分析中，某种形式的预白化或对非白噪声效应的校正是必不可少的。在随机效应组分析的第一级应用校正不太重要，但可能是谨慎的做法。我们还评估了高通滤波的效果。我们发现，在固定效应分析中，随着滤波器截止频率的增加，假阳性的数量显著增加。因此，高通滤波带来的敏感性益处可能会被假阳性数量的显著增加部分抵消。
