Allefeld Carsten, Görgen Kai, Haynes John-Dylan
Bernstein Center for Computational Neuroscience, Berlin Center of Advanced Neuroimaging, Department of Neurology, and Excellence Cluster NeuroCure, Charité - Universitätsmedizin Berlin, Germany.
Bernstein Center for Computational Neuroscience, Berlin Center of Advanced Neuroimaging, Department of Neurology, and Excellence Cluster NeuroCure, Charité - Universitätsmedizin Berlin, Germany.
Neuroimage. 2016 Nov 1;141:378-392. doi: 10.1016/j.neuroimage.2016.07.040. Epub 2016 Jul 20.
In multivariate pattern analysis of neuroimaging data, 'second-level' inference is often performed by entering classification accuracies into a t-test vs chance level across subjects. We argue that while the random-effects analysis implemented by the t-test does provide population inference if applied to activation differences, it fails to do so in the case of classification accuracy or other 'information-like' measures, because the true value of such measures can never be below chance level. This constraint changes the meaning of the population-level null hypothesis being tested, which becomes equivalent to the global null hypothesis that there is no effect in any subject in the population. Consequently, rejecting it only allows to infer that there are some subjects in which there is an information effect, but not that it generalizes, rendering it effectively equivalent to fixed-effects analysis. This statement is supported by theoretical arguments as well as simulations. We review possible alternative approaches to population inference for information-based imaging, converging on the idea that it should not target the mean, but the prevalence of the effect in the population. One method to do so, 'permutation-based information prevalence inference using the minimum statistic', is described in detail and applied to empirical data.
在神经影像数据的多变量模式分析中，“二级”推断通常是通过将分类准确率纳入跨受试者与机遇水平的t检验来进行的。我们认为，虽然t检验所实施的随机效应分析如果应用于激活差异确实能提供总体推断，但在分类准确率或其他“类信息”测量的情况下却无法做到，因为此类测量的真实值永远不会低于机遇水平。这种限制改变了所检验的总体水平零假设的含义，该假设变得等同于总体中任何受试者都无效应的全局零假设。因此，拒绝它仅能推断出在某些受试者中存在信息效应，但不能推断出该效应具有普遍性，这实际上使其等同于固定效应分析。这一观点得到了理论论证以及模拟的支持。我们回顾了基于信息的成像总体推断的可能替代方法，得出的结论是，它不应以均值为目标，而应以总体中效应的发生率为目标。详细描述了一种这样的方法，即“使用最小统计量的基于排列的信息发生率推断”，并将其应用于实证数据。
