Valid population inference for information-based imaging: From the second-level t-test to prevalence inference
Valid population inference for information-based imaging: From the second-level t-test to prevalence inference
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DOI：
10.1016/j.neuroimage.2016.07.040
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发表时间：
2016-11-01
期刊：
影响因子：
5.7
通讯作者：
Haynes, John-Dylan
中科院分区：
文献类型：
--
作者：
Allefeld, Carsten;Goergen, Kai;Haynes, John-Dylan
在神经影像学数据的多变量模式分析中，通常通过将分类准确度输入到跨受试者的t检验与机会水平中来执行“第二级”推断。我们认为，虽然由t检验实现的随机效应分析确实提供了人口推断，如果适用于激活差异，它不能这样做的情况下，分类准确性或其他“类信息”的措施，因为这些措施的真实价值永远不会低于机会水平。该约束改变了正在检验的群体水平零假设的含义，其等同于在群体中的任何受试者中没有影响的全局零假设。因此，拒绝它只允许推断有一些科目，其中有一个信息效应，但不是它的推广，使其有效地等同于固定效应分析。这一说法得到了理论论证和模拟的支持。我们审查可能的替代方法，人口推断的信息为基础的成像，收敛的想法，它不应该针对的意思，但在人口中的影响的患病率。这样做的一种方法，“置换为基础的信息流行的推断使用最小的矩阵”，详细描述和应用于经验数据。(C)2016 Elsevier Inc. All rights reserved.
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. (C) 2016 Elsevier Inc. All rights reserved.