The multiple testing problem arises not only when there are many voxels or vertices in an image representation of the brain, but also when multiple contrasts of parameter estimates (that represent hypotheses) are tested in the same general linear model. We argue that a correction for this multiplicity must be performed to avoid excess of false positives. Various methods for correction have been proposed in the literature, but few have been applied to brain imaging. Here we discuss and compare different methods to make such correction in different scenarios, showing that one classical and well known method is invalid, and argue that permutation is the best option to perform such correction due to its exactness and flexibility to handle a variety of common imaging situations.

中文翻译：

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脑成像的多重测试校正过对比

多重测试问题不仅在大脑的图像表示中有许多体素或顶点时出现，而且在相同的一般线性模型中测试参数估计的多个对比（代表假设）时也会出现。我们认为必须对这种多重性进行校正以避免过多的误报。文献中提出了各种校正方法，但很少应用于脑成像。在这里，我们讨论并比较了在不同场景下进行此类校正的不同方法，表明一种经典且众所周知的方法是无效的，并认为置换是执行此类校正的最佳选择，因为它具有处理各种常见问题的准确性和灵活性。成像情况。