Graduate Program in Electrical and Computer Engineering, Universidade Tecnológica Federal Do Paraná, Curitiba, PR, Brazil.
Li-Ka Shing Big Data Institute, University of Oxford, UK.
Neuroimage. 2020 Aug 1;216:116760. doi: 10.1016/j.neuroimage.2020.116760. Epub 2020 Mar 19.
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.
当大脑图像表示中有许多体素或顶点时，或者当在相同的一般线性模型中测试多个参数估计（表示假设）的对比时，就会出现多重检验问题。我们认为，必须进行这种多重性的校正，以避免出现过多的假阳性。文献中已经提出了各种校正方法，但很少应用于脑成像。在这里，我们讨论并比较了不同方法在不同情况下进行这种校正的方法，结果表明一种经典而众所周知的方法是无效的，并认为由于其精确性和处理各种常见成像情况的灵活性，置换是进行这种校正的最佳选择。
