论文标题
随机字段简介
Introduction to Random Fields
论文作者
论文摘要
通用线性模型（GLM）通常在脑成像中的体素水平的统计推断中构建和使用。在本文中，我们探讨了随机字段的基础知识和随机字段上的多个比较，这对于在特定统计显着性水平上正确地正确地阈值统计图是正确的。多重比较对于确定整个大脑的相关测试统计数据的总体统计显着性至关重要。实际上，相邻体素的T或F统计量相关。因此，存在多个比较的问题，到目前为止，我们已经忽略了这一点。对于多个说明了空间相关的测试统计数据的比较，提出了各种方法：Bonferroni校正，随机场理论，错误发现率和置换测试。其中，我们将探讨随机字段方法。
General linear models (GLM) are often constructed and used in statistical inference at the voxel level in brain imaging. In this paper, we explore the basics of random fields and the multiple comparisons on the random fields, which are necessary to properly threshold statistical maps for the whole image at specific statistical significance level. The multiple comparisons are crucial in determining overall statistical significance in correlated test statistics over the whole brain. In practice, t- or F-statistics in adjacent voxels are correlated. So there is the problem of multiple comparisons, which we have simply neglected up to now. For multiple comparisons that account for spatially correlated test statistics, various methods were proposed: Bonferroni correction, random field theory, false discovery rates and permutation tests. Among them, we will explore the random field approach.
