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Combining voxel intensity and cluster extent with a permutation test framework

机译:将体素强度和聚类范围与排列测试框架相结合

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Voxel intensity-based tests provide good sensitivity for high intensity signals, whereas cluster extent-based tests are sensitive to spatially extended signals. To benefit from the strength of both, we consider combining intensity and extent information. We generalize previous work by proposing the use of weighted combining functions. Using a combining framework with permutation tests, we consider a variety of ways of combining voxel intensity and cluster extent information without knowing their distribution. Further, we propose meta-combining, a combining function of combining functions, which integrates strengths of multiple combining functions into one single statistic. Using real data, we demonstrate that combined tests can be more sensitive than voxel or cluster size test alone. Though not necessarily sensitive than individual combining functions, the meta-combining function is sensitive to all types of signals, thus can be uses as a single test summarizing all the combining functions.
机译:基于体素强度的测试为高强度信号提供了良好的灵敏度,而基于簇范围的测试对空间扩展的信号敏感。为了从两者的优势中受益,我们考虑结合强度和范围信息。我们通过建议使用加权合并函数来概括以前的工作。通过使用具有排列检验的组合框架,我们考虑了在不知道其分布的情况下组合体素强度和聚类范围信息的多种方法。此外,我们提出了元合并功能,即合并功能的合并功能,它将多个合并功能的优势整合到一个单一的统计数据中。使用真实数据,我们证明组合测试比单独的体素或簇大小测试更敏感。虽然不一定比单个合并功能敏感,但是元合并功能对所有类型的信号都敏感,因此可以用作总结所有合并功能的单个测试。

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