This article describes a hybrid method to threshold functional magnetic resonance imaging (FMRI) group statistical maps derived from voxel-wise second-level statistical analyses. The proposed "Equitable Thresholding and Clustering" (ETAC) approach seeks to reduce the dependence of clustering results on arbitrary parameter values by using multiple subtests, each equivalent to a standard FMRI clustering analysis, to make decisions about which groups of voxels are potentially significant. The union of these subtest results decides which voxels are accepted. The approach adjusts the cluster-thresholding parameter of each subtest in an equitable way, so that the individual false-positive rates (FPRs) are balanced across subtests to achieve a desired final FPR (e.g., 5%). ETAC utilizes resampling methods to estimate the FPR and thus does not rely on parametric assumptions about the spatial correlation of FMRI noise. The approach was validated with pseudotask timings in resting-state brain data. In addition, a task FMRI data collection was used to compare ETACs true positive detection power versus a standard cluster detection method, demonstrating that ETAC is able to detect true results and control false positives while reducing reliance on arbitrary analysis parameters.

中文翻译：

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公平阈值化和聚类：AFNI中功能性磁共振成像聚类的新方法。

本文介绍了一种混合方法，可对从体素明智的第二级统计分析得出的功能性磁共振成像（FMRI）组统计图进行阈值处理。提议的“公平阈值和聚类”（ETAC）方法试图通过使用多个子测试（每个子测试均等同于标准FMRI聚类分析）来减少聚类结果对任意参数值的依赖性，以决定哪些体素组可能具有重要意义。这些子测试结果的并集决定接受哪些体素。该方法以公平的方式调整每个子测试的聚类阈值参数，以使各个子测试之间的假阳性率（FPR）达到平衡，以实现所需的最终FPR（例如5％）。ETAC利用重采样方法来估计FPR，因此不依赖于有关FMRI噪声的空间相关性的参数假设。该方法已在静止状态大脑数据中使用伪任务计时进行了验证。此外，使用任务FMRI数据收集将ETAC的真实阳性检测能力与标准簇检测方法进行比较，证明ETAC能够检测真实结果并控制假阳性，同时减少了对任意分析参数的依赖。