Clusterwise statistical inference is the most widely used technique for functional magnetic resonance imaging (fMRI) data analyses. Clusterwise statistical inference consists of two steps: (i) primary thresholding that excludes less significant voxels by a prespecified cut-off (eg, $p<.001$); and (ii) clusterwise thresholding that controls the familywise error rate caused by clusters consisting of false positive suprathreshold voxels. The selection of the primary threshold is critical because it determines both statistical power and false discovery rate (FDR). However, in most existing statistical packages, the primary threshold is selected based on prior knowledge (eg, $p<.001$) without taking into account the information in the data. In this article, we propose a data-driven approach to algorithmically select the optimal primary threshold based on an empirical Bayes framework. We evaluate the proposed model using extensive simulation studies and real fMRI data. In the simulation, we show that our method can effectively increase statistical power by 20% to over 100% while effectively controlling the FDR. We then investigate the brain response to the dose-effect of chlorpromazine in patients with schizophrenia by analyzing fMRI scans and generate consistent results.

中文翻译：

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聚类功能磁共振成像推断中初级阈值的贝叶斯估计

聚类统计推断是功能磁共振成像 (fMRI) 数据分析中使用最广泛的技术。聚类统计推断包括两个步骤：(i) 初级阈值化，通过预先指定的截止值排除不太重要的体素（例如，$p<.001$); (ii) clusterwise thresholding 控制由误报超阈值体素组成的集群引起的 familywise 错误率。主要阈值的选择至关重要，因为它决定了统计功效和错误发现率 (FDR)。然而，在大多数现有的统计包中，主要阈值是根据先验知识（例如，$p<.001$) 而不考虑数据中的信息。在本文中，我们提出了一种基于经验贝叶斯框架的数据驱动方法，通过算法选择最佳主阈值。我们使用广泛的模拟研究和真实的 fMRI 数据评估所提出的模型。在仿真中，我们表明我们的方法可以有效地将统计功效提高 20% 到 100% 以上，同时有效地控制 FDR。然后，我们通过分析 fMRI 扫描并生成一致的结果来研究精神分裂症患者对氯丙嗪剂量效应的大脑反应。