Evaluation of Second-Level Inference in fMRI Analysis.
Evaluation of Second-Level Inference in fMRI Analysis.
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DOI：
10.1155/2016/1068434
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发表时间：
2016
影响因子：
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通讯作者：
Moerkerke B
中科院分区：
文献类型：
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作者：
Roels SP;Loeys T;Moerkerke B
我们研究了在功能磁共振成像的第二水平(即，对受试者)推理过程中的决策对(1)假阳性和假阴性之间的平衡和(2)数据分析稳定性的影响，这两个指标都代表了结果的再现性。基于质量单变量方法的二级分析通常由3个阶段组成。首先，通过测试图像的一般线性模型进行处理，该模型由来自不同受试者的集合信息组成。我们评估考虑到第一级(受试者内)可变性的模型和没有考虑这种可变性的模型。其次，通过基于参数假设的推理或基于置换的推理来进行。第三，我们评估了3种解决多重测试问题的常用方法：家族错误率校正、错误发现率(FDR)校正和最小聚类大小的两步法。基于仿真研究和实际数据，我们发现具有最小聚类大小的两步法得到的结果最稳定，其次是家庭误码率校正。对于基于排列的推理和参数推理，FDR都会产生最可变的结果。在使用参数推理时，对特定于受试者的可变性进行建模可以在假阳性和假阴性之间产生更好的平衡。
We investigate the impact of decisions in the second-level (i.e., over subjects) inferential process in functional magnetic resonance imaging on (1) the balance between false positives and false negatives and on (2) the data-analytical stability, both proxies for the reproducibility of results. Second-level analysis based on a mass univariate approach typically consists of 3 phases. First, one proceeds via a general linear model for a test image that consists of pooled information from different subjects. We evaluate models that take into account first-level (within-subjects) variability and models that do not take into account this variability. Second, one proceeds via inference based on parametrical assumptions or via permutation-based inference. Third, we evaluate 3 commonly used procedures to address the multiple testing problem: familywise error rate correction, False Discovery Rate (FDR) correction, and a two-step procedure with minimal cluster size. Based on a simulation study and real data we find that the two-step procedure with minimal cluster size results in most stable results, followed by the familywise error rate correction. The FDR results in most variable results, for both permutation-based inference and parametrical inference. Modeling the subject-specific variability yields a better balance between false positives and false negatives when using parametric inference.