Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates
Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates
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DOI：
10.1002/hbm.24350
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发表时间：
2019-05-01
影响因子：
4.8
通讯作者：
Nichols, Thomas E.
中科院分区：
文献类型：
--
作者：
Eklund, Anders;Knutsson, Hans;Nichols, Thomas E.
方法论研究很少产生广泛的兴趣，但我们的工作的有效性集群推理方法的功能性磁共振成像（fMRI）创建激烈的讨论，我们的方法的细节和它的影响的纪律。在本书中，我们接受了对我们工作的各种批评，并进一步探讨了我们原始工作的局限性。我们解决的问题，我们使用的特定事件相关的设计，考虑到多个事件类型和受试者之间的事件随机化。我们认为缺乏有效性发现与单样本排列（符号翻转）测试，调查了一些方法来提高这个广泛使用的程序的假阳性控制。我们发现，双侧检验和使用伊卡FIX清理数据的组合导致所有数据集的标称假阳性率，这意味着数据清理不仅对静息状态fMRI很重要，而且对任务fMRI也很重要。最后，我们讨论了我们的工作对fMRI文献的影响，估计至少有10%的fMRI研究使用了最有问题的聚类推理方法（p = 0.01聚类定义阈值），以及如何根据我们的发现解释个别研究。这些额外的结果强调了我们最初的结论，即数据共享和对现实零数据统计方法进行彻底评估的重要性。
Methodological research rarely generates a broad interest, yet our work on the validity of cluster inference methods for functional magnetic resonance imaging (fMRI) created intense discussion on both the minutia of our approach and its implications for the discipline. In the present work, we take on various critiques of our work and further explore the limitations of our original work. We address issues about the particular event-related designs we used, considering multiple event types and randomization of events between subjects. We consider the lack of validity found with one-sample permutation (sign flipping) tests, investigating a number of approaches to improve the false positive control of this widely used procedure. We found that the combination of a two-sided test and cleaning the data using ICA FIX resulted in nominal false positive rates for all data sets, meaning that data cleaning is not only important for resting state fMRI, but also for task fMRI. Finally, we discuss the implications of our work on the fMRI literature as a whole, estimating that at least 10% of the fMRI studies have used the most problematic cluster inference method (p = .01 cluster defining threshold), and how individual studies can be interpreted in light of our findings. These additional results underscore our original conclusions, on the importance of data sharing and thorough evaluation of statistical methods on realistic null data.