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Abstract：
In recent years, there has been significant criticism of functional magnetic resonance imaging (fMRI) studies with small sample sizes. The argument is that such studies have low statistical power, as well as reduced likelihood for statistically significant results to be true effects. The prevalence of these studies has led to a situation where a large number of published results are not replicable and likely false. Despite this growing body of evidence, small sample fMRI studies continue to be regularly performed; likely due to the high cost of scanning. In this report we investigate the use of a moderated t-statistic for performing group-level fMRI analysis to help alleviate problems related to small sample sizes. The proposed approach, implemented in the popular R-package LIMMA (linear models for microarray data), has found wide usage in the genomics literature for dealing with similar issues. Utilizing task-based fMRI data from the Human Connectome Project (HCP), we compare the performance of the moderated t-statistic with the standard t-statistic, as well as the pseudo t-statistic commonly used in non-parametric fMRI analysis. We find that the moderated t-test significantly outperforms both alternative approaches for studies with sample sizes less than 40 subjects. Further, we find that the results were consistent both when using voxel-based and cluster-based thresholding. We also introduce an R-package, LIMMI (linear models for medical images), that provides a quick and convenient way to apply the method to fMRI data.
摘要：
近年来,对于小样本量的功能磁共振成像(fMRI)研究一直存在显著的批评.论点是这样的研究具有较低的统计能力，以及降低具有统计学意义的结果成为真实效果的可能性。这些研究的普遍性导致了大量发表的结果不可复制并且可能是错误的情况。尽管有越来越多的证据,小样本功能磁共振成像研究继续定期进行;可能是由于扫描成本高。在本报告中，我们研究了使用适度的t统计量进行组水平的fMRI分析，以帮助缓解与小样本量相关的问题。拟议的方法，在流行的R包LIMMA(微阵列数据的线性模型)中实现，在基因组学文献中发现了处理类似问题的广泛用途。利用人类连接体项目(HCP)的基于任务的功能磁共振成像数据，我们比较了适度t统计量与标准t统计量的性能，以及非参数功能磁共振成像分析中常用的伪t统计量。我们发现，对于样本量小于40名受试者的研究，适度的t检验显着优于两种替代方法。Further,我们发现,当使用基于体素和基于聚类的阈值时,结果是一致的.我们还介绍了一个R包，LIMMI（医学图像的线性模型），这提供了一种快速便捷的方法来将该方法应用于fMRI数据。
