Does parametric fMRI analysis with SPM yield valid results?-An empirical study of 1484 rest datasets
Does parametric fMRI analysis with SPM yield valid results?-An empirical study of 1484 rest datasets
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DOI：
10.1016/j.neuroimage.2012.03.093
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发表时间：
2012-07-02
期刊：
影响因子：
5.7
通讯作者：
Knutsson, Hans
中科院分区：
文献类型：
--
作者：
Eklund, Anders;Andersson, Mats;Knutsson, Hans
参数功能性磁共振成像（fMRI）分析的有效性只报道了模拟数据。计算机科学和数据共享的最新进展使得分析大量的真实的fMRI数据成为可能。在这项研究中，1484休息数据集已在SPM 8中进行了分析，以估计真正的家庭错误率。对于5%的家族显著性阈值，在1484个休息数据集中的1%-70%中发现了显著的活性，这取决于重复时间、范例和参数设置。这意味着SPM中的参数显著性阈值可以是保守的或非常自由的。高的族误差率的主要原因似乎是SPM中的全局AR（1）自相关校正未能对残差的谱进行建模，特别是对于短重复时间。在这项研究中报告的结果不能概括为一般的参数fMRI分析，其他软件包可能会给出不同的结果。通过使用图形处理单元（GPU）的计算能力。其余1484个数据集也用随机排列检验进行分析。然后在1%-19%的数据集中发现了显著的活性。这些发现说明了在fMRI时间序列中需要一个更好的时间相关性模型。(c)2012 Elsevier Inc. All rights reserved.
The validity of parametric functional magnetic resonance imaging (fMRI) analysis has only been reported for simulated data. Recent advances in computer science and data sharing make it possible to analyze large amounts of real fMRI data. In this study, 1484 rest datasets have been analyzed in SPM8, to estimate true familywise error rates. For a familywise significance threshold of 5%, significant activity was found in 1%-70% of the 1484 rest datasets, depending on repetition time, paradigm and parameter settings. This means that parametric significance thresholds in SPM both can be conservative or very liberal. The main reason for the high familywise error rates seems to be that the global AR(1) auto correlation correction in SPM fails to model the spectra of the residuals, especially for short repetition times. The findings that are reported in this study cannot be generalized to parametric fMRI analysis in general, other software packages may give different results. By using the computational power of the graphics processing unit (GPU). the 1484 rest datasets were also analyzed with a random permutation test. Significant activity was then found in 1%-19% of the datasets. These findings speak to the need for a better model of temporal correlations in fMRI timeseries. (c) 2012 Elsevier Inc. All rights reserved.