Does Parametric fMRI Analysis with SPM Yield Valid Results? - An Empirical Study of 1484 Rest Datasets

A presentation of our NeuroImage paper "Does Parametric fMRI Analysis with SPM Yield Valid Results? - An Empirical Study of 1484 Rest Datasets" Abstract 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.

我们发表于《NeuroImage》的论文《基于SPM的参数化功能磁共振成像（functional magnetic resonance imaging, fMRI）分析是否可得有效结果？——基于1484例静息态数据集的实证研究》的摘要如下：参数化fMRI分析的有效性此前仅在模拟数据中得到验证。近年来计算机科学与数据共享领域的发展，使得大规模真实fMRI数据的分析成为现实。本研究使用统计参数映射（Statistical Parametric Mapping, SPM）8对1484例静息态数据集进行分析，以估算真实的全族错误率（familywise error rate）。当全族显著性阈值设定为5%时，根据重复时间、实验范式与参数设置的差异，1484例静息态数据集中有1%~70%被检测到存在显著激活。这表明SPM中的参数化显著性阈值既可能过于保守，也可能过度宽松。导致全族错误率偏高的主要原因，似乎是SPM中采用的全局AR(1)自相关校正无法准确拟合残差的频谱特征，尤其在较短重复时间的场景下问题更为突出。本研究的结论仅适用于本次分析所使用的分析流程，无法推广至所有参数化fMRI分析场景，其他软件工具可能得到不同的分析结果。借助图形处理器（graphics processing unit, GPU）的计算能力，本研究同时采用随机置换检验（random permutation test）对1484例静息态数据集进行了分析，最终仅在1%~19%的数据集中检测到显著激活。上述研究结果凸显了针对fMRI时间序列构建更优时间相关性模型的迫切需求。