基于随机场理论的p值:对SPM实现的回顾

D. Ostwald, Sebastian C. Schneider, R. Bruckner, Lilla Horvath
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引用次数: 4

摘要

p值和零假设显著性检验是功能神经影像学中常用的数据分析工具。受静息状态fMRI数据分析的启发,最近人们对使用SPM软件评估的一些p值的有效性重新产生了兴趣。SPM中报告的默认参数p值是基于将随机场理论的结果应用于统计参数图(我们称之为RFP的框架)。虽然RFP是在近20年前建立的,并且已经应用于大量的功能磁共振成像研究中,但在SPM的当前版本中实现的RFP的数学和计算基础并没有统一的文档。在这里,我们提供这样一份文件,目的是为当代在功能性神经成像中实现更高水平的计算透明度做出贡献。
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Random field theory-based p-values: a review of the SPM implementation
P-values and null-hypothesis significance testing are popular data-analytical tools in functional neuroimaging. Sparked by the analysis of resting-state fMRI data, there has recently been a resurgence of interest in the validity of some of the p-values evaluated with the widely used software SPM. The default parametric p-values reported in SPM are based on the application of results from random field theory to statistical parametric maps, a framework we refer to as RFP. While RFP was established almost two decades ago and has since been applied in a plethora of fMRI studies, there does not exist a unified documentation of the mathematical and computational underpinnings of RFP as implemented in current versions of SPM. Here, we provide such a documentation with the aim of contributing to contemporary efforts towards higher levels of computational transparency in functional neuroimaging.
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