Nonparametric permutation tests for functional neuroimaging: A primer with examples
Nonparametric permutation tests for functional neuroimaging: A primer with examples
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DOI：
10.1002/hbm.1058
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发表时间：
2002-01-01
影响因子：
4.8
通讯作者：
Holmes, AP
中科院分区：
文献类型：
--
作者：
Nichols, TE;Holmes, AP
非参数排列检验只需要最小的假设有效性，提供了一个灵活和直观的方法，从功能神经影像学实验的数据进行统计分析，在一些计算费用。Holmes等人（[1996]：J Cereb Blood Flow Metab 16：7-22）将置换方法引入功能性神经影像学文献中，该方法很容易解释标准逐体素假设检验框架中隐含的多重比较问题。当适当的假设成立时，非参数置换方法给出的结果类似于从可比的统计参数映射方法使用一般线性模型与来自随机场理论的多重比较校正获得的结果。对于自由度较低的分析，例如评估群体效应的单个受试者PET/SPECT实验或多个受试者PET/SPECT或fMRI设计，采用局部合并（平滑）方差估计的非参数方法可以优于可比的统计参数映射方法。因此，这些非参数技术可以用来验证计算成本较低的参数方法的有效性。尽管不同的作者已经讨论了置换方法的理论和相对优势，但是还没有对该方法的可访问的解释，也没有自由分发的软件实现它。因此，该技术的实际应用很少。本文以及随附的MATLAB软件试图解决这些问题。标准的非参数随机化和排列检验的想法是在一个可访问的水平，使用功能性神经影像学的实际例子，和所描述的多重比较的扩展。三个工作的例子，从PET和功能磁共振成像，与讨论，并在适当的情况下与标准参数的方法进行比较。贯穿全文的实际考虑，并在附录中阐述了相关的统计概念。(C)2001 Wiley-Liss，Inc.
Requiring only minimal assumptions for validity, nonparametric permutation testing provides a flexible and intuitive methodology for the statistical analysis of data from functional neuroimaging experiments, at some computational expense. Introduced into the functional neuroimaging literature by Holmes et al. ([1996]: J Cereb Blood Flow Metab 16:7-22), the permutation approach readily accounts for the multiple comparisons problem implicit in the standard voxel-by-voxel hypothesis testing framework. When the appropriate assumptions hold, the nonparametric permutation approach gives results similar to those obtained from a comparable Statistical Parametric Mapping approach using a general linear model with multiple comparisons corrections derived from random field theory. For analyses with low degrees of freedom, such as single subject PET/SPECT experiments or multi-subject PET/SPECT or fMRI designs assessed for population effects, the nonparametric approach employing a locally pooled (smoothed) variance estimate can outperform the comparable Statistical Parametric Mapping approach. Thus, these nonparametric techniques can be used to verify the validity of less computationally expensive parametric approaches. Although the theory and relative advantages of permutation approaches have been discussed by various authors, there has been no accessible explication of the method, and no freely distributed software implementing it. Consequently, there have been few practical applications of the technique. This article, and the accompanying MATLAB software, attempts to address these issues. The standard nonparametric randomization and permutation testing ideas are developed at an accessible level, using practical examples from functional neuroimaging, and the extensions for multiple comparisons described. Three worked examples from PET and fMRI are presented, with discussion, and comparisons with standard parametric approaches made where appropriate. Practical considerations are given throughout, and relevant statistical concepts are expounded in appendices. (C) 2001 Wiley-Liss, Inc.