Analysis of family-wise error rates in statistical parametric mapping using random field theory

被引:134
作者
Flandin, Guillaume [1 ]
Friston, Karl J. [1 ]
机构
[1] UCL, Inst Neurol, Wellcome Ctr Human Neuroimaging, 12 Queen Sq, London WC1N 3BG, England
关键词
family-wise error rate; statistical parametric mapping; topological inference; random field theory; CLUSTER-SIZE INFERENCE; EXTENT;
D O I
10.1002/hbm.23839
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
This technical report revisits the analysis of family-wise error rates in statistical parametric mapping-using random field theory-reported in (Eklund et al. []: arXiv 1511.01863). Contrary to the understandable spin that these sorts of analyses attract, a review of their results suggests that they endorse the use of parametric assumptions-and random field theory-in the analysis of functional neuroimaging data. We briefly rehearse the advantages parametric analyses offer over nonparametric alternatives and then unpack the implications of (Eklund et al. []: arXiv 1511.01863) for parametric procedures. Hum Brain Mapp, 40:2052-2054, 2019. (c) 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
引用
收藏
页码:2052 / 2054
页数:3
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