2015/11/05 by Anders Eklund, Thomas E. Nichols, Thomas Nichols +1 · 1 voice · 4 citations
Mathematics · Medicine · Neuroscience · #Advanced MRI Techniques and Applications #Artificial intelligence #Biology #Cluster (spacecraft) #Computer science #Data mining #False discovery rate #Functional Brain Connectivity Studies #Inference #Magnetic resonance imaging #Mathematics #Medicine #Multiple comparisons problem #Neural dynamics and brain function #Nonparametric statistics #Parametric statistics #Pattern recognition (psychology) #Permutation (music) #Resampling #Statistical hypothesis testing #Statistical inference #Statistical parametric mapping #Statistics #Voxel #math.ST #stat.AP #stat.CO #stat.ME #stat.TH
paper · pdf · doi:10.1073/pnas.1602413113
published as PNAS (2016), vol. 113 no. 28, 7900 - 7905
arxiv created 2015/11/05 · openalex publication_date 2016/06/28 · arxiv updated 2016/07/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
The most widely used task functional magnetic resonance imaging (fMRI) analyses use parametric statistical methods that depend on a variety of assumptions. In this work, we use real resting-state data and a total of 3 million random task group analyses to compute empirical familywise error rates for the fMRI software packages SPM, FSL, and AFNI, as well as a nonparametric permutation method. For a nominal familywise error rate of 5%, the parametric statistical methods are shown to be conservative for voxelwise inference and invalid for clusterwise inference. Our results suggest that the principal cause of the invalid cluster inferences is spatial autocorrelation functions that do not follow the assumed Gaussian shape. By comparison, the nonparametric permutation test is found to produce nominal results for voxelwise as well as clusterwise inference. These findings speak to the need of validating the statistical methods being used in the field of neuroimaging.