Family Wise Error Fmri
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an excellent introduction to the issue of FWE in neuroimaging in very readable fashion. You're encouraged to check it out. Many scientific fields have had to confront the problem
Family Wise Error Correction
of assessing statistical significance in the context of multiple tests. With family wise error calculator a single statistical test, the standard conventionally dictates a statistic is significant if it is less than 5% can parametric statistical methods be trusted for fmri based group studies? likely to occur by chance - a p-threshold of 0.05. But in fields like DNA microassays or neuroimaging, many thousands of tests are done at once. Each voxel in
Familywise Error Rate Anova
the brain constitutes a separate test, which usually means tens of thousands of tests for a given subject. If the conventional p-threshold of 0.05 is applied on a voxelwise basis, then, just by chance you're almost guaranteed to have many hundreds of false-positive voxels. In order to avoid any false positives, then, researchers generally correct their p-threshold to
Experiment Wise Error Rate
account for how many tests they're performing. This type of correction prevents Type I error across the whole family of tests you're doing - a familwise error correction, or FWE correction. The standard approach to FWE correction has been the Bonferroni correction - simply divide the desired p-threshold by the number of tests, and you'll maintain correct control over the FWE rate. In general, the Bonferroni correction is a pretty conservative correction, and it suffers from a fatal flaw with neuroimaging data. The Bonferroni correction demands that all the tests be independent from each other, and that demand is manifestly not fulfilled in neuroimaging data, where there is a complex, substantial and generally unknown structure of spatial correlations in the data. Essentially, the Bonferroni correction assumes there are more spatial 'degrees of freedom' than there really are; one voxel is not independent from the next, and so one only needs to correct for the 'true' number of independent tests you're doing. This effort, though, is tricky, and so a good deal of
has been posted to the ArXiv that has very important implications and should be required reading comparison wise error rate for all fMRI researchers. Anders Eklund, Tom Nichols, and Hans
Family Wise Error Rate Post Hoc
Knutsson applied task fMRI analyses to a large number of resting fMRI datasets, in order to identify per comparison error rate the empirical corrected “familywise” Type I error rates observed under the null hypothesis for both voxel-wise and cluster-wise inference. What they found is shocking: While voxel-wise error http://mindhive.mit.edu/book/export/html/90 rates were valid, nearly all cluster-based parametric methods (except for FSL’s FLAME 1) have greatly inflated familywise Type I error rates. This inflation was worst for analyses using lower cluster-forming thresholds (e.g. p=0.01) compared to higher thresholds, but even with higher thresholds there was serious inflation. This should be a sobering wake-up call for fMRI http://reproducibility.stanford.edu/big-problems-for-common-fmri-thresholding-methods/ researchers, as it suggests that the methods used in a large number of previous publications suffer from exceedingly high false positive rates (sometimes greater than 50%). Figure 1 from Eklund et al., showing substantial inflation of familywise error rates for most common cluster-based thresholding methods. They also examined the commonly used heuristic correction (what they call “ad-hoc cluster inference”) of p=0.001 and a cluster extent threshold of 80 mm^3. This method showed a shockingly high rate of false positives, up to 90% familywise error in some cases. Hopefully this paper will serve as the deathknell for such heuristic corrections. Figure 7 from Eklund et al., showing massive inflation of familywise Type I error rate using ad-hoc clustering inference. Another set of serious concerns were raised about the simulation-based methods implemented in AFNI’s 3DclustSim tool: Firstly, AFNI estimates the spatial group smoothness differently compared to SPM and FSL. AFNI averages smoothness estimates from the first level analysis, whereas SPM and FSL e
My Basket My Account Social Cognitive & Affective Neurosci About This Journal Contact This http://scan.oxfordjournals.org/content/4/4/417.full Journal Subscriptions View Current Issue (Volume 11 Issue 10 October 2016) Archive Search Oxford Journals Medicine & Health Social Cognitive & Affective Neurosci Volume 4 Issue 4 Pp. 417-422. The principled control of false positives in neuroimaging Craig M. Bennett1, George L. Wolford2 and Michael B. Miller1 1Department of Psychology, University of California, Santa Barbara, wise error California, 93106 and 2Department of Psychological and Brain Sciences, Moore Hall, Dartmouth College, Hanover, New Hampshire 03755, USA Correspondence should be addressed to Craig M. Bennett, Department of Psychology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA. E-mail: bennett{at}psych.ucsb.edu Received November 7, 2009. Accepted December 7, 2009. Next Section Abstract An incredible amount of family wise error data is generated in the course of a functional neuroimaging experiment. The quantity of data gives us improved temporal and spatial resolution with which to evaluate our results. It also creates a staggering multiple testing problem. A number of methods have been created that address the multiple testing problem in neuroimaging in a principled fashion. These methods place limits on either the familywise error rate (FWER) or the false discovery rate (FDR) of the results. These principled approaches are well established in the literature and are known to properly limit the amount of false positives across the whole brain. However, a minority of papers are still published every month using methods that are improperly corrected for the number of tests conducted. These latter methods place limits on the voxelwise probability of a false positive and yield no information on the global rate of false positives in the results. In this commentary, we argue in favor of a principled approach to the multiple testing problem—one
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