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Reply to BROWN AND BEHRMANN, COX ET AL., AND KESSLER ET AL.: Data and code sharing is the way forward for fMRI
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Department of Statistics, University of Warwick, UK / WMG, University of Warwick, UK.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9091-4724
2017 (English)In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, 1-2 p.Article in journal, Letter (Other academic) Published
Abstract [en]

We are glad that our paper (1) has generated intense discussions in the fMRI field (2⇓–4), on how to analyze fMRI data, and how to correct for multiple comparisons. The goal of the paper was not to disparage any specific fMRI software, but to point out that parametric statistical methods are based on a number of assumptions that are not always valid for fMRI data, and that nonparametric statistical methods (5) are a good alternative. Through AFNI’s introduction of nonparametric statistics in the function 3dttest++ (3, 6), the three most common fMRI softwares now all support nonparametric group inference [SPM through the toolbox SnPM (www2.warwick.ac.uk/fac/sci/statistics/staff/academic-research/nichols/software/snpm), and FSL through the function randomise].

Cox et al. (3) correctly point out that the bug in the AFNI function 3dClustSim only had a minor impact on the false-positive rate (FPR). This was also covered in our original paper (1): “We note that FWE [familywise error] rates are lower with the bug-fixed 3dClustSim function. As an example, the updated function reduces the degree of false …

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2017. 1-2 p.
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Medical Engineering
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URN: urn:nbn:se:liu:diva-136541DOI: 10.1073/pnas.1620285114OAI: oai:DiVA.org:liu-136541DiVA: diva2:1089371
Available from: 2017-04-19 Created: 2017-04-19 Last updated: 2017-04-28Bibliographically approved

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