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A defense of using resting state fMRI as null data for estimating false positive rates
University of Warwick, England.
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. 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). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-9091-4724
2017 (English)In: Cognitive Neuroscience, ISSN 1758-8928, E-ISSN 1758-8936Article in journal (Refereed) Accepted
Abstract [en]

A recent Editorial by Slotnick (2017) reconsiders the findings of our paper on the accuracy of false positive rate control with cluster inference in fMRI (Eklund et al, 2016), in particular criticising our use of resting state fMRI data as a source for null data in the evaluation of task fMRI methods. We defend this use of resting fMRI data, as while there is much structure in this data, we argue it is representative of task data noise and such analysis software should be able to accommodate this noise. We also discuss a potential problem with Slotnick’s own method.

Place, publisher, year, edition, pages
Taylor & Francis, 2017.
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-134435DOI: 10.1080/17588928.2017.1287069OAI: oai:DiVA.org:liu-134435DiVA: diva2:1073806
Available from: 2017-02-13 Created: 2017-02-13 Last updated: 2017-02-20

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The full text will be freely available from 2018-01-31 14:59
Available from 2018-01-31 14:59

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Eklund, AndersKnutsson, Hans
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