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Empirically Investigating the Statistical Validity of SPM, FSL and AFNI for Single Subject fMRI Analysis
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, Statistics.
Department of Statistics, University of Warwick, England.
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. 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
2015 (English)In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), IEEE conference proceedings, 2015, 1376-1380 p.Conference paper, Published paper (Refereed)
Abstract [en]

The software packages SPM, FSL and AFNI are the most widely used packages for the analysis of functional magnetic resonance imaging (fMRI) data. Despite this fact, the validity of the statistical methods has only been tested using simulated data. By analyzing resting state fMRI data (which should not contain specific forms of brain activity) from 396 healthy con- trols, we here show that all three software packages give in- flated false positive rates (4%-96% compared to the expected 5%). We isolate the sources of these problems and find that SPM mainly suffers from a too simple noise model, while FSL underestimates the spatial smoothness. These results highlight the need of validating the statistical methods being used for fMRI. 

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015. 1376-1380 p.
Series
IEEE International Symposium on Biomedical Imaging, ISSN 1945-7928
Keyword [en]
fMRI, statistics, neuroimaging, random field theory
National Category
Computer Science Probability Theory and Statistics Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-119755DOI: 10.1109/ISBI.2015.7164132ISI: 000380546000331ISBN: 978-1-4799-2374-8 (print)OAI: oai:DiVA.org:liu-119755DiVA: diva2:826782
Conference
IEEE 12th International Symposium on Biomedical Imaging
Available from: 2015-06-25 Created: 2015-06-25 Last updated: 2016-09-19Bibliographically approved

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Eklund, AndersAndersson, MatsKnutsson, Hans

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Eklund, AndersAndersson, MatsKnutsson, Hans
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