Empirically investigating the statistical validity of SPM, FSL and AFNI for single subject fMRI analysis

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 no...

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Bibliographic Details
Published inProceedings (International Symposium on Biomedical Imaging) pp. 1376 - 1380
Main Authors Eklund, Anders, Nichols, Thomas, Andersson, Mats, Knutsson, Hans
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2015
Subjects
Biomedical imaging
Brain modeling
Noise
Smoothing methods
Software packages
Spectral analysis
Statistical analysis
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Summary: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 controls, we here show that all three software packages give inflated 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.
ISSN:1945-7928
DOI:10.1109/ISBI.2015.7164132