Journal of Serendipitous and Unexpected Results Neural Correlates of Interspecies Perspective Taking in the Post-Mortem Atlantic Salmon: An Argument For Proper Multiple Comparisons Correction

With the extreme dimensionality of functional neuroimaging data comes extreme risk for false positives. Across the 130, 000 voxels in a typical fMRI volume the probability of at least one false positive is almost certain. Proper correction for multiple comparisons should be completed during the anal...

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Bibliographic Details
Main Authors: Craig M. Bennett, Abigail A. Baird, Michael B. Miller, George L
Other Authors: The Pennsylvania State University CiteSeerX Archives
Format: Text
Language:English
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Online Access:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.366.2250
http://www.jsur.org/ar/jsur_ben102010.pdf
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Summary:With the extreme dimensionality of functional neuroimaging data comes extreme risk for false positives. Across the 130, 000 voxels in a typical fMRI volume the probability of at least one false positive is almost certain. Proper correction for multiple comparisons should be completed during the analysis of these datasets, but is often ignored by investigators. To highlight the danger of this practice we completed an fMRI scanning session with a post-mortem Atlantic Salmon as the subject. The salmon was shown the same social perspectivetaking task that was later administered to a group of human subjects. Statistics that were uncorrected for multiple comparisons showed active voxel clusters in the salmon’s brain cavity and spinal column. Statistics controlling for the familywise error rate (FWER) and false discovery rate (FDR) both indicated that no active voxels were present, even at relaxed statistical thresholds. We argue that relying on standard statistical thresholds (p < 0.001) and low minimum cluster sizes (k> 8) is an ineffective control for multiple comparisons. We further argue that the vast majority of fMRI studies should be utilizing proper multiple comparisons correction as standard practice when thresholding their data.