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arXiv · 1702.04846

FMRI Clustering and False Positive Rates

Robert W. Cox·Gang Chen·Daniel R. Glen·Richard C. Reynolds·Paul A. Taylor
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Abstract

Recently, Eklund et al. (2016) analyzed clustering methods in standard FMRI packages: AFNI (which we maintain), FSL, and SPM [1]. They claimed: 1) false positive rates (FPRs) in traditional approaches are greatly inflated, questioning the validity of "countless published fMRI studies"; 2) nonparametric methods produce valid, but slightly conservative, FPRs; 3) a common flawed assumption is that the spatial autocorrelation function (ACF) of FMRI noise is Gaussian-shaped; and 4) a 15-year-old bug in AFNI's 3dClustSim significantly contributed to producing "particularly high" FPRs compared to other software. We repeated simulations from [1] (Beijing-Zang data [2], see [3]), and comment on each point briefly.

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Robert W. Cox, Gang Chen, Daniel R. Glen, Richard C. Reynolds, Paul A. Taylor. 2017-02-16. FMRI Clustering and False Positive Rates. https://doi.org/10.1073/pnas.1614961114

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