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Cited by 862

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

FMRI Clustering in AFNI: False-Positive Rates Redux

Open access
Cox, Robert W., Chen, Gang, Glen, Daniel R., Reynolds, Richard C., Taylor, Paul A.·2017·Brain Connectivity
DOI 10.1089/brain.2016.0475

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Abstract

Recent reports of inflated false-positive rates (FPRs) in FMRI group analysis tools by Eklund and associates in 2016 have become a large topic within (and outside) neuroimaging. They concluded that existing parametric methods for determining statistically significant clusters had greatly inflated FPRs ("up to 70%," mainly due to the faulty assumption that the noise spatial autocorrelation function is Gaussian shaped and stationary), calling into question potentially "countless" previous results; in contrast, nonparametric methods, such as their approach, accurately reflected nominal 5% FPRs. They also stated that AFNI showed "particularly high" FPRs compared to other software, largely due to a bug in 3dClustSim. We comment on these points using their own results and figures and by repeating some of their simulations. Briefly, while parametric methods show some FPR inflation in those tests (and assumptions of Gaussian-shaped spatial smoothness also appear to be generally incorrect), their emphasis on reporting the single worst result from thousands of simulation cases greatly exaggerated the scale of the problem. Importantly, FPR statistics depends on "task" paradigm and voxelwise p value threshold; as such, we show how results of their study provide useful suggestions for FMRI study design and analysis, rather than simply a catastrophic downgrading of the field's earlier results. Regarding AFNI (which we maintain), 3dClustSim's bug effect was greatly overstated-their own results show that AFNI results were not "particularly" worse than others. We describe further updates in AFNI for characterizing spatial smoothness more appropriately (greatly reducing FPRs, although some remain >5%); in addition, we outline two newly implemented permutation/randomization-based approaches producing FPRs clustered much more tightly about 5% for voxelwise p ≤ 0.01.

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Research works printed but not linked: 4

  1. 10.1073/pnas.0911855107 · DOI · Paper page
  2. 10.1016/j.neuroimage.2013.01.047 · DOI · Paper page
  3. 10.1073/pnas.1602413113 · DOI · Paper page
  4. 10.1002/(SICI)1097-0193(1996)4:1<58::AID-HBM4>3.0.CO;2-O · doi:10.1002/(sici)1097-0193(1996)4:1<58::aid-hbm4>3.0.co;2-o

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Metadata

Authors
Cox, R. W., Chen, G., Glen, D. R., Reynolds, R. C., & Taylor, P. A.
Venue
Brain Connectivity
Publisher
SAGE Publications
Published
2017-04
Volume
7
Issue
3
Pages
152-171
ISSN
  • 2158-0014 (ISSN-L)
  • 2158-0022
DOI
10.1089/brain.2016.0475
OpenAlex
W2588860206
Cited by
862
References
1
FWCI
63.70
Type
journal article
Language
en
Open access
green

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