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112127 |
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|a dc
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|a Behrmann, Marlene
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|a Massachusetts Institute of Technology. Institute for Medical Engineering & Science
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|a Harvard University-
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|a Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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|a Brown, Emery Neal
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|a Brown, Emery Neal
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|a Controversy in statistical analysis of functional magnetic resonance imaging data
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|b National Academy of Sciences (U.S.),
|c 2017-11-02T19:00:09Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/112127
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|a To test the validity of statistical methods for fMRI data analysis, Eklund et al. (1) used, for the first time, large-scale experimental data rather than simulated data. Using resting-state fMRI measurements to represent a null hypothesis of no task-induced activation, the authors compare familywise error rates for voxel-based and cluster-based inferences for both parametric and nonparametric methods. Eklund et al.'s study used three fMRI statistical analysis packages. They found that, for a target familywise error rate of 5%, the parametric methods gave invalid cluster-based inferences and conservative voxel-based inferences.
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|a Article
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|t Proceedings of the National Academy of Sciences
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