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Finding objective and effective thresholds for voxelwise statistics derived from neuroimaging data has been a long-standing problem. With at least one test performed for every voxel in an image, some correction of the thresholds is needed to control the error rates, but standard procedures for multiple hypothesis testing (e.g., Bonferroni) tend to not be sensitive enough to be useful in this context. This paper introduces to the neuroscience literature statistical procedures for controlling the false discovery rate (FDR). Recent theoretical work in statistics suggests that FDR-controlling procedures will be effective for the analysis of neuroimaging data. These procedures operate simultaneously on all voxelwise test statistics to determine which tests should be considered statistically significant. The innovation of the procedures is that they control the expected proportion of the rejected hypotheses that are falsely rejected. We demonstrate this approach using both simulations and functional magnetic resonance imaging data from two simple experiments.

Original publication

DOI

10.1006/nimg.2001.1037

Type

Journal article

Journal

Neuroimage

Publication Date

04/2002

Volume

15

Pages

870 - 878

Addresses

Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA.

Keywords

Cerebral Cortex, Humans, Magnetic Resonance Imaging, Artifacts, Brain Mapping, Motor Activity, Mathematical Computing, Reference Values, Computer Simulation, Image Processing, Computer-Assisted, Adult, Female, Male