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**Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate**

**C.R. Genovese, N.A. Lazar and T. Nichols**

### Abstract:

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.

*Heidi Sestrich*

*4/16/2001*
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