This paper introduces statistical procedures for controlling the false discovery rate (FDR) in the analysis of neuroimaging data. Traditional methods for multiple hypothesis testing, such as the Bonferroni correction, are too conservative and lead to an excessive number of false positives. The FDR approach provides a more sensitive and effective method for identifying significant voxels in neuroimaging data. The FDR method controls the expected proportion of rejected hypotheses that are falsely rejected, allowing for a balance between false positives and true positives. The procedures operate simultaneously on all voxelwise test statistics to determine which tests should be considered statistically significant. The FDR method is demonstrated using simulations and functional magnetic resonance imaging (fMRI) data from two experiments. The method is shown to be effective in identifying active voxels while controlling the false discovery rate. The FDR approach is adaptive, meaning that the thresholds are automatically adjusted to the strength of the signal. This makes it particularly useful for neuroimaging data, where the number of tests is large and the data can vary significantly between subjects. The FDR method is compared to the Bonferroni correction, which controls the probability of having any false positives. While the Bonferroni correction is conservative and has low power, the FDR method is more powerful and provides a better balance between false positives and true positives. The FDR method is also more flexible, as it does not require strict assumptions about the distribution of the P values. The paper also discusses the practical issues in the use of FDR, including the choice of the FDR bound and the impact of data smoothing. The FDR method is shown to be effective in both simulated and real data examples, including fMRI data from studies of finger opposition and auditory stimulation. The results demonstrate that the FDR method provides a more accurate and sensitive approach to identifying significant voxels in neuroimaging data.This paper introduces statistical procedures for controlling the false discovery rate (FDR) in the analysis of neuroimaging data. Traditional methods for multiple hypothesis testing, such as the Bonferroni correction, are too conservative and lead to an excessive number of false positives. The FDR approach provides a more sensitive and effective method for identifying significant voxels in neuroimaging data. The FDR method controls the expected proportion of rejected hypotheses that are falsely rejected, allowing for a balance between false positives and true positives. The procedures operate simultaneously on all voxelwise test statistics to determine which tests should be considered statistically significant. The FDR method is demonstrated using simulations and functional magnetic resonance imaging (fMRI) data from two experiments. The method is shown to be effective in identifying active voxels while controlling the false discovery rate. The FDR approach is adaptive, meaning that the thresholds are automatically adjusted to the strength of the signal. This makes it particularly useful for neuroimaging data, where the number of tests is large and the data can vary significantly between subjects. The FDR method is compared to the Bonferroni correction, which controls the probability of having any false positives. While the Bonferroni correction is conservative and has low power, the FDR method is more powerful and provides a better balance between false positives and true positives. The FDR method is also more flexible, as it does not require strict assumptions about the distribution of the P values. The paper also discusses the practical issues in the use of FDR, including the choice of the FDR bound and the impact of data smoothing. The FDR method is shown to be effective in both simulated and real data examples, including fMRI data from studies of finger opposition and auditory stimulation. The results demonstrate that the FDR method provides a more accurate and sensitive approach to identifying significant voxels in neuroimaging data.