In the context of neuroimaging experiments, it is essential to account for the multiple comparisons problem when thresholding statistical mappings. Various methods are in use to deal with this issue, but they differ in their signal detection power for small- and large-scale effects. In this paper, we comprehensively describe a new method that is based on control of the false discovery rate (FDR). Our method increases sensitivity by exploiting the spatially clustered nature of neuroimaging effects. This is achieved by using a sliding window technique, in which FDR-control is first applied at a regional level. Thus, a new statistical map that is related to the regionally achieved FDR is derived from the available voxelwise P-values. On the basis of receiver operating characteristic (ROC) curves, thresholding based on this map is demonstrated to have better discriminatory power than conventional thresholding based on P-values. Secondly, it is shown that the resulting maps can be thresholded at a level that results in control of the global FDR. By means of statistical arguments and numerical simulations under widely varying conditions, our method is validated, characterized, and compared to some other common voxel-based methods (uncorrected thresholding, Bonferroni correction, and conventional FDR-control). It is found that our method shows considerably higher sensitivity as compared to conventional FDR-control, while still controlling the achieved FDR at the same level or better. Finally, our method is applied to two diverse neuroimaging experiments to assess its practical merits, resulting in substantial improvements as compared to the other methods.