To date, most functional imaging centers have relied on ultrafast imaging approaches such as echo-planar imaging (EPI) techniques for acquiring functional brain activation data. These methods require specialized hardware and are not yet installed widely on clinical MR imagers, thus limiting the application of functional MR imaging at many sites. EPI is used to limit motion artifacts and to collect multiple images under different task paradigms in order to distinguish reliably true signal changes from noise. However, it suffers from poor signal to noise ratio because of the high sampling bandwidth employed. This work presents an approach for increasing the efficiency of functional studies that use conventional gradient echo imaging. In this approach, small numbers of image data sets are acquired and recombined to generate composite datasets with minimized motion artifacts. The technique is introduced, and several algorithms for combining the data are explored. A receiver operator characteristic analysis and in vivo studies are performed to examine the efficacy of this approach for improving functional MR imaging studies.