GRAPPA has been widely used in fMRI recently to improve spatial resolution. A drawback of GRAPPA for fMRI is that head motion in the reference scans can result in significant artifact for all the images in a run and higher temporal noise level. This problem can be solved by TGRAPPA using time interleaved sampling scheme. Separate reconstruction is needed for the interleaved k-space to minimize signal variation from volume to volume caused by phase errors. Although TGRAPPA has less statistical power than GRAPPA, the ability of retrospective motion correction makes it appealing in some application.