Functional magnetic resonance imaging (fMRI) has become an exceedingly popular technique for studies of human brain activity. Typically, fMRI is performed with >3-mm sampling, so that the imaging data can be regarded as two-dimensional samples that roughly average through the typically 1.5-4-mm thickness of cerebral cortex. The use of higher spatial resolutions, <1.5-mm sampling, complicates the use of fMRI, as one must now consider activity variations within the depth of the brain. We present a set of surface-based methods to exploit the use of high-resolution fMRI for depth analysis. These methods utilize white-matter segmentations coupled with deformable-surface algorithms to create a smooth surface representation at the gray-white interface. These surfaces provide vertex positions and surface normals, vector references for depth calculations. That information enables averaging schemes that can increase contrast-to-noise ratio, as well as permitting the direct analysis of depth profiles of functional activity in the human brain.