Functional magnetic resonance imaging (fMRI) has become a 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 average through the 1.5–4-mm thickness of cerebral cortex. The increasing use of higher spatial resolutions, <1.5-mm sampling, complicates the analysis of fMRI, as one must now consider activity variations within the depth of the brain tissue. 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 and pial membrane. These surfaces provide vertex positions and normals for depth calculations, enabling 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.

功能磁共振成像 (fMRI) 已成为研究人类大脑活动的流行技术。通常，fMRI 的采样大于 3 毫米，因此成像数据可以被视为二维样本，平均穿过大脑皮层的 1.5-4 毫米厚度。越来越多地使用更高的空间分辨率，<1.5 毫米采样，使 fMRI 的分析变得复杂，因为现在必须考虑脑组织深度内的活动变化。我们提出了一组基于表面的方法来利用高分辨率 fMRI 进行深度分析。这些方法利用白质分割与可变形表面算法相结合，在灰白色界面和软脑膜处创建平滑的表面表示。这些表面为深度计算提供顶点位置和法线，