AFNI Tutorial #9: Surface-Based Analysis with SUMA¶
Our analyses so far have been volume-based - that is, we have preprocessed and calculated statistics for each voxel of a three-dimensional cube. Our goal in this chapter is to do the preprocessing and statistical modeling on a two-dimensional surface of the cortex. This gives you several advantages, including:
- The ability to visualize activity along the surfaces of the gyri and sulci, which gives you a better picture of where the activity is localized;
- This also avoids the partial voluming problem of a voxel that encompasses the edges of multiple gyri, making it impossible to determine which part of the cortex the activity comes from;
- The ability to use larger smoothing kernels to increase your signal-to-noise ratio.
We will run this analysis with SUMA, a package that comes with your AFNI installation. Technically, SUMA is a separate package that can be run on its own, but we will be using it in conjunction with the AFNI viewer and with AFNI commands.