I've been experimenting more with Camtasia, and I've uploaded a new video showing how masks are drawn on an actual human, rubber brain, which involves the use of R studio, Excel, and colored pens. My hope is that this makes the learning experience more interactive; in addition, you get to see what my mug looks like.
Now that we have covered how to create masks using three separate software packages - FSL, SPM, and AFNI - I should probably take a step back and talk about what masks are all about. When I first read about masks, all I heard was a bunch of mumbo jumbo about zeros and ones, and unhelpful saran wrap metaphors. While this did remind me to purchase valuable kitchen supplies, it was unhelpful in understanding what a mask was, exactly, and how it was used.
Simply put, a mask is a subset of voxels you wish to analyze. Let's say I'm only interested in the right hemisphere of the brain; to create a mask of the right hemisphere, imagine using a papercutter to split the brain in half, and only taking the right hemisphere for further analysis, while discarding the left hemisphere into the trash can. The generation of masks follows this same logic - only focus on a specific part of the brain, and discard the rest.
Fortunately, we have come a long way since using office supplies to create masks, and now we have computers to do it for us. In order to create a mask using any of the listed software packages, usually you will use a tool to insert "1's" into the voxels that you wish to analyze, and "0's" everywhere else. Then, say that you want to do an ROI analysis only on those voxels that contain "1's". If you are trying to extract contrast estimates for a subject, the contrast estimate at each voxel will be multiplied by the mask, and you will be left with the contrast estimates in the "1's" voxels (since each estimate is being multiplied by 1), and zeros everywhere else.
Furthermore, ROI extraction within a mask often averages the contrast (or parameter) estimates across all of the voxels inside the mask. It is also possible to extract estimates from single voxels or a single triplet of coordinates - just think of this as ROI analysis of a very small mask.
I hope that this clarifies things a bit; I know that it took me a couple of years to wrap my head around the whole concept of masks and ROIs and severing hemispheres from each other. However, once you understand this, the whole process of ROI interrogation becomes much simpler and more intuitive, and analyses become easier to carry out. ROI analysis is the foundation for carrying out more complex analyses, such as double dissociations and connectivity analyses, and it is well to become familiar with this before tackling larger game.