FSL Tutorial: Featquery_gui

Now that we've created our masks, we can go ahead and extract data using FSL's featquery tool. You may want to run it from the command line when batching large numbers of subjects, but this tutorial will focus on Featquery_gui, a graphical interface for loading subjects and ROIs, and then performing data extraction from that ROI. The procedure is similar to Marsbar, and I hope that the video is clear on how to do this.

Also, I've attached a Black Dynamite video for your enjoyment. Nothing to do with ROIs, really, but we all need a break now and then.

A Note about FMRI Masks

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.

Creating Masks In FSL

Due to a high number of requests (three), I have made some walkthroughs about how to create masks in FSL. There are a few different ways to do this:

1. Anatomical ROI: These masks are generated from anatomical regions labeled by atlases. For example, you may decide to focus only on voxels within the V1 area of visual cortex. Using an atlas will create a mask of that region, based on the atlas-defined anatomical boundaries in a standardized space.
2. Functional ROI (or contrast ROI): This is a mask created from a contrast thresholded at a specific statistic value. For example, you may wish to focus only on voxels that pass cluster correction for the contrast of left button presses minus right button presses.
3. Painting ROIs: This is where the real fun starts; instead of being confined by the limitations of anatomical or contrast boundaries, let your imagination run wild and simply paint where you want to do an ROI analysis. Similar to what you did in first grade, but more high-tech and with less puking after eating your crayons. (Is it my fault that Razzmatazz Red sounds so delicious?)

Demonstrations of each approach can be found in the following videos:

 Anatomical ROIs

Functional ROIs

 ROIs created from FSLview. Pretend like you're Bob Ross.