|Pictured: FSL User|
Finally, we have arrived at the end of the FEAT interface. The last two tabs, post-stats and registration, allow the user to specify how the results will be visualized, what kinds of multiple comparison corrections to carry out, and how to register and normalize the data.
One might wonder why FSL chooses to perform coregistration and normalization as the last step, instead of at a previous step in the preprocessing pipeline as do other software analysis packages. The reasoning is that because these steps introduce spatial correlations, it is better to introduce them after having run the statistical analysis, in order to prevent any sort of biases that may be introduced into the data as a result of applying these steps. Personally, I don't think it matters that much either way, since you have to do it at some point; however, that is the way it is built into the FSL stream, and if you don't like it, tough bananas.
Most of the defaults are fine; the only tab that requires any input before you can move forward is the Registration tab, which requires a skullstripped brain to normalize to a standardized space. This includes atlases such as Talairach or Montreal Neurological Institute (MNI), although I believe FSL only uses MNI. The point of normalization is that every subject's brain will be twisted, rotated, warped, and undergo various other uncomfortable transformations until it is located within a box that has equal dimensions to the standard space. Furthermore, certain anatomical landmarks will be at a specific coordinate position relative to every other part of the brain; for example, in Talairach space, the anterior commissure - a bundle of nerve fibers connecting the hemispheres, located at the base of the anterior columns of the fornix - will be positioned at coordinates 0, 0, 0. Thus, according to the Talairach atlas in this example, any other brain regions can be defined based on their distance from this origin (although the researcher should always check to make sure that what the atlas says matches up with what is directly in front of him).
A couple of other useful options are in the post-stats tab. For example, Pre-threshold masking allows the user to perform region of interest (ROI) analyses which define an a priori region either based on anatomical regions defined by an atlas or a binary mask generated by a program like Marsbar. Contrast masking has a similar role, masking out certain regions of the brain based on whether they are covered by another contrast in the analysis; although caution should be exercised here as well, in order to make sure that the masking contrast is orthogonal to the one being investigated. For more information about ROI analyses, as well as potential pitfalls, see an earlier post about the topic.
More tutorials will be up soon to guide the user through what all those HTML output files mean, as well as looking at and interpreting results.