One of the more advanced features of MRtrix is Fixel-Based Analysis (FBA), a technique to measure both the fiber density and the fiber cross-section of a given piece of white matter. The developers of the package invented the term “fixel” to rhyme with “voxel” (kind of), indicating that they both contain values representing a metric of brain activity or brain structure. The typical voxels we think of contain a single number representing contrast - either the contrast between grey and white matter or other tissue types, in the case of a T1-weighted anatomical image, or the contrast between the intensity of the BOLD signal in a T2-weighted functional image.
Fixels, on the other hand, are MRtrix-calculated values that are stored in voxels; they are the smallest unit of resolution for measuring white-matter related metrics, such as fiber density or fiber cross-section. These terms are defined in more detail in the Raffelt et al. 2017 paper, in which fiber density refers to the overall number of fibers compressed into a single voxel. Fiber cross-section, on the other hand, refers to the amount of the voxel that is occupied by the fiber bundle. These differences are illustrated in the following figure, taken from the Raffelt et al. 2017 study:
The goal of Fixel-Based Analysis is to compare groups and determine which fixels show a difference in fiber density, cross-section, or a combination of the two (referred to as Fiber Density & Cross-Section, or FDC). Many patient populations, such as persons with Alzheimer’s or other age-related dementias, have markedly different fiber density and cross-sections in major white-matter pathways, and the technique described above is a way to visualize and quantify these differences.
I have written a tutorial demonstrating how to do this for the BTC Preop dataset, available on OpenNeuro, which includes glioma patients as well as controls. As there are 36 participants in total, I recommend running this analysis on a supercomputing cluster. In fact, you probably won’t be able to run this analysis without a computing cluster, because with 36 datasets, commands such as “population_template” and “fixelcfestats” can take dozens if not hundreds of hours to run. The datasets that are generated are also huge. All of this points to using a powerful supercomputing cluster with plenty of storage in order to run the analyses, and then downloading the final product to visualize on your local computer, or mounting a volume of the computing cluster on your machine.
The supercomputing code for Fixel-Based Analysis, adapted from the code outlined on the MRtrix FBA tutorial page, can be found here. The tutorial may be updated to reflect better supercomputing practices - for example, using an array instead of creating an individual template file and submitting it for each subject - but it should work for most purposes.