Introduction to Diffusion Tensor Imaging: From Hospital Horror Story to Neuroimaging

It is well known that one of the deepest human fears is to be a patient in a hospital late at night, all alone, while a psychotic nurse injects you with a paralyzing agent, opens up your skull with a bone saw, and begins peeling away layers of your cortex while you are still awake.*

As horrifying as this nightmare scenario may be, it also lends an important insight into an increasingly popular neuroimaging method, diffusion tensor imaging (DTI; pronounced "diddy"). To be more gruesomely specific, the psychotic nurse is able to peel away strips of your brain because it has preferred tear directions - just like string cheese. These strips follow the general direction of fascicles, or bundles of nerves, comprising grey and white matter pathways; and of these pathways, it is white matter that tends to exhibit a curious phenomenon called anisotropy.

Imagine, for example, that I release a gas - such as, let's say, deadly neurotoxin - into a spherical compartment, such as a balloon. The gas, through a process called Brownian motion (not to be confused with the Dr. Will Brown frequently mentioned here) will begin to diffuse randomly in all directions; in other words, as though it is unconstrained.

However, release the same gas into a cylindrical or tube-shaped compartment, such as one of those cardboard tubes you used to fight with when you were a kid,** and the gas particles will tend to move along the direction of the tube. This is what is known as anisotropy, meaning that the direction of the diffusion tends to go in a particular direction, as opposed to isotropy, where diffusion occurs in all directions with equal probability.

Left two figures: Ellipsoids showing different amounts of anisotropy, with lambda 1, 2, and 3 symbolizing eigenvalues. Eigenvalues represent the amount of diffusion along a particular direction. Right: A sphere representing isotropy, where diffusion occurs with equal probability in all directions.

This diffusion can be measured in DTI scans, which in turn can be used to indirectly measure white matter integrity and structural connectivity between different areas of the brain. A common use of DTI is to compare different populations, such as young and old, and to observe where fractional anisotropy (FA) differs between groups, possibly with the assumption that less FA can be indicative of less efficient communication between cortical regions. There are other applications as well, but this is the one we will focus on for the remainder of the tutorials.

The data that we will be using can be found on the FSL course website, after scrolling down to Data Files and downloading File 2 (melodic and diffusion). I haven't been able to find any good online repositories for DTI data, so we'll be working with a relatively small sample of three subjects in one group, and three subjects in the other. Also note that while we will focus on FSL, there are many other tools that process DTI data, including some new commands in AFNI, and also a program called TORTOISE. As with most things I post about, these methods have already been covered in detail by others; and in particular I recommend a blog called blog.cogneurostats.com, which covers both the AFNI and TORTOISE approaches to DTI, along with other tutorials that I thought I had been the first to cover, but which have actually already been discussed in detail. I encourage you to check it out - but also to come back, eventually.

*Or maybe that's just me.
**And maybe you still do!

Regional Homogeneity Analysis with 3dReHo, Part 1: Introduction

Learning a new method, such as regional homogeneity analysis, can be quite difficult, and one often asks whether there is an easier, quicker method to become enlightened. Unfortunately, such learning can only be accomplished through large, dense books. Specifically, you should go to the library, check out the largest, heaviest book on regional homogeneity analysis you can find, and then go to the lab of someone smarter than you and threaten to smash their computer with the book unless they do the analysis for you.

If for some reason that isn't an option, the next best way is to read how others have implemented the same analysis; such as me, for example. Just because I haven't published anything on this method, and just because I am learning it for the first time, doesn't mean you should go do something rash, such as try to figure it out on your own. Rather, come along as we attempt to unravel the intriguing mystery of regional homogeneity analysis, and hide from irate postdocs whose computers we have destroyed. In addition to the thrills and danger of finding things out, if you follow all of the steps outlined in this multi-part series, I promise that you will be the first one to learn this technique from a blog. And surely, that must count for something.

With regional homogeneity analysis (or ReHo), researchers ask similar questions as with functional connectivity analysis; however, in the case of ReHo, we correlate the timecourse in one voxel with its immediate neighbors, or with a range of neighbors within a specified radius, instead of using a single voxel or seed region and testing for correlations with every other voxel in the brain, as in standard functional connectivity analysis.

As an analogy, think of ReHo as searching for similarities in the timecourse of the day's temperature between different counties across a country. One area's temperature timecourse will be highly correlated with neighboring counties's temperatures, and the similarity will tend to decrease the further away you go from the county you started in. Functional connectivity analysis, on the other hand, looks at any other county that shows a similar temperature timecourse to the county you are currently in.

Similarly, when ReHo is applied to functional data, we look for differences in local connectivity; that is, whether there are differences in connectivity within small areas or cortical regions. For example, when comparing patient groups to control groups, there may be significantly less or significantly more functional connectivity in anterior and posterior cingulate areas, possibly pointing towards some deficiency or overexcitation of communication within those areas. (Note that any differences found in any brain area with the patient group implies that there is obviously something "wrong" with that particular area compared to the control group, and that the opposite can never be true. While I stand behind this arbitrary judgment one hundred percent, I would also appreciate it if you never quoted me on this.)

As with the preprocessing step of smoothing, ReHo is applied to all voxels simultaneously, and that the corresponding correlation statistic in each voxel quantifies how much it correlates with its neighboring voxels. This correlation statistic is called Kendall's W, and ranges between 0 (no correlation at all between the specified voxel and its neighbors) and 1 (perfect correlation with all neighbors). Once these maps are generated, they can then be normalized and entered into t-tests, producing similar maps that we used with our functional connectivity analysis.

Now that we have covered this technique in outline, in our next post we will move on to the second, more difficult part: Kidnapping a senior research assistant and forcing him to do the analysis for us.

No, wait! What I meant was, we will review some papers that have used ReHo, and attempt to apply the same steps to our own analysis. If you have already downloaded and processed the KKI data that we used for our previous tutorial on functional connectivity, we will be applying a slightly different variation to create our ReHo analysis stream - one which will, I hope, not include federal crimes or destroying property.

FSL Tutorial: Part 1 (of many)

I recently started testing out FSL to see if it has any advantages over other fMRI analysis packages, and decided to document everything on Youtube as I go along. The concepts are the same as any other package (AFNI, SPM, etc), but the terminology is slightly different, and driving it from the command line is not as intuitive as you would think. Plus, they use a ton of acronyms for everything, which, to be honest, kind of pisses me off; I don't like it when they try to be cute and funny like that. The quotes and sonnets generated by AFNI after exiting the program, however, are sophisticated and endearing. One of my favorites: "Each goodbye makes the next hello closer!"

In any case, here is the first, introductory tutorial I made about FSL. I realized from searching around on Youtube that hardly any fMRI analysis tutorial videos exist, and that this is a market that sorely needs to be filled. A series of walkthroughs and online lessons using actual data, in my opinion, would be far more useful at illustrating the fundamentals of fMRI data analysis than only having manuals (although those are extremely important as well, and I would recommend that anyone getting started in the field read them so that they can needlessly suffer as I did).

I will attempt to upload more on a regular basis, and start to get some coherent lesson plan going which allows the beginner to get off the ground and understand what the hell is going on. True story: It took me at least three years to fully comprehend what a beta weight was. Three years. I'm not going to blame it all on Smirnoff Ice, but it certainly didn't help.

Note: I suggest hitting fullscreen mode and viewing at a higher resolution (360p or 480p) in order to better see the text in the terminal window.

Also, the example data for these tutorials can be found here.