Andy's Brain Blog

In the beginning, a young man is placed upon the scanning table as if in sacrifice. He is afraid; there are loud noises; he performs endless repetitions of a task incomprehensible. He thinks only of the coercively high amount of money he is promised in exchange for an hour of meaningless existence.

The scanner sits in silent judgment and marks off the time. The sap of life rushes to the brain, the gradients flip with terrible precision, and all is seen and all is recorded.

Such is the prologue for data collection. Sent straight into the logs of the server: Every slice, every volume, every run. All this should be marked well, as these native elements shall evolve into something far greater.

You will require three ingredients for converting raw scanner data into a basic AFNI dataset. First, the number of slices: Each volume comprises several slices, each of which measures a separate plane. Second, the number of volumes: Each run of data comprises several volumes, each of which measures a separate timepoint. Third, the repetition time: Each volume is acquired after a certain amount of time has elapsed.

Once you have assembled your materials, use to3d to convert the raw data into a BRIK/HEAD dataset. A sample command:

to3d -prefix r01 -time:zt 50 206 3000 alt+z *000006_*.dcm

This command means: "AFNI, I implore you: Label my output dataset r01; there are 50 slices per volume, 206 volumes per run, and each volume is acquired every 3000 milliseconds; slices are acquired interleaved in the z-direction; and harvest all volumes which contain the pattern 000006_ and end in dcm. Alert me when the evolution is complete."

More details and an interactive example can be found in the following video.

As promised, we now begin our series of AFNI tutorials. These walkthroughs will be more in-depth than the FSL series, as I am more familiar with AFNI and use it for a greater number of tasks; accordingly, more advanced tools and concepts will be covered.

Using AFNI requires a solid understanding of Unix; the user should know how to write and read conditional statements and for loops, as well as know how to interpret scripts written by others. Furthermore, when confronted with a new or unfamiliar script or command, the user should be able to make an educated guess about what it does. AFNI also demands a sophisticated knowledge of fMRI preprocessing steps and statistical analysis, as AFNI allows the user more opportunity to customize his script.

A few other points about AFNI:

1) There is no release schedule. This means that there is no fixed date for the release of new versions or patches; rather, AFNI responds to user demands on an ad hoc basis. In a sense, all users are beta testers for life. The advantage is that requests are addressed quickly; I once made a feature request at an AFNI bootcamp, and the developers updated the software before I returned home the following week.

2) AFNI is almost entirely run from the command line. In order to make the process less painful, the developers have created "uber" scripts which allow the user to input experiment information through a graphical user interface and generate a preprocessing script. However, these should be treated as templates subject to further alteration.

3) AFNI has a quirky, strange, and, at times, shocking sense of humor. Through clicking on a random hotspot on the AFNI interface, one can choose their favorite Shakespeare sonnet; read through the Declaration of Independence; generate an inspirational quote or receive kind and thoughtful parting words. Do not let this deter you. As you become more proficient with AFNI, and as you gain greater life experience and maturity, the style of the software will become more comprehensible, even enjoyable. It is said that one knows he going insane when what used to be nonsensical gibberish starts to take on profound meaning. So too with AFNI.

The next video will cover the to3d command and the conversion of raw volumetric data into AFNI's BRIK/HEAD format; study this alongside data conversion through mricron, as both produce a similar result and can be used to complement each other. As we progress, we will methodically work through the preprocessing stream and how to visualize the output with AFNI, with an emphasis on detecting artifacts and understanding what is being done at each step. Along the way different AFNI tools and scripts will be broken down and discussed.

At long last my children, we shall take that which is rightfully ours. We shall become as gods among fMRI researchers - wise as serpents, harmless as doves. Seek to understand AFNI with an open heart, and I will gather you unto my terrible and innumerable flesh and hasten your annihilation.