Resting State Analysis, Part III: Automating Your Analysis

Once you've set up a resting-state analysis script, either through or following example #9 in the documentation, I highly recommend that you set up some sort of higher-level script to automate running that script in each subject's directory. This is especially useful in the dataset we are using, since each individual analysis doesn't necessarily take that long, but we have a large number of subjects.

To begin, navigate to the downloaded KKI directory and use the following command to list each directory without trailing slashes:

ls -d */ | cut -f1 -d'/' > subjList.txt

You can then redirect this output to a text file, which can then be later edited at your leisure; in the above example, I used a redirect command to place all of the directories in a file called subjList.txt.

A for loop can then be used to do the analysis for each subject. (You can use any shell you want, but in this example I will use the t-shell.) Simply use the output of the text file as a variable, then use the for loop to execute the analysis for each subject, e.g.:

setenv subject `cat subjList.txt`
foreach subj ($subject)
cp $subj/session_1
cd $subj/session_1
tcsh $subj
cd ../..

The script, generated from the interface used in the last tutorial, can be found here. Note that I use a motion cutoff threshold of 0.3mm, which is slightly different from the standard 0.2mm cutoff; feel free to alter this if you like.

This should take care of all of your analyses while you go do something else, such as reading a book or shopping for tupperware and nosehair trimmers.* Of course, you will want to examine the output of your commands for any errors, but this menial task can usually be designated to one of your undergraduate RAs slated for resting-state data summoning immolation.

*Or maybe that's just me.

One Weird Trick to Boost Brain Connectivity

Answer: Take an LSAT course.

At least, that seems to contribute to changes in resting-state functional connectivity between distinct brain regions, according to a new study in the Journal of Neuroscience by Mackey et al (2013). The researchers took two groups of pre-law students and divided them into a training group, which was taking an LSAT prep course, and a control group, which intended to take the LSAT but was not enrolled in the prep course. After matching the subjects on demographics and intelligence metrics, functional connectivity was measured during a resting state scan (which, if you remember from a previous post, is a measure of correlation between timecourses between regions, rather than physical connectivity per se).

Taking the LSAT prep course was associated with increased correlations between the rostro-lateral prefrontal cortex (RLPFC; a few centimeters inside your skull if you take your finger and press it against your forehead just to the lateral side of your eye) and regions of parietal cortex (located more to the rear of your skull, slightly forward and lateral of the bump in the back of your head). The RLPFC seems to help integrate abstract relations, such as detecting flaws in your spouse's arguments, while the parietal cortex processes individual relations between items. Overall, when they combine forces, as shown by a concomitant increase in functional connectivity and test scores, your LSAT skills become unstoppable.

The parietal cortices and striatal regions, particularly the caudate and putamen nuclei, showed a stronger coupling as a result of taking the prep course; presumably because of the strong dopaminergic inputs from the basal nuclei and striatum, which emit bursts of pleasure whenever you make a breakthrough in reasoning or learning. This should come as no surprise to classical scholars, as Aristotle once observed that the two greatest peaks of human pleasure are 1) thinking, and 2) hanky-panky. (Or something like that.)

Taken to the extreme, these results suggest efficient ways to manufacture super-lawyers, or at least to strengthen connectivity between disparate regions, and alter resting state dynamics. This touches on the concept of neuroplasticity, which suggests that our brains are adaptive and malleable throuhgout life, as opposed to traditional views that cognitive stability and capacity plateaus sometime in early adulthood, and from there makes a slow decline to the grave. Instead, regularly engaging your thinking muscles and trying new cognitive tasks, such as mathematics, music, and fingerpainting, as well as grappling with some of the finest and most profound philosophical minds humanity has produced - Kant, Kierkegaard, Hegel, Nietzsche, Andy's Brain Blog, et alia - will continue to change and transmogrify your brain in ways unimaginable and calamitous beyond reckoning.

Thanks to Simon Parker. (LSAT professors hate him!)

Head Motion and Functional Connectivity

Yesterday as I was listening to a talk about diffusion tensor imaging, a professor asked about the influences of head motion on DTI data, and whether it could lead to spurious effects. Another professor vigorously denied it, stating that it was much more of a problem for bread and butter FMRI analyses, and in particular resting state functional connectivity analyses. At one point he became so animated that his monocle fell off, his collar stud came undone, and eventually he had to be physically restrained by the people sitting next to him. It was then that I knew that I should pay heed, for it is unlikely that a scientist becomes highly excited and talkative over matters that are trivial; in short, I could sense that he was talking about something important.

I have done few functional connectivity analyses in my brief life, but what I understand about them is this: You take the timecourse of one voxel - or the average timecourse over a group of voxels, also known as a "seed" - and then compare that timecourse with the timecourse of every other voxel in the brain. (When I speak of timecourses, I mean the sampled signal over time.) If it is a good fit - in other words, if there is a significantly high correlation between the timecourses - then we say that the two regions are functionally connected. This is a bit of a misnomer, as we cannot make any direct inferences about any "real" connectivity from two correlated timecourses; but it can serve as a good starting point for more sophisticated analyses, such as psychophysiological interactions (PPI; also known as context-dependent correlations) which measure changes in functional connectivity as a function of task context. For example: Does the timecourse correlation between cognitive control regions and reward-related areas change depending on whether the subject is instructed to upregulate or downregulate their gut reactions to rewarding stimuli?

One of the most popular variations of functional connectivity is something called resting state functional connectivity (rsFC), where a subject is simply instructed to relax and listen to Barry Manilow* while undergoing scanning. Functional connectivity maps are then calculated, and usually a comparison is made between a control group and an experimental or patient group, such as schizophrenics. For us FMRI researchers, this is about as close as we can get to simulating a person's natural environment where they would be relaxing and thinking about nothing in particular; except that they're in an extremely tight-fitting, noisy tube, and unable to move in any direction more than a few millimeters. Other than that, though, it's pretty normal.

These types of experiments have become all the rage in recent years, with several studies claiming to have found meaningful resting-state differences between healthy controls and several different patients populations such as schizophrenics, depressives, Nickelback fans, and drug addicts. However, a few publications have called into question some of these results, stating that many of these differences could be due to head motion. As we've talked about before, head motion can be a particularly insidious confound in any experiment, but it is especially troublesome for functional connectivity analyses. This can arise due to systematic differences between control and patient populations that are possibly confounded with motion. Take, for example, an experiment contrasting young versus older populations. Older populations are known to move more, and any observed differences in functional connectivity may be due solely to this increased motion, not underlying neural hemodynamics.

A study by Van Dijk, Sabuncu, & Bruckner (2012) looked at this in detail by scanning over a thousand (!) subjects, and binning them into ten groups based on increasing amounts of motion (e.g., group 1 had the least amount of motion, while group 10 had the most motion). The authors found decreased functional connectivity in the "default network" of the brain - usually referring to the functional connectivity between the medial prefrontal cortex and retrosplenial cingulate cortex -, decreased connectivity in the frontal-parietal network, and slightly increased local connectivity among clustered voxels, simply based on motion alone. (Keep in mind that each bin of subjects were matched as closely as possible on all other demographic measures.) Furthermore, even when comparing bins of subjects closely matched for motion (e.g., bins 5 and 6), small but significant differences in functional connectivity were seen.

Figure 3 from Van Dijk et al (2012). Functional connectivity among different networks measured as a function of head motion. Both linear and nonlinear (quadratic) terms were modeled to fit the data.

Figure 4 from Van Dijk et al (2012). Note the comparison on the far right between groups 5 and 6; the mean motion difference between these two groups is a few thousandths of a millimeter, but noticeable functional connectivity differences are still seen between the two groups.

Lastly, a subset of subjects were rescanned in order to see whether motion was reliable; in other words, if a subject that moved a large amount on one day had the same tendency to move a large amount on the next day. A clear correlation was found between scanning subjects, suggesting that motion might need to be treated as a trait or individual difference, just like any other.

Figure 5 from Van Dijk et al (2012). There is a robust correlation between the movement of scanning sessions, even with the outliers removed (marked in diamonds).

So, what to do? A few recommendations are to match subjects for motion, correct motion prospectively (Ward et al, 2000), and regress out motion when performing a group-level analysis, as you would any other covariate. Apparently traditional methods of motion correction on a subject-by-subject basis are not enough, and increasing awareness of the pitfalls of between-subject motion is important for evaluating current functional connectivity analyses, and for conducting your own experiments.

This study hit me in the face like a wet mackerel since I am beginning to investigate a recent AFNI tool, 3dReHo, to do local functional connectivity analyses for publicly available datasets on the ABIDE website. However, as far as I can tell, motion limits were not used as exclusionary criteria, which may be a possible confound when examining, say, autistic children to controls. More to come soon. Or not.

*I Don't Want to Walk Without You