Resting-State Analysis Part IV: Generating a Seed Region for Resting-State Analysis

Part of the resting-state pipeline includes warping each individual anatomical image into a standardized space, so that results can be compared across subjects. This also means that we can place a seed voxel or seed region into one location of the brain, and it will be roughly in the same spot for every subject in our analysis.

To do this, we will focus on one of the core components of the so-called "resting-state network," which is a reliable pattern of connectivity observed when subjects are at rest. Several studies have revealed patterns of correlative activity between the ventromedial prefrontal cortex (vmPFC) and retrosplenial cortex, which is the network we will be focusing on for this tutorial series; our aim will be to compare correlations between these nodes across persons with autism and a control group without autism.

First, however, we will need to create and place the seed region appropriately. We can place a seed voxel in the vmPFC using the XYZ coordinates 0, -50, -5 (similar to MNI coordinates of 0, +50, -5), and a correlation coefficient will be estimated for every other voxel in the brain. The resulting correlation maps will be created for each subject, and then collapsed across groups and statistically compared against each other.

The procedure for generating an ROI is exactly the same as what was done in a previous post about 3dUndump; we simply put the coordinates into a text file, and tell 3dUndump how large a sphere we want to create around those coordinates.


echo "0, -50, -5" > tmp.txt
3dUndump -prefix vmPFC -master errts.0050783+tlrc -srad 5 -xyz tmp.txt


This will then create a sphere with a 5mm radius around those coordinates, and information about that time series can then be extracted and correlated with other time series in every other voxel in the brain.

Resting-State Functional Connectivity Analysis, Part II: Setting Up Your Analysis

Once you have downloaded the KKI dataset discussed in the last resting-state post, you have most of what you need, sacrificial undergraduate RA notwithstanding. Also, as I mentioned, we will be using AFNI for this, specifically AFNI's uber_subject.py script which includes an option for preprocessing and analyzing resting-state data.

First, type uber_subject.py from your terminal to open up the GUI (rhymes with "whey"), and select the resting-state option from the preprocessing selections. This will automatically fill in a series of preprocessing steps which our AFNI overlords, in their wisdom, have decided is best for resting-state analyses. Of course, you can change this, but that would be an unbelievably stupid decision, on par with doing something like asking out your girlfriend's sister.

Notice that with resting-state experiments, we avoid several of the annoying, persnickety details endemic to typical FMRI experiments, such as having to actually design an experiment. You simply place the subject inside the scanner, set the scanner to 350 degrees, and leave it for ten minutes. When you come back, you will have a slightly charred piece of carbon that used to be a human being. After framing someone else, such as your FMRI technician, you should then realize that you are simply not cut out for actually carrying out a resting-state scan, and download someone else's data instead from the Internet like I recommended in the first place.

Notice that much of the preprocessing and setting up the design matrix is the similar to usual FMRI analyses. However, there is an important difference in the design matrix setup, because you do not have any tasks or events to model. Instead, the only things you need to model are potential sources of noise, which may include heart rate or respiration data, if you have it, and always motion data, since this can be an insidious confound in any FMRI analysis, but particularly for resting-state analyses.

The upshot of all this is that, whereas in a traditional FMRI analysis AFNI saves the beta estimates and contrasts to a statistics dataset and everything else that wasn't modeled into an error or residual dataset (usually labelled "errts+orig"), in resting-state analyses we are interested in everything that wasn't explicitly modeled - in other words, we will want to focus on what gets output to the errts+orig dataset, since that will contain the timecourses of voxel activity that we are interested in. You can then place ROIs or other seed regions within that dataset, and generate correlation maps from those seed regions.

In the next chapter of the series, we will look more closely at converting these correlation values into z-maps for comparison across groups, as well as where to find more undergraduate RAs if the ones who were working in your lab have already been burnt as offerings to the FMRI gods.

Introduction to Resting-State Functional Connectivity Analysis: You Can Do It! (Maybe)

A while ago I promised to provide a series of tutorials on resting-state connectivity, and I'm happy to announce the first part after a brief delay of, give or take, fourteen months. But in my defense, I wanted to wait until I had everything well prepared and ready to go, by which I mean until I had a new suit, which, obviously, is a critical part of the education process.

For this series, in order to follow along I recommend downloading resting-state data from the ABIDE website, which collects resting-state data from autistic subjects and controls. The end result will be a simple comparison of resting-state networks across both populations, which, although the interpretation of such results has a large farrago of its own issues, should provide a solid platform from which to launch your own resting-state analyses. We will be using AFNI for this demonstration, although in the future I may include an identical analysis using FSL.

First, you need to jump through the usual administrative hoops in order to download the dataset. This involves a simple series of steps, such as registering as a member, filling out a form with your username and password, and immolating a virginal undergraduate RA. The resulting aroma will summon the resting-state data, which can then be downloaded to your computer.

First, register at the NITRC website: http://www.nitrc.org/account/register.php
Two, request access from the following link: http://fcon_1000.projects.nitrc.org/indi/req_access.html
Last, go to the ABIDE website and download a resting-state dataset (I am using data from the Kennedy Krieger Institute): http://fcon_1000.projects.nitrc.org/indi/abide/

As for finding and sacrificing the virginal undergraduate RA, you're on your own.