Cello Unchained: Public Recital

Tomorrow at Boxcar Books in Bloomington, there will be a public studio recital featuring cellists from the Jacobs School of Music. The pieces range from virtuosic showpieces (such as the Popper etudes) to lyrical songs without words, and I will be accompanying several of them. So if you're in the area, feel free to stop by!

Boxcar Books Recital  

When: Monday, November 26th, at 7:00pm
Where: Boxcar Books, 408 E 6th St (right next to Runcible Spoon)
Who: The entire cello studio of Emilio Colón
Link to Facebook invite

Coregistration Demonstrations

Coregistration - the alignment of two separate modalities, such as T1-weighted and T2-weighted images - is an important precursor to normalization. This is because 1) It aligns both the anatomical and functional images into the same space and orientation; and 2) Because any warps applied to the anatomical image can then be accurately applied to the functional images as well. You can create a homemade demonstration of this yourself, using nothing more than a deck of playing cards, a lemon, and a belt.

However, before doing either coregistration or normalization, often it is useful to manually set the coordinates of the anatomical image (or whichever image you will be warping to a standardized space) so that it is in as close an alignment with the template image as possible. Since the origins of both MNI and Talairach standardized spaces are located approximately at the anterior commissure, the origin of the anatomical image should be placed there as well; this provides a better starting point for the normalization process, and increases the likelihood of success. The following tutorial shows you how to do this, as well as what the anterior commissure looks like.

Once this is done, you are ready to proceed with the coregistration step. Usually the average EPI image - output from the realignment step - will be used as the source image, while the anatomical image will be used as the reference image (the image that is moved around). Then, these warps are applied to the functional images to bring everything into harmonious alignment.

Let's Talk about Masks (Live Video)

I've been experimenting more with Camtasia, and I've uploaded a new video showing how masks are drawn on an actual human, rubber brain, which involves the use of R studio, Excel, and colored pens. My hope is that this makes the learning experience more interactive; in addition, you get to see what my mug looks like.

FSL Tutorial: Featquery_gui

Now that we've created our masks, we can go ahead and extract data using FSL's featquery tool. You may want to run it from the command line when batching large numbers of subjects, but this tutorial will focus on Featquery_gui, a graphical interface for loading subjects and ROIs, and then performing data extraction from that ROI. The procedure is similar to Marsbar, and I hope that the video is clear on how to do this.

Also, I've attached a Black Dynamite video for your enjoyment. Nothing to do with ROIs, really, but we all need a break now and then.

A Note about FMRI Masks

Now that we have covered how to create masks using three separate software packages - FSL, SPM, and AFNI - I should probably take a step back and talk about what masks are all about. When I first read about masks, all I heard was a bunch of mumbo jumbo about zeros and ones, and unhelpful saran wrap metaphors. While this did remind me to purchase valuable kitchen supplies, it was unhelpful in understanding what a mask was, exactly, and how it was used.

Simply put, a mask is a subset of voxels you wish to analyze. Let's say I'm only interested in the right hemisphere of the brain; to create a mask of the right hemisphere, imagine using a papercutter to split the brain in half, and only taking the right hemisphere for further analysis, while discarding the left hemisphere into the trash can. The generation of masks follows this same logic - only focus on a specific part of the brain, and discard the rest.

Fortunately, we have come a long way since using office supplies to create masks, and now we have computers to do it for us. In order to create a mask using any of the listed software packages, usually you will use a tool to insert "1's" into the voxels that you wish to analyze, and "0's" everywhere else. Then, say that you want to do an ROI analysis only on those voxels that contain "1's". If you are trying to extract contrast estimates for a subject, the contrast estimate at each voxel will be multiplied by the mask, and you will be left with the contrast estimates in the "1's" voxels (since each estimate is being multiplied by 1), and zeros everywhere else.

Furthermore, ROI extraction within a mask often averages the contrast (or parameter) estimates across all of the voxels inside the mask. It is also possible to extract estimates from single voxels or a single triplet of coordinates - just think of this as ROI analysis of a very small mask.

I hope that this clarifies things a bit; I know that it took me a couple of years to wrap my head around the whole concept of masks and ROIs and severing hemispheres from each other. However, once you understand this, the whole process of ROI interrogation becomes much simpler and more intuitive, and analyses become easier to carry out. ROI analysis is the foundation for carrying out more complex analyses, such as double dissociations and connectivity analyses, and it is well to become familiar with this before tackling larger game.

Creating Masks In FSL

Due to a high number of requests (three), I have made some walkthroughs about how to create masks in FSL. There are a few different ways to do this:

1. Anatomical ROI: These masks are generated from anatomical regions labeled by atlases. For example, you may decide to focus only on voxels within the V1 area of visual cortex. Using an atlas will create a mask of that region, based on the atlas-defined anatomical boundaries in a standardized space.
2. Functional ROI (or contrast ROI): This is a mask created from a contrast thresholded at a specific statistic value. For example, you may wish to focus only on voxels that pass cluster correction for the contrast of left button presses minus right button presses.
3. Painting ROIs: This is where the real fun starts; instead of being confined by the limitations of anatomical or contrast boundaries, let your imagination run wild and simply paint where you want to do an ROI analysis. Similar to what you did in first grade, but more high-tech and with less puking after eating your crayons. (Is it my fault that Razzmatazz Red sounds so delicious?)

Demonstrations of each approach can be found in the following videos:

 Anatomical ROIs

Functional ROIs

 ROIs created from FSLview. Pretend like you're Bob Ross.

Parameter Extraction with MarsBar

Marsbar, a region of interest (ROI) tool interfacing with SPM, is a swiss-army knife of programs for ROI manipulation and data extraction. The most commonly used features of Marsbar are 1) The creation of ROIs from spheres or boxes centered on specified coordinates, and 2) The extraction of parameter or contrast estimates from ROIs. The following video tutorial focuses on the latter, in which parameter estimates for each subject are dumped out from a defined ROI.

For example, say you have two ROIs placed in distinct locations, and you wish to extract parameter estimates from the contrast A-B from each of those ROIs. Marsbar can do this easily, even flippantly, such a saucy and irreverent child it is. After your ROIs have been created, simply specify the SPM design you wish to extract parameter estimates from. In the case of second-level analyses, the SPM.mat files generated by these analyses will contain a number of time points equal to the number of subjects that went into that analysis; where Marsbar comes in is taking all of the parameter estimates for each subject and averages them over the entire ROI, generating a list of averaged parameter values for each subject.

Once this is done, save the results to a .mat file, load the file into memory, and check the output of SPM.marsY.Y (as in, "Why, Black Dynamite? Why?").


More deets can be found in the following tutorial; for a text-based walkthrough, complete with pictures, check out this link. I believe that both of these approaches are valid with both SPM5 and SPM8 distributions; if not, I apologize.

Unlike when Black Dynamite was denied the chance to apologize for the life he took so needlessly.

SPM Realign

I've covered motion correction in a previous post, and the concept is the same in SPM as it is in the other major software analysis packages. One difference, however, is that SPM realigns the first volume in each run to the first volume of the first run, and then registers each image in each run to the first volume of that run. This may not seem optimal if the anatomical scan is taken after the last functional run, and thus would be spatially closer to the very last image of the last functional scan; but it's the way SPM operates, and hey, I didn't make it - so deal with it, wuss.

spm_realign and spm_reslice are the command line options to run motion correction, and both the command line and GUI approaches will output a graph of motion parameters in the x-, y-, and z-directions, as well as pitch, roll and raw estimates for each run. The motion of each volume relative to the first volume in that run is output into an rp_*.txt file, which can be used for nuisance regressors to soak up any variance associated with motion. (Does anybody else notice how often people use cleaning metaphors when discussing variance? As though it is some messy substance that needs to be mopped up or soaked up, as opposed to appreciated, cared for, and loved.)

Although most of the defaults are fine, you may want to turn up the interpolation order a few notches if you have the computing power to do it, and don't mind waiting longer for the realignment to complete. The higher the interpolation order you use, the better results you get, but the benefits get smaller the higher you go, as though the return on your realignment begins to diminish. Someone should come up with a name for that phenomenon.

Anyway, here's some sample commands for running realignment from the command line:

P = spm_select('ExtList', pwd, '^ar01.nii', 1:165);
spm_realign(P);
spm_reslice(P);

More details, along with my soothing, anodyne voice, can be found in the following screencasts.

SPM Realign from the GUI

SPM Estimate & Reslice from the command line

Introduction to SPM Marsbar

Marsbar is an extraction tool designed to output beta estimates or contrast estimates from a region of interest (ROI), a cluster of voxels defined either anatomically, or through an independent contrast. I covered this in an earlier post, but thought that this would lend itself better to a bright, vibrant, visual tutorial, rather than the musty arrow charts.

How to define ROIs from coordinates

How to define ROIs from other contrasts

SPM Jobman

Now that we have created our own .mat files from the SPM GUI and seen how it can be written to the disk, altered, and reloaded back into SPM, the hour is at hand for using the command spm_jobman. This is a command for those eager to disenthrall themselves from the tyranny of graphical interfaces through batching SPM processes from the command line.

I first met spm_jobman - also known as Tim - a few weeks ago at a conference, when I was at the nadir of my sorrows, despairing over whether I would ever be able to run SPM commands without the GUI. Suddenly, like a judge divinely sent in answer to the lamentations of the oppressed, spm_jobman appeared by my side, trig and smartly dressed, and said he would be more than happy to help out; and from my first impression of his bearing and demeanor, I believed I was in the presence of an able and reliable ally. Anyone who has ever met spm_jobman, I believe, has felt the same thing. However, as I learned too late, far from being a delight, he is a charmless psychopath; and he continues to infect my dreams with nameless horrors and the unrelenting screams of the abattoir.

spm_jobman has three main options to choose from: Interactive, serial, and run. After choosing one of these options, for the second argument you enter your jobs structure, which is automatically populated after loading the .mat file from the command line. Interactive will load the traditional GUI with the options filled in from the jobs structure, which you can then modify and execute as you please; Serial will prompt the user to fill in each field, with the defaults set to the values in the jobs structure; and Run will execute the jobs structure without cuing the GUI. For most purposes, if you decide to run spm_jobman at all, you will want to use the Run command, as this allows you to loop processes over subjects without pause, allowing you to do more useful tasks, such as Googling the history of the lint roller.

Saving .mat files from SPM is immensely helpful in understanding the relationship between the .mat files created by SPM, and what exactly goes into them; and this will in turn reinforce your understanding of and ability to manipulate Matlab structures. The following tutorials show how the .mat file is generated from the SPM interface, which can then be used as a template for spm_jobman. I've been working with SPM for years now, but found out about this only recently; and I hope that it helps ease the burden of your SPM endeavors.