Group (Level 2) fmri Data Analysis - Lab 4

Group (Level 2) fmri Data Analysis - Lab 4 Index Goals of this Lab Before Getting Started The Chosen Ten Checking Data Quality Create a Mean Anatomical of the Group Group Analysis: One-Sample T-Test Examine Results in SPM Visualization of Results in MRIcron Region of Interest Analyses Non-parametric model estimation Goals of this lab 1. Learn how to evaluate aspects of data quality that affect group analyses. 2. Learn how to run group analyses on the 10 subjects included in the course dataset. 3. Learn how to correct your results for multiple comparisons at both the voxel and cluster levels. 4. Learn how to visualize and explore your results. 5. Learn how to define regions of interest (ROI) and use them in a group analysis. Before Getting Started Today's lab will require use of several 3rd-party extensions to spm12 analysis in MATLAB. These are available in a folder called extensions that should be present in the folder containing the data for your 10 subjects. Before you get started, add that folder - along with all of its subfolders - to your MATLAB search path. Once you are finished, check to make sure it worked by running the following in the MATLAB command window (for each MATLAB should print the path to the file - if not, try adding the path again): which spm which files which peak_nii which nee_roi

The Chosen Ten By now you will have estimated (at least) two distinct models of the fmri timeseries for the subject you just happened to choose to examine during the first SPM lab on preprocessing. Now, briefly close your eyes and imagine that, magically, the same models were estimated for the remaining nine subjects in the dataset. Now, open your eyes and behold the precooked folder containing a normalized anatomical image (wanat_hires.nii) and folders for the two models estimated in the single-subject lab. IMPORTANT: Use the precooked folders to estimate your group level analyses for EVERY subject, including the one you've already done some processing on. This will ensure that everyone is starting with the same set of data.

Checking Data Quality There are at at least two questions you need to answer about your individual subject data before you begin a group analysis: Is everyone in the same stereotaxic space? Use check reg in SPM to verify that all 10 sets of images are in the atlas space. Before you do that, note that for this lab there will be many instances in which you will have to select a set of 10 images from each one of The Chosen Ten. To learn how to DRAMATICALLY speed up the process of selecting multiple files, read this tutorial before moving on. (Seriously, it's mandatory.) Once you're ready, examine 10 wanat_hires.nii's and see how the anatomy lines up (if the orthogonal viewers are too small, view the first 5 and then the second 5 images). Are there any subjects who seem out of registration with the others? Is anyone missing functional data? For this, use check reg to compare the 10 "mask.img" file located in your single subject analysis folders. (Do this just for the 2x2 model, but know that it is generally good practice to do this for

every model that you estimate.) SPM automatically generates these images to show the User (that's you!) which voxels were included in their analysis. This is called masking, in which only a subset of all voxels in the image are singled out for computation. When SPM decides which voxels to include and which to exclude (which it does by default), the masking is known as "implicit" since it happens without the User having to "explicitly" specify a mask. You can learn more about "implicit" versus "explicit" masking in the SPM manual, or here. This is very important to know, for the following reason: By default, SPM will only include a voxel in your group analysis if and only if EVERY individual subject has data at that voxel. If 9/10 have it, it will be excluded. With that in mind, check out your group's mask images, playing close attention to ventral regions of the frontal and temporal lobe that are notoriously susceptible to signal distortion and signal loss. Are there any subjects who seem to have more signal loss than others? Create a Mean Anatomical for the Group Let's create an anatomical reference image using the ImCalc tool:

1. Go to Options: Data Matrix. Click Yes-read images into data matrix. This will allow ImCalc to read all of the images you are about to specify into matrix X. 2. Now go to Input Images and select the 10 wanat_hires.nii which are subjects' normalized anatomical images. 3. In Output Filename type wanat_hires_mean.img. In Output Directory specify the directory into which you want the mean anatomical to be written. In Expression write mean(x). This expression will create an average of the anatomical images. 4. Run the batch. 5. Use the Check Reg utility to compare the mean anatomical to the template image. Does it look like the group is, on average, in good registration with the template? Now take a quick look at this tutorial to see how to do what you just did in less than 10 seconds using the function FILES and a second function you'll learn about called BSPM_IMCALC. Group Analysis: One-Sample T-Test Start by making an output folder for each of the analyses you plan on running. If I were you, I'd make a folder called "groupstats" in your data directory (use "mkdir groupstats" in the MATLAB command window to do this). Then, enter that folder with "cd groupstats" and make two additional folders called "2x2" and "Parametric". Finally, enter the 2x2 folder with "cd 2x2" and make folders

called "Why-How" and "Face-Hand". These correspond to con_0001.img and con_0002.img in your individual subject folders (you'll use the precooked models, even for your own subject). Now, let's use the Batch Editor to specify and estimate a one-sample t-test on the first of these images: 1. Open the Batch Editor 2. Open a Stats: Factorial Design Specification module 3. For Directory, select the analysis directory you created (i.e., the Why-How directory). 4. Click on "Design" and observe that you can select from a variety of statistical models; the one we need, "One Sample t test", is already selected. Expand it, then for Scans, select the ten (10) con_0001 contrast images. Tip: use the files command to create a list of all contrast images that you can copy and paste into the 'Scans' dialog when you click edit:

5. Add an Estimation job using the Batch Editor (as we did on Monday). Specify a dependency with the SPM.mat created from the first job. 6. Add a Contrast Manager job. Define two contrasts. First, "Why-How" requires a single contrast weight: +1. Then, "How-Why" requires the inverse: -1. 7. Save this Job.

8. Repeat the above for the Face-Hand contrast. 9. Save that job, too. 10. Load both jobs using "Load" from the Batch Editor menubar. The Module List should look like this: 11. When you're ready, run the jobs. The SPM figure window should look like this:

Examine Results in SPM The time has come to peer inside the group mind of The Chosen Ten. Using the Results button in the SPM menu, load in the SPM.mat file for the 2x2 model and select the Why > How contrast for viewing. For now, threshold it with:

1. Do not apply masking. 2. P-value adjusment should be "none" 3. Threshold should be left at.001 4. Extent threshold at 0 Once you see the "glass brain", use the overlay menu (using 'sections') to use the mean anatomical as an underlay for the thresholded statistical map. Use the "whole brain" option under p-values to produce a report showing the peaks wihtin each identified cluster of activity. Below, you can see this table. The circled part at the bottom (FWEc) shows the minimum extent (which we've so far been leaving at 0) required for a cluster to considered "significant" at a family-wise error rate of.05. This is one of the forms of cluster-level correction for multiple comparisons that SPM offers.

To apply this correction, use the drop down menu to change the thresholding you're using, as shown below:

Enter all the same values except the last one, where it asks for the "extent threshold". Instead of entering "0", enter the value necessary for cluster-level correction. Does the resulting thresholding statistic map look less noisy to you? Now, let's change the threshold again but this time apply a voxel level correction for multiple comparisons. To do this, use the change threshold procedure as above. When you get "P-value adjusment", choose the option for family-wise error (FWE). Then, stick with the default threshold of.05 (which in this case refers to the FWE corrected threshold), and do not apply an extent threshold (that is, leave the last input at 0). Did this change the number of "supra" threshold voxels? At this point, this walkthrough challenges you to do the following: 1. Use SPM to load the results for the Face > Hand contrast. 2. Save a CSV file that reports those regions that survive an uncorrected voxel-wise threshold of.001 and a family-wise error corrected cluster-level threshold of.05. Which regions survived? If you did things correctly, you should see bilateral activation in some noname region of the brain that no one has every heard of. (Just in case it wasn't clear, that last sentence was meant to be ironic.) As shown below, SPM offers a number of different options for saving all or part of your results as image files. Given that we'll shortly be discussing region of interest analyses (ROIs), use the "Save current cluster" option shown below to save an image of just the right amygdala (for this, you'll need to have the crosshair positioned on a voxel in the

cluster). Use the Display utility to take a look at the "functional" ROI you've just created:

Visualization of Results in MRIcron To visualize the data, we'll use MRICron. MRIcron has already been installed on your machine and can be found here. You can launch MRIcron from Windows by doube-clicking the mricron icon. Once it is open, click on Open Templates and load in the chi2better.nii.gz. This is a skullstripped version of a canonical template in MNI space.

Next, click on Overlay -> Add and open the spm_t map located in your group analysis of the Why > How contrast in the 2x2 model. Once loaded, we can change the color map and the upper and lower bounds of the color map:

Let's start with the "Multislice" feature. Go to Window -> Multislice Under View, you can change which slices you want to look at (Slices...), how much the slices visually overlap (Overslice), and various other features. When you re happy with the figure, you can go to File -> Save as bitmap... to cherish forever.

Not satisfied? Try the Window -> Render option that is much more flexible than SPM: The Azimuth and Elevation change the view. Make sure to check the option Overlay -> Search -> Infront/below BG surface so that deep activations are not mysteriously brought to the surface. You

can control how much activations are brought to the surface under Overlay -> Search Depth. Finally, we can peel back chunks of the brain to observe activations lurking in inner structures. Click on View -> Cutout and then click the Show cutout checkbox. The lower and upper limits of X, Y, and Z will determine what parts of the brain are cutout. Feel free to play with those values and perform different cuts. Brain surgery made simple... When you re satisfied with your cuts, you can click OK in the cutout window. Finally, click the

double red arrow at the top of the render window to generate a high resolution rendering. Note, that doing this multiple times may cause MRIcron to crash. It appears to be good for one high resolution rendering per use of the program. Now, you can click on File -> Save as bitmap... for a nice figure. Region of Interest Analyses You've already seen above how you can easily make functional ROIs from activation maps in SPM. In this tutorial, you'll see how to make ROIs based on the peak coordinates reported in previous neuroimaging studies. Finally, let s extract data from ROIs. First, gather the contrast images for the Face > Hand contrast. You can use the function files again, or you can find them neatly stored in the Group Level SPM.mat file under the field SPM.xY.P. Next, we ll extract ROI averaged data for each subject. P = SPM.xY.P; % alternatively, set P to the images you gathered using files ROI = 'ROI_Sphere9_-50_9_30_L_IFG.nii'; % Or you can use a different ROI file roidata = nee_roi(p,roi); roidata This returns an n x 1 vector with the contrast estimate averaged across the ROI for each subject. You can perform a t-test on it: [h,p,ci,stats] = ttest(roidata) Next, you can do the same analysis on the functional ROI you created above. That analysis

should be highly significant. But we already knew that since the functional ROI was defined as a region that showed a significant effect (and reporting it as if it were another independent test in a paper is one form of double-dipping). However, extracting data from a functional ROI can sometimes be useful to serve as a correlate (e.g. with behavior or another ROI). Non-parametric model estimation (SnPM) With the Statistical nonparametric Mapping (SnPM) toolbox you can estimate your models using a non-parametric permutation-based approach. Non-parametric model estimation has the advantage that it does not have any assumptions about the distribution of your outcome measure (i.e., your voxel values) and is therefore also appropriate for non-normally distributed residuals. You can start the SnPM toolbox by typing snpm_ui, or by selecting a design from the batch editor: In the sample below we will run the same one-sample t-test as we ran previously, but now using SnPM. For this, create a new folder to store your model and results. Next, select MultiSub: One Sample T test on diffs/contrasts. Specify your analysis directory, and the contrast images of the contrast you want to analyze:

You can select your first level parametric contrast images for this second level non-parametric group level analysis. For this example, we will select each second contrast image (Face-Hand) of the parametric 1st level analysis of all 10 subjects (i.e, con_0002.img). SnPM has a couple of options that you have to set. As you know by now, SPM (and SnPM as well) provide a lot of good information about the options in the batch editor in the bottom part of the batch editor. Below, we will briefly go over the most important options. For more information see the text boxes in the SnPM batch window: Number of permutations: The number of permutations that is being conducted can be exhaustive, but this normally takes a very long time to complete. This is why you can select to run a random subset of the possible permutations. The default is 5000. You can reduce the number of permutations for

testing. Generally, for publication you should set the number of permutations to 10,000 or more. The more permutations, the more precise the p-value. For now, let's set the number of permutations to: 1000. (Note that for a one-sample t-test the non-parametric sign flip test is used instead of permutation tests). Variance smoothing: This is particularly useful if you have a small sample (i.e., n<20). In this case SnPM regularizes your variance image. In general, the smoothing kernel that you select for your variance maps should not exceed the smoothing kernel that you selected for your data (i.e., 8mm in our case). For now, let's set the variance smoothing to: [8 8 8]. Additional background information about variance smoothing by Thomas Nichols can be found HERE. Cluster inference: By default cluster inference is not performed because it takes a long time to run. If you want to easily switch between different cluster size inferences it is recommended to set cluster inference to: Yes (slow, may create huge SnPM_ST.mat file). However, for now we are going to use cluster inference with a predefined statistical threshold. For this, select Yes, set cluster-forming threshold now (fast) and set it to 0.001 Now that we have created a model, we are going to add add compute to our batch (this is the equivalent of estimate in the parametric analysis). To add compute to your batch select it from the batch menu: SPM --> Tools --> SnPM --> Compute. Click on dependency and select the design that you just created. Finally, add inference to your batch by selecting it from the batch menu: SPM --> Tools --> SnPM --> Inference. Inference is the equivalent for results in SPM. Set the SnPM.mat results file to dependency and select: Compute: SnPM.mat results file. For type of thresholding you can select either voxel wise or cluster wise inference. Both are valid options, but for now we are going to select Cluster-Level Inference. We will leave the Cluster-Forming Threshold at NaN, because we have already set it to p<0.001 when we were defining our model. Now, keep the default value for all other options, save your batch, and run it! Results: Your results overview should look like this (click to enlarge):

If you still have time, compare these results to the results from the parametric One-Sample T-Test of the second contrast (Face-Hand). Do they differ a lot? If so, what could be an explanation? Note that SnPM does not offer all the tools in the results window that SPM offers. However, SnPM stores the pseudo-t image in the file "snpmt-.img". You can open this file in MRICron and use the T-threshold that you will find in your SnPM output, under "Cluster-defining thresh".