Fortunately, there is a simply way to automate all of this - provided that each subject has the same number of runs, and that the regressors in each run are structured the same way. If they are, though, the following approach will work.

First, open up SPM and click on the TASKS button in the upper right corner of the Graphics window. The button is marked "TASKS" in capital letters, because they really, really want you to use this thing, and mitigate all of the damage and harm in your life caused by doing things manually. You then select the Stats menu, then Contrast Manager. The options from there are straightforward, similar to what you would do when opening up the Results section from the GUI and typing in contrasts manually.

When specifying the contrast vector, take note of how many runs there are per subject. This is because we want to take the average parameter estimate for each regressor we are considering; one can imagine a scenario where one of the regressors occurs in every run, but the other regressor only happens in a subset of runs, and this more or less puts them on equal footing. In addition, comparing the average parameter or contrast estimate across subjects is easier to interpret.

Once you have the settings to your satisfaction, save it out as a .mat file - for example, 'RunContrasts.mat'. This can then be loaded from the command line:

load('RunContrasts')

Which will put a structure called "jobs" in your workspace, which contains all of the code needed to run a first-level contrast. The only part of it we need to change when looping over subjects is the spmmat field, which can be done with code like the following:

subjList=[207 208]; %And so on, including however many subjects you want

for subj=subjList

jobs{1}.stats{1}.con.spmmat = {['/data/hammer/space4/MultiOutcome2/fmri/' num2str(subj) '/RESULTS/model_multiSess/SPM.mat']} %This could be modified so that the path is a variable reflecting where you put your SPM.mat file

spm_jobman('run', jobs)

end

This is demonstrated in the following pair of videos; the first, showing the general setup, and the second showing the execution from the command line.

You don't even have to create your .mat with a gui if in the end you're using a Matlab script. For example here is what i used for a two-sample t-test between patients and control groups:

ReplyDeletematlabbatch = [];

matlabbatch{1}.spm.stats.con.spmmat = cellstr(strcat(save_folder,'/SPM.mat'));

matlabbatch{1}.spm.stats.con.consess{1}.tcon.name = 'Control > Patients';

matlabbatch{1}.spm.stats.con.consess{1}.tcon.convec = [1 -1];

matlabbatch{1}.spm.stats.con.consess{1}.tcon.sessrep = 'none';

matlabbatch{1}.spm.stats.con.consess{2}.tcon.name = 'Patients > Control';

matlabbatch{1}.spm.stats.con.consess{2}.tcon.convec = [-1 1];

matlabbatch{1}.spm.stats.con.consess{2}.tcon.sessrep = 'none';

matlabbatch{1}.spm.stats.con.delete = 1;

spm_jobman('run',matlabbatch);

Of course this is valid for subject level contrasts. Makes it easier when you have a little thing to change as well.

Hey Rodolphe,

DeleteThat's true, and very useful; I show it with the GUI since for newcomers that seems to be a more intuitive way to set up the structure. But either way would work.

-Andy

Dear Andrew Jahn,

ReplyDeleteFirst of all, thank you for all your efforts.

I was wondering about the weight contrasts in first level...

I'v seen that in several papers the rule is perform a [1 -1] (condition 1 > condition) and then use this contrasts in a second level analysis.

But, my question relay if I can perform contrasts with a weight of [1 0] to identify voxels whose activation increased in one condition and then use this img contrasts in second level.

Is this correct?

Thank you in advance!

Cheers!

Hi Richard,

DeleteYes, that's right; if you simply have a [1 0] contrast vector, then that will give you the equivalent of a simple effect, i.e. only looking at a single condition (or cell, if your design is factorial). However, contrasts between conditions are more highly recommended since if the conditions are controlled for confounds and differ only on a particular quality of interest, than the resulting contrast isolates that quality. This is also referred to as the assumption of pure insertion, which isn't perfect, but is often a better approach than just looking at simple effects.

Best,

-Andy

Hey Andrew, thanks for all the posts, it's been very informative for getting used to interacting with SPM through the command line. I was wondering though if you could elaborate a bit more on setting the contrasts to .2 or -.2 versus 1 or -1. I understand you are summing them up across the number of runs so, you end up with 1 or -1 versus -5 or 5. But are your results going to be much different if you ran the contrasts at -1 and 1 over 5 runs than -.2 or .2 over 5 runs? I realize I could answer my own question with a bit of time but wanted to know more about why you do it that way. Is that common in the field?

ReplyDeleteThanks a bunch!

Hey Tony,

DeleteI think the reason is that if you have different numbers of runs for different subjects, it can be biased. I'm not 100% sure on this, but the convention is to have the weights sum to 1 and -1, and I believe that is why.

-Andy

Hi Andrew,

ReplyDeleteThanks for your wonderful efforts in order to help us getting better at this complicated thing that is neuroimaging.

I am relatively comfortable with writing T-contrasts, but I am trying for the very first time to write an F contrast and I can't find my answer anywhere.

I ran a longitudinal VBM analysis using the VBM8 manual protocol. I have two groups of individuals that have been scanned twice over 2 years. I chose the flexible factorial design with factors 1: Subject, 2: Group, 3: Time, and I also entered ICV as a covariate.

In the contrast manager, I entered a T contrast for the Main Group Effect (0 1), a T contrast for the Main Time Effect (0 0 1), but I don't know how to write the Group X Time interaction. Do you know how to write this?

Also, am I supposed to include my covariate in my contrasts?

Best regards,

Nick

Hey Nick,

DeleteI do what I can! For these kinds of interactions, one of the best guides I know of is written by Glascher and Gitelman; if you include subject as a factor and then have a 2x2 design, your interaction term would look something like -0.5 0.5 0.5 -0.5 (nonlinear, parabola-shaped in this case). Check out the Design 1 graphic on page 8; I think that should give you what you want: http://www.sbirc.ed.ac.uk/cyril/download/Contrast_Weighting_Glascher_Gitelman_2008.pdf

If you have a single number covariate, such as age (or ICV, in your case), then yes, you can add it as a covariate at the 2nd level. If you wanted to test whether any of the betas correlate significantly with ICV, you would just put a 1 in that column, and leave the rest as zeros.

Best,

-Andy

Hi Andy,

ReplyDeleteHow would the weights be affected if the size of the regressors within each run changes? I.e. A subject sees 4 images fitting Regressor A, but 5 images fitting Regressor B in run 1, and then sees 6 images for Reg A and 5 images for Reg B in run 2.

Would A > B be set up as [0.5 -0.5 0.5 -0.5] or would it have to account for the variance within each run?

Thanks

Hi there,

DeleteYou can still use that contrast vector; it will take the average of the parameter estimate across the runs, and any variability not explained by the parameter will go into the error term.

Best,

-Andy

Hi, Andy. I've just started doing cognitive neuroscience research, and my advisor asked me to find out what contrast analysis we can do on fMRI data to discover trends (using fMRI contrasts). Any help?

ReplyDeleteHey there,

DeleteAre you talking about trends as in linear and quadratic trends? It depends on how many regressors you have, but assuming that you have 4, a linear trend analysis would look like this:

[-1.5 -0.5 0.5 1.5]

Note that this is the result of the command (1,2,3,4) - mean(1,2,3,4), a formula you can apply to any number of regressors.

For more details, see this PDF by Jeanette Mumford: http://mumford.bol.ucla.edu/lin_cont_illus.pdf

Best,

-Andy

Hi Andy,

ReplyDeleteI'm a fMRI beginner.

I read some information from the Internet and my own lab from prior students about the contrast.

However, there're some different at the first level contrast manager.

In most information from the Internet, they set contrasts equal to conditions, it means, if I have 2 condition, my contrast will be [condition 1, condition 2].

However, in my lab's manual, they have conditions and time derivatives, it means, if I have 2 condition, my contrast will be [condition 1, condition 1 time derivatives, condition 2, condition 2 time derivatives].

I rarely found information (because I haven't read enough papers) about the time derivatives.

Thanks....

Hey Miss Life,

DeleteA time derivative is a regressor that captures any variability in the onset of the HRF. (I can't visualize what a time derivative would look like, so I don't understand it that well.) If you think there could be a lot of variability in the onset of the HRF in whatever region you're looking at, then including the time derivative could give you a better model fit.

However, a paper from Della-Maggiore (2002) showed that including the time derivative decreases power, suggesting that just using the canonical HRF may be good enough to explain most BOLD responses. That paper is pretty old though, and I haven't kept up with what has happened since then.

I would try doing a couple of models both with and without the time derivative, and see what your contrast maps look like. If there's no significant difference, I would omit the time derivatives, since I like to simplify things.

-Andy