Video Introduction to ROI Analyses

About forty years ago certain persons went up to Laputa, either upon business or diversion, and upon their return began to dislike the management of everything below, and fell into schemes of putting all arts, sciences, languages, and mechanics upon a new foot. To this end they procured a royal patent for erecting an Academy of Projectors in Lagado. Every room hath in it one or more projectors. The first man I saw had been eight years extracting sunbeams out of cucumbers.

--Swift: Gulliver's Travels, Part III, chs. 4-5


I'm updating my videos on fMRI basics, starting with ROI analysis. This is low-hanging fruit, yes, but delicious fruit, fruit packed with nutrients and sugars and vitamins and knowledge, fruit that will cure the scurvy of ignorance and halt the spreading gangrene of frustration.

In these videos you will observe a greater emphasis on illustration and analogy, two of the most effective ways to have concepts like ROI analysis take root inside your mind; to make them have a real, visceral presence when you think about them, and not to exist merely as words that happened to impinge on your retina. These videos take longer to make, but are all the more rewarding. And if they help you to think differently than you did before, if they help you, even without my knowing it, to see the world as I understand it, then I will have taken a significant step toward fulfilling my purpose here on this earth.

SPM Smoothing: A Reader Writes

The angry red pustule of the Gaussian normal distribution

I love questions, because questions get answers. One question you may be asking is, "Why are my smoothness estimates in SPM so whack?" To which the obvious response is, "How much whack are we talking about here? Whiggidy-whack, or just the regular kind?" Details matter.

If the former, then the following code snippet may help. In the absence of a gold standard for calculating smoothness estimates, often we have to resort to our own ingenuity and cunning, by which I mean: Copy what other people are doing. One alert reader, Tamara, noticed that the standard SPM function for estimating smoothness, spm_est_smoothness, is so whack that all the other SPM functions want nothing to do with it. Which is kind of the goal of life, when you think about it - to not be that guy everyone else wants to avoid.

In any case, if you are having issues with it, the following code may help. I've also included the rest of the email, just to make you aware that I can and will publish your correspondence without your consent.

Hi Andy, I never figured out why spm_est_smoothness is not working, although other people have had the same issue with getting estimates in the thousands.  Ultimately, I ended up using this simple code to estimate each individual's smoothness, and then averaged across subjects.  Jim Lee posted this on the SPM listserv along with this note:  

The smoothness estimates in SPM.xVol.FWHM are in units of VOXELS, so you need to multiply by the voxel dimensions to get them in mm. Something like this:

load SPM.mat; M = SPM.xVol.M; VOX = sqrt(diag(M(1:3,1:3)'*M(1:3,1:3)))'; FWHM = SPM.xVol.FWHM; FWHMmm= FWHM.*VOX; disp(FWHMmm);

Thanks again for your help!!

Converting T-Maps to Z-Maps

Mankind craves unity - the peace that comes with knowing that everyone thinks and feels the same. Religious, political, social endeavors have all been directed toward this same end; that all men have the same worldview, the same Weltanschauung. Petty squabbles about things such as guns and abortion matter little when compared to the aim of these architects. See, for example, the deep penetration into our bloodstream by words such as equality, lifestyle, value - words of tremendous import, triggering automatic and powerful reactions without our quite knowing why, and with only a dim awareness of where these words came from. That we use and respond to them constantly is one of the most astounding triumphs of modern times; that we could even judge whether this is a good or bad thing has already been rendered moot. Best not to try.

It is only fitting, therefore, that we as neuroimagers all "get on the same page" and learn "the right way to do things," and, when possible, make "air quotes." This is another way of saying that this blog is an undisguised attempt to dominate the thoughts and soul of every neuroimager - in short, to ensure unity. And I can think of no greater emblem of unity than the normal distribution, also known as the Z-distribution - the end, the omega, the seal of all distributions. The most vicious of arguments, the most controversial of ideas are quickly resolved by appeal to this monolith; it towers over all research questions like a baleful phallus.

There will be no end to bantering about whether to abolish the arbitrary nature of p less than 0.05, but the bantering will be just that. The standard exists for a reason - it is clear, simple, understood by nearly everyone involved, and is as good a standard as any. A multitude of standards, a deviation from what has become so steeped in tradition, would be chaos, mayhem, a catastrophe. Again, best not to try.

I wish to clear away your childish notions that the Z-distribution is unfair or silly. On the contrary, it will dominate your research life until the day you die. Best to get along with it. The following SPM code will allow you to do just that - convert any output to the normal distribution, so that your results can be understood by anyone. Even by those who disagree, or wish to disagree, with the nature of this thing, will be forced to accept it. A shared Weltanschauung is a powerful thing. The most powerful.

=============

The following Matlab snippet was created by my adviser, Josh Brown. I take no credit for it, but I use it frequently, and believe others will get some use out of it. The calculators in each of the major statistical packages - SPM, AFNI, FSL - all do the same thing, and this is merely one application of it. The more one gets used to applying these transformations to achieve a desired result, the more intuitive it becomes to work with the data at any stage - registration, normalization, statistics, all.


%
% Usage:  convert_spm_stat(conversion, infile, outfile, dof)
%
% This script uses a template .mat batch script object to
% convert an SPM (e.g. SPMT_0001.hdr,img) to a different statistical rep.
% (Requires matlab stats toolbox)
%
%  Args:
%  conversion -- one of 'TtoZ', 'ZtoT', '-log10PtoZ', 'Zto-log10P',
%               'PtoZ', 'ZtoP'
%  infile -- input file stem (may include full path)
%  outfile -- output file stem (may include full pasth)
%  dof -- degrees of freedom
%
% Created by:           Josh Brown 
% Modification date:    Aug. 3, 2007
% Modified: 8/21/2009 Adam Krawitz - Added '-log10PtoZ' and 'Zto-log10P'
% Modified: 2/10/2010 Adam Krawitz - Added 'PtoZ' and 'ZtoP'

function completed=convert_spm_stat(conversion, infile, outfile, dof)

old_dir = cd();

if strcmp(conversion,'TtoZ')
    expval = ['norminv(tcdf(i1,' num2str(dof) '),0,1)'];
elseif strcmp(conversion,'ZtoT')
    expval = ['tinv(normcdf(i1,0,1),' num2str(dof) ')'];
elseif strcmp(conversion,'-log10PtoZ')
    expval = 'norminv(1-10.^(-i1),0,1)';
elseif strcmp(conversion,'Zto-log10P')
    expval = '-log10(1-normcdf(i1,0,1))';
elseif strcmp(conversion,'PtoZ')
    expval = 'norminv(1-i1,0,1)';
elseif strcmp(conversion,'ZtoP')
    expval = '1-normcdf(i1,0,1)';
else
    disp(['Conversion "' conversion '" unrecognized']);
    return;
end
    
if isempty(outfile)
    outfile = [infile '_' conversion];
end

if strcmp(conversion,'ZtoT')
    expval = ['tinv(normcdf(i1,0,1),' num2str(dof) ')'];
elseif strcmp(conversion,'-log10PtoZ')
    expval = 'norminv(1-10.^(-i1),0,1)';
end

%%% Now load into template and run
jobs{1}.util{1}.imcalc.input{1}=[infile '.img,1'];
jobs{1}.util{1}.imcalc.output=[outfile '.img'];
jobs{1}.util{1}.imcalc.expression=expval;

% run it:
spm_jobman('run', jobs);

cd(old_dir)
disp(['Conversion ' conversion ' complete.']);
completed = 1;



Assuming you have a T-map generated by SPM, and 25 subjects that went into the analysis, a sample command might be:

convert_spm_stat('TtoZ', 'spmT_0001', 'spmZ_0001', '24')

Note that the last argument is degrees of freedom, or N-1.

Slice Analysis of FMRI Data with SPM

Slice analysis is a simple procedure - first you take a jar of peanut butter and a jar of Nutella, and then use a spoon to take some Nutella and then use the same spoon to mix it with the peanut butter. Eat and repeat until you go into insulin shock, and then...

No, wait! I was describing my midnight snack. The actual slice analysis method, although less delicious, is infinitely more helpful in determining regional dissociations of activity, as well as avoiding diabetes. (Although who says they can't both be done at the same time?)

The first step is to extract contrast estimates for each slice from a region of interest (ROI, also pronounced "ROY") and then average across all the voxels in that slice for the subject. Of course, there is no way you would be able to do this step on your own, so we need to copy someone else's code from the Internet and adapt it to our needs; one of John Ashburner's code snippets (#23, found here) is a good template to start with. Here is my adaptation:



rootdir = '/data/drill/space10/PainStudy/fmri/'; %Change these to reflect your directory structure
glmdir = '/RESULTS/model_RTreg/'; %Path to SPM.mat and mask files

subjects = [202:209 211:215 217 219 220:222 224:227 229 230 232 233];
%subjects = 202:203;

Conditions.names = {'stroopSurpriseConStats', 'painSurpriseConStats'}; %Replace with your own conditions
Masks = {'stroopSurpriseMask.img', 'painSurpriseMask.img'}; %Replace with your own masks; should be the product of a binary ROI multiplied by your contrast of interest
Conditions.Contrasts = {'', ''};

ConStats = [];
Condition1 = [];
Condition2 = [];

for i=subjects
    
    cd([rootdir num2str(i) glmdir])
    outputPath = [rootdir num2str(i) glmdir]; %Should contain both SPM.mat file and mask files
    
    for maskIdx = 1:length(Masks)
      
    P = [outputPath Masks{(maskIdx)}];

    V=spm_vol(P);

    tmp2 = [];
    
     [x,y,z] = ndgrid(1:V.dim(1),1:V.dim(2),0);
     for i=1:V.dim(3),
       z   = z + 1;
       tmp = spm_sample_vol(V,x,y,z,0);
       msk = find(tmp~=0 & isfinite(tmp));
       if ~isempty(msk),
         tmp = tmp(msk);
         xyz1=[x(msk)'; y(msk)'; z(msk)'; ones(1,length(msk))];
         xyzt=V.mat(1:3,:)*xyz1;
         for j=1:length(tmp),
           tmp2 = [tmp2; xyzt(1,j), xyzt(2,j), xyzt(3,j), tmp(j)];
         end;
       end;
     end;

         xyzStats = sortrows(tmp2,2); %Sort relative to second column (Y column); 1 = X, 3 = Z
         minY = min(xyzStats(:,2));
         maxY = max(xyzStats(:,2));

         ConStats = [];

     for idx = minY:2:maxY
         x = find(xyzStats(:,2)==idx); %Go in increments of 2, since most images are warped to this dimension; however, change if resolution is different
         ConStats = [ConStats; mean(xyzStats(min(x):max(x),4))];
     end

    if maskIdx == 1
        Condition1 = [ConStats Condition1];
    elseif maskIdx == 2
        Condition2 = [ConStats Condition2];
    end

    end
end

Conditions.Contrasts{1} = Condition1;
Conditions.Contrasts{2} = Condition2;


This script assumes that there are only two conditions; more can be added, but care should be taken to reflect this, especially with the if/else statement near the end of the script. I could refine it to work with any amount of conditions, but that would require effort and talent.

Once these contrasts are loaded into your structure, you can then put them in an Excel spreadsheet or any other program that will allow you to format and save the contrasts in a tab-delimited text format. The goal is to prepare them for analysis in R, where you can test for main effects and interactions across the ROI for your contrasts. In Excel, I like to format it in the following four-column format:


Subject Condition Position  Contrast
202 Stroop 0 -0.791985669
202 Stroop 2 -0.558366941
202 Stroop 4 -0.338829942
202 Pain 0 0.17158524
202 Pain 2 0.267789503
202 Pain 4 0.192473782
203 Stroop 0 0.596162455
203 Stroop 2 0.44917655
203 Stroop 4 0.410870348
203 Pain 0 0.722974284
203 Pain 2 0.871030304
203 Pain 4 1.045700207


And so on, depending on how many subjects, conditions, and slices you have. (Note here that I have position in millimeters from the origin in the y-direction; this will depend on your standardized space resolution, which in this case is 2mm per slice.)

Once you export that to a tab-delimited text file, you can then read it into R and analyze it with code like the following:

setwd("~/Desktop")
x = read.table("SliceAnalysis.txt", header=TRUE)
x$Subject <- as.factor="" font="" ubject="" x="">
aov.x = aov(Contrast~(Condition*Position)+Error(Subject/(Condition*Position)),x)
summary(aov.x)
interaction.plot(x$Position, x$Condition, x$Contrast)


This will output statistics for main effects and interactions, as well as plotting the contrasts against each other as a function of position.

That's it! Enjoy your slices, crack open some jars of sugary products, and have some wild times!

Automating SPM Contrasts

Manually typing in contrasts in SPM is a grueling process that can have a wide array of unpleasant side effects, including diplopia, lumbago, carpal tunnel syndrome, psychosis, violent auditory and visual hallucinations, hives, and dry mouth. These symptoms are only compounded by the number of regressors in your model, and the number of subjects in your study.

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.

Parametric Modulation with SPM: Why Order Matters

Those who are the youngest children in the family are keenly aware of the disadvantages of being last in the pecking order; the quality of toys, clothes, and nutrition is often best for the eldest children in the family, and gradually tapers off as you reach the last kids, who are usually left with what are called, in parent euphemisms, "hand-me-downs."

In reality, these are things that should have been discarded several years ago due to being broken, unsafe, containing asbestos, or clearly being overused, such as pairs of underwear that have several holes in them so large that you're unsure of where exactly your legs go.

In SPM land, a similar thing happens to parametric modulators, with later modulators getting the dregs of the variance not wanted by the first modulators. Think of variance as toys and cake, and parametric modulators as children; everybody wants the most variance, but only the first modulators get the lion's share of it; the last modulators get whatever wasn't given to the first modulators.

The reason this happens is because when GLMs are set up, SPM uses a command, spm_orth, to orthogonalize the modulators. The first modulator is essentially untouched, but all other modulators coming after it are orthogonalized with respect to the first; and this proceeds in a stepwise function, with the second modulator getting whatever was unassigned to the first, the third getting whatever was unassigned to the second, and so on.

(You can see this for yourself by doing a couple of quick simulations in Matlab. Type x1=rand(10,1), and x2=rand(10,1), and then combine the two into a matrix with xMat = [x1 x2]. Type spm_orth(xMat), and notice that the first column is unchanged, while the second is greatly altered. You can then flip it around by creating another matrix, xMatRev = [x2 x1], and using spm_orth again to see what effect this change has.)

Be aware of this, as this can dramatically alter your results depending on how your modulators were ordered. (If they were completely orthogonal by design, then the spm_orth command should have virtually no impact; but this scenario is rare.) This came to my attention when looking at a contrast of a parametric modulator, which was one of three assigned to a main regressor. In one GLM, the modulator was last, and correspondingly explained almost no variance in the data; while in another GLM, the modulator was first, and loaded on much more of the brain. The following two figures show the difference, using exactly the same contrast and correction threshold, but simply reversing the order of the modulators:

Results window when parametric modulator was the last one in order.

Results window when parametric modulator was the first one in order.

This can be dealt with in a couple of ways:

1. Comment out the spm_orth call in two scripts used in setting up the GLM: line 229 in spm_get_ons.m, and lines 285-287 in spm_fMRI_design.m. However, this might affect people who actually do want the orthogonalization on, and also this increases the probability that you will screw something up. Also note that doing this means that the modulators will not get de-meaned when entered into the GLM.
2. Create your own modulators by de-meaning the regressor, inserting your value at the TR where the modulator occurred, then convolving this with an HRF (usually the one in SPM.xBF.bf). If needed, downsample the output to match the original TR.

It is well to be aware of all this, not only for your own contrasts, but to see how this is done in other papers. I recall reading one a long time ago - I can't remember the name - where several different models were used, in particular different models where the modulators were entered in different orders. Not surprisingly, the results were vastly different, although why this was the case wasn't clearly explained. Just something to keep in mind, as both a reader and a reviewer.

I should also point out that this orthogonalization does not occur in AFNI; the order of modulators has no influence on how they are estimated. A couple of simulations should help illustrate this:

Design matrices from AFNI with simulated data (one regressor, two parametric modulators.) Left: PM1 entered first, PM2 second. Right: Order reversed. The values are the same in both windows, just transposed to illustrate which one was first in the model.

More information on the topic can be found here, and detailed instructions on how to construct your own modulators can be found on Tor Wager's lab website.

Quick and Efficient ROI Analysis Using spm_get_data

For any young cognitive neuroscientist out for the main chance, ROI analyses are an indispensable part of the trade, along with having a cool, marketable name, such as Moon Unit or Dweezil Zappa. Therefore, you will find yourself doing a lot of such ROI analyses; and the quicker and more efficient you can do them, with a minimum of error, will allow you to succeed wildly and be able to embark upon an incredible, interesting career for the next four or five decades of your life before you die.

Most ROI analyses in SPM are carried out through the Marsbar toolbox, and for most researchers, that is all they will ever need. However, for those who feel more comfortable with the command line, there is a simple command within the SPM library - spm_get_data - that will make all of your ROI fantasies come true. All the command needs is an array of paths leading to the images you wish to extract data from, along with a matrix of coordinates representing the ROI.

First, the ROI coordinates can be gathered by loading up an arbitrary contrast and selecting an ROI created through, say, Marsbar or wfu_pickatlas. Next, set your corrected p-value threshold to 1; this will guarantee that every voxel in that ROI is "active," which will be recorded by a structure automatically generated each time the Results GUI is opened, a structure called xSPM. One of the fields, xSPM.XYZ, contains the coordinates for each voxel within that ROI. This can then be assigned to a variable, and the same procedure done for however many ROIs you have. The best part, however, is that you only need to do this once for each ROI; once you have assigned it to a variable, you can simply store it in a new .mat file with the "save" command (e.g., save('myROIs.mat', 'M1', 'ACC', 'V1')). These can then be restored to the Matlab workspace at any time by loading that .mat file.

Note: An alternative way to get these coordinates just from the command line would be the following:

Y = spm_read_vols(spm_vol(seedroi),1);
indx = find(Y>0);
[x,y,z] = ind2sub(size(Y),indx);

XYZ = [x y z]';

Where the variable "seedroi" can be looped over a cell containing the paths to each of your ROIs.


The next step is to create your array of images you wish to extract data from - which, conveniently, is stored within the SPM.mat file that is created any time you run a second-level analysis. For example, let's say that I carried out a couple of 2nd-level t-tests, one for left button presses, the other for right button presses. If I go into the folder for left button presses that has already estimated an run a second-level analysis, all of the contrast images that went into that analysis are now stored in SPM.xY.P, which is available in your workspace after simply navigating to the directory containing your SPM.mat file and typing "load SPM".

Lastly, spm_get_data is called to do the ROI analysis by extracting data from each voxel in the ROI for each subject for each contrast, and these are averaged across all of the voxels in that ROI using the "mean" function. Sticking with the current example, let's say have a left M1 region and a right M1 region, the coordinates of which have been extracted using the procedure detailed above, and which have been saved into variables called left_M1 and right_M1, respectively. I then navigate to the left button presses second-level directory, load SPM, and type the following command:

right_M1_leftButtonPress = mean(spm_get_data(SPM.xY.P, right_M1),2)

which returns an array of one value per subject for that contrast averaged across the ROI. You can then easily navigate to another second-level directory - say, right button presses - and, after loading the SPM.mat file, do the same thing:

right_M1_rightButtonPress = mean(spm_get_data(SPM.xY.P, right_M1),2)

T-tests can then be carried out between the sets of parameter or contrast estimates with the t_test function:

[h, p, ci, stats] = ttest(right_M1_leftButtonPress, right_M1_rightButtonPress)

which will return the p-value, confidence interval, and t-value that you would then report in your results.

Multiple Regression in SPM

A couple of videos have been posted about multiple regression in SPM, both at the first level and second level. Technically, almost all of the GLMs researchers usually set up use multiple linear regression, where a linear combination of weighted regressors is fit to the timecourse of each voxel. However, in SPM parlance, regressors are distinct from conditions: Conditions are typically convolved with some sort of basis function (usually a hemodynamic response function), whereas regressors are not convolved with anything. For example, someone may enter motion parameters as regressors, since they are not convolved with any hemodynamic shape, but still may account for some of the variance observed in the signal (and, we hope, the signal that is primarily due to motion). Another example would be entering in the timecourse extracted from an ROI to do a functional connectivity analysis, or inserting the interaction term from a PPI analysis as a regressor.

At the second level SPM also gives the option for doing multiple regression; but this is simply the entering of covariates which can be tested for correlations with the signal change in contrasts across subjects. In most cases, however, you can select any other design, and enter in covariates regardless of which design that you choose; which I believe to be the better option, since it is more flexible.

In any case, most of the rest of the details can be found in the following videos. Speak hands for me.

Updated SPM Command Line, Including Beta Series Option

I've updated the code of the previously discussed previous script in the previous post, previously, including the addition of more help text to explain certain options and termination of the script if the timing file is not found.

However, the biggest change is a block of code to convert the timings files so that each trial is now a regressor, before entering them into the GLM. With so many regressors, the GLM will look a little funky; possibly something like this:

Note that, since there are so many regressors and so many scans, not all of them are going to be labeled individually; but it should look as though there is only one estimation per column, or blip of white underneath each regressor. Also due to space constraints the parameter estimability may not be completely visible, but when each trial is being estimated individually, you should still get a beta for each trial. Check the output directory to make sure that you have the number of betas you think you should have: one for each trial, plus the amount of session constants (usually equal to the number of runs for the subject).

The updated script can be found here; as always, send me any comments if you encounter any serious issues or bugs. I recommend using it as a replacement for the previously discussed previous script, since it can either estimate each trial individually or average them together, depending on whether you set the DO_BETASERIES flag to 1 or 0.

All of this is explained clearly in the following video:

Running SPM First-Level Analyses from the Command Line

For most people, doing first-level analyses in SPM is tedious in the extreme; for other people, these analyses are manageable through command line scripting in Matlab. And there are still other people who don't even think about first-level analyses or SPM or Matlab or any of that. This last class of people are your college classmates who majored in something else, like economics, and are living significantly less stressful lives than you, making scads of money, and regularly eating out at restaurants where they serve shrimp the size of Mike Tyson's forearm.

Assuming that you belong to the first category, however, first-level analyses are invariably a crashing bore; and, worse, completely impractical for something like beta-series analysis, where a separate regressor is needed for individual events.

The following script will do all of that for you, although it makes many assumptions about your timing files and where everything is stored. First, your directory needs to be set up with a root directory containing all of the subjects, as well as a separate timing directory containing your onsets files; and second, that the timing files are written in a four-column format of runs, regressors, onsets (in seconds), and duration. Once you have all of that, though, all it takes is simply modifications of the script to run your analysis.

As always, let me know if this actually helps, or whether there are sections of code that can be cleaned up. I will be making a modification tomorrow allowing for beta series analysis by converting all of the timing events into individual regressors, which should help things out.

Link to script: http://mypage.iu.edu/~ajahn/docs/Run_SPM_FirstLevel.m

% INPUT:
%  rootDir        - Path to where subjects are located
%  subjects       - Vector containing list of subjects (can concatenate with brackets)
%  spmDir         - Path to folder where you want to move the multiple conditions .mat files and where to the output SPM file (directory is created if it doesn't
%  exist)
%  timingDir      - Path to timing directory, relative to rootDir
%  timingSuffix   - Suffix appended to timing files
%  matPrefix      - Will be prefixed to output .mat files.
%  dataDir        - Directory storing functional runs.
%
% OUTPUT:
%  One multiple condition .mat file per run. Also specifies the GLM and runs
%  beta estimation.
%       
%
%       Note that this script assumes that your timing files are organized
%       with columns in the following order: Run, ConditionName, Onset (in
%       seconds), Duration (in seconds)
%
%       Ideally, this timing file should be written out from your
%       presentation software, e.g., E-Prime
%
%       Also note how the directory tree is structured in the following example. You may have to
%       change this script to reflect your directory structure.
%       
%       Assume that:
%         rootdir = '/server/studyDirectory/'
%         spmDir = '/modelOutput/'
%         timingDir = '/timings/onsets/'
%         subjects = [101]
%         timingSuffix = '_timing.txt'
%       1) Sample path to SPM directory:
%       '/server/studyDirectory/101/modelOutput/'
%       2) Sample path to timing file in the timing directory:
%       '/server/studyDirectory/timings/onsets/101_timing.txt'
%
% Andrew Jahn, Indiana University, June 2014
% andysbrainblog.blogspot.com

clear all

%%%----Things you will want to change for your study----%%%

rootDir = '/data/hammer/space5/ImagineMove/fmri/';
subjects = [214 215];
spmDir = '/RESULTS/testMultis/';
timingDir = 'fMRI_behav/matlab_input/';
timingSuffix = '_spm_mixed.txt';
matPrefix = 'testMulti';
dataDir = '/RESULTS/data/';

%Change these parameters to reflect study-specific information about number
%of scans and discarded acquisitions (disacqs) per run
funcs = {'swuadf0006.nii', 'swuadf0007.nii', 'swuadf0008.nii'}; %You can add more functional runs; just remember to separate them with a comma
numScans = [240 240 240]; %This if the number of scans that you acquire per run
disacqs = 2; %This is the number of scans you later discard during preprocessing
numScans = numScans-disacqs;
TR = 2; %Repetition time, in seconds

%Estimating the GLM can take some time, particularly if you have a lot of betas. If you just want to specify your
%design matrix so that you can assess it for singularities, turn this to 0.
%If you wish to do it later, estimating the GLM through the GUI is very
%quick.
ESTIMATE_GLM = 1;


%%%-----------------------------------------------------%%%



%For each subject, create timing files and jobs structure
for subject = subjects
    
    %See whether output directory exists; if it doesn't, create it
    outputDir = [rootDir num2str(subject) spmDir];
    
    if ~exist(outputDir)
        mkdir(outputDir)
    end
     
    %---Navigate to timing directory and create .mat files for each run---%
    cd([rootDir timingDir]);
    
    fid = fopen([num2str(subject) timingSuffix], 'rt');
    T = textscan(fid, '%f %s %f %f', 'HeaderLines', 1); %Columns should be 1)Run, 2)Regressor Name, 3) Onset Time (in seconds, relative to start of each run), and 4)Duration, in seconds
    fclose(fid);
    
    clear runs nameList names onsets durations sizeOnsets %Remove these variables if left over from previous analysis
    
    runs = unique(T{1});

    %Begin creating jobs structure
    jobs{1}.stats{1}.fmri_spec.dir = cellstr(outputDir);
    jobs{1}.stats{1}.fmri_spec.timing.units = 'secs';
    jobs{1}.stats{1}.fmri_spec.timing.RT = TR;
    jobs{1}.stats{1}.fmri_spec.timing.fmri_t = 16;
    jobs{1}.stats{1}.fmri_spec.timing.fmri_t0 = 1;

    %Create multiple conditions .mat file for each run
    for runIdx = 1:size(runs, 1)
            nameList = unique(T{2});
            names = nameList';
            onsets = cell(1, size(nameList,1));
            durations = cell(1, size(nameList,1));
            sizeOnsets = size(T{3}, 1);
        for nameIdx = 1:size(nameList,1)
            for idx = 1:sizeOnsets
                if isequal(T{2}{idx}, nameList{nameIdx}) && T{1}(idx) == runIdx
                    onsets{nameIdx} = double([onsets{nameIdx} T{3}(idx)]);
                    durations{nameIdx} = double([durations{nameIdx} T{4}(idx)]);
                end
            end
            onsets{nameIdx} = (onsets{nameIdx} - (TR*disacqs)); %Adjust timing for discarded acquisitions
        end

        save ([matPrefix '_' num2str(subject) '_' num2str(runIdx)], 'names', 'onsets', 'durations')
        
        %Grab frames for each run using spm_select, and fill in session
        %information within jobs structure
        files = spm_select('ExtFPList', [rootDir num2str(subject) dataDir], ['^' funcs{runIdx}], 1:numScans);

        jobs{1}.stats{1}.fmri_spec.sess(runIdx).scans = cellstr(files);
        jobs{1}.stats{1}.fmri_spec.sess(runIdx).cond = struct('name', {}, 'onset', {}, 'duration', {}, 'tmod', {}, 'pmod', {});
        jobs{1}.stats{1}.fmri_spec.sess(runIdx).multi = cellstr([outputDir matPrefix '_' num2str(subject) '_' num2str(runIdx) '.mat']);
        jobs{1}.stats{1}.fmri_spec.sess(runIdx).regress = struct('name', {}, 'val', {});
        jobs{1}.stats{1}.fmri_spec.sess(runIdx).multi_reg = {''};
        jobs{1}.stats{1}.fmri_spec.sess(runIdx).hpf = 128;
        
                
    end
    
    movefile([matPrefix '_' num2str(subject) '_*.mat'], outputDir)
    
    %Fill in the rest of the jobs fields
    jobs{1}.stats{1}.fmri_spec.fact = struct('name', {}, 'levels', {});
    jobs{1}.stats{1}.fmri_spec.bases.hrf = struct('derivs', [0 0]);
    jobs{1}.stats{1}.fmri_spec.volt = 1;
    jobs{1}.stats{1}.fmri_spec.global = 'None';
    jobs{1}.stats{1}.fmri_spec.mask = {''};
    jobs{1}.stats{1}.fmri_spec.cvi = 'AR(1)';
    
    %Navigate to output directory, specify and estimate GLM
    cd(outputDir);
    spm_jobman('run', jobs)
    
    if ESTIMATE_GLM == 1
        load SPM;
        spm_spm(SPM);
    end
    
end

%Uncomment the following line if you want to debug
%keyboard

Writing Out Timing Files from E-Prime

Now, I promised you all something that would help make running SPM analyses easier; for although we would all like something that is both transparent and usable, in most FMRI packages they are polar of each other. SPM in many ways is very usable; but this tends to be more hindrance than asset when running large groups of subjects, when manually selecting each session and copying and pasting by hand become not only tedious, but dangerous, as the probability of human error is compounded with each step. Best to let the machines do the work for you when you can. Anything else is an affront to them; you'll wake more than just the dogs.

Do assist our future overlords in their work, command lines for each step should be used whenever possible. But, rummily enough, SPM offers little documentation about this, and it more or less needs to be puzzled out on your own. For most processing this wouldn't be too much of a hassle, but there is one step where it is indispensable, more indispensable, if possible, than Nutella on a banana sprinkled with chopped walnuts. Or possibly warm Nutella drizzled on top of vanilla ice cream and left standing until it cools and hardens into a shell. Has anyone tried this? Could you let me know how it tastes? Thanks!

Where was I? Right; the one step where you need batching is first-level analysis, where timing information is entered for each session (or run) for each subject. Back in the autumn of aught eight during my days as a lab technician I used to be just like you, arriving to work in the morning and easing my falstaffian bulk into my chair before starting my mind-numbing task of copying and pasting onset times into each condition for each run, invariably tiring of it after a few minutes but keeping at it, and maybe completing a few subjects by the end of the day. Not the most exciting or fulfilling work, and I probably screwed a lot of things up without noticing it.

SPM has tried to anticipate problems like this by specifying a Multiple Condition option through their interface, where you supply a .mat file for each session that contains all of the names, onsets, and durations for that session, and everything is automatically filled in for you. Which sounds great, until you realize that creating Matlab files is, like, hard, and then you go back to your old routine. (The manual also says that these .mat files can be generated automatically by presentation software such as COGENT, but I have never met anyone who has ever used COGENT; this is, in all likelihood, a fatcat move by Big Science to get you to buy a piece of software that you don't need.)

What we will be doing, then, is trying to make this as straightforward and simple of a process as possible. However, this assumes that you have your onsets organized in a certain way; and first we will talk about how to create those onsets from your stimulus presentation program. This will allow you much more flexibility, as you can choose what you want to write into a simple text file without having to go through copying and pasting data from the E-Prime DataAid program into Excel for further processing.  (I use E-Prime, and will be reviewing that here, but I believe that most presentation programs will allow you to write out data on the fly.)

Within E-Prime, you will need to know exactly when the scanner started, and use this as time zero for your onsets files. I usually do this by having a screen that says "Waiting for scanner..." which only terminates once the scanner sends a trigger pulse through a USB cord hooked up to the presentation laptop. We have a Siemens Trio scanner, which sends a backtick ( ` ); check whether your scanner does the same.

Note that I have an inline script (i.e., an object that contains E-Basic code) right after the WaitScanner slide that contains the following code:

StartTimestamp = CLng(c.GetAttrib("WaitScanner.RTTime")
c.SetAttrib "StartTimestamp", StartTimestamp

if c.GetAttrib("Session") = 1 Then

Open "OnsetTimes_" & c.GetAttrib("Subject") & ".txt" For Output as #1
Close #1

Open "OnsetTimes_" & c.GetAttrib("Subject") & ".txt" For Append As #1
      Print #1, "Run", "Event", "Onset", "Dur"
Close #1

end if


Also, in the duration/input tab for the properties of waitscanner, I have the Keyboard accepting ( ` ) as an input, and termination when it receives that backtick. The StartTimestamp will simply record when the scanner first started, and the if/then statement ensures that the file does not get overwritten when the next run is started.

After that, the StartTimestamp variable and the newly create text file can be used to record information after each event, with the specified condition being determined by you. To calculate the timing for each event, all you have to do is grab the onset time of a condition through the c.GetAttrib command, and subtract the StartTimestamp variable from that:

Additional runs will continue to append to the text file, until you have all of the timing information that you need for your subject. This can then be parsed by a script and divided into multiple condition .mat files, which we will discuss tomorrow.

Beta Series Analysis in SPM

The following is a script to run beta series analysis in SPM; which, conceptually, is identical to beta series analysis in AFNI, just made more complicated by the use of Matlab commands, many of which I would otherwise remain totally ignorant of.

Also, if you believe that I did this on my own, you are a scoundrel and a fool. Virtually all of the code is from my colleague Derek Nee, a postdoc who used to work in my lab and who remains my superior in all things, scientific and otherwise. He also once got me with the old "If you say gullible slowly, it sounds like orange" joke.

In any case, all that I did was repackage it into a function to be more accessible by people outside of our lab. You may see a couple snippets of code that are useful outside of just doing beta series analysis; for example, the spm_get_data command, which can be used for parameter extraction from the command line without using marsbar. Also note that the script assumes that each trial for a condition has been labeled separately with the regressor name followed by a "_" and then the occurrence of the trial. So, for example, if I were looking at 29 instances of pressing the left button, and the regressor name was LeftButtonPress, I would have LeftButtonPress_1, LeftButtonPress_2, LeftButtonPress_3...LeftButtonPress_29.

Obviously, this is very tedious to do by hand. The Multiple Conditions option through the SPM GUI for setting up 1st level analyses is indispensable; but to cover that is another tutorial, one that will probably cross over into our discussion of GLMs.


The script:

function DoBetaSeries(rootdir, subjects, spmdir, seedroi, Conds, MapNames)
% function BetaSeries(rootdir, subjects, spmdir, seedroi, Conds, MapNames)
% INPUT:
%  rootdir  - Path to where subjects are stored
%  subjects - List of subjects (can concatenate with brackets)
%  spmdir   - Path to folder containing SPM file
%  seedroi  - Absolute path to file containing ROI in NIFTI format
%  Conds    - List of conditions for beta series analysis
%  MapNames - Output for list of maps, one per Conds cell
%       
%   Example use:
%       BetaSeries('/data/study1/fmri/', [101 102 103],
%       'model/BetaSeriesDir/', '/data/study1/Masks/RightM1.nii',
%       {'TapLeft'}, {'TapLeft_RightM1'})
%
%       conditions to be averaged together should be placed together in a cell
%       separate correlation maps will be made for each cell
%       Conds = {'Incongruent' 'Congruent' {'Error' 'Late'}}; 
%
%       For MapNames, there should be one per Conds cell above
%       e.g., with the Conds above, MapNames = {'Incongruent_Map',
%       'Congruent_Map', 'Error_Late_Map'}
%
%       Once you have z-maps, these can be entered into a 2nd-level
%       1-sample t-test, or contrasts can be taken between z-maps and these
%       contrasts can be taken to a 1-sample t-test as well.
%
% 

if nargin < 5
 disp('Need rootdir, subjects, spmdir, seedroi, Conds, MapNames. See "help BetaSeries" for more information.')
    return
end


%Find XYZ coordinates of ROI
Y = spm_read_vols(spm_vol(seedroi),1);
indx = find(Y>0);
[x,y,z] = ind2sub(size(Y),indx);
XYZ = [x y z]';


%Find each occurrence of a trial for a given condition
%These will be stacked together in the Betas array
for i = 1:length(subjects)
    subj = num2str(subjects(i));
    disp(['Loading SPM for subject ' subj]);
    %Can change the following line of code to CD to the directory
    %containing your SPM file, if your directory structure is different
    cd([rootdir subj filesep spmdir]);
    load SPM;
    
    for cond = 1:length(Conds)
        Betas = [];
        currCond = Conds{cond};
        if ~iscell(currCond)
            currCond = {currCond};
        end
        for j = 1:length(SPM.Vbeta) 
            for k = 1:length(currCond)
                if ~isempty(strfind(SPM.Vbeta(j).descrip,[currCond{k} '_'])) 
                    Betas = strvcat(Betas,SPM.Vbeta(j).fname);
                end
            end
        end
              

    %Extract beta series time course from ROI
    %This will be correlated with every other voxel in the brain
    if ischar(Betas)
        P = spm_vol(Betas);
    end

    est = spm_get_data(P,XYZ);
    est = nanmean(est,2);

        
        
        %----Do beta series correlation between ROI and rest of brain---%

            MapName = MapNames{cond};
            disp(['Performing beta series correlation for ' MapName]);
            
            Vin = spm_vol(Betas);
            nimgo = size(Vin,1);
            nslices = Vin(1).dim(3);

            % create new header files
            Vout_r = Vin(1);   
            Vout_p = Vin(1);
            [pth,nm,xt] = fileparts(deblank(Vin(1).fname));
            Vout_r.fname = fullfile(pth,[MapNames{cond} '_r.img']);
            Vout_p.fname = fullfile(pth,[MapNames{cond} '_p.img']);

            Vout_r.descrip = ['correlation map'];
            Vout_p.descrip = ['p-value map'];

            Vout_r.dt(1) = 16;
            Vout_p.dt(1) = 16;

            Vout_r = spm_create_vol(Vout_r);
            Vout_p = spm_create_vol(Vout_p);

            % Set up large matrix for holding image info
            % Organization is time by voxels
            slices = zeros([Vin(1).dim(1:2) nimgo]);
            stack = zeros([nimgo Vin(1).dim(1)]);
            out_r = zeros(Vin(1).dim);
            out_p = zeros(Vin(1).dim);


            for i = 1:nslices
                B = spm_matrix([0 0 i]);
                %creates plane x time
                for j = 1:nimgo
                    slices(:,:,j) = spm_slice_vol(Vin(j),B,Vin(1).dim(1:2),1);
                end

                for j = 1:Vin(1).dim(2)
                    stack = reshape(slices(:,j,:),[Vin(1).dim(1) nimgo])';
                    [r p] = corr(stack,est);
                    out_r(:,j,i) = r;
                    out_p(:,j,i) = p;

                end

                Vout_r = spm_write_plane(Vout_r,out_r(:,:,i),i);
                Vout_p = spm_write_plane(Vout_p,out_p(:,:,i),i);

            end

                
            %Convert correlation maps to z-scores
            %NOTE: If uneven number of trials in conditions you are
            %comparing, leave out the "./(1/sqrt(n-3)" term in the zdata
            %variable assignment, as this can bias towards conditions which
            %have more trials
            
                disp(['Converting correlation maps for subject ' subj ', condition ' MapNames{cond} ' to z-scores'])
                P = [MapNames{cond} '_r.img'];
                n = size(Betas,1);
                Q = MapNames{cond};
                Vin = spm_vol([MapNames{cond} '_r.img']);

                % create new header files
                Vout = Vin;   

                [pth,nm,xt] = fileparts(deblank(Vin(1).fname));
                Vout.fname = fullfile(pth,[Q '_z.img']);

                Vout.descrip = ['z map'];

                Vout = spm_create_vol(Vout);

                data = spm_read_vols(Vin);
                zdata = atanh(data)./(1/sqrt(n-3));

                spm_write_vol(Vout,zdata);

    end
end



This can either be copied and pasted into a script, or downloaded as a .m file here.

The Next Three Months

Comrades, friends, visionaries, thinkers, and fortune-hunters,

This is, barring any unforeseen circumstances, such as meteor strike, death by Nutella overdose, or floss strangulation, my last summer of graduate school at Indiana University; and I will be spending the majority of that time working on my dissertation to escape this durance vile.

However, I have also been thinking (which should worry you), and it appears to me that what this site has become is a pastiche of training and tutorials which deal with small, isolated problems: How to do this for your data, what it means, and then moving on to the next, often without a clear link between the two, and often with a joke or two thrown in for good measure as a diversion to briefly suspend the terror that haunts your guilt-ridden minds. No doubt some come here only to receive what goods they need and then ride on, I no more than a digital manciple supplying a growing, ravenous demand for answers. Some may come here to merely carp and backbite at my charlatanry; others out of a morbid curiosity for ribald stories and for whatever details about my personal life they can divine; and still others, perhaps, see something here that warms something inside of them.

What is needed, however, is a focus on what ties them all together; not only technique and methods, but ways of thinking that will stand you in good stead for whatever you wish to figure out, neuroimaging or otherwise. And, as new wine requires new bottles, a higher-order website is needed to serve as a hub where everything that I have created, along with other materials, lies together; where one is able to quickly find the answers that they need, whether through videos or links to the appropriate tools tools and scripts, along with educational material about the most important concepts to grasp before moving forward with a particular problem. Problem-solution flowcharts, perhaps. Applets that quiz you and record scores, possibly. And, of course, links to my haberdashers. (You think I am making a fortune? Ascots and cufflinks cost more, and without them one would not be in good taste.)

So much for exhortations. Actually putting this into practice is the difficult part, and is an effort that could, quite possibly, kill me from exhaustion, or at least cause me to procrastinate endlessly. We shall see. At least I have taken a few steps towards starting, which includes looking at some HTML code and prodding at it thoughtfully through the computer screen with my finger. I'm not exactly sure how HTML works; but at least whenever I am working with it, I take pains to look very concerned and deep in thought.

However, before this endeavor there are still a few matters left to discuss, which I had earlier promised but still haven't delivered yet. In some states I would be hanged for such effrontery; but your continued patience means that either 1) You really, really don't want to read the manual (and who can blame you?), or 2) You find it worth the wait. Regardless, and whatever my behavior suggests to the contrary, I do want to help out with these concepts, but I do not always get to everyone's questions. Sometimes too much time passes, and they are gradually swept into the vast oubliette of other stuff I tend to forget; other times, some emails do in fact miss me, and aren't opened until much later, when it would seem too late to reply. In any case, I apologize if I have forgotten any of you. Below is a brief schedule of what I plan to cover:

1. Independent Components Analysis with FSL
2. Beta Series Analysis in SPM
3. Explanation of GLMs
4. Multiple Regression in SPM
5. MVPA in AFNI
6. An explanation of GSReg, and why it's fashionable to hate it, using example data on the AFNI website
7. Overview of 3dTproject

And a couple of minor projects which I plan to get to at some point:

1. Comparison of cluster correction algorithms in SPM and AFNI
2. How to simulate Kohonen nets in Matlab, which will make your nerd cred go through the roof

We will start tomorrow with Independent Components Analysis, a method considerably different from seed-based analysis, starting with the differences between the two and terminating at a destination calamitous beyond all reckoning; and then moving on to Beta Series analysis in SPM, which means doing something extremely complicated through Matlab, and plagiarizing code from one of my mentors; and then the rest of it in no set order, somewhere within the mists beyond right knowing where the eye rolls crazily and the lip jerks and drools.

Saving Cluster-Corrected Images in SPM

Once you've loaded up a contrast in SPM and thresholded it at a particular correction threshold, such as cluster corrected or FWE corrected, you can then simply save that image into a .hdr/.img file for use in another viewer, such as MRIcron. Just push the save button in the results window, and there you have it. This image can also be used for other purposes, such as doing an ROI analysis within only those voxels passing a corrected threshold for a given contrast.

Cluster-corrected image in the SPM interface

Same image, but saved out into .hdr/.img format and viewed in MRIcron.

In case it isn't clear how to do this from the above, I've also made an instructional video about how to do it, which - if you can believe it - was done in only one take. I have my little victories.

What's in an SPM.mat File?

A while back I attempted to write up a document that summarized everything someone would need to know about using SPM. It was called, I believe, the SPM User's Ultimate Guide: For Alpha Males, By Alpha Males. The title was perhaps a little ambitious, since I stopped updating it after only a few dozen pages. In particular I remember a section where I attempted to tease apart everything contained within the SPM.mat files output after first- and second-level analyses. To me this was the most important section, since complex model setups could be executed fairly easily by someone with a good understanding of Matlab code, but I never completed it.

Fortunately, however, there is someone out there who already vivisected the SPM.mat file and publicly displayed its gruesome remains in the online piazza. Researcher Nikki Sullivan has written an excellent short summary of what each field means, broken down into neat, easily digestible categories. You can find it on her website here, and I have also copied and pasted the information below. It makes an excellent shorthand reference, especially if you've forgotten, for example, where contrast weights are stored in the structure, and don't want to go through the tedium of typing SPM, then SPM.xY, then SPM.xY.VY, and so on.

But if you've forgotten how to rock that body? Girl, ain't no remedy for that.

========================

details on experiment:

 

SPM.xY.RT - TR length (RT ="repeat time")
SPM.xY.P - matrix of file names
SPM.xY.VY - # of runs x 1 struct array of mapped image volumes (.img file info)
SPM.modality - the data you're using (PET, FMRI, EEG)
SPM.stats.[modality].UFp - critical F-threshold for selecting voxels over which the non-sphericity is estimated (if required) [default: 0.001]
SPM. stats.maxres - maximum number of residual images for smoothness estimation
SPM. stats.maxmem - maximum amount of data processed at a time (in bytes)
SPM.SPMid - version of SPM used
SPM.swd - directory for SPM.mat and img files. default is pwd

basis function:

 

SPM.xBF.name - name of basis function
SPM.xBF.length - length in seconds of basis
SPM.xBF.order - order of basis set
SPM.xBF.T - number of subdivisions of TR
SPM.xBF.T0 - first time bin (see slice timing)
SPM.xBF.UNITS - options: 'scans'|'secs' for onsets
SPM.xBF.Volterra - order of convolution
SPM.xBF.dt - length of time bin in seconds
SPM.xBF.bf - basis set matrix

Session Stucture:

 

user-specified covariates/regressors (e.g. motion)
SPM.Sess([sesssion]).C.C - [nxc double] regressor (c#covariates,n#sessions)
SPM.Sess([sesssion]).C.name - names of covariates
conditions & modulators specified - i.e. input structure array
SPM.Sess([sesssion]).U(condition).dt: - time bin length {seconds}
SPM.Sess([sesssion]).U(condition).name - names of conditions
SPM.Sess([sesssion]).U(condition).ons - onset for condition's trials
SPM.Sess([sesssion]).U(condition).dur - duration for condition's trials
SPM.Sess([sesssion]).U(condition).u - (t x j) inputs or stimulus function matrix
SPM.Sess([sesssion]).U(condition).pst - (1 x k) peri-stimulus times (seconds)
parameters/modulators specified
SPM.Sess([sesssion]).U(condition).P - parameter structure/matrix
SPM.Sess([sesssion]).U(condition).P.name - names of modulators/parameters
SPM.Sess([sesssion]).U(condition).P.h - polynomial order of modulating parameter (order of polynomial expansion where 0none)
SPM.Sess([sesssion]).U(condition).P.P - vector of modulating values
SPM.Sess([sesssion]).U(condition).P.P.i - sub-indices of U(i).u for plotting
scan indices for sessions
SPM.Sess([sesssion]).row
effect indices for sessions
SPM.Sess([sesssion]).col
F Contrast information for input-specific effects
SPM.Sess([sesssion]).Fc
SPM.Sess([sesssion]).Fc.i - F Contrast columns for input-specific effects
SPM.Sess([sesssion]).Fc.name - F Contrast names for input-specific effects
SPM.nscan([session]) - number of scans per session (or if e.g. a t-test, total number of con*.img files)

global variate/normalization details

 

SPM.xGX.iGXcalc - either "none" or "scaling." for fMRI usually is "none" (no global normalization). if global normalization is "Scaling", see spm_fmri_spm_ui for parameters that will then appear under SPM.xGX.

design matrix information:

 

SPM.xX.X - Design matrix (raw, not temporally smoothed)
SPM.xX.name - cellstr of parameter names corresponding to columns of design matrix
SPM.xX.I - nScan x 4 matrix of factor level indicators. first column is the replication number. other columns are the levels of each experimental factor. SPM.xX.iH - vector of H partition (indicator variables) indices
SPM.xX.iC - vector of C partition (covariates) indices
SPM.xX.iB - vector of B partition (block effects) indices
SPM.xX.iG - vector of G partition (nuisance variables) indices
SPM.xX.K - cell. low frequency confound: high-pass cutoff (secs)
SPM.xX.K.HParam - low frequency cutoff value
SPM.xX.K.X0 - cosines (high-pass filter)
SPM.xX.W - Optional whitening/weighting matrix used to give weighted least squares estimates (WLS).

• if not specified spm_spm will set this to whiten the data and render the OLS estimates maximum likelihood i.e. W*W' inv(xVi.V).

SPM.xX.xKXs - space structure for K*W*X, the 'filtered and whitened' design matrix
SPM.xX.xKXs.X - Mtx - matrix of trials and betas (columns) in each trial
SPM.xX.xKXs.tol - tolerance
SPM.xX.xKXs.ds - vectors of singular values
SPM.xX.xKXs.u - u as in X u*diag(ds)*v'
SPM.xX.xKXs.v - v as in X u*diag(ds)*v'
SPM.xX.xKXs.rk - rank
SPM.xX.xKXs.oP - orthogonal projector on X
SPM.xX.xKXs.oPp - orthogonal projector on X'
SPM.xX.xKXs.ups - space in which this one is embedded
SPM.xX.xKXs.sus - subspace
SPM.xX.pKX - pseudoinverse of K*W*X, computed by spm_sp
SPM.xX.Bcov - xX.pKX*xX.V*xX.pKX - variance-covariance matrix of parameter estimates (when multiplied by the voxel-specific hyperparameter ResMS of the parameter estimates (ResSS/xX.trRV ResMS) )
SPM.xX.trRV - trace of R*V
SPM.xX.trRVRV - trace of RVRV
SPM.xX.erdf - effective residual degrees of freedom (trRV^2/trRVRV)
SPM.xX.nKX - design matrix (xX.xKXs.X) scaled for display (see spm_DesMtx('sca',... for details) SPM.xX.sF - cellstr of factor names (columns in SPM.xX.I, i think) SPM.xX.D - struct, design definition SPM.xX.xVi - correlation constraints (see non-sphericity below) SPM.xC - struct. array of covariate info

header info

 

SPM.P - a matrix of filenames
SPM.V - a vector of structures containing image volume information.
SPM.V.fname - the filename of the image.
SPM.V.dim - the x, y and z dimensions of the volume
SPM.V.dt - A 1x2 array. First element is datatype (see spm_type). The second is 1 or 0 depending on the endian-ness.
SPM.V.mat- a 4x4 affine transformation matrix mapping from voxel coordinates to real world coordinates.
SPM.V.pinfo - plane info for each plane of the volume.
SPM.V.pinfo(1,:) - scale for each plane
SPM.V.pinfo(2,:) - offset for each plane The true voxel intensities of the jth image are given by: val*V.pinfo(1,j) + V.pinfo(2,j)
SPM.V.pinfo(3,:) - offset into image (in bytes).If the size of pinfo is 3x1, then the volume is assumed to be contiguous and each plane has the same scalefactor and offset.

structure describing intrinsic temporal non-sphericity

 

SPM.xVi.I - typically the same as SPM.xX.I SPM.xVi.h - hyperparameters
SPM.xVi.V xVi.h(1)*xVi.Vi{1} + ...
SPM.xVi.Cy - spatially whitened (used by ReML to estimate h)
SPM.xVi.CY - <(Y - )*(Y - )'>(used by spm_spm_Bayes)
SPM.xVi.Vi - array of non-sphericity components

• defaults to {speye(size(xX.X,1))} - i.ii.d.
• specifying a cell array of contraints ((Qi)
• These contraints invoke spm_reml to estimate hyperparameters assuming V is constant over voxels that provide a high precise estimate of xX.V

SPM.xVi.form - form of non-sphericity (either 'none' or 'AR(1)')
SPM.xX.V - Optional non-sphericity matrix. CCov(e)sigma^2*V.

• If not specified spm_spm will compute this using a 1st pass to identify signifcant voxels over which to estimate V. A 2nd pass is then used to re-estimate the parameters with WLS and save the ML estimates (unless xX.W is already specified)

 

filtering information

 

SPM.K - filter matrix or filtered structure:

• SPM.K(s) - struct array containing partition-specific specifications
• SPM.K(s).RT - observation interval in seconds
• SPM.K(s).row - row of Y constituting block/partitions
• SPM.K(s).HParam - cut-off period in seconds
• SPM.K(s).X0 - low frequencies to be removed (DCT)
• SPM.Y - filtered data matrix

 

masking information

 

SPM.xM - Structure containing masking information, or a simple column vector of thresholds corresponding to the images in VY.
SPM.xM.T - [n x 1 double] - Masking index
SPM.xM.TH - nVar x nScan matrix of analysis thresholds, one per image
SPM.xM.I - Implicit masking (0 --> none; 1 --> implicit zero/NaN mask)
SPM.xM.VM - struct array of mapped explicit mask image volumes
SPM.xM.xs - [1x1 struct] cellstr description

design information (self-explanatory names, for once) 

 

SPM.xsDes.Basis_functions - type of basis function
SPM.xsDes.Number_of_sessions
SPM.xsDes.Trials_per_session
SPM.xsDes.Interscan_interval
SPM.xsDes.High_pass_Filter
SPM.xsDes.Global_calculation
SPM.xsDes.Grand_mean_scaling
SPM.xsDes.Global_normalisation

details on scannerdata (e.g. smoothness) 

 

SPM.xVol - structure containing details of volume analyzed
SPM.xVol.M- 4x4 voxel --> mm transformation matrix
SPM.xVol.iM - 4x4 mm --> voxel transformation matrix
SPM.xVol.DIM - image dimensions - column vector (in voxels)
SPM.xVol.XYZ - 3 x S vector of in-mask voxel coordinates
SPM.xVol.S- Lebesgue measure or volume (in voxels)
SPM.xVol.R- vector of resel counts (in resels)
SPM.xVol.FWHM - Smoothness of components - FWHM, (in voxels)

info on beta files:

 

SPM.Vbeta - struct array of beta image handles
SPM.Vbeta.fname - beta img file names
SPM.Vbeta.descrip - names for each beta file

info on variance of the error

 

SPM.VResMS - file struct of ResMS image handle
SPM.VResMS.fname - variance of error file name

info on mask

 

SPM.VM - file struct of Mask image handle
SPM.VM.fname - name of mask img file

contrast details (added after running contrasts)

 

SPM.xCon - Contrast definitions structure array

• (see also spm_FcUtil.m for structure, rules &handling)

SPM.xCon.name - Contrast name
SPM.xCon.STAT - Statistic indicator character ('T', 'F' or 'P')
SPM.xCon.c - Contrast weights (column vector contrasts)
SPM.xCon.X0 - Reduced design matrix data (spans design space under Ho)

• Stored as coordinates in the orthogonal basis of xX.X from spm_sp
• (Matrix in SPM99b)
• Extract using X0 spm_FcUtil('X0',...
  

SPM.xCon.iX0 - Indicates how contrast was specified:

• If by columns for reduced design matrix then iX0 contains the column indices.
• Otherwise, it's a string containing the spm_FcUtil 'Set' action: Usually one of {'c','c+','X0'} defines the indices of the columns that will not be tested. Can be empty.

SPM.xCon.X1o - Remaining design space data (X1o is orthogonal to X0)

• Stored as coordinates in the orthogonal basis of xX.X from spm_sp (Matrix in SPM99b) Extract using X1o spm_FcUtil('X1o',...

SPM.xCon.eidf - Effective interest degrees of freedom (numerator df)

• Or effect-size threshold for Posterior probability

SPM.xCon.Vcon - Name of contrast (for 'T's) or ESS (for 'F's) image
SPM.xCon.Vspm - Name of SPM image