SPM/Timeseries extraction

From Wikibooks, open books for an open world
Jump to navigation Jump to search

Various functional MRI analyses, such as PPI or DCM, begin by extracting representative timeseries from selected brain regions. These are sometimes called Volumes of Interest (VOIs) or Regions of Interest (ROIs). There are manual and automated ways of doing this with SPM, detailed below. Before doing this, we recommend defining an 'Effects of Interest' F-contrast, which tells SPM which regressors (columns) in the design matrix are interesting. Any others regressors, such as head motion or the mean of the signal, will be regressed out of the timeseries during VOI extraction.

Preliminary step - defining effects of interest[edit | edit source]

The effects of interest F-contrast is defined like any other contrast - either manually (by pressing Define New Contrast when viewing SPM results) or using the Contrast Manager batch. Make sure to create an F-contrast rather than a T-contrast, and for clarity name it Effects of Interest.

The F-contrast matrix typically has one row per effect of interest. For example, if the first three regressors in the design matrix are interesting, then the contrast matrix will be:

Alternatively, if only the first and third regressors are interesting, then the matrix is:

In both cases, any effects encoded in the fourth column onwards of the design matrix will be regressed out during timeseries extraction.

Manual timeseries extraction[edit | edit source]

  1. View your SPM results in the usual way by clicking Results, selecting your SPM.mat file, and following the questions that appear until the results are displayed.
  2. Place the crosshairs / cursor where you want the VOI to be located. This is done most easily by clicking on one of the rows of the SPM results table (on the mm coordinates at the right hand side).
  3. Click eigenvariate in the Results window. Note: If you cursor is not positioned at a peak, it will jump to the nearest peak.
  4. Give a name for the region
  5. Click the "adjust data" dropdown menu and select the Effects of Interest F-contrast you defined earlier.
  6. Select the criteria for which voxels to include for generating the representative timeseries for this region. You can choose between a sphere centred on the crosshairs, or a box, or the thresholded activation cluster located at the crosshairs, or a predefined mask. Note that voxels will only be included which exceed the threshold for significance chosen in Step 1.
  7. Answer any additional questions about the shape of the VOI, and press Done. A file will be saved in the same directory as the SPM.mat, called VOI_XX_sess.mat, where sess is the session or run number. It contains a variable called Y, which is a representative timeseries for the region. More specifically, it is the first principal component or eigenvariate of the pre-whitened, high-pass filtered and confounded-corrected timeseries in the selected region.

Batch timeseries extraction (GUI)[edit | edit source]

The batch editor provides additional flexibility for defining VOIs, and also enables scripting VOI extraction over subjects. Here we'll walk through creating a VOI based on a sphere which jumps to the peak for each subject, within a certain maximum radius.

  1. From the main SPM window, click Batch, then using the menu at the top, click SPM -> Util -> Volume of Interest.
  2. Choose the estimated SPM.mat
  3. For Adjust data, select the index of the Effects of Interest F-contrast defined above. For example, if the F-contrast was the first contrast added to the SPM, and so is first contrast when viewing the list of contrasts, type: 1 and press OK.
  4. For Which session, select the session or run number for which you want to extract a VOI.
  5. Type a short Name of VOI.
  6. Define the shape of the VOI by mixing and matching different components under Regions of Interest. For this example we'll need three components: 1) a map of which voxels exceed a statistical significance of p < 0.001 uncorrected 2) a large sphere which is in a fixed position across subjects 3) a smaller, mobile sphere which can jump automatically to the peak of the larger sphere. To create these components, Click: "New Thresholded SPM", "New: Sphere" and then "New: Sphere" again. (We'll refer to these three components as i1, i2 and i3 in the steps which follow.)
  7. For "Select SPM.mat" under "Thresholded SPM", you can leave this blank - it will pick up the SPM defined in step 2 automatically.
  8. For Contrast, type in the index of the contrast you want to use for finding the peak. E.g. type the number 2 and press OK to use the second contrast.
  9. For Centre under the first sphere, type the coordinates in mm. This will be the outer sphere which constrains the movement of the smaller inner sphere. For Radius, set the desired radius to cover the anatomy of interest, e.g. 15 (in units of mm).
  10. For Centre under the second sphere, set the Centre to any value, e.g. [0 0 0] (as it will move automatically). Set the Radius to the size of the desired ROI, e.g. 8mm. Set Movement of centre to Global maximum. Set SPM index to 1 (if it was the first element you selected in step 6) and for Mask expression, type: "i2" (without the quotes) . That tells it to use the outer sphere to limit the movement of the inner sphere.
  11. Finally, for Expression at the bottom, type: "i1 & i3" (without quotes). That will include all voxels in the first and third components added in Step 6 - i.e. the thresholded SPM and the mobile sphere.

You can now run the batch, and the timeseries will be saved in a file named VOI_XX_sess.mat, where XX is the VOI name and sess is the session number. As for the manual example above, the file will contain a variable called Y, which is a representative timeseries for the region. More specifically, it is the first principal component or eigenvariate of the pre-whitened, high-pass filtered and confounded-corrected timeseries in the selected region.

Batch timeseries extraction (script)[edit | edit source]

A Matlab script corresponding to the steps above is as follows. This can easily be placed in a loop over subjects. Make sure to check each of the settings carefully and adjust as needed for each experiment.

% Insert the subject's SPM .mat filename here
spm_mat_file = '';

% Start batch
clear matlabbatch;
matlabbatch{1}.spm.util.voi.spmmat  = cellstr(spm_mat_file);
matlabbatch{1}.spm.util.voi.adjust  = 1;                    % Effects of interest contrast number
matlabbatch{1}.spm.util.voi.session = 1;                    % Session index
matlabbatch{1}.spm.util.voi.name    = 'name';               % VOI name

% Define thresholded SPM for finding the subject's local peak response
matlabbatch{1}.spm.util.voi.roi{1}.spm.spmmat      = {''};
matlabbatch{1}.spm.util.voi.roi{1}.spm.contrast    = 2;     % Index of contrast for choosing voxels
matlabbatch{1}.spm.util.voi.roi{1}.spm.conjunction = 1;
matlabbatch{1}.spm.util.voi.roi{1}.spm.threshdesc  = 'none';
matlabbatch{1}.spm.util.voi.roi{1}.spm.thresh      = 0.001;
matlabbatch{1}.spm.util.voi.roi{1}.spm.extent      = 0;
matlabbatch{1}.spm.util.voi.roi{1}.spm.mask ...
    = struct('contrast', {}, 'thresh', {}, 'mtype', {});

% Define large fixed outer sphere
matlabbatch{1}.spm.util.voi.roi{2}.sphere.centre     = [-15 -37 59]; % Set coordinates here
matlabbatch{1}.spm.util.voi.roi{2}.sphere.radius     = 15;           % Radius (mm)
matlabbatch{1}.spm.util.voi.roi{2}.sphere.move.fixed = 1;

% Define smaller inner sphere which jumps to the peak of the outer sphere
matlabbatch{1}.spm.util.voi.roi{3}.sphere.centre           = [0 0 0]; % Leave this at zero
matlabbatch{1}.spm.util.voi.roi{3}.sphere.radius           = 8;       % Set radius here (mm)
matlabbatch{1}.spm.util.voi.roi{3}.sphere.move.global.spm  = 1;       % Index of SPM within the batch
matlabbatch{1}.spm.util.voi.roi{3}.sphere.move.global.mask = 'i2';    % Index of the outer sphere within the batch

% Include voxels in the thresholded SPM (i1) and the mobile inner sphere (i3)
matlabbatch{1}.spm.util.voi.expression = 'i1 & i3'; 

% Run the batch