BrainVoyager version: 22.4
Datasets used: ses-01_task-Localizer_run-01_bold and ses-01_task-Localizer_run-02_bold of sub-01 and sub-02 of the newbi4fmri datasets (Jody Culham and Kevin Stubbs and Ethan Jackson and Rebekka Lagace Cusiac (2020). newbi4fmri2020 Localizer. OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds003433.v1.0.1, accessed 31 January 2023)
Close any open projects and open the MNI VMR project provided by BrainVoyager via File -> Open MNI VMR. In the Analysis menu, select General Linear Model: Multi Study, Multi Subject ... item. This will invoke a dialog entitled General Linear Model: Multi Study, Multi Subject.
The text field of the dialog shows the multi study list box which is empty when you invoke the dialog. When filled, each row of this list box will refer to a single study, i.e. a functional run (VTC file name) together with an associated single-study design matrix (SDM file name). The multi study list box is filled sequentially with pairs of single-study data and design matrix files. In this example, we will include four studies, two runs from two subjects. Click the Add to List button to specify the necessary files for the first study.
The appearing Open dialog asks for a VTC file. Select the file "sub-01_ses-01_task-Localizer_run-01_bold_3DMCTS_SCCTBL_256_sinc_2x1.0_MNI_THPGLMF2c_SD3DVSS4.00mm.vtc" which contains the data of the first run of the first subject and click the Open button. A second Open dialog appears asking for a single study design matrix (SDM) file. Select the file "sub-01_ses-01_task-Localizer_run-01_TASK.sdm" and click the Open button. The first row of the multi study list box will now be filled as shown below:
The first column refers to the VTC data to be included in the multi study GLM. The second column contains the single study design matrix filename (SDM) specifying the statistical model to be used for the data file in the first column. The last column lists the names of the predictors found in the SDM file. The predictor names are shown so that you can check that each included study (SDM) uses the same predictors in the same order.
Important: Each included study (functional run) has to use the same predictors, defined in the same order, so that the program can combine predictors with the same "meaning" (i.e. equal conditions) across studies. However, the defined time course of each single study predictor can be different across studies (e.g. condition Body in study 1 and condition Body in study 2). This is for example the case when the presentation order of stimulus conditions (Body, Face, Hand, Scrambled) is randomized across participants and/or across functional runs.
At any time we can check the current state of the internally built design matrix. Simply click the Design Matrix... button in the right lower part of the dialog to invoke the General Linear Model - Design Matrix dialog.
In the design matrix display, time (represented in volumes / TRs) runs from top to bottom. Each column represents one predictor. The first column contains a graphical representation of the first defined predictor with its name, i.e. Body, shown in the column header. In this representation, numerical values within each cell are color-coded by different grey values ranging from -1.0 (black) to 1.0 (white). All values in between -1.0 and 1.0 are color-coded with different shades of grey. The numerical values are made visible by clicking the Numerical button on the right.
These values are the results of the convolution of the box car function with the hemodynamic response function. If you visualize the design matrix prior to pressing the HRF button, you would see only 1.0 and 0.0 values in the first four columns (see screenshot below):
The fifth predictor with a constant value of "1" (entirely white) has been added automatically to the design matrix and is necessary for the GLM to estimate the level of the fMRI signal at each voxel. Such a constant predictor will be added automatically for each study. You can use the scroll bar(s) to browse to any position within the design matrix or you can zoom in and out. You can also increase the size of the dialog by using the size grip in the right lower corner.
If you would like to generate a Design Matrix Plot of the entire model, you can use the respective button in the General Linear Model - Design Matrix dialog:
In the design matrix plot you will see the number of volumes represented as rows and the number of predictors represented as columns.
Note: The design matrix is used exactly as shown in the General Linear Model - Design Matrix dialog for the subsequent GLM computation. In order to know precisely what statistical model is constructed and used by BrainVoyager, you can always check the design matrix in this dialog. In addition, the design matrix is also saved to disk automatically at the beginning of a GLM computation ("DesignMatrix.txt"). It can thus be used easily in external calculations, e.g. for computing the intercorrelations between the GLM predictors.
The design matrix is identical to the one we have created interactively in the Single Study General Linear Model dialog. This is to be expected since we have included only a single study so far. If we would run the multi study GLM right now, it would, thus, compute exactly the same result as for the single study GLM.
Click the Close button to close the design matrix dialog. We now add three more studies by clicking the Add button three more times, every time specifying another VTC / SDM file pair. So, we add the VTC file for run 2 of subject "sub-01" with the respective design matrix file. Then we add the files for runs 1 and 2 of subject "sub-02". There also exist files for 16 additional subjects (sub-03 to sub-18 ), but we will not use these files here. The dialog should now look as shown in the figure below:
Let's now inspect the internally built multi study design matrix again by clicking the Design Matrix... button. In order to see the time courses of all defined predictors, please click now on Design Matrix Plot. The design matrix should look as shown in the figure below:
You will see that eight predictors have been built.
The four main predictors are shown in the first four columns as before, however they extend now over all four studies/runs. In other words, the multi study design matrix has been created by concatenating the four single study design matrices. We have, thus, one set of predictors across all studies. This is one of three ways how the program constructs a multi study design matrix, the other two ways using separate sets of predictors for each study or subject will be described in another article. Please note that BrainVoyager has added four signal level "confound" predictors (Study 1- 4: Constant) automatically. Each of these predictors are set to a value of 1.0 (white color) for all time points of a study and to 0.0 (grey color) for the remaining studies.
Note: The different constant terms for the different studies allow each study to have a different signal level. This is very important since often different runs of different subjects - and sometimes even runs of the same subject in the same scanning session - produce different signal levels. A visualization of these study level effects can be obtained for any region-of-interest with the ROI GLM analysis tool. If you check the z-transform option in the General Linear Model: Multi Study, Multi Subject dialog, the signal level confounds will be estimated as 0.0 because the mean of the signal time courses will become zero after the transformation. If you uncheck the z-transform option, each study constant predictor will be estimated as the mean of the data points belonging to the respective study.
The concatenation of the task predictors means that the signal changes at a voxel time course are estimated by the same four values (beta weights of the four main predictors) across the concatenated data points. This is also reflected in the resulting GLM where you will find the four main predictors for specifying contrasts. To check this, run the GLM by clicking the GO button. If you then invoke the Overlay General Linear Model dialog from the Analysis, it will look as below:
The first four filled rows represent the four main predictors of the multi study design matrix. You can now specify contrasts and compute statistical maps showing voxels where a contrast reaches significance across the four studies.
The concatenation approach to build a multi study design matrix is a reasonable approach if the included studies are multiple runs of the same subject.