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)
To obtain better insights into how multiple studies contribute to an overall statistical map, it is possible to estimate a separate set of predictors for each study included. After running the GLM, this allows you to compute statistical maps for each individual study as well as for any set of combined studies. In addition, this approach gives you the opportunity to specify study x predictor interaction effects (which isn't common, but can be quite useful!).
The only change we have to do to switch from the concatenation (FFX) approach to the separate study predictors approach is to check the Separate study predictors option in the General Linear Model: Multi Study, Multi Subject dialog:
We can now inspect the multi study design matrix reflecting the separate study predictor settings. Please click 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 similar to the one shown below:
As you can see, there are now four sets of the four main predictors. Each predictor set defines a time course (non-zero values) only for one study but contains zero values (grey color) for all other studies. Therefore it can be said that each study has its own set of separated predictors.
The separation of predictors for each study means that the signal changes of a voxel time course are estimated by 16 (4*4) values (beta weights of the main predictors) plus the respective constant terms. Since the predictors for a particular study contain, however, only zero values for the other studies, only five values (beta weights of four main predictors plus constant term) actually estimate the time course of a single study.
This is also reflected in the resulting GLM where you will find 16 main predictors for specifying contrasts. To check this, close the design matrix dialog and run the GLM by clicking the GO button. If you then invoke the Overlay GLM Contrasts dialog, it will look as shown below:
The 16 filled rows represent the four main predictors for each of the four studies of the multi study design matrix. The four main predictors are appropriately labeled to reflect the study to which they belong. You can now specify contrasts within any single study or across any set of studies providing more flexibility than was available with the concatenation approach. Any confound predictors, in the current case only the signal level confound predictors (constant term for each study) are shown in orange.
Note: To simplify the specification of the same contrast for each study, hold down the CTRL key while specifying with the left mouse button the respective contrast for one study. The pressed CTRL key ensures that the defined contrast is copied to all other studies.