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The flow chart below illustrates possible pathways for multi-subject designs with missing condition(s). For missing parametric conditions, please see the workaround below.

multisubject multirun missingconditions v02 pg1

The resulting design matrices and beta values are illustrated below:

multisubject multirun missingconditions v02 pg2

Workaround for missing parametric condition

How to make this parametric design matrix working when a condition does not appear in all protocols (provided intra-session alignment or FMR-VMR alignment have been applied):

For conditions that do not appear in all protocols, one creates an empty condition with 0 intervals for multi-subject studies. However, a condition with 0 intervals in a parametric protocol does not generate a parametric design matrix (*.sdm); in that case, add an empty predictor in the "Single Study General Linear Model"
dialog by clicking "Add Pred" and give it the same name as the parametric predictors in the design matrices of the other subjects. When creating an *.mdm, all protocols will have the same number of predictors.
For advice on parametric modulation, please see http://support.brainvoyager.com/functional-analysis-statistics/35-glm-modelling-a-single-study/174-parametric-modulation.html.

Special case: sparse predictors

In case the responses of subjects are included in the design matrix and one subject has no errors, one can split the response values for all subjects in two predictors:

- the 'good' values become a predictor of interest
- the `wrong' values become a confound predictor

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