Dear Leonardo,
I would analyse this data as described below. Classically, there are 8 effects you might wish to test for (see below) - in what i've described you set up 4 different 2nd-level designs and enter two 2nd level contrasts for each. That is, you create 4 new directories for SPM analyses, and each uses a different set of first-level contrast images as data. So, step (1) is to create the necessary first level con images, step (2) create the 4 second level design matrices, assign con images and fit the models, step (3) is to enter the 2nd level contrasts to test for the effects you are interested in.
I'm assuming you have two within-subject factors here (A) Task, with two levels: question 1 or question 2 and (B) Emotional valence, with three levels: (1) positive, (2) neutral and (3) negative.
To make this concrete lets say you have 18 people in the patient group and 17 in the controls, making a total of 35.
Let's also say you have set up a first level design matrix for each subject with columns arranged in the order A1B1, A1B2, A1B3, A2B1, A2B2 and A2,B3.
To test (1) the overall effect I would use a [1 1 1 1 1 1] contrast for each subject and take the resulting 35 con images into a one-sample t-test at the second level. Then you would specify a '1' F-contrast (at the second level) to test for significantly non-zero BOLD responses related to your paradigm. Using the same first level contrast images in a two-sample t-test design at the second level, split into the 18 patients and 17 controls, will let you test for group effects (using a second-level F-contrast [1 -1] to test for differences). This is (2) the main effect of group.
To test for (3) the main effect of Factor A (the one with two levels) I would use a [1 1 1 -1 -1 -1] contrast for each subject and take the resulting 35 con images into a one-sample t-test at the second level. Similarly, using the same first level contrast images in a two-sample t-test design at the second level, split into the 18 patients and 17 controls, will let you test for group effects (using a second-level F-contrast [1 -1] to test for differences). This will test for (4) the group x task interaction.
To test for (5) the main effect of Factor B I would use two contrasts per subject [1 -1 0 1 -1 0] and [0 1 -1 0 1 -1] and take the resulting 70 con images (two per subject) into a two-sample t-test design at the second level. I would then use a [1 0; 0 1] F-contrast to test for this main effect. Similarly, using the same first level contrast images in a one-way ANOVA design at the second level (with 4 "levels"; first two for patients, second two for controls), split into the 18 patients and 17 controls, will let you test for group effects (using a second-level F-contrast [1 0 -1 0; 0 1 0 -1] to test for differences). This will test for (6) the group x valence interaction.
To test for (7) the interaction between Factors A and B I would use two contrasts per subject [1 -1 0 -1 1 0] and [0 1 -1 0 -1 1] and take the resulting 70 con images (two per subject) into a two-sample t-test design at the second level. I would then use a [1 0; 0 1] F-contrast to test for this interaction effect. Similarly, using the same contrasts in a one-way ANOVA design at the second level (with 4 "levels"; first two for patients, second two for controls), split into the 18 patients and 17 controls, will let you test for group effects (using a second-level F-contrast [1 0 -1 0; 0 1 0 -1] to test for differences). This will test for (8) the group x valence x task interaction.
That's it in terms of the factorial nature of your design: for a factorial design with 3 factors there are 8 effects to test for: an overall effect, 3 main effects, 3 two-way interactions and one 3-way interaction - and you can test for them using the approaches numbered (1) to (8) above.
As an aside, SPM can help you with the contrast you need to test for effects in factorial designs. Use the function
> Con = spm_make_contrasts ([k1 k2]) for a k1 by k2 design (eg. 2 by 3) . If the required contrast has multiple rows you'll need to take up multiple con images to the second level.
For any of the second level designs you can also enter covariates, such as age. If you have a one-sample t-test design at the second level then this is straightforward, as long as your age variable is centred - i.e. zero mean (otherwise it will be collinear
with the other effect you are testing, and make it disappear). A [0 1] F-contrast will then test for regions with BOLD responses dependent on age. For more complex second level designs e.g. two-sample t-tests, you can include an interaction term - this will
create two new columns e.g. age of patients, age of controls. A [0 0 1 0; 0 0 0 1] F-contrast then tests for any effect of age, a [0 0 1 -1] F-contrast then tests for regions where the BOLD vs age effect is different for patients versus controls.
All the best,