Hello,
I have been reading the posts about factorial DCM design and I am still a little confused as how to optimally design my DCM
In my block-design experiment, there are two main trial types, self and charity, and two conditions, high value and neutral. There are two questions I am interested in:
1. the structure of the network in the self trials and the bilinear effects from the high value condition
2. how the network for these trials compares to the network for the charity trials.
I can think of two possible ways to proceed:
- Construct a model where self-high and self-neutral cues are combined and modeled as the driving input and the self-high condition acts as contextual, modulatory variable. The t-contrast I would use for extracting VOIs would be (high – neutral) self. Then I construct a second set of models exactly the same way, except replacing self with charity.
- Construct one model which has all stimuli as driving inputs and two modulatory contexts: self high and charity high. In this case, it is not clear to me which contrast I should use for extracting VOIs. I’ve read that one should include all experimental conditions in a single DCM, but it seems like this is ignoring the interesting possibility that the network structure may change according to the type of task.
Thanks for any help,
Ian