Dear all!


I'm modeling an event-related memory recognition task.

In this fMRI model we have 3 kind of trials:

-NEW PICTURES-->50% of all inputs.

-PICTURES corresponding to the Vertex Condition --> 25% (pictures that during the encoding were presented with Vertex repetitive Transcranial Magnetic Stimulation).

-PICTURES corresponding to the DLPFC Condition --> 25% (pictures that during the encoding were presented with Dorsolateral Prefrontal Cortex repetitive Transcranial Magnetic Stimulation).


For all of these events we have correct answers (hits and correct rejections) and wrong answers (misses and false alarms). And, of course the onset of all the events.


We have then 12 subjects with fMRI onsets for Hits in two categories (Vertex and DLPFC).

My question is: Which one of the next models is better fitted to the data?

1) 12 inputs and 4 explanatory variables : Hits Vertex, Hits DLPFC, Misses Vertex, Misses DLPFC. This model let me do contrasts with  hits vs misses activations or interacions.

2) 2 separate models of 24 inputs with Hits or Misses. The first 12 inputs are related with Vertex condition and the next 12 for the same 12 subjects in the DLPFC condition, like in a repeated measures model. In this model i can add an EV for each 12 subjects to control for each subject activation. I can't contrast in the same model Hits and Misses.

I'd really appreciate your help!!!


Many thanks