Hi everyone,
We are trying to set up a task based fMRI GLM analysis in nilearn with a repeated measure 2x2 factorial design. We are not sure on how to set up the first and second level contrasts (similarly to this question:fMRI Group analysis (2nd-level) - Which 1st-level contrasts for which model?).
This example (Understanding parameters of the first-level model - Nilearn) indicates how to build the F-contrast at the first level, but we are not sure on how to test for the interaction at the second-level.
Should we build the F contrast at the first level testing for the interaction and then perform a t-test at the second level with the maps from each subject?
Thank you very much for any suggestions!
1 Like
The easiest way in my opinion is to estimate the interaction at the 1st level and then just do a one-sample t-test at the 2nd level.
Let’s take the tutorial you linked:
“left - right button press”: (
contrasts[“audio_left_hand_button_press”]
- contrasts[“audio_right_hand_button_press”]
+ contrasts[“visual_left_hand_button_press”]
- contrasts[“visual_right_hand_button_press”]
)
this is a contrast for main effect of direction, right?
“audio- visual button press”: (
contrasts[“audio_left_hand_button_press”]
+ contrasts[“audio_right_hand_button_press”]
- contrasts[“visual_left_hand_button_press”]
- contrasts[“visual_right_hand_button_press”]
)
this is a contrast for main effect of modality…
“modalityXdirection”: (
contrasts[“audio_left_hand_button_press”]
- contrasts[“audio_right_hand_button_press”]
- contrasts[“visual_left_hand_button_press”]
+ contrasts[“visual_right_hand_button_press”]
)
this is a contrast for the interaction.
Once you have these for each of your participant, you can get away with just doing a one-sample t-test on the resulting Z-maps like we see here:
Thank you very much for your suggestion @foldes.andrei . That sounds like a good approach to me!