Hello Gang,

I am working with Christine on these data and we have a question about how to interpret AM2 regressors that were put into a LME. I modified what you suggested above to modulate rt and confidence at the trial level, and accuracy at the group level in a LME.

1) Obtain the *overall* mean for each condition (Hour, Day, Week, Month) across all subjects for each of the two variables (Confidence, Response Time);
2) Subtract the *overall* mean for each condition (Hour, Day, Week, Month) from the original number for each variable within condition (Confidence, Response Time)
3) Create the four modulation (Hour, Day, Week, Month) regressors for each condition yourself by using the values from 2) (the reason for this step is that 3dDeconvolve automatically removes the within-subject mean when generating the modulation regressor) [I used 1dMarry to create the regressors]
4) Stick the regressors from 3) in 3dDeconvolve

Q1: Are three regressors made separately? 1) modulated activity 2) activity tracking rt 3) activity tracking confidence
Q2 Is it possible to run an F test on regressor 2 (i.e. activity that ONLY tracks rt) and 3 separately (i.e. activity that ONLY tracks confidence)

Q3: Does LME only use the 1) modulated activity regressors for F-tests? And is there a way to run an F test with regressors type 2 and 3 separately?

It becomes a bit tricky as I ran a LME with Percent Correct as a variable of interest based on a previous discussion we had: [afni.nimh.nih.gov]

>> 3dLME -prefix myOutput -jobs 8 \
-model "PerCorr" \
-qVars "PerrCorr" \
-ranEff '~1+PerrCorr' \

Q4: We are looking at subbrick #2, which is the Cond F-test with AM2 regressors. The significant clusters represent regions that do not track or change with behavior. Is this a correct interpretation?



Edited 1 time(s). Last edit at 02/21/2020 08:54PM by Catherine Tallman.