Hi FSL'ers,
I'm analyzing data from a drug study, where the drug is likely to change the
mean BOLD signal in a region-specific manner, in addition to altering
functional responses to stimulation. To account for any possible
region-specific mean BOLD signal changes as a function of drug level, I
would like to:
1. Normalize each data set (or cope image) by its temporal mean (like
"mean_func.nii.gz"), essentially creating a "percent signal change image."
This would allow me to compare drug-level effects in my group analysis in
terms of percent signal changes.
--I tried doing this on some simulated data by scaling the first-level
"cope1" by 100/mean_func and varcope1 by (100/mean_func)^2, but the flame12
2nd-level pe's and copes ended up being identical to the unscaled case
(????). What could be happening here? Is there a straightforward way to
accomplish this?
2. Run a flame12 analysis on the mean BOLD signal as a function of drug level.
--Is there an easy way to get an unscaled temporal mean and variance
from the first-level analysis that can be passed up to flame12?
Any suggestions on how to do this? Thanks a lot for your help!
--Patrick
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