I am looking at how to compare activations in dorsolateral prefrontal cortex between two timepoints (i.e. conditions) within one session. A bit new to AFNI and its functions and looking for help anywhere!
I recorded resting-state scans in nonhuman primates, with processing using AFNI’s animal warper script and afni.proc.py. I performed a seed-based analysis using dlPFC as the seed (to look at functional connectivity) but I was also interested to see how activation of the dlPFC region alone differed between by pre- and post-experimental condition within the session.
Any help would be greatly appreciated!
how to compare activations in dorsolateral prefrontal cortex between two timepoints (i.e. conditions) within one session
How many sessions do you have? Do you want to make inference about the comparison at the individual or population level?
So each monkey had one session. In the session, we were looking at pre- vs. post-injection of a substance and effect on resting state FC.
In the session there were 2 runs pre-inj and 3 runs post-inj.
For the dlPFC seed-based analysis, I ran afni_proc.py on each timepoint (so one pre with the two scans and one post with the three scans) and then went from there. I figured I may have to adjust something if I am interested in just comparing dlPFC activation post vs. pre injection.
Thanks for the help and let me know any other info you’d like!
If I understand it correctly, what you have done is to perform the seed-based correlation analysis for pre-injection runs and post-injection runs separately. There might have better solutions to compare the voxel-wise correlation values between pre- and post-injection for each monkey, but here is one possibility I cannot think of:
Standardize the seed time series for pre- and post-injection runs separately: remove the mean, and divide by the standard deviation
Create two separate regressors by appending to the end of the seed time series of the pre-injection runs with the same number of 0s as the number of time points in the post-injection runs, and by adding the same number of 0s as the number of time points in the pre-injection runs to the beginning of the seed time series of the post-injection runs
Standardize the EPI time series
– Remove the mean from each run separately: 3dTstat -mean
– Detrend each run separately: 3dDetrend
– Compute the standard deviation for pre- and post-injection runs: 3dTstat -stdev
– Standardize the data: 3dcalc
Compare the correlations between pre- and post-injection runs
– Create a 3dDeconvolve script with -polort 0 and with standardized data from pre- and post-injection runs concatenated as input
– Use the two standardized seed time series as two regressors
– The two regression coefficients would be roughly the seed-based correlations
– Specify the contrast of the two correlations with -gltsym