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:
1) Standardize the seed time series for pre- and post-injection runs separately: remove the mean, and divide by the standard deviation
2) 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
3) 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
4) 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
Gang
Edited 1 time(s). Last edit at 07/02/2021 04:33AM by Gang.
