I am pretty much a newbie in statistics and I hope you can help. I have an experimental design where I analyze three time-windows before the stimulus onset and three windows after. I have three conditions under which the brain responses are obtained. I want to see to what extent does the pre1 predict the post1 (the same for pre2 -> post2 and pre3 -> post3) in different conditions and whether the regression coefficients between the conditions differ significantly.

What is better, to run a linear regression for two conditions at a time (1 vs 2, 2vs 3, 1 vs 3) and then comparing the coefficients by creating a dummy variable and creating an interaction between it and the predictor

or to run a multivariate regression (e.g. DV = post1, post2 and predictors are pre1 and pre2) in order to correct the possible correlations between the predictors (as I understand it, pre1 can correlate with pre2 because all the data is obtained from the same participants and the brain response in pre1 correlate with the brain response in pre2 in one person)? However, I still didn't find whether it's possible to compare regression coefficients in a multivariate regression.

I am a little bit confused and hope I've explained it understandable enough.