Hello together,
At the moment I am trying to
interpret my beta weights from an fMRI study of motor preparation and
execution. Overall our experiment had 4 different conditions that each
consisted of a preparatory phase and an execution phase with either the
left or the right hand. Thus I builded my GLM with overall 16 regressors
of interest (2 hands x 2 phases x 4 conditions).
Unfortunately,
it was not possible to completely delineate the regressors for
preparation and execution and they correlated to some extent (one pair
with r=77 the other pairs around r=40-50).
Afterwards I
entered my regressors in a flexible factorial ANOVA. Hereby, I skipped
the contrast images for the condition in which preparation and execution
was highly correlated, because the design matrix looked not like
expected for an ANOVA.
When I run the 2(right hand/left
hand)x 6 (preparation cond 1, preparation cond 2, prepartaion cond 3,
execution cond 1, execution cond 2, execution cond 3) and plot the beta
weights for different areas depending on condition I get somehow
mirrored beta weights for the preparation and execution. That means when
the voxel exhibited a positive beta during preparation it was negative
during execution and the other way round.
For me this
looks like a statistical problem. So far, however, I do not have an idea
why it occurs. Even though my regressors were correlated, I did not
think that this may be the problem, since this should just decrease my
statistical power (shared explained variance enters into error term) and
my results should be valid.
I would be really glad if some one could help.
I
have attached an example of the beta weights