Dear SPMers,
When analyzing fMRI data, I normally realign the functional
images using the 'coregister only' option. According to my
understanding, this procedure writes a 6-parameter affine transformation
into the .mat file associated with each functional image. The
affine transformation describes how to translate and rotate any given
functional image such that it is realigned with the reference image.
After realignment, I usually normalize the functional images to MNI
space. From my reading of the SPM documentation, it seems to be the case
that the spatial normalization module looks at the .mat file associated
with each functional image. Further, the online help states that it is
possible to normalize the functional images without having resliced them
first. When normalizing functional images that have been realigned with
the 'coregister only' option, does the normalization module use the .mat
file associated with each functional image to create normalized images that
are realigned with one another, in addition to being warped to MRI space?
The reason I ask is that we recently found that including motion
parameters as regressors during model estimation gets rid of a lot of
spurious-looking activations (e.g., activations around the edge of the
brain). This is certainly a good result. But, I'm wondering why
entering motion parameters (produced for each session during realignment)
as regressors during model estimation should be so helpful if, in fact, the
functional images were already realigned with each other during
normalization. That is, if the motion has already been corrected, how
could the motion parameters account for much variance during model
estimation? Any advice would be greatly appreciated!
Thanks in advance,
Daniel Weissman
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|