Hi Matthew,
We have been going back and forth wondering which methods is the "best"
method to normalize our data? As mentioned in my earlier mails, our data
has significant drop outs in the orbito-frontal cortex as well as the
temporal lobes.
You sent me the link to the "masked normalization method"
(http://imaging.mrc-cbu.cam.ac.uk/imaging/MaskedEpiNormalization)
suggesting using a mask of the drop out areas while normalization and I
did try that. I got some good results as well.
My questions for you are:
1. Is the masking method better or worse than using the structural image
of the subject (well coregistered to an EPI to estimate the parameters
for the affine transformation which can be further applied to the EPI's
to bring them over to the template space)?
2. Using the structural to find the normalizing parameters seems to be a
very common approach that the functional community uses. Is the masking
approach also equally common and reliable? If so, can you cite some
publications that you might know of?
I appreciate you responding to my mails and sharing your expertise.
I shall be waiting to hear back from you soon.
Thanks a bunch!
Warm regards
Sunaina
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