I'd like to preface my questions by saying I really like the concept
of DARTEL and the results presented in the Neuroimage paper. My
questions are driven by my desire to learn more about how the steps in
DARTEL are implemented and how they might compare to optimized VBM.
I have several questions about how DARTEL operates:
(1) From the user guide, it seems that the initial import only uses
the affine portion of the seg_sn.mat file.... is this correct?
(2) Has anyone investigated the effect of linear versus non-linear
segmentation seg_sn.mat files on the initial import (e.g. in unified
segmentation in SPM5, does including non-linear terms alter the affine
component)? Can one simply use the normalization routine to get an
Affine transformation (and insert the prior list matrix into the mat
file), if not why?
(3) How is DARTEL performing the segmentations? Are the outputs
posterior probabilities? Does the procedure include bias correction?
Can you used customized priors? Are the priors determined from the
seg_sn.mat?
(4) Does the segmentation use Hidden Markov Random Fields?
(5) When using previous segments, did you mean to say use the segments
in native space that are unmodulated?
(6) I'm interested in looking at the CSF segments, particularly around
the ventricles; you mentioned that including CSF in the processing is
not an good idea because CSF segmentation is variable (especially
around the edge of the brain). Can one apply another flow field to the
CSF segments?
Thank you very much in advance for your responses.
--
Best Regards, Donald McLaren
=====================
D.G. McLaren
University of Wisconsin - Madison
Neuroscience Training Program
Tel: (773) 406 2464
=====================
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