| I have been working with the spatial  normalization  routines  in
| spm99  and  have run into a few difficulties.  First, I realigned
| all the data sets creating a mean image (we are working with  145
| mm  coverage 3.75 x 3.75 x 5.00 resolution x 200+ scans) for each
| session.  Then I  tried  to  normalize  (using  all  the  default
| options)  these  mean  images  to  the EPI template provided with
| spm99.  I ran about 100 subjects last night and in about  25%  of
| them  the  normalization  produced  a  poor  fit.  The problem is
| largely in front of the AC line in that the front of the brain is
| stretched  out  much  farther  than  it  should  be  (I can email
| postscript output if anyone wants to see).  I then played  around
| (as the online help suggests) trying to re-orient the mean images
| (after deleting the .mat files of course) such that they  matched
| (visually)  the  template's  orientation to hopefully provide the
| algorithm with a better starting  estimate.   At  most  the  data
| needed to be rotated about .3 degrees. This did not help.  Then I
| looked more closely at all the mean images in  which  there  were
| difficulties  and realized that most of them were slightly bigger
| brains (in the y direction) than the template.    I then tried to
| rescale them into smaller brain using the new display facility in
| spm99.  This did not help.  Then I tried  to  normalize  them  in
| spm96  (using the spm99 EPI template) and it worked great.   Then
| I went ahead and plugged in the same defaults as spm96 into spm99
| (mainly 4.x5 x 4 nonlinear basis functions, instead of 7x8x7) and
| it still failed.    Then I  removed  the  'masking'  option  and,
| still  using  4x5x4  basis  functions and it worked great.  Every
| brain was properly normalized  (as  best  I  can  tell  with  our
| display  utilities).  I am wondering if anyone else may have seen
| this before and whether I should just go ahead and  proceed  with
| stats.    I  would  appreciate any comments the spm authors might
| have on this as well.

I would recommend disabling the brain masking option for spatially
normalising images with very little signal outside the brain.  The
default settings are best for T1 weighted images, but may not be
optimal for EPI images, or even PET images.  Brain masking is
intended for weighting the spatial normalisation so that it is
based only on information within the brain, and not on matching the
scalps.  This is useful because variations in skull thickness can
not be modelled particularly well with only about 1000 smooth basis
functions

| While I am  at  it,  I  was  curious  about  a  couple  of  other
| functions.  If I realign my data (origins set to AC) and create a
| mean image - and then decide that I would prefer to reorient them
| (b/c of this rotation we seem to have in all our data) - is there
| any reason that  I  can't  re-orient  the  realigned  images?  My
| perusal  of the code suggests that the re-orienting process first
| reads all the .mat files (from the realignment) and then  applies
| the new orientation parameters.  So, my guess is that it will not
| matter.  Confirmation of this would save about 100 hours  of  cpu
| time.  Thanks.

You can quite happily re-orient realigned images via the <Display>
button.  The code pre-multiplies the existing .mat files with
a matrix derived from the specified rotations and translations.
This means that when the images are transformed, their relative
positions with respect to each other remain consistent.

| Next, I was thinking of changing the  spatial  normalization  (to
| save  disk space and time) so that it did not have to reslice the
| images until after smoothing had been applied. That is, can't one
| just take the spatial normalization parameters determined for the
| mean image and create new  .mat  files  for  each  image  in  the
| session?   Then  modify  the  smoothing code to look for the .mat
| file and apply it before smoothing.  Alternatively, perhaps  just
| apply smoothing at the normalization step?  Are there any reasons
| not to do this?  It would save a lot  of  time  and  disk  space.
| Thanks in advance.

I don't think this would work.  The spatial normalization parameters
are stored in the _sn3d.mat file rather than just the .mat files,
and contain information about nonlinear warping rather than just an
affine transformation.  The simpler solution for saving disk space
(but not time) would be to modify the smoothing code so that it
deletes the original file after it has been smoothed.

All the best,
-John




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