> I'm working on a project with musician dystonia patients, trying to examine
> the effects of therapy on the activation and mapping of the digits within
> sensorimotor and premotor areas. To maximize the resolution and keep the
> TR short, we would like to prescribe a series of slices that extend from
> the top of the brain to a location just above the corpus callosum. Because
> we want to compare the mapping of these patients with that of normal
> subjects, we will need to coregister and normalize the data.
>
> I am not clear from the archives how to do this. How do I coregister the
> anatomical and functional images, and how will I then normalize the
> functional data? Also, will the use of partial volumes compromise the
> realignment procedure?
The online manual should give you a few ideas of the overall procedure:
http://www.fil.ion.ucl.ac.uk/spm/course/manual/spatial.htm
Coregistration can be by using the default procedure, or you can do it
using Mutual Information (MI). The <Defaults> button allows you to select
which option you go for. If you wish to use MI, then I would strongly
suggest downloading the latest patches from:
ftp://ftp.fil.ion.ucl.ac.uk/spm/spm99_updates
(That is unless you wish to use it under Windows and Matlab 5.3, in which
case you will need to somehow compile spm_hist2.dll).
I can't really say what the best way of spatially normalising your images
is. There are two options:
1) Estimate the warps from the anatomical and apply to the functional images.
2) Estimate the warps from one of the functional images and apply to the
functionals.
3) Segment the anatomical, estimate warps from grey matter and apply to the
functional images (as described in the Good et al paper).
If you use the 2nd or 3rd options, then I would strongly suggest disabling
"brain masking" (via the defaults button). The method that works best
depends on a number of factors, particularly the contrast in your images,
the amount of coverage and the amount of artifacts/distortions. Remember
that the template you select should have as similar contrast as possible
to the image you are normalising. You can also select a number of templates
and the algorithm will try to find the optimal linear combination of templates
that best approximates the intensities of your images. The images in spm's
apriori directory can also be used as templates (recommended for option 3 above).
Best regards,
-John
--
Dr John Ashburner.
Functional Imaging Lab., 12 Queen Square, London WC1N 3BG, UK.
tel: +44 (0)20 78337491 or +44 (0)20 78373611 x4381
fax: +44 (0)20 78131420 http://www.fil.ion.ucl.ac.uk/~john
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