Hi Stephen,
my impression that SPM2 overregularizes less than SPM99
(given the default values of both) stems from just practically
comparing the normalization procedure of SPM99 and SPM2
(using the EPI-template) in the case of a whole brain
functional image with a great deal of signal loss in the
frontal orbital cortex. This presents a similar problem,
because the normalized EPI-Templates contains tissue in this area.
In effect the brain normalized with SPM99 'grew' tissue into
the non-existent area (approx an area of 5 cm!)
This did not happen with SPM2 for whatever reason.
I assumed that the reason for this is the new cut-off value option
in SPM2 (the regularization option is present in both).
But, of course, certainly more has changed between SPM99 and SPM2
regarding normalization than just that.
Hope that clarifies the matter
Best,
Anja
Dr. Anja Ischebeck
Innsbruck Medical University
Clinical Department of Neurology
Anichstrasse 35
A-6020 Innsbruck - Austria
tel.: +43 (0) 512 504 23661
>>> "Stephen J. Fromm" <[log in to unmask]> 07.10.2005 15:41 >>>
On Fri, 7 Oct 2005 10:03:53 +0200, Anja Ischebeck
<[log in to unmask]> wrote:
>Hi Philipp,
>
>Overnormalization is less likely in SPM2 than in SPM99.
>There is a cutoff-value in the defaults (which you could
>change yourself to an even more conservative value)
>that prevents the normalized brain to extend beyond
>the area covered by the scans.
>Using the EPI template should therefore work ok.
What cutoff is that? AFAICT there's a similar cutoff in SPM99. It was
just named differently (number of basis functions instead of size in
mm).
My impression is that if part of a source brain is missing,
normalization
algorithms will invariably try to "grow it" towards the edge of the
template. (One way to try to deal with that is to change the
regularization, as someone just posted.)
Cheers,
S
>However, using the EPI-template can introduce
>spatial insecurities of approx. 2 cm! We tested both
>ways of normalization (using T1 or EPI template)
>with a tumor data set - with this quite alarming result,
>especially if you are interested in small brain areas
>(hippocampus and the like).
>
>If coregistration fails (it does in 50% of all cases with MI
>at least for us) you can work around this in three ways:
>
>Use a deskulled anatomical
>(but don't use this image for normalisation!
>so copy the .mat file gained and rename it so it applies
>to the original anatomy)
>
>Use the coregistration in SPM99 (the segmentation approach
>is worth (several) tries, if MI fails. If MI fails, it will fail
>consistently
>whatever the starting parameters and default options -
>at least to my experience. When Segmentation fails you get at least
>different results for different starting parameters (.mat file).
>
>If you get a near but not perfect match, e.g. the funcs 2mm lower
>than the anatomicals, use display to enter the displacement value
>by hand and reorient the anatomical.
>
>Hope that helps,
>Best,
>
>Anja
>
>
>
>
>
>
>Dr. Anja Ischebeck
>Innsbruck Medical University
>Clinical Department of Neurology
>Anichstrasse 35
>A-6020 Innsbruck - Austria
>tel.: +43 (0) 512 504 23661
>
>>>> Philipp Saemann <[log in to unmask]> 07.10.2005 01:49 >>>
>Hi,
>
>for an fMRI series which does not cover the whole brain (e. g. 20
>slices 4 mm
>thick), how is spatial normalisation best achieved?
>
>Can object masking (be recommended, e. g. a binary mask of the
volume)
>to keep
>the influence of the missing parts and the border zones low ?
>
>Should the starting position be optimized manually (SPM2)?
>
>(Coregistering on T1 did not work properly using MI, otherwise we
would
>have
>used this option)
>
>Grateful for any hints on this,
>
>Philipp
>=========================================================================
|