I'm not 100% sure without re-reading the paper, but I assume that Tina used
spatial normalisation based on grey matter for grey matter VBM, and spatial
normalisation from white matter for white matter VBM.
Best regards,
-John
> sorry for bringing this up again, I know that there has been lots on the
> list already - but there are a couple of things about the optimized VBM
> protocol in Good et al (2001) that I just can't get straight.
>
> the paper talks about normalising the segemented grey and white matter
> images to their respective templates - I assume white.img and gray.img
> from the apriori directory. can I assume from the mail below that only
> the parameters from the grey matter are applied to the whole-brain
> structural files in native space? are the parameters from normalizing
> the white matter ever used?
>
> from John Ashburner's recent
> mail on the topic...
>
> 2) Estimate spatial normalisation parameters from the grey matter image.
> The result of the segmentation includes a grey matter image, which can
> then
> be used to estimate spatial normalisation parameters from by matching it
> to a
> grey matter template (typically the grey.img in the apriori directory).
> You
> can estimate better parameter estimates for spatially normalising grey
> matter
> by estimating them from grey matter, as these images do not include lots
> of
> other confounding information from other tissues. Disable brain masking
> when
> you do this, as it would not help.
>
>
> 3) Apply these spatial normalisation parameters to the T1
> Use these estimated warps for spatially normalising the original T1 image,
> writing the spatially normalised image at a higher resolution (eg.
> 1x1x1mm).
>
> thanks
> chris summerfield
> psychology
> columbia university
>
>
>
>
>
>
>
>
>
>
>
>
> 4) Segment the spatially normalised T1.
>
> 5) then maybe a few additional steps to clean up the segmented image.
>
> Best regards,
> -John
>
> > having read the "new" Good et al NeuroImage (14,21-36,2001)VBM paper,
> > and wanting to implement the method myself, I have turned to the group,
> > only to be confused by recent posts. If we all pretend for a minute that
> > I didn't write spm, then how can I use this method. In particular, as I
> > understand it (hmm...) one :
> > 1. first affine registerster the T1 to a T1 template (okay, I can manage
> > that!)
> > 2. segments this into GM and WM volumes (ditto)
> > 3. performs a new (non-linear, affine+warp) registration to standard
> > space of the GM and WM vols (do I first do an inverse of the first
> > transform??how?)
> > 4. apply parameters to the native space T1 (okay as long as the GM and
> > WM vols were inverse transformed, or can do i apply it to the result of
> > 1 above, and if so, doesnt this lead to a double resampling???)
> > 5. Segment the wonderfully registered T1 image.
> >
> > okay, you guys see that I'm floundering here...tips PLEASE!
>
> --
> Dr John Ashburner.
> Wellcome Department of Cognitive Neurology.
> 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
> mail: [log in to unmask]
--
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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