Hi John,

if the global size differences are now encoded in the deformation field, is there any easy way to 
extract the deformation field into affine and non-linear components? The procrustes 
decomposition only extracts the rigid body term (?), but I am interested in the affine term. I intend 
to use the determinant of the affine term to calculate the global volume change and will use this 
term as nuisance parameter rather than the total volume or GM volume extracted from the 
segmented images. I guess the global volume change is more appropriate to use to control for the 
changes due to affine normalisation. This would approximate a modulation with non-linear 
components only which would fit to most studies where the user is interested in relative volume 
changes rather than comparing raw volumes.

Best regards,

Christian


____________________________________________________________________________

Christian Gaser, Ph.D.
Assistant Professor of Computational Neuroscience
Department of Psychiatry
Friedrich-Schiller-University of Jena
Philosophenweg 3, D-07743 Jena, Germany
Tel: ++49-3641-935805	Fax:   ++49-3641-935280
e-mail: [log in to unmask]
http://dbm.neuro.uni-jena.de

On Thu, 18 Jan 2007 11:44:36 +0100, Ashburner John (PSYCHOLOGY) 
<[log in to unmask]> wrote:

>It is true that the *_seg_sn.mat files contain an affine transformation
>that is actually rigid-body.  Any global size differences that would
>previously have been accounted for by the affine transform are now
>encoded by the nonlinear deformations instead.  "Modulation" should
>still be correct (you can try this by summing up the volume of a
>native-space grey matter image, and a modulated spatially normalized
>grey matter image).
>
>The reasons for doing things this way were as follows:
>
>The deformations estimated by the segmentation are actually a mapping
>from the native space image to the tissue probability images (normally
>in the spm5/tpm directory).  Writing spatially normalized images
>requires a mapping from the TPMs (template) to the individual images.
>Therefore, the deformations need to be inverted.  Doing this properly is
>not as trivial as often perceived.  The way it is done here, is to
>create a full deformation field from the parameters (affine and DCT),
>invert this deformation field using the piecewise affine model described
>in the appendix of one of my papers, and then re-parameterise with an
>affine transform and a bunch of DCT basis functions.
>
>A few people are interested in applying "Deformation-based Morphometry"
>methods.  One way to do this is to do a Procrustes decomposition of the
>deformation in order to factor out the pose (and sometimes the size) of
>the individual subjects brains.  What is left in the deformations should
>then reflect the shape and size of the individual brains.
>
>Because the deformations were generated in full, I figured that it would
>be reasonably trivial to incorporate this Procrustes decomposition.
>This is why the affine transform encodes a rigid body mapping (i.e. the
>pose of the subject).  The advantage of this is that anyone wishing to
>do some sort of DBM can analyze the coefficients of the DCT transform
>using standard multivariate methods.
>
>Best regards,
>-John
>
>-----Original Message-----
>From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]]
>On Behalf Of Christian Gaser
>Sent: Thursday, January 18, 2007 10:17 AM
>To: [log in to unmask]
>Subject: [SPM] Volume change due to affine registration
>
>Dear SPMers,
>
>while trying to calculate the size/volume change due to affine
>registration in the segmentation 
>approach of SPM5 I have noticed that the calculation of the volume
>change in spm_write_sn.m 
>always results in a value of 1. I have checked this using the formula:
>    detAff = det(prm.VF.mat*prm.Affine/prm.VG(1).mat)
>and indeed this always returns 1. However the normalisation function
>(outside segmentation) is 
>providing the right value and the effect seems to be restricted to the
>normalisation used in the 
>segmentation approach. This will affect the calculation of the modulated
>images. Only local 
>volume changes due to non-linear normalisation will be applied and the
>affine parts will be 
>omitted (which makes sense for most cases, because usually people are
>interested in analyzing  
>local volumes which are corrected for whole brain volume). This is in
>contrast to the SPM2 
>approach, where the affine (as constant value) and the non-linear volume
>changes were included 
>in the modulation step.
>If this is correct (and intended), the statistical analysis of modulated
>images should be always 
>applied without total or GM volume as nuisance parameter (which makes
>life easier). Is this 
>correct?
>
>Best regards,
>
>Christian
>
>________________________________________________________________________
>____
>
>Christian Gaser, Ph.D.
>Assistant Professor of Computational Neuroscience
>Department of Psychiatry
>Friedrich-Schiller-University of Jena
>Philosophenweg 3, D-07743 Jena, Germany
>Tel: ++49-3641-935805    Fax:   ++49-3641-935280
>e-mail: [log in to unmask]
>http://dbm.neuro.uni-jena.de