Hi John and Nikos,
the modulated images of the RAVENS and the DARTEL methods seem to be very similiar, which is
not surprising because both approaches are based on a diffeomorphic high-dimensional spatial
registration. However, DARTEL is using a more tricky approach to create a population template
based on the given sample.
The erroneous smoothness estimation of the RAVENS maps was caused by an undefined scaling
factor in the RAVENS maps (and SPM5 is then using the default value of 1). This caused rounding
errors for the uint8 data, because the maximum values were around 30-50 and after smoothing
there were only a few different (integer) values existent.
Regards,
Christian
____________________________________________________________________________
Christian Gaser, Ph.D.
Assistant Professor of Computational Neuroscience
Department of Psychiatry
Friedrich-Schiller-University of Jena
Jahnstrasse 3, D-07743 Jena, Germany
Tel: ++49-3641-934752 Fax: ++49-3641-934755
e-mail: [log in to unmask]
http://dbm.neuro.uni-jena.de
On Mon, 31 Mar 2008 13:40:56 +0000, John Ashburner <[log in to unmask]> wrote:
>The RAVENS registration is more precise than the older spatial normalisation
>routines of SPM. In SPM, there are only about 1000 parameters to describe
>the warps, whereas RAVENS uses millions. Generally, if the residuals are
>very smooth, then it could imply that the model is underfitting the data. The
>original concept behind VBM was to treat the spatial normalisation a bit like
>high pass filtering in an SPM analysis (ie to remove "macroscopic"
>differences) so that "mesoscopic" differences could be detected.
>
>Although the introduction of "modulation" (Jacobian transformation) confused a
>lot of people, it meant that the values actually had a more meaningful
>interpretation, and that a VBM analysis was not simply an examination of
>registration error. "Morphometry" is the study of variation and change in
>the form (size and shape) of organisms. With the introduction of the Jacobian
>transform, the use of the term became slightly closer to its dictionary
>definition, as it involved comparing the regional volumes of grey matter.
>This also meant that more accurate spatial normalisation could be used.
>RAVENS maps have always implicitly preserved the tissue volumes in the warped
>images.
>
>I eventually got around to improving the inter-subject registration model in
>SPM. In the latest updates of SPM5, there is a DARTEL toolbox, which can be
>used to obtain more precise inter-subject alignment for VBM studies.
>Experience of DARTEL in the FIL is generally positive. VBM results produce
>higher t stats for the difference that are detected than for the other SPM
>spatial normalisation approaches. I would expect the RAVENS results to be
>similar.
>
>Best regards,
>-John
>
>On Sunday 30 March 2008 09:54, Nikolaos Koutsouleris wrote:
>> Dear SPMers,
>>
>> recently, I tried the HAMMER normalization algorithm of Davatzikos et al.
>> on data that had been segmented with SPM5. I observed that the smoothness
>> of the obtained RAVENS maps is significantly lower compared the modulated,
>> low-dimensionally normalized volumes of SPM5 (see example of the same
>> subject in the attachments). Correct me if I am wrong, but this may be due
>> to the fact that the HAMMER algorithm uses high-dimensional elastic warping
>> which retains a higher degree of anatomical information compared to the SPM
>> normalization.
>>
>> When I did cluster-level statistical inference on the 10mm-smoothed RAVENS
>> maps (using Satoru's non-stationarity correction toolbox), I noticed that
>> the resulting smoothness estimate was about 5 mm, compared to the
>> SPM-normalized data which was about 11 mm. In the RAVENS analysis a primary
>> threshold of p<0.05 together with FWE-corrected extent threshold of p<0.05
>> produced a minimum number of 1500 voxels, whereas the extent threshold for
>> the SPM -normalized data was around 20000 voxels. This is a dramatic
>> difference which make clusters significant at a much lower spatial
>> threshold. So, I wonder if this difference is really due to the "roughness"
>> of the RAVENS maps or if the smoothness estimation in SPM is not valid for
>> RAVENS data. Does anybody have similar experiences or possible
>> explanations?
>>
>> Thanks in advance and sorry for this lengthy email!
>>
>> Cheers,
>>
>> Nikos Koutsouleris
>>
>> Imaging Workgroup
>> Department of Psychiatry and Psychotherapy,
>> Ludwig-Maximilians-University,
>> Munich
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