(hopefully) quick question about the method described below: why
modulate again in step f? It seems like deformations from the
intra-subject warping is the interesting part and inter-subject warping
is just to put things in a common space for logistical ease of doing
stats. So why input inter-subject warping info into the images by
Stanford Systems Neuroscience and Pain Lab
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John Ashburner wrote:
> The suggestion I made was about how to use DARTEL to do the intra-subject
> alignment, so I was basically just answering the question. I have no
> empirical evidence either way, but (for the reasons I mentioned) I suspect
> that HDW may do a marginally better job with most serial scans.
> Best regards,
> On Friday 22 May 2009 11:15, Benetti, Stefania wrote:
>> Thank you John for your answers and suggestions. However, I am not sure to
>> understand what you are suggesting about HDW approach that Kipps used.
>> "In general, I think I would still suggest the HDW approach that Kipps and
>> others have used...For inter-subject alignment, the residuals are rarely
>> i.i.d. Gaussian, which is why I chose to align tissue class data
>> instead.... However, the segmentation errors may be relatively large
>> compared with the volumetric differences between the serial scans, which
>> would make the DARTEL approach less accurate"
>> My understanding was that the procedure you suggested to Reinders
>> consisted in an adaptation of Kipps' approach for DARTEL, in which the
>> within subject alignment approach replaced the HDW. Am I completely wrong?
>> Did you mean that this longitudinal DARTEL procedure may be less accurate
>> than the procedure that Kipps actually used?
>> Many thanks again
>> Stefania Benetti
>> King's College - Institute of Psychiatry
>> Neuroimaging Section
>> -----Messaggio originale-----
>> Da: John Ashburner [mailto:[log in to unmask]]
>> Inviato: gio 21/05/2009 13.50
>> A: Benetti, Stefania; [log in to unmask]
>> Oggetto: Re: [SPM] Longitudinal DARTEL spm8
>>> We pre-processed a small longitudinal dataset (26 subjects,T1=baseline
>>> T2=follow-up) using DARTEL and the procedure suggested in:
>>> a) both T1 and T2 manually reoriented. T2 co-registered to T1,no
>>> re-slicing. b) segmentation of both T1 and T2 images (section 1.1 DARTEL
>>> manual). c) create within-subject template (smoothing parameter to NONE)
>>> d) generate modulated warped GM an WM using the within-subject flow
>>> e) create an inter-subject template
>>> f) generated modulated warped(mwmwc1T1 and mwmwc1T2) GM and WM using the
>>> inter-subject flow fields. g) smoothing and statistical analysis using
>>> both a flexible factorial design.
>>> However when we pre-processed the same database using optimised VBM for
>>> serial scans and then the same statistical analyses, we obtained a
>>> completely different result. GM changes were found in regions where no
>>> significant effects were detected with the DARTEL approach.
>>> Would you expect to find such a difference?
>> I would expect different models to give different results, so I'm not
>> really surprised. I would suggest checking out the contrast images
>> generated from the GLM to see if the general trends are similar.
>>> Could this difference in terms
>>> of localisation be attributable to differences in normalisation?
>> Very likely, and also differences in tissue classification.
>>> Is it
>>> sensible to rely on the DARTEL approach rather than the optimised one?
>> I haven't tested the various approaches to know what works "best", and the
>> most sucessful approach is likely to be dependent on things like the
>> contrast in the images, the image artifacts and the stability of the
>> scanner. However, I would expect that some form of within subject alignment
>> approach may provide more sensitivity to differences. For longitudinal
>> analyses, you are identifying tiny volumetric differences of the order of a
>> percentage or so, so the details really do matter.
>> In general, I think I would still suggest the HDW approach that Kipps and
>> others have used. There are issues with HDW, which relate to the algorithm
>> fully converging and it is also asymmetric (so registering early with late
>> will give different results from doing it the other way around), but it is
>> probably the more accurate of the SPM procedures to use for longitudinal
>> studies. A histogram of the difference between the registered images should
>> approximately indicate that the residuals are i.i.d. Gaussian, which would
>> make the mean-squares difference (used by HDW) a suitable objective
>> function to use. For inter-subject alignment, the residuals are rarely
>> i.i.d. Gaussian, which is why I chose to align tissue class data instead.
>> However, the segmentation errors may be relatively large compared with the
>> volumetric differences between the serial scans, which would make the
>> DARTEL approach less accurate.
>>> One more question about step d) in the procedure mentioned above. Is this
>>> step necessary? I am probably missing something, but I was wondering if
>>> warping the T1 and T2 images using the within-subject flow fields, and
>>> then warping the obtained images again with the inter-subject flow fields
>>> (step f) may reduce local differences we are actually interested in
>>> since we are dealing with serial scans.
>> The transforms could be composed and then used, but I'm not sure how much
>> difference it would make in practice.
>> Best regards,