Dear Jonathan
I thank you very much for your answer. I apologize for the inaccurate
question. I try again.
Question 1 concerns mainly the option "Normalise to MNI space". The question
is: Instead of a group template ("Template_6.nii,1") can I select in this
option the template of a single subject (e.g. for subject 1 I take
"subject1_Tempate_6.nii,1"), which was made based on 2 Anatomies of this
subject?. Then I would perform the 1st level statistics and I would repeat
this procedure for every subject. Finally, I would take the resulting con*
images to the second level. Am I allow to analyze the data in this way or is
there any reason for not doing it?
Again, thanks a lot.
All the best,
Natalia
-----Ursprüngliche Nachricht-----
Von: Jonathan Peelle [mailto:[log in to unmask]]
Gesendet: Mittwoch, 21. September 2011 15:18
An: Natalia Estévez
Cc: [log in to unmask]
Betreff: Re: [SPM] Dartel template for normalizing function image comment
Dear Natalia,
> 1. Jonathan you wrote that when using the “normalize to MNI space”
> option, she should select the template created for the group. Is there
> any reason way you should not at all use the “normalize to MNI space”
> on subject template (for example the template of 2 anatomies) and then
> use these files for group analysis (I mean to first use the files in
> first level and then in second level analysis)?
I'm afraid that I don't exactly understand the question. In an fMRI study,
you have two options (assuming you want your final results in MNI space):
1. Normalize all of the functional images to MNI space before performing 1st
level statistics, and then take the resulting con* images to the second
level.
2. Conduct 1st level analyses in subject space, and then normalize the
resulting con* images to MNI space, which get taken to the second level.
I'm not aware of any systematic comparison of the two. Option 1 is more
comparable to how most people analyze fMRI data (as far as I can tell),
option 2 will save disk space.
In either case, under the "normalise to MNI" module, you would select the
group template (Template_6.nii,1) as the DARTEL template, and then for each
subject (a) their flow fields (describing the warp from subject-space to
Template_6) and (b) images to normalize (either all functional images, or
the con* files, depending on the option you choose).
For structural image analysis, you would just normalize all of the
rc1* (or rc2*, for white matter) files to MNI space, and conduct your
statistics on those.
Does that help at all?
> 2. I’m analyzing data of stroke patients and when I use the “New
> Segment” option the lesion is also shown as gray matter and some parts
> of the lesion as white matter. But this happens with the segmentation too.
> Attached you can find a picture, the first two images are the c1 and
> c2 after “Segmentation”. The next two images are c1 and c2 after “New
Segment”
> and the last image is the anatomy of the patient, where you can see
> the lesion. Can these files be used or is it maybe better to mask out
> the lesion?
I did not see the attachment, so I can't comment on your particular case.
Also, I have not worked with stroke patient data before, so hopefully
someone else can comment more knowledgeably. However, it does not surprise
me that segmentations might fail (or be a bit odd) on these images, because
they do not conform to what the segmentation algorithm "expects" (based on
tissue probability maps). You may want to have a look at Seghier et al.
(2008), who found that including an additional tissue class to model the
lesion was successful.
Otherwise, I would think that masking out the lesion is the best approach
(in the case where the standard approach is not working).
Seghier ML, Ramlackhansingh A, Crinion JT, Leff AP, Price CJ (2008) Lesion
identification using unified segmentation-normalisation models and fuzzy
clustering. NeuroImage 41:1253-1266.
Best regards,
Jonathan
--
Dr. Jonathan Peelle
Department of Neurology
University of Pennsylvania
3 West Gates
3400 Spruce Street
Philadelphia, PA 19104
USA
http://jonathanpeelle.net/
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