Dear Marko,
Cyril is absolutely right that the only way to correct for TIV in your case is to use global scaling. Because I found this step not very self-explaining you can use the following steps:
o Global Calculation → User <-X → Define here the TIV values
o Global Normalization
• Overall grand mean scaling → Yes
o Normalization → User → Proportional
Please note that the global normalization will also affect the absolute threshold for the masking because your images will be now scaled to a global value of 50. While usually an absolute threshold of 0.1..0.25 is recommended, the scaled values will be now smaller by a factor of around 30:
o If the mean TIV is 1500 all images are globally scaled to a value of 50. Thus, the overall scaling is 50/1500 = 1/30
o To get the (old) absolute threshold of 0.1 (0.2) now use 0.1/30 (0.2/30)
Best,
Christian
PS: This topic is also covered in the CAT12 manual at "Checking for design orthogonality"...
On Tue, 5 Jul 2016 15:24:24 +0200, Marko Wilke <[log in to unmask]> wrote:
>Dear All,
>
>I have a question that has recently come up in a discussion. Background
>is, we are doing a VBM study of three groups, say A/B/C. We want to
>perform an analysis on "modulated" GM maps, as derived by a DARTEL
>procedure. As such, it is usually recommended to include a global
>covariate, either total GM volume or total intracranial volume, as a
>covariate. Our groups, however, due to the nature of the underlying
>condition, have different globals, i.e., group A has higher globals than
>group B and/or C.
>
>I understand that including the globals will change the interpretation
>of the resulting group differences. It may also, due to the group
>difference in the globals, "take away" some effects that may exist
>between the groups because the variance may be shared.
>
>One idea that then came up was whether it is possible to use an
>orthogonalization on total GM / TIV, w.r.t. group status. This seemed
>like a worthwhile idea at the time as the aim was to not explain group
>differences by this orthogonalized variable that are already explained
>by group. We tried it and the effect was substantial, to say the least.
>
>I am not sure, though, that this is a good (or even valid) idea from a
>statistical point of view. For one, I have seen several mails in the
>archives that mention that othogonalization of a nuisance variable is
>not a good idea. One could of course argue that it is not really a
>nuisance variable, it "only" changes the interpretation of the results
>by (interpreted in a very naive way) rescaling. But then again, I could
>be totally wrong and not see the forest for the trees. So as always,
>your insights into this matter are much appreciated.
>
>Cheers
>Marko
>
>--
>____________________________________________________
>Prof. Dr. med. Marko Wilke
> Facharzt für Kinder- und Jugendmedizin
> Leiter, Experimentelle Pädiatrische Neurobildgebung
> Universitäts-Kinderklinik
> Abt. III (Neuropädiatrie)
>
>Marko Wilke, MD, PhD
> Pediatrician
> Head, Experimental Pediatric Neuroimaging
> University Children's Hospital
> Dept. III (Pediatric Neurology)
>
>Hoppe-Seyler-Str. 1
> D - 72076 Tübingen, Germany
> Tel. +49 7071 29-83416
> Fax +49 7071 29-5473
> [log in to unmask]
>
> http://www.medizin.uni-tuebingen.de/kinder/epn/
>____________________________________________________
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