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Re: TIV/GM covariate question

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Mon, 18 Jul 2016 13:38:15 +0100

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 ```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/ >____________________________________________________ ```