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Dear Michael, I'm afraid this is not correct. It may be the case for other types of statistical test, but not for the GLM. In the GLM (as typically implemented in FSL and other neuroimaging packages) there is no distinction between continuous and discrete variables. Everything is treated as a regressor and you are doing multiple regression. The consequence of this is that if two regressors each contain a non-zero mean, then any true non-zero mean in the data will tend to be split across these regressors (especially as the mean is often a strong signal). So it makes a big difference to the estimated parameters (the coefficients associated with the regressors) whether you remove the mean from one of them or not. It is true that if you span the same space (assuming that some set of regressors adds up to a flat mean). However, it is the fact that the mean signal will get shared between the regressors which causes a problem and *will* have an effect on the parameters associated with the "mean" regressors, which is normally what is of interest and hence a big issue. All the best, Mark On 11 Feb 2011, at 18:02, Michael Harms wrote: > Hi Gwenaelle, > Why does gender need to be demeaned? You should get identical results > either way because the intercept and gender terms together model the > same space, regardless of whether gender is demeaned. Demeaning really > only matters when trying to interpret a main effect when that effect is > also included as part of an interaction term with a continuous variable. > > cheers, > -MH > > > On Fri, 2011-02-11 at 17:52 +0000, Gwenaëlle DOUAUD wrote: >> Hi, >> >> gender needs to be demeaned. It is not necessary to split the age per group, unless you expect an interaction of age with group... >> >> Cheers, >> Gwenaelle >> >> >>> De: Stijn Michielse <[log in to unmask]> >>> Objet: [FSL] 3 groups randomise >>> À: [log in to unmask] >>> Date: Vendredi 11 février 2011, 14h57 >>> Dear FSL Experts, >>> >>> The project I'm working on has 258 subjects in the >>> population divided over 3 groups. Processing in TBSS is >>> straightforward and I have some questions regarding the >>> randomise tool. >>> >>> Using the randomise tool, I first started creating the >>> design matrix and contrast matrix (named design.mat and >>> design.con). For performing a simple T-test everything is >>> straightforward with contrasts 1 and -1 for corresponding >>> groups. But things get complicated with the introduction of >>> confounders. Our groups are not matched since we would like >>> to include as many individuals as possible. Now we would >>> like to add age, gender and handedness as a confounder in >>> the model. >>> >>> Checking the JISCMail FSL Archives clue's regarding the >>> demeaning of confounders pop up. Demeaning per group is >>> necessary since our groups are not matched. Gender is a >>> bi-directional (being either female or male) variable and >>> doesn't need to get demeaned. In our case we have demeaned >>> handedness since we apply an Oldfield scale (-100 is fully >>> left-handed, +100 is fully right-handed, with value's in >>> between). To know sure we do the right thing in analysing, I >>> attached our design matrix and contrast matrix. In the >>> design matrix the first column is group 1, second column is >>> group 2 and the third column is group 3. As you might >>> notice, row 48 has a group change since this individual is >>> classified as patient after TBSS processing (some more >>> changes are seen further on). >>> For investigating the influence of confounders I added >>> three extra columns; column 4 for age (demeanded per group), >>> column 5 for gender (not demeaned) and column 6 for >>> handedness (demeaned per group). Is it necessary to add >>> specified columns per group for age, padding the other >>> groups with 0? Later we may add more confounders if it >>> survives. >>> >>> Executing the randomise tool with the two designs goes like >>> this: >>> randomise -i all_FA_skeletonised -o tbss -m >>> mean_FA_skeleton_mask -d design.mat -t design.con -n 5000 >>> --T2 -V >>> >>> Can someone please review the attached design matrix and >>> contrast matrix and give some advice? >>> >>> >>> Kind regards, >>> >>> Stijn Michielse >>> Research Assistant >>> Dept. Psychiatry and Neuropsychology >>> Maastricht University >>> E-mail: [log in to unmask] >> >> -------------------------------------------------------------------- >> >> Gwenaëlle Douaud, PhD >> >> FMRIB Centre, University of Oxford >> John Radcliffe Hospital, Headington OX3 9DU Oxford UK >> >> Tel: +44 (0) 1865 222 523 Fax: +44 (0) 1865 222 717 >> >> www.fmrib.ox.ac.uk/~douaud >> >> -------------------------------------------------------------------- >> >> >> >