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Hi, see below On Mon, May 13, 2013 at 9:34 AM, Iwo Bohr <[log in to unmask]> wrote: > Dear FSL experts,****** > I know that this topic has already been dealt with (partially) by Steve > Smith on this list (Item #8709 (1 Sep 2006 04:59) - Re: 2nd level > covariate of interest<https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind06&L=fsl&P=R194216&1=fsl&9=A&I=-3&J=on&d=No+Match%3BMatch%3BMatches&z=4>), > but I would like to double check I got right his response.**** > I would like to include different measures of lesion severity as > covariates of interest in a 2nd level analysis (one group).**** > So in my model I should:**** > 1. include group mean regressors (column of ones) next to > EACH covariate but WITHOUT demeaning actual scores? > Demeaning won't hurt you and is only necessary if you're also looking at the overall mean (interpreting the column of 1's). Basically you can extend this example for your design http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/GLM#Single-Group_Average_with_Additional_Covariate > **** > 2. in addition: orthogonalize covariates with respect of each > other? It’s important since I would expect quite some degree of correlation > between them since they measure similar things (not identical though; want > to know which; if any; is the best predictor of BOLD activations) > The GLM's p-values automatically reflect the unique variability due to each regressor. Orthogonalizing basically defeats the purpose of adding additional regressors. Typically you add additional regressors to adjust your analysis for those effects. When you orthogonalize you're removing the ability of one regressor's inference to be adjusted for another. It makes a strong assumption that the shared variability truly belongs to one EV over the other and in 99% of the cases there's no way to make that argument. Just let the GLM naturally parse out the unique portions for each covariate without orthogonalizing. > **** > 3. orthogonalize each covariate wrt to the principal regressor > (first level BOLD statistics)? > I'm not sure what you mean here, but as I said above, orthogonalization is not necessary. Unfortunately if you're EVs are highly collinear, it is what it is and collinearity is not the answer. Hope that helps, Jeanette > **** > Many thanks in advance,** ** > Iwo**** >