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This question is posed to Jean-Baptiste Poline and concerns your recent technical note on the problem of correlations between covariates (Andrade et al., NeuroImage 10:483, 1999). You propose a method to remove the correlation between conditions and task performance that occurr when task performance increases across scan conditions. I understand the problem, but not the solution. 1) Could you explain why your method keeps the effects that are common to both the condition and the peformance covariate from being removed from the general linear equation ? The method you propose does not remove the correlation between the condition covariate and the performance covariate, nor does it alter the slope of the regression between the two covariates. All it does is add a scaling factor to the regression, i.e. the regression equation between the two covariates now has a non-zero intercept. I do not understand why this procedure, therefore, removes redundancies between the covariates ? 2) Could you explain why only changing the values of the performance covariate only changes the contrasts assocatiated with the condition covariate. Presumably, your procedure restores effects common to the both covariates to the performance covariate as well as the condition covariate. 3) Was a condition-specific fit used for the performance variable in either the original model (M) or the model with the modified performance covariates (M*). Thanks sg %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%