 |
 |
Dear Angela, >I have two variables of interest that are potentially confounded. One is >RT and the other is an attentional variable. That is, the attentional >might be expected to light up both because the attentional variable is >requiring them to attend more, or because they're attending longer for >whatever reasons. Now it turns out that when the attentional variable >requires them to attend more, the RT is on average slower as well. >Hence the potential confound. > >What I tried first was to enter two regressors, the first for RT, and the >second for the attention variable. When you look at the orthogonality of >the DM, the correlation coefficient is around 0.1, so not too high. And I >see that the same set of areas lighting up for both contrasts. Should I >be worried? No; this is perfectly OK and suggests that there are attentional effects that cannot be explained by RT and RT effects that cannot be explained by attention. Interestingly these effects co-localize in the areas that are common to both contrasts. Using both explanatory variables in the same linear model means that any test for the effect of one (in the presence of the other) is testing only or effects that can be explained uniquely. I am assuming here that you specified one event-type and used two regressors; RT and attention as parametric modulators in a first-level design. SPM will orthogonalise RT w.r.t. attention. This means the results for attention will cover any redundancy between the two variables. The reason you have a small correlation between the regressors is that the orthogonalisation is implemented before convolution with the HRF. >Well, I also tried doing it a different way, which is to make the basic >variable (target onset) to have a duration of RT for that trial instead of >0, and then only one regressor for the attention variable, and there again >I see the attentional areas light up for the attention variable contrast. >These two methods should be fairly similar, right? Yes, they should give very similar results because parametric modulation of a fixed event-related response by RT is almost identical to convolving a variable duration response. This is because the RT is small in relation to the length of the hemodynamic response function. However, if you modulated this variable -uration stimulus function with attention, you are effectively modeling the interaction between RT and attention. Because the RT was not mean corrected (when specifying the durations) this interaction will contain the main effect of attention. >Then I tried a third way (just to convince myself), which is to swap the >order of the regressors (now with the attentional variable first, and RT >second), and now although I still get the same general areas lighting up >for both, the p-val's for both contrasts are much worse! How bizzare. >Why should that have happened? If the p-values got better for one, and >worse than the other, then I might be able to understand -- since >presumably the order of orthogonalization matters. But how can they both >get worse?? This should not happen. As you point out the order can only affect the orthgonalization. You can check this by performing an F-test across both effects. You should get the same results. irrespective of the order. I am guessing there is some other error in model specification that has crept in here. I would use the first analysis because it is the simplest. I hope this heslps - Karl