Hello AFNI gurus -

We have a two part experiment in which subjects were asked to read a story about a fictional character named Bob (unintentional conincidence) who enounters various moral (care and justice) dilemmas and nonmoral (strategic and tactical) problems throughout his day. Neutral statements that had no moral conflict or nonmoral problem were also included in the story.

In the first experiment, the subjects were simply asked to identify any topic or issue they felt was 'important'. In the second experiment, we selected three passages from each of the categories (care, justice, strategic, tactical, neutral) within the story and asked the subject to rate each statement based on how important it was to them within the context of the story (1 - not important, 4 - very important). In other words we have three ratings for each category.

We analyzed the first experiment with SPM99 and have found some interesting activation patterns relating to the "implicit recognition" of moral dilemmas and nonmoral problems. The second experiment has proven to be more problematic. Behaviorally we accomplised an initial goal which was to create neutral passages that were indeed neutral relative to the conditions of interest. And statistically the neutral ratings were MUCH lower that the other four categories. As a result, we feel we need (and would like) to include each subjects rating into the analysis as a covariate to see how rating a dilemma or problem as more or less 'important' affects neural activation - if at all. However, due to what seems to be a peculiarly limited capacity within SPM, I have been unable to adequately account for subject rating, and as such was curious to know if this was feasible with AFNI. Specifically, which toolbox would be best suited for such an analysis?

Just for some quick background, I am moderately familair with the AFNI package, but havent used it to any large extent, so any input at all would be greatly appreciated.

Thanks...

keith