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On Wed, Jan 18, 2012 at 9:51 AM, Kris Baetens <[log in to unmask]> wrote: > Dear SPM’ers, > > I'm sorry for the somewhat basic questions, but I'm very confused by the > second-level analysis of some parametric modulations, and haven't found > another post that was completely comparable. > > Participants conducted four different tasks during an experiment. After > each trial they rated how difficult they had experienced this trial to be. > > On the first level I defined four conditions with a parametric modulator > each (the difficulty rating). As helpfully suggested earlier on this list, > I have asked a contrast for each condition and two contrasts for each > parametric modulator (positive - negative regression slope). > 1. How are the resulting images of the condition contrasts – if at > all – influenced by including the parametric modulators in the first level? > Is it useful to compare these images to those that result from a > first-level analysis without modulators? > In theory, these should be the same since one is orthogonal to the other; however, in practice they will be slighltly different because of multiple tasks. I would stick with one model. > On the second level, I conducted a within-subjects one-way ANOVA (with > equal variance, no independence) to investigate the effect of the > conditions themselves. > To investigate the effect of difficulty on a group level, I made use of a > flexible factorial design. I am absolutely uncertain whether what I did > here makes sense. I specified two factors (condition and positive/negative > difficulty regressor slope). I specified conditions/subject in an 8x2 > double (1 1 , 2 1, 3 1, 4 1, 1 2, 2 2, 3 2, 4 2), meaning that I only input > the 8 con images of the first level representing the parametric modulators. > Then in the contrast manager, I specified a t-contrast for each of the 8 > parametric modulations (4 conditions x 2). For example, for increasing > difficulty ratings in the first condition, I entered the vector 1 0 0 0 1 0 > 0 0 0 0 0 0. > 2. Is this approach correct? > No. The positive and negative slopes have a perfect correlation. Just use the ones with a positive slope. To test negative slopes invert the contrast. > 3. Is it correct to interpret the results as representing regions in > which there was a significant interaction between a given condition and > difficulty? > In the new model yes. Make sure you have columns for each subject as well. > 4. Is it normal that the condition contrast images are not required > in this step of the analysis? > For the question you are asking, yes. > 5. Is it correct to input “No independence” and “Equal variance” here > for both factors? > Yes. But you only need one factor as described above. > 6. Is it customary to investigate the impact of the conditions > themselves and the parametric modulators in two different analyses? Can > they be combined? > You will want one first level analysis and two second level analyses. > > Thanks in advance for any help, > Kris >