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Sorry for the rather naive question, but I just want to make sure I am doing things properly, so any feedback will be greatly appreciated.
I use SPM for memory-based fMRI experiments mostly. So many of my contrasts of interest involve looking at the difference in activation between 'old' items and 'new' items (usually old > new). How I usually set up my design is in the the following way:
at the first (subject) level, I create a beta weight for 'old' items and also one for 'new' items. I then create a con image for each subject using 1 for the 'old' beta weight and -1 for the 'new' beta weight. This leaves me with a con image that shows the differential activity between 'old' items and 'new items' for each subject (old > new).
then I take these con images up to the second (group) level of analysis and run a one-sample t-test against zero to see if this 'old > new' contrast is significant at the group level.
All is fine and good.
However, recently I've included a different factor in the experimental design. Now there are 2 conditions that each subject goes through - a 'High Probability' condition and a 'Low Probability' condition. I do the same analysis that I described above, separately for the High and Low conditions. Now I have two one-sample t-tests that show me the 'old>new' contrast at the second level for each condition separately.
But what I want to do is compare these two 'old>new' contrasts between the two conditions. Basically I want to see:
Is the 'old > new' activity during the Low Probability condition significantly different than the 'old > new' activity during the High Probability condition?
As far as I can see, there are a few ways to get at this, but I'm not sure which (if any) are statistically the most correct. Here's what I was thinking as options:
1. Run a paired-samples t-test at the second level of analysis. To do this, I would pair each subject's 'old>new' Low Probability con image with their 'old > new' High Probability con image. This seems to be the most straight-forward way to go about this
2. Use ImCalc to create a 'difference image' for each subject. Simply subtract each subject's 'old >new' High Probability Condition con image from their 'old > new' Low Probability con image. Then bring those 'difference images' up to the second level and run a one-sample t-test against zero on those.
3. Use a flexible factorial to run a one-way ANOVA (setting the independence parameter to 'No') and put Condition as a factor with two levels (High and Low) and then input the 'old > new' con images for each subject for both conditions. I would then create a t contrast that directly compared the two levels to each other.
Are these the right way to go about this? Is there one method that is statistically better than the other? I get slightly different results depending on what method I use (due to differences in degrees of freedom which lead to differences in thresholding), so I just wanted to reach out and see what you thought
Thanks in advance for any advice!
~ Amy
