Hi SPM experts,
This question has puzzled me for a while and I hope some experts could help.
In my experiment, I have one decision task (i.e. D). I want to identify neural correlates related to D. A hypothesis suggest that if a brain region is related to decision, its activity should covary with task difficulty. So I designed 2 types of difficulty levels (each repeated for 20 times) and collected data from 20 subjects. Behavioral data showed that subjects did report significantly different difficulty ratings for the 2 types of task levels. Below is the proposed three methods to isolate decision-related regions:
Method (1): Subject's first-level model contained 1 EV representing D epoch. Each subject has an average difficulty score obtained from the mean of the 2 levels. In group-level model, I perform a regression analysis using each subject's average difficulty score as a covariate.
Method (2): Subject's first-level model contained 2 EVs corresponding to the 2 difficulty conditions of D epoch. In contrast manager, I used "-1 1" to identify activity related to task difficulty in each subject. Finally, in group-level model, one-sample t-test was performed.
Method (3): Subject's first-level model contained 1 EV representing D epoch. In the first-level model, I entered each trial's difficulty rating in "parametric modulations" under "condition" (i.e., 40 numbers were entered in this EV; 1st order modulation selected). In contrast manager, I used "1", which I think would identify activity correlated with trial-by-trial task difficulty. Finally, in group-level model, one-sample t-test was performed.
Question 1: Which method is correct or better?
Question 2: If I did not design 2 or more levels of task difficulty (i.e., with only one type of task), can I still identify neural correlates related to decision, based on the hypothesis mentioned above? I mean, even though the design has just one difficulty level, different difficulty ratings could be reported across subjects. Then I use Methods (1) and (3) to get the neural correlates.
Thanks.
Mike
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