 |
 |
I have a question regarding the correct statistical technique to use in SPM to analyze data associated with a parametric regressor. First, here's a little bit about my task: I have 4 conditions listed in the model of my task: Condition A, Condition B, Control Condition, and Fixation. I have already looked at t-tests comparing activation between sets of these conditions (i.e. ConditionA-B, A-Control, and B-Control). In addition, I am constructing another model in spm that has the previous 4 conditions and a parametric regressor for Condition A. This regressor is the subject's response during this condition (ranges from 0 to 6). So this is the same model as above except in addition Condition A gets one parametric regressor (and included in this are both 1st and 2nd order poly). Now basically I am interested in whether activation in Condition A is related to the parametric regressor (both 1st and 2nd order terms). I'm not sure what the best way to look at this would be. I could do F test with the following: [1 0 0 ...], [0 1 0 ...], [0 0 1...] where the terms are [Condition A, Con. A Parametric Regressor Poly 1, and Con. A Parametric Regressor Poly 2...], but I'm not sure if this is correct. But I have heard that I should always subtract another condition out when doing f test, so that data will show activation associated with a condition. Is this correct? And if so, how would I do this with F test and what would be best to subtract out? Another possibility is masking the F test with something else. Would this be correct, and if so what would I mask it with? Thanks. I'm a relative novice with this and could use some advice. Brian