Dear fellow SPMŽers, I am currently analyzing the data from a Stop-signal task in which we have multiple sessions, plus some problems with regards to the quality of the data. Allow me to delineate the task: The subjects are instructed to respond to a visual cue with a button press. 75% of the trials within a session consists of these trials. The remainder of the time they are presented with a visual cue, followed by a stop signal prompting them to inhibit their initial response. IŽve divided these trials into 4 regressors, respectively Go, NoGo, Hit (Successful inhibition) and Miss (Unsuccessful inhibition), where NoGo is the onset of the initial visual cue before the stop signal. Of course, there were subjects who had a skewed proportion of "Hit"/"Miss" trials within any given session, so we had to run multiple sessions to ensure that they had ~50% of each. The mode of sessions used to achieve this was 3. However, achieving this entailed running one or even two additional sessions for some of the subjects. I am using [-1 0 1 0] and a [-1 0 0 1] vectors replicated that I compare at the second level through a 2- tailed T-test. Išve gathered that replicating vectors across sessions is equivalent to calculating the mean difference between the conditions, which obviates the need to use different weightings based on proportions (provided the total proportion of hit vs miss trials is reasonable, i.e. .50 +/-5), and also corrects for the different number of Go trials used as -1. My first question is whether or not I am right in assuming this, or if I should use session specific weighting. My second question is whether or not it is valid to compare data on the 2nd level when different subjects have different numbers of sessions, and hence differ with regard to the number of trials used in to render the FFX contrasts. My third question is regarding my 2nd level analysis: Ideally I would want to construct a first-level contrast contrasting hit and miss trials after they both have been subtracted from Go trials, i.e. [-1 0 1 0] - [-1 0 0 1]. As I mentioned I am currently doing this through a two-tailed T-test on the 2nd level, with Independence specified as No. Is this the correct approach, or is there some simpler way to achieve this on the first level? Lastly, we have data on both normal controls and patients with substantial brain lesions. We have manually drawn masks of the lesions. Would it be appropriate to include these as explicit masks in the first level analysis, so as to remove noise from the data? In addition, we have some instances of dropout, probably as a consequence of magnetic disturbances from surgical clips/screws (see attached .jpg). How should I go about handling these? Would masking them out be appropriate, or should I leave them be? And to what degree can I trust the data from the areas surrounding these magnetic artifacts? Any help would be greatly appreciated, Best wishes Haakon Center For The Study of Human Cognition, University of Oslo