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Hi Daniel If there were null events in the design then the session-specific baseline (i.e., y-intercept term of the multiple regression) for each run would represent the activity during null events, which usually is activity during fixation. In that case, one could do a one-sample t-test on the beta image for each condition separately, testing whether each differs significantly from zero as you suggest. And, one could look at t-contrasts of 1 and -1 to get positive and negative activations (relative to baseline, e.g., fixation), as you suggest. The problem comes about if there are no null events in the design. In that case, the session-specific baseline no longer reflects activity during a low-level control condition, such as fixation. Rather, the baseline activity is much higher, and much closer to (though not exactly equal to) the mean activity across the entire run. In this case, since the baseline represents activity that exceeds a low-level control condition value, it is difficult to assess what should count as a positive or a negative activation. Hope this helps! Daniel _____ From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf Of Daniel H. Mathalon Sent: Sunday, May 29, 2005 7:00 PM To: [log in to unmask] Subject: Re: [SPM] Inference fo contrast Assuming that there are some time periods that are not modelled (i.e., null events), or even if there aren't, couldn't amit do a one sample t-test on the beta images for each condition separately, testing whether beta values are significantly different from zero? By looking at t contrasts of 1, then -1, wouldn't he be able to assess whether beta values at a particular voxel location are significantly positive or significantly negative relative to implicit baseline? Hi Amit, ----- Original Message ----- From: "Amit" < <mailto:[log in to unmask]> [log in to unmask]> To: < <mailto:[log in to unmask]> [log in to unmask]> Sent: Sunday, May 29, 2005 4:57 PM Subject: [SPM] Inference fo contrast > Dear SPMers, > > The experimental contrasts (a difference between two conditions A and B) > is interpreted in usual SPM analysis as indicating that condition A shows > a larger response than condition B. However, difference measures, > as conceptualized in SPM could have three potential ways in which > conditions may differ: positive activity in A may be greater than positive > activity in B, positive activity in A may be greater than negative activity > in B, and finally, negative activity in B may be greater than negative > activity in A. All lead to a positive difference between conditions. Yes, absolutely. But implicit in your explanations is a third conditiion C to which you are comparing A and B. > > Is there a way of teasing out these three differences? If you have a meaningful C in your experimental design, then you can compute 2 new contrasts to get your answers: (1) A minus C and (2) B minus C. > > Also, how does one get a contrast for areas which are getting inhibited by > a particular task - does one just reverse the contrast for the active > versus control condition in a block-design experiment or is there a more > sophisticated way for doing this? The use of the word "inhibited" has connotations that I am not sure you intended to convey. But if you simply meant "how do see where the signal in B is greater than that in A?", then yes just compute the contrast B minus A. Eric