 |
 |
On a related issue, if we do model multiple scans as one long scan (including appropriate scan regressors and linear drift regressors for each scan), how do we then deal with high pass filtering? Shall we not use it? And what about AR? your help is much appreciated. lila On Jul 27, 2005, at 11:18 AM, Nicole David wrote: > Thanks, Anja! > > Concerning the second issue, what if a condition/type of event only > appears once in one scanning run and then 10 times in another scanning > run? doesn't that mean that the parameter estimates will be highly > unstable and very influenced by noise. Would it be possible to analyze > everything as one run and include extra covariates for each scanning > session? And, if so, how would I do this or is there another solution > (e.g. including global signal scaling)? > > Nicole > > > > <[log in to unmask]>: > >> Hi Nicole, >> ad 1) in my experience, it is better, if you model errors as an >> additional error condition (but do not differentiate it further >> into different errors or you lose too many degrees of freedom) >> you coudl also choose to ignore errors and only model correct trials. >> I usually try both and choose the better versions (i.e. the one with >> higher T-values). Leaving the errors in is usually the worst option. >> >> ad 2) it might be difficult to get good results, because when >> the scanner starts anew it will have a different setoff/mean value. >> So the analysis over all runs will not yield much. So you should do it >> as a FFX Model with 4 different sessions. Then SPM will estimate all >> predictors for each session separately and insert a session >> mean as a covariate of no interest. >> >> Hope that helps >> >> Anja > >> >>>>> Nicole David <[log in to unmask]> 27.07.2005 13:00 >>> >> hi, >> i have two questions: >> >> 1) i have an event-related design. each event (a stimulus) is coupled > with >> a rating and categorized according to it (into two conditions). > sometimes, >> subjects missed a rating, i.e. yielded no score. How do i deal with > these >> events that weren't rated? do i have to model them out, i.e. as a >> third "error" condition, so that they don't fall into the iti (and >> thus >> the baseline)? fortunately, they didn't occur often... or would it be >> legitimate to ignore those only specifying a model with the events >> that >> were rated? >> >> 2) i had 4 scanning sessions. would it be o.k. to treat everything as >> one long scan? >> >> any ideas? >> thanks, >> nicole > > Lila Davachi Assistant Professor Department of Psychology New York University 6 Washington Place, Rm 866B New York, NY 10003 212-992-9612 [log in to unmask]