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Hi Steve I'm going to restate what I think the problem is to make sure we are on the same page. You can let me know if I've misunderstood. Let's say you have 6 scans and a covariate for each of them (10,20,30,40,50,60). You enter these in your design, and by default SPM will mean-center this vector, so instead of [10 20 30 40 50 60] you have [-25 -15 -5 5 15 25]. This is what you want, because a high parameter estimate for this regressor would tell you that the first scan contributed less than average, and the last scan more than average---the relative differences of all your covariates. The problem arises when you don't have values for all scans. So now, say you have 6 scans, but only 3 numbers (10,20,30). If you then enter [10 20 30 0 0 0], mean-centering gives you [0 10 20 -10 -10 -10], so a high parameter estimate would indicate that the last 3 scans contribute less to the mean than the first three----not what you want. The solution is to set up your vector so that the values for the scans you want are mean-centered before adding the 0s for the other scans. In other words, mean-center [10 20 30], which gives you [-10 0 10], and then add the extra zeros, for [-10 0 10 0 0 0]. Now, data in the last 3 scans can't have any influence on the fitting of this parameter estimate, because no matter how large the beta value is, it is multiplied by 0. [Note that, since you have mean-centered this vector yourself outside of SPM, it makes no difference whether you tell SPM to mean-center it or not.] The columns of your design matrix relating to subjects will scale to fit variance associated with a subject across both scans; this covariate will reflect the values over the first scans. I think this will answer the question you are asking...hopefully someone will correct me if I'm wrong. :) Hope this helps, Jonathan On Thu, Mar 12, 2009 at 4:23 PM, Cramer, Steven <[log in to unmask]> wrote: > Hi > Thanks for below. There is another layer of complexity. The covariates are > scan-specific, and the covariate score exists for the first scan only. > When prompted by SPM5 for the pair of covariate values, if one enters the > covariate value for scan 1 and a zero for scan 2, this is inaccurate, as the > model would use the difference (zero minus scan 1 covar value) as a change > in score, which is not accurate, as the scan 1 value is the score at > baseline not the change over time. > Is there a way to enter the covariate value at time 1 when no value exists > at time 2, in a manner that does not suggest that the time 1 value is a > change over time? > Thanks, > Steve > > > At 10:25 PM +0000 3/10/09, Jonathan Peelle wrote: > > Hi Steve > > Are your covariates subject-specific, or scan specific? I.e. do you > enter the same number for the subject on both scan 1 and scan 2? With > a paired samples t-test, I think any part of your data that can be > explained in a subject-specific manner would be modeled out; so, if > your covariates are subject-specific, they aren't helping you explain > any more of your data. This would explain why your results are the > same. > > If your covariates aren't set up this way, then something else is > likely to be the culprit... > > Hope this helps, > Jonathan > > On Tue, Mar 10, 2009 at 8:22 PM, Steve Cramer <[log in to unmask]> wrote: >> I have scanned 24 subjects with fMRI twice per person. I am trying to >> examine a paired t-test while controlling for effects of two covariates. >> I >> am able to generate a paired t-test in SPM5 without the covariates, and >> the >> activation looks proper. >> >> However, the results (glass brain, cluster analysis) do not change when I >> add 1 or 2 covariates to the model. When I made the second model (paired >> t-test that has the two covariates), I used all the same choices (same >> pairs, no change in Grand mean scaling choices, etc) for the paired >> t-test, >> and note too that the covariates are entered without error (vector entered >> ok, variable named OK, no interactions, etc). In the second model (with 2 >> covariates), I entered a zero for both of these covariates in the contrast >> manager. >> >> Thus, it appears that in SPM5, a paired t-test with no covariates produces >> identical results as a paired t-test with 2 covariates properly specified. >> >> I would expect that the second model, with the two covariates that have a >> zero in contrast manager, would have a different result than the first >> model, with the difference reflecting removal of the signal accounted for >> by >> these two covariates. What am I missing, or doing wrong ? Thanks--Steve >