 |
 |
Marian, The general problem of analyzing fMRI data from a small group of subjects with a lot of movement may not be yet be solved, but I would suggest the following. The ArtRepair toolbox has a slice repair feature that is mostly designed for weird transient scanner noise, and your data probably doesn't need it. (By the way, there is very little redundancy between the slice and volume repairs, and if one type of repair fixes a problem, the second does not do anything to the fixed data.) Large movement subjects often exhibit both large amplitude movements and rapid movements, so the goal is to fix both types of problems. The Volterra motion regressors will catch spin history artifacts, so I believe they are better than six motion regressors. But rapid motions may also cause image distortions, which is a problem different from spin history, so the analysis needs to do something with the rapid motion scans. One approach is the one by Lemieux that adds "null regressors" near the times of jerky head motions. Alternatively, the ArtRepair volume repair function replaces error prone data by interpolating through volumes where there was high scan-to-scan motion. It's designed to be automatic, to simplify running large numbers of subjects. So, I would propose that the ArtRepair volume repair plus the Volterra motion regressors would be a good analysis method. OR, follow the procedure in the Lemieux paper and add null regressors as needed to the design matrices. But even with the "best" method, how does one know if the result is correct? This problem is tricky, because the GLM could give false activations from task- correlated motion, consequently, higher activations are not necessarily better. One suggestion is to quality check the estimates that come out of the single subject analyses. (The estimates are the con images, not the activation spmT images). ArtRepair version3 (just released) has new tools to perform a quality check on those estimates. If the estimates are unusual, then the single subject analysis may not have been successful. The software will also suggest outlier subjects to be excluded from a group analysis. (http://cibsr.stanford.edu/tools/ArtRepair/ArtRepair.htm) One controversial point is whether the interpolation by ArtRepair will compromise the single subject activation map. For group analyses, only the estimates are passed up to the group level, so it doesn't matter. For single subject analyses, the toolbox includes a deweight function that essentially removes the repaired scans from the GLM estimation, and SPM will correspondingly reduce the number of degrees of freedom. Good luck, Paul ----- Original Message ----- From: "Marian Michielsen" <[log in to unmask]> To: [log in to unmask] Sent: Wednesday, April 1, 2009 12:54:25 PM GMT -08:00 US/Canada Pacific Subject: [SPM] modelling out task related movement Dear SPMers, I have a data set in which quite a few subjects show task correlated head movements. As people of the list have commented earlier, the most valid option would be to just throw away those subjects, but if I do that I have very little data left. I am now looking at different options to deal with this problem. Just adding the realignment regressors into the model seems too conservative; if I do that, in some sessions I have almost no activation left. Because of that, I tried some other approaches, which as far as my understanding goes (from reading other posts in this list and from looking at my own data), range from very conservative to very unconservative: - Modelling with realignment regressors -> this leaves me with almost no activation - Modelling the volterra expansion of the realignment regressors (as described for instance in Lemieux, 2007 ) -> this seems to work out slightly better then using just the six primary realignment regressors, with this approach my contrast maps show a bit more activation - Unwarping the data instead of modelling the realignment regressors -> this results in quite a lot of activation, in task related areas but as it seems also quite a lot of noise - No unwarping and neither modelling the realignment regressors -> results in most activation, and will probably generate a lot of false positives As for those four options one of the middle two is probably most valid. However, I also just starting looking a bit into the ArtRepair toolbox (by Paul Mazaika). It wonder if it makes sense to use this toolbox in combination with one of the former mentioned approaches. With this toolbox, it is possible to detect and repair artifacts both at the slice and at the volume level. The first would be done before any preprocessing steps, the second just before estimating the model. Does anybody know if it makes sense to repair artifacts at both those level in one session (i.e. both within and between volumes), or is that redundant? If you would be very precise, you could for instance opt for the following approach: 1. use artrepair to repair bad slices 2. realign 3. unwarp 4. coregister 5. normalize 6. create first level model (without realignment regressors) 7. use artrepair to repair bad volumes 8. estimate results from the repaired data Or use the same steps but choose to include the realignment regressors instead of unwarping. However, maybe some of those steps make some of the other steps redundant. Has anyone any thoughts on this? Or maybe tried out other approaches? Any help would be very much appreciated! Kind regards, Marian -- Paul K. Mazaika, PhD. Center for Interdisciplinary Brain Sciences Research Stanford University School of Medicine Office: (650)724-6646 Cell: (650)799-8319 CONFIDENTIALITY NOTICE: Information contained in this message and any attachments is intended only for the addressee(s). If you believe that you have received this message in error, please notify the sender immediately by return electronic mail, and please delete it without further review, disclosure, or copying.