Reply-To: | | [log in to unmask][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 dÆ6„\b |