Hi Michael, indeed ..well done for germany today! :). Thanks for the reply and the suggestion on KNN I should have been more clear that for each subject I have the following *block *sequences ababbaabbaabbaba in TASK 1 ababbaabbaabbaba in TASK 2
this explains that I have 8 a-betas and 8 b-betas for each task AND for each subject..so if i concatenate & normalize all the beta data across subjects I will have 8 x 19 (subjects)= 152 beta images for class a and the same for class b then could I use SVM searchlight trained to discriminate a from b in task1 betas and tested in the task2 betas? cheers ds Hey, sorry for the delay... Aren't you watching the world cup? ;-) On Thu, Jun 26, 2014 at 02:11:00PM +0100, David Soto wrote: >* The design is simple, basically I have 2 tasks, S and I and each task has 2 *>* conditions: a and b *> >* Each task occurs on a separate fMRI run and the conditions a & b are *>* blocked such as 'ababbaabbaabbaba' (each block is 4 trials each *> >* Data has been preprocessed in FSL (as part of univariate-based analyses), *>* including a 5 mm smoothing. I have derived parameter estimates for each *>* task condition a & b....so have 8 betas per subject per condition. * I don't fully understand how two conditions time two tasks make 8 betas... >* Basically I would like to train a SVM classifier to discriminate *>* conditions a & b in task S and then test it on the independent dataset *>* from the different task I. *> >* For this I thought to normalise to MNI and concatenate all the arameter *>* estimates for a & b for task S across all subjects and in principle use *>* whole-brain classification, with the intention of trying searchligh *>* analyses later on... *> >* Does this make sense? or would it be better to do it differently? Any *>* advise or pointers would be much appreciated! * The general approach is sane. However, I don't know if that SVM can be trained properly with 8 training samples. Doing it in a searchlight brings the number of features closer to the number of samples. You could also consider a simple k-nearest-neighbor approach (prediction determined by the closest (eucl./corr-distane) training dataset sample). However, the latter is not really applicable in the full-brain case, as the distance measure will be dominated/contaminated by thousands of noise voxels... HTH, Michael -- J.-Prof. Dr. Michael Hanke Psychoinformatik Labor, Institut für Psychologie II Otto-von-Guericke-Universität Magdeburg, Universitätsplatz 2, Geb.24 Tel.: +49(0)391-67-18481 Fax: +49(0)391-67-11947 GPG: 4096R/7FFB9E9B
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