I don’t think your rig, as it’s set up, will do what you want. I attempted to do something similar in my own work, and I think I have a solution for you.
First, merge your two tasks together into 1 bold image fslmerge -t bold_taska.nii.gz bold_taskb.nii.gz bold_taskab.nii.gz Also double up your attr.txt, so it corresponds to the new double-length bold image Now you have a dataset with shape (608, whatever) ds=fmri_dataset(samples=‘bold_taskab.nii.gz’, targets=attr.targets, chunks=attr.chunks, mask=‘mask.nii.gz’) now, do this: ds.sa[‘task’]=np.repeat([‘A’,’B’], 304) which will label the first half of your data as “A” and the second half as “B" Now zscore, making sure you’re conscious of your task assignment zscore(ds, chunks_attr=‘task’) Now set up the SL clf=LinearCSVMC() partitioner=HalfPartitioner(count=2, selection_strategy=‘first’, attr=‘task’) cv=CrossValidation(clf, partitioner) sl=sphere_searchlight(cv, radius=3, postproc=mean_sample(), nproc=16) res=sl(ds) The HalfPartitioner as its set up will split you data into 2 chunks based on your new ‘task’ attribute. It’ll train on task A and test on task B, then visa versa. Usually you want the average errors of that, but if you’re really set on just training on A and testing on B, then omit the postproc=mean_sample() bit, and you’ll get per-fold error in the res dataset, and you can find the fold corresponding to what you want. Anyway, hope that helps. - Gavin ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Gavin Hanson, B.S. Research Assistant Department of Psychology University of Kansas 1415 Jayhawk Blvd., 426 Fraser Hall Lawrence, KS 66045 On Jul 15, 2014, at 6:11 PM, David Soto <[email protected]<mailto:[email protected]>> wrote: Hi, I hope you have enjoyed the worldcup :) I am trying a searchlight pipeline for the first time now, it has been running for some 6-8 hours and remains on with little RAM and CPU used . To recapitulate, I am training a SVM on FSL copes from task A regarding classes X & Y and then testing the model on FSL copes from task B regarding the same classes. The shape of training and testing datasets is (304, 902629) My searchlight pipeline is the following, would you please let me know if this is OK? cheers, ds from mvpa2.suite import * datapath1='/home/dsoto/Documents/fmri/rawprepro_wmintrosp' attr = SampleAttributes(os.path.join(datapath1, 'attr.txt')) ds = fmri_dataset(samples=os.path.join(datapath1, 'bold_taska.nii.gz'), targets=attr.targets, chunks=attr.chunks) ts = fmri_dataset(samples=os.path.join(datapath1, 'bold_taskb.nii.gz'), targets=attr.targets, chunks=attr.chunks) zscore(ds) zscore(ts) clf= LinearCSVMC() clf.train(ds) predictions = clf.predict(ts.samples) #validation= np.mean(predictions== ts.sa.targets) sl = sphere_searchlight(predictions, radius=3, space='voxel_indices', postproc=mean_sample()) sl_map = sl(ds) the ipython gui currently says [SLC] DBG: Starting off 4 child processes for nblocks=4 On Fri, Jul 4, 2014 at 2:44 PM, David Soto <[email protected]<mailto:[email protected]>> wrote: great thanks! best of luck in the semifinals! cheers ds On Fri, Jul 4, 2014 at 2:33 PM, Michael Hanke <[email protected]<mailto:[email protected]>> wrote: Hi, On Tue, Jul 01, 2014 at 12:25:40AM +0100, David Soto wrote: > 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 Ah, I guess you model each task with two regressors (hrf + derivative?). You can also use a basis function set and get even more betas... > > then could I use SVM searchlight trained to discriminate a from b in task1 > betas and tested in the task2 betas? yes, no problem. Cheers, Michael PS: Off to enjoy the quarter finals ... ;-) -- Michael Hanke http://mih.voxindeserto.de<http://mih.voxindeserto.de/> _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected]<mailto:[email protected]> http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa -- http://www1.imperial.ac.uk/medicine/people/d.soto/ -- http://www1.imperial.ac.uk/medicine/people/d.soto/ _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected]<mailto:[email protected]> http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
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