[pymvpa] searchlight for data with different runs with different masks

[Kaustubh Patil]

Thanks a lot Yaroslav. I am following a procedure as described below please
let  me know if it has any clear or potential problems. I am also throwing
in another questions here but can start another thread if its worth.

1) Alignment procedure: Align all the runs to the middle volume of run1
(example_func from fsl). Use the mask that was generated by fsl form run1.

2) MVPA: do the classifiers give balanced accuracy as my datasets are not
balanced? Also, is it recommended to run searchlight on betamap (after
fitting hrf) or zscored raw data? If betamap after fitting hrf then I using
the provided function I get only one parameter per target per run, is that
how its supposed to be?

Again, thanks a lot.

Best Regards


On Sat, Dec 19, 2015 at 4:52 PM, Yaroslav Halchenko <debian at onerussian.com>
wrote:

>
> On Sat, 19 Dec 2015, Kaustubh Patil wrote:
>
> > Thanks a lot for your reply.
>
> > The preprocessing was done by someone else and yes FSL was used.
>
> > If I am to do the preprocessing (realignment and motion correction) what
> would
> > you recommend?
>
> sorry but not sure what particular recommendation I could give you.  The
> only thing is to reiterate that you need to motion-correct/align
> to the same volume across runs.  There is multiple ways to do it.  You
> can as well redo your preprocessing with FSL just specify to use a
> specific volume for mcflirt.  If your data was e.g. in openfmri layout I
> could recommend/provide nipype workflow to carry out all the
> preprocessing (with your parameters)... but otherwise -- don't want to
> impose anything.
>
> as for a quick one, once again, you could either just reslice all your
> bold data per run into some common space one way (just apply
> transformations to common space) or another (rerunning mcflirt but
> pointing to some volume you consider the target one, e.g. the
> example_func.nii.gz FSL might have prepared for you already)
>
> > As I mentioned before, there are 10 runs per subject, and I
> > would like all the volumes (108 in total) to be aligned.
>
> 108 doesn't divide by 10 (and would anyways be too few per run) -- so I
> guess you meant 108 per each run.
>
> --
> Yaroslav O. Halchenko
> Center for Open Neuroscience     http://centerforopenneuroscience.org
> Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
> Phone: +1 (603) 646-9834                       Fax: +1 (603) 646-1419
> WWW:   http://www.linkedin.com/in/yarik
>
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