[pymvpa] searchlight for data with different runs with different masks
debian at onerussian.com
Sat Jan 16 04:08:58 UTC 2016
On Fri, 15 Jan 2016, Kaustubh Patil wrote:
> Thanks Yaroslav.
> I tried your solution and it seems to work for this particular dataset but
> unfortunately not for other datasets as the labels cannot be balanced
> Maybe it's possible to directly calculate balanced measures in the CV? I
> guess I will have to change the code to do that, any suggestions where to
some toolboxes compute 'mean of within class accuracies' (not mean
overall accuracy) which allows to account for disbalance. I guess we
could code it quite easily if you like
BUT the problem really would remain: with small number of samples
classifier might just take the "majority" label since it would minimize
error more than low performace decision. So you would hurt yourself
more than help.
another solution is to try a classifier which provides weighting
to the classes, e.g. as GNB with default prior setting does. you could
try it and see how it goes. It is not the greatest classifier but a
start. then you could add similar class weighting to some other
classifiers supporting that.
> Best regards
> On Sat, Dec 19, 2015 at 3:51 PM, Yaroslav Halchenko
> <debian at onerussian.com> wrote:
> On Sat, 19 Dec 2015, Kaustubh Patil wrote:
> > Hi,
> > I want to use PyMVPA for whole-brain searchlight analysis on some
> > data. The data has been already preprocessed (skull stripping, motion
> > correction etc.). Each subject data contains 10 runs and each run was
> > separately, so there is a separate full brain boolean mask for each
> > My question is what is the recommended/correct a way to use this data
> > perform run-wise cross-validation searchlight?
> you have a problem here, since you have done per run preprocessing, in
> particular motion-correction, your volumes are misaligned across runs.
> (used FSL, didn't you? )
> ideally, you redo preprocessing while motion correcting to the same
> volume across all the runs.A Alternatively, you reslice all the runs
> into the same space (could well be the common space your toolkit used
> for analysis across runs -- common anatomical or MNI) and then do
> analysis there, while again unifying your mask, which must be the same
> across all the runs.
> > As I understand, each run has to be in the same space (same number of
> > so that training and test can be performed, so the whole brain masks
> have to be
> > somehow aligned. How would you recommend doing this?
> it is not a mere 'number of voxels' problem but rather that you have
> misaligned across runs volumes.A if just voxel number -- choose
> intersection of all masks.
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
More information about the Pkg-ExpPsy-PyMVPA