[pymvpa] Using searchlight for quick spherical ROIs?

[Yaroslav Halchenko]

sorry for just a brief reply... 

>    the dataset with a new mask for each ROI. However, I wonder if there is
>    a quicker option, using the Searchlight and the "center id" parameter?
iirc... not

>    The idea would be to start with a list of peak voxel coordinates for
>    each ROI from the nifti image, map these to feature IDs somehow, and
>    pass the list of ROI IDs to the Searchlight. The result would then be a
>    (fast!) searchlight analysis, that runs a spherical classifier centered
>    on each ROI in turn. The resulting classification map should only
>    contain one voxel per ROI.
since I don't know how you set things up, if you assume that
ds is full bold NiftiDataset you want to extract roi's from
roi_map -- just silly dataset where you loaded your rois with the same
mask as you had in ds

then just use smth like

for roi_coord in [(0,1,2), (0,3,1)]:
 # to do voxel -> feature_id mapping
 roi_featureid = ds.mapper.getOutId(roi_coord)
 # just remember that NiftiImage has reverted coordinates
 # so you need to use (z,y,x) here

 # figure out the 'color' of ROI
 roi_id = roi_map.samples[roi_featureid] 
 # extract all the features from that ROI
 ds_roi = ds.selectFeatures(N.where(roi_map == roi_id)[0])
 # assess what you like to assess on that ds_roi


it might not work... just coding right in the email... but it might give
you hints... 

for mapping back and forth of coordinates between voxels/features use
ds.mapper.getOutId / getInId

hope this helps

>    Has anyone attempted something similar? I can't quite figure out how to
>    map the ROI coordinates in the original nifti to the feature ID that
>    the classifier runs on.
>    Any help would be greatly appreciated.
>    Johan

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-- 
Yaroslav Halchenko
Research Assistant, Psychology Department, Rutgers-Newark
Student  Ph.D. @ CS Dept. NJIT
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