Hi Johan, On Sun, Mar 15, 2009 at 03:43:51PM +0000, Johan Carlin wrote: > Hi all, > > I have just started using PyMVPA for some fMRI analysis. It has worked like > a charm so far. :) Thanks very much for developing such a useful tool.
I'm glad you like it! > I would like to run a clasifier on a number of ROIs that were defined in a > univariate SPM analysis. The most obvious solution is to re-load 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? > > 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. > > 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. I cannot look into the issue in detail right now, but with respect to the "translation" problem I might provide some help. Let's look at the following code. The first snippet loads the example dataset, which shall for now be a replacement for your dataset -- the important bit is however the type being 'NiftiDataset': >>> from mvpa.suite import * >>> attr = SampleAttributes(os.path.join(mvpa.pymvpa_dataroot,'attributes.txt')) >>> ds = NiftiDataset(samples=os.path.join(mvpa.pymvpa_dataroot,'bold.nii.gz'), ... labels=attr.labels, chunks=attr.chunks) The dataset apparently has 1452 features >>> ds <Dataset / int16 1452 x 800 uniq: 12 chunks 9 labels> If you now want to know which feature corresponds to a particular coordinate, you can simply query the mapper instance of the dataset. # feature id for coordinate z=0 y=10 x=20 >>> ds.mapper.getOutId((0,10,20)) 420 That should get you started. You can even go a bit further and compile the list of neighbouring voxels yourself (again with the mappers help): >>> ds.mapper.getNeighbors(420, radius=5) [379, 380, 381, 419, 420, 421, 459, 460, 461] which return you a list of feature ids all in a 5mm range around "420". Does that help? Michael -- GPG key: 1024D/3144BE0F Michael Hanke http://apsy.gse.uni-magdeburg.de/hanke ICQ: 48230050 _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected] http://lists.alioth.debian.org/mailman/listinfo/pkg-exppsy-pymvpa
