Hi Shane, I agree with Nick, I would use AFNI's 3dresample command. Make sure to use the -master flag followed by the name of your fMRI data, e.g.,
3dresample -master bold.nii -prefix rois_in_fmri_space -inset your_ROI_filename_here.nii (you may get an error with gzipped data... if so first gzip -d bold.nii.gz) Then AFNI's 3dAFNItoNIFTI command will get the resultant file into NIFTI format for you: 3dAFNItoNIFTI rois_in_fmri_space+orig. If you cannot install AFNI on your local machine, you could always install the Neurodebian virtual machine: http://neuro.debian.net/vm.html Cheers, Derek On Sat, Aug 31, 2013 at 8:46 PM, Shane Hoversten <[email protected]>wrote: > Howdy - > > Let me preface this msg by saying that I only _barely_ know what I'm > doing. This will probably become evident immediately. > > In an experiment whose data I'm trying to analyze, I'd like to > localize the analysis based on the results of various masks, including > Brodmann masks created with the wfu pickatlas tool > (http://fmri.wfubmc.edu/software/PickAtlas). I can use these masks in > the course of univariate analyses with SPM despite the fact that the > masks have different numbers of voxels than do our acquired volumes -- > the NIFTI headers apparently contain enough info to match up the mask > and the volumes in physical space, and SPM just does the right thing, > which was a pleasant surprise. > > But now I'd like to use these masks with PyMVPA. I've been working > through the tutorials with the Haxby dataset, and part 2 of the > tutorial demonstrates loading the Haxby BOLD data with a mask, like > so: > > ds = fmri_dataset(os.path.join(path, 'bold.nii.gz'), > mask=os.path.join(path, 'mask_vt.nii.gz')) > > As it happens, the shape of the voxel matrix for this BOLD data, and > for this mask, are the same. That's a luxury we don't have in our > dataset, but I crossed my fingers and tried to load one of our volumes > with the mask, but to no avail. I got this error: > > > /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/mvpa2/mappers/flatten.pyc > in _forward_data(self, data) > 103 raise ValueError("FlattenMapper has not been > trained for data " > 104 "shape '%s' (known only '%s')." > --> 105 % (str(sshape), str(oshape))) > 106 ## input matches the shape of a single sample > 107 #if sshape == oshape: > > ValueError: FlattenMapper has not been trained for data shape '(91, > 109, 91)' (known only '(96, 96, 37)'). > > The (91,109,91) are the dimensions of the masks produced by wfu > pickatlas; our data is the (96, 96, 37). So my question is, how does > one deal with these situations? I've googled as best as I'm able but > haven't found the issue addressed. Can anyone point me in a good > direction? > > Thanks very much, > > S > > _______________________________________________ > Pkg-ExpPsy-PyMVPA mailing list > [email protected] > http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa >
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