This dataset is a compilation of data and results for PyMVPA Tutorial.
At the moment dataset is based on data for a single subject from a study published by Haxby et al. (2001). The full (raw) dataset of this study is also available. However, in constrast to the full data this single subject datasets has been preprocessed to a degree that should allow people without prior fMRI experience to perform meaningful analyses. Moreover, it should not require further preprocessing with external tools.
All preprocessing has been performed using tools from FSL. Specifically, the 4D fMRI timeseries has been motion-corrected by applying MCFLIRT to a skull-stripped and thresholded timeseries (to zero-out non-brain voxels, using a brain outline estimate significantly larger than the brain, to prevent removal of edge voxels actually covering brain tissue). The estimated motion parameters have been subsequently applied to the original (unthresholded, unstripped) timeseries. For simplicity the T1-weighed anatomical image has also been projected and resampled into the subjects functional space.
The orginal authors of Haxby et al. (2001) hold the copyright of this dataset and made it available under the terms of the Creative Commons Attribution-Share Alike 3.0 license. The PyMVPA authors have preprocessed the data and released this derivative work under the same licensing terms.
Contains data files:
>>> from mvpa2.suite import * >>> datapath = os.path.join(pymvpa_datadbroot, 'tutorial_data', ... 'tutorial_data', 'data') >>> attrs = SampleAttributes(os.path.join(datapath, 'attributes.txt')) >>> ds = fmri_dataset(samples=os.path.join(datapath, 'bold.nii.gz'), ... targets=attrs.targets, chunks=attrs.chunks, ... mask=os.path.join(datapath, 'mask_brain.nii.gz')) >>> print ds.shape (1452, 39912) >>> print ds.a.voxel_dim (40, 64, 64) >>> print ds.a.voxel_eldim (3.5, 3.75, 3.75) >>> print ds.a.mapper <Chain: <Flatten>-<StaticFeatureSelection>> >>> print ds.uniquetargets ['bottle' 'cat' 'chair' 'face' 'house' 'rest' 'scissors' 'scrambledpix' 'shoe']
Haxby, J., Gobbini, M., Furey, M., Ishai, A., Schouten, J., and Pietrini, pl. (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425–2430.
- Removed tutorial_lib.py which is superseeded by using mvpa2.tutorial_suite
- Updated tutorial code to work with PyMVPA 0.6
- Removed dependency on PyNIfTI and use NiBabel instead.
- Initial release.