Multivariate Pattern Analysis in Python

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# Tutorial Data: Block-design fMRI

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.

## Terms Of Use

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.

## Download

A tarball is available at:

> http://data.pymvpa.org/datasets/tutorial_data

## Tarball Content

data/

  Contains data files:

  bold.nii.gz
    The motion-corrected 4D timeseries (1452 volumes with 40 x 64 x 64 voxels, corresponding to a voxel size of 3.5 x 3.75 x 3.75 mm and a volume repetition time of 2.5 seconds). The timeseries contains all 12 runs of the original experiment, concatenated in a single file. Please note, that the timeseries signal is not detrended.
  bold_mc.par
    The motion correction parameters. This is a 6-column textfile with three rotation and three translation parameters respectively. This information can be used e.g. as additional regressors for motion-aware timeseries detrending.
  mask*.nii.gz
    A number of mask images in the subjects functional space, including a full-brain mask.
  attributes.txt
    A two-column text file with the stimulation condition and the corresponding experimental run for each volume in the timeseries image. The labels are given in literal form (e.g. ‘face’).
  anat.nii.gz
    An anatomical image of the subject, projected and resampled into the same space as the functional images, hence also of the same spatial resolution. The image is not skull-stripped.
results/
  Some analyses presented in the tutorial takes non-negligible time to compute. Therefore, we provide results of some analysis so they could simply be loaded while following the tutorial (commands to load them are embedded in the code snippets through out tutorial and prefixed with ``# alt: ``).
start_tutorial_session.sh
  Helper shell script to start an interactive session within IPython to proceed with the tutorial code.

## Instructions

    >>> 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']

## References

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.

## Changelog

0.3

> - Removed tutorial_lib.py which is superseeded by using mvpa2.tutorial_suite

0.2

> - Updated tutorial code to work with PyMVPA 0.6
> - Removed dependency on PyNIfTI and use NiBabel instead.

0.1

> - Initial release.

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