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Basic (f)MRI plotting

Estimate basic univariate sensitivity (ANOVA) an plot it overlayed on top of the anatomical.

We start with basic steps: loading PyMVPA and the example fMRI dataset, basic preprocessing, estimation of the ANOVA scores and plotting.

from mvpa.suite import *

# load PyMVPA example dataset
attr = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes_literal.txt'))
dataset = NiftiDataset(samples=os.path.join(pymvpa_dataroot, 'bold.nii.gz'),
                       labels=attr.labels,
                       labels_map=True,
                       chunks=attr.chunks,
                       mask=os.path.join(pymvpa_dataroot, 'mask.nii.gz'))

# since we don't have a proper anatomical -- lets overlay on BOLD
nianat = NiftiImage(dataset.O[0], header=dataset.niftihdr)

# do chunkswise linear detrending on dataset
detrend(dataset, perchunk=True, model='linear')

# define sensitivity analyzer
sensana = OneWayAnova(transformer=N.abs)
sens = sensana(dataset)

It might be convinient to pre-define common arguments for multiple calls to plotMRI

::

mri_args = {

‘background’ : nianat, # could be a filename ‘background_mask’ : os.path.join(pymvpa_dataroot, ‘mask.nii.gz’), ‘overlay_mask’ : os.path.join(pymvpa_dataroot, ‘mask.nii.gz’), ‘do_stretch_colors’ : False, ‘cmap_bg’ : ‘gray’, ‘cmap_overlay’ : ‘autumn’, # YlOrRd_r # P.cm.autumn ‘fig’ : None, # create new figure

‘interactive’ : cfg.getboolean(‘examples’, ‘interactive’, True),

}

fig = plotMRI(overlay=dataset.map2Nifti(sens),

vlim=(0.5, None), #vlim_type=”symneg_z”, **mri_args)

Output of the example analysis:

See also

The full source code of this example is included in the PyMVPA source distribution (doc/examples/mri_plot.py).