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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
Example of (step-by-step) GLM application and result creation.
More specifically,
1. A sequence of fMRI volumes are loaded
2. A design matrix describing all the effects related to the data is computed
3. a mask of the useful brain volume is computed
4. A GLM is applied to the dataset (effect/covariance,
then contrast estimation)
Note that this corresponds to a single session
Author : Bertrand Thirion, 2010
"""
print __doc__
import numpy as np
import os.path as op
import matplotlib.pylab as mp
import pylab
from nipy.neurospin.utils.mask import compute_mask_files
from nipy.io.imageformats import load, save, Nifti1Image
import get_data_light
import nipy.neurospin.glm
import nipy.neurospin.utils.design_matrix as dm
import tempfile
#######################################
# Data and analysis parameters
#######################################
# volume mask
get_data_light.get_localizer_dataset()
data_path = op.expanduser(op.join('~', '.nipy', 'tests', 'data',
's12069_swaloc1_corr.nii.gz'))
paradigm_file = op.expanduser(op.join('~', '.nipy', 'tests', 'data',
'localizer_paradigm.csv'))
# timing
n_scans = 128
tr = 2.4
# paradigm
frametimes = np.linspace(0, (n_scans-1)*tr, n_scans)
conditions = [ 'damier_H', 'damier_V', 'clicDaudio', 'clicGaudio',
'clicDvideo', 'clicGvideo', 'calculaudio', 'calculvideo', 'phrasevideo',
'phraseaudio' ]
# confounds
hrf_model = 'Canonical With Derivative'
drift_model = "Cosine"
hfcut = 128
# write directory
swd = tempfile.mkdtemp()
########################################
# Design matrix
########################################
paradigm = dm.load_protocol_from_csv_file(paradigm_file, session=0)
design_matrix = dm.DesignMatrix( frametimes, paradigm, hrf_model=hrf_model,
drift_model=drift_model, hfcut=hfcut,
cond_ids= conditions)
design_matrix.show()
# design_matrix.save(...)
########################################
# Mask the data
########################################
mask_path = op.join(swd, 'mask.nii')
mask_array = compute_mask_files( data_path, mask_path, False, 0.4, 0.9)
########################################
# Perform a GLM analysis
########################################
fmri_image = load(data_path)
Y = fmri_image.get_data()[mask_array]
model = "ar1"
method = "kalman"
my_glm = nipy.neurospin.glm.glm()
glm = my_glm.fit(Y.T, design_matrix.matrix,
method="kalman", model="ar1")
#########################################
# Specify the contrasts
#########################################
# simplest ones
contrasts = {}
contrast_id = conditions
for i in range(len(conditions)):
contrasts['%s' % conditions[i]]= np.eye(len(design_matrix.names))[2*i]
# and more complex/ interesting ones
contrasts["audio"] = contrasts["clicDaudio"] + contrasts["clicGaudio"] +\
contrasts["calculaudio"] + contrasts["phraseaudio"]
contrasts["video"] = contrasts["clicDvideo"] + contrasts["clicGvideo"] + \
contrasts["calculvideo"] + contrasts["phrasevideo"]
contrasts["left"] = contrasts["clicGaudio"] + contrasts["clicGvideo"]
contrasts["right"] = contrasts["clicDaudio"] + contrasts["clicDvideo"]
contrasts["computation"] = contrasts["calculaudio"] +contrasts["calculvideo"]
contrasts["sentences"] = contrasts["phraseaudio"] + contrasts["phrasevideo"]
contrasts["H-V"] = contrasts["damier_H"] - contrasts["damier_V"]
contrasts["V-H"] =contrasts["damier_V"] - contrasts["damier_H"]
contrasts["left-right"] = contrasts["left"] - contrasts["right"]
contrasts["right-left"] = contrasts["right"] - contrasts["left"]
contrasts["audio-video"] = contrasts["audio"] - contrasts["video"]
contrasts["video-audio"] = contrasts["video"] - contrasts["audio"]
contrasts["computation-sentences"] = contrasts["computation"] - \
contrasts["sentences"]
contrasts["reading-visual"] = contrasts["sentences"]*2 - \
contrasts["damier_H"] - contrasts["damier_V"]
#########################################
# Estimate the contrasts
#########################################
for contrast_id in contrasts:
lcontrast = my_glm.contrast(contrasts[contrast_id])
#
contrast_path = op.join(swd, '%s_z_map.nii'% contrast_id)
write_array = mask_array.astype(np.float)
write_array[mask_array] = lcontrast.zscore()
contrast_image = Nifti1Image(write_array, fmri_image.get_affine() )
save(contrast_image, contrast_path)
#########################################
# End
#########################################
print "All the results were witten in %s" %swd
pylab.show()
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