contrasts["audio-video"] = contrasts["audio"] - contrasts["video"]
contrasts["video-audio"] = -contrasts["audio-video"]
contrasts["computation-sentences"] = contrasts["computation"] - contrasts["sentences"]
contrasts["reading-visual"] = contrasts["phrasevideo"] - contrasts["damier_H"]
# keep only interesting contrasts
interesting_contrasts = [
"H-V",
"V-H",
"computation-sentences",
"reading-visual",
"video-audio",
"audio-video",
"left-right",
]
contrasts = dict([(key, contrasts[key]) for key in interesting_contrasts])
for index, (contrast_id, contrast_val) in enumerate(contrasts.items()):
print(" Contrast % 2i out of %i: %s" % (index + 1, len(contrasts), contrast_id))
# save the z_image
image_path = path.join(write_dir, "%s_z_map.nii" % contrast_id)
z_map, = fmri_glm.contrast(contrast_val, con_id=contrast_id, output_z=True)
save(z_map, image_path)
# Create snapshots of the contrasts
vmax = max(-z_map.get_data().min(), z_map.get_data().max())
display = plotting.plot_stat_map(z_map, display_mode="z", threshold=3.0, title=contrast_id)
display.savefig(path.join(write_dir, "%s_z_map.png" % contrast_id))
plt.show()
mask='compute')
fmri_glm.fit(do_scaling=True, model='ar1')
# save computed mask
mask_path = os.path.join(output_dir, "mask.nii.gz")
print("Saving mask image %s" % mask_path)
nibabel.save(fmri_glm.mask, mask_path)
# compute bg unto which activation will be projected
mean_img = mean_img(subject_data.func)
print("Computing contrasts ..")
for contrast_id, contrast_val in contrasts.items():
print("\tcontrast id: %s" % contrast_id)
z_map, t_map, eff_map, var_map = fmri_glm.contrast(
contrasts[contrast_id], con_id=contrast_id, output_z=True,
output_stat=True, output_effects=True, output_variance=True)
# store stat maps to disk
for dtype, out_map in zip(['z', 't', 'effects', 'variance'],
[z_map, t_map, eff_map, var_map]):
map_dir = os.path.join(output_dir, '%s_maps' % dtype)
if not os.path.exists(map_dir):
os.makedirs(map_dir)
map_path = os.path.join(map_dir, '%s.nii.gz' % contrast_id)
nibabel.save(out_map, map_path)
print("\t\t%s map: %s" % (dtype, map_path))
# plot activation map
if contrast_id == 'active-rest':
display = plot_stat_map(z_map, bg_img=mean_img, threshold=3.0,
display_mode='z', cut_coords=3, black_bg=True,
contrast['DSt_minus_SSt_for_DSp'] = _pad_vector([0, - 1, 0, 1], n_columns)
contrast['DSp_minus_SSp_for_DSt'] = _pad_vector([0, 0, - 1, 1], n_columns)
contrast['Deactivation'] = _pad_vector([- 1, - 1, - 1, - 1, 4], n_columns)
contrast['Effects_of_interest'] = np.eye(n_columns)[:5]
return contrast
# compute fixed effects of the two runs and compute related images
n_columns = np.load(design_files[0])['X'].shape[1]
contrasts = make_fiac_contrasts(n_columns)
print('Computing contrasts...')
mean_img = multi_session_model.means[0] # for display
for index, (contrast_id, contrast_val) in enumerate(contrasts.items()):
print(' Contrast % 2i out of %i: %s' % (
index + 1, len(contrasts), contrast_id))
z_image_path = path.join(write_dir, '%s_z_map.nii' % contrast_id)
z_map, = multi_session_model.contrast(
[contrast_val] * 2, con_id=contrast_id, output_z=True)
nib.save(z_map, z_image_path)
# make a snapshot of the contrast activation
if contrast_id == 'Effects_of_interest':
vmax = max(- z_map.get_data().min(), z_map.get_data().max())
vmin = - vmax
display = plot_stat_map(z_map, bg_img=mean_img, threshold=2.5,
title=contrast_id)
display.savefig(path.join(write_dir, '%s_z_map.png' % contrast_id))
print('All the results were witten in %s' % write_dir)
plt.show()
