def group_one_sample_t_test(masks, effects_maps, contrasts, output_dir,
start_time=base_reporter.pretty_time(),
**kwargs):
"""
Runs a one-sample t-test procedure for group analysis. Here, we are
for each experimental condition, only interested refuting the null
hypothesis H0: "The average effect accross the subjects is zero!"
Parameters
----------
masks: list of strings or nibabel image objects
subject masks, one per subject
effects_maps: list of dicts of lists
effects maps from subject-level GLM; each entry is a dictionary;
each entry (indexed by condition id) of this dictionary is the
filename (or correspinding nibabel image object) for the effects
maps for that condition (aka contrast),for that subject
contrasts: dictionary of array_likes
contrasts vectors, indexed by condition id
kwargs: dict_like
kwargs for plot_stats_map API
"""
# make output directory
if not os.path.exists(output_dir):
os.makedirs(output_dir)
assert len(masks) == len(effects_maps), (len(masks), len(effects_maps))
# compute group mask
group_mask = intersect_masks(masks)
# construct design matrix (only one covariate, namely the "mean effect")
design_matrix = np.ones(len(effects_maps)
)[:, np.newaxis] # only the intercept
group_level_z_maps = {}
group_level_t_maps = {}
for contrast_id in contrasts:
print "\tcontrast id: %s" % contrast_id
# effects maps will be the input to the second level GLM
first_level_image = nibabel.concat_images(
[x[contrast_id] for x in effects_maps])
# fit 2nd level GLM for given contrast
group_model = FirstLevelGLM(first_level_image,
design_matrix, group_mask)
group_model.fit(do_scaling=False, model='ols')
# specify and estimate the contrast
contrast_val = np.array(([[1.]])
) # the only possible contrast !
z_map, t_map = group_model.contrast(
contrast_val, con_id='one_sample %s' % contrast_id, output_z=True,
output_stat=True)
# save map
for map_type, map_img in zip(["z", "t"], [z_map, t_map]):
map_dir = os.path.join(output_dir, '%s_maps' % map_type)
if not os.path.exists(map_dir):
os.makedirs(map_dir)
map_path = os.path.join(map_dir, 'group_level_%s.nii.gz' % (
contrast_id))
print "\t\tWriting %s ..." % map_path
nibabel.save(map_img, map_path)
if map_type == "z":
group_level_z_maps[contrast_id] = map_path
elif map_type == "t":
group_level_z_maps[contrast_id] = map_path
# do stats report
stats_report_filename = os.path.join(output_dir, "report_stats.html")
generate_subject_stats_report(stats_report_filename, contrasts,
group_level_z_maps, group_mask,
start_time=start_time,
**kwargs)
print "\r\nStatistic report written to %s\r\n" % (
stats_report_filename)
return group_level_z_maps
_, matrix, names = check_design_matrix(design_matrix)
contrasts = {}
n_columns = len(names)
I = np.eye(len(names))
for i in xrange(2):
contrasts['%s' % names[2 * i]] = I[2 * i]
"""more interesting contrasts"""
contrasts['EV1>EV2'] = contrasts['EV1'] - contrasts['EV2']
contrasts['EV2>EV1'] = contrasts['EV2'] - contrasts['EV1']
contrasts['effects_of_interest'] = contrasts['EV1'] + contrasts['EV2']
"""fit GLM"""
print('\r\nFitting a GLM (this takes time) ..')
fmri_glm = FirstLevelGLM()
fmri_glm.fit(fmri_files, design_matrix)
"""save computed mask"""
mask_path = os.path.join(subject_data.output_dir, "mask.nii.gz")
print("Saving mask image %s" % mask_path)
nibabel.save(fmri_glm.masker_.mask_img_, mask_path)
# compute bg unto which activation will be projected
mean_fmri_files = compute_mean_3D_image(fmri_files)
print("Computing contrasts ..")
z_maps = {}
for contrast_id, contrast_val in contrasts.items():
print("\tcontrast id: %s" % contrast_id)
z_map, t_map, eff_map, var_map = fmri_glm.transform(
con_vals=contrasts[contrast_id],
contrast_name=contrast_id,
def group_one_sample_t_test(masks,
effects_maps,
contrasts,
output_dir,
start_time=base_reporter.pretty_time(),
**kwargs):
"""
Runs a one-sample t-test procedure for group analysis. Here, we are
for each experimental condition, only interested refuting the null
hypothesis H0: "The average effect accross the subjects is zero!"
Parameters
----------
masks: list of strings or nibabel image objects
subject masks, one per subject
effects_maps: list of dicts of lists
effects maps from subject-level GLM; each entry is a dictionary;
each entry (indexed by condition id) of this dictionary is the
filename (or correspinding nibabel image object) for the effects
maps for that condition (aka contrast),for that subject
contrasts: dictionary of array_likes
contrasts vectors, indexed by condition id
kwargs: dict_like
kwargs for plot_stats_map API
"""
# make output directory
if not os.path.exists(output_dir):
os.makedirs(output_dir)
assert len(masks) == len(effects_maps), (len(masks), len(effects_maps))
# compute group mask
group_mask = intersect_masks(masks)
# construct design matrix (only one covariate, namely the "mean effect")
design_matrix = np.ones(
len(effects_maps))[:, np.newaxis] # only the intercept
group_level_z_maps = {}
group_level_t_maps = {}
for contrast_id in contrasts:
print("\tcontrast id: %s" % contrast_id)
# effects maps will be the input to the second level GLM
first_level_image = nibabel.concat_images(
[x[contrast_id] for x in effects_maps])
# fit 2nd level GLM for given contrast
group_model = FirstLevelGLM(first_level_image, design_matrix,
group_mask)
group_model.fit(do_scaling=False, model='ols')
# specify and estimate the contrast
contrast_val = np.array(([[1.]])) # the only possible contrast !
z_map, t_map = group_model.contrast(contrast_val,
con_id='one_sample %s' %
contrast_id,
output_z=True,
output_stat=True)
# save map
for map_type, map_img in zip(["z", "t"], [z_map, t_map]):
map_dir = os.path.join(output_dir, '%s_maps' % map_type)
if not os.path.exists(map_dir):
os.makedirs(map_dir)
map_path = os.path.join(map_dir,
'group_level_%s.nii.gz' % (contrast_id))
print("\t\tWriting %s ..." % map_path)
nibabel.save(map_img, map_path)
if map_type == "z":
group_level_z_maps[contrast_id] = map_path
elif map_type == "t":
group_level_z_maps[contrast_id] = map_path
# do stats report
stats_report_filename = os.path.join(output_dir, "report_stats.html")
generate_subject_stats_report(stats_report_filename,
contrasts,
group_level_z_maps,
group_mask,
start_time=start_time,
**kwargs)
print("\r\nStatistic report written to %s\r\n" % stats_report_filename)
return group_level_z_maps
_, matrix, names = check_design_matrix(design_matrix)
contrasts = {}
n_columns = len(names)
I = np.eye(len(names))
for i in xrange(2):
contrasts['%s' % names[2 * i]] = I[2 * i]
"""more interesting contrasts"""
contrasts['EV1>EV2'] = contrasts['EV1'] - contrasts['EV2']
contrasts['EV2>EV1'] = contrasts['EV2'] - contrasts['EV1']
contrasts['effects_of_interest'] = contrasts['EV1'] + contrasts['EV2']
"""fit GLM"""
print('\r\nFitting a GLM (this takes time) ..')
fmri_glm = FirstLevelGLM()
fmri_glm.fit(fmri_files, design_matrix)
"""save computed mask"""
mask_path = os.path.join(subject_data.output_dir, "mask.nii.gz")
print "Saving mask image %s" % mask_path
nibabel.save(fmri_glm.masker_.mask_img_, mask_path)
# compute bg unto which activation will be projected
mean_fmri_files = compute_mean_3D_image(fmri_files)
print "Computing contrasts .."
z_maps = {}
for contrast_id, contrast_val in contrasts.iteritems():
print "\tcontrast id: %s" % contrast_id
z_map, t_map, eff_map, var_map = fmri_glm.transform(
con_vals=contrasts[contrast_id],
contrast_name=contrast_id,
