# additional ``kwargs`` for more informative report
paradigm=paradigm.__dict__,
TR=tr,
n_scans=n_scans,
hfcut=hfcut,
frametimes=frametimes,
drift_model=drift_model,
hrf_model=hrf_model,
)
ProgressReport().finish_dir(subject_session_output_dir)
print "Statistic report written to %s\r\n" % stats_report_filename
# compute a population-level mask as the intersection of individual masks
print "Inter-session GLM"
grp_mask = nibabel.Nifti1Image(intersect_masks(mask_images).astype(np.int8),
nibabel.load(mask_images[0]).get_affine())
second_level_z_maps = {}
design_matrix = np.ones(len(first_level_effects_maps)
)[:, np.newaxis] # only the intercept
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 first_level_effects_maps])
# fit 2nd level GLM for given contrast
grp_model = FMRILinearModel(first_level_image, design_matrix, grp_mask)
grp_model.fit(do_scaling=False, model='ols')
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
parameters can be regular `nipy.labs.viz.plot_map` parameters
(e.g slicer="y") or any parameter we want be reported (e.g
fwhm=[5, 5, 5])
"""
# 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 = nibabel.Nifti1Image(
intersect_masks(masks).astype(np.int8),
(nibabel.load(masks[0]) if isinstance(
masks[0], basestring) else masks[0]).get_affine())
# 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 = FMRILinearModel(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
-1)))
if n_jobs > 1:
subjects = Parallel(n_jobs=n_jobs, verbose=100)(
delayed(run_suject_level1_glm)(subject_data, **kwargs)
for subject_data in subjects)
else:
subjects = [
run_suject_level1_glm(subject_data, **kwargs)
for subject_data in subjects
]
# level 2
stats_start_time = pretty_time()
mask_images = [subject_data.mask for subject_data in subjects]
group_mask = nibabel.Nifti1Image(
intersect_masks(mask_images).astype(np.int8),
nibabel.load(mask_images[0]).get_affine())
nibabel.save(group_mask, os.path.join(task_output_dir, "mask.nii.gz"))
print "... done.\r\n"
print "Group GLM"
contrasts = subjects[0].contrasts
subjects_effects_maps = [
subject_data.effects_maps for subject_data in subjects
]
group_one_sample_t_test(
mask_images,
subjects_effects_maps,
contrasts,
task_output_dir,
subjects = Parallel(
n_jobs=n_jobs, verbose=100)(delayed(
run_suject_level1_glm)(
subject_data,
**kwargs) for subject_data in subjects)
else:
subjects = [run_suject_level1_glm(subject_data,
**kwargs)
for subject_data in subjects]
subjects = [subject for subject in subjects if subject]
# level 2
stats_start_time = pretty_time()
mask_images = [subject_data.mask for subject_data in subjects]
group_mask = nibabel.Nifti1Image(
intersect_masks(mask_images).astype(np.int8),
nibabel.load(mask_images[0]).get_affine())
nibabel.save(group_mask, os.path.join(
task_output_dir, "mask.nii.gz"))
print "... done.\r\n"
print "Group GLM"
contrasts = subjects[0].contrasts
subjects_effects_maps = [subject_data.effects_maps
for subject_data in subjects]
group_one_sample_t_test(
mask_images,
subjects_effects_maps,
contrasts,
task_output_dir,
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
parameters can be regular `nipy.labs.viz.plot_map` parameters
(e.g slicer="y") or any parameter we want be reported (e.g
fwhm=[5, 5, 5])
"""
# 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 = nibabel.Nifti1Image(
intersect_masks(masks).astype(np.int8),
(nibabel.load(masks[0])
if isinstance(masks[0], basestring) else masks[0]).get_affine())
# 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 = FMRILinearModel(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