]
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,
threshold=threshold,
cluster_th=cluster_th,
start_time=stats_start_time,
subjects=[subject_data.subject_id for subject_data in subjects],
title='Group GLM for HCP fMRI %s protocol (%i subjects)' %
(protocol, len(subjects)),
slicer=slicer)
# paradigm=paradigm, frametimes=frametimes,
# drift_model=drift_model, hrf_model=hrf_model)
# ProgressReport().finish_dir(subject_output_dir)
return dict(subject_id=subject_id,
mask=mask_path,
effects_maps=effects_maps,
z_maps=z_maps,
contrasts=contrasts)
# first level GLM
mem = Memory(os.path.join(output_dir, "cache_dir"))
n_jobs = min(n_jobs, len(subject_ids))
first_levels = Parallel(n_jobs=n_jobs)(
delayed(mem.cache(do_subject_glm))(subject_id)
for subject_id in subject_ids)
# run second-level GLM
group_zmaps = group_one_sample_t_test(
[subject_data["mask"] for subject_data in first_levels],
[subject_data["effects_maps"] for subject_data in first_levels],
first_levels[0]["contrasts"],
output_dir,
threshold=2.)
plot_prob_atlas([zmap for zmap in group_zmaps.values() if "_minus_" in zmap],
threshold=1.2,
view_type="filled_contours")
plt.savefig("group_zmaps.png")
show()
if not os.path.isfile(eff_map):
skip = True
break
subject_effects_maps[condition] = eff_map
if skip:
continue
effects_maps.append(subject_effects_maps)
if anat is None:
tmp = os.path.join(subject_dir, "wT1w_acpc_dc_restore_brain.nii")
if os.path.isfile(tmp):
anat = tmp
contrasts = json.load(open("/tmp/contrasts.json"))
contrasts = dict(
(k, v) for k, v in contrasts.iteritems() if k in conditions)
group_one_sample_t_test(
masks,
effects_maps,
contrasts,
"/tmp",
slicer="ortho",
threshold=3.,
anat=nibabel.load(anat).get_data() if anat else None,
anat_affine=nibabel.load(anat).get_affine() if anat else None,
title="HCP group GLM (MOTOR protocol, %i subjects)" % len(masks))
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,
threshold=threshold,
cluster_th=cluster_th,
start_time=stats_start_time,
subjects=[subject_data.subject_id for subject_data in subjects],
title='Group GLM for HCP fMRI %s protocol (%i subjects)' % (
protocol, len(subjects)),
slicer=slicer
)
if not os.path.isfile(eff_map):
skip = True
break
subject_effects_maps[condition] = eff_map
if skip:
continue
effects_maps.append(subject_effects_maps)
if anat is None:
tmp = os.path.join(subject_dir, "wT1w_acpc_dc_restore_brain.nii")
if os.path.isfile(tmp):
anat = tmp
contrasts = json.load(open("/tmp/contrasts.json"))
contrasts = dict((k, v) for k, v in contrasts.iteritems() if k in conditions)
group_one_sample_t_test(
masks,
effects_maps,
contrasts,
"/tmp",
slicer="ortho",
threshold=3.0,
anat=nibabel.load(anat).get_data() if anat else None,
anat_affine=nibabel.load(anat).get_affine() if anat else None,
title="HCP group GLM (MOTOR protocol, %i subjects)" % len(masks),
)
# stats_report_filename = os.path.join(subject_output_dir, "reports",
# "report_stats.html")
# generate_subject_stats_report(
# stats_report_filename, contrasts, z_maps, fmri_glm.mask, anat=anat,
# threshold=2.3, cluster_th=15, design_matrices=design_matrices, TR=tr,
# subject_id="sub001", n_scans=n_scans, hfcut=hfcut,
# paradigm=paradigm, frametimes=frametimes,
# drift_model=drift_model, hrf_model=hrf_model)
# ProgressReport().finish_dir(subject_output_dir)
return dict(subject_id=subject_id, mask=mask_path,
effects_maps=effects_maps, z_maps=z_maps, contrasts=contrasts)
# first level GLM
mem = Memory(os.path.join(output_dir, "cache_dir"))
n_jobs = min(n_jobs, len(subject_ids))
first_levels = Parallel(n_jobs=n_jobs)(delayed(mem.cache(do_subject_glm))(
subject_id) for subject_id in subject_ids)
# run second-level GLM
group_zmaps = group_one_sample_t_test(
[subject_data["mask"] for subject_data in first_levels],
[subject_data["effects_maps"] for subject_data in first_levels],
first_levels[0]["contrasts"],
output_dir, threshold=2.)
plot_prob_atlas([zmap for zmap in group_zmaps.values() if "_minus_" in zmap],
threshold=1.2, view_type="filled_contours")
plt.savefig("group_zmaps.png")
show()
