"""specify contrasts"""
_, 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 = FMRILinearModel(fmri_files, matrix, mask='compute')
fmri_glm.fit(do_scaling=True, model='ar1')
"""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.mask, 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.contrast(
contrasts[contrast_id],
def do_subject_glm(subject_id):
subject_output_dir = os.path.join(output_dir, subject_id)
# make design matrices
design_matrices = []
func = []
anat = os.path.join(subject_output_dir, "anatomy", "whighres001_brain.nii")
for run_path in sorted(
glob.glob(
os.path.join(data_dir, subject_id,
"model/model001/onsets/task*"))):
run_id = os.path.basename(run_path)
run_func = glob.glob(
os.path.join(subject_output_dir, "BOLD", run_id, "wrbold*.nii"))
assert len(run_func) == 1
run_func = run_func[0]
run_onset_paths = sorted(
glob.glob(
os.path.join(data_dir, subject_id,
"model/model001/onsets/%s/*" % run_id)))
onsets = map(np.loadtxt, run_onset_paths)
conditions = np.hstack([[condition_keys["cond%03i" % (c + 1)]] *
len(onsets[c])
for c in range(len(run_onset_paths))])
onsets = np.vstack((onsets))
onsets *= tr
run_func = nibabel.load(run_func)
func.append(run_func)
n_scans = run_func.shape[-1]
onset, duration, modulation = onsets.T
frametimes = np.linspace(0, (n_scans - 1) * tr, n_scans)
paradigm = pd.DataFrame(
dict(name=conditions,
onset=onset,
duration=duration,
modulation=modulation))
design_matrix = make_design_matrix(frametimes,
paradigm,
hrf_model=hrf_model,
drift_model=drift_model,
period_cut=hfcut)
design_matrices.append(design_matrix)
n_runs = len(func)
# specify contrasts
_, _, names = check_design_matrix(design_matrix)
n_columns = len(names)
contrast_matrix = np.eye(n_columns)
contrasts = {}
for c in range(len(condition_keys)):
contrasts[names[2 * c]] = contrast_matrix[2 * c]
contrasts["avg"] = np.mean(contrasts.values(), axis=0)
# more interesting contrasts
contrasts_ = {}
for contrast, val in contrasts.items():
if not contrast == "avg":
contrasts_["%s_minus_avg" % contrast] = val - contrasts["avg"]
contrasts = contrasts_
# fit GLM
from nilearn.image import smooth_img
func = smooth_img(func, fwhm=8.)
print('Fitting a GLM (this takes time)...')
fmri_glm = FMRILinearModel(func, [
check_design_matrix(design_matrix)[1]
for design_matrix in design_matrices
],
mask='compute')
fmri_glm.fit(do_scaling=True, model='ar1')
# save computed mask
mask_path = os.path.join(subject_output_dir, "mask.nii")
print("Saving mask image to %s ..." % mask_path)
nibabel.save(fmri_glm.mask, mask_path)
# compute contrast maps
z_maps = {}
effects_maps = {}
for contrast_id, contrast_val in contrasts.items():
print("\tcontrast id: %s" % contrast_id)
z_map, t_map, effects_map, var_map = fmri_glm.contrast(
[contrast_val] * n_runs,
con_id=contrast_id,
output_z=True,
output_stat=True,
output_effects=True,
output_variance=True)
for map_type, out_map in zip(['z', 't', 'effects', 'variance'],
[z_map, t_map, effects_map, var_map]):
map_dir = os.path.join(subject_output_dir, '%s_maps' % map_type)
if not os.path.exists(map_dir):
os.makedirs(map_dir)
map_path = os.path.join(map_dir, '%s.nii.gz' % contrast_id)
print("\t\tWriting %s ..." % map_path)
nibabel.save(out_map, map_path)
if map_type == 'z':
z_maps[contrast_id] = map_path
if map_type == 'effects':
effects_maps[contrast_id] = map_path
# # generate stats report
# 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)
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 = 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
def execute_glm(doc, out_dir, contrast_definitions=None,
outputs=None, glm_model='ar1',
):
"""Function to execute GLM for one subject --and perhaps multiple
sessions thereof
"""
stats_start_time = time.ctime()
# study_dir = os.path.join(out_dir, doc['study'])
if outputs is None:
outputs = {'maps': False,
'data': False,
'mask': True,
'model': True,
}
else:
outputs['maps'] = False
subject_id = doc['subject']
subject_output_dir = os.path.join(
out_dir, subject_id)
_export(doc, subject_output_dir, outputs=outputs)
params = load_glm_params(doc)
# instantiate GLM
fmri_glm = FMRILinearModel(params['data'],
params['design_matrices'],
mask=doc['mask'])
# fit GLM
fmri_glm.fit(do_scaling=True, model=glm_model)
# save beta-maps to disk
beta_map_dir = os.path.join(subject_output_dir, 'beta_maps')
if not os.path.exists(beta_map_dir):
os.makedirs(beta_map_dir)
for j, glm in zip(range(len(fmri_glm.glms)), fmri_glm.glms):
# XXX save array in some compressed format
np.savetxt(os.path.join(beta_map_dir, "beta_map_%i.txt" % j),
glm.get_beta(), # array has shape (n_conditions, n_voxels)
)
# define contrasts
if contrast_definitions is not None:
params['contrasts'] = make_contrasts(params, contrast_definitions)
contrasts = sorted(params['contrasts'][0].keys())
_contrasts = {}
z_maps = {}
# compute stats maps
for index, contrast_id in enumerate(contrasts):
print ' study[%s] subject[%s] contrast [%s]: %i/%i' % (
doc['study'], doc['subject'],
contrast_id, index + 1, len(contrasts)
)
contrast = [c[contrast_id] for c in params['contrasts']]
contrast_name = contrast_id.replace(' ', '_')
z_map, t_map, c_map, var_map = fmri_glm.contrast(
contrast,
con_id=contrast_id,
output_z=True,
output_stat=True,
output_effects=True,
output_variance=True,)
for dtype, out_map in zip(['z', 't', 'c', 'variance'],
[z_map, t_map, c_map, var_map]):
map_dir = os.path.join(subject_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_name)
nb.save(out_map, map_path)
# collect z map
if dtype == 'z':
_contrasts[contrast_name] = contrast
z_maps[contrast_name] = map_path
# invoke a single API to handle plotting and html business for you
subject_stats_report_filename = os.path.join(
subject_output_dir, "report_stats.html")
glm_reporter.generate_subject_stats_report(
subject_stats_report_filename,
_contrasts,
z_maps,
doc['mask'],
design_matrices=list(params['design_matrices']),
subject_id=doc['subject'],
cluster_th=15, # 15 voxels
start_time=stats_start_time,
TR=doc['TR'],
n_scans=doc['n_scans'],
n_sessions=doc['n_sessions'],
model=glm_model,
)
print "Report for subject %s written to %s" % (
doc['subject'],
subject_stats_report_filename)
def execute_glm(
doc,
out_dir,
contrast_definitions=None,
outputs=None,
glm_model='ar1',
):
"""Function to execute GLM for one subject --and perhaps multiple
sessions thereof
"""
stats_start_time = time.ctime()
# study_dir = os.path.join(out_dir, doc['study'])
if outputs is None:
outputs = {
'maps': False,
'data': False,
'mask': True,
'model': True,
}
else:
outputs['maps'] = False
subject_id = doc['subject']
subject_output_dir = os.path.join(out_dir, subject_id)
_export(doc, subject_output_dir, outputs=outputs)
params = load_glm_params(doc)
# instantiate GLM
fmri_glm = FMRILinearModel(params['data'],
params['design_matrices'],
mask=doc['mask'])
# fit GLM
fmri_glm.fit(do_scaling=True, model=glm_model)
# save beta-maps to disk
beta_map_dir = os.path.join(subject_output_dir, 'beta_maps')
if not os.path.exists(beta_map_dir):
os.makedirs(beta_map_dir)
for j, glm in zip(range(len(fmri_glm.glms)), fmri_glm.glms):
# XXX save array in some compressed format
np.savetxt(
os.path.join(beta_map_dir, "beta_map_%i.txt" % j),
glm.get_beta(), # array has shape (n_conditions, n_voxels)
)
# define contrasts
if contrast_definitions is not None:
params['contrasts'] = make_contrasts(params, contrast_definitions)
contrasts = sorted(params['contrasts'][0].keys())
_contrasts = {}
z_maps = {}
# compute stats maps
for index, contrast_id in enumerate(contrasts):
print ' study[%s] subject[%s] contrast [%s]: %i/%i' % (
doc['study'], doc['subject'], contrast_id, index + 1,
len(contrasts))
contrast = [c[contrast_id] for c in params['contrasts']]
contrast_name = contrast_id.replace(' ', '_')
z_map, t_map, c_map, var_map = fmri_glm.contrast(
contrast,
con_id=contrast_id,
output_z=True,
output_stat=True,
output_effects=True,
output_variance=True,
)
for dtype, out_map in zip(['z', 't', 'c', 'variance'],
[z_map, t_map, c_map, var_map]):
map_dir = os.path.join(subject_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_name)
nb.save(out_map, map_path)
# collect z map
if dtype == 'z':
_contrasts[contrast_name] = contrast
z_maps[contrast_name] = map_path
# invoke a single API to handle plotting and html business for you
subject_stats_report_filename = os.path.join(subject_output_dir,
"report_stats.html")
glm_reporter.generate_subject_stats_report(
subject_stats_report_filename,
_contrasts,
z_maps,
doc['mask'],
design_matrices=list(params['design_matrices']),
subject_id=doc['subject'],
cluster_th=15, # 15 voxels
start_time=stats_start_time,
TR=doc['TR'],
n_scans=doc['n_scans'],
n_sessions=doc['n_sessions'],
model=glm_model,
)
print "Report for subject %s written to %s" % (
doc['subject'], subject_stats_report_filename)
_, matrix, names = check_design_matrix(design_matrix)
contrasts = {}
n_columns = len(names)
contrast_matrix = np.eye(n_columns)
for i in xrange(2):
contrasts[names[2 * i]] = contrast_matrix[2 * i]
# more interesting contrasts
contrasts['faces-scrambled'] = contrasts['faces'] - contrasts['scrambled']
contrasts['scrambled-faces'] = -contrasts['faces-scrambled']
contrasts['effects_of_interest'] = contrasts['faces'] + contrasts['scrambled']
# fit GLM
print 'Fitting a GLM (this takes time)...'
fmri_glm = FMRILinearModel(
[nibabel.concat_images(x) for x in subject_data.func],
[check_design_matrix(design_matrix)[1]
for design_matrix in design_matrices], mask='compute')
fmri_glm.fit(do_scaling=True, model='ar1')
# 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.mask, mask_path)
mask_images.append(mask_path)
# compute contrast maps
z_maps = {}
effects_maps = {}
for contrast_id, contrast_val in contrasts.items():
print "\tcontrast id: %s" % contrast_id
z_map, t_map, effects_map, var_map = fmri_glm.contrast(
plt.savefig(dmat_outfile, bbox_inches="tight", dpi=200)
# specify contrasts
contrasts = {}
_, matrix, names = check_design_matrix(design_matrix)
contrast_matrix = np.eye(len(names))
for i in range(len(names)):
contrasts[names[i]] = contrast_matrix[i]
# more interesting contrasts"""
contrasts = {'active-rest': contrasts['active'] - contrasts['rest']}
# fit GLM
print('\r\nFitting a GLM (this takes time) ..')
fmri_glm = FMRILinearModel(nibabel.concat_images(subject_data.func[0]),
matrix,
mask='compute')
fmri_glm.fit(do_scaling=True, model='ar1')
# 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.mask, mask_path)
# compute bg unto which activation will be projected
anat_img = nibabel.load(subject_data.anat)
print "Computing contrasts .."
z_maps = {}
effects_maps = {}
for contrast_id, contrast_val in contrasts.iteritems():
fmri_files = results[0]['func']
anat_file = results[0]['anat']
"""specify contrasts"""
_, 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 = FMRILinearModel(fmri_files, matrix, mask='compute')
fmri_glm.fit(do_scaling=True, model='ar1')
"""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.mask, 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.contrast(
contrasts[contrast_id],
con_id=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 = 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
def do_subject_glm(subject_id):
subject_output_dir = os.path.join(output_dir, subject_id)
# make design matrices
design_matrices = []
func = []
anat = os.path.join(subject_output_dir, "anatomy", "whighres001_brain.nii")
for run_path in sorted(glob.glob(os.path.join(
data_dir, subject_id, "model/model001/onsets/task*"))):
run_id = os.path.basename(run_path)
run_func = glob.glob(os.path.join(subject_output_dir, "BOLD", run_id,
"wrbold*.nii"))
assert len(run_func) == 1
run_func = run_func[0]
run_onset_paths = sorted(glob.glob(os.path.join(
data_dir, subject_id, "model/model001/onsets/%s/*" % run_id)))
onsets = map(np.loadtxt, run_onset_paths)
conditions = np.hstack(
[[condition_keys["cond%03i" % (c + 1)]] * len(onsets[c])
for c in range(len(run_onset_paths))])
onsets = np.vstack((onsets))
onsets *= tr
run_func = nibabel.load(run_func)
func.append(run_func)
n_scans = run_func.shape[-1]
onset, duration, modulation = onsets.T
frametimes = np.linspace(0, (n_scans - 1) * tr, n_scans)
paradigm = pd.DataFrame(dict(name=conditions, onset=onset,
duration=duration, modulation=modulation))
design_matrix = make_design_matrix(frametimes, paradigm,
hrf_model=hrf_model,
drift_model=drift_model,
period_cut=hfcut)
design_matrices.append(design_matrix)
n_runs = len(func)
# specify contrasts
_, _, names = check_design_matrix(design_matrix)
n_columns = len(names)
contrast_matrix = np.eye(n_columns)
contrasts = {}
for c in range(len(condition_keys)):
contrasts[names[2 * c]] = contrast_matrix[2 * c]
contrasts["avg"] = np.mean(contrasts.values(), axis=0)
# more interesting contrasts
contrasts_ = {}
for contrast, val in contrasts.items():
if not contrast == "avg":
contrasts_["%s_minus_avg" % contrast] = val - contrasts["avg"]
contrasts = contrasts_
# fit GLM
from nilearn.image import smooth_img
func = smooth_img(func, fwhm=8.)
print 'Fitting a GLM (this takes time)...'
fmri_glm = FMRILinearModel(func, [check_design_matrix(design_matrix)[1]
for design_matrix in design_matrices],
mask='compute')
fmri_glm.fit(do_scaling=True, model='ar1')
# save computed mask
mask_path = os.path.join(subject_output_dir, "mask.nii")
print "Saving mask image to %s ..." % mask_path
nibabel.save(fmri_glm.mask, mask_path)
# compute contrast maps
z_maps = {}
effects_maps = {}
for contrast_id, contrast_val in contrasts.items():
print "\tcontrast id: %s" % contrast_id
z_map, t_map, effects_map, var_map = fmri_glm.contrast(
[contrast_val] * n_runs, con_id=contrast_id, output_z=True,
output_stat=True, output_effects=True, output_variance=True)
for map_type, out_map in zip(['z', 't', 'effects', 'variance'],
[z_map, t_map, effects_map, var_map]):
map_dir = os.path.join(subject_output_dir, '%s_maps' % map_type)
if not os.path.exists(map_dir):
os.makedirs(map_dir)
map_path = os.path.join(
map_dir, '%s.nii.gz' % contrast_id)
print "\t\tWriting %s ..." % map_path
nibabel.save(out_map, map_path)
if map_type == 'z':
z_maps[contrast_id] = map_path
if map_type == 'effects':
effects_maps[contrast_id] = map_path
# # generate stats report
# 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)