def first_level(subject_dic):
# experimental paradigm meta-params
stats_start_time = time.ctime()
tr = 2.4
drift_model = 'blank'
hrf_model = 'canonical' # hemodynamic reponse function
hfcut = 128.
n_scans = 128
# make design matrices
mask_images = []
design_matrices = []
fmri_files = subject_dic['func']
for x in xrange(len(fmri_files)):
paradigm = paradigm_contrasts.localizer_paradigm()
# build design matrix
frametimes = np.linspace(0, (n_scans - 1) * tr, n_scans)
design_matrix = make_dmtx(
frametimes,
paradigm,
hrf_model=hrf_model,
drift_model=drift_model,
hfcut=hfcut,
)
design_matrices.append(design_matrix)
# Specify contrasts
contrasts = paradigm_contrasts.localizer_contrasts(design_matrix)
#create output directory
subject_session_output_dir = os.path.join(subject_dic['output_dir'],
'res_stats')
if not os.path.exists(subject_session_output_dir):
os.makedirs(subject_session_output_dir)
# Fit GLM
print 'Fitting a GLM (this takes time)...'
fmri_glm = FMRILinearModel(
fmri_files,
[design_matrix.matrix 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_session_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 contrasts
z_maps = {}
effects_maps = {}
for contrast_id, contrast_val in contrasts.iteritems():
print "\tcontrast id: %s" % contrast_id
z_map, t_map, effects_map, var_map = fmri_glm.contrast(
[contrast_val] * 1,
con_id=contrast_id,
output_z=True,
output_stat=True,
output_effects=True,
output_variance=True)
# store stat maps to disk
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_session_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%s.nii.gz' % (subject_dic['subject_id'], contrast_id))
print "\t\tWriting %s ..." % map_path
nibabel.save(out_map, map_path)
# collect zmaps for contrasts we're interested in
if map_type == 'z':
z_maps[contrast_id] = map_path
if map_type == 'effects':
effects_maps[contrast_id] = map_path
# do stats report
anat_img = nibabel.load(subject_dic['anat'])
stats_report_filename = os.path.join(subject_session_output_dir,
"report_stats.html")
generate_subject_stats_report(
stats_report_filename,
contrasts,
z_maps,
fmri_glm.mask,
threshold=2.3,
cluster_th=15,
anat=anat_img.get_data(),
anat_affine=anat_img.get_affine(),
design_matrices=design_matrix,
subject_id="sub001",
start_time=stats_start_time,
title="GLM for subject %s" % subject_dic['session_id'],
# 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
return z_maps
# do stats report
anat_img = nibabel.load(subject_data.anat)
stats_report_filename = os.path.join(subject_data.output_dir, "reports",
"report_stats.html")
generate_subject_stats_report(
stats_report_filename,
contrasts,
z_maps,
fmri_glm.mask,
threshold=2.3,
cluster_th=15,
anat=anat_img.get_data(),
anat_affine=anat_img.get_affine(),
design_matrices=design_matrix,
subject_id="sub001",
start_time=stats_start_time,
title="GLM for subject %s" % subject_data.subject_id,
# 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_data.output_dir)
print "Statistic report written to %s\r\n" % stats_report_filename
reports_dir = os.path.join(subject_data.output_dir, "reports")
stats_report_filename = os.path.join(reports_dir, "report_stats.html")
contrasts = dict((contrast_id, contrasts[contrast_id])
for contrast_id in z_maps.keys())
generate_subject_stats_report(
stats_report_filename,
contrasts,
z_maps,
fmri_glm.masker_.mask_img_,
design_matrices=[design_matrix],
subject_id=subject_data.subject_id,
anat=anat_file,
cluster_th=50, # we're only interested in this 'large' clusters
start_time=stats_start_time,
# additional ``kwargs`` for more informative report
paradigm=paradigm,
TR=TR,
n_scans=n_scans,
hfcut=hfcut,
frametimes=frametimes,
drift_model=drift_model,
hrf_model=hrf_model,
slicer='z'
)
# shutdown main report page
ProgressReport().finish_dir(output_dir)
print "\r\nStatistic report written to %s\r\n" % stats_report_filename
def first_level(subject_id):
'''
Launch the first level analysis for one subject
ROOTDIR is an variable of environnmenent where your data are stored.
ROOTDIR needs to be set up by the user.
See: https://github.com/neurospin/pypreprocess/
Tape: python first_level.py
Keyword arguments:
subject_id -- Name of the subject
'''
# Configure paths
data_dir = os.path.join(os.environ["ROOTDIR"], "dataset", "bids_dataset",
subject_id)
output_dir = os.path.join(os.environ["ROOTDIR"], "processed_data",
subject_id)
subject_session_output_dir = os.path.join(output_dir, 'res_stats')
if not os.path.exists(subject_session_output_dir):
os.makedirs(subject_session_output_dir)
# Experimental paradigm meta-params
stats_start_time = time.ctime()
tr = 2.4
drift_model = 'blank'
#hrf_model = 'canonical' # hemodynamic reponse function
hrf_model = 'spm' # hemodynamic reponse function
hfcut = 128.
n_scans = 128
# Preparation of paradigm
events_file = glob.glob(
os.path.join(data_dir, 'func/*_task-standartloc_events.tsv'))[0]
print events_file
paradigm = paradigm_contrasts.localizer_paradigm(events_file)
# Build design matrix
frametimes = np.linspace(0, (n_scans - 1) * tr, n_scans)
design_matrix = make_design_matrix(
frametimes,
paradigm,
hrf_model=hrf_model,
drift_model=drift_model,
period_cut=hfcut,
)
# Specify contrasts
contrasts = paradigm_contrasts.localizer_contrasts(design_matrix)
# Fit GLM
fmri_file = glob.glob(
os.path.join(output_dir, 'func/wra*_task-standartloc_bold.nii.gz'))[0]
print 'Fitting a GLM (this takes time)...'
# fmri_glm = FirstLevelGLM(noise_model='ar1', standardize=False).fit(fmri_files[0],
# [design_matrix for design_matrix in design_matrices]
# )
# fmri_glm = FirstLevelGLM(noise_model='ar1', standardize=False).fit(fmri_file,
# [design_matrix for design_matrix in design_matrices]
# )
fmri_glm = FirstLevelGLM(noise_model='ar1',
standardize=False).fit(fmri_file, design_matrix)
# Save computed mask
mask_images = []
mask_path = os.path.join(subject_session_output_dir, "mask.nii.gz")
nibabel.save(fmri_glm.masker_.mask_img_, mask_path)
mask_images.append(mask_path)
# Compute contrasts
z_maps = {}
effects_maps = {}
for contrast_id, contrast_val in contrasts.iteritems():
print "\tcontrast id: %s" % contrast_id
z_map, t_map, effects_map, var_map = fmri_glm.transform(
[contrast_val] * 1,
contrast_name=contrast_id,
output_z=True,
output_stat=True,
output_effects=True,
output_variance=True)
# Store stat maps to disk
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_session_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%s.nii.gz' % (subject_id, contrast_id))
print "\t\tWriting %s ..." % map_path
nibabel.save(out_map, map_path)
# collect zmaps for contrasts we're interested in
if map_type == 'z':
z_maps[contrast_id] = map_path
if map_type == 'effects':
effects_maps[contrast_id] = map_path
# Do stats report
anat_file = glob.glob(os.path.join(output_dir, 'anat/w*_T1w.nii.gz'))[0]
anat_img = nibabel.load(anat_file)
stats_report_filename = os.path.join(subject_session_output_dir,
"report_stats.html")
generate_subject_stats_report(
stats_report_filename,
contrasts,
z_maps,
fmri_glm.masker_.mask_img_,
threshold=2.3,
cluster_th=15,
anat=anat_img,
anat_affine=anat_img.get_affine(),
design_matrices=[design_matrix],
paradigm=paradigm,
subject_id=subject_id,
start_time=stats_start_time,
title="GLM for subject %s" % subject_id,
# additional ``kwargs`` for more informative report
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
return z_maps
def run_suject_level1_glm(
subject_data,
readout_time=.01392, # seconds
tr=.72,
dc=True,
hrf_model="Canonical with Derivative",
drift_model="Cosine",
hfcut=100,
regress_motion=True,
slicer='ortho',
cut_coords=None,
threshold=3.,
cluster_th=15,
normalize=True,
fwhm=0.,
protocol="MOTOR",
func_write_voxel_sizes=None,
anat_write_voxel_sizes=None,
**other_preproc_kwargs):
"""
Function to do preproc + analysis for a single HCP subject (task fMRI)
"""
add_regs_files = None
n_motion_regressions = 6
subject_data.n_sessions = 2
subject_data.tmp_output_dir = os.path.join(subject_data.output_dir, "tmp")
if not os.path.exists(subject_data.tmp_output_dir):
os.makedirs(subject_data.tmp_output_dir)
if not os.path.exists(subject_data.output_dir):
os.makedirs(subject_data.output_dir)
mem = Memory(os.path.join(subject_data.output_dir, "cache_dir"),
verbose=100)
# glob design files (.fsf)
subject_data.design_files = [
os.path.join(subject_data.data_dir,
("MNINonLinear/Results/tfMRI_%s_%s/"
"tfMRI_%s_%s_hp200_s4_level1.fsf") %
(protocol, direction, protocol, direction))
for direction in ['LR', 'RL']
]
assert len(subject_data.design_files) == 2
for df in subject_data.design_files:
assert os.path.isfile(df), df
if 0x0:
subject_data = _do_fmri_distortion_correction(
subject_data,
dc=dc,
fwhm=fwhm,
readout_time=readout_time,
**other_preproc_kwargs)
# chronometry
stats_start_time = pretty_time()
# merged lists
paradigms = []
frametimes_list = []
design_matrices = []
# fmri_files = []
n_scans = []
# for direction, direction_index in zip(['LR', 'RL'], xrange(2)):
for sess in xrange(subject_data.n_sessions):
direction = ['LR', 'RL'][sess]
# glob the design file
# design_file = os.path.join(# _subject_data_dir, "tfMRI_%s_%s" % (
# protocol, direction),
design_file = subject_data.design_files[sess]
# "tfMRI_%s_%s_hp200_s4_level1.fsf" % (
# protocol, direction))
if not os.path.isfile(design_file):
print "Can't find design file %s; skipping subject %s" % (
design_file, subject_data.subject_id)
return
# read the experimental setup
print "Reading experimental setup from %s ..." % design_file
fsl_condition_ids, timing_files, fsl_contrast_ids, contrast_values = \
read_fsl_design_file(design_file)
print "... done.\r\n"
# fix timing filenames
timing_files = [
tf.replace("EVs", "tfMRI_%s_%s/EVs" % (protocol, direction))
for tf in timing_files
]
# make design matrix
print "Constructing design matrix for direction %s ..." % direction
_n_scans = nibabel.load(subject_data.func[sess]).shape[-1]
n_scans.append(_n_scans)
add_regs_file = add_regs_files[
sess] if not add_regs_files is None else None
design_matrix, paradigm, frametimes = make_dmtx_from_timing_files(
timing_files,
fsl_condition_ids,
n_scans=_n_scans,
tr=tr,
hrf_model=hrf_model,
drift_model=drift_model,
hfcut=hfcut,
add_regs_file=add_regs_file,
add_reg_names=[
'Translation along x axis', 'Translation along yaxis',
'Translation along z axis', 'Rotation along x axis',
'Rotation along y axis', 'Rotation along z axis',
'Differential Translation along x axis',
'Differential Translation along yaxis',
'Differential Translation along z axis',
'Differential Rotation along x axis',
'Differential Rotation along y axis',
'Differential Rotation along z axis'
][:n_motion_regressions] if not add_regs_files is None else None,
)
print "... done."
paradigms.append(paradigm)
frametimes_list.append(frametimes)
design_matrices.append(design_matrix)
# convert contrasts to dict
contrasts = dict((
contrast_id,
# append zeros to end of contrast to match design
np.hstack((
contrast_value,
np.zeros(len(design_matrix.names) - len(contrast_value)))))
for contrast_id, contrast_value in zip(
fsl_contrast_ids, contrast_values))
# more interesting contrasts
if protocol == 'MOTOR':
contrasts['RH-LH'] = contrasts['RH'] - contrasts['LH']
contrasts['LH-RH'] = -contrasts['RH-LH']
contrasts['RF-LF'] = contrasts['RF'] - contrasts['LF']
contrasts['LF-RF'] = -contrasts['RF-LF']
contrasts['H'] = contrasts['RH'] + contrasts['LH']
contrasts['F'] = contrasts['RF'] + contrasts['LF']
contrasts['H-F'] = contrasts['RH'] + contrasts['LH'] - (
contrasts['RF'] - contrasts['LF'])
contrasts['F-H'] = -contrasts['H-F']
contrasts = dict((k, v) for k, v in contrasts.iteritems() if "-" in k)
# replicate contrasts across sessions
contrasts = dict((cid, [cval] * 2) for cid, cval in contrasts.iteritems())
cache_dir = cache_dir = os.path.join(subject_data.output_dir, 'cache_dir')
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
nipype_mem = NipypeMemory(base_dir=cache_dir)
if 0x0:
if np.sum(fwhm) > 0.:
subject_data.func = nipype_mem.cache(spm.Smooth)(
in_files=subject_data.func,
fwhm=fwhm,
ignore_exception=False,
).outputs.smoothed_files
# fit GLM
def tortoise(*args):
print args
print(
'Fitting a "Fixed Effect" GLM for merging LR and RL '
'phase-encoding directions for subject %s ...' %
(subject_data.subject_id))
fmri_glm = FMRILinearModel(
subject_data.func,
[design_matrix.matrix for design_matrix in design_matrices],
mask='compute')
fmri_glm.fit(do_scaling=True, model='ar1')
print "... done.\r\n"
# save computed mask
mask_path = os.path.join(subject_data.output_dir, "mask.nii")
print "Saving mask image to %s ..." % mask_path
nibabel.save(fmri_glm.mask, mask_path)
print "... done.\r\n"
z_maps = {}
effects_maps = {}
map_dirs = {}
for contrast_id, contrast_val in contrasts.iteritems():
print "\tcontrast id: %s" % contrast_id
z_map, eff_map = fmri_glm.contrast(contrast_val,
con_id=contrast_id,
output_z=True,
output_effects=True)
# store stat maps to disk
for map_type, out_map in zip(['z', 'effects'], [z_map, eff_map]):
map_dir = os.path.join(subject_data.output_dir,
'%s_maps' % map_type)
map_dirs[map_type] = map_dir
if not os.path.exists(map_dir):
os.makedirs(map_dir)
map_path = os.path.join(map_dir,
'%s_%s.nii' % (map_type, contrast_id))
print "\t\tWriting %s ..." % map_path
nibabel.save(out_map, map_path)
# collect zmaps for contrasts we're interested in
if map_type == 'z':
z_maps[contrast_id] = map_path
if map_type == 'effects':
effects_maps[contrast_id] = map_path
return effects_maps, z_maps, mask_path, map_dirs
# compute native-space maps and mask
effects_maps, z_maps, mask_path, map_dirs = mem.cache(tortoise)(
subject_data.func, subject_data.anat)
# do stats report
if 0x0:
anat_img = nibabel.load(subject_data.anat)
stats_report_filename = os.path.join(subject_data.output_dir,
"reports", "report_stats.html")
generate_subject_stats_report(
stats_report_filename,
contrasts,
z_maps,
nibabel.load(mask_path),
anat=anat_img.get_data(),
anat_affine=anat_img.get_affine(),
threshold=threshold,
cluster_th=cluster_th,
slicer=slicer,
cut_coords=cut_coords,
design_matrices=design_matrices,
subject_id=subject_data.subject_id,
start_time=stats_start_time,
title="GLM for subject %s" % subject_data.subject_id,
# additional ``kwargs`` for more informative report
TR=tr,
n_scans=n_scans,
hfcut=hfcut,
drift_model=drift_model,
hrf_model=hrf_model,
paradigm={
'LR': paradigms[0].__dict__,
'RL': paradigms[1].__dict__
},
frametimes={
'LR': frametimes_list[0],
'RL': frametimes_list[1]
},
fwhm=fwhm)
ProgressReport().finish_dir(subject_data.output_dir)
print "\r\nStatistic report written to %s\r\n" % stats_report_filename
# remove repeated contrasts
contrasts = dict((cid, cval[0]) for cid, cval in contrasts.iteritems())
import json
json.dump(
dict((k, list(v)) for k, v in contrasts.iteritems()),
open(os.path.join(subject_data.tmp_output_dir, "contrasts.json"), "w"))
subject_data.contrasts = contrasts
if normalize:
assert hasattr(subject_data, "parameter_file")
subject_data.native_effects_maps = effects_maps
subject_data.native_z_maps = z_maps
subject_data.native_mask_path = mask_path
# warp effects maps and mask from native to standard space (MNI)
apply_to_files = [
v for _, v in subject_data.native_effects_maps.iteritems()
] + [subject_data.native_mask_path]
tmp = nipype_mem.cache(spm.Normalize)(
parameter_file=getattr(subject_data, "parameter_file"),
apply_to_files=apply_to_files,
write_bounding_box=[[-78, -112, -50], [78, 76, 85]],
write_voxel_sizes=func_write_voxel_sizes,
write_wrap=[0, 0, 0],
write_interp=1,
jobtype='write',
ignore_exception=False,
).outputs.normalized_files
subject_data.mask = hard_link(tmp[-1], subject_data.output_dir)
subject_data.effects_maps = dict(
zip(effects_maps.keys(), hard_link(tmp[:-1], map_dirs["effects"])))
# warp anat image
subject_data.anat = hard_link(
nipype_mem.cache(spm.Normalize)(
parameter_file=getattr(subject_data, "parameter_file"),
apply_to_files=subject_data.anat,
write_bounding_box=[[-78, -112, -50], [78, 76, 85]],
write_voxel_sizes=anat_write_voxel_sizes,
write_wrap=[0, 0, 0],
write_interp=1,
jobtype='write',
ignore_exception=False,
).outputs.normalized_files, subject_data.anat_output_dir)
else:
subject_data.mask = mask_path
subject_data.effects_maps = effects_maps
subject_data.z_maps = z_maps
return subject_data
def first_level(subject_dic,
additional_regressors=None,
compcorr=False,
smooth=None,
surface=False,
mask_img=None):
""" Run the first-level analysis (GLM fitting + statistical maps)
in a given subject
Parameters
----------
subject_dic: dict,
exhaustive description of an individual acquisition
additional_regressors: dict or None,
additional regressors provided as an already sampled
design_matrix
dictionary keys are session_ids
compcorr: Bool, optional,
whether confound estimation and removal should be done or not
smooth: float or None, optional,
how much the data should spatially smoothed during masking
"""
start_time = time.ctime()
# experimental paradigm meta-params
motion_names = ['tx', 'ty', 'tz', 'rx', 'ry', 'rz']
hrf_model = subject_dic['hrf_model']
hfcut = subject_dic['hfcut']
drift_model = subject_dic['drift_model']
tr = subject_dic['TR']
if not surface and (mask_img is None):
mask_img = masking(subject_dic['func'], subject_dic['output_dir'])
if additional_regressors is None:
additional_regressors = dict([
(session_id, None) for session_id in subject_dic['session_id']
])
for session_id, fmri_path, onset, motion_path in zip(
subject_dic['session_id'], subject_dic['func'],
subject_dic['onset'], subject_dic['realignment_parameters']):
paradigm_id = _session_id_to_task_id([session_id])[0]
if surface:
from nibabel.gifti import read
n_scans = np.array(
[darrays.data for darrays in read(fmri_path).darrays]).shape[0]
else:
n_scans = nib.load(fmri_path).shape[3]
# motion parameters
motion = np.loadtxt(motion_path)
# define the time stamps for different images
frametimes = np.linspace(0, (n_scans - 1) * tr, n_scans)
if paradigm_id == 'audio':
mask = np.array([1, 0, 1, 1, 0, 1, 1, 0, 1, 1])
n_cycles = 28
cycle_duration = 20
t_r = 2
cycle = np.arange(0, cycle_duration, t_r)[mask > 0]
frametimes = np.tile(cycle, n_cycles) +\
np.repeat(np.arange(n_cycles) * cycle_duration, mask.sum())
frametimes = frametimes[:-2] # for some reason...
if surface:
compcorr = False # XXX Fixme
if compcorr:
confounds = high_variance_confounds(fmri_path, mask_img=mask_img)
confounds = np.hstack((confounds, motion))
confound_names = ['conf_%d' % i for i in range(5)] + motion_names
else:
confounds = motion
confound_names = motion_names
if onset is None:
warnings.warn('Onset file not provided. Trying to guess it')
task = os.path.basename(fmri_path).split('task')[-1][4:]
onset = os.path.join(
os.path.split(os.path.dirname(fmri_path))[0], 'model001',
'onsets', 'task' + task + '_run001', 'task%s.csv' % task)
if not os.path.exists(onset):
warnings.warn('non-existant onset file. proceeding without it')
paradigm = None
else:
paradigm = make_paradigm(onset, paradigm_id)
# handle manually supplied regressors
add_reg_names = []
if additional_regressors[session_id] is None:
add_regs = confounds
else:
df = read_csv(additional_regressors[session_id])
add_regs = []
for regressor in df:
add_reg_names.append(regressor)
add_regs.append(df[regressor])
add_regs = np.array(add_regs).T
add_regs = np.hstack((add_regs, confounds))
add_reg_names += confound_names
# create the design matrix
design_matrix = make_first_level_design_matrix(
frametimes,
paradigm,
hrf_model=hrf_model,
drift_model=drift_model,
period_cut=hfcut,
add_regs=add_regs,
add_reg_names=add_reg_names)
_, dmtx, names = check_design_matrix(design_matrix)
# create the relevant contrasts
contrasts = make_contrasts(paradigm_id, names)
if surface:
subject_session_output_dir = os.path.join(
subject_dic['output_dir'], 'res_surf_%s' % session_id)
else:
subject_session_output_dir = os.path.join(
subject_dic['output_dir'], 'res_stats_%s' % session_id)
if not os.path.exists(subject_session_output_dir):
os.makedirs(subject_session_output_dir)
np.savez(os.path.join(subject_session_output_dir, 'design_matrix.npz'),
design_matrix=design_matrix)
if surface:
run_surface_glm(design_matrix, contrasts, fmri_path,
subject_session_output_dir)
else:
z_maps = run_glm(design_matrix,
contrasts,
fmri_path,
mask_img,
subject_dic,
subject_session_output_dir,
tr=tr,
smoothing_fwhm=smooth)
# do stats report
anat_img = nib.load(subject_dic['anat'])
stats_report_filename = os.path.join(subject_session_output_dir,
'report_stats.html')
generate_subject_stats_report(
stats_report_filename,
contrasts,
z_maps,
mask_img,
threshold=3.,
cluster_th=15,
anat=anat_img,
anat_affine=anat_img.affine,
design_matrices=[design_matrix],
subject_id=subject_dic['subject_id'],
start_time=start_time,
title="GLM for subject %s" % session_id,
# additional ``kwargs`` for more informative report
TR=tr,
n_scans=n_scans,
hfcut=hfcut,
frametimes=frametimes,
drift_model=drift_model,
hrf_model=hrf_model,
)
if not surface:
ProgressReport().finish_dir(subject_session_output_dir)
print("Statistic report written to %s\r\n" % stats_report_filename)
def _preprocess_and_analysis_subject(subject_data,
slicer='z',
cut_coords=6,
threshold=3.,
cluster_th=15,
**preproc_params):
"""
Preprocesses the subject and then fits (mass-univariate) GLM thereup.
"""
# sanitize run_ids:
# Sub14/BOLD/Run_02/fMR09029-0004-00010-000010-01.nii is garbage,
# for example
run_ids = range(9)
if subject_data['subject_id'] == "Sub14":
run_ids = [0] + range(2, 9)
subject_data['func'] = [subject_data['func'][0]
] + subject_data['func'][2:]
subject_data['session_id'] = [subject_data['session_id'][0]
] + subject_data['session_id'][2:]
# sanitize subject output dir
if not 'output_dir' in subject_data:
subject_data['output_dir'] = os.path.join(output_dir,
subject_data['subject_id'])
# preprocess the data
subject_data = do_subject_preproc(subject_data, **preproc_params)
# chronometry
stats_start_time = pretty_time()
# to-be merged lists, one item per run
paradigms = []
frametimes_list = []
design_matrices = [] # one
list_of_contrast_dicts = [] # one dict per run
n_scans = []
for run_id in run_ids:
_n_scans = len(subject_data.func[run_id])
n_scans.append(_n_scans)
# make paradigm
paradigm = make_paradigm(getattr(subject_data, 'timing')[run_id])
# make design matrix
tr = 2.
drift_model = 'Cosine'
hrf_model = 'Canonical With Derivative'
hfcut = 128.
frametimes = np.linspace(0, (_n_scans - 1) * tr, _n_scans)
design_matrix = make_dmtx(
frametimes,
paradigm,
hrf_model=hrf_model,
drift_model=drift_model,
hfcut=hfcut,
add_regs=np.loadtxt(
getattr(subject_data, 'realignment_parameters')[run_id]),
add_reg_names=[
'Translation along x axis', 'Translation along yaxis',
'Translation along z axis', 'Rotation along x axis',
'Rotation along y axis', 'Rotation along z axis'
])
# import matplotlib.pyplot as plt
# design_matrix.show()
# plt.show()
paradigms.append(paradigm)
design_matrices.append(design_matrix)
frametimes_list.append(frametimes)
n_scans.append(_n_scans)
# specify contrasts
contrasts = {}
n_columns = len(design_matrix.names)
for i in xrange(paradigm.n_conditions):
contrasts['%s' %
design_matrix.names[2 * i]] = np.eye(n_columns)[2 * i]
# more interesting contrasts"""
contrasts['Famous-Unfamiliar'] = contrasts['Famous'] - contrasts[
'Unfamiliar']
contrasts['Unfamiliar-Famous'] = -contrasts['Famous-Unfamiliar']
contrasts[
'Famous-Scrambled'] = contrasts['Famous'] - contrasts['Scrambled']
contrasts['Scrambled-Famous'] = -contrasts['Famous-Scrambled']
contrasts['Unfamiliar-Scrambled'] = contrasts[
'Unfamiliar'] - contrasts['Scrambled']
contrasts['Scrambled-Unfamiliar'] = -contrasts['Unfamiliar-Scrambled']
list_of_contrast_dicts.append(contrasts)
# importat maps
z_maps = {}
effects_maps = {}
# fit GLM
print('\r\nFitting a GLM (this takes time) ..')
fmri_glm = FMRILinearModel(
[nibabel.concat_images(sess_func) for sess_func in subject_data.func],
[design_matrix.matrix for design_matrix in design_matrices],
mask='compute')
fmri_glm.fit(do_scaling=True, model='ar1')
print "... done.\r\n"
# save computed mask
mask_path = os.path.join(subject_data.output_dir, "mask.nii.gz")
print "Saving mask image to %s ..." % mask_path
nibabel.save(fmri_glm.mask, mask_path)
print "... done.\r\n"
# replicate contrasts across runs
contrasts = dict(
(cid, [contrasts[cid] for contrasts in list_of_contrast_dicts])
for cid, cval in contrasts.iteritems())
# compute effects
for contrast_id, contrast_val in contrasts.iteritems():
print "\tcontrast id: %s" % contrast_id
z_map, eff_map = fmri_glm.contrast(contrast_val,
con_id=contrast_id,
output_z=True,
output_stat=False,
output_effects=True,
output_variance=False)
# store stat maps to disk
for map_type, out_map in zip(['z', 'effects'], [z_map, eff_map]):
map_dir = os.path.join(subject_data.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)
# collect zmaps for contrasts we're interested in
if map_type == 'z':
z_maps[contrast_id] = map_path
if map_type == 'effects':
effects_maps[contrast_id] = map_path
# remove repeated contrasts
contrasts = dict((cid, cval[0]) for cid, cval in contrasts.iteritems())
# do stats report
stats_report_filename = os.path.join(
getattr(subject_data, 'reports_output_dir', subject_data.output_dir),
"report_stats.html")
generate_subject_stats_report(
stats_report_filename,
contrasts,
z_maps,
fmri_glm.mask,
threshold=threshold,
cluster_th=cluster_th,
slicer=slicer,
cut_coords=cut_coords,
design_matrices=design_matrices,
subject_id=subject_data.subject_id,
start_time=stats_start_time,
title="GLM for subject %s" % subject_data.subject_id,
# additional ``kwargs`` for more informative report
TR=tr,
n_scans=n_scans,
hfcut=hfcut,
drift_model=drift_model,
hrf_model=hrf_model,
paradigm=dict(("Run_%02i" % (run_id + 1), paradigms[run_id])
for run_id in run_ids),
frametimes=dict(("Run_%02i" % (run_id + 1), frametimes_list[run_id])
for run_id in run_ids),
# fwhm=fwhm
)
ProgressReport().finish_dir(subject_data.output_dir)
print "\r\nStatistic report written to %s\r\n" % stats_report_filename
return contrasts, effects_maps, z_maps, mask_path
