def _delete_orientation(self):
"""
Delete orientation metadata. Garbage orientation metadata can lead to
severe mis-registration trouble.
"""
# prepare for smart caching
cache_dir = os.path.join(self.output_dir, 'cache_dir')
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
mem = Memory(cachedir=cache_dir, verbose=5)
# deleteorient for func
for attr in ['n_sessions', 'session_output_dirs']:
if getattr(self, attr) is None:
warnings.warn("'%s' attribute of is None! Skipping" % attr)
break
else:
self.func = [mem.cache(delete_orientation)(
self.func[sess], self.session_output_dirs[sess])
for sess in range(self.n_sessions)]
# deleteorient for anat
if not self.anat is None:
self.anat = mem.cache(delete_orientation)(
self.anat, self.anat_output_dir)
def _niigz2nii(self):
"""
Convert .nii.gz to .nii (crucial for SPM).
"""
cache_dir = os.path.join(self.scratch, 'cache_dir')
mem = Memory(cache_dir, verbose=100)
self._sanitize_session_output_dirs()
if not None in [self.func, self.n_sessions, self.session_output_dirs]:
self.func = [mem.cache(do_niigz2nii)(
self.func[sess], output_dir=self.session_output_dirs[sess])
for sess in range(self.n_sessions)]
if not self.anat is None:
self.anat = mem.cache(do_niigz2nii)(
self.anat, output_dir=self.anat_output_dir)
def _niigz2nii(self):
"""
Convert .nii.gz to .nii (crucial for SPM).
"""
cache_dir = os.path.join(self.scratch, 'cache_dir')
mem = Memory(cache_dir, verbose=100)
self.func = [
mem.cache(do_niigz2nii)(self.func[sess],
output_dir=self.session_output_dirs[sess])
for sess in xrange(self.n_sessions)
]
if not self.anat is None:
self.anat = mem.cache(do_niigz2nii)(
self.anat, output_dir=self.anat_output_dir)
def _delete_orientation(self):
"""
Delete orientation metadata. Garbage orientation metadata can lead to
severe mis-registration trouble.
"""
# prepare for smart caching
cache_dir = os.path.join(self.output_dir, 'cache_dir')
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
mem = Memory(cachedir=cache_dir, verbose=5)
# deleteorient for func
self.func = [mem.cache(delete_orientation)(
self.func[sess],
self.session_output_dirs[sess])
for sess in xrange(self.n_sessions)]
# deleteorient for anat
if not self.anat is None:
self.anat = mem.cache(delete_orientation)(
self.anat, self.anat_output_dir)
def _delete_orientation(self):
"""
Delete orientation metadata. Garbage orientation metadata can lead to
severe mis-registration trouble.
"""
# prepare for smart caching
cache_dir = os.path.join(self.output_dir, 'cache_dir')
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
mem = Memory(cachedir=cache_dir, verbose=5)
# deleteorient for func
self.func = [
mem.cache(delete_orientation)(self.func[sess],
self.session_output_dirs[sess])
for sess in xrange(self.n_sessions)
]
# deleteorient for anat
if not self.anat is None:
self.anat = mem.cache(delete_orientation)(self.anat,
self.anat_output_dir)
def do_subject_preproc(subject_id,
output_dir,
func,
anat,
do_bet=True,
do_mc=True,
do_coreg=True,
do_normalize=True,
cmd_prefix="fsl5.0-",
**kwargs
):
"""
Preprocesses subject data using FSL.
Parameters
----------
"""
output = {'func': func,
'anat': anat
}
# output dir
subject_output_dir = os.path.join(output_dir, subject_id)
if not os.path.exists(subject_output_dir):
os.makedirs(subject_output_dir)
# prepare for smart-caching
cache_dir = os.path.join(output_dir, "cache_dir")
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
nipype_mem = NipypeMemory(base_dir=cache_dir)
joblib_mem = JoblibMemory(cache_dir, verbose=100)
# sanitize input files
if not isinstance(output['func'], basestring):
output['func'] = joblib_mem.cache(do_fsl_merge)(
func, subject_output_dir, output_prefix='Merged',
cmd_prefix=cmd_prefix)
######################
# Brain Extraction
######################
if do_bet:
if not fsl.BET._cmd.startswith("fsl"):
fsl.BET._cmd = cmd_prefix + fsl.BET._cmd
bet = nipype_mem.cache(fsl.BET)
bet_results = bet(in_file=output['anat'],
)
output['anat'] = bet_results.outputs.out_file
#######################
# Motion correction
#######################
if do_mc:
if not fsl.MCFLIRT._cmd.startswith("fsl"):
fsl.MCFLIRT._cmd = cmd_prefix + fsl.MCFLIRT._cmd
mcflirt = nipype_mem.cache(fsl.MCFLIRT)
mcflirt_results = mcflirt(in_file=output['func'],
cost='mutualinfo',
save_mats=True, # save mc matrices
save_plots=True # save mc params
)
output['motion_parameters'] = mcflirt_results.outputs.par_file
output['motion_matrices'] = mcflirt_results.outputs.mat_file
output['func'] = mcflirt_results.outputs.out_file
###################
# Coregistration
###################
if do_coreg:
if not fsl.FLIRT._cmd.startswith("fsl"):
fsl.FLIRT._cmd = cmd_prefix + fsl.FLIRT._cmd
flirt1 = nipype_mem.cache(fsl.FLIRT)
flirt1_results = flirt1(in_file=output['func'],
reference=output['anat']
)
if not do_normalize:
output['func'] = flirt1_results.outputs.out_file
##########################
# Spatial normalization
##########################
if do_normalize:
if not fsl.FLIRT._cmd.startswith("fsl"):
fsl.FLIRT._cmd = cmd_prefix + fsl.FLIRT._cmd
# T1 normalization
flirt2 = nipype_mem.cache(fsl.FLIRT)
flirt2_results = flirt2(in_file=output['anat'],
reference=FSL_T1_TEMPLATE)
output['anat'] = flirt2_results.outputs.out_file
# concatenate 'func -> anat' and 'anat -> standard space'
# transformation matrices to obtaun 'func -> standard space'
# transformation matrix
if do_coreg:
if not fsl.ConvertXFM._cmd.startswith("fsl"):
fsl.ConvertXFM._cmd = cmd_prefix + fsl.ConvertXFM._cmd
convertxfm = nipype_mem.cache(fsl.ConvertXFM)
convertxfm_results = convertxfm(
in_file=flirt1_results.outputs.out_matrix_file,
in_file2=flirt2_results.outputs.out_matrix_file,
concat_xfm=True
)
# warp func data into standard space by applying
# 'func -> standard space' transformation matrix
if not fsl.ApplyXfm._cmd.startswith("fsl"):
fsl.ApplyXfm._cmd = cmd_prefix + fsl.ApplyXfm._cmd
applyxfm = nipype_mem.cache(fsl.ApplyXfm)
applyxfm_results = applyxfm(
in_file=output['func'],
in_matrix_file=convertxfm_results.outputs.out_file,
reference=FSL_T1_TEMPLATE
)
output['func'] = applyxfm_results.outputs.out_file
return output
def do_subject_glm(subject_data):
"""FE analysis for a single subject."""
subject_id = subject_data['subject_id']
output_dir = subject_data["output_dir"]
func_files = subject_data['func']
anat = subject_data['anat']
onset_files = subject_data['onset']
# subject_id = os.path.basename(subject_dir)
# subject_output_dir = os.path.join(output_dir, subject_id)
mem = Memory(os.path.join(output_dir, "cache"))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# glob files: anat, session func files, session onset files
# anat = glob.glob(os.path.join(subject_dir, anat_wildcard))
# assert len(anat) == 1
# anat = anat[0]
# onset_files = sorted([glob.glob(os.path.join(subject_dir, session))[0]
# for session in session_onset_wildcards])
# func_files = sorted([sorted(glob.glob(os.path.join(subject_dir, session)))
# for session in session_func_wildcards])
### Preprocess data #######################################################
if 0:
subject_data = mem.cache(do_subject_preproc)(
dict(func=func_files, anat=anat, output_dir=output_dir))
func_files = subject_data['func']
anat = subject_data['anat']
# reslice func images
func_files = [mem.cache(reslice_vols)(
sess_func,
target_affine=nibabel.load(sess_func[0]).get_affine())
for sess_func in func_files]
### GLM: loop on (session_bold, onse_file) pairs over the various sessions
design_matrices = []
for session, (func_file, onset_file) in enumerate(zip(func_files,
onset_files)):
if isinstance(func_file, str):
bold = nibabel.load(func_file)
else:
if len(func_file) == 1:
func_file = func_file[0]
bold = nibabel.load(func_file)
assert len(bold.shape) == 4
n_scans = bold.shape[-1]
del bold
else:
n_scans = len(func_file)
frametimes = np.linspace(0, (n_scans - 1) * tr, n_scans)
conditions, onsets, durations, amplitudes = parse_onset_file(
onset_file)
onsets *= tr
durations *= tr
paradigm = BlockParadigm(con_id=conditions, onset=onsets,
duration=durations, amplitude=amplitudes)
design_matrices.append(make_dmtx(
frametimes,
paradigm, hrf_model=hrf_model,
drift_model=drift_model, hfcut=hfcut))
# specify contrasts
n_columns = len(design_matrices[0].names)
contrasts = {}
for i in xrange(paradigm.n_conditions):
contrasts['%s' % design_matrices[0].names[2 * i]
] = np.eye(n_columns)[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']
# effects of interest F-test
diff_contrasts = []
for i in xrange(paradigm.n_conditions - 1):
a = contrasts[design_matrices[0].names[2 * i]]
b = contrasts[design_matrices[0].names[2 * (i + 1)]]
diff_contrasts.append(a - b)
contrasts["diff"] = diff_contrasts
# fit GLM
print 'Fitting a GLM (this takes time)...'
fmri_glm = FMRILinearModel([nibabel.concat_images(sess_func,
check_affines=False)
for sess_func in func_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(output_dir, "mask.nii.gz")
print "Saving mask image %s" % mask_path
nibabel.save(fmri_glm.mask, mask_path)
# compute contrasts
z_maps = {}
effects_maps = {}
for contrast_id, contrast_val in contrasts.iteritems():
print "\tcontrast id: %s" % contrast_id
if np.ndim(contrast_val) > 1:
contrast_type = "t"
else:
contrast_type = "F"
z_map, t_map, effects_map, var_map = fmri_glm.contrast(
[contrast_val] * 2,
con_id=contrast_id,
contrast_type=contrast_type,
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(
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
return subject_id, anat, effects_maps, z_maps, contrasts, fmri_glm.mask
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
return subject_id, anat, effects_maps, z_maps, contrasts, fmri_glm.mask
if __name__ == "__maih__":
mem = Memory(os.path.join(output_dir, "cache"))
first_level_glms = map(mem.cache(do_subject_glm), subject_dirs)
# plot stats (per subject)
import matplotlib.pyplot as plt
import nipy.labs.viz as viz
all_masks = []
all_effects_maps = []
for (subject_id, anat, effects_maps, z_maps,
contrasts, mask) in first_level_glms:
all_masks.append(mask)
anat_img = nibabel.load(anat)
z_map = nibabel.load(z_maps.values()[0])
all_effects_maps.append(effects_maps)
for contrast_id, z_map in z_maps.iteritems():
z_map = nibabel.load(z_map)
def _do_fmri_distortion_correction(
subject_data,
# i'm unsure of the readout time,
# but this is constant across both PE
# directions and so can be scaled to 1
# (or any other nonzero float)
protocol="MOTOR",
readout_time=.01392,
realign=True,
coregister=True,
coreg_func_to_anat=True,
dc=True,
segment=False,
normalize=False,
func_write_voxel_sizes=None,
anat_write_voxel_sizes=None,
report=False,
**kwargs):
"""
Function to undistort task fMRI data for a given HCP subject.
"""
directions = ['LR', 'RL']
subject_data.sanitize()
if dc:
acq_params = [[1, 0, 0, readout_time], [-1, 0, 0, readout_time]]
acq_params_file = os.path.join(subject_data.output_dir,
"b0_acquisition_params.txt")
np.savetxt(acq_params_file, acq_params, fmt='%f')
fieldmap_files = [
os.path.join(
os.path.dirname(subject_data.func[sess]),
"%s_3T_SpinEchoFieldMap_%s.nii.gz" %
(subject_data.subject_id, directions[sess]))
for sess in xrange(subject_data.n_sessions)
]
sbref_files = [
sess_func.replace(".nii", "_SBRef.nii")
for sess_func in subject_data.func
]
# prepare for smart caching
mem = Memory(os.path.join(subject_data.output_dir, "cache_dir"))
for x in [fieldmap_files, sbref_files, subject_data.func]:
assert len(x) == 2
for y in x:
assert os.path.isfile(y), y
# fslroi
zeroth_fieldmap_files = []
for fieldmap_file in fieldmap_files:
if not os.path.isfile(fieldmap_file):
print "Can't find fieldmap file %s; skipping subject %s" % (
fieldmap_file, subject_data.subject_id)
return
# peel 0th volume of each fieldmap
zeroth_fieldmap_file = os.path.join(
subject_data.output_dir,
"0th_%s" % os.path.basename(fieldmap_file))
fslroi_cmd = "fsl5.0-fslroi %s %s 0 1" % (fieldmap_file,
zeroth_fieldmap_file)
print "\r\nExecuting '%s' ..." % fslroi_cmd
print mem.cache(commands.getoutput)(fslroi_cmd)
zeroth_fieldmap_files.append(zeroth_fieldmap_file)
# merge the 0th volume of both fieldmaps
merged_zeroth_fieldmap_file = os.path.join(
subject_data.output_dir, "merged_with_other_direction_%s" %
(os.path.basename(zeroth_fieldmap_files[0])))
fslmerge_cmd = "fsl5.0-fslmerge -t %s %s %s" % (
merged_zeroth_fieldmap_file, zeroth_fieldmap_files[0],
zeroth_fieldmap_files[1])
print "\r\nExecuting '%s' ..." % fslmerge_cmd
print mem.cache(commands.getoutput)(fslmerge_cmd)
# do topup (learn distortion model)
topup_results_basename = os.path.join(subject_data.output_dir,
"topup_results")
topup_cmd = ("fsl5.0-topup --imain=%s --datain=%s --config=b02b0.cnf "
"--out=%s" % (merged_zeroth_fieldmap_file,
acq_params_file, topup_results_basename))
print "\r\nExecuting '%s' ..." % topup_cmd
print mem.cache(commands.getoutput)(topup_cmd)
# apply learn deformations to absorb distortion
dc_fmri_files = []
for sess in xrange(2):
# merge SBRef + task BOLD for current PE direction
assert len(subject_data.func) == 2, subject_data
fourD_plus_sbref = os.path.join(
subject_data.output_dir,
"sbref_plus_" + os.path.basename(subject_data.func[sess]))
fslmerge_cmd = "fsl5.0-fslmerge -t %s %s %s" % (
fourD_plus_sbref, sbref_files[sess], subject_data.func[sess])
print "\r\nExecuting '%s' ..." % fslmerge_cmd
print mem.cache(commands.getoutput)(fslmerge_cmd)
# realign task BOLD to SBRef
sess_output_dir = subject_data.session_output_dirs[sess]
rfourD_plus_sbref = _do_subject_realign(SubjectData(
func=[fourD_plus_sbref],
output_dir=subject_data.output_dir,
n_sessions=1,
session_output_dirs=[sess_output_dir]),
report=False).func[0]
# apply topup to realigned images
dc_rfourD_plus_sbref = os.path.join(
subject_data.output_dir,
"dc" + os.path.basename(rfourD_plus_sbref))
applytopup_cmd = (
"fsl5.0-applytopup --imain=%s --verbose --inindex=%i "
"--topup=%s --out=%s --datain=%s --method=jac" %
(rfourD_plus_sbref, sess + 1, topup_results_basename,
dc_rfourD_plus_sbref, acq_params_file))
print "\r\nExecuting '%s' ..." % applytopup_cmd
print mem.cache(commands.getoutput)(applytopup_cmd)
# recover undistorted task BOLD
dc_rfmri_file = dc_rfourD_plus_sbref.replace("sbref_plus_", "")
fslroi_cmd = "fsl5.0-fslroi %s %s 1 -1" % (dc_rfourD_plus_sbref,
dc_rfmri_file)
print "\r\nExecuting '%s' ..." % fslroi_cmd
print mem.cache(commands.getoutput)(fslroi_cmd)
# sanity tricks
if dc_rfmri_file.endswith(".nii"):
dc_rfmri_file = dc_rfmri_file + ".gz"
dc_fmri_files.append(dc_rfmri_file)
subject_data.func = dc_fmri_files
if isinstance(subject_data.func, basestring):
subject_data.func = [subject_data.func]
# continue preprocessing
subject_data = do_subject_preproc(
subject_data,
realign=realign,
coregister=coregister,
coreg_anat_to_func=not coreg_func_to_anat,
segment=True,
normalize=False,
report=report)
# ok for GLM now
return subject_data
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
"""
:Synopsis: Step-by-step example usage of purepython_preroc_pipeline module
:Author: DOHMATOB Elvis Dopgima <*****@*****.**>
"""
from pypreprocess.datasets import fetch_spm_auditory_data
from pypreprocess.slice_timing import fMRISTC
from pypreprocess.realign import MRIMotionCorrection
from pypreprocess.coreg import Coregister
from pypreprocess.external.joblib import Memory
import os
# create cache
mem = Memory('/tmp/stepwise_cache', verbose=100)
# fetch input data
sd = fetch_spm_auditory_data(
os.path.join(os.environ['HOME'], "CODE/datasets/spm_auditory"))
n_sessions = 1 # this dataset has 1 session (i.e 1 fMRI acquisiton or run)
do_subject_preproc(sd.__dict__(),
concat=False,
coregister=True,
stc=True,
cv_tc=True,
realign=True,
report=True)
"""
:Synopsis: Step-by-step example usage of purepython_preroc_pipeline module
:Author: DOHMATOB Elvis Dopgima <*****@*****.**>
"""
from pypreprocess.datasets import fetch_spm_auditory_data
from pypreprocess.slice_timing import fMRISTC
from pypreprocess.realign import MRIMotionCorrection
from pypreprocess.coreg import Coregister
from pypreprocess.external.joblib import Memory
import os
# create cache
mem = Memory('/tmp/stepwise_cache', verbose=100)
# fetch input data
sd = fetch_spm_auditory_data(os.path.join(
os.environ['HOME'],
"CODE/datasets/spm_auditory"))
n_sessions = 1 # this dataset has 1 session (i.e 1 fMRI acquisiton or run)
# ouput dict
output = {'func': [sd.func], # one fMRI 4D datum per session
'anat': sd.anat, 'n_sessions': n_sessions}
##################################
# Slice-Timing Correction (STC)
##################################
for sess_func, sess_id in zip(output['func'], xrange(n_sessions)):
# session fit
Demo script for the coreg.py module (coregistration in pure python).
It demos coregistration on a variety of datasets including:
SPM single-subject auditory, NYU rest, ABIDE, etc.
"""
import os
import glob
import matplotlib.pyplot as plt
from pypreprocess.datasets import fetch_spm_auditory, fetch_nyu_rest
from pypreprocess.reporting.check_preprocessing import plot_registration
from pypreprocess.coreg import Coregister
from pypreprocess.external.joblib import Memory
# misc
mem = Memory("demos_cache")
def _run_demo(func, anat):
# fit
coreg = Coregister().fit(anat, func)
# apply coreg
VFk = coreg.transform(func)
# QA
plot_registration(anat, VFk, title="before coreg")
plot_registration(VFk, anat, title="after coreg")
plt.show()
def _do_fmri_distortion_correction(subject_data,
# i'm unsure of the readout time,
# but this is constant across both PE
# directions and so can be scaled to 1
# (or any other nonzero float)
protocol="MOTOR",
readout_time=.01392,
realign=True,
coregister=True,
coreg_func_to_anat=True,
dc=True,
segment=False,
normalize=False,
func_write_voxel_sizes=None,
anat_write_voxel_sizes=None,
report=False,
**kwargs
):
"""
Function to undistort task fMRI data for a given HCP subject.
"""
directions = ['LR', 'RL']
subject_data.sanitize()
if dc:
acq_params = [[1, 0, 0, readout_time], [-1, 0, 0, readout_time]]
acq_params_file = os.path.join(subject_data.output_dir,
"b0_acquisition_params.txt")
np.savetxt(acq_params_file, acq_params, fmt='%f')
fieldmap_files = [os.path.join(os.path.dirname(
subject_data.func[sess]),
"%s_3T_SpinEchoFieldMap_%s.nii.gz" % (
subject_data.subject_id, directions[sess]))
for sess in xrange(subject_data.n_sessions)]
sbref_files = [sess_func.replace(".nii", "_SBRef.nii")
for sess_func in subject_data.func]
# prepare for smart caching
mem = Memory(os.path.join(subject_data.output_dir, "cache_dir"))
for x in [fieldmap_files, sbref_files, subject_data.func]:
assert len(x) == 2
for y in x:
assert os.path.isfile(y), y
# fslroi
zeroth_fieldmap_files = []
for fieldmap_file in fieldmap_files:
if not os.path.isfile(fieldmap_file):
print "Can't find fieldmap file %s; skipping subject %s" % (
fieldmap_file, subject_data.subject_id)
return
# peel 0th volume of each fieldmap
zeroth_fieldmap_file = os.path.join(
subject_data.output_dir, "0th_%s" % os.path.basename(
fieldmap_file))
fslroi_cmd = "fsl5.0-fslroi %s %s 0 1" % (
fieldmap_file, zeroth_fieldmap_file)
print "\r\nExecuting '%s' ..." % fslroi_cmd
print mem.cache(commands.getoutput)(fslroi_cmd)
zeroth_fieldmap_files.append(zeroth_fieldmap_file)
# merge the 0th volume of both fieldmaps
merged_zeroth_fieldmap_file = os.path.join(
subject_data.output_dir, "merged_with_other_direction_%s" % (
os.path.basename(zeroth_fieldmap_files[0])))
fslmerge_cmd = "fsl5.0-fslmerge -t %s %s %s" % (
merged_zeroth_fieldmap_file, zeroth_fieldmap_files[0],
zeroth_fieldmap_files[1])
print "\r\nExecuting '%s' ..." % fslmerge_cmd
print mem.cache(commands.getoutput)(fslmerge_cmd)
# do topup (learn distortion model)
topup_results_basename = os.path.join(subject_data.output_dir,
"topup_results")
topup_cmd = (
"fsl5.0-topup --imain=%s --datain=%s --config=b02b0.cnf "
"--out=%s" % (merged_zeroth_fieldmap_file, acq_params_file,
topup_results_basename))
print "\r\nExecuting '%s' ..." % topup_cmd
print mem.cache(commands.getoutput)(topup_cmd)
# apply learn deformations to absorb distortion
dc_fmri_files = []
for sess in xrange(2):
# merge SBRef + task BOLD for current PE direction
assert len(subject_data.func) == 2, subject_data
fourD_plus_sbref = os.path.join(
subject_data.output_dir, "sbref_plus_" + os.path.basename(
subject_data.func[sess]))
fslmerge_cmd = "fsl5.0-fslmerge -t %s %s %s" % (
fourD_plus_sbref, sbref_files[sess], subject_data.func[sess])
print "\r\nExecuting '%s' ..." % fslmerge_cmd
print mem.cache(commands.getoutput)(fslmerge_cmd)
# realign task BOLD to SBRef
sess_output_dir = subject_data.session_output_dirs[sess]
rfourD_plus_sbref = _do_subject_realign(SubjectData(
func=[fourD_plus_sbref],
output_dir=subject_data.output_dir,
n_sessions=1, session_output_dirs=[sess_output_dir]),
report=False).func[0]
# apply topup to realigned images
dc_rfourD_plus_sbref = os.path.join(
subject_data.output_dir, "dc" + os.path.basename(
rfourD_plus_sbref))
applytopup_cmd = (
"fsl5.0-applytopup --imain=%s --verbose --inindex=%i "
"--topup=%s --out=%s --datain=%s --method=jac" % (
rfourD_plus_sbref, sess + 1, topup_results_basename,
dc_rfourD_plus_sbref, acq_params_file))
print "\r\nExecuting '%s' ..." % applytopup_cmd
print mem.cache(commands.getoutput)(applytopup_cmd)
# recover undistorted task BOLD
dc_rfmri_file = dc_rfourD_plus_sbref.replace("sbref_plus_", "")
fslroi_cmd = "fsl5.0-fslroi %s %s 1 -1" % (
dc_rfourD_plus_sbref, dc_rfmri_file)
print "\r\nExecuting '%s' ..." % fslroi_cmd
print mem.cache(commands.getoutput)(fslroi_cmd)
# sanity tricks
if dc_rfmri_file.endswith(".nii"):
dc_rfmri_file = dc_rfmri_file + ".gz"
dc_fmri_files.append(dc_rfmri_file)
subject_data.func = dc_fmri_files
if isinstance(subject_data.func, basestring):
subject_data.func = [subject_data.func]
# continue preprocessing
subject_data = do_subject_preproc(
subject_data,
realign=realign,
coregister=coregister,
coreg_anat_to_func=not coreg_func_to_anat,
segment=True,
normalize=False,
report=report)
# ok for GLM now
return subject_data
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