else:
dataset_dir = os.path.join(this_dir, "spm_multimodal_faces")
# fetch spm multimodal_faces data
subject_data = fetch_spm_multimodal_fmri()
dataset_dir = os.path.dirname(os.path.dirname(os.path.dirname(
subject_data.anat)))
# preprocess the data
subject_id = "sub001"
subject_data = SubjectData(
output_dir=os.path.join(dataset_dir, "pypreprocess_output", subject_id),
subject_id=subject_id, func=[subject_data.func1, subject_data.func2],
anat=subject_data.anat, trials_ses1=subject_data.trials_ses1,
trials_ses2=subject_data.trials_ses2, session_ids=["Session1", "Session2"])
subject_data = do_subject_preproc(subject_data, realign=True, coregister=True,
segment=True, normalize=True)
# experimental paradigm meta-params
stats_start_time = time.ctime()
tr = 2.
drift_model = 'Cosine'
hrf_model = 'spm + derivative'
hfcut = 128.
# make design matrices
first_level_effects_maps = []
mask_images = []
design_matrices = []
for x in xrange(2):
if not os.path.exists(subject_data.output_dir):
os.makedirs(subject_data.output_dir)
if not os.path.exists(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR)
# fetch the data
sd = fetch_spm_multimodal_fmri_data(DATA_DIR)
subject_id = "sub001"
subject_data = SubjectData(subject_id=subject_id,
session_id=["Session1", "Session2"],
func=[sd.func1, sd.func2],
anat=sd.anat,
trials_ses1=sd.trials_ses1,
trials_ses2=sd.trials_ses2,
output_dir=os.path.join(OUTPUT_DIR, subject_id)
)
# preprocess the data
subject_data = do_subject_preproc(subject_data, do_normalize=False, fwhm=[8.])
# collect preprocessed data
anat_img = nibabel.load(subject_data.anat)
# experimental paradigm meta-params
stats_start_time = time.ctime()
tr = 2.
drift_model = 'Cosine'
hrf_model = 'Canonical With Derivative'
hfcut = 128.
# make design matrices
first_level_effects_maps = []
mask_images = []
for x in xrange(2):
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 _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 do_preproc(funcfile, anatfile, subject, decimate=False):
""" Function that performs the preprocessing.
Parameters
----------
funcfile: str
the functional volume.
anatfile: str
the anatomical volume.
subject: str
the subject identifier.
decimate: bool, default False
if set reduce the input functional volume size (loose information).
Returns
-------
subject_data: object
a structure that contains the output results.
"""
# Grab the data
splitpath = anatfile.split(os.sep)
outdir = os.path.join(BIDS_DATA_DIR, "derivatives",
"spmpreproc_{0}".format(splitpath[-3]), subject)
if not os.path.isdir(outdir):
os.makedirs(outdir)
local_funcfile = os.path.join(outdir, os.path.basename(funcfile))
if not os.path.isfile(local_funcfile):
shutil.copy2(funcfile, local_funcfile)
if decimate:
im = nibabel.load(local_funcfile)
dec_im = nibabel.Nifti1Image(im.get_data()[..., :3], im.affine)
nibabel.save(dec_im, local_funcfile)
local_anatfile = os.path.join(outdir, os.path.basename(anatfile))
if not os.path.isfile(local_anatfile):
shutil.copy2(anatfile, local_anatfile)
#cwd = os.getcwd()
os.chdir(outdir)
subject_data = SubjectData(subject_id=subject,
func=local_funcfile,
anat=local_anatfile,
output_dir=outdir,
caching=True)
# Start processing
print('Starting pre-processing...')
subject_data = do_subject_preproc(subject_data,
deleteorient=False,
slice_timing=False,
ref_slice=0,
TR=2.5,
TA=None,
realign=True,
realign_reslice=True,
register_to_mean=True,
realign_software="spm",
coregister=True,
coregister_reslice=True,
coreg_anat_to_func=False,
coregister_software="spm",
segment=False,
normalize=True,
fwhm=0,
anat_fwhm=0,
func_write_voxel_sizes=[3, 3, 3],
anat_write_voxel_sizes=[1, 1, 1],
hardlink_output=True,
report=False,
tsdiffana=True,
parent_results_gallery=None,
last_stage=True,
preproc_undergone=None,
prepreproc_undergone="",
caching=True)
return subject_data
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
os.path.dirname(os.path.dirname(subject_data.anat)))
# preprocess the data
subject_id = "sub001"
subject_data = SubjectData(output_dir=os.path.join(dataset_dir,
"pypreprocess_output",
subject_id),
subject_id=subject_id,
func=[subject_data.func1, subject_data.func2],
anat=subject_data.anat,
trials_ses1=subject_data.trials_ses1,
trials_ses2=subject_data.trials_ses2,
session_ids=["Session1", "Session2"])
subject_data = do_subject_preproc(subject_data,
realign=True,
coregister=True,
segment=True,
normalize=True)
# experimental paradigm meta-params
stats_start_time = time.ctime()
tr = 2.
drift_model = 'Cosine'
hrf_model = 'spm + derivative'
hfcut = 1. / 128
# make design matrices
first_level_effects_maps = []
mask_images = []
design_matrices = []
for x in range(2):
do_subject_preproc(
subject_data, #subject data class
deleteorient=False, #
slice_timing=True,
slice_order="ascending",
interleaved=True,
refslice=1,
TR=2.4,
TA=2.3,
slice_timing_software="spm",
realign=True,
realign_reslice=False,
register_to_mean=True,
realign_software="spm",
coregister=True,
coregister_reslice=False,
coreg_anat_to_func=True,
coregister_software="spm",
segment=False,
normalize=True,
dartel=False,
fwhm=3.,
anat_fwhm=0.,
func_write_voxel_sizes=3.,
anat_write_voxel_sizes=1.,
hardlink_output=True,
report=True,
cv_tc=True,
parent_results_gallery=None,
last_stage=True,
preproc_undergone=None,
prepreproc_undergone="",
generate_preproc_undergone=True,
caching=True,
)
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
def run_suject_level1_glm(subject_data_dir, subject_output_dir, task_id,
readout_time=.01392, # seconds
tr=.72,
do_preproc=False,
do_realign=False,
do_normalize=False,
fwhm=0.,
report=False,
hrf_model="Canonical with Derivative",
drift_model="Cosine",
hfcut=100,
regress_motion=True,
slicer='y',
cut_coords=6,
threshold=3.,
cluster_th=15
):
"""
Function to do preproc + analysis for a single HCP subject (task fMRI)
"""
# sanitize subject data_dir
subject_id = int(os.path.basename(subject_data_dir))
subject_data_dir = os.path.abspath(subject_data_dir)
_subject_data_dir = os.path.join(subject_data_dir,
"MNINonLinear/Results/")
add_regs_files = None
if do_preproc:
if not os.path.exists(subject_output_dir):
os.makedirs(subject_output_dir)
# glob fmri files
fmri_files = [os.path.join(
subject_data_dir,
"unprocessed/3T/tfMRI_%s_%s/%s_3T_tfMRI_%s_%s.nii.gz" % (
task_id, direction, subject_id,
task_id, direction))
for direction in ["LR", "RL"]]
assert len(fmri_files) == 2
# glob anat file
anat_file = os.path.join(subject_data_dir,
"T1w/T1w_acpc_dc_restore_brain.nii.gz")
# assert os.path.isfile(anat_file)
if not os.path.isfile(anat_file):
anat_file = None
# distortion correction ?
dc_output = _do_fmri_distortion_correction(
fmri_files, subject_data_dir,
subject_output_dir,
subject_id, task_id,
readout_time=readout_time,
report=report
)
if dc_output is None:
return
else:
fmri_files, realignment_parameters = dc_output
# preprocess the data
preproc_subject_data = do_subject_preproc(SubjectData(
func=fmri_files, anat=anat_file,
output_dir=subject_output_dir),
do_realign=True,
do_normalize=do_normalize,
fwhm=fwhm,
report=report
)
fmri_files = preproc_subject_data.func
n_motion_regressions = 6
if do_realign and regress_motion:
add_regs_files = realignment_parameters
else:
n_motion_regressions = 12
# glob fmri files
fmri_files = []
for direction in ['LR', 'RL']:
fmri_file = os.path.join(
_subject_data_dir, "tfMRI_%s_%s/tfMRI_%s_%s.nii.gz" % (
task_id, direction, task_id, direction))
if not os.path.isfile(fmri_file):
print "Can't find task fMRI file %s; skipping subject %s" % (
fmri_file, subject_id)
return
else:
fmri_files.append(fmri_file)
# glob movement confounds
if regress_motion:
add_regs_files = [os.path.join(_subject_data_dir,
"tfMRI_%s_%s" % (
task_id, direction),
"Movement_Regressors.txt")
for direction in ["LR", "RL"]]
# smooth images
if np.sum(fwhm) > 0:
print "Smoothing fMRI data (fwhm = %s)..." % fwhm
fmri_files = _do_subject_smooth(SubjectData(
func=fmri_files, output_dir=subject_output_dir),
fwhm=fwhm,
report=False
).func
print "... done.\r\n"
# sanitize subject_output_dir
if not os.path.exists(subject_output_dir):
os.makedirs(subject_output_dir)
# 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)):
# glob the design file
design_file = os.path.join(_subject_data_dir, "tfMRI_%s_%s" % (
task_id, direction),
"tfMRI_%s_%s_hp200_s4_level1.fsf" % (
task_id, direction))
if not os.path.isfile(design_file):
print "Can't find design file %s; skipping subject %s" % (
design_file, 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_design_fsl_design_file(design_file)
print "... done.\r\n"
# fix timing filenames
timing_files = _insert_directory_in_file_name(
timing_files, "tfMRI_%s_%s" % (task_id, direction), 1)
# make design matrix
print "Constructing design matrix for direction %s ..." % direction
_n_scans = nibabel.load(fmri_files[direction_index]).shape[-1]
n_scans.append(_n_scans)
add_regs_file = add_regs_files[
direction_index] 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 task_id == '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']
# importat maps
z_maps = {}
effects_maps = {}
# replicate contrasts across sessions
contrasts = dict((cid, [cval] * 2)
for cid, cval in contrasts.iteritems())
# compute effects
mask_path = os.path.join(subject_output_dir, "mask.nii.gz")
skip = os.path.isfile(mask_path)
if skip:
for contrast_id, contrast_val in contrasts.iteritems():
for map_type in ['z', 'effects']:
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)
if not os.path.exists(map_path):
skip = 0
break
# 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
if skip:
print "Skipping subject %s..." % (
subject_id)
# fit GLM
if not skip:
print (
'Fitting a "Fixed Effect" GLM for merging LR and RL phase-encoding '
'directions for subject %s ...' % subject_id)
fmri_glm = FMRILinearModel(fmri_files,
[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_output_dir, "mask.nii.gz")
print "Saving mask image to %s ..." % mask_path
nibabel.save(fmri_glm.mask, mask_path)
print "... done.\r\n"
# 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_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_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
if 0x0:
anat_img = load_specific_vol(fmri_files[0], 0)[0]
stats_report_filename = os.path.join(subject_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_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,
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_output_dir)
print "\r\nStatistic report written to %s\r\n" % stats_report_filename
return contrasts, effects_maps, z_maps, mask_path
