def __init__(self, in_file='path', **options):
from nipype.interfaces.nipy.preprocess import Trim
trim = Trim()
trim.inputs.in_file = in_file
for ef in options:
setattr(trim.inputs, ef, options[ef])
self.res = trim.run()
def check_mask_coverage(epi,brainmask):
from os.path import abspath
from nipype import config, logging
config.enable_debug_mode()
logging.update_logging(config)
from nilearn import plotting
from nipype.interfaces.nipy.preprocess import Trim
trim = Trim()
trim.inputs.in_file = epi
trim.inputs.end_index = 1
trim.inputs.out_file = 'epi_vol1.nii.gz'
trim.run()
epi_vol = abspath('epi_vol1.nii.gz')
maskcheck_filename='maskcheck.png'
display = plotting.plot_anat(epi_vol, display_mode='ortho',
draw_cross=False,
title = 'brainmask coverage')
display.add_contours(brainmask,levels=[.5], colors='r')
display.savefig(maskcheck_filename)
display.close()
maskcheck_file = abspath(maskcheck_filename)
return(maskcheck_file)
def test_Trim_outputs():
output_map = dict(out_file=dict(), )
outputs = Trim.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def __repr__(self):
return "<Subject Dir: sub{:0>3d}>".format(self._subject_code)
def remove_volumes_from_bold(self, model_num, bold_suffix, task_to_trim, num_of_volumes):
from nipype.interfaces.nipy.preprocess import Trim
for run_dir in self.dir_tree('functional')[task_to_trim]:
file = glob(run_dir + "/bold{}.nii.gz".format(bold_suffix))[0]
out_file = file.replace('.nii.gz', '_{}trim.nii.gz'.format(num_of_volumes))
if os.path.isfile(out_file):
print "removing volumes already ran!"
return
trim = Trim()
trim.inputs.in_file = file
trim.inputs.begin_index = num_of_volumes # remove X first volumes
trim.inputs.out_file = out_file
res = trim.run()
def test_Trim_outputs():
output_map = dict(out_file=dict(),
)
outputs = Trim.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def trim(begin_idx=6):
"""Simple interface to trim a few volumes from a 4d fmri nifti file.
:param begin_idx: int
Remove first `begin_idx` volumes.
Notes
-----
http://www.mit.edu/~satra/nipype-nightly/interfaces/generated/nipype.interfaces.nipy.preprocess.html#trim
"""
trim = Trim()
#trim.inputs.in_file = 'functional.nii'
trim.inputs.begin_index = begin_idx
return trim
def test_Trim_inputs():
input_map = dict(
begin_index=dict(usedefault=True, ),
end_index=dict(usedefault=True, ),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
in_file=dict(mandatory=True, ),
out_file=dict(),
suffix=dict(usedefault=True, ),
)
inputs = Trim.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_Trim_inputs():
input_map = dict(begin_index=dict(usedefault=True,
),
end_index=dict(usedefault=True,
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
in_file=dict(mandatory=True,
),
out_file=dict(),
suffix=dict(usedefault=True,
),
)
inputs = Trim.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def prepro_func(i):
try:
subj = i
for s in (['session2']):
# Define input files: 2xfMRI + 1xMPRAGE
func1 = data_path + subj + '/Functional_scans/' + s[:-2] + s[
-1] + '_a/epi.nii.gz' #choose this for patients
func2 = data_path + subj + '/Functional_scans/' + s[:-2] + s[
-1] + '_b/epi.nii.gz' #choose this for patients
#anat = glob.glob(anat_path + subj +'/'+ s + '/anat/reorient/anat_*.nii.gz') #choose this for session 1
lesion_mask_file = anat_path + subj + '/session1/anat/reorient/lesion_seg.nii.gz'
old_lesion_mask_file = glob.glob(
anat_path + subj +
'/session1/anat/reorient/old_lesion_seg.nii.gz'
) #choose this for ones with no old lesion
#old_lesion_mask_file = anat_path + subj +'/session1/anat/reorient/old_lesion_seg.nii.gz' #choose this for ones with old lesion
anat = glob.glob(anat_path + subj + '/' + s +
'/anat/anat2hr/anat_*.nii.gz'
) #choose this for sessions 2 and 3
anat_CSF = glob.glob(
anat_path + subj +
'/session1/seg_anat/segmentation/anat_*_pve_0.nii.gz'
) # don't change, same for all sessions
anat_WM = glob.glob(
anat_path + subj +
'/session1/seg_anat/segmentation/anat_*_pve_2.nii.gz'
) # don't change, same for all sessions
anat_GM = glob.glob(
anat_path + subj +
'/session1/seg_anat/segmentation/anat_*_pve_1.nii.gz'
) # don't change, same for all sessions
anat2MNI_fieldwarp = glob.glob(
anat_path + subj +
'/session1/anat/nonlinear_reg/anat_*_fieldwarp.nii.gz'
) # don't change, same for all sessions
if not os.path.isdir(data_path + subj + '/' + s): # No data exists
continue
if not os.path.isfile(func1):
print '1. functional file ' + func1 + ' not found. Skipping!'
continue
if not os.path.isfile(func2):
print '2. functional file ' + func2 + ' not found. Skipping!'
continue
if not anat:
print 'Preprocessed anatomical file not found. Skipping!'
continue
if len(anat) > 1:
print 'WARNING: found multiple files of preprocessed anatomical image!'
continue
anat = anat[0]
if not anat2MNI_fieldwarp:
print 'Anatomical registration to MNI152-space field file not found. Skipping!'
continue
if len(anat2MNI_fieldwarp) > 1:
print 'WARNING: found multiple files of anat2MNI fieldwarp!'
continue
anat2MNI_fieldwarp = anat2MNI_fieldwarp[0]
if not anat_CSF:
anat_CSF = glob.glob(
anat_path + subj + '/' + s +
'/seg_anat/segmentation/anat_*_pve_0.nii.gz')
if not anat_CSF:
print 'Anatomical segmentation CSF file not found. Skipping!'
continue
if len(anat_CSF) > 1:
print 'WARNING: found multiple files of anatomical CSF file!'
continue
anat_CSF = anat_CSF[0]
if not anat_WM:
anat_WM = glob.glob(
anat_path + subj + '/' + s +
'/seg_anat/segmentation/anat_*_pve_2.nii.gz')
if not anat_WM:
print 'Anatomical segmentation WM file not found. Skipping!'
continue
if len(anat_WM) > 1:
print 'WARNING: found multiple files of anatomical WM file!'
continue
anat_WM = anat_WM[0]
if not anat_GM:
anat_GM = glob.glob(
anat_path + subj + '/' + s +
'/seg_anat/segmentation/anat_*_pve_1.nii.gz')
if not anat_GM:
print 'Anatomical segmentation GM file not found. Skipping!'
continue
if len(anat_GM) > 1:
print 'WARNING: found multiple files of anatomical GM file!'
continue
anat_GM = anat_GM[0]
if not os.path.isdir(results_path + subj):
os.mkdir(results_path + subj)
if not os.path.isdir(results_path + subj + '/' + s):
os.mkdir(results_path + subj + '/' + s)
for data in acquisitions:
os.chdir(results_path + subj + '/' + s)
print "Currently processing subject: " + subj + '/' + s + ' ' + data
#Initialize workflows
workflow = pe.Workflow(name=data)
workflow.base_dir = '.'
inputnode = pe.Node(
interface=util.IdentityInterface(fields=['source_file']),
name='inputspec')
outputnode = pe.Node(
interface=util.IdentityInterface(fields=['result_func']),
name='outputspec')
if data == 'func1':
inputnode.inputs.source_file = func1
else:
inputnode.inputs.source_file = func2
# Remove n_dummies first volumes
trim = pe.Node(interface=Trim(begin_index=n_dummies),
name='trim')
workflow.connect(inputnode, 'source_file', trim, 'in_file')
# Motion correction + slice timing correction
realign4d = pe.Node(interface=SpaceTimeRealigner(),
name='realign4d')
#realign4d.inputs.ignore_exception=True
realign4d.inputs.slice_times = 'asc_alt_siemens'
realign4d.inputs.slice_info = 2 # horizontal slices
realign4d.inputs.tr = mytr # TR in seconds
workflow.connect(trim, 'out_file', realign4d, 'in_file')
# Reorient
#deoblique = pe.Node(interface=afni.Warp(deoblique=True, outputtype='NIFTI_GZ'), name='deoblique') #leave out if you don't need this
#workflow.connect(realign4d, 'out_file', deoblique, 'in_file')
reorient = pe.Node(
interface=fsl.Reorient2Std(output_type='NIFTI_GZ'),
name='reorient')
workflow.connect(realign4d, 'out_file', reorient, 'in_file')
# AFNI skullstrip and mean image skullstrip
tstat1 = pe.Node(interface=afni.TStat(args='-mean',
outputtype="NIFTI_GZ"),
name='tstat1')
automask = pe.Node(interface=afni.Automask(
dilate=1, outputtype="NIFTI_GZ"),
name='automask')
skullstrip = pe.Node(interface=afni.Calc(
expr='a*b', outputtype="NIFTI_GZ"),
name='skullstrip')
tstat2 = pe.Node(interface=afni.TStat(args='-mean',
outputtype="NIFTI_GZ"),
name='tstat2')
workflow.connect(reorient, 'out_file', tstat1, 'in_file')
workflow.connect(tstat1, 'out_file', automask, 'in_file')
workflow.connect(automask, 'out_file', skullstrip, 'in_file_b')
workflow.connect(reorient, 'out_file', skullstrip, 'in_file_a')
workflow.connect(skullstrip, 'out_file', tstat2, 'in_file')
# Register to anatomical space #can be changed
#mean2anat = pe.Node(fsl.FLIRT(bins=40, cost='normmi', dof=7, interp='nearestneighbour', searchr_x=[-180,180], searchr_y=[-180,180], searchr_z=[-180,180]), name='mean2anat')
mean2anat = pe.Node(fsl.FLIRT(bins=40,
cost='normmi',
dof=7,
interp='nearestneighbour'),
name='mean2anat')
#mean2anat = pe.Node(fsl.FLIRT(no_search=True), name='mean2anat')
mean2anat.inputs.reference = anat
workflow.connect(tstat2, 'out_file', mean2anat, 'in_file')
# Transform mean functional image
warpmean = pe.Node(interface=fsl.ApplyWarp(), name='warpmean')
warpmean.inputs.ref_file = MNI_brain
warpmean.inputs.field_file = anat2MNI_fieldwarp
workflow.connect(mean2anat, 'out_matrix_file', warpmean,
'premat')
workflow.connect(tstat2, 'out_file', warpmean, 'in_file')
# ----- inversion matrix and eroded brain mask for regression -----
# create inverse matrix from mean2anat registration
invmat = pe.Node(fsl.ConvertXFM(), name='invmat')
invmat.inputs.invert_xfm = True
workflow.connect(mean2anat, 'out_matrix_file', invmat,
'in_file')
# erode functional brain mask
erode_brain = pe.Node(fsl.ImageMaths(), name='erode_brain')
erode_brain.inputs.args = '-kernel boxv 3 -ero'
workflow.connect(automask, 'out_file', erode_brain, 'in_file')
# register GM mask to functional image space, this is done for quality control
reg_GM = pe.Node(fsl.preprocess.ApplyXFM(), name='register_GM')
reg_GM.inputs.apply_xfm = True
reg_GM.inputs.in_file = anat_GM
workflow.connect(tstat2, 'out_file', reg_GM, 'reference')
workflow.connect(invmat, 'out_file', reg_GM, 'in_matrix_file')
# --------- motion regression and censor signals ------------------
# normalize motion parameters
norm_motion = pe.Node(interface=Function(
input_names=['in_file'],
output_names=['out_file'],
function=normalize_motion_data),
name='normalize_motion')
workflow.connect(realign4d, 'par_file', norm_motion, 'in_file')
# create censor file, for censoring motion
get_censor = pe.Node(afni.OneDToolPy(), name='motion_censors')
get_censor.inputs.set_nruns = 1
get_censor.inputs.censor_motion = (censor_thr, 'motion')
get_censor.inputs.show_censor_count = True
if overwrite: get_censor.inputs.args = '-overwrite'
workflow.connect(norm_motion, 'out_file', get_censor,
'in_file')
# compute motion parameter derivatives (for use in regression)
deriv_motion = pe.Node(afni.OneDToolPy(), name='deriv_motion')
deriv_motion.inputs.set_nruns = 1
deriv_motion.inputs.derivative = True
if overwrite: deriv_motion.inputs.args = '-overwrite'
deriv_motion.inputs.out_file = 'motion_derivatives.txt'
workflow.connect(norm_motion, 'out_file', deriv_motion,
'in_file')
# scale motion parameters and get quadratures
quadr_motion = pe.Node(interface=Function(
input_names=['in_file', 'multicol'],
output_names=['out_file', 'out_quadr_file'],
function=scale_and_quadrature),
name='quadr_motion')
quadr_motion.inputs.multicol = True
workflow.connect(norm_motion, 'out_file', quadr_motion,
'in_file')
# scale motion derivatives and get quadratures
quadr_motion_deriv = pe.Node(interface=Function(
input_names=['in_file', 'multicol'],
output_names=['out_file', 'out_quadr_file'],
function=scale_and_quadrature),
name='quadr_motion_deriv')
quadr_motion_deriv.inputs.multicol = True
workflow.connect(deriv_motion, 'out_file', quadr_motion_deriv,
'in_file')
# -------- CSF regression signals ---------------
# threshold and erode CSF mask
erode_CSF_mask = pe.Node(fsl.ImageMaths(),
name='erode_CSF_mask')
erode_CSF_mask.inputs.args = '-thr 0.5 -kernel boxv 3 -ero'
erode_CSF_mask.inputs.in_file = anat_CSF
# register CSF mask to functional image space
reg_CSF_mask = pe.Node(fsl.preprocess.ApplyXFM(),
name='register_CSF_mask')
reg_CSF_mask.inputs.apply_xfm = True
workflow.connect(tstat2, 'out_file', reg_CSF_mask, 'reference')
workflow.connect(invmat, 'out_file', reg_CSF_mask,
'in_matrix_file')
# inverse lesion mask and remove it from CSF mask #remove this if you don't have a lesion mask
inverse_lesion_mask = pe.Node(fsl.ImageMaths(),
name='inverse_lesion_mask')
inverse_lesion_mask.inputs.args = '-add 1 -rem 2'
inverse_lesion_mask.inputs.in_file = lesion_mask_file
rem_lesion = pe.Node(fsl.ImageMaths(), name='remove_lesion')
workflow.connect(erode_CSF_mask, 'out_file', rem_lesion,
'in_file')
workflow.connect(inverse_lesion_mask, 'out_file', rem_lesion,
'mask_file')
'''
# Transform lesion mask to MNI152 space #remove if lesion masks are already in MNI152 space
warp_lesion = pe.Node(interface=fsl.ApplyWarp(), name='warp_lesion')
warp_lesion.inputs.ref_file = MNI_brain
warp_lesion.inputs.field_file = anat2MNI_fieldwarp
warp_lesion.inputs.in_file = lesion_mask_file
warp_lesion.inputs.out_file = anat_path + subj +'/'+ s + '/anat/nonlinear_reg/lesion_seg_warp.nii.gz'
warp_lesion.run()
'''
# inverse old lesion mask and remove it from CSF mask #remove this if you don't have a lesion mask
if old_lesion_mask_file:
inverse_old_lesion_mask = pe.Node(
fsl.ImageMaths(), name='inverse_old_lesion_mask')
inverse_old_lesion_mask.inputs.args = '-add 1 -rem 3'
#inverse_old_lesion_mask.inputs.in_file = old_lesion_mask_file[0]
inverse_old_lesion_mask.inputs.in_file = old_lesion_mask_file
rem_old_lesion = pe.Node(fsl.ImageMaths(),
name='remove_old_lesion')
workflow.connect(rem_lesion, 'out_file', rem_old_lesion,
'in_file')
workflow.connect(inverse_old_lesion_mask, 'out_file',
rem_old_lesion, 'mask_file')
workflow.connect(rem_old_lesion, 'out_file', reg_CSF_mask,
'in_file')
'''
# Transform old lesion mask to MNI152 space #remove if lesion masks are already in MNI152 space
warp_old_lesion = pe.Node(interface=fsl.ApplyWarp(), name='warp_old_lesion')
warp_old_lesion.inputs.ref_file = MNI_brain
warp_old_lesion.inputs.field_file = anat2MNI_fieldwarp
warp_old_lesion.inputs.in_file = old_lesion_mask_file
warp_old_lesion.inputs.out_file = anat_path + subj +'/'+ s + '/anat/nonlinear_reg/old_lesion_seg_warp.nii.gz'
warp_old_lesion.run()
'''
else:
workflow.connect(rem_lesion, 'out_file', reg_CSF_mask,
'in_file')
# threshold CSF mask and intersect with functional brain mask
thr_CSF_mask = pe.Node(fsl.ImageMaths(),
name='threshold_CSF_mask')
thr_CSF_mask.inputs.args = '-thr 0.25'
workflow.connect(reg_CSF_mask, 'out_file', thr_CSF_mask,
'in_file')
workflow.connect(erode_brain, 'out_file', thr_CSF_mask,
'mask_file')
# extract CSF values
get_CSF_noise = pe.Node(fsl.ImageMeants(),
name='get_CSF_noise')
workflow.connect(skullstrip, 'out_file', get_CSF_noise,
'in_file')
workflow.connect(thr_CSF_mask, 'out_file', get_CSF_noise,
'mask')
# compute CSF noise derivatives
deriv_CSF = pe.Node(afni.OneDToolPy(), name='deriv_CSF')
deriv_CSF.inputs.set_nruns = 1
deriv_CSF.inputs.derivative = True
if overwrite: deriv_CSF.inputs.args = '-overwrite'
deriv_CSF.inputs.out_file = 'CSF_derivatives.txt'
workflow.connect(get_CSF_noise, 'out_file', deriv_CSF,
'in_file')
# scale SCF noise and get quadratures
quadr_CSF = pe.Node(interface=Function(
input_names=['in_file', 'multicol'],
output_names=['out_file', 'out_quadr_file'],
function=scale_and_quadrature),
name='quadr_CSF')
quadr_CSF.inputs.multicol = False
workflow.connect(get_CSF_noise, 'out_file', quadr_CSF,
'in_file')
# scale CSF noise derivatives and get quadratures
quadr_CSF_deriv = pe.Node(interface=Function(
input_names=['in_file', 'multicol'],
output_names=['out_file', 'out_quadr_file'],
function=scale_and_quadrature),
name='quadr_CSF_deriv')
quadr_CSF_deriv.inputs.multicol = False
workflow.connect(deriv_CSF, 'out_file', quadr_CSF_deriv,
'in_file')
# -------- WM regression signals -----------------
# threshold and erode WM mask
erode_WM_mask = pe.Node(fsl.ImageMaths(), name='erode_WM_mask')
erode_WM_mask.inputs.args = '-thr 0.5 -kernel boxv 7 -ero'
erode_WM_mask.inputs.in_file = anat_WM
# registrer WM mask to functional image space
reg_WM_mask = pe.Node(fsl.preprocess.ApplyXFM(),
name='register_WM_mask')
reg_WM_mask.inputs.apply_xfm = True
workflow.connect(tstat2, 'out_file', reg_WM_mask, 'reference')
workflow.connect(invmat, 'out_file', reg_WM_mask,
'in_matrix_file')
workflow.connect(erode_WM_mask, 'out_file', reg_WM_mask,
'in_file')
# create inverse nonlinear registration MNI2anat
invwarp = pe.Node(fsl.InvWarp(output_type='NIFTI_GZ'),
name='invwarp')
invwarp.inputs.warp = anat2MNI_fieldwarp
invwarp.inputs.reference = anat
# transform ventricle mask to functional space
reg_ventricles = pe.Node(fsl.ApplyWarp(),
name='register_ventricle_mask')
reg_ventricles.inputs.in_file = ventricle_mask
workflow.connect(tstat2, 'out_file', reg_ventricles,
'ref_file')
workflow.connect(invwarp, 'inverse_warp', reg_ventricles,
'field_file')
workflow.connect(invmat, 'out_file', reg_ventricles, 'postmat')
# threshold WM mask and intersect with functional brain mask
thr_WM_mask = pe.Node(fsl.ImageMaths(),
name='threshold_WM_mask')
thr_WM_mask.inputs.args = '-thr 0.25'
workflow.connect(reg_WM_mask, 'out_file', thr_WM_mask,
'in_file')
workflow.connect(erode_brain, 'out_file', thr_WM_mask,
'mask_file')
# remove ventricles from WM mask
exclude_ventricles = pe.Node(fsl.ImageMaths(),
name='exclude_ventricles')
workflow.connect(thr_WM_mask, 'out_file', exclude_ventricles,
'in_file')
workflow.connect(reg_ventricles, 'out_file',
exclude_ventricles, 'mask_file')
# check that WM is collected from both hemispheres
check_WM_bilat = pe.Node(interface=Function(
input_names=['in_file'],
output_names=['errors'],
function=check_bilateralism),
name='check_WM_bilateralism')
workflow.connect(exclude_ventricles, 'out_file',
check_WM_bilat, 'in_file')
# extract WM values
get_WM_noise = pe.Node(fsl.ImageMeants(), name='get_WM_noise')
workflow.connect(skullstrip, 'out_file', get_WM_noise,
'in_file')
workflow.connect(exclude_ventricles, 'out_file', get_WM_noise,
'mask')
# compute WM noise derivatives
deriv_WM = pe.Node(afni.OneDToolPy(), name='deriv_WM')
deriv_WM.inputs.set_nruns = 1
deriv_WM.inputs.derivative = True
if overwrite: deriv_WM.inputs.args = '-overwrite'
deriv_WM.inputs.out_file = 'WM_derivatives.txt'
workflow.connect(get_WM_noise, 'out_file', deriv_WM, 'in_file')
# scale WM noise and get quadratures
quadr_WM = pe.Node(interface=Function(
input_names=['in_file', 'multicol'],
output_names=['out_file', 'out_quadr_file'],
function=scale_and_quadrature),
name='quadr_WM')
quadr_WM.inputs.multicol = False
workflow.connect(get_WM_noise, 'out_file', quadr_WM, 'in_file')
# scale WM noise derivatives and get quadratures
quadr_WM_deriv = pe.Node(interface=Function(
input_names=['in_file', 'multicol'],
output_names=['out_file', 'out_quadr_file'],
function=scale_and_quadrature),
name='quadr_WM_deriv')
quadr_WM_deriv.inputs.multicol = False
workflow.connect(deriv_WM, 'out_file', quadr_WM_deriv,
'in_file')
# ---------- global regression signals ----------------
if global_reg:
# register anatomical whole brain mask to functional image space
reg_glob_mask = pe.Node(fsl.preprocess.ApplyXFM(),
name='register_global_mask')
reg_glob_mask.inputs.apply_xfm = True
reg_glob_mask.inputs.in_file = anat
workflow.connect(tstat2, 'out_file', reg_glob_mask,
'reference')
workflow.connect(invmat, 'out_file', reg_glob_mask,
'in_matrix_file')
# threshold anatomical brain mask and intersect with functional brain mask
thr_glob_mask = pe.Node(fsl.ImageMaths(),
name='threshold_global_mask')
thr_glob_mask.inputs.args = '-thr -0.1'
workflow.connect(reg_glob_mask, 'out_file', thr_glob_mask,
'in_file')
workflow.connect(erode_brain, 'out_file', thr_glob_mask,
'mask_file')
# extract global signal values
get_glob_noise = pe.Node(fsl.ImageMeants(),
name='get_global_noise')
workflow.connect(skullstrip, 'out_file', get_glob_noise,
'in_file')
workflow.connect(thr_glob_mask, 'out_file', get_glob_noise,
'mask')
# compute global noise derivative
deriv_glob = pe.Node(afni.OneDToolPy(),
name='deriv_global')
deriv_glob.inputs.set_nruns = 1
deriv_glob.inputs.derivative = True
if overwrite: deriv_glob.inputs.args = '-overwrite'
deriv_glob.inputs.out_file = 'global_derivatives.txt'
workflow.connect(get_glob_noise, 'out_file', deriv_glob,
'in_file')
# scale global noise and get quadratures
quadr_glob = pe.Node(interface=Function(
input_names=['in_file', 'multicol'],
output_names=['out_file', 'out_quadr_file'],
function=scale_and_quadrature),
name='quadr_glob')
quadr_glob.inputs.multicol = False
workflow.connect(get_glob_noise, 'out_file', quadr_glob,
'in_file')
# scale global noise derivatives and get quadratures
quadr_glob_deriv = pe.Node(interface=Function(
input_names=['in_file', 'multicol'],
output_names=['out_file', 'out_quadr_file'],
function=scale_and_quadrature),
name='quadr_glob_deriv')
quadr_glob_deriv.inputs.multicol = False
workflow.connect(deriv_glob, 'out_file', quadr_glob_deriv,
'in_file')
# ---------- regression matrix ----------
# create bandpass regressors, can not be easily implemented to workflow
get_bandpass = pe.Node(interface=Function(
input_names=['minf', 'maxf', 'example_file', 'tr'],
output_names=['out_tuple'],
function=bandpass),
name='bandpass_regressors')
get_bandpass.inputs.minf = myminf
get_bandpass.inputs.maxf = mymaxf
get_bandpass.inputs.tr = mytr
workflow.connect(norm_motion, 'out_file', get_bandpass,
'example_file')
# concatenate regressor time series
cat_reg_name = 'cat_regressors'
if global_reg: cat_reg_name = cat_reg_name + '_global'
cat_reg = pe.Node(interface=Function(
input_names=[
'mot', 'motd', 'motq', 'motdq', 'CSF', 'CSFd', 'CSFq',
'CSFdq', 'WM', 'WMd', 'WMq', 'WMdq', 'include_global',
'glob', 'globd', 'globq', 'globdq'
],
output_names=['reg_file_args'],
function=concatenate_regressors),
name=cat_reg_name)
cat_reg.inputs.include_global = global_reg
workflow.connect(quadr_motion, 'out_file', cat_reg, 'mot')
workflow.connect(quadr_motion_deriv, 'out_file', cat_reg,
'motd')
workflow.connect(quadr_motion, 'out_quadr_file', cat_reg,
'motq')
workflow.connect(quadr_motion_deriv, 'out_quadr_file', cat_reg,
'motdq')
workflow.connect(quadr_CSF, 'out_file', cat_reg, 'CSF')
workflow.connect(quadr_CSF_deriv, 'out_file', cat_reg, 'CSFd')
workflow.connect(quadr_CSF, 'out_quadr_file', cat_reg, 'CSFq')
workflow.connect(quadr_CSF_deriv, 'out_quadr_file', cat_reg,
'CSFdq')
workflow.connect(quadr_WM, 'out_file', cat_reg, 'WM')
workflow.connect(quadr_WM_deriv, 'out_file', cat_reg, 'WMd')
workflow.connect(quadr_WM, 'out_quadr_file', cat_reg, 'WMq')
workflow.connect(quadr_WM_deriv, 'out_quadr_file', cat_reg,
'WMdq')
if global_reg:
workflow.connect(quadr_glob, 'out_file', cat_reg, 'glob')
workflow.connect(quadr_glob_deriv, 'out_file', cat_reg,
'globd')
workflow.connect(quadr_glob, 'out_quadr_file', cat_reg,
'globq')
workflow.connect(quadr_glob_deriv, 'out_quadr_file',
cat_reg, 'globdq')
else:
cat_reg.inputs.glob = None
cat_reg.inputs.globd = None
cat_reg.inputs.globq = None
cat_reg.inputs.globdq = None
# create regression matrix
deconvolve_name = 'deconvolve'
if global_reg: deconvolve_name = deconvolve_name + '_global'
deconvolve = pe.Node(afni.Deconvolve(), name=deconvolve_name)
deconvolve.inputs.polort = 2 # contstant, linear and quadratic background signals removed
deconvolve.inputs.fout = True
deconvolve.inputs.tout = True
deconvolve.inputs.x1D_stop = True
deconvolve.inputs.force_TR = mytr
workflow.connect(cat_reg, 'reg_file_args', deconvolve, 'args')
workflow.connect(get_bandpass, 'out_tuple', deconvolve,
'ortvec')
workflow.connect([(skullstrip, deconvolve,
[(('out_file', str2list), 'in_files')])])
# regress out motion and other unwanted signals
tproject_name = 'tproject'
if global_reg: tproject_name = tproject_name + '_global'
tproject = pe.Node(afni.TProject(outputtype="NIFTI_GZ"),
name=tproject_name)
tproject.inputs.TR = mytr
tproject.inputs.polort = 0 # use matrix created with 3dDeconvolve, higher order polynomials not needed
tproject.inputs.cenmode = 'NTRP' # interpolate removed time points
workflow.connect(get_censor, 'out_file', tproject, 'censor')
workflow.connect(skullstrip, 'out_file', tproject, 'in_file')
workflow.connect(automask, 'out_file', tproject, 'mask')
workflow.connect(deconvolve, 'x1D', tproject, 'ort')
# Transform all images
warpall_name = 'warpall'
if global_reg: warpall_name = warpall_name + '_global'
warpall = pe.Node(interface=fsl.ApplyWarp(), name=warpall_name)
warpall.inputs.ref_file = MNI_brain
warpall.inputs.field_file = anat2MNI_fieldwarp
workflow.connect(mean2anat, 'out_matrix_file', warpall,
'premat')
workflow.connect(tproject, 'out_file', warpall, 'in_file')
workflow.connect(warpall, 'out_file', outputnode,
'result_func')
# Run workflow
workflow.write_graph()
workflow.run()
print "FUNCTIONAL PREPROCESSING DONE! Results in ", results_path + subj + '/' + s
except:
print "Error with patient: ", subj
traceback.print_exc()
outputtype="NIFTI_GZ"),
name='automask')
skullstrip = pe.Node(interface=afni.Calc(expr='a*b',
outputtype="NIFTI_GZ"),
name='skullstrip')
tstat2 = pe.Node(interface=afni.TStat(args='-mean', outputtype="NIFTI_GZ"),
name='tstat2')
workflow2.connect(inputnode2, 'drifter_result', tstat1, 'in_file')
workflow2.connect(tstat1, 'out_file', automask, 'in_file')
workflow2.connect(automask, 'out_file', skullstrip, 'in_file_b')
workflow2.connect(inputnode2, 'drifter_result', skullstrip, 'in_file_a')
workflow2.connect(skullstrip, 'out_file', tstat2, 'in_file')
# Remove n (3) first volumes
trim = pe.Node(interface=Trim(begin_index=3), name='trim')
workflow2.connect(skullstrip, 'out_file', trim, 'in_file')
# Spatial smoothing, kernel sigma 2.00 mm (5 mm is too much)
#smooth = pe.Node(interface=fsl.maths.SpatialFilter(operation='mean', terminal_output='stream', kernel_shape='gauss', kernel_size=1.5, nan2zeros=True), name='smooth')
#workflow2.connect(trim,'out_file', smooth, 'in_file')
# Normalize the median value of each run to 10000
intnorm = pe.Node(interface=fsl.ImageMaths(op_string='-ing 10000',
suffix='_intnorm'),
name='intnorm')
workflow2.connect(trim, 'out_file', intnorm, 'in_file')
# Register to standard space
#mean2anat = pe.Node(fsl.FLIRT(bins=40, cost='normmi', dof=12, interp='nearestneighbour'), name='mean2anat')
def TV_Preproc_Pipeline(base_dir=None,
output_dir=None,
subject_id=None,
spm_path=None):
""" Create a preprocessing workflow for the Couples Conflict Study using nipype
Args:
base_dir: path to data folder where raw subject folder is located
output_dir: path to where key output files should be saved
subject_id: subject_id (str)
spm_path: path to spm folder
Returns:
workflow: a nipype workflow that can be run
"""
import nipype.interfaces.io as nio
import nipype.interfaces.utility as util
from nipype.interfaces.utility import Merge as Merge_List
from nipype.pipeline.engine import Node, Workflow
from nipype.interfaces.fsl.maths import UnaryMaths
from nipype.interfaces.nipy.preprocess import Trim
from nipype.algorithms.rapidart import ArtifactDetect
from nipype.interfaces import spm
from nipype.interfaces.spm import Normalize12
from nipype.algorithms.misc import Gunzip
from nipype.interfaces.nipy.preprocess import ComputeMask
import nipype.interfaces.matlab as mlab
from nltools.utils import get_resource_path, get_vox_dims, get_n_volumes
from nltools.interfaces import Plot_Coregistration_Montage, PlotRealignmentParameters, Create_Covariates, Plot_Quality_Control
import os
import glob
########################################
## Setup Paths and Nodes
########################################
# Specify Paths
canonical_file = os.path.join(spm_path, 'canonical', 'single_subj_T1.nii')
template_file = os.path.join(spm_path, 'tpm', 'TPM.nii')
# Set the way matlab should be called
mlab.MatlabCommand.set_default_matlab_cmd("matlab -nodesktop -nosplash")
mlab.MatlabCommand.set_default_paths(spm_path)
# Get File Names for different types of scans. Parse into separate processing streams
datasource = Node(interface=nio.DataGrabber(infields=['subject_id'],
outfields=['struct', 'func']),
name='datasource')
datasource.inputs.base_directory = base_dir
datasource.inputs.template = '*'
datasource.inputs.field_template = {
'struct': '%s/T1.nii.gz',
'func': '%s/*ep*.nii.gz'
}
datasource.inputs.template_args = {
'struct': [['subject_id']],
'func': [['subject_id']]
}
datasource.inputs.subject_id = subject_id
datasource.inputs.sort_filelist = True
# iterate over functional scans to define paths
func_source = Node(interface=util.IdentityInterface(fields=['scan']),
name="func_source")
func_source.iterables = ('scan',
glob.glob(
os.path.join(base_dir, subject_id,
'*ep*nii.gz')))
########################################
## Preprocessing
########################################
# Trim - remove first 5 TRs
n_vols = 5
trim = Node(interface=Trim(), name='trim')
trim.inputs.begin_index = n_vols
#Realignment - 6 parameters - realign to first image of very first series.
realign = Node(interface=spm.Realign(), name="realign")
realign.inputs.register_to_mean = True
#Coregister - 12 parameters
coregister = Node(interface=spm.Coregister(), name="coregister")
coregister.inputs.jobtype = 'estwrite'
#Plot Realignment
plot_realign = Node(interface=PlotRealignmentParameters(),
name="plot_realign")
#Artifact Detection
art = Node(interface=ArtifactDetect(), name="art")
art.inputs.use_differences = [True, False]
art.inputs.use_norm = True
art.inputs.norm_threshold = 1
art.inputs.zintensity_threshold = 3
art.inputs.mask_type = 'file'
art.inputs.parameter_source = 'SPM'
# Gunzip - unzip the functional and structural images
gunzip_struc = Node(Gunzip(), name="gunzip_struc")
gunzip_func = Node(Gunzip(), name="gunzip_func")
# Normalize - normalizes functional and structural images to the MNI template
normalize = Node(interface=Normalize12(jobtype='estwrite',
tpm=template_file),
name="normalize")
#Plot normalization Check
plot_normalization_check = Node(interface=Plot_Coregistration_Montage(),
name="plot_normalization_check")
plot_normalization_check.inputs.canonical_img = canonical_file
#Create Mask
compute_mask = Node(interface=ComputeMask(), name="compute_mask")
#remove lower 5% of histogram of mean image
compute_mask.inputs.m = .05
#Smooth
#implicit masking (.im) = 0, dtype = 0
smooth = Node(interface=spm.Smooth(), name="smooth")
smooth.inputs.fwhm = 6
#Create Covariate matrix
make_cov = Node(interface=Create_Covariates(), name="make_cov")
#Plot Quality Control Check
quality_control = Node(interface=Plot_Quality_Control(),
name='quality_control')
# Create a datasink to clean up output files
datasink = Node(interface=nio.DataSink(), name='datasink')
datasink.inputs.base_directory = output_dir
datasink.inputs.container = subject_id
########################################
# Create Workflow
########################################
workflow = Workflow(name='Preprocessed')
workflow.base_dir = os.path.join(base_dir, subject_id)
workflow.connect([
(datasource, gunzip_struc, [('struct', 'in_file')]),
(func_source, trim, [('scan', 'in_file')]),
(trim, gunzip_func, [('out_file', 'in_file')]),
(gunzip_func, realign, [('out_file', 'in_files')]),
(realign, quality_control, [('realigned_files', 'dat_img')]),
(gunzip_struc, coregister, [('out_file', 'source')]),
(coregister, normalize, [('coregistered_source', 'image_to_align')]),
(realign, coregister, [('mean_image', 'target'),
('realigned_files', 'apply_to_files')]),
(realign, normalize, [(('mean_image', get_vox_dims),
'write_voxel_sizes')]),
(coregister, normalize, [('coregistered_files', 'apply_to_files')]),
(normalize, smooth, [('normalized_files', 'in_files')]),
(realign, compute_mask, [('mean_image', 'mean_volume')]),
(compute_mask, art, [('brain_mask', 'mask_file')]),
(realign, art, [('realignment_parameters', 'realignment_parameters'),
('realigned_files', 'realigned_files')]),
(realign, plot_realign, [('realignment_parameters',
'realignment_parameters')]),
(normalize, plot_normalization_check, [('normalized_files', 'wra_img')
]),
(realign, make_cov, [('realignment_parameters',
'realignment_parameters')]),
(art, make_cov, [('outlier_files', 'spike_id')]),
(normalize, datasink, [('normalized_files', 'structural.@normalize')]),
(coregister, datasink, [('coregistered_source', 'structural.@struct')
]),
(smooth, datasink, [('smoothed_files', 'functional.@smooth')]),
(plot_realign, datasink, [('plot', 'functional.@plot_realign')]),
(plot_normalization_check, datasink,
[('plot', 'functional.@plot_normalization')]),
(make_cov, datasink, [('covariates', 'functional.@covariates')]),
(quality_control, datasink, [('plot', 'functional.@quality_control')])
])
return workflow
def init_clean_wf(
in_files,
parcellation,
out_dir,
out_path_base,
space,
parcellation_space,
source_file,
work_dir,
save_clean_dtseries=False,
save_clean_ptseries=False,
save_clean_pconn=False,
save_clean_covariance=False,
polort=-1,
passband=None,
stopband=None,
desc_entity='concatenated',
name='clean_wf'
):
"""
Generates a parcellated connectome.
Example:
wf = init_clean_wf(
in_files = {'cifti': ['sub-001_task-rest_run-1_space-fsLR32k_bold.nii.gz',
'sub-001_task-rest_run-2_space-fsLR32k_bold.nii.gz'],
'source_files: ['sub-001_task-rest_run-1_bold.nii.gz',
'sub-001_tesk-rest_run-2_bold.nii.gz'],
'censor': ['sub-001_task-rest_run-1_censor.1D',
'sub-001_task-rest_run-2_censor.1D'],
'ort': ['sub-001_task-rest_run-1_ort.1D',
'sub-001_task-rest_run-2_ort.1D'],
'dt': [0.8, 0.8],
'trim': [2, 3]},
parcellation = 'glasser_parcellation.dlabel.nii',
out_dir = '/path/to/out_dir',
out_path_base = 'some_folder_name',
space = 'fsLR32k',
parcellation_space = 'glasser',
source_file = 'sub-EC1008_task-rest_bold.nii.gz',
work_dir = '/path/to/work_dir',
save_clena_dtseries = False,
save_clean_ptseries = False,
save_clean_pconn = False,
save_clean_covariance = False,
polort=2,
passband=[0.01, 0.1],
stopband=None,
desc_entity='concatenated',
name='really_cool_name'
)
Parameters
----------
in_files : dict
keys - cifti, source_files, censor, ort, dt
cifti is a list of preprocessed cifti files
source_files are used for naming output modified bold files
censor is a list of afni censor 1D files or None
ort is a list of ort 1D files or None
dt is a list of floats for the repetition times
trim removes these number of volumes from the begining before any
preprocessing. Number of volumes after trimming must match
censor and ort volumes. This number is zero-based. Use 0
if you do not want to trim any beginning frames.
parcellation : str
path to cifti parcellation
out_dir : str
the output directory
out_path_base : str
the new directory for the output, to be crteate within out_dir
space : str
the space for the cift files
parcellation_space : str
the space alias for the parcellation, so keep it terse
source_file : str
a filename for output naming purposes
work_dir : str
the working directory for the workflow
save_clean_dtseries : bool
save the clean dtseries for each in cifti
save_clean_ptseries : bool
save the clean ptseries for each in cifti
save_clean_pconn : bool
save the pconn for each in cifti
save_clean_covariance : bool
save the covariance for each in cifti
polort : int
remove polynomials up to and including degree polynomial
passband : list
fbot, ftop
stopband : list
sbot, stop
desc_entity : str
desc entity value in final output file names
name : str
workflow name
Returns
-------
workflow : nipype workflow
Here are the workflow steps:
1. convert cifti to nifti.
2. Remove nuisance regressors with 3dTproject.
3. Convert 'cleaned' nifti back to cifti.
4. Parcellate cifti.
a) Calculate task connectome.
5. Merge all task ciftis.
6. Calculate subject connectome.
"""
cifti = in_files['cifti']
source_files = in_files['source_files']
censor = in_files['censor']
ort = in_files['ort']
dt = in_files['dt']
trim = in_files['trim']
DerivativesDataSink = bids.DerivativesDataSink
DerivativesDataSink.out_path_base = out_path_base
write_verbose = (ort is not None
or polort > 0
or passband is not None
or stopband is not None)
run_tproject = (censor is not None
or write_verbose)
tproject_iterfields = ['in_file', 'TR']
if censor is not None:
tproject_iterfields.append('censor')
if ort is not None:
tproject_iterfields.append('ort')
# start workflow now
clean_wf = Workflow(name=name, base_dir=work_dir)
cifti_to_nifti = MapNode(CiftiConvertToNifti(
out_file='fakenifti.nii.gz'),
name='cifti_to_nifti', iterfield=['in_file'])
cifti_to_nifti.inputs.in_file = cifti
trim_begin = MapNode(Trim(),
name='trim_begin', iterfield=['in_file', 'begin_index'])
trim_begin.inputs.begin_index = trim
tproject = MapNode(TProject(
out_file='clean.nii.gz',
polort=polort,
verb=True), name='tproject', iterfield=tproject_iterfields)
tproject.inputs.TR = dt
if 'censor' in tproject_iterfields:
tproject.inputs.censor = censor
if 'ort' in tproject_iterfields:
tproject.inputs.ort = ort
nifti_to_cifti = MapNode(CiftiConvertFromNifti(
out_file='converted.dtseries.nii'),
name='nifti_to_cifti',
iterfield=['in_file', 'cifti_template', 'reset_timepoints'])
nifti_to_cifti.inputs.cifti_template = cifti
nifti_to_cifti.inputs.reset_timepoints = [(x, 0) for x in dt]
cifti_parcellate = MapNode(CiftiParcellate(
cifti_label=parcellation,
direction='COLUMN',
out_file='parcellated.ptseries.nii'),
name='cifti_parcellate', iterfield=['in_file'])
task_rvals = MapNode(CiftiCorrelation(
out_file='task_rvals.pconn.nii'),
name='task_rvals', iterfield=['in_file'])
task_zvals = MapNode(CiftiCorrelation(
out_file='task_zvals.pconn.nii',
fisher_z=True),
name='task_zvals', iterfield=['in_file'])
task_cov = MapNode(CiftiCorrelation(
out_file='task_cov.pconn.nii',
covariance=True),
name='task_cov', iterfield=['in_file'])
task_merge = Node(CiftiMerge(
out_file='merge.ptseries.nii'),
name='task_merge')
merge_rvals = Node(CiftiCorrelation(
out_file='merge_rvals.pconn.nii'),
name='merge_rvals')
merge_zvals = Node(CiftiCorrelation(
out_file='merge_zvals.pconn.nii',
fisher_z=True),
name='merge_zvals')
merge_cov = Node(CiftiCorrelation(
out_file='merge_cov.pconn.nii',
covariance=True),
name='merge_cov')
# derivatives
ds_clean_dtseries = MapNode(DerivativesDataSink(
base_directory=out_dir,
desc='clean',
space=space,
suffix='bold.dtseries'),
iterfield=['in_file', 'source_file'],
name='ds_clean_dtseries',
run_without_submitting=True)
ds_clean_dtseries.inputs.source_file = source_files
ds_task_ptseries = MapNode(DerivativesDataSink(
base_directory=out_dir,
desc='clean',
space=parcellation_space,
suffix='bold.ptseries'),
iterfield=['in_file', 'source_file'],
name='ds_task_ptseries',
run_without_submitting=True)
ds_task_ptseries.inputs.source_file = source_files
ds_task_zvals = MapNode(DerivativesDataSink(
base_directory=out_dir,
space=parcellation_space,
suffix='zvals.pconn'),
iterfield=['in_file', 'source_file'],
name='ds_task_zvals',
run_without_submitting=True)
ds_task_zvals.inputs.source_file = source_files
ds_task_rvals = MapNode(DerivativesDataSink(
base_directory=out_dir,
space=parcellation_space,
suffix='rvals.pconn'),
iterfield=['in_file', 'source_file'],
name='ds_task_rvals',
run_without_submitting=True)
ds_task_rvals.inputs.source_file = source_files
ds_task_cov = MapNode(DerivativesDataSink(
base_directory=out_dir,
space=parcellation_space,
suffix='cov.pconn'),
iterfield=['in_file', 'source_file'],
name='ds_task_cov',
run_without_submitting=True)
ds_task_cov.inputs.source_file = source_files
ds_subject_zvals = Node(DerivativesDataSink(
base_directory=out_dir,
desc=desc_entity,
space=parcellation_space,
suffix='zvals.pconn',
source_file=source_file),
name='ds_subject_zvals', run_without_submitting=True)
ds_subject_rvals = Node(DerivativesDataSink(
base_directory=out_dir,
desc=desc_entity,
space=parcellation_space,
suffix='rvals.pconn',
source_file=source_file),
name='ds_subject_rvals', run_without_submitting=True)
ds_subject_cov = Node(DerivativesDataSink(
base_directory=out_dir,
desc=desc_entity,
space=parcellation_space,
suffix='cov.pconn',
source_file=source_file),
name='ds_subject_cov', run_without_submitting=True)
ds_ort = MapNode(DerivativesDataSink(
base_directory=out_dir,
desc='confounds',
suffix='tproject'),
iterfield=['in_file', 'source_file'],
name='ds_ort',
run_without_submittting=True)
ds_ort.inputs.source_file = source_files
ds_sval = MapNode(DerivativesDataSink(
base_directory=out_dir,
desc='sval',
suffix='tproject'),
iterfield=['in_file', 'source_file'],
name='ds_sval',
run_without_submitting=True)
ds_sval.inputs.source_file = source_files
ds_psinv = MapNode(DerivativesDataSink(
base_directory=out_dir,
desc='psinv',
suffix='tproject'),
iterfield=['in_file', 'source_file'],
name='ds_psinv',
run_without_submitting=True)
ds_psinv.inputs.source_file = source_files
if run_tproject:
clean_wf.connect([
(cifti_to_nifti, trim_begin, [('out_file', 'in_file')]),
(trim_begin, tproject, [('out_file', 'in_file')]),
(tproject, nifti_to_cifti, [('out_file', 'in_file')]),
(nifti_to_cifti, cifti_parcellate, [('out_file', 'in_file')]),
])
if write_verbose:
clean_wf.connect([
(tproject, ds_ort, [('matrix', 'in_file')]),
(tproject, ds_sval, [('singular_values', 'in_file')]),
(tproject, ds_psinv, [('pseudo_inv', 'in_file')]),
])
else:
clean_wf.connect([
(cifti_to_nifti, trim_begin, [('out_file', 'in_file')]),
(trim_begin, nifti_to_cifti, [('out_file', 'in_file')]),
(nifti_to_cifti, cifti_parcellate, [('out_file', 'in_file')]),
])
clean_wf.connect([
(cifti_parcellate, task_merge, [('out_file', 'in_files')]),
(task_merge, merge_rvals, [('out_file', 'in_file')]),
(task_merge, merge_zvals, [('out_file', 'in_file')]),
(task_merge, merge_cov, [('out_file', 'in_file')]),
# derivatives
(merge_rvals, ds_subject_rvals, [('out_file', 'in_file')]),
(merge_zvals, ds_subject_zvals, [('out_file', 'in_file')]),
(merge_cov, ds_subject_cov, [('out_file', 'in_file')]),
])
if save_clean_dtseries:
clean_wf.connect([
(nifti_to_cifti, ds_clean_dtseries, [('out_file', 'in_file')]),
])
if save_clean_ptseries:
clean_wf.connect([
(cifti_parcellate, ds_task_ptseries, [('out_file', 'in_file')]),
])
if save_clean_pconn:
clean_wf.connect([
(cifti_parcellate, task_rvals, [('out_file', 'in_file')]),
(cifti_parcellate, task_zvals, [('out_file', 'in_file')]),
# derivatives
(task_rvals, ds_task_rvals, [('out_file', 'in_file')]),
(task_zvals, ds_task_zvals, [('out_file', 'in_file')]),
])
if save_clean_covariance:
clean_wf.connect([
(cifti_parcellate, task_cov, [('out_file', 'in_file')]),
# derivatives
(task_cov, ds_task_cov, [('out_file', 'in_file')]),
])
return clean_wf
def Couple_Preproc_Pipeline(base_dir=None,
output_dir=None,
subject_id=None,
spm_path=None):
""" Create a preprocessing workflow for the Couples Conflict Study using nipype
Args:
base_dir: path to data folder where raw subject folder is located
output_dir: path to where key output files should be saved
subject_id: subject_id (str)
spm_path: path to spm folder
Returns:
workflow: a nipype workflow that can be run
"""
from nipype.interfaces.dcm2nii import Dcm2nii
from nipype.interfaces.fsl import Merge, TOPUP, ApplyTOPUP
import nipype.interfaces.io as nio
import nipype.interfaces.utility as util
from nipype.interfaces.utility import Merge as Merge_List
from nipype.pipeline.engine import Node, Workflow
from nipype.interfaces.fsl.maths import UnaryMaths
from nipype.interfaces.nipy.preprocess import Trim
from nipype.algorithms.rapidart import ArtifactDetect
from nipype.interfaces import spm
from nipype.interfaces.spm import Normalize12
from nipype.algorithms.misc import Gunzip
from nipype.interfaces.nipy.preprocess import ComputeMask
import nipype.interfaces.matlab as mlab
from nltools.utils import get_resource_path, get_vox_dims, get_n_volumes
from nltools.interfaces import Plot_Coregistration_Montage, PlotRealignmentParameters, Create_Covariates
import os
import glob
########################################
## Setup Paths and Nodes
########################################
# Specify Paths
canonical_file = os.path.join(spm_path, 'canonical', 'single_subj_T1.nii')
template_file = os.path.join(spm_path, 'tpm', 'TPM.nii')
# Set the way matlab should be called
mlab.MatlabCommand.set_default_matlab_cmd("matlab -nodesktop -nosplash")
mlab.MatlabCommand.set_default_paths(spm_path)
# Get File Names for different types of scans. Parse into separate processing streams
datasource = Node(interface=nio.DataGrabber(
infields=['subject_id'], outfields=['struct', 'ap', 'pa']),
name='datasource')
datasource.inputs.base_directory = base_dir
datasource.inputs.template = '*'
datasource.inputs.field_template = {
'struct': '%s/Study*/t1w_32ch_mpr_08mm*',
'ap': '%s/Study*/distortion_corr_32ch_ap*',
'pa': '%s/Study*/distortion_corr_32ch_pa*'
}
datasource.inputs.template_args = {
'struct': [['subject_id']],
'ap': [['subject_id']],
'pa': [['subject_id']]
}
datasource.inputs.subject_id = subject_id
datasource.inputs.sort_filelist = True
# iterate over functional scans to define paths
scan_file_list = glob.glob(
os.path.join(base_dir, subject_id, 'Study*', '*'))
func_list = [s for s in scan_file_list if "romcon_ap_32ch_mb8" in s]
func_list = [s for s in func_list
if "SBRef" not in s] # Exclude sbref for now.
func_source = Node(interface=util.IdentityInterface(fields=['scan']),
name="func_source")
func_source.iterables = ('scan', func_list)
# Create Separate Converter Nodes for each different type of file. (dist corr scans need to be done before functional)
ap_dcm2nii = Node(interface=Dcm2nii(), name='ap_dcm2nii')
ap_dcm2nii.inputs.gzip_output = True
ap_dcm2nii.inputs.output_dir = '.'
ap_dcm2nii.inputs.date_in_filename = False
pa_dcm2nii = Node(interface=Dcm2nii(), name='pa_dcm2nii')
pa_dcm2nii.inputs.gzip_output = True
pa_dcm2nii.inputs.output_dir = '.'
pa_dcm2nii.inputs.date_in_filename = False
f_dcm2nii = Node(interface=Dcm2nii(), name='f_dcm2nii')
f_dcm2nii.inputs.gzip_output = True
f_dcm2nii.inputs.output_dir = '.'
f_dcm2nii.inputs.date_in_filename = False
s_dcm2nii = Node(interface=Dcm2nii(), name='s_dcm2nii')
s_dcm2nii.inputs.gzip_output = True
s_dcm2nii.inputs.output_dir = '.'
s_dcm2nii.inputs.date_in_filename = False
########################################
## Setup Nodes for distortion correction
########################################
# merge output files into list
merge_to_file_list = Node(interface=Merge_List(2),
infields=['in1', 'in2'],
name='merge_to_file_list')
# fsl merge AP + PA files (depends on direction)
merger = Node(interface=Merge(dimension='t'), name='merger')
merger.inputs.output_type = 'NIFTI_GZ'
# use topup to create distortion correction map
topup = Node(interface=TOPUP(), name='topup')
topup.inputs.encoding_file = os.path.join(get_resource_path(),
'epi_params_APPA_MB8.txt')
topup.inputs.output_type = "NIFTI_GZ"
topup.inputs.config = 'b02b0.cnf'
# apply topup to all functional images
apply_topup = Node(interface=ApplyTOPUP(), name='apply_topup')
apply_topup.inputs.in_index = [1]
apply_topup.inputs.encoding_file = os.path.join(get_resource_path(),
'epi_params_APPA_MB8.txt')
apply_topup.inputs.output_type = "NIFTI_GZ"
apply_topup.inputs.method = 'jac'
apply_topup.inputs.interp = 'spline'
# Clear out Zeros from spline interpolation using absolute value.
abs_maths = Node(interface=UnaryMaths(), name='abs_maths')
abs_maths.inputs.operation = 'abs'
########################################
## Preprocessing
########################################
# Trim - remove first 10 TRs
n_vols = 10
trim = Node(interface=Trim(), name='trim')
trim.inputs.begin_index = n_vols
#Realignment - 6 parameters - realign to first image of very first series.
realign = Node(interface=spm.Realign(), name="realign")
realign.inputs.register_to_mean = True
#Coregister - 12 parameters
coregister = Node(interface=spm.Coregister(), name="coregister")
coregister.inputs.jobtype = 'estwrite'
#Plot Realignment
plot_realign = Node(interface=PlotRealignmentParameters(),
name="plot_realign")
#Artifact Detection
art = Node(interface=ArtifactDetect(), name="art")
art.inputs.use_differences = [True, False]
art.inputs.use_norm = True
art.inputs.norm_threshold = 1
art.inputs.zintensity_threshold = 3
art.inputs.mask_type = 'file'
art.inputs.parameter_source = 'SPM'
# Gunzip - unzip the functional and structural images
gunzip_struc = Node(Gunzip(), name="gunzip_struc")
gunzip_func = Node(Gunzip(), name="gunzip_func")
# Normalize - normalizes functional and structural images to the MNI template
normalize = Node(interface=Normalize12(jobtype='estwrite',
tpm=template_file),
name="normalize")
#Plot normalization Check
plot_normalization_check = Node(interface=Plot_Coregistration_Montage(),
name="plot_normalization_check")
plot_normalization_check.inputs.canonical_img = canonical_file
#Create Mask
compute_mask = Node(interface=ComputeMask(), name="compute_mask")
#remove lower 5% of histogram of mean image
compute_mask.inputs.m = .05
#Smooth
#implicit masking (.im) = 0, dtype = 0
smooth = Node(interface=spm.Smooth(), name="smooth")
smooth.inputs.fwhm = 6
#Create Covariate matrix
make_cov = Node(interface=Create_Covariates(), name="make_cov")
# Create a datasink to clean up output files
datasink = Node(interface=nio.DataSink(), name='datasink')
datasink.inputs.base_directory = output_dir
datasink.inputs.container = subject_id
########################################
# Create Workflow
########################################
workflow = Workflow(name='Preprocessed')
workflow.base_dir = os.path.join(base_dir, subject_id)
workflow.connect([
(datasource, ap_dcm2nii, [('ap', 'source_dir')]),
(datasource, pa_dcm2nii, [('pa', 'source_dir')]),
(datasource, s_dcm2nii, [('struct', 'source_dir')]),
(func_source, f_dcm2nii, [('scan', 'source_dir')]),
(ap_dcm2nii, merge_to_file_list, [('converted_files', 'in1')]),
(pa_dcm2nii, merge_to_file_list, [('converted_files', 'in2')]),
(merge_to_file_list, merger, [('out', 'in_files')]),
(merger, topup, [('merged_file', 'in_file')]),
(topup, apply_topup, [('out_fieldcoef', 'in_topup_fieldcoef'),
('out_movpar', 'in_topup_movpar')]),
(f_dcm2nii, trim, [('converted_files', 'in_file')]),
(trim, apply_topup, [('out_file', 'in_files')]),
(apply_topup, abs_maths, [('out_corrected', 'in_file')]),
(abs_maths, gunzip_func, [('out_file', 'in_file')]),
(gunzip_func, realign, [('out_file', 'in_files')]),
(s_dcm2nii, gunzip_struc, [('converted_files', 'in_file')]),
(gunzip_struc, coregister, [('out_file', 'source')]),
(coregister, normalize, [('coregistered_source', 'image_to_align')]),
(realign, coregister, [('mean_image', 'target'),
('realigned_files', 'apply_to_files')]),
(realign, normalize, [(('mean_image', get_vox_dims),
'write_voxel_sizes')]),
(coregister, normalize, [('coregistered_files', 'apply_to_files')]),
(normalize, smooth, [('normalized_files', 'in_files')]),
(realign, compute_mask, [('mean_image', 'mean_volume')]),
(compute_mask, art, [('brain_mask', 'mask_file')]),
(realign, art, [('realignment_parameters', 'realignment_parameters'),
('realigned_files', 'realigned_files')]),
(realign, plot_realign, [('realignment_parameters',
'realignment_parameters')]),
(normalize, plot_normalization_check, [('normalized_files', 'wra_img')
]),
(realign, make_cov, [('realignment_parameters',
'realignment_parameters')]),
(art, make_cov, [('outlier_files', 'spike_id')]),
(normalize, datasink, [('normalized_files', 'structural.@normalize')]),
(coregister, datasink, [('coregistered_source', 'structural.@struct')
]),
(topup, datasink, [('out_fieldcoef', 'distortion.@fieldcoef')]),
(topup, datasink, [('out_movpar', 'distortion.@movpar')]),
(smooth, datasink, [('smoothed_files', 'functional.@smooth')]),
(plot_realign, datasink, [('plot', 'functional.@plot_realign')]),
(plot_normalization_check, datasink,
[('plot', 'functional.@plot_normalization')]),
(make_cov, datasink, [('covariates', 'functional.@covariates')])
])
return workflow
def fmri_cleanup_wf(wf_name="fmri_cleanup"):
""" Run the resting-state fMRI pre-processing workflow against the rest files in `data_dir`.
Tasks:
- Trim first 6 volumes of the rs-fMRI file.
- Slice Timing correction.
- Motion and nuisance correction.
- Calculate brain mask in fMRI space.
- Bandpass frequency filtering for resting-state fMRI.
- Smoothing.
- Tissue maps co-registration to fMRI space.
Parameters
----------
wf_name: str
Nipype Inputs
-------------
rest_input.in_file: traits.File
The resting-state fMRI file.
rest_input.anat: traits.File
Path to the high-contrast anatomical image.
rest_input.tissues: list of traits.File
Paths to the tissue segmentations in anatomical space.
Expected to have this order: GM, WM and CSF.
rest_input.highpass_sigma:traits.Float
Band pass timeseries filter higher bound in Hz.
rest_input.lowpass_sigma: traits.Float
Band pass timeseries filter lower bound in Hz.
Nipype Outputs
--------------
rest_output.smooth: traits.File
The isotropically smoothed time filtered nuisance corrected image.
rest_output.nuis_corrected: traits.File
The nuisance corrected fMRI file.
rest_output.motion_params: traits.File
The affine transformation file.
rest_output.time_filtered: traits.File
The bandpass time filtered fMRI file.
rest_output.epi_brain_mask: traits.File
An estimated brain mask from mean EPI volume.
rest_output.tissues_brain_mask: traits.File
A brain mask calculated from the addition of coregistered
GM, WM and CSF segmentation volumes from the anatomical
segmentation.
rest_output.tissues: list of traits.File
The tissues segmentation volume in fMRI space.
Expected to have this order: GM, WM and CSF.
rest_output.anat: traits.File
The T1w image in fMRI space.
rest_output.avg_epi: traits.File
The average EPI image in fMRI space after slice-time and motion correction.
rest_output.motion_regressors: traits.File
rest_output.compcor_regressors: traits.File
rest_output.art_displacement_files
One image file containing the voxel-displacement timeseries.
rest_output.art_intensity_files
One file containing the global intensity values determined from the brainmask.
rest_output.art_norm_files
One file containing the composite norm.
rest_output.art_outlier_files
One file containing a list of 0-based indices corresponding to outlier volumes.
rest_output.art_plot_files
One image file containing the detected outliers.
rest_output.art_statistic_files
One file containing information about the different types of artifacts and if design info is provided then
details of stimulus correlated motion and a listing or artifacts by event type.
Returns
-------
wf: nipype Workflow
"""
# Create the workflow object
wf = pe.Workflow(name=wf_name)
# specify input and output fields
in_fields = [
"in_file",
"anat",
"atlas_anat",
"coreg_target",
"tissues",
"lowpass_freq",
"highpass_freq",
]
out_fields = [
"motion_corrected",
"motion_params",
"tissues",
"anat",
"avg_epi",
"time_filtered",
"smooth",
"tsnr_file",
"epi_brain_mask",
"tissues_brain_mask",
"motion_regressors",
"compcor_regressors",
"gsr_regressors",
"nuis_corrected",
"art_displacement_files",
"art_intensity_files",
"art_norm_files",
"art_outlier_files",
"art_plot_files",
"art_statistic_files",
]
# input identities
rest_input = setup_node(IdentityInterface(fields=in_fields, mandatory_inputs=True),
name="rest_input")
# rs-fMRI preprocessing nodes
trim = setup_node(Trim(), name="trim")
stc_wf = auto_spm_slicetime()
realign = setup_node(nipy_motion_correction(), name='realign')
# average
average = setup_node(
Function(
function=mean_img,
input_names=["in_file"],
output_names=["out_file"],
imports=['from neuro_pypes.interfaces.nilearn import ni2file']
),
name='average_epi'
)
mean_gunzip = setup_node(Gunzip(), name="mean_gunzip")
# co-registration nodes
coreg = setup_node(spm_coregister(cost_function="mi"), name="coreg_fmri")
brain_sel = setup_node(Select(index=[0, 1, 2]), name="brain_sel")
# brain mask made with EPI
epi_mask = setup_node(ComputeMask(), name='epi_mask')
# brain mask made with the merge of the tissue segmentations
tissue_mask = setup_node(fsl.MultiImageMaths(), name='tissue_mask')
tissue_mask.inputs.op_string = "-add %s -add %s -abs -kernel gauss 4 -dilM -ero -kernel gauss 1 -dilM -bin"
tissue_mask.inputs.out_file = "tissue_brain_mask.nii.gz"
# select tissues
gm_select = setup_node(Select(index=[0]), name="gm_sel")
wmcsf_select = setup_node(Select(index=[1, 2]), name="wmcsf_sel")
# noise filter
noise_wf = rest_noise_filter_wf()
wm_select = setup_node(Select(index=[1]), name="wm_sel")
csf_select = setup_node(Select(index=[2]), name="csf_sel")
# bandpass filtering
bandpass = setup_node(
Function(
input_names=['files', 'lowpass_freq', 'highpass_freq', 'tr'],
output_names=['out_files'],
function=bandpass_filter
),
name='bandpass'
)
# smooth
smooth = setup_node(
Function(
function=smooth_img,
input_names=["in_file", "fwhm"],
output_names=["out_file"],
imports=['from neuro_pypes.interfaces.nilearn import ni2file']
),
name="smooth"
)
smooth.inputs.fwhm = get_config_setting('fmri_smooth.fwhm', default=8)
smooth.inputs.out_file = "smooth_std_{}.nii.gz".format(wf_name)
# output identities
rest_output = setup_node(IdentityInterface(fields=out_fields), name="rest_output")
# Connect the nodes
wf.connect([
# trim
(rest_input, trim, [("in_file", "in_file")]),
# slice time correction
(trim, stc_wf, [("out_file", "stc_input.in_file")]),
# motion correction
(stc_wf, realign, [("stc_output.timecorrected_files", "in_file")]),
# coregistration target
(realign, average, [("out_file", "in_file")]),
(average, mean_gunzip, [("out_file", "in_file")]),
(mean_gunzip, coreg, [("out_file", "target")]),
# epi brain mask
(average, epi_mask, [("out_file", "mean_volume")]),
# coregistration
(rest_input, coreg, [("anat", "source")]),
(rest_input, brain_sel, [("tissues", "inlist")]),
(brain_sel, coreg, [(("out", flatten_list), "apply_to_files")]),
# tissue brain mask
(coreg, gm_select, [("coregistered_files", "inlist")]),
(coreg, wmcsf_select, [("coregistered_files", "inlist")]),
(gm_select, tissue_mask, [(("out", flatten_list), "in_file")]),
(wmcsf_select, tissue_mask, [(("out", flatten_list), "operand_files")]),
# nuisance correction
(coreg, wm_select, [("coregistered_files", "inlist",)]),
(coreg, csf_select, [("coregistered_files", "inlist",)]),
(realign, noise_wf, [("out_file", "rest_noise_input.in_file",)]),
(tissue_mask, noise_wf, [("out_file", "rest_noise_input.brain_mask")]),
(wm_select, noise_wf, [(("out", flatten_list), "rest_noise_input.wm_mask")]),
(csf_select, noise_wf, [(("out", flatten_list), "rest_noise_input.csf_mask")]),
(realign, noise_wf, [("par_file", "rest_noise_input.motion_params",)]),
# temporal filtering
(noise_wf, bandpass, [("rest_noise_output.nuis_corrected", "files")]),
# (realign, bandpass, [("out_file", "files")]),
(stc_wf, bandpass, [("stc_output.time_repetition", "tr")]),
(rest_input, bandpass, [
("lowpass_freq", "lowpass_freq"),
("highpass_freq", "highpass_freq"),
]),
(bandpass, smooth, [("out_files", "in_file")]),
# output
(epi_mask, rest_output, [("brain_mask", "epi_brain_mask")]),
(tissue_mask, rest_output, [("out_file", "tissues_brain_mask")]),
(realign, rest_output, [
("out_file", "motion_corrected"),
("par_file", "motion_params"),
]),
(coreg, rest_output, [
("coregistered_files", "tissues"),
("coregistered_source", "anat"),
]),
(noise_wf, rest_output, [
("rest_noise_output.motion_regressors", "motion_regressors"),
("rest_noise_output.compcor_regressors", "compcor_regressors"),
("rest_noise_output.gsr_regressors", "gsr_regressors"),
("rest_noise_output.nuis_corrected", "nuis_corrected"),
("rest_noise_output.tsnr_file", "tsnr_file"),
("rest_noise_output.art_displacement_files", "art_displacement_files"),
("rest_noise_output.art_intensity_files", "art_intensity_files"),
("rest_noise_output.art_norm_files", "art_norm_files"),
("rest_noise_output.art_outlier_files", "art_outlier_files"),
("rest_noise_output.art_plot_files", "art_plot_files"),
("rest_noise_output.art_statistic_files", "art_statistic_files"),
]),
(average, rest_output, [("out_file", "avg_epi")]),
(bandpass, rest_output, [("out_files", "time_filtered")]),
(smooth, rest_output, [("out_file", "smooth")]),
])
return wf
def init_ica_aroma_wf(
dt,
aroma_melodic_dim=-200,
err_on_aroma_warn=False,
susan_fwhm=6.0,
name='ica_aroma_wf',
):
"""
Build a workflow that runs `ICA-AROMA`_.
This workflow wraps `ICA-AROMA`_ to identify and remove motion-related
independent components from a BOLD time series.
The following steps are performed:
#. Remove non-steady state volumes from the bold series.
#. Smooth data using FSL `susan`, with a kernel width FWHM=6.0mm.
#. Run FSL `melodic` outside of ICA-AROMA to generate the report
#. Run ICA-AROMA
#. Aggregate identified motion components (aggressive) to TSV
#. Return ``classified_motion_ICs`` and ``melodic_mix`` for user to complete
non-aggressive denoising in T1w space
#. Calculate ICA-AROMA-identified noise components
(columns named ``AROMAAggrCompXX``)
There is a current discussion on whether other confounds should be extracted
before or after denoising `here
<http://nbviewer.jupyter.org/github/nipreps/fmriprep-notebooks/blob/922e436429b879271fa13e76767a6e73443e74d9/issue-817_aroma_confounds.ipynb>`__.
.. _ICA-AROMA: https://github.com/maartenmennes/ICA-AROMA
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from ecp.workflows.confounds import init_ica_aroma_wf
wf = init_ica_aroma_wf(
dt=1.0)
Parameters
----------
dt : :obj:`float`
bold repetition time
aroma_melodic_dim : :obj:`int`
Set the dimensionality of the MELODIC ICA decomposition.
Negative numbers set a maximum on automatic dimensionality estimation.
Positive numbers set an exact number of components to extract.
(default: -200, i.e., estimate <=200 components)
err_on_aroma_warn : :obj:`bool`
Do not fail on ICA-AROMA errors
susan_fwhm : :obj:`float`
Kernel width (FWHM in mm) for the smoothing step with
FSL ``susan`` (default: 6.0mm)
name : :obj:`str`
Name of workflow (default: ``ica_aroma_wf``)
Inputs
------
bold_std
BOLD series NIfTI file in MNI152NLin6Asym space
bold_mask_std
BOLD mask for MNI152NLin6Asym space
movpar_file
movement parameter file
skip_vols
number of non steady state volumes
Outputs
-------
aroma_confounds
TSV of confounds identified as noise by ICA-AROMA
aroma_noise_ics
CSV of noise components identified by ICA-AROMA
melodic_mix
FSL MELODIC mixing matrix
aroma_metatdata
metadata
out_report
aroma out report
"""
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.segmentation import ICA_AROMARPT
from niworkflows.interfaces.utility import KeySelect
from niworkflows.interfaces.utils import TSV2JSON
workflow = Workflow(name=name)
workflow.__postdesc__ = """\
Automatic removal of motion artifacts using independent component analysis
[ICA-AROMA, @aroma] was performed on the *preprocessed BOLD on MNI space*
time-series after removal of non-steady state volumes and spatial smoothing
with an isotropic, Gaussian kernel of 6mm FWHM (full-width half-maximum).
The "aggressive" noise-regressors were collected and placed
in the corresponding confounds file.
"""
inputnode = pe.Node(niu.IdentityInterface(fields=[
'bold_std',
'bold_mask_std',
'movpar_file',
'skip_vols',
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'aroma_confounds', 'aroma_noise_ics', 'melodic_mix', 'aroma_metadata',
'out_report'
]),
name='outputnode')
# extract out to BOLD base
rm_non_steady_state = pe.Node(Trim(), name='rm_nonsteady')
trim_movement = pe.Node(TrimMovement(), name='trim_movement')
calc_median_val = pe.Node(fsl.ImageStats(op_string='-k %s -p 50'),
name='calc_median_val')
calc_bold_mean = pe.Node(fsl.MeanImage(), name='calc_bold_mean')
def _getusans_func(image, thresh):
return [tuple([image, thresh])]
getusans = pe.Node(niu.Function(function=_getusans_func,
output_names=['usans']),
name='getusans',
mem_gb=0.01)
smooth = pe.Node(fsl.SUSAN(fwhm=susan_fwhm), name='smooth')
# melodic node
melodic = pe.Node(fsl.MELODIC(no_bet=True,
tr_sec=dt,
mm_thresh=0.5,
out_stats=True,
dim=aroma_melodic_dim),
name="melodic")
# ica_aroma node
ica_aroma = pe.Node(ICA_AROMARPT(denoise_type='no',
generate_report=True,
TR=dt,
args='-np'),
name='ica_aroma')
# extract the confound ICs from the results
ica_aroma_confound_extraction = pe.Node(
ICAConfounds(err_on_aroma_warn=err_on_aroma_warn),
name='ica_aroma_confound_extraction')
ica_aroma_metadata_fmt = pe.Node(TSV2JSON(index_column='IC',
output=None,
enforce_case=True,
additional_metadata={
'Method': {
'Name':
'ICA-AROMA',
'Version':
getenv(
'AROMA_VERSION',
'n/a')
}
}),
name='ica_aroma_metadata_fmt')
def _getbtthresh(medianval):
return 0.75 * medianval
# connect the nodes
workflow.connect([
(inputnode, ica_aroma, [('movpar_file', 'motion_parameters')]),
(inputnode, rm_non_steady_state, [('skip_vols', 'begin_index')]),
(inputnode, rm_non_steady_state, [('bold_std', 'in_file')]),
(inputnode, calc_median_val, [('bold_mask_std', 'mask_file')]),
(inputnode, trim_movement, [('movpar_file', 'movpar_file')]),
(inputnode, trim_movement, [('skip_vols', 'skip_vols')]),
(rm_non_steady_state, calc_median_val, [('out_file', 'in_file')]),
(rm_non_steady_state, calc_bold_mean, [('out_file', 'in_file')]),
(calc_bold_mean, getusans, [('out_file', 'image')]),
(calc_median_val, getusans, [('out_stat', 'thresh')]),
# Connect input nodes to complete smoothing
(rm_non_steady_state, smooth, [('out_file', 'in_file')]),
(getusans, smooth, [('usans', 'usans')]),
(calc_median_val, smooth, [(('out_stat', _getbtthresh),
'brightness_threshold')]),
# connect smooth to melodic
(smooth, melodic, [('smoothed_file', 'in_files')]),
(inputnode, melodic, [('bold_mask_std', 'mask')]),
# connect nodes to ICA-AROMA
(smooth, ica_aroma, [('smoothed_file', 'in_file')]),
(inputnode, ica_aroma, [('bold_mask_std', 'report_mask'),
('bold_mask_std', 'mask')]),
(melodic, ica_aroma, [('out_dir', 'melodic_dir')]),
# generate tsvs from ICA-AROMA
(ica_aroma, ica_aroma_confound_extraction, [('out_dir', 'in_directory')
]),
(inputnode, ica_aroma_confound_extraction, [('skip_vols', 'skip_vols')
]),
(ica_aroma_confound_extraction, ica_aroma_metadata_fmt,
[('aroma_metadata', 'in_file')]),
# output for processing and reporting
(ica_aroma_confound_extraction,
outputnode, [('aroma_confounds', 'aroma_confounds'),
('aroma_noise_ics', 'aroma_noise_ics'),
('melodic_mix', 'melodic_mix')]),
(ica_aroma_metadata_fmt, outputnode, [('output', 'aroma_metadata')]),
(ica_aroma, outputnode, [('out_report', 'out_report')]),
])
return workflow
def builder(subject_id,
subId,
project_dir,
data_dir,
output_dir,
output_final_dir,
output_interm_dir,
layout,
anat=None,
funcs=None,
fmaps=None,
task_name='',
session=None,
apply_trim=False,
apply_dist_corr=False,
apply_smooth=False,
apply_filter=False,
mni_template='2mm',
apply_n4=True,
ants_threads=8,
readable_crash_files=False,
write_logs=True):
"""
Core function that returns a workflow. See wfmaker for more details.
Args:
subject_id: name of subject folder for final outputted sub-folder name
subId: abbreviate name of subject for intermediate outputted sub-folder name
project_dir: full path to root of project
data_dir: full path to raw data files
output_dir: upper level output dir (others will be nested within this)
output_final_dir: final preprocessed sub-dir name
output_interm_dir: intermediate preprcess sub-dir name
layout: BIDS layout instance
"""
##################
### PATH SETUP ###
##################
if session is not None:
session = int(session)
if session < 10:
session = '0' + str(session)
else:
session = str(session)
# Set MNI template
MNItemplate = os.path.join(get_resource_path(),
'MNI152_T1_' + mni_template + '_brain.nii.gz')
MNImask = os.path.join(get_resource_path(),
'MNI152_T1_' + mni_template + '_brain_mask.nii.gz')
MNItemplatehasskull = os.path.join(get_resource_path(),
'MNI152_T1_' + mni_template + '.nii.gz')
# Set ANTs files
bet_ants_template = os.path.join(get_resource_path(),
'OASIS_template.nii.gz')
bet_ants_prob_mask = os.path.join(
get_resource_path(), 'OASIS_BrainCerebellumProbabilityMask.nii.gz')
bet_ants_registration_mask = os.path.join(
get_resource_path(), 'OASIS_BrainCerebellumRegistrationMask.nii.gz')
#################################
### NIPYPE IMPORTS AND CONFIG ###
#################################
# Update nipype global config because workflow.config[] = ..., doesn't seem to work
# Can't store nipype config/rc file in container anyway so set them globaly before importing and setting up workflow as suggested here: http://nipype.readthedocs.io/en/latest/users/config_file.html#config-file
# Create subject's intermediate directory before configuring nipype and the workflow because that's where we'll save log files in addition to intermediate files
if not os.path.exists(os.path.join(output_interm_dir, subId, 'logs')):
os.makedirs(os.path.join(output_interm_dir, subId, 'logs'))
log_dir = os.path.join(output_interm_dir, subId, 'logs')
from nipype import config
if readable_crash_files:
cfg = dict(execution={'crashfile_format': 'txt'})
config.update_config(cfg)
config.update_config({
'logging': {
'log_directory': log_dir,
'log_to_file': write_logs
},
'execution': {
'crashdump_dir': log_dir
}
})
from nipype import logging
logging.update_logging(config)
# Now import everything else
from nipype.interfaces.io import DataSink
from nipype.interfaces.utility import Merge, IdentityInterface
from nipype.pipeline.engine import Node, Workflow
from nipype.interfaces.nipy.preprocess import ComputeMask
from nipype.algorithms.rapidart import ArtifactDetect
from nipype.interfaces.ants.segmentation import BrainExtraction, N4BiasFieldCorrection
from nipype.interfaces.ants import Registration, ApplyTransforms
from nipype.interfaces.fsl import MCFLIRT, TOPUP, ApplyTOPUP
from nipype.interfaces.fsl.maths import MeanImage
from nipype.interfaces.fsl import Merge as MERGE
from nipype.interfaces.fsl.utils import Smooth
from nipype.interfaces.nipy.preprocess import Trim
from .interfaces import Plot_Coregistration_Montage, Plot_Quality_Control, Plot_Realignment_Parameters, Create_Covariates, Down_Sample_Precision, Create_Encoding_File, Filter_In_Mask
##################
### INPUT NODE ###
##################
# Turn functional file list into interable Node
func_scans = Node(IdentityInterface(fields=['scan']), name='func_scans')
func_scans.iterables = ('scan', funcs)
# Get TR for use in filtering below; we're assuming all BOLD runs have the same TR
tr_length = layout.get_metadata(funcs[0])['RepetitionTime']
#####################################
## TRIM ##
#####################################
if apply_trim:
trim = Node(Trim(), name='trim')
trim.inputs.begin_index = apply_trim
#####################################
## DISTORTION CORRECTION ##
#####################################
if apply_dist_corr:
# Get fmap file locations
fmaps = [
f.filename for f in layout.get(
subject=subId, modality='fmap', extensions='.nii.gz')
]
if not fmaps:
raise IOError(
"Distortion Correction requested but field map scans not found..."
)
# Get fmap metadata
totalReadoutTimes, measurements, fmap_pes = [], [], []
for i, fmap in enumerate(fmaps):
# Grab total readout time for each fmap
totalReadoutTimes.append(
layout.get_metadata(fmap)['TotalReadoutTime'])
# Grab measurements (for some reason pyBIDS doesn't grab dcm_meta... fields from side-car json file and json.load, doesn't either; so instead just read the header using nibabel to determine number of scans)
measurements.append(nib.load(fmap).header['dim'][4])
# Get phase encoding direction
fmap_pe = layout.get_metadata(fmap)["PhaseEncodingDirection"]
fmap_pes.append(fmap_pe)
encoding_file_writer = Node(interface=Create_Encoding_File(),
name='create_encoding')
encoding_file_writer.inputs.totalReadoutTimes = totalReadoutTimes
encoding_file_writer.inputs.fmaps = fmaps
encoding_file_writer.inputs.fmap_pes = fmap_pes
encoding_file_writer.inputs.measurements = measurements
encoding_file_writer.inputs.file_name = 'encoding_file.txt'
merge_to_file_list = Node(interface=Merge(2),
infields=['in1', 'in2'],
name='merge_to_file_list')
merge_to_file_list.inputs.in1 = fmaps[0]
merge_to_file_list.inputs.in1 = fmaps[1]
# Merge AP and PA distortion correction scans
merger = Node(interface=MERGE(dimension='t'), name='merger')
merger.inputs.output_type = 'NIFTI_GZ'
merger.inputs.in_files = fmaps
merger.inputs.merged_file = 'merged_epi.nii.gz'
# Create distortion correction map
topup = Node(interface=TOPUP(), name='topup')
topup.inputs.output_type = 'NIFTI_GZ'
# Apply distortion correction to other scans
apply_topup = Node(interface=ApplyTOPUP(), name='apply_topup')
apply_topup.inputs.output_type = 'NIFTI_GZ'
apply_topup.inputs.method = 'jac'
apply_topup.inputs.interp = 'spline'
###################################
### REALIGN ###
###################################
realign_fsl = Node(MCFLIRT(), name="realign")
realign_fsl.inputs.cost = 'mutualinfo'
realign_fsl.inputs.mean_vol = True
realign_fsl.inputs.output_type = 'NIFTI_GZ'
realign_fsl.inputs.save_mats = True
realign_fsl.inputs.save_rms = True
realign_fsl.inputs.save_plots = True
###################################
### MEAN EPIs ###
###################################
# For coregistration after realignment
mean_epi = Node(MeanImage(), name='mean_epi')
mean_epi.inputs.dimension = 'T'
# For after normalization is done to plot checks
mean_norm_epi = Node(MeanImage(), name='mean_norm_epi')
mean_norm_epi.inputs.dimension = 'T'
###################################
### MASK, ART, COV CREATION ###
###################################
compute_mask = Node(ComputeMask(), name='compute_mask')
compute_mask.inputs.m = .05
art = Node(ArtifactDetect(), name='art')
art.inputs.use_differences = [True, False]
art.inputs.use_norm = True
art.inputs.norm_threshold = 1
art.inputs.zintensity_threshold = 3
art.inputs.mask_type = 'file'
art.inputs.parameter_source = 'FSL'
make_cov = Node(Create_Covariates(), name='make_cov')
################################
### N4 BIAS FIELD CORRECTION ###
################################
if apply_n4:
n4_correction = Node(N4BiasFieldCorrection(), name='n4_correction')
n4_correction.inputs.copy_header = True
n4_correction.inputs.save_bias = False
n4_correction.inputs.num_threads = ants_threads
n4_correction.inputs.input_image = anat
###################################
### BRAIN EXTRACTION ###
###################################
brain_extraction_ants = Node(BrainExtraction(), name='brain_extraction')
brain_extraction_ants.inputs.dimension = 3
brain_extraction_ants.inputs.use_floatingpoint_precision = 1
brain_extraction_ants.inputs.num_threads = ants_threads
brain_extraction_ants.inputs.brain_probability_mask = bet_ants_prob_mask
brain_extraction_ants.inputs.keep_temporary_files = 1
brain_extraction_ants.inputs.brain_template = bet_ants_template
brain_extraction_ants.inputs.extraction_registration_mask = bet_ants_registration_mask
brain_extraction_ants.inputs.out_prefix = 'bet'
###################################
### COREGISTRATION ###
###################################
coregistration = Node(Registration(), name='coregistration')
coregistration.inputs.float = False
coregistration.inputs.output_transform_prefix = "meanEpi2highres"
coregistration.inputs.transforms = ['Rigid']
coregistration.inputs.transform_parameters = [(0.1, ), (0.1, )]
coregistration.inputs.number_of_iterations = [[1000, 500, 250, 100]]
coregistration.inputs.dimension = 3
coregistration.inputs.num_threads = ants_threads
coregistration.inputs.write_composite_transform = True
coregistration.inputs.collapse_output_transforms = True
coregistration.inputs.metric = ['MI']
coregistration.inputs.metric_weight = [1]
coregistration.inputs.radius_or_number_of_bins = [32]
coregistration.inputs.sampling_strategy = ['Regular']
coregistration.inputs.sampling_percentage = [0.25]
coregistration.inputs.convergence_threshold = [1e-08]
coregistration.inputs.convergence_window_size = [10]
coregistration.inputs.smoothing_sigmas = [[3, 2, 1, 0]]
coregistration.inputs.sigma_units = ['mm']
coregistration.inputs.shrink_factors = [[4, 3, 2, 1]]
coregistration.inputs.use_estimate_learning_rate_once = [True]
coregistration.inputs.use_histogram_matching = [False]
coregistration.inputs.initial_moving_transform_com = True
coregistration.inputs.output_warped_image = True
coregistration.inputs.winsorize_lower_quantile = 0.01
coregistration.inputs.winsorize_upper_quantile = 0.99
###################################
### NORMALIZATION ###
###################################
# Settings Explanations
# Only a few key settings are worth adjusting and most others relate to how ANTs optimizer starts or iterates and won't make a ton of difference
# Brian Avants referred to these settings as the last "best tested" when he was aligning fMRI data: https://github.com/ANTsX/ANTsRCore/blob/master/R/antsRegistration.R#L275
# Things that matter the most:
# smoothing_sigmas:
# how much gaussian smoothing to apply when performing registration, probably want the upper limit of this to match the resolution that the data is collected at e.g. 3mm
# Old settings [[3,2,1,0]]*3
# shrink_factors
# The coarseness with which to do registration
# Old settings [[8,4,2,1]] * 3
# >= 8 may result is some problems causing big chunks of cortex with little fine grain spatial structure to be moved to other parts of cortex
# Other settings
# transform_parameters:
# how much regularization to do for fitting that transformation
# for syn this pertains to both the gradient regularization term, and the flow, and elastic terms. Leave the syn settings alone as they seem to be the most well tested across published data sets
# radius_or_number_of_bins
# This is the bin size for MI metrics and 32 is probably adequate for most use cases. Increasing this might increase precision (e.g. to 64) but takes exponentially longer
# use_histogram_matching
# Use image intensity distribution to guide registration
# Leave it on for within modality registration (e.g. T1 -> MNI), but off for between modality registration (e.g. EPI -> T1)
# convergence_threshold
# threshold for optimizer
# convergence_window_size
# how many samples should optimizer average to compute threshold?
# sampling_strategy
# what strategy should ANTs use to initialize the transform. Regular here refers to approximately random sampling around the center of the image mass
normalization = Node(Registration(), name='normalization')
normalization.inputs.float = False
normalization.inputs.collapse_output_transforms = True
normalization.inputs.convergence_threshold = [1e-06]
normalization.inputs.convergence_window_size = [10]
normalization.inputs.dimension = 3
normalization.inputs.fixed_image = MNItemplate
normalization.inputs.initial_moving_transform_com = True
normalization.inputs.metric = ['MI', 'MI', 'CC']
normalization.inputs.metric_weight = [1.0] * 3
normalization.inputs.number_of_iterations = [[1000, 500, 250, 100],
[1000, 500, 250, 100],
[100, 70, 50, 20]]
normalization.inputs.num_threads = ants_threads
normalization.inputs.output_transform_prefix = 'anat2template'
normalization.inputs.output_inverse_warped_image = True
normalization.inputs.output_warped_image = True
normalization.inputs.radius_or_number_of_bins = [32, 32, 4]
normalization.inputs.sampling_percentage = [0.25, 0.25, 1]
normalization.inputs.sampling_strategy = ['Regular', 'Regular', 'None']
normalization.inputs.shrink_factors = [[8, 4, 2, 1]] * 3
normalization.inputs.sigma_units = ['vox'] * 3
normalization.inputs.smoothing_sigmas = [[3, 2, 1, 0]] * 3
normalization.inputs.transforms = ['Rigid', 'Affine', 'SyN']
normalization.inputs.transform_parameters = [(0.1, ), (0.1, ),
(0.1, 3.0, 0.0)]
normalization.inputs.use_histogram_matching = True
normalization.inputs.winsorize_lower_quantile = 0.005
normalization.inputs.winsorize_upper_quantile = 0.995
normalization.inputs.write_composite_transform = True
# NEW SETTINGS (need to be adjusted; specifically shink_factors and smoothing_sigmas need to be the same length)
# normalization = Node(Registration(), name='normalization')
# normalization.inputs.float = False
# normalization.inputs.collapse_output_transforms = True
# normalization.inputs.convergence_threshold = [1e-06, 1e-06, 1e-07]
# normalization.inputs.convergence_window_size = [10]
# normalization.inputs.dimension = 3
# normalization.inputs.fixed_image = MNItemplate
# normalization.inputs.initial_moving_transform_com = True
# normalization.inputs.metric = ['MI', 'MI', 'CC']
# normalization.inputs.metric_weight = [1.0]*3
# normalization.inputs.number_of_iterations = [[1000, 500, 250, 100],
# [1000, 500, 250, 100],
# [100, 70, 50, 20]]
# normalization.inputs.num_threads = ants_threads
# normalization.inputs.output_transform_prefix = 'anat2template'
# normalization.inputs.output_inverse_warped_image = True
# normalization.inputs.output_warped_image = True
# normalization.inputs.radius_or_number_of_bins = [32, 32, 4]
# normalization.inputs.sampling_percentage = [0.25, 0.25, 1]
# normalization.inputs.sampling_strategy = ['Regular',
# 'Regular',
# 'None']
# normalization.inputs.shrink_factors = [[4, 3, 2, 1]]*3
# normalization.inputs.sigma_units = ['vox']*3
# normalization.inputs.smoothing_sigmas = [[2, 1], [2, 1], [3, 2, 1, 0]]
# normalization.inputs.transforms = ['Rigid', 'Affine', 'SyN']
# normalization.inputs.transform_parameters = [(0.1,),
# (0.1,),
# (0.1, 3.0, 0.0)]
# normalization.inputs.use_histogram_matching = True
# normalization.inputs.winsorize_lower_quantile = 0.005
# normalization.inputs.winsorize_upper_quantile = 0.995
# normalization.inputs.write_composite_transform = True
###################################
### APPLY TRANSFORMS AND SMOOTH ###
###################################
merge_transforms = Node(Merge(2),
iterfield=['in2'],
name='merge_transforms')
# Used for epi -> mni, via (coreg + norm)
apply_transforms = Node(ApplyTransforms(),
iterfield=['input_image'],
name='apply_transforms')
apply_transforms.inputs.input_image_type = 3
apply_transforms.inputs.float = False
apply_transforms.inputs.num_threads = 12
apply_transforms.inputs.environ = {}
apply_transforms.inputs.interpolation = 'BSpline'
apply_transforms.inputs.invert_transform_flags = [False, False]
apply_transforms.inputs.reference_image = MNItemplate
# Used for t1 segmented -> mni, via (norm)
apply_transform_seg = Node(ApplyTransforms(), name='apply_transform_seg')
apply_transform_seg.inputs.input_image_type = 3
apply_transform_seg.inputs.float = False
apply_transform_seg.inputs.num_threads = 12
apply_transform_seg.inputs.environ = {}
apply_transform_seg.inputs.interpolation = 'MultiLabel'
apply_transform_seg.inputs.invert_transform_flags = [False]
apply_transform_seg.inputs.reference_image = MNItemplate
###################################
### PLOTS ###
###################################
plot_realign = Node(Plot_Realignment_Parameters(), name="plot_realign")
plot_qa = Node(Plot_Quality_Control(), name="plot_qa")
plot_normalization_check = Node(Plot_Coregistration_Montage(),
name="plot_normalization_check")
plot_normalization_check.inputs.canonical_img = MNItemplatehasskull
############################################
### FILTER, SMOOTH, DOWNSAMPLE PRECISION ###
############################################
# Use cosanlab_preproc for down sampling
down_samp = Node(Down_Sample_Precision(), name="down_samp")
# Use FSL for smoothing
if apply_smooth:
smooth = Node(Smooth(), name='smooth')
if isinstance(apply_smooth, list):
smooth.iterables = ("fwhm", apply_smooth)
elif isinstance(apply_smooth, int) or isinstance(apply_smooth, float):
smooth.inputs.fwhm = apply_smooth
else:
raise ValueError("apply_smooth must be a list or int/float")
# Use cosanlab_preproc for low-pass filtering
if apply_filter:
lp_filter = Node(Filter_In_Mask(), name='lp_filter')
lp_filter.inputs.mask = MNImask
lp_filter.inputs.sampling_rate = tr_length
lp_filter.inputs.high_pass_cutoff = 0
if isinstance(apply_filter, list):
lp_filter.iterables = ("low_pass_cutoff", apply_filter)
elif isinstance(apply_filter, int) or isinstance(apply_filter, float):
lp_filter.inputs.low_pass_cutoff = apply_filter
else:
raise ValueError("apply_filter must be a list or int/float")
###################
### OUTPUT NODE ###
###################
# Collect all final outputs in the output dir and get rid of file name additions
datasink = Node(DataSink(), name='datasink')
if session:
datasink.inputs.base_directory = os.path.join(output_final_dir,
subject_id)
datasink.inputs.container = 'ses-' + session
else:
datasink.inputs.base_directory = output_final_dir
datasink.inputs.container = subject_id
# Remove substitutions
data_dir_parts = data_dir.split('/')[1:]
if session:
prefix = ['_scan_'] + data_dir_parts + [subject_id] + [
'ses-' + session
] + ['func']
else:
prefix = ['_scan_'] + data_dir_parts + [subject_id] + ['func']
func_scan_names = [os.path.split(elem)[-1] for elem in funcs]
to_replace = []
for elem in func_scan_names:
bold_name = elem.split(subject_id + '_')[-1]
bold_name = bold_name.split('.nii.gz')[0]
to_replace.append(('..'.join(prefix + [elem]), bold_name))
datasink.inputs.substitutions = to_replace
#####################
### INIT WORKFLOW ###
#####################
# If we have sessions provide the full path to the subject's intermediate directory
# and only rely on workflow init to create the session container *within* that directory
# Otherwise just point to the intermediate directory and let the workflow init create the subject container within the intermediate directory
if session:
workflow = Workflow(name='ses_' + session)
workflow.base_dir = os.path.join(output_interm_dir, subId)
else:
workflow = Workflow(name=subId)
workflow.base_dir = output_interm_dir
############################
######### PART (1a) #########
# func -> discorr -> trim -> realign
# OR
# func -> trim -> realign
# OR
# func -> discorr -> realign
# OR
# func -> realign
############################
if apply_dist_corr:
workflow.connect([(encoding_file_writer, topup, [('encoding_file',
'encoding_file')]),
(encoding_file_writer, apply_topup,
[('encoding_file', 'encoding_file')]),
(merger, topup, [('merged_file', 'in_file')]),
(func_scans, apply_topup, [('scan', 'in_files')]),
(topup, apply_topup,
[('out_fieldcoef', 'in_topup_fieldcoef'),
('out_movpar', 'in_topup_movpar')])])
if apply_trim:
# Dist Corr + Trim
workflow.connect([(apply_topup, trim, [('out_corrected', 'in_file')
]),
(trim, realign_fsl, [('out_file', 'in_file')])])
else:
# Dist Corr + No Trim
workflow.connect([(apply_topup, realign_fsl, [('out_corrected',
'in_file')])])
else:
if apply_trim:
# No Dist Corr + Trim
workflow.connect([(func_scans, trim, [('scan', 'in_file')]),
(trim, realign_fsl, [('out_file', 'in_file')])])
else:
# No Dist Corr + No Trim
workflow.connect([
(func_scans, realign_fsl, [('scan', 'in_file')]),
])
############################
######### PART (1n) #########
# anat -> N4 -> bet
# OR
# anat -> bet
############################
if apply_n4:
workflow.connect([(n4_correction, brain_extraction_ants,
[('output_image', 'anatomical_image')])])
else:
brain_extraction_ants.inputs.anatomical_image = anat
##########################################
############### PART (2) #################
# realign -> coreg -> mni (via t1)
# t1 -> mni
# covariate creation
# plot creation
###########################################
workflow.connect([
(realign_fsl, plot_realign, [('par_file', 'realignment_parameters')]),
(realign_fsl, plot_qa, [('out_file', 'dat_img')]),
(realign_fsl, art, [('out_file', 'realigned_files'),
('par_file', 'realignment_parameters')]),
(realign_fsl, mean_epi, [('out_file', 'in_file')]),
(realign_fsl, make_cov, [('par_file', 'realignment_parameters')]),
(mean_epi, compute_mask, [('out_file', 'mean_volume')]),
(compute_mask, art, [('brain_mask', 'mask_file')]),
(art, make_cov, [('outlier_files', 'spike_id')]),
(art, plot_realign, [('outlier_files', 'outliers')]),
(plot_qa, make_cov, [('fd_outliers', 'fd_outliers')]),
(brain_extraction_ants, coregistration, [('BrainExtractionBrain',
'fixed_image')]),
(mean_epi, coregistration, [('out_file', 'moving_image')]),
(brain_extraction_ants, normalization, [('BrainExtractionBrain',
'moving_image')]),
(coregistration, merge_transforms, [('composite_transform', 'in2')]),
(normalization, merge_transforms, [('composite_transform', 'in1')]),
(merge_transforms, apply_transforms, [('out', 'transforms')]),
(realign_fsl, apply_transforms, [('out_file', 'input_image')]),
(apply_transforms, mean_norm_epi, [('output_image', 'in_file')]),
(normalization, apply_transform_seg, [('composite_transform',
'transforms')]),
(brain_extraction_ants, apply_transform_seg,
[('BrainExtractionSegmentation', 'input_image')]),
(mean_norm_epi, plot_normalization_check, [('out_file', 'wra_img')])
])
##################################################
################### PART (3) #####################
# epi (in mni) -> filter -> smooth -> down sample
# OR
# epi (in mni) -> filter -> down sample
# OR
# epi (in mni) -> smooth -> down sample
# OR
# epi (in mni) -> down sample
###################################################
if apply_filter:
workflow.connect([(apply_transforms, lp_filter, [('output_image',
'in_file')])])
if apply_smooth:
# Filtering + Smoothing
workflow.connect([(lp_filter, smooth, [('out_file', 'in_file')]),
(smooth, down_samp, [('smoothed_file', 'in_file')
])])
else:
# Filtering + No Smoothing
workflow.connect([(lp_filter, down_samp, [('out_file', 'in_file')])
])
else:
if apply_smooth:
# No Filtering + Smoothing
workflow.connect([
(apply_transforms, smooth, [('output_image', 'in_file')]),
(smooth, down_samp, [('smoothed_file', 'in_file')])
])
else:
# No Filtering + No Smoothing
workflow.connect([(apply_transforms, down_samp, [('output_image',
'in_file')])])
##########################################
############### PART (4) #################
# down sample -> save
# plots -> save
# covs -> save
# t1 (in mni) -> save
# t1 segmented masks (in mni) -> save
# realignment parms -> save
##########################################
workflow.connect([
(down_samp, datasink, [('out_file', 'functional.@down_samp')]),
(plot_realign, datasink, [('plot', 'functional.@plot_realign')]),
(plot_qa, datasink, [('plot', 'functional.@plot_qa')]),
(plot_normalization_check, datasink,
[('plot', 'functional.@plot_normalization')]),
(make_cov, datasink, [('covariates', 'functional.@covariates')]),
(normalization, datasink, [('warped_image', 'structural.@normanat')]),
(apply_transform_seg, datasink, [('output_image',
'structural.@normanatseg')]),
(realign_fsl, datasink, [('par_file', 'functional.@motionparams')])
])
if not os.path.exists(os.path.join(output_dir, 'pipeline.png')):
workflow.write_graph(dotfilename=os.path.join(output_dir, 'pipeline'),
format='png')
print(f"Creating workflow for subject: {subject_id}")
if ants_threads != 8:
print(
f"ANTs will utilize the user-requested {ants_threads} threads for parallel processing."
)
return workflow
def wfmaker(project_dir,
raw_dir,
subject_id,
task_name='',
apply_trim=False,
apply_dist_corr=False,
apply_smooth=False,
apply_filter=False,
mni_template='2mm',
apply_n4=True,
ants_threads=8,
readable_crash_files=False):
"""
This function returns a "standard" workflow based on requested settings. Assumes data is in the following directory structure in BIDS format:
*Work flow steps*:
1) EPI Distortion Correction (FSL; optional)
2) Trimming (nipy)
3) Realignment/Motion Correction (FSL)
4) Artifact Detection (rapidART/python)
5) Brain Extraction + N4 Bias Correction (ANTs)
6) Coregistration (rigid) (ANTs)
7) Normalization to MNI (non-linear) (ANTs)
8) Low-pass filtering (nilearn; optional)
8) Smoothing (FSL; optional)
9) Downsampling to INT16 precision to save space (nibabel)
Args:
project_dir (str): full path to the root of project folder, e.g. /my/data/myproject. All preprocessed data will be placed under this foler and the raw_dir folder will be searched for under this folder
raw_dir (str): folder name for raw data, e.g. 'raw' which would be automatically converted to /my/data/myproject/raw
subject_id (str/int): subject ID to process. Can be either a subject ID string e.g. 'sid-0001' or an integer to index the entire list of subjects in raw_dir, e.g. 0, which would process the first subject
apply_trim (int/bool; optional): number of volumes to trim from the beginning of each functional run; default is None
task_name (str; optional): which functional task runs to process; default is all runs
apply_dist_corr (bool; optional): look for fmap files and perform distortion correction; default False
smooth (int/list; optional): smoothing to perform in FWHM mm; if a list is provided will create outputs for each smoothing kernel separately; default False
apply_filter (float/list; optional): low-pass/high-freq filtering cut-offs in Hz; if a list is provided will create outputs for each filter cut-off separately. With high temporal resolution scans .25Hz is a decent value to capture respitory artifacts; default None/False
mni_template (str; optional): which mm resolution template to use, e.g. '3mm'; default '2mm'
apply_n4 (bool; optional): perform N4 Bias Field correction on the anatomical image; default true
ants_threads (int; optional): number of threads ANTs should use for its processes; default 8
readable_crash_files (bool; optional): should nipype crash files be saved as txt? This makes them easily readable, but sometimes interferes with nipype's ability to use cached results of successfully run nodes (i.e. picking up where it left off after bugs are fixed); default False
Examples:
>>> from cosanlab_preproc.wfmaker import wfmaker
>>> # Create workflow that performs no distortion correction, trims first 5 TRs, no filtering, 6mm smoothing, and normalizes to 2mm MNI space. Run it with 16 cores.
>>>
>>> workflow = wfmaker(
project_dir = '/data/project',
raw_dir = 'raw',
apply_trim = 5)
>>>
>>> workflow.run('MultiProc',plugin_args = {'n_procs': 16})
>>>
>>> # Create workflow that performs distortion correction, trims first 25 TRs, no filtering and filtering .25hz, 6mm and 8mm smoothing, and normalizes to 3mm MNI space. Run it serially (will be super slow!).
>>>
>>> workflow = wfmaker(
project_dir = '/data/project',
raw_dir = 'raw',
apply_trim = 25,
apply_dist_corr = True,
apply_filter = [0, .25],
apply_smooth = [6.0, 8.0],
mni = '3mm')
>>>
>>> workflow.run()
"""
##################
### PATH SETUP ###
##################
if mni_template not in ['1mm', '2mm', '3mm']:
raise ValueError("MNI template must be: 1mm, 2mm, or 3mm")
data_dir = os.path.join(project_dir, raw_dir)
output_dir = os.path.join(project_dir, 'preprocessed')
output_final_dir = os.path.join(output_dir, 'final')
output_interm_dir = os.path.join(output_dir, 'intermediate')
log_dir = os.path.join(project_dir, 'logs', 'nipype')
if not os.path.exists(output_final_dir):
os.makedirs(output_final_dir)
if not os.path.exists(output_interm_dir):
os.makedirs(output_interm_dir)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
# Set MNI template
MNItemplate = os.path.join(get_resource_path(),
'MNI152_T1_' + mni_template + '_brain.nii.gz')
MNImask = os.path.join(get_resource_path(),
'MNI152_T1_' + mni_template + '_brain_mask.nii.gz')
MNItemplatehasskull = os.path.join(get_resource_path(),
'MNI152_T1_' + mni_template + '.nii.gz')
# Set ANTs files
bet_ants_template = os.path.join(get_resource_path(),
'OASIS_template.nii.gz')
bet_ants_prob_mask = os.path.join(
get_resource_path(), 'OASIS_BrainCerebellumProbabilityMask.nii.gz')
bet_ants_registration_mask = os.path.join(
get_resource_path(), 'OASIS_BrainCerebellumRegistrationMask.nii.gz')
#################################
### NIPYPE IMPORTS AND CONFIG ###
#################################
# Update nipype global config because workflow.config[] = ..., doesn't seem to work
# Can't store nipype config/rc file in container anyway so set them globaly before importing and setting up workflow as suggested here: http://nipype.readthedocs.io/en/latest/users/config_file.html#config-file
from nipype import config
if readable_crash_files:
cfg = dict(execution={'crashfile_format': 'txt'})
config.update_config(cfg)
config.update_config(
{'logging': {
'log_directory': log_dir,
'log_to_file': True
}})
from nipype import logging
logging.update_logging(config)
# Now import everything else
from nipype.interfaces.io import DataSink
from nipype.interfaces.utility import Merge, IdentityInterface
from nipype.pipeline.engine import Node, Workflow
from nipype.interfaces.nipy.preprocess import ComputeMask
from nipype.algorithms.rapidart import ArtifactDetect
from nipype.interfaces.ants.segmentation import BrainExtraction, N4BiasFieldCorrection
from nipype.interfaces.ants import Registration, ApplyTransforms
from nipype.interfaces.fsl import MCFLIRT, TOPUP, ApplyTOPUP
from nipype.interfaces.fsl.maths import MeanImage
from nipype.interfaces.fsl import Merge as MERGE
from nipype.interfaces.fsl.utils import Smooth
from nipype.interfaces.nipy.preprocess import Trim
from .interfaces import Plot_Coregistration_Montage, Plot_Quality_Control, Plot_Realignment_Parameters, Create_Covariates, Down_Sample_Precision, Create_Encoding_File, Filter_In_Mask
##################
### INPUT NODE ###
##################
layout = BIDSLayout(data_dir)
# Dartmouth subjects are named with the sub- prefix, handle whether we receive an integer identifier for indexing or the full subject id with prefixg
if isinstance(subject_id, six.string_types):
subId = subject_id[4:]
elif isinstance(subject_id, int):
subId = layout.get_subjects()[subject_id]
subject_id = 'sub-' + subId
else:
raise TypeError("subject_id should be a string or integer")
#Get anat file location
anat = layout.get(subject=subId, type='T1w',
extensions='.nii.gz')[0].filename
#Get functional file locations
if task_name:
funcs = [
f.filename for f in layout.get(subject=subId,
type='bold',
task=task_name,
extensions='.nii.gz')
]
else:
funcs = [
f.filename for f in layout.get(
subject=subId, type='bold', extensions='.nii.gz')
]
#Turn functional file list into interable Node
func_scans = Node(IdentityInterface(fields=['scan']), name='func_scans')
func_scans.iterables = ('scan', funcs)
#Get TR for use in filtering below; we're assuming all BOLD runs have the same TR
tr_length = layout.get_metadata(funcs[0])['RepetitionTime']
#####################################
## TRIM ##
#####################################
if apply_trim:
trim = Node(Trim(), name='trim')
trim.inputs.begin_index = apply_trim
#####################################
## DISTORTION CORRECTION ##
#####################################
if apply_dist_corr:
#Get fmap file locations
fmaps = [
f.filename for f in layout.get(
subject=subId, modality='fmap', extensions='.nii.gz')
]
if not fmaps:
raise IOError(
"Distortion Correction requested but field map scans not found..."
)
#Get fmap metadata
totalReadoutTimes, measurements, fmap_pes = [], [], []
for i, fmap in enumerate(fmaps):
# Grab total readout time for each fmap
totalReadoutTimes.append(
layout.get_metadata(fmap)['TotalReadoutTime'])
# Grab measurements (for some reason pyBIDS doesn't grab dcm_meta... fields from side-car json file and json.load, doesn't either; so instead just read the header using nibabel to determine number of scans)
measurements.append(nib.load(fmap).header['dim'][4])
# Get phase encoding direction
fmap_pe = layout.get_metadata(fmap)["PhaseEncodingDirection"]
fmap_pes.append(fmap_pe)
encoding_file_writer = Node(interface=Create_Encoding_File(),
name='create_encoding')
encoding_file_writer.inputs.totalReadoutTimes = totalReadoutTimes
encoding_file_writer.inputs.fmaps = fmaps
encoding_file_writer.inputs.fmap_pes = fmap_pes
encoding_file_writer.inputs.measurements = measurements
encoding_file_writer.inputs.file_name = 'encoding_file.txt'
merge_to_file_list = Node(interface=Merge(2),
infields=['in1', 'in2'],
name='merge_to_file_list')
merge_to_file_list.inputs.in1 = fmaps[0]
merge_to_file_list.inputs.in1 = fmaps[1]
#Merge AP and PA distortion correction scans
merger = Node(interface=MERGE(dimension='t'), name='merger')
merger.inputs.output_type = 'NIFTI_GZ'
merger.inputs.in_files = fmaps
merger.inputs.merged_file = 'merged_epi.nii.gz'
#Create distortion correction map
topup = Node(interface=TOPUP(), name='topup')
topup.inputs.output_type = 'NIFTI_GZ'
#Apply distortion correction to other scans
apply_topup = Node(interface=ApplyTOPUP(), name='apply_topup')
apply_topup.inputs.output_type = 'NIFTI_GZ'
apply_topup.inputs.method = 'jac'
apply_topup.inputs.interp = 'spline'
###################################
### REALIGN ###
###################################
realign_fsl = Node(MCFLIRT(), name="realign")
realign_fsl.inputs.cost = 'mutualinfo'
realign_fsl.inputs.mean_vol = True
realign_fsl.inputs.output_type = 'NIFTI_GZ'
realign_fsl.inputs.save_mats = True
realign_fsl.inputs.save_rms = True
realign_fsl.inputs.save_plots = True
###################################
### MEAN EPIs ###
###################################
#For coregistration after realignment
mean_epi = Node(MeanImage(), name='mean_epi')
mean_epi.inputs.dimension = 'T'
#For after normalization is done to plot checks
mean_norm_epi = Node(MeanImage(), name='mean_norm_epi')
mean_norm_epi.inputs.dimension = 'T'
###################################
### MASK, ART, COV CREATION ###
###################################
compute_mask = Node(ComputeMask(), name='compute_mask')
compute_mask.inputs.m = .05
art = Node(ArtifactDetect(), name='art')
art.inputs.use_differences = [True, False]
art.inputs.use_norm = True
art.inputs.norm_threshold = 1
art.inputs.zintensity_threshold = 3
art.inputs.mask_type = 'file'
art.inputs.parameter_source = 'FSL'
make_cov = Node(Create_Covariates(), name='make_cov')
################################
### N4 BIAS FIELD CORRECTION ###
################################
if apply_n4:
n4_correction = Node(N4BiasFieldCorrection(), name='n4_correction')
n4_correction.inputs.copy_header = True
n4_correction.inputs.save_bias = False
n4_correction.inputs.num_threads = ants_threads
n4_correction.inputs.input_image = anat
###################################
### BRAIN EXTRACTION ###
###################################
brain_extraction_ants = Node(BrainExtraction(), name='brain_extraction')
brain_extraction_ants.inputs.dimension = 3
brain_extraction_ants.inputs.use_floatingpoint_precision = 1
brain_extraction_ants.inputs.num_threads = ants_threads
brain_extraction_ants.inputs.brain_probability_mask = bet_ants_prob_mask
brain_extraction_ants.inputs.keep_temporary_files = 1
brain_extraction_ants.inputs.brain_template = bet_ants_template
brain_extraction_ants.inputs.extraction_registration_mask = bet_ants_registration_mask
brain_extraction_ants.inputs.out_prefix = 'bet'
###################################
### COREGISTRATION ###
###################################
coregistration = Node(Registration(), name='coregistration')
coregistration.inputs.float = False
coregistration.inputs.output_transform_prefix = "meanEpi2highres"
coregistration.inputs.transforms = ['Rigid']
coregistration.inputs.transform_parameters = [(0.1, ), (0.1, )]
coregistration.inputs.number_of_iterations = [[1000, 500, 250, 100]]
coregistration.inputs.dimension = 3
coregistration.inputs.num_threads = ants_threads
coregistration.inputs.write_composite_transform = True
coregistration.inputs.collapse_output_transforms = True
coregistration.inputs.metric = ['MI']
coregistration.inputs.metric_weight = [1]
coregistration.inputs.radius_or_number_of_bins = [32]
coregistration.inputs.sampling_strategy = ['Regular']
coregistration.inputs.sampling_percentage = [0.25]
coregistration.inputs.convergence_threshold = [1e-08]
coregistration.inputs.convergence_window_size = [10]
coregistration.inputs.smoothing_sigmas = [[3, 2, 1, 0]]
coregistration.inputs.sigma_units = ['mm']
coregistration.inputs.shrink_factors = [[4, 3, 2, 1]]
coregistration.inputs.use_estimate_learning_rate_once = [True]
coregistration.inputs.use_histogram_matching = [False]
coregistration.inputs.initial_moving_transform_com = True
coregistration.inputs.output_warped_image = True
coregistration.inputs.winsorize_lower_quantile = 0.01
coregistration.inputs.winsorize_upper_quantile = 0.99
###################################
### NORMALIZATION ###
###################################
# Settings Explanations
# Only a few key settings are worth adjusting and most others relate to how ANTs optimizer starts or iterates and won't make a ton of difference
# Brian Avants referred to these settings as the last "best tested" when he was aligning fMRI data: https://github.com/ANTsX/ANTsRCore/blob/master/R/antsRegistration.R#L275
# Things that matter the most:
# smoothing_sigmas:
# how much gaussian smoothing to apply when performing registration, probably want the upper limit of this to match the resolution that the data is collected at e.g. 3mm
# Old settings [[3,2,1,0]]*3
# shrink_factors
# The coarseness with which to do registration
# Old settings [[8,4,2,1]] * 3
# >= 8 may result is some problems causing big chunks of cortex with little fine grain spatial structure to be moved to other parts of cortex
# Other settings
# transform_parameters:
# how much regularization to do for fitting that transformation
# for syn this pertains to both the gradient regularization term, and the flow, and elastic terms. Leave the syn settings alone as they seem to be the most well tested across published data sets
# radius_or_number_of_bins
# This is the bin size for MI metrics and 32 is probably adequate for most use cases. Increasing this might increase precision (e.g. to 64) but takes exponentially longer
# use_histogram_matching
# Use image intensity distribution to guide registration
# Leave it on for within modality registration (e.g. T1 -> MNI), but off for between modality registration (e.g. EPI -> T1)
# convergence_threshold
# threshold for optimizer
# convergence_window_size
# how many samples should optimizer average to compute threshold?
# sampling_strategy
# what strategy should ANTs use to initialize the transform. Regular here refers to approximately random sampling around the center of the image mass
normalization = Node(Registration(), name='normalization')
normalization.inputs.float = False
normalization.inputs.collapse_output_transforms = True
normalization.inputs.convergence_threshold = [1e-06, 1e-06, 1e-07]
normalization.inputs.convergence_window_size = [10]
normalization.inputs.dimension = 3
normalization.inputs.fixed_image = MNItemplate
normalization.inputs.initial_moving_transform_com = True
normalization.inputs.metric = ['MI', 'MI', 'CC']
normalization.inputs.metric_weight = [1.0] * 3
normalization.inputs.number_of_iterations = [[1000, 500, 250, 100],
[1000, 500, 250, 100],
[100, 70, 50, 20]]
normalization.inputs.num_threads = ants_threads
normalization.inputs.output_transform_prefix = 'anat2template'
normalization.inputs.output_inverse_warped_image = True
normalization.inputs.output_warped_image = True
normalization.inputs.radius_or_number_of_bins = [32, 32, 4]
normalization.inputs.sampling_percentage = [0.25, 0.25, 1]
normalization.inputs.sampling_strategy = ['Regular', 'Regular', 'None']
normalization.inputs.shrink_factors = [[4, 3, 2, 1]] * 3
normalization.inputs.sigma_units = ['vox'] * 3
normalization.inputs.smoothing_sigmas = [[2, 1], [2, 1], [3, 2, 1, 0]]
normalization.inputs.transforms = ['Rigid', 'Affine', 'SyN']
normalization.inputs.transform_parameters = [(0.1, ), (0.1, ),
(0.1, 3.0, 0.0)]
normalization.inputs.use_histogram_matching = True
normalization.inputs.winsorize_lower_quantile = 0.005
normalization.inputs.winsorize_upper_quantile = 0.995
normalization.inputs.write_composite_transform = True
###################################
### APPLY TRANSFORMS AND SMOOTH ###
###################################
merge_transforms = Node(Merge(2),
iterfield=['in2'],
name='merge_transforms')
# Used for epi -> mni, via (coreg + norm)
apply_transforms = Node(ApplyTransforms(),
iterfield=['input_image'],
name='apply_transforms')
apply_transforms.inputs.input_image_type = 3
apply_transforms.inputs.float = False
apply_transforms.inputs.num_threads = 12
apply_transforms.inputs.environ = {}
apply_transforms.inputs.interpolation = 'BSpline'
apply_transforms.inputs.invert_transform_flags = [False, False]
apply_transforms.inputs.reference_image = MNItemplate
# Used for t1 segmented -> mni, via (norm)
apply_transform_seg = Node(ApplyTransforms(), name='apply_transform_seg')
apply_transform_seg.inputs.input_image_type = 3
apply_transform_seg.inputs.float = False
apply_transform_seg.inputs.num_threads = 12
apply_transform_seg.inputs.environ = {}
apply_transform_seg.inputs.interpolation = 'MultiLabel'
apply_transform_seg.inputs.invert_transform_flags = [False]
apply_transform_seg.inputs.reference_image = MNItemplate
###################################
### PLOTS ###
###################################
plot_realign = Node(Plot_Realignment_Parameters(), name="plot_realign")
plot_qa = Node(Plot_Quality_Control(), name="plot_qa")
plot_normalization_check = Node(Plot_Coregistration_Montage(),
name="plot_normalization_check")
plot_normalization_check.inputs.canonical_img = MNItemplatehasskull
############################################
### FILTER, SMOOTH, DOWNSAMPLE PRECISION ###
############################################
#Use cosanlab_preproc for down sampling
down_samp = Node(Down_Sample_Precision(), name="down_samp")
#Use FSL for smoothing
if apply_smooth:
smooth = Node(Smooth(), name='smooth')
if isinstance(apply_smooth, list):
smooth.iterables = ("fwhm", apply_smooth)
elif isinstance(apply_smooth, int) or isinstance(apply_smooth, float):
smooth.inputs.fwhm = apply_smooth
else:
raise ValueError("apply_smooth must be a list or int/float")
#Use cosanlab_preproc for low-pass filtering
if apply_filter:
lp_filter = Node(Filter_In_Mask(), name='lp_filter')
lp_filter.inputs.mask = MNImask
lp_filter.inputs.sampling_rate = tr_length
lp_filter.inputs.high_pass_cutoff = 0
if isinstance(apply_filter, list):
lp_filter.iterables = ("low_pass_cutoff", apply_filter)
elif isinstance(apply_filter, int) or isinstance(apply_filter, float):
lp_filter.inputs.low_pass_cutoff = apply_filter
else:
raise ValueError("apply_filter must be a list or int/float")
###################
### OUTPUT NODE ###
###################
#Collect all final outputs in the output dir and get rid of file name additions
datasink = Node(DataSink(), name='datasink')
datasink.inputs.base_directory = output_final_dir
datasink.inputs.container = subject_id
# Remove substitutions
data_dir_parts = data_dir.split('/')[1:]
prefix = ['_scan_'] + data_dir_parts + [subject_id] + ['func']
func_scan_names = [os.path.split(elem)[-1] for elem in funcs]
to_replace = []
for elem in func_scan_names:
bold_name = elem.split(subject_id + '_')[-1]
bold_name = bold_name.split('.nii.gz')[0]
to_replace.append(('..'.join(prefix + [elem]), bold_name))
datasink.inputs.substitutions = to_replace
#####################
### INIT WORKFLOW ###
#####################
workflow = Workflow(name=subId)
workflow.base_dir = output_interm_dir
############################
######### PART (1a) #########
# func -> discorr -> trim -> realign
# OR
# func -> trim -> realign
# OR
# func -> discorr -> realign
# OR
# func -> realign
############################
if apply_dist_corr:
workflow.connect([(encoding_file_writer, topup, [('encoding_file',
'encoding_file')]),
(encoding_file_writer, apply_topup,
[('encoding_file', 'encoding_file')]),
(merger, topup, [('merged_file', 'in_file')]),
(func_scans, apply_topup, [('scan', 'in_files')]),
(topup, apply_topup,
[('out_fieldcoef', 'in_topup_fieldcoef'),
('out_movpar', 'in_topup_movpar')])])
if apply_trim:
# Dist Corr + Trim
workflow.connect([(apply_topup, trim, [('out_corrected', 'in_file')
]),
(trim, realign_fsl, [('out_file', 'in_file')])])
else:
# Dist Corr + No Trim
workflow.connect([(apply_topup, realign_fsl, [('out_corrected',
'in_file')])])
else:
if apply_trim:
# No Dist Corr + Trim
workflow.connect([(func_scans, trim, [('scan', 'in_file')]),
(trim, realign_fsl, [('out_file', 'in_file')])])
else:
# No Dist Corr + No Trim
workflow.connect([
(func_scans, realign_fsl, [('scan', 'in_file')]),
])
############################
######### PART (1n) #########
# anat -> N4 -> bet
# OR
# anat -> bet
############################
if apply_n4:
workflow.connect([(n4_correction, brain_extraction_ants,
[('output_image', 'anatomical_image')])])
else:
brain_extraction_ants.inputs.anatomical_image = anat
##########################################
############### PART (2) #################
# realign -> coreg -> mni (via t1)
# t1 -> mni
# covariate creation
# plot creation
###########################################
workflow.connect([
(realign_fsl, plot_realign, [('par_file', 'realignment_parameters')]),
(realign_fsl, plot_qa, [('out_file', 'dat_img')]),
(realign_fsl, art, [('out_file', 'realigned_files'),
('par_file', 'realignment_parameters')]),
(realign_fsl, mean_epi, [('out_file', 'in_file')]),
(realign_fsl, make_cov, [('par_file', 'realignment_parameters')]),
(mean_epi, compute_mask, [('out_file', 'mean_volume')]),
(compute_mask, art, [('brain_mask', 'mask_file')]),
(art, make_cov, [('outlier_files', 'spike_id')]),
(art, plot_realign, [('outlier_files', 'outliers')]),
(plot_qa, make_cov, [('fd_outliers', 'fd_outliers')]),
(brain_extraction_ants, coregistration, [('BrainExtractionBrain',
'fixed_image')]),
(mean_epi, coregistration, [('out_file', 'moving_image')]),
(brain_extraction_ants, normalization, [('BrainExtractionBrain',
'moving_image')]),
(coregistration, merge_transforms, [('composite_transform', 'in2')]),
(normalization, merge_transforms, [('composite_transform', 'in1')]),
(merge_transforms, apply_transforms, [('out', 'transforms')]),
(realign_fsl, apply_transforms, [('out_file', 'input_image')]),
(apply_transforms, mean_norm_epi, [('output_image', 'in_file')]),
(normalization, apply_transform_seg, [('composite_transform',
'transforms')]),
(brain_extraction_ants, apply_transform_seg,
[('BrainExtractionSegmentation', 'input_image')]),
(mean_norm_epi, plot_normalization_check, [('out_file', 'wra_img')])
])
##################################################
################### PART (3) #####################
# epi (in mni) -> filter -> smooth -> down sample
# OR
# epi (in mni) -> filter -> down sample
# OR
# epi (in mni) -> smooth -> down sample
# OR
# epi (in mni) -> down sample
###################################################
if apply_filter:
workflow.connect([(apply_transforms, lp_filter, [('output_image',
'in_file')])])
if apply_smooth:
# Filtering + Smoothing
workflow.connect([(lp_filter, smooth, [('out_file', 'in_file')]),
(smooth, down_samp, [('smoothed_file', 'in_file')
])])
else:
# Filtering + No Smoothing
workflow.connect([(lp_filter, down_samp, [('out_file', 'in_file')])
])
else:
if apply_smooth:
# No Filtering + Smoothing
workflow.connect([
(apply_transforms, smooth, [('output_image', 'in_file')]),
(smooth, down_samp, [('smoothed_file', 'in_file')])
])
else:
# No Filtering + No Smoothing
workflow.connect([(apply_transforms, down_samp, [('output_image',
'in_file')])])
##########################################
############### PART (4) #################
# down sample -> save
# plots -> save
# covs -> save
# t1 (in mni) -> save
# t1 segmented masks (in mni) -> save
##########################################
workflow.connect([
(down_samp, datasink, [('out_file', 'functional.@down_samp')]),
(plot_realign, datasink, [('plot', 'functional.@plot_realign')]),
(plot_qa, datasink, [('plot', 'functional.@plot_qa')]),
(plot_normalization_check, datasink,
[('plot', 'functional.@plot_normalization')]),
(make_cov, datasink, [('covariates', 'functional.@covariates')]),
(normalization, datasink, [('warped_image', 'structural.@normanat')]),
(apply_transform_seg, datasink, [('output_image',
'structural.@normanatseg')])
])
if not os.path.exists(os.path.join(output_dir, 'pipeline.png')):
workflow.write_graph(dotfilename=os.path.join(output_dir, 'pipeline'),
format='png')
print(f"Creating workflow for subject: {subject_id}")
if ants_threads == 8:
print(
f"ANTs will utilize the default of {ants_threads} threads for parallel processing."
)
else:
print(
f"ANTs will utilize the user-requested {ants_threads} threads for parallel processing."
)
return workflow
function=utils.tsv2subjectinfo,
input_names=['events_file', 'exclude', 'confounds_file', 'trim_indices'],
output_names=['subject_info']),
name="tsv2subjinfo",
iterfield=['events_file', 'confounds_file'])
modelspec = pe.MapNode(interface=model.SpecifyModel(),
name="modelspec",
iterfield=['subject_info'])
level1design = pe.MapNode(interface=fsl.Level1Design(),
name="level1design",
iterfield=['session_info'])
modelgen = pe.MapNode(interface=fsl.FEATModel(),
name='modelgen',
iterfield=["fsf_file", "ev_files"])
trim = pe.MapNode(interface=Trim(), name="trim", iterfield=['in_file'])
applymask = pe.MapNode(interface=fsl.ApplyMask(),
name="applymask",
iterfield=["in_file", "mask_file"])
modelestimate = pe.MapNode(interface=fsl.FILMGLS(),
name='modelestimate',
iterfield=['design_file', 'in_file', 'tcon_file'])
# combine copes, varcopes, and masks across multiple sessions
copemerge = pe.MapNode(interface=fsl.Merge(dimension='t'),
iterfield=['in_files'],
name="copemerge")
varcopemerge = pe.MapNode(interface=fsl.Merge(dimension='t'),
iterfield=['in_files'],
name="varcopemerge")
def build_pipeline(model_def):
# create pointers to needed values from
# the model dictionary
# TODO - this could be refactored
TR = model_def['TR']
subject_list = model_def['subject_list']
JSON_MODEL_FILE = model_def['model_path']
working_dir = model_def['working_dir']
output_dir = model_def['output_dir']
SUBJ_DIR = model_def['SUBJ_DIR']
PROJECT_DIR = model_def['PROJECT_DIR']
TASK_NAME = model_def['TaskName']
RUNS = model_def['Runs']
MODEL_NAME = model_def['ModelName']
PROJECT_NAME = model_def['ProjectID']
BASE_DIR = model_def['BaseDirectory']
SERIAL_CORRELATIONS = "AR(1)" if not model_def.get(
'SerialCorrelations') else model_def.get('SerialCorrelations')
RESIDUALS = model_def.get('GenerateResiduals')
# SpecifyModel - Generates SPM-specific Model
modelspec = pe.Node(model.SpecifySPMModel(concatenate_runs=False,
input_units='secs',
output_units='secs',
time_repetition=TR,
high_pass_filter_cutoff=128),
output_units='scans',
name="modelspec")
# #### Level 1 Design node
#
# ** TODO -- get the right matching template file for fmriprep **
#
# * ??do we need a different mask than:
#
# `'/data00/tools/spm8/apriori/brainmask_th25.nii'`
# Level1Design - Generates an SPM design matrix
level1design = pe.Node(
spm.Level1Design(
bases={'hrf': {
'derivs': [0, 0]
}},
timing_units='secs',
interscan_interval=TR,
# model_serial_correlations='AR(1)', # [none|AR(1)|FAST]',
# 8/21/20 mbod - allow for value to be set in JSON model spec
model_serial_correlations=SERIAL_CORRELATIONS,
# TODO - allow for specified masks
mask_image=BRAIN_MASK_PATH,
global_intensity_normalization='none'),
name="level1design")
# #### Estimate Model node
# EstimateModel - estimate the parameters of the model
level1estimate = pe.Node(
spm.EstimateModel(
estimation_method={'Classical': 1},
# 8/21/20 mbod - allow for value to be set in JSON model spec
write_residuals=RESIDUALS),
name="level1estimate")
# #### Estimate Contrasts node
# EstimateContrast - estimates contrasts
conestimate = pe.Node(spm.EstimateContrast(), name="conestimate")
# ## Setup pipeline workflow for level 1 model
# Initiation of the 1st-level analysis workflow
l1analysis = pe.Workflow(name='l1analysis')
# Connect up the 1st-level analysis components
l1analysis.connect([
(modelspec, level1design, [('session_info', 'session_info')]),
(level1design, level1estimate, [('spm_mat_file', 'spm_mat_file')]),
(level1estimate, conestimate, [('spm_mat_file', 'spm_mat_file'),
('beta_images', 'beta_images'),
('residual_image', 'residual_image')])
])
# ## Set up nodes for file handling and subject selection
# ### `getsubjectinfo` node
#
# * Use `get_subject_info()` function to generate spec data structure for first level model design matrix
# Get Subject Info - get subject specific condition information
getsubjectinfo = pe.Node(util.Function(
input_names=['subject_id', 'model_path'],
output_names=['subject_info', 'realign_params', 'condition_names'],
function=get_subject_info),
name='getsubjectinfo')
makecontrasts = pe.Node(util.Function(
input_names=['subject_id', 'condition_names', 'model_path'],
output_names=['contrasts'],
function=make_contrast_list),
name='makecontrasts')
if model_def.get('ExcludeDummyScans'):
ExcludeDummyScans = model_def['ExcludeDummyScans']
else:
ExcludeDummyScans = 0
#if DEBUG:
# print(f'Excluding {ExcludeDummyScans} dummy scans.')
trimdummyscans = pe.MapNode(Trim(begin_index=ExcludeDummyScans),
name='trimdummyscans',
iterfield=['in_file'])
# ### `infosource` node
#
# * iterate over list of subject ids and generate subject ids and produce list of contrasts for subsequent nodes
# Infosource - a function free node to iterate over the list of subject names
infosource = pe.Node(util.IdentityInterface(
fields=['subject_id', 'model_path', 'resolution', 'smoothing']),
name="infosource")
try:
fwhm_list = model_def['smoothing_list']
except:
fwhm_list = [4, 6, 8]
try:
resolution_list = model_def['resolutions']
except:
resolution_list = ['low', 'medium', 'high']
infosource.iterables = [
('subject_id', subject_list),
('model_path', [JSON_MODEL_FILE] * len(subject_list)),
('resolution', resolution_list),
('smoothing', ['fwhm_{}'.format(s) for s in fwhm_list])
]
# SelectFiles - to grab the data (alternativ to DataGrabber)
## TODO: here need to figure out how to incorporate the run number and task name in call
templates = {
'func':
'{subject_id}/{resolution}/{smoothing}/sr{subject_id}_task-' +
TASK_NAME + '_run-0*_*MNI*preproc*.nii'
}
selectfiles = pe.Node(nio.SelectFiles(
templates,
base_directory='{}/{}/derivatives/nipype/resampled_and_smoothed'.
format(BASE_DIR, PROJECT_NAME)),
working_dir=working_dir,
name="selectfiles")
# ### Specify datasink node
#
# * copy files to keep from various working folders to output folder for model for subject
# Datasink - creates output folder for important outputs
datasink = pe.Node(
nio.DataSink(
base_directory=SUBJ_DIR,
parameterization=True,
#container=output_dir
),
name="datasink")
datasink.inputs.base_directory = output_dir
# Use the following DataSink output substitutions
substitutions = []
subjFolders = [(
'_model_path.*resolution_(low|medium|high)_smoothing_(fwhm_\\d{1,2})_subject_id_sub-.*/(.*)$',
'\\1/\\2/\\3')]
substitutions.extend(subjFolders)
datasink.inputs.regexp_substitutions = substitutions
# datasink connections
datasink_in_outs = [('conestimate.spm_mat_file', '@spm'),
('level1estimate.beta_images', '@betas'),
('level1estimate.mask_image', '@mask'),
('conestimate.spmT_images', '@spmT'),
('conestimate.con_images', '@con'),
('conestimate.spmF_images', '@spmF')]
if model_def.get('GenerateResiduals'):
datasink_in_outs.append(
('level1estimate.residual_images', '@residuals'))
# ---------
# ## Set up workflow for whole process
pipeline = pe.Workflow(
name='first_level_model_{}_{}'.format(TASK_NAME.upper(), MODEL_NAME))
pipeline.base_dir = os.path.join(SUBJ_DIR, working_dir)
pipeline.connect([
(infosource, selectfiles, [('subject_id', 'subject_id'),
('resolution', 'resolution'),
('smoothing', 'smoothing')]),
(infosource, getsubjectinfo, [('subject_id', 'subject_id'),
('model_path', 'model_path')]),
(infosource, makecontrasts, [('subject_id', 'subject_id'),
('model_path', 'model_path')]),
(getsubjectinfo, makecontrasts, [('condition_names', 'condition_names')
]),
(getsubjectinfo, l1analysis,
[('subject_info', 'modelspec.subject_info'),
('realign_params', 'modelspec.realignment_parameters')]),
(makecontrasts, l1analysis, [('contrasts', 'conestimate.contrasts')]),
# (selectfiles, l1analysis, [('func',
# 'modelspec.functional_runs')]),
(selectfiles, trimdummyscans, [('func', 'in_file')]),
(trimdummyscans, l1analysis, [('out_file', 'modelspec.functional_runs')
]),
(infosource, datasink, [('subject_id', 'container')]),
(l1analysis, datasink, datasink_in_outs)
])
return pipeline