def main(sourcedata,
derivatives,
tmp_dir,
subject=None,
session=None,
run=None):
print(subject, session, run)
layout = BIDSLayout(sourcedata)
derivatives_layout = BIDSLayout('/derivatives/spynoza', validate=False)
cortex_l = get_derivative(derivatives,
'nighres',
'anat',
subject,
'dseg',
session='anat',
space='average',
description='cortex',
hemi='left')
cortex_r = get_derivative(derivatives,
'nighres',
'anat',
subject,
'dseg',
session='anat',
space='average',
description='cortex',
hemi='left')
mask = derivatives_layout.get(subject=subject,
session=session,
suffix='mask',
return_type='file')
mask = sorted(mask)
assert (len(mask) == 1)
mask = mask[0]
bold = derivatives_layout.get(subject=subject,
session=session,
suffix='preproc',
return_type='file')
bold = sorted(bold)
print('BOLD: {}'.format(bold))
print('MASK: {}'.format(mask))
inputnode = pe.Node(
niu.IdentityInterface(fields=['cortex_l', 'cortex_r', 'bold', 'mask']),
name='inputnode')
inputnode.inputs.cortex_l = cortex_l
inputnode.inputs.cortex_r = cortex_r
inputnode.inputs.bold = bold
inputnode.inputs.mask = mask
get_masks = pe.MapNode(niu.Function(
function=get_brain_regions_cruise,
input_names=['cortex_l', 'cortex_r', 'type'],
output_names=['out']),
iterfield=['type'],
name='get_masks')
get_masks.inputs.type = ['csf', 'wm']
wf = pe.Workflow(name='get_confounds_{}_{}'.format(subject, session),
base_dir='/workflow_folders')
wf.connect(inputnode, 'cortex_l', get_masks, 'cortex_l')
wf.connect(inputnode, 'cortex_r', get_masks, 'cortex_r')
resampler = pe.MapNode(niu.Function(
function=resample_img,
input_names=['input_image', 'ref_image', 'interpolation'],
output_names=['resampled_image'],
),
iterfield=['input_image'],
name='resampler')
wf.connect(inputnode, ('bold', pickfirst), resampler, 'ref_image')
wf.connect(get_masks, 'out', resampler, 'input_image')
compcorr = pe.MapNode(ACompCor(merge_method='union'),
iterfield=['realigned_file'],
name='acompcorr')
wf.connect(resampler, 'resampled_image', compcorr, 'mask_files')
wf.connect(inputnode, 'bold', compcorr, 'realigned_file')
dvars = pe.MapNode(ComputeDVARS(), iterfield=['in_file'], name='dvars')
wf.connect(inputnode, 'mask', dvars, 'in_mask')
wf.connect(inputnode, 'bold', dvars, 'in_file')
add_header = pe.MapNode(AddTSVHeader(columns=["dvars"]),
iterfield=['in_file'],
name="add_header_dvars")
wf.connect(dvars, 'out_std', add_header, 'in_file')
concat = pe.MapNode(GatherConfounds(),
iterfield=['acompcor', 'dvars'],
name="concat")
wf.connect(add_header, 'out_file', concat, 'dvars')
wf.connect(compcorr, 'components_file', concat, 'acompcor')
ds_confounds = pe.MapNode(DerivativesDataSink(out_path_base='spynoza',
suffix='confounds_compcor',
base_directory=derivatives),
iterfield=['in_file', 'source_file'],
name='ds_reg_report')
wf.connect(inputnode, 'bold', ds_confounds, 'source_file')
wf.connect(concat, 'confounds_file', ds_confounds, 'in_file')
wf.run(plugin='MultiProc', plugin_args={'n_procs': 10})
def create_workflow(files,
target_file,
subject_id,
TR,
slice_times,
norm_threshold=1,
num_components=5,
vol_fwhm=None,
surf_fwhm=None,
lowpass_freq=-1,
highpass_freq=-1,
subjects_dir=None,
sink_directory=os.getcwd(),
target_subject=['fsaverage3', 'fsaverage4'],
name='resting'):
wf = Workflow(name=name)
# Rename files in case they are named identically
name_unique = MapNode(Rename(format_string='rest_%(run)02d'),
iterfield=['in_file', 'run'],
name='rename')
name_unique.inputs.keep_ext = True
name_unique.inputs.run = list(range(1, len(files) + 1))
name_unique.inputs.in_file = files
realign = Node(nipy.SpaceTimeRealigner(), name="spacetime_realign")
realign.inputs.slice_times = slice_times
realign.inputs.tr = TR
realign.inputs.slice_info = 2
realign.plugin_args = {'sbatch_args': '-c%d' % 4}
# Compute TSNR on realigned data regressing polynomials up to order 2
tsnr = MapNode(TSNR(regress_poly=2), iterfield=['in_file'], name='tsnr')
wf.connect(realign, "out_file", tsnr, "in_file")
# Compute the median image across runs
calc_median = Node(CalculateMedian(), name='median')
wf.connect(tsnr, 'detrended_file', calc_median, 'in_files')
"""Segment and Register
"""
registration = create_reg_workflow(name='registration')
wf.connect(calc_median, 'median_file', registration, 'inputspec.mean_image')
registration.inputs.inputspec.subject_id = subject_id
registration.inputs.inputspec.subjects_dir = subjects_dir
registration.inputs.inputspec.target_image = target_file
"""Quantify TSNR in each freesurfer ROI
"""
get_roi_tsnr = MapNode(fs.SegStats(default_color_table=True),
iterfield=['in_file'], name='get_aparc_tsnr')
get_roi_tsnr.inputs.avgwf_txt_file = True
wf.connect(tsnr, 'tsnr_file', get_roi_tsnr, 'in_file')
wf.connect(registration, 'outputspec.aparc', get_roi_tsnr, 'segmentation_file')
"""Use :class:`nipype.algorithms.rapidart` to determine which of the
images in the functional series are outliers based on deviations in
intensity or movement.
"""
art = Node(interface=ArtifactDetect(), name="art")
art.inputs.use_differences = [True, True]
art.inputs.use_norm = True
art.inputs.norm_threshold = norm_threshold
art.inputs.zintensity_threshold = 9
art.inputs.mask_type = 'spm_global'
art.inputs.parameter_source = 'NiPy'
"""Here we are connecting all the nodes together. Notice that we add the merge node only if you choose
to use 4D. Also `get_vox_dims` function is passed along the input volume of normalise to set the optimal
voxel sizes.
"""
wf.connect([(name_unique, realign, [('out_file', 'in_file')]),
(realign, art, [('out_file', 'realigned_files')]),
(realign, art, [('par_file', 'realignment_parameters')]),
])
def selectindex(files, idx):
import numpy as np
from nipype.utils.filemanip import filename_to_list, list_to_filename
return list_to_filename(np.array(filename_to_list(files))[idx].tolist())
mask = Node(fsl.BET(), name='getmask')
mask.inputs.mask = True
wf.connect(calc_median, 'median_file', mask, 'in_file')
# get segmentation in normalized functional space
def merge_files(in1, in2):
out_files = filename_to_list(in1)
out_files.extend(filename_to_list(in2))
return out_files
# filter some noise
# Compute motion regressors
motreg = Node(Function(input_names=['motion_params', 'order',
'derivatives'],
output_names=['out_files'],
function=motion_regressors,
imports=imports),
name='getmotionregress')
wf.connect(realign, 'par_file', motreg, 'motion_params')
# Create a filter to remove motion and art confounds
createfilter1 = Node(Function(input_names=['motion_params', 'comp_norm',
'outliers', 'detrend_poly'],
output_names=['out_files'],
function=build_filter1,
imports=imports),
name='makemotionbasedfilter')
createfilter1.inputs.detrend_poly = 2
wf.connect(motreg, 'out_files', createfilter1, 'motion_params')
wf.connect(art, 'norm_files', createfilter1, 'comp_norm')
wf.connect(art, 'outlier_files', createfilter1, 'outliers')
filter1 = MapNode(fsl.GLM(out_f_name='F_mcart.nii.gz',
out_pf_name='pF_mcart.nii.gz',
demean=True),
iterfield=['in_file', 'design', 'out_res_name'],
name='filtermotion')
wf.connect(realign, 'out_file', filter1, 'in_file')
wf.connect(realign, ('out_file', rename, '_filtermotart'),
filter1, 'out_res_name')
wf.connect(createfilter1, 'out_files', filter1, 'design')
createfilter2 = MapNode(ACompCor(),
iterfield=['realigned_file', 'extra_regressors'],
name='makecompcorrfilter')
createfilter2.inputs.components_file = 'noise_components.txt'
createfilter2.inputs.num_components = num_components
wf.connect(createfilter1, 'out_files', createfilter2, 'extra_regressors')
wf.connect(filter1, 'out_res', createfilter2, 'realigned_file')
wf.connect(registration, ('outputspec.segmentation_files', selectindex, [0, 2]),
createfilter2, 'mask_file')
filter2 = MapNode(fsl.GLM(out_f_name='F.nii.gz',
out_pf_name='pF.nii.gz',
demean=True),
iterfield=['in_file', 'design', 'out_res_name'],
name='filter_noise_nosmooth')
wf.connect(filter1, 'out_res', filter2, 'in_file')
wf.connect(filter1, ('out_res', rename, '_cleaned'),
filter2, 'out_res_name')
wf.connect(createfilter2, 'components_file', filter2, 'design')
wf.connect(mask, 'mask_file', filter2, 'mask')
bandpass = Node(Function(input_names=['files', 'lowpass_freq',
'highpass_freq', 'fs'],
output_names=['out_files'],
function=bandpass_filter,
imports=imports),
name='bandpass_unsmooth')
bandpass.inputs.fs = 1. / TR
bandpass.inputs.highpass_freq = highpass_freq
bandpass.inputs.lowpass_freq = lowpass_freq
wf.connect(filter2, 'out_res', bandpass, 'files')
"""Smooth the functional data using
:class:`nipype.interfaces.fsl.IsotropicSmooth`.
"""
smooth = MapNode(interface=fsl.IsotropicSmooth(), name="smooth", iterfield=["in_file"])
smooth.inputs.fwhm = vol_fwhm
wf.connect(bandpass, 'out_files', smooth, 'in_file')
collector = Node(Merge(2), name='collect_streams')
wf.connect(smooth, 'out_file', collector, 'in1')
wf.connect(bandpass, 'out_files', collector, 'in2')
"""
Transform the remaining images. First to anatomical and then to target
"""
warpall = MapNode(ants.ApplyTransforms(), iterfield=['input_image'],
name='warpall')
warpall.inputs.input_image_type = 3
warpall.inputs.interpolation = 'Linear'
warpall.inputs.invert_transform_flags = [False, False]
warpall.terminal_output = 'file'
warpall.inputs.reference_image = target_file
warpall.inputs.args = '--float'
warpall.inputs.num_threads = 2
warpall.plugin_args = {'sbatch_args': '-c%d' % 2}
# transform to target
wf.connect(collector, 'out', warpall, 'input_image')
wf.connect(registration, 'outputspec.transforms', warpall, 'transforms')
mask_target = Node(fsl.ImageMaths(op_string='-bin'), name='target_mask')
wf.connect(registration, 'outputspec.anat2target', mask_target, 'in_file')
maskts = MapNode(fsl.ApplyMask(), iterfield=['in_file'], name='ts_masker')
wf.connect(warpall, 'output_image', maskts, 'in_file')
wf.connect(mask_target, 'out_file', maskts, 'mask_file')
# map to surface
# extract aparc+aseg ROIs
# extract subcortical ROIs
# extract target space ROIs
# combine subcortical and cortical rois into a single cifti file
#######
# Convert aparc to subject functional space
# Sample the average time series in aparc ROIs
sampleaparc = MapNode(freesurfer.SegStats(default_color_table=True),
iterfield=['in_file', 'summary_file',
'avgwf_txt_file'],
name='aparc_ts')
sampleaparc.inputs.segment_id = ([8] + list(range(10, 14)) + [17, 18, 26, 47] +
list(range(49, 55)) + [58] + list(range(1001, 1036)) +
list(range(2001, 2036)))
wf.connect(registration, 'outputspec.aparc',
sampleaparc, 'segmentation_file')
wf.connect(collector, 'out', sampleaparc, 'in_file')
def get_names(files, suffix):
"""Generate appropriate names for output files
"""
from nipype.utils.filemanip import (split_filename, filename_to_list,
list_to_filename)
import os
out_names = []
for filename in files:
path, name, _ = split_filename(filename)
out_names.append(os.path.join(path, name + suffix))
return list_to_filename(out_names)
wf.connect(collector, ('out', get_names, '_avgwf.txt'),
sampleaparc, 'avgwf_txt_file')
wf.connect(collector, ('out', get_names, '_summary.stats'),
sampleaparc, 'summary_file')
# Sample the time series onto the surface of the target surface. Performs
# sampling into left and right hemisphere
target = Node(IdentityInterface(fields=['target_subject']), name='target')
target.iterables = ('target_subject', filename_to_list(target_subject))
samplerlh = MapNode(freesurfer.SampleToSurface(),
iterfield=['source_file'],
name='sampler_lh')
samplerlh.inputs.sampling_method = "average"
samplerlh.inputs.sampling_range = (0.1, 0.9, 0.1)
samplerlh.inputs.sampling_units = "frac"
samplerlh.inputs.interp_method = "trilinear"
samplerlh.inputs.smooth_surf = surf_fwhm
# samplerlh.inputs.cortex_mask = True
samplerlh.inputs.out_type = 'niigz'
samplerlh.inputs.subjects_dir = subjects_dir
samplerrh = samplerlh.clone('sampler_rh')
samplerlh.inputs.hemi = 'lh'
wf.connect(collector, 'out', samplerlh, 'source_file')
wf.connect(registration, 'outputspec.out_reg_file', samplerlh, 'reg_file')
wf.connect(target, 'target_subject', samplerlh, 'target_subject')
samplerrh.set_input('hemi', 'rh')
wf.connect(collector, 'out', samplerrh, 'source_file')
wf.connect(registration, 'outputspec.out_reg_file', samplerrh, 'reg_file')
wf.connect(target, 'target_subject', samplerrh, 'target_subject')
# Combine left and right hemisphere to text file
combiner = MapNode(Function(input_names=['left', 'right'],
output_names=['out_file'],
function=combine_hemi,
imports=imports),
iterfield=['left', 'right'],
name="combiner")
wf.connect(samplerlh, 'out_file', combiner, 'left')
wf.connect(samplerrh, 'out_file', combiner, 'right')
# Sample the time series file for each subcortical roi
ts2txt = MapNode(Function(input_names=['timeseries_file', 'label_file',
'indices'],
output_names=['out_file'],
function=extract_subrois,
imports=imports),
iterfield=['timeseries_file'],
name='getsubcortts')
ts2txt.inputs.indices = [8] + list(range(10, 14)) + [17, 18, 26, 47] +\
list(range(49, 55)) + [58]
ts2txt.inputs.label_file = \
os.path.abspath(('OASIS-TRT-20_jointfusion_DKT31_CMA_labels_in_MNI152_'
'2mm_v2.nii.gz'))
wf.connect(maskts, 'out_file', ts2txt, 'timeseries_file')
######
substitutions = [('_target_subject_', ''),
('_filtermotart_cleaned_bp_trans_masked', ''),
('_filtermotart_cleaned_bp', ''),
]
substitutions += [("_smooth%d" % i, "") for i in range(11)[::-1]]
substitutions += [("_ts_masker%d" % i, "") for i in range(11)[::-1]]
substitutions += [("_getsubcortts%d" % i, "") for i in range(11)[::-1]]
substitutions += [("_combiner%d" % i, "") for i in range(11)[::-1]]
substitutions += [("_filtermotion%d" % i, "") for i in range(11)[::-1]]
substitutions += [("_filter_noise_nosmooth%d" % i, "") for i in range(11)[::-1]]
substitutions += [("_makecompcorfilter%d" % i, "") for i in range(11)[::-1]]
substitutions += [("_get_aparc_tsnr%d/" % i, "run%d_" % (i + 1)) for i in range(11)[::-1]]
substitutions += [("T1_out_brain_pve_0_maths_warped", "compcor_csf"),
("T1_out_brain_pve_1_maths_warped", "compcor_gm"),
("T1_out_brain_pve_2_maths_warped", "compcor_wm"),
("output_warped_image_maths", "target_brain_mask"),
("median_brain_mask", "native_brain_mask"),
("corr_", "")]
regex_subs = [('_combiner.*/sar', '/smooth/'),
('_combiner.*/ar', '/unsmooth/'),
('_aparc_ts.*/sar', '/smooth/'),
('_aparc_ts.*/ar', '/unsmooth/'),
('_getsubcortts.*/sar', '/smooth/'),
('_getsubcortts.*/ar', '/unsmooth/'),
('series/sar', 'series/smooth/'),
('series/ar', 'series/unsmooth/'),
('_inverse_transform./', ''),
]
# Save the relevant data into an output directory
datasink = Node(interface=DataSink(), name="datasink")
datasink.inputs.base_directory = sink_directory
datasink.inputs.container = subject_id
datasink.inputs.substitutions = substitutions
datasink.inputs.regexp_substitutions = regex_subs # (r'(/_.*(\d+/))', r'/run\2')
wf.connect(realign, 'par_file', datasink, 'resting.qa.motion')
wf.connect(art, 'norm_files', datasink, 'resting.qa.art.@norm')
wf.connect(art, 'intensity_files', datasink, 'resting.qa.art.@intensity')
wf.connect(art, 'outlier_files', datasink, 'resting.qa.art.@outlier_files')
wf.connect(registration, 'outputspec.segmentation_files', datasink, 'resting.mask_files')
wf.connect(registration, 'outputspec.anat2target', datasink, 'resting.qa.ants')
wf.connect(mask, 'mask_file', datasink, 'resting.mask_files.@brainmask')
wf.connect(mask_target, 'out_file', datasink, 'resting.mask_files.target')
wf.connect(filter1, 'out_f', datasink, 'resting.qa.compmaps.@mc_F')
wf.connect(filter1, 'out_pf', datasink, 'resting.qa.compmaps.@mc_pF')
wf.connect(filter2, 'out_f', datasink, 'resting.qa.compmaps')
wf.connect(filter2, 'out_pf', datasink, 'resting.qa.compmaps.@p')
wf.connect(registration, 'outputspec.min_cost_file', datasink, 'resting.qa.mincost')
wf.connect(tsnr, 'tsnr_file', datasink, 'resting.qa.tsnr.@map')
wf.connect([(get_roi_tsnr, datasink, [('avgwf_txt_file', 'resting.qa.tsnr'),
('summary_file', 'resting.qa.tsnr.@summary')])])
wf.connect(bandpass, 'out_files', datasink, 'resting.timeseries.@bandpassed')
wf.connect(smooth, 'out_file', datasink, 'resting.timeseries.@smoothed')
wf.connect(createfilter1, 'out_files',
datasink, 'resting.regress.@regressors')
wf.connect(createfilter2, 'components_file',
datasink, 'resting.regress.@compcorr')
wf.connect(maskts, 'out_file', datasink, 'resting.timeseries.target')
wf.connect(sampleaparc, 'summary_file',
datasink, 'resting.parcellations.aparc')
wf.connect(sampleaparc, 'avgwf_txt_file',
datasink, 'resting.parcellations.aparc.@avgwf')
wf.connect(ts2txt, 'out_file',
datasink, 'resting.parcellations.grayo.@subcortical')
datasink2 = Node(interface=DataSink(), name="datasink2")
datasink2.inputs.base_directory = sink_directory
datasink2.inputs.container = subject_id
datasink2.inputs.substitutions = substitutions
datasink2.inputs.regexp_substitutions = regex_subs # (r'(/_.*(\d+/))', r'/run\2')
wf.connect(combiner, 'out_file',
datasink2, 'resting.parcellations.grayo.@surface')
return wf
def create_compcor_workflow(name='compcor'):
""" Creates A/T compcor workflow. """
input_node = pe.Node(interface=IdentityInterface(fields=[
'in_file', 'fast_files', 'highres2epi_mat', 'n_comp_tcompcor',
'n_comp_acompcor', 'output_directory', 'sub_id'
]),
name='inputspec')
output_node = pe.Node(interface=IdentityInterface(
fields=['tcompcor_file', 'acompcor_file', 'epi_mask']),
name='outputspec')
extract_task = pe.MapNode(interface=Extract_task,
iterfield=['in_file'],
name='extract_task')
rename_acompcor = pe.MapNode(interface=Rename(
format_string='task-%(task)s_acompcor.tsv', keepext=True),
iterfield=['task', 'in_file'],
name='rename_acompcor')
datasink = pe.Node(DataSink(), name='sinker')
datasink.inputs.parameterization = False
average_func = pe.MapNode(interface=fsl.maths.MeanImage(dimension='T'),
name='average_func',
iterfield=['in_file'])
epi_mask = pe.MapNode(interface=fsl.BET(frac=.3,
mask=True,
no_output=True,
robust=True),
iterfield=['in_file'],
name='epi_mask')
wm2epi = pe.MapNode(fsl.ApplyXFM(interp='nearestneighbour'),
iterfield=['reference'],
name='wm2epi')
csf2epi = pe.MapNode(fsl.ApplyXFM(interp='nearestneighbour'),
iterfield=['reference'],
name='csf2epi')
erode_csf = pe.MapNode(interface=Erode_mask,
name='erode_csf',
iterfield=['epi_mask', 'in_file'])
erode_csf.inputs.erosion_mm = 0
erode_csf.inputs.epi_mask_erosion_mm = 30
erode_wm = pe.MapNode(interface=Erode_mask,
name='erode_wm',
iterfield=['epi_mask', 'in_file'])
erode_wm.inputs.erosion_mm = 6
erode_wm.inputs.epi_mask_erosion_mm = 10
merge_wm_and_csf_masks = pe.MapNode(Merge(2),
name='merge_wm_and_csf_masks',
iterfield=['in1', 'in2'])
# This should be fit on the 30mm eroded mask from CSF
tcompcor = pe.MapNode(TCompCor(components_file='tcomcor_comps.txt'),
iterfield=['realigned_file', 'mask_files'],
name='tcompcor')
# WM + CSF mask
acompcor = pe.MapNode(ACompCor(components_file='acompcor_comps.txt',
merge_method='union'),
iterfield=['realigned_file', 'mask_files'],
name='acompcor')
compcor_wf = pe.Workflow(name=name)
compcor_wf.connect(input_node, 'in_file', extract_task, 'in_file')
compcor_wf.connect(extract_task, 'task_name', rename_acompcor, 'task')
compcor_wf.connect(acompcor, 'components_file', rename_acompcor, 'in_file')
compcor_wf.connect(input_node, 'sub_id', datasink, 'container')
compcor_wf.connect(input_node, 'output_directory', datasink,
'base_directory')
compcor_wf.connect(input_node, ('fast_files', pick_wm), wm2epi, 'in_file')
compcor_wf.connect(epi_mask, 'mask_file', wm2epi, 'reference')
compcor_wf.connect(input_node, 'highres2epi_mat', wm2epi, 'in_matrix_file')
compcor_wf.connect(input_node, ('fast_files', pick_csf), csf2epi,
'in_file')
compcor_wf.connect(epi_mask, 'mask_file', csf2epi, 'reference')
compcor_wf.connect(input_node, 'highres2epi_mat', csf2epi,
'in_matrix_file')
compcor_wf.connect(input_node, 'n_comp_tcompcor', tcompcor,
'num_components')
compcor_wf.connect(input_node, 'n_comp_acompcor', acompcor,
'num_components')
compcor_wf.connect(input_node, 'in_file', average_func, 'in_file')
compcor_wf.connect(average_func, 'out_file', epi_mask, 'in_file')
compcor_wf.connect(epi_mask, 'mask_file', erode_csf, 'epi_mask')
compcor_wf.connect(epi_mask, 'mask_file', erode_wm, 'epi_mask')
compcor_wf.connect(wm2epi, 'out_file', erode_wm, 'in_file')
compcor_wf.connect(csf2epi, 'out_file', erode_csf, 'in_file')
compcor_wf.connect(erode_wm, 'roi_eroded', merge_wm_and_csf_masks, 'in1')
compcor_wf.connect(erode_csf, 'roi_eroded', merge_wm_and_csf_masks, 'in2')
compcor_wf.connect(merge_wm_and_csf_masks, 'out', acompcor, 'mask_files')
compcor_wf.connect(input_node, 'in_file', acompcor, 'realigned_file')
compcor_wf.connect(input_node, 'in_file', tcompcor, 'realigned_file')
compcor_wf.connect(erode_csf, 'epi_mask_eroded', tcompcor, 'mask_files')
#compcor_wf.connect(tcompcor, 'components_file', output_node, 'acompcor_file')
#compcor_wf.connect(acompcor, 'components_file', output_node, 'tcompcor_file')
compcor_wf.connect(epi_mask, 'mask_file', output_node, 'epi_mask')
compcor_wf.connect(rename_acompcor, 'out_file', datasink, 'acompcor_file')
#compcor_wf.connect(tcompcor, 'components_file', combine_files, 'tcomp')
#compcor_wf.connect(acompcor, 'components_file', combine_files, 'acomp')
#compcor_wf.connect(combine_files, 'out_file', datasink, 'confounds')
return compcor_wf