def create_prep(name='preproc'):
""" Base preprocessing workflow for task and resting state fMRI
Parameters
----------
name : name of workflow. Default = 'preproc'
Inputs
------
inputspec.fssubject_id :
inputspec.fssubject_dir :
inputspec.func :
inputspec.highpass :
inputspec.num_noise_components :
inputspec.ad_normthresh :
inputspec.ad_zthresh :
inputspec.tr :
inputspec.interleaved :
inputspec.sliceorder :
inputspec.compcor_select :
inputspec.highpass_sigma :
inputspec.lowpass_sigma :
inputspec.reg_params :
inputspec.FM_TEdiff :
inputspec.FM_Echo_spacing :
inputspec.FM_sigma :
Outputs
-------
outputspec.reference :
outputspec.motion_parameters :
outputspec.realigned_files :
outputspec.mask :
outputspec.smoothed_files :
outputspec.highpassed_files :
outputspec.mean :
outputspec.combined_motion :
outputspec.outlier_files :
outputspec.mask :
outputspec.reg_cost :
outputspec.reg_file :
outputspec.noise_components :
outputspec.tsnr_file :
outputspec.stddev_file :
outputspec.filter_file :
outputspec.scaled_files :
outputspec.z_img :
outputspec.motion_plots :
outputspec.FM_unwarped_mean :
outputspec.FM_unwarped_epi :
Returns
-------
workflow : preprocessing workflow
"""
import nipype.interfaces.fsl as fsl # fsl
import nipype.algorithms.rapidart as ra # rapid artifact detection
from nipype.workflows.smri.freesurfer.utils import create_getmask_flow
from modular_nodes import create_mod_smooth, mod_realign, mod_despike
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
preproc = pe.Workflow(name=name)
# Compcorr node
compcor = create_compcorr()
# Input node
inputnode = pe.Node(util.IdentityInterface(fields=[
'fssubject_id', 'fssubject_dir', 'func', 'highpass',
'num_noise_components', 'ad_normthresh', 'ad_zthresh', 'tr',
'do_slicetime', 'sliceorder', 'compcor_select', 'highpass_freq',
'lowpass_freq', 'reg_params', 'FM_TEdiff', 'FM_Echo_spacing',
'FM_sigma', 'motion_correct_node', 'smooth_type', 'surface_fwhm',
'filter_type', 'timepoints_to_remove', 'do_whitening',
'regress_before_PCA', 'realign_parameters', 'do_despike', 'anatomical'
]),
name='inputspec')
# Separate input node for FWHM
inputnode_fwhm = pe.Node(util.IdentityInterface(fields=['fwhm']),
name='fwhm_input')
# strip ids
strip_rois = pe.MapNode(fsl.ExtractROI(),
name='extractroi',
iterfield='in_file')
strip_rois.inputs.t_size = -1
preproc.connect(inputnode, 'timepoints_to_remove', strip_rois, 't_min')
# convert BOLD images to float
img2float = pe.MapNode(interface=fsl.ImageMaths(out_data_type='float',
op_string='',
suffix='_dtype'),
iterfield=['in_file'],
name='img2float')
#afni despike
despike = pe.MapNode(util.Function(input_names=['in_file', "do_despike"],
output_names=["out_file"],
function=mod_despike),
name="despike",
iterfield=["in_file"])
preproc.connect(inputnode, "do_despike", despike, "do_despike")
# define the motion correction node
#motion_correct = pe.Node(interface=FmriRealign4d(),
# name='realign')
motion_correct = pe.Node(util.Function(
input_names=[
'node', 'in_file', 'tr', 'do_slicetime', 'sliceorder', "parameters"
],
output_names=['out_file', 'par_file', 'parameter_source'],
function=mod_realign),
name="mod_realign")
preproc.connect(inputnode, 'motion_correct_node', motion_correct, 'node')
# construct motion plots
#plot_motion = pe.MapNode(interface=fsl.PlotMotionParams(in_source='fsl'),
# name='plot_motion',
# iterfield=['in_file'])
# rapidArt for artifactual timepoint detection
ad = pe.Node(ra.ArtifactDetect(save_plot=False), name='artifactdetect')
# extract the mean volume if the first functional run
meanfunc = art_mean_workflow()
# generate a freesurfer workflow that will return the mask
getmask = create_getmask_flow()
# create a SUSAN smoothing workflow, and smooth each run with
# 75% of the median value for each run as the brightness
# threshold.
smooth = create_mod_smooth(name="modular_smooth", separate_masks=False)
preproc.connect(inputnode, 'smooth_type', smooth, 'inputnode.smooth_type')
# choose susan function
"""
The following node selects smooth or unsmoothed data
depending on the fwhm. This is because SUSAN defaults
to smoothing the data with about the voxel size of
the input data if the fwhm parameter is less than 1/3 of
the voxel size.
"""
choosesusan = pe.Node(util.Function(
input_names=['fwhm', 'motion_files', 'smoothed_files'],
output_names=['cor_smoothed_files'],
function=choose_susan),
name='select_smooth')
# scale the median value of each run to 10,000
meanscale = pe.MapNode(interface=fsl.ImageMaths(suffix='_gms'),
iterfield=['in_file', 'op_string'],
name='scale_median')
# determine the median value of the MASKED functional runs
medianval = pe.MapNode(interface=fsl.ImageStats(op_string='-k %s -p 50'),
iterfield=['in_file'],
name='compute_median_val')
# temporal highpass filtering
highpass = pe.MapNode(interface=fsl.ImageMaths(suffix='_tempfilt'),
iterfield=['in_file'],
name='highpass')
# Calculate the z-score of output
zscore = pe.MapNode(interface=util.Function(
input_names=['image', 'outliers'],
output_names=['z_img'],
function=z_image),
name='z_score',
iterfield=['image', 'outliers'])
# declare some node inputs...
#plot_motion.iterables = ('plot_type', ['rotations', 'translations'])
#ad.inputs.parameter_source = 'FSL'
meanfunc.inputs.inputspec.parameter_source = 'FSL'
ad.inputs.mask_type = 'file'
ad.inputs.use_differences = [True, False]
getmask.inputs.inputspec.contrast_type = 't2'
getmask.inputs.register.out_fsl_file = True
fssource = getmask.get_node('fssource')
# make connections...
preproc.connect(inputnode, 'fssubject_id', getmask, 'inputspec.subject_id')
preproc.connect(inputnode, 'ad_normthresh', ad, 'norm_threshold')
preproc.connect(inputnode, 'ad_zthresh', ad, 'zintensity_threshold')
preproc.connect(inputnode, 'tr', motion_correct, 'tr')
preproc.connect(inputnode, 'realign_parameters', motion_correct,
'parameters')
preproc.connect(motion_correct, 'parameter_source', ad, 'parameter_source')
preproc.connect(inputnode, 'do_slicetime', motion_correct, 'do_slicetime')
preproc.connect(inputnode, 'sliceorder', motion_correct, 'sliceorder')
preproc.connect(inputnode, 'compcor_select', compcor, 'inputspec.selector')
preproc.connect(inputnode, 'fssubject_dir', getmask,
'inputspec.subjects_dir')
#preproc.connect(inputnode, 'func',
# img2float, 'in_file')
preproc.connect(inputnode, 'func', strip_rois, 'in_file')
preproc.connect(strip_rois, 'roi_file', img2float, 'in_file')
preproc.connect(img2float, 'out_file', despike, "in_file")
preproc.connect(despike, "out_file", motion_correct, 'in_file')
#preproc.connect(motion_correct, 'par_file',
# plot_motion, 'in_file')
preproc.connect(motion_correct, 'out_file', meanfunc,
'inputspec.realigned_files')
preproc.connect(motion_correct, 'par_file', meanfunc,
'inputspec.realignment_parameters')
preproc.connect(meanfunc, 'outputspec.mean_image', getmask,
'inputspec.source_file')
preproc.connect(inputnode, 'num_noise_components', compcor,
'inputspec.num_components')
preproc.connect(inputnode, 'regress_before_PCA', compcor,
'inputspec.regress_before_PCA')
preproc.connect(motion_correct, 'out_file', compcor,
'inputspec.realigned_file')
preproc.connect(meanfunc, 'outputspec.mean_image', compcor,
'inputspec.mean_file')
preproc.connect(fssource, 'aseg', compcor, 'inputspec.fsaseg_file')
preproc.connect(getmask, ('outputspec.reg_file', pickfirst), compcor,
'inputspec.reg_file')
preproc.connect(ad, 'outlier_files', compcor, 'inputspec.outlier_files')
preproc.connect(motion_correct, 'par_file', compcor,
'inputspec.realignment_parameters')
preproc.connect(motion_correct, 'out_file', ad, 'realigned_files')
preproc.connect(motion_correct, 'par_file', ad, 'realignment_parameters')
preproc.connect(getmask, ('outputspec.mask_file', pickfirst), ad,
'mask_file')
preproc.connect(getmask, ('outputspec.mask_file', pickfirst), medianval,
'mask_file')
preproc.connect(inputnode_fwhm, 'fwhm', smooth, 'inputnode.fwhm')
preproc.connect(motion_correct, 'out_file', smooth, 'inputnode.in_files')
preproc.connect(getmask, ('outputspec.mask_file', pickfirst), smooth,
'inputnode.mask_file')
preproc.connect(getmask, ('outputspec.reg_file', pickfirst), smooth,
'inputnode.reg_file')
preproc.connect(inputnode, 'surface_fwhm', smooth,
'inputnode.surface_fwhm')
preproc.connect(inputnode, 'fssubject_dir', smooth, 'inputnode.surf_dir')
preproc.connect(smooth, 'outputnode.smoothed_files', choosesusan,
'smoothed_files')
preproc.connect(motion_correct, 'out_file', choosesusan, 'motion_files')
preproc.connect(inputnode_fwhm, 'fwhm', choosesusan, 'fwhm')
preproc.connect(choosesusan, 'cor_smoothed_files', meanscale, 'in_file')
preproc.connect(choosesusan, 'cor_smoothed_files', medianval, 'in_file')
preproc.connect(medianval, ('out_stat', getmeanscale), meanscale,
'op_string')
preproc.connect(inputnode, ('highpass', highpass_operand), highpass,
'op_string')
preproc.connect(meanscale, 'out_file', highpass, 'in_file')
preproc.connect(highpass, 'out_file', zscore, 'image')
preproc.connect(ad, 'outlier_files', zscore, 'outliers')
# create output node
outputnode = pe.Node(interface=util.IdentityInterface(fields=[
'mean', 'motion_parameters', 'realigned_files', 'smoothed_files',
'highpassed_files', 'combined_motion', 'outlier_files',
'outlier_stat_files', 'mask', 'reg_cost', 'reg_file', 'reg_fsl_file',
'noise_components', 'tsnr_file', 'stddev_file', 'tsnr_detrended',
'filter_file', 'scaled_files', 'unmasked_fullspectrum', 'z_img',
'motion_plots', 'FM_unwarped_epi', 'FM_unwarped_mean', 'vsm_file',
'bandpassed_file', 'intensity_files', 'noise_mask', 'csf_mask'
]),
name='outputspec')
# make output connection
preproc.connect(meanfunc, 'outputspec.mean_image', outputnode, 'mean')
preproc.connect(motion_correct, 'par_file', outputnode,
'motion_parameters')
preproc.connect(motion_correct, 'out_file', outputnode, 'realigned_files')
preproc.connect(highpass, 'out_file', outputnode, 'highpassed_files')
preproc.connect(ad, 'norm_files', outputnode, 'combined_motion')
preproc.connect(ad, 'outlier_files', outputnode, 'outlier_files')
preproc.connect(ad, 'intensity_files', outputnode, 'intensity_files')
preproc.connect(ad, 'statistic_files', outputnode, 'outlier_stat_files')
preproc.connect(compcor, 'outputspec.noise_components', outputnode,
'noise_components')
preproc.connect(compcor, 'outputspec.noise_mask', outputnode, 'noise_mask')
preproc.connect(compcor, 'outputspec.csf_mask', outputnode, 'csf_mask')
preproc.connect(getmask, 'outputspec.mask_file', outputnode, 'mask')
preproc.connect(getmask, 'register.out_fsl_file', outputnode,
'reg_fsl_file')
preproc.connect(getmask, 'outputspec.reg_file', outputnode, 'reg_file')
preproc.connect(getmask, 'outputspec.reg_cost', outputnode, 'reg_cost')
preproc.connect(choosesusan, 'cor_smoothed_files', outputnode,
'smoothed_files')
preproc.connect(compcor, 'outputspec.tsnr_file', outputnode, 'tsnr_file')
preproc.connect(compcor, 'outputspec.stddev_file', outputnode,
'stddev_file')
preproc.connect(compcor, 'outputspec.tsnr_detrended', outputnode,
'tsnr_detrended')
preproc.connect(zscore, 'z_img', outputnode, 'z_img')
#preproc.connect(plot_motion,'out_file',
# outputnode,'motion_plots')
return preproc
def create_prep(name='preproc'):
""" Base preprocessing workflow for task and resting state fMRI
Parameters
----------
name : name of workflow. Default = 'preproc'
Inputs
------
inputspec.fssubject_id :
inputspec.fssubject_dir :
inputspec.func :
inputspec.highpass :
inputspec.num_noise_components :
inputspec.ad_normthresh :
inputspec.ad_zthresh :
inputspec.tr :
inputspec.interleaved :
inputspec.sliceorder :
inputspec.compcor_select :
inputspec.highpass_sigma :
inputspec.lowpass_sigma :
inputspec.reg_params :
inputspec.FM_TEdiff :
inputspec.FM_Echo_spacing :
inputspec.FM_sigma :
Outputs
-------
outputspec.reference :
outputspec.motion_parameters :
outputspec.realigned_files :
outputspec.mask :
outputspec.smoothed_files :
outputspec.highpassed_files :
outputspec.mean :
outputspec.combined_motion :
outputspec.outlier_files :
outputspec.mask :
outputspec.reg_cost :
outputspec.reg_file :
outputspec.noise_components :
outputspec.tsnr_file :
outputspec.stddev_file :
outputspec.filter_file :
outputspec.scaled_files :
outputspec.z_img :
outputspec.motion_plots :
outputspec.FM_unwarped_mean :
outputspec.FM_unwarped_epi :
Returns
-------
workflow : preprocessing workflow
"""
import nipype.interfaces.fsl as fsl # fsl
import nipype.algorithms.rapidart as ra # rapid artifact detection
from nipype.workflows.smri.freesurfer.utils import create_getmask_flow
from modular_nodes import create_mod_smooth, mod_realign, mod_despike
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
preproc = pe.Workflow(name=name)
# Compcorr node
compcor = create_compcorr()
# Input node
inputnode = pe.Node(util.IdentityInterface(fields=['fssubject_id',
'fssubject_dir',
'func',
'highpass',
'num_noise_components',
'ad_normthresh',
'ad_zthresh',
'tr',
'do_slicetime',
'sliceorder',
'compcor_select',
'highpass_freq',
'lowpass_freq',
'reg_params',
'FM_TEdiff',
'FM_Echo_spacing',
'FM_sigma',
'motion_correct_node',
'smooth_type',
'surface_fwhm',
'filter_type',
'timepoints_to_remove',
'do_whitening',
'regress_before_PCA',
'realign_parameters',
'do_despike',
'anatomical']),
name='inputspec')
# Separate input node for FWHM
inputnode_fwhm = pe.Node(util.IdentityInterface(fields=['fwhm']),
name='fwhm_input')
# strip ids
strip_rois = pe.MapNode(fsl.ExtractROI(),name='extractroi',iterfield='in_file')
strip_rois.inputs.t_size = -1
preproc.connect(inputnode,'timepoints_to_remove',strip_rois,'t_min')
# convert BOLD images to float
img2float = pe.MapNode(interface=fsl.ImageMaths(out_data_type='float',
op_string='',
suffix='_dtype'),
iterfield=['in_file'],
name='img2float')
#afni despike
despike=pe.MapNode(util.Function(input_names=['in_file',"do_despike"],
output_names=["out_file"],
function=mod_despike),
name="despike",iterfield=["in_file"])
preproc.connect(inputnode,"do_despike",despike,"do_despike")
# define the motion correction node
#motion_correct = pe.Node(interface=FmriRealign4d(),
# name='realign')
motion_correct = pe.Node(util.Function(input_names=['node','in_file','tr',
'do_slicetime','sliceorder',"parameters"],
output_names=['out_file','par_file','parameter_source'],
function=mod_realign),
name="mod_realign")
preproc.connect(inputnode,'motion_correct_node',
motion_correct, 'node')
# construct motion plots
#plot_motion = pe.MapNode(interface=fsl.PlotMotionParams(in_source='fsl'),
# name='plot_motion',
# iterfield=['in_file'])
# rapidArt for artifactual timepoint detection
ad = pe.Node(ra.ArtifactDetect(save_plot=False),
name='artifactdetect')
# extract the mean volume if the first functional run
meanfunc = art_mean_workflow()
# generate a freesurfer workflow that will return the mask
getmask = create_getmask_flow()
# create a SUSAN smoothing workflow, and smooth each run with
# 75% of the median value for each run as the brightness
# threshold.
smooth = create_mod_smooth(name="modular_smooth",
separate_masks=False)
preproc.connect(inputnode,'smooth_type', smooth,'inputnode.smooth_type')
# choose susan function
"""
The following node selects smooth or unsmoothed data
depending on the fwhm. This is because SUSAN defaults
to smoothing the data with about the voxel size of
the input data if the fwhm parameter is less than 1/3 of
the voxel size.
"""
choosesusan = pe.Node(util.Function(input_names=['fwhm',
'motion_files',
'smoothed_files'],
output_names=['cor_smoothed_files'],
function=choose_susan),
name='select_smooth')
# scale the median value of each run to 10,000
meanscale = pe.MapNode(interface=fsl.ImageMaths(suffix='_gms'),
iterfield=['in_file',
'op_string'],
name='scale_median')
# determine the median value of the MASKED functional runs
medianval = pe.MapNode(interface=fsl.ImageStats(op_string='-k %s -p 50'),
iterfield=['in_file'],
name='compute_median_val')
# temporal highpass filtering
highpass = pe.MapNode(interface=fsl.ImageMaths(suffix='_tempfilt'),
iterfield=['in_file'],
name='highpass')
# Calculate the z-score of output
zscore = pe.MapNode(interface=util.Function(input_names=['image','outliers'],
output_names=['z_img'],
function=z_image),
name='z_score',
iterfield=['image','outliers'])
# declare some node inputs...
#plot_motion.iterables = ('plot_type', ['rotations', 'translations'])
#ad.inputs.parameter_source = 'FSL'
meanfunc.inputs.inputspec.parameter_source = 'FSL'
ad.inputs.mask_type = 'file'
ad.inputs.use_differences = [True, False]
getmask.inputs.inputspec.contrast_type = 't2'
getmask.inputs.register.out_fsl_file = True
fssource = getmask.get_node('fssource')
# make connections...
preproc.connect(inputnode, 'fssubject_id',
getmask, 'inputspec.subject_id')
preproc.connect(inputnode, 'ad_normthresh',
ad, 'norm_threshold')
preproc.connect(inputnode, 'ad_zthresh',
ad, 'zintensity_threshold')
preproc.connect(inputnode, 'tr',
motion_correct, 'tr')
preproc.connect(inputnode, 'realign_parameters',
motion_correct, 'parameters')
preproc.connect(motion_correct,'parameter_source',
ad,'parameter_source')
preproc.connect(inputnode, 'do_slicetime',
motion_correct, 'do_slicetime')
preproc.connect(inputnode, 'sliceorder',
motion_correct, 'sliceorder')
preproc.connect(inputnode, 'compcor_select',
compcor, 'inputspec.selector')
preproc.connect(inputnode, 'fssubject_dir',
getmask, 'inputspec.subjects_dir')
#preproc.connect(inputnode, 'func',
# img2float, 'in_file')
preproc.connect(inputnode, 'func', strip_rois, 'in_file')
preproc.connect(strip_rois, 'roi_file', img2float, 'in_file')
preproc.connect(img2float, 'out_file', despike, "in_file")
preproc.connect(despike,"out_file", motion_correct, 'in_file')
#preproc.connect(motion_correct, 'par_file',
# plot_motion, 'in_file')
preproc.connect(motion_correct, 'out_file',
meanfunc, 'inputspec.realigned_files')
preproc.connect(motion_correct, 'par_file',
meanfunc, 'inputspec.realignment_parameters')
preproc.connect(meanfunc, 'outputspec.mean_image',
getmask, 'inputspec.source_file')
preproc.connect(inputnode, 'num_noise_components',
compcor, 'inputspec.num_components')
preproc.connect(inputnode, 'regress_before_PCA',
compcor, 'inputspec.regress_before_PCA')
preproc.connect(motion_correct, 'out_file',
compcor, 'inputspec.realigned_file')
preproc.connect(meanfunc, 'outputspec.mean_image',
compcor, 'inputspec.mean_file')
preproc.connect(fssource, 'aseg',
compcor, 'inputspec.fsaseg_file')
preproc.connect(getmask, ('outputspec.reg_file', pickfirst),
compcor, 'inputspec.reg_file')
preproc.connect(ad, 'outlier_files',
compcor, 'inputspec.outlier_files')
preproc.connect(motion_correct, 'par_file',
compcor, 'inputspec.realignment_parameters')
preproc.connect(motion_correct, 'out_file',
ad, 'realigned_files')
preproc.connect(motion_correct, 'par_file',
ad, 'realignment_parameters')
preproc.connect(getmask, ('outputspec.mask_file', pickfirst),
ad, 'mask_file')
preproc.connect(getmask, ('outputspec.mask_file', pickfirst),
medianval, 'mask_file')
preproc.connect(inputnode_fwhm, 'fwhm',
smooth, 'inputnode.fwhm')
preproc.connect(motion_correct, 'out_file',
smooth, 'inputnode.in_files')
preproc.connect(getmask, ('outputspec.mask_file',pickfirst),
smooth, 'inputnode.mask_file')
preproc.connect(getmask, ('outputspec.reg_file', pickfirst),
smooth, 'inputnode.reg_file')
preproc.connect(inputnode,'surface_fwhm',
smooth, 'inputnode.surface_fwhm')
preproc.connect(inputnode, 'fssubject_dir',
smooth, 'inputnode.surf_dir')
preproc.connect(smooth, 'outputnode.smoothed_files',
choosesusan, 'smoothed_files')
preproc.connect(motion_correct, 'out_file',
choosesusan, 'motion_files')
preproc.connect(inputnode_fwhm, 'fwhm',
choosesusan, 'fwhm')
preproc.connect(choosesusan, 'cor_smoothed_files',
meanscale, 'in_file')
preproc.connect(choosesusan, 'cor_smoothed_files',
medianval, 'in_file')
preproc.connect(medianval, ('out_stat', getmeanscale),
meanscale, 'op_string')
preproc.connect(inputnode, ('highpass', highpass_operand),
highpass, 'op_string')
preproc.connect(meanscale, 'out_file',
highpass, 'in_file')
preproc.connect(highpass, 'out_file',
zscore, 'image')
preproc.connect(ad, 'outlier_files',
zscore, 'outliers')
# create output node
outputnode = pe.Node(interface=util.IdentityInterface(
fields=['mean',
'motion_parameters',
'realigned_files',
'smoothed_files',
'highpassed_files',
'combined_motion',
'outlier_files',
'outlier_stat_files',
'mask',
'reg_cost',
'reg_file',
'reg_fsl_file',
'noise_components',
'tsnr_file',
'stddev_file',
'tsnr_detrended',
'filter_file',
'scaled_files','unmasked_fullspectrum',
'z_img',
'motion_plots',
'FM_unwarped_epi',
'FM_unwarped_mean',
'vsm_file',
'bandpassed_file',
'intensity_files',
'noise_mask',
'csf_mask']),
name='outputspec')
# make output connection
preproc.connect(meanfunc, 'outputspec.mean_image',
outputnode, 'mean')
preproc.connect(motion_correct, 'par_file',
outputnode, 'motion_parameters')
preproc.connect(motion_correct, 'out_file',
outputnode, 'realigned_files')
preproc.connect(highpass, 'out_file',
outputnode, 'highpassed_files')
preproc.connect(ad, 'norm_files',
outputnode, 'combined_motion')
preproc.connect(ad, 'outlier_files',
outputnode, 'outlier_files')
preproc.connect(ad, 'intensity_files',
outputnode, 'intensity_files')
preproc.connect(ad, 'statistic_files',
outputnode, 'outlier_stat_files')
preproc.connect(compcor, 'outputspec.noise_components',
outputnode, 'noise_components')
preproc.connect(compcor, 'outputspec.noise_mask',
outputnode, 'noise_mask')
preproc.connect(compcor, 'outputspec.csf_mask',
outputnode, 'csf_mask')
preproc.connect(getmask, 'outputspec.mask_file',
outputnode, 'mask')
preproc.connect(getmask, 'register.out_fsl_file',
outputnode, 'reg_fsl_file')
preproc.connect(getmask, 'outputspec.reg_file',
outputnode, 'reg_file')
preproc.connect(getmask, 'outputspec.reg_cost',
outputnode, 'reg_cost')
preproc.connect(choosesusan, 'cor_smoothed_files',
outputnode, 'smoothed_files')
preproc.connect(compcor, 'outputspec.tsnr_file',
outputnode, 'tsnr_file')
preproc.connect(compcor, 'outputspec.stddev_file',
outputnode, 'stddev_file')
preproc.connect(compcor, 'outputspec.tsnr_detrended',
outputnode, 'tsnr_detrended')
preproc.connect(zscore,'z_img',
outputnode,'z_img')
#preproc.connect(plot_motion,'out_file',
# outputnode,'motion_plots')
return preproc
