.. AUTO-GENERATED FILE -- DO NOT EDIT!

.. _example_rsfmri_vol_surface_preprocessing:


====================================
rsfMRI: ANTS, FS, FSL, SPM, aCompCor
====================================


A preprocessing workflow for Siemens resting state data.

This workflow makes use of:

- ANTS
- FreeSurfer
- FSL
- SPM
- CompCor

For example::

  python rsfmri_preprocessing.py -d /data/12345-34-1.dcm -f /data/Resting.nii
      -s subj001 -o output -p PBS --plugin_args "dict(qsub_args='-q many')"

  or

  python rsfmri_vol_surface_preprocessing.py -f SUB_1024011/E?/func/rest.nii
      -t OASIS-30_Atropos_template_in_MNI152_2mm.nii.gz --TR 2 -s SUB_1024011
      --subjects_dir fsdata --slice_times 0 17 1 18 2 19 3 20 4 21 5 22 6 23
      7 24 8 25 9 26 10 27 11 28 12 29 13 30 14 31 15 32 16 -o .

This workflow takes resting timeseries and a Siemens dicom file corresponding
to it and preprocesses it to produce timeseries coordinates or grayordinates.

This workflow also requires 2mm subcortical atlas and templates that are
available from:

http://mindboggle.info/data.html

specifically the 2mm versions of:

- `Joint Fusion Atlas <http://mindboggle.info/data/atlases/jointfusion/OASIS-TRT-20_jointfusion_DKT31_CMA_labels_in_MNI152_2mm_v2.nii.gz>`_
- `MNI template <http://mindboggle.info/data/templates/ants/OASIS-30_Atropos_template_in_MNI152_2mm.nii.gz>`_

::
  
  
  import os
  
  from nipype.interfaces.base import CommandLine
  CommandLine.set_default_terminal_output('allatonce')
  
  
  from dicom import read_file
  
  from nipype.interfaces import (spm, fsl, Function, ants, freesurfer)
  from nipype.interfaces.c3 import C3dAffineTool
  
  fsl.FSLCommand.set_default_output_type('NIFTI')
  
  from nipype import Workflow, Node, MapNode
  from nipype.interfaces import matlab as mlab
  
  mlab.MatlabCommand.set_default_matlab_cmd("matlab -nodisplay")
  # If SPM is not in your MATLAB path you should add it here
  # mlab.MatlabCommand.set_default_paths('/software/matlab/spm12')
  
  from nipype.algorithms.rapidart import ArtifactDetect
  from nipype.algorithms.misc import TSNR
  from nipype.interfaces.utility import Rename, Merge, IdentityInterface
  from nipype.utils.filemanip import filename_to_list
  from nipype.interfaces.io import DataSink, FreeSurferSource
  
  import numpy as np
  import scipy as sp
  import nibabel as nb
  
  imports = ['import os',
             'import nibabel as nb',
             'import numpy as np',
             'import scipy as sp',
             'from nipype.utils.filemanip import filename_to_list, list_to_filename, split_filename',
             'from scipy.special import legendre'
             ]
  
  
  def get_info(dicom_files):
      from dcmstack.extract import default_extractor
      """Given a Siemens dicom file return metadata
  
      Returns
      -------
      RepetitionTime
      Slice Acquisition Times
      Spacing between slices
      """
      meta = default_extractor(read_file(filename_to_list(dicom_files)[0],
                                         stop_before_pixels=True,
                                         force=True))
      return (meta['RepetitionTime']/1000., meta['CsaImage.MosaicRefAcqTimes'],
              meta['SpacingBetweenSlices'])
  
  
  def median(in_files):
      """Computes an average of the median of each realigned timeseries
  
      Parameters
      ----------
  
      in_files: one or more realigned Nifti 4D time series
  
      Returns
      -------
  
      out_file: a 3D Nifti file
      """
      import numpy as np
      import nibabel as nb
      average = None
      for idx, filename in enumerate(filename_to_list(in_files)):
          img = nb.load(filename)
          data = np.median(img.get_data(), axis=3)
          if average is None:
              average = data
          else:
              average = average + data
      median_img = nb.Nifti1Image(average/float(idx + 1),
                                  img.get_affine(), img.get_header())
      filename = os.path.join(os.getcwd(), 'median.nii.gz')
      median_img.to_filename(filename)
      return filename
  
  
  def bandpass_filter(files, lowpass_freq, highpass_freq, fs):
      """Bandpass filter the input files
  
      Parameters
      ----------
      files: list of 4d nifti files
      lowpass_freq: cutoff frequency for the low pass filter (in Hz)
      highpass_freq: cutoff frequency for the high pass filter (in Hz)
      fs: sampling rate (in Hz)
      """
      from nipype.utils.filemanip import split_filename, list_to_filename
      import numpy as np
      import nibabel as nb
      out_files = []
      for filename in filename_to_list(files):
          path, name, ext = split_filename(filename)
          out_file = os.path.join(os.getcwd(), name + '_bp' + ext)
          img = nb.load(filename)
          timepoints = img.shape[-1]
          F = np.zeros((timepoints))
          lowidx = timepoints/2 + 1
          if lowpass_freq > 0:
              lowidx = np.round(lowpass_freq / fs * timepoints)
          highidx = 0
          if highpass_freq > 0:
              highidx = np.round(highpass_freq / fs * timepoints)
          F[highidx:lowidx] = 1
          F = ((F + F[::-1]) > 0).astype(int)
          data = img.get_data()
          if np.all(F == 1):
              filtered_data = data
          else:
              filtered_data = np.real(np.fft.ifftn(np.fft.fftn(data) * F))
          img_out = nb.Nifti1Image(filtered_data, img.get_affine(),
                                   img.get_header())
          img_out.to_filename(out_file)
          out_files.append(out_file)
      return list_to_filename(out_files)
  
  
  def motion_regressors(motion_params, order=0, derivatives=1):
      """Compute motion regressors upto given order and derivative
  
      motion + d(motion)/dt + d2(motion)/dt2 (linear + quadratic)
      """
      import numpy as np
      out_files = []
      for idx, filename in enumerate(filename_to_list(motion_params)):
          params = np.genfromtxt(filename)
          out_params = params
          for d in range(1, derivatives + 1):
              cparams = np.vstack((np.repeat(params[0, :][None, :], d, axis=0),
                                   params))
              out_params = np.hstack((out_params, np.diff(cparams, d, axis=0)))
          out_params2 = out_params
          for i in range(2, order + 1):
              out_params2 = np.hstack((out_params2, np.power(out_params, i)))
          filename = os.path.join(os.getcwd(), "motion_regressor%02d.txt" % idx)
          np.savetxt(filename, out_params2, fmt="%.10f")
          out_files.append(filename)
      return out_files
  
  
  def build_filter1(motion_params, comp_norm, outliers, detrend_poly=None):
      """Builds a regressor set comprisong motion parameters, composite norm and
      outliers
  
      The outliers are added as a single time point column for each outlier
  
  
      Parameters
      ----------
  
      motion_params: a text file containing motion parameters and its derivatives
      comp_norm: a text file containing the composite norm
      outliers: a text file containing 0-based outlier indices
      detrend_poly: number of polynomials to add to detrend
  
      Returns
      -------
      components_file: a text file containing all the regressors
      """
      import numpy as np
      import nibabel as nb
      from scipy.special import legendre
      out_files = []
      for idx, filename in enumerate(filename_to_list(motion_params)):
          params = np.genfromtxt(filename)
          norm_val = np.genfromtxt(filename_to_list(comp_norm)[idx])
          out_params = np.hstack((params, norm_val[:, None]))
          try:
              outlier_val = np.genfromtxt(filename_to_list(outliers)[idx])
          except IOError:
              outlier_val = np.empty((0))
          for index in np.atleast_1d(outlier_val):
              outlier_vector = np.zeros((out_params.shape[0], 1))
              outlier_vector[index] = 1
              out_params = np.hstack((out_params, outlier_vector))
          if detrend_poly:
              timepoints = out_params.shape[0]
              X = np.ones((timepoints, 1))
              for i in range(detrend_poly):
                  X = np.hstack((X, legendre(
                      i + 1)(np.linspace(-1, 1, timepoints))[:, None]))
              out_params = np.hstack((out_params, X))
          filename = os.path.join(os.getcwd(), "filter_regressor%02d.txt" % idx)
          np.savetxt(filename, out_params, fmt="%.10f")
          out_files.append(filename)
      return out_files
  
  
  def extract_noise_components(realigned_file, mask_file, num_components=5,
                               extra_regressors=None):
      """Derive components most reflective of physiological noise
  
      Parameters
      ----------
      realigned_file: a 4D Nifti file containing realigned volumes
      mask_file: a 3D Nifti file containing white matter + ventricular masks
      num_components: number of components to use for noise decomposition
      extra_regressors: additional regressors to add
  
      Returns
      -------
      components_file: a text file containing the noise components
      """
      from scipy.linalg.decomp_svd import svd
      import numpy as np
      import nibabel as nb
      import os
      imgseries = nb.load(realigned_file)
      components = None
      for filename in filename_to_list(mask_file):
          mask = nb.load(filename).get_data()
          if len(np.nonzero(mask > 0)[0]) == 0:
              continue
          voxel_timecourses = imgseries.get_data()[mask > 0]
          voxel_timecourses[np.isnan(np.sum(voxel_timecourses, axis=1)), :] = 0
          # remove mean and normalize by variance
          # voxel_timecourses.shape == [nvoxels, time]
          X = voxel_timecourses.T
          stdX = np.std(X, axis=0)
          stdX[stdX == 0] = 1.
          stdX[np.isnan(stdX)] = 1.
          stdX[np.isinf(stdX)] = 1.
          X = (X - np.mean(X, axis=0))/stdX
          u, _, _ = svd(X, full_matrices=False)
          if components is None:
              components = u[:, :num_components]
          else:
              components = np.hstack((components, u[:, :num_components]))
      if extra_regressors:
          regressors = np.genfromtxt(extra_regressors)
          components = np.hstack((components, regressors))
      components_file = os.path.join(os.getcwd(), 'noise_components.txt')
      np.savetxt(components_file, components, fmt="%.10f")
      return components_file
  
  
  def rename(in_files, suffix=None):
      from nipype.utils.filemanip import (filename_to_list, split_filename,
                                          list_to_filename)
      out_files = []
      for idx, filename in enumerate(filename_to_list(in_files)):
          _, name, ext = split_filename(filename)
          if suffix is None:
              out_files.append(name + ('_%03d' % idx) + ext)
          else:
              out_files.append(name + suffix + ext)
      return list_to_filename(out_files)
  
  
  def get_aparc_aseg(files):
      """Return the aparc+aseg.mgz file"""
      for name in files:
          if 'aparc+aseg.mgz' in name:
              return name
      raise ValueError('aparc+aseg.mgz not found')
  
  
  def extract_subrois(timeseries_file, label_file, indices):
      """Extract voxel time courses for each subcortical roi index
  
      Parameters
      ----------
  
      timeseries_file: a 4D Nifti file
      label_file: a 3D file containing rois in the same space/size of the 4D file
      indices: a list of indices for ROIs to extract.
  
      Returns
      -------
      out_file: a text file containing time courses for each voxel of each roi
          The first four columns are: freesurfer index, i, j, k positions in the
          label file
      """
      from nipype.utils.filemanip import split_filename
      import nibabel as nb
      import os
      img = nb.load(timeseries_file)
      data = img.get_data()
      roiimg = nb.load(label_file)
      rois = roiimg.get_data()
      prefix = split_filename(timeseries_file)[1]
      out_ts_file = os.path.join(os.getcwd(), '%s_subcortical_ts.txt' % prefix)
      with open(out_ts_file, 'wt') as fp:
          for fsindex in indices:
              ijk = np.nonzero(rois == fsindex)
              ts = data[ijk]
              for i0, row in enumerate(ts):
                  fp.write('%d,%d,%d,%d,' % (fsindex, ijk[0][i0],
                                             ijk[1][i0], ijk[2][i0]) +
                           ','.join(['%.10f' % val for val in row]) + '\n')
      return out_ts_file
  
  
  def combine_hemi(left, right):
      """Combine left and right hemisphere time series into a single text file
      """
      import os
      import numpy as np
      lh_data = nb.load(left).get_data()
      rh_data = nb.load(right).get_data()
  
      indices = np.vstack((1000000 + np.arange(0, lh_data.shape[0])[:, None],
                           2000000 + np.arange(0, rh_data.shape[0])[:, None]))
      all_data = np.hstack((indices, np.vstack((lh_data.squeeze(),
                                                rh_data.squeeze()))))
      filename = left.split('.')[1] + '_combined.txt'
      np.savetxt(filename, all_data,
                 fmt=','.join(['%d'] + ['%.10f'] * (all_data.shape[1] - 1)))
      return os.path.abspath(filename)
  
  
  def create_reg_workflow(name='registration'):
      """Create a FEAT preprocessing workflow together with freesurfer
  
      Parameters
      ----------
  
      ::
  
          name : name of workflow (default: 'registration')
  
      Inputs::
  
          inputspec.source_files : files (filename or list of filenames to register)
          inputspec.mean_image : reference image to use
          inputspec.anatomical_image : anatomical image to coregister to
          inputspec.target_image : registration target
  
      Outputs::
  
          outputspec.func2anat_transform : FLIRT transform
          outputspec.anat2target_transform : FLIRT+FNIRT transform
          outputspec.transformed_files : transformed files in target space
          outputspec.transformed_mean : mean image in target space
      """
  
      register = Workflow(name=name)
  
      inputnode = Node(interface=IdentityInterface(fields=['source_files',
                                                           'mean_image',
                                                           'subject_id',
                                                           'subjects_dir',
                                                           'target_image']),
                       name='inputspec')
  
      outputnode = Node(interface=IdentityInterface(fields=['func2anat_transform',
                                                            'out_reg_file',
                                                            'anat2target_transform',
                                                            'transforms',
                                                            'transformed_mean',
                                                            'segmentation_files',
                                                            'anat2target',
                                                            'aparc'
                                                            ]),
                        name='outputspec')
  
      # Get the subject's freesurfer source directory
      fssource = Node(FreeSurferSource(),
                      name='fssource')
      fssource.run_without_submitting = True
      register.connect(inputnode, 'subject_id', fssource, 'subject_id')
      register.connect(inputnode, 'subjects_dir', fssource, 'subjects_dir')
  
      convert = Node(freesurfer.MRIConvert(out_type='nii'),
                     name="convert")
      register.connect(fssource, 'T1', convert, 'in_file')
  
      # Coregister the median to the surface
      bbregister = Node(freesurfer.BBRegister(),
                      name='bbregister')
      bbregister.inputs.init = 'fsl'
      bbregister.inputs.contrast_type = 't2'
      bbregister.inputs.out_fsl_file = True
      bbregister.inputs.epi_mask = True
      register.connect(inputnode, 'subject_id', bbregister, 'subject_id')
      register.connect(inputnode, 'mean_image', bbregister, 'source_file')
      register.connect(inputnode, 'subjects_dir', bbregister, 'subjects_dir')
  

Estimate the tissue classes from the anatomical image. But use spm's segment
as FSL appears to be breaking.


::
  
      stripper = Node(fsl.BET(), name='stripper')
      register.connect(convert, 'out_file', stripper, 'in_file')
      fast = Node(fsl.FAST(), name='fast')
      register.connect(stripper, 'out_file', fast, 'in_files')
  

Binarize the segmentation


::
  
      binarize = MapNode(fsl.ImageMaths(op_string='-nan -thr 0.9 -ero -bin'),
                         iterfield=['in_file'],
                         name='binarize')
      register.connect(fast, 'partial_volume_files', binarize, 'in_file')
  

Apply inverse transform to take segmentations to functional space


::
  
      applyxfm = MapNode(freesurfer.ApplyVolTransform(inverse=True,
                                                      interp='nearest'),
                         iterfield=['target_file'],
                         name='inverse_transform')
      register.connect(inputnode, 'subjects_dir', applyxfm, 'subjects_dir')
      register.connect(bbregister, 'out_reg_file', applyxfm, 'reg_file')
      register.connect(binarize, 'out_file', applyxfm, 'target_file')
      register.connect(inputnode, 'mean_image', applyxfm, 'source_file')
  

Apply inverse transform to aparc file


::
  
      aparcxfm = Node(freesurfer.ApplyVolTransform(inverse=True,
                                                   interp='nearest'),
                      name='aparc_inverse_transform')
      register.connect(inputnode, 'subjects_dir', aparcxfm, 'subjects_dir')
      register.connect(bbregister, 'out_reg_file', aparcxfm, 'reg_file')
      register.connect(fssource, ('aparc_aseg', get_aparc_aseg),
                       aparcxfm, 'target_file')
      register.connect(inputnode, 'mean_image', aparcxfm, 'source_file')
  

Convert the BBRegister transformation to ANTS ITK format


::
  
      convert2itk = Node(C3dAffineTool(), name='convert2itk')
      convert2itk.inputs.fsl2ras = True
      convert2itk.inputs.itk_transform = True
      register.connect(bbregister, 'out_fsl_file', convert2itk, 'transform_file')
      register.connect(inputnode, 'mean_image',convert2itk, 'source_file')
      register.connect(stripper, 'out_file', convert2itk, 'reference_file')
  

Compute registration between the subject's structural and MNI template
This is currently set to perform a very quick registration. However, the
registration can be made significantly more accurate for cortical
structures by increasing the number of iterations
All parameters are set using the example from:
#https://github.com/stnava/ANTs/blob/master/Scripts/newAntsExample.sh


::
  
      reg = Node(ants.Registration(), name='antsRegister')
      reg.inputs.output_transform_prefix = "output_"
      reg.inputs.transforms = ['Rigid', 'Affine', 'SyN']
      reg.inputs.transform_parameters = [(0.1,), (0.1,), (0.2, 3.0, 0.0)]
      reg.inputs.number_of_iterations = [[10000, 11110, 11110]] * 2 + [[100, 30, 20]]
      reg.inputs.dimension = 3
      reg.inputs.write_composite_transform = True
      reg.inputs.collapse_output_transforms = True
      reg.inputs.initial_moving_transform_com = True
      reg.inputs.metric = ['Mattes'] * 2 + [['Mattes', 'CC']]
      reg.inputs.metric_weight = [1] * 2 + [[0.5, 0.5]]
      reg.inputs.radius_or_number_of_bins = [32] * 2 + [[32, 4]]
      reg.inputs.sampling_strategy = ['Regular'] * 2 + [[None, None]]
      reg.inputs.sampling_percentage = [0.3] * 2 + [[None, None]]
      reg.inputs.convergence_threshold = [1.e-8] * 2 + [-0.01]
      reg.inputs.convergence_window_size = [20] * 2 + [5]
      reg.inputs.smoothing_sigmas = [[4, 2, 1]] * 2 + [[1, 0.5, 0]]
      reg.inputs.sigma_units = ['vox'] * 3
      reg.inputs.shrink_factors = [[3, 2, 1]]*2 + [[4, 2, 1]]
      reg.inputs.use_estimate_learning_rate_once = [True] * 3
      reg.inputs.use_histogram_matching = [False] * 2 + [True]
      reg.inputs.winsorize_lower_quantile = 0.005
      reg.inputs.winsorize_upper_quantile = 0.995
      reg.inputs.args = '--float'
      reg.inputs.output_warped_image = 'output_warped_image.nii.gz'
      reg.inputs.num_threads = 4
      reg.plugin_args = {'qsub_args': '-l nodes=1:ppn=4'}
      register.connect(stripper, 'out_file', reg, 'moving_image')
      register.connect(inputnode,'target_image', reg,'fixed_image')
  

Concatenate the affine and ants transforms into a list


::
  
      pickfirst = lambda x: x[0]
  
      merge = Node(Merge(2), iterfield=['in2'], name='mergexfm')
      register.connect(convert2itk, 'itk_transform', merge, 'in2')
      register.connect(reg, ('composite_transform', pickfirst), merge, 'in1')
  

Transform the mean image. First to anatomical and then to target


::
  
      warpmean = Node(ants.ApplyTransforms(), name='warpmean')
      warpmean.inputs.input_image_type = 3
      warpmean.inputs.interpolation = 'BSpline'
      warpmean.inputs.invert_transform_flags = [False, False]
      warpmean.inputs.terminal_output = 'file'
      warpmean.inputs.args = '--float'
      warpmean.inputs.num_threads = 4
  
      register.connect(inputnode,'target_image', warpmean,'reference_image')
      register.connect(inputnode, 'mean_image', warpmean, 'input_image')
      register.connect(merge, 'out', warpmean, 'transforms')
  

Assign all the output files


::
  
      register.connect(reg, 'warped_image', outputnode, 'anat2target')
      register.connect(warpmean, 'output_image', outputnode, 'transformed_mean')
      register.connect(applyxfm, 'transformed_file',
                       outputnode, 'segmentation_files')
      register.connect(aparcxfm, 'transformed_file',
                       outputnode, 'aparc')
      register.connect(bbregister, 'out_fsl_file',
                       outputnode, 'func2anat_transform')
      register.connect(bbregister, 'out_reg_file',
                       outputnode, 'out_reg_file')
      register.connect(reg, 'composite_transform',
                       outputnode, 'anat2target_transform')
      register.connect(merge, 'out', outputnode, 'transforms')
  
      return register
  
  

Creates the main preprocessing workflow

::
  
  
  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 = range(1, len(files) + 1)
      name_unique.inputs.in_file = files
  
      realign = Node(interface=spm.Realign(), name="realign")
      realign.inputs.jobtype = 'estwrite'
  
      num_slices = len(slice_times)
      slice_timing = Node(interface=spm.SliceTiming(), name="slice_timing")
      slice_timing.inputs.num_slices = num_slices
      slice_timing.inputs.time_repetition = TR
      slice_timing.inputs.time_acquisition = TR - TR/float(num_slices)
      slice_timing.inputs.slice_order = (np.argsort(slice_times) + 1).tolist()
      slice_timing.inputs.ref_slice = int(num_slices/2)
  
      # Comute TSNR on realigned data regressing polynomials upto order 2
      tsnr = MapNode(TSNR(regress_poly=2), iterfield=['in_file'], name='tsnr')
      wf.connect(slice_timing, 'timecorrected_files', tsnr, 'in_file')
  
      # Compute the median image across runs
      calc_median = Node(Function(input_names=['in_files'],
                                  output_names=['median_file'],
                                  function=median,
                                  imports=imports),
                         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
  
  

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 = 'SPM'
  
  

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_files')]),
                  (realign, slice_timing, [('realigned_files', 'in_files')]),
                  (slice_timing, art, [('timecorrected_files', 'realigned_files')]),
                  (realign, art, [('realignment_parameters', '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, 'realignment_parameters', 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',
                                out_pf_name='pF_mcart.nii',
                                demean=True),
                        iterfield=['in_file', 'design', 'out_res_name'],
                        name='filtermotion')
  
      wf.connect(slice_timing, 'timecorrected_files', filter1, 'in_file')
      wf.connect(slice_timing, ('timecorrected_files', rename, '_filtermotart'),
                 filter1, 'out_res_name')
      wf.connect(createfilter1, 'out_files', filter1, 'design')
  
  
      createfilter2 = MapNode(Function(input_names=['realigned_file', 'mask_file',
                                                    'num_components',
                                                    'extra_regressors'],
                                       output_names=['out_files'],
                                       function=extract_noise_components,
                                       imports=imports),
                              iterfield=['realigned_file', 'extra_regressors'],
                              name='makecompcorrfilter')
      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',
                                out_pf_name='pF.nii',
                                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, 'out_files', 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.spm.Smooth`.


::
  
      smooth = Node(interface=spm.Smooth(), name="smooth")
      smooth.inputs.fwhm = vol_fwhm
  
      wf.connect(bandpass, 'out_files', smooth, 'in_files')
  
      collector = Node(Merge(2), name='collect_streams')
      wf.connect(smooth, 'smoothed_files', 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 = 'BSpline'
      warpall.inputs.invert_transform_flags = [False, False]
      warpall.inputs.terminal_output = 'file'
      warpall.inputs.reference_image = target_file
      warpall.inputs.args = '--float'
      warpall.inputs.num_threads = 1
  
      # 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] + range(10, 14) + [17, 18, 26, 47] +
                                       range(49, 55) + [58] + range(1001, 1036) +
                                       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)
          out_names = []
          for filename in files:
              _, name, _ = split_filename(filename)
              out_names.append(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] + range(10, 14) + [17, 18, 26, 47] +\
                              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', '')
                       ]
      regex_subs = [('_ts_masker.*/sar', '/smooth/'),
                    ('_ts_masker.*/ar', '/unsmooth/'),
                    ('_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, 'realignment_parameters', 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(bandpass, 'out_files', datasink, 'resting.timeseries.@bandpassed')
      wf.connect(smooth, 'smoothed_files', datasink, 'resting.timeseries.@smoothed')
      wf.connect(createfilter1, 'out_files',
                 datasink, 'resting.regress.@regressors')
      wf.connect(createfilter2, 'out_files',
                 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
  
  

Creates the full workflow including getting information from dicom files

::
  
  
  def create_resting_workflow(args, name=None):
      TR = args.TR
      slice_times = args.slice_times
      if args.dicom_file:
          TR, slice_times, slice_thickness = get_info(args.dicom_file)
          slice_times = (np.array(slice_times)/1000.).tolist()
      if name is None:
          name = 'resting_' + args.subject_id
      kwargs = dict(files=[os.path.abspath(filename) for filename in args.files],
                    target_file=os.path.abspath(args.target_file),
                    subject_id=args.subject_id,
                    TR=TR,
                    slice_times=slice_times,
                    vol_fwhm=args.vol_fwhm,
                    surf_fwhm=args.surf_fwhm,
                    norm_threshold=2.,
                    subjects_dir=os.path.abspath(args.fsdir),
                    target_subject=args.target_surfs,
                    lowpass_freq=args.lowpass_freq,
                    highpass_freq=args.highpass_freq,
                    sink_directory=os.path.abspath(args.sink),
                    name=name)
      wf = create_workflow(**kwargs)
      return wf
  
  if __name__ == "__main__":
      from argparse import ArgumentParser, RawTextHelpFormatter
      defstr = ' (default %(default)s)'
      parser = ArgumentParser(description=__doc__,
                              formatter_class=RawTextHelpFormatter)
      parser.add_argument("-d", "--dicom_file", dest="dicom_file",
                          help="an example dicom file from the resting series")
      parser.add_argument("-f", "--files", dest="files", nargs="+",
                          help="4d nifti files for resting state",
                          required=True)
      parser.add_argument("-t", "--target", dest="target_file",
                          help=("Target in MNI space. Best to use the MindBoggle "
                                "template - "
                                "OASIS-30_Atropos_template_in_MNI152_2mm.nii.gz"),
                          required=True)
      parser.add_argument("-s", "--subject_id", dest="subject_id",
                          help="FreeSurfer subject id", required=True)
      parser.add_argument("--subjects_dir", dest="fsdir",
                          help="FreeSurfer subject directory", required=True)
      parser.add_argument("--target_surfaces", dest="target_surfs", nargs="+",
                          default=['fsaverage5'],
                          help="FreeSurfer target surfaces" + defstr)
      parser.add_argument("--TR", dest="TR", default=None, type=float,
                          help="TR if dicom not provided in seconds")
      parser.add_argument("--slice_times", dest="slice_times", nargs="+",
                          type=float, help="Slice onset times in seconds")
      parser.add_argument('--vol_fwhm', default=6., dest='vol_fwhm',
                          type=float, help="Spatial FWHM" + defstr)
      parser.add_argument('--surf_fwhm', default=15., dest='surf_fwhm',
                          type=float, help="Spatial FWHM" + defstr)
      parser.add_argument("-l", "--lowpass_freq", dest="lowpass_freq",
                          default=0.1, help="Low pass frequency (Hz)" + defstr)
      parser.add_argument("-u", "--highpass_freq", dest="highpass_freq",
                          default=0.01, help="High pass frequency (Hz)" + defstr)
      parser.add_argument("-o", "--output_dir", dest="sink",
                          help="Output directory base", required=True)
      parser.add_argument("-w", "--work_dir", dest="work_dir",
                          help="Output directory base")
      parser.add_argument("-p", "--plugin", dest="plugin",
                          default='Linear',
                          help="Plugin to use")
      parser.add_argument("--plugin_args", dest="plugin_args",
                          help="Plugin arguments")
      args = parser.parse_args()
  
      wf = create_resting_workflow(args)
  
      if args.work_dir:
          work_dir = os.path.abspath(args.work_dir)
      else:
          work_dir = os.getcwd()
  
      wf.base_dir = work_dir
      if args.plugin_args:
          wf.run(args.plugin, plugin_args=eval(args.plugin_args))
      else:
          wf.run(args.plugin)


.. include:: ../../links_names.txt


        
.. admonition:: Example source code

   You can download :download:`the full source code of this example <../../../examples/rsfmri_vol_surface_preprocessing.py>`.
   This same script is also included in the Nipype source distribution under the
   :file:`examples` directory.

