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

.. _example_rsfmri_fsl_compcorr:


==============================
rsfMRI: FSL, Nipype, tCompCorr
==============================

Performs preprocessing for resting state data based on the tCompCorr method
described in Behzadi et al. (2007).

Tell python where to find the appropriate functions.

::
  
  import os                                    # system functions
  
  import nipype.interfaces.io as nio           # Data i/o
  import nipype.pipeline.engine as pe          # pypeline engine
  import nipype.interfaces.utility as util
  
  #####################################################################
  # Preliminaries
  
  from nipype.workflows.fmri.fsl import create_resting_preproc
  

Set up parameters for the resting state preprocessing workflow.

::
  
  TR = 3.0
  restingflow = create_resting_preproc()
  restingflow.inputs.inputspec.num_noise_components = 6
  restingflow.inputs.inputspec.highpass_sigma = 100/(2*TR)
  restingflow.inputs.inputspec.lowpass_sigma = 12.5/(2*TR)
  
  # Specify the location of the data.
  data_dir = os.path.abspath('data')
  # Specify the subject directories
  subject_list = ['s1']
  
  infosource = pe.Node(interface=util.IdentityInterface(fields=['subject_id']),
                       name="infosource")
  

Here we set up iteration over all the subjects.

::
  
  infosource.iterables = ('subject_id', subject_list)
  

Preprocessing pipeline nodes
----------------------------

Now we create a :class:`nipype.interfaces.io.DataSource` object and
fill in the information from above about the layout of our data.

::
  
  datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
                                                 outfields=['func', 'struct']),
                       name = 'datasource')
  datasource.inputs.base_directory = data_dir
  datasource.inputs.template = '%s/%s.nii'
  
  # Map field names to individual subject runs.
  info = dict(func=[['subject_id', ['f3',]]])
  
  datasource.inputs.template_args = info
  datasource.inputs.sort_filelist = True
  

Store significant result-files in a special directory

::
  
  datasink = pe.Node(interface=nio.DataSink(parameterization=False),
                     name='datasink')
  datasink.inputs.base_directory = os.path.abspath('./fslresting/compcorred')
  

Set up complete workflow
------------------------

::
  
  def get_substitutions(subject_id):
      '''Replace output names of files with more meaningful ones
      '''
      return [('vol0000_warp_merged_detrended_regfilt_filt',
               '%s_filtered'%subject_id),
              ('vol0000_warp_merged_tsnr_stddev_thresh',
               '%s_noisyvoxels'%subject_id)]
  
  l1pipeline = pe.Workflow(name= "resting")
  l1pipeline.base_dir = os.path.abspath('./fslresting/workingdir')
  l1pipeline.connect([(infosource, datasource, [('subject_id', 'subject_id')]),
                      (datasource, restingflow, [('func', 'inputspec.func')]),
                      (infosource, datasink, [('subject_id', 'container'),
                                              (('subject_id', get_substitutions),
                                                'substitutions')]),
                      (restingflow, datasink, [('outputspec.noise_mask_file',
                                                '@noisefile'),
                                                ('outputspec.filtered_file',
                                                 '@filteredfile')])
                      ])
  
  if __name__ == '__main__':
      l1pipeline.run()
      l1pipeline.write_graph()


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


        
.. admonition:: Example source code

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

