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

.. _example_fsl_tutorial2:


A workflow that uses fsl to perform a first level analysis on the nipype
tutorial data set::

python fsl_tutorial2.py


First tell python where to find the appropriate functions.

::

  import os                                    # system functions

  import nipype.interfaces.io as nio           # Data i/o
  import nipype.interfaces.fsl as fsl          # fsl
  import nipype.interfaces.utility as util     # utility
  import nipype.pipeline.engine as pe          # pypeline engine
  import nipype.algorithms.modelgen as model   # model generation
  import nipype.algorithms.rapidart as ra      # artifact detection

  from nibabel import load



Preliminaries
-------------

Confirm package dependencies are installed.  (This is only for the tutorial,
rarely would you put this in your own code.)

::

  from nipype.utils.misc import package_check

  package_check('numpy', '1.3', 'tutorial1')
  package_check('scipy', '0.7', 'tutorial1')
  package_check('networkx', '1.0', 'tutorial1')
  package_check('IPython', '0.10', 'tutorial1')


Setup any package specific configuration. The output file format for FSL
routines is being set to compressed NIFTI.

::

  fsl.FSLCommand.set_default_output_type('NIFTI_GZ')


Setting up workflows
--------------------

In this tutorial we will be setting up a hierarchical workflow for fsl
analysis. This will demonstrate how pre-defined workflows can be setup and
shared across users, projects and labs.


Setup preprocessing workflow
----------------------------

This is a generic fsl feat preprocessing workflow encompassing skull stripping,
motion correction and smoothing operations.


::

  preproc = pe.Workflow(name='preproc')


Set up a node to define all inputs required for the preprocessing workflow

::

  inputnode = pe.Node(interface=util.IdentityInterface(fields=['func',
                                                               'struct',]),
                      name='inputspec')


Convert functional images to float representation. Since there can be more than
one functional run we use a MapNode to convert each run.

::

  img2float = pe.MapNode(interface=fsl.ImageMaths(out_data_type='float',
                                               op_string = '',
                                               suffix='_dtype'),
                         iterfield=['in_file'],
                         name='img2float')
  preproc.connect(inputnode, 'func', img2float, 'in_file')


Extract the middle volume of the first run as the reference

::

  extract_ref = pe.Node(interface=fsl.ExtractROI(t_size=1),
                        name = 'extractref')


Define a function to pick the first file from a list of files

::

  def pickfirst(files):
      if isinstance(files, list):
          return files[0]
      else:
          return files

  preproc.connect(img2float, ('out_file', pickfirst), extract_ref, 'in_file')


Define a function to return the 1 based index of the middle volume

::

  def getmiddlevolume(func):
      funcfile = func
      if isinstance(func, list):
          funcfile = func[0]
      _,_,_,timepoints = load(funcfile).get_shape()
      return (timepoints/2)-1

  preproc.connect(inputnode, ('func', getmiddlevolume), extract_ref, 't_min')


Realign the functional runs to the middle volume of the first run

::

  motion_correct = pe.MapNode(interface=fsl.MCFLIRT(save_mats = True,
                                                    save_plots = True),
                              name='realign',
                              iterfield = ['in_file'])
  preproc.connect(img2float, 'out_file', motion_correct, 'in_file')
  preproc.connect(extract_ref, 'roi_file', motion_correct, 'ref_file')



Plot the estimated motion parameters

::

  plot_motion = pe.MapNode(interface=fsl.PlotMotionParams(in_source='fsl'),
                          name='plot_motion',
                          iterfield=['in_file'])
  plot_motion.iterables = ('plot_type', ['rotations', 'translations'])
  preproc.connect(motion_correct, 'par_file', plot_motion, 'in_file')


Extract the mean volume of the first functional run

::

  meanfunc = pe.Node(interface=fsl.ImageMaths(op_string = '-Tmean',
                                              suffix='_mean'),
                     name='meanfunc')
  preproc.connect(motion_correct, ('out_file', pickfirst), meanfunc, 'in_file')


Strip the skull from the mean functional to generate a mask

::

  meanfuncmask = pe.Node(interface=fsl.BET(mask = True,
                                           no_output=True,
                                           frac = 0.3),
                         name = 'meanfuncmask')
  preproc.connect(meanfunc, 'out_file', meanfuncmask, 'in_file')


Mask the functional runs with the extracted mask

::

  maskfunc = pe.MapNode(interface=fsl.ImageMaths(suffix='_bet',
                                                 op_string='-mas'),
                        iterfield=['in_file'],
                        name = 'maskfunc')
  preproc.connect(motion_correct, 'out_file', maskfunc, 'in_file')
  preproc.connect(meanfuncmask, 'mask_file', maskfunc, 'in_file2')



Determine the 2nd and 98th percentile intensities of each functional run

::

  getthresh = pe.MapNode(interface=fsl.ImageStats(op_string='-p 2 -p 98'),
                         iterfield = ['in_file'],
                         name='getthreshold')
  preproc.connect(maskfunc, 'out_file', getthresh, 'in_file')



Threshold the first run of the functional data at 10% of the 98th percentile

::

  threshold = pe.Node(interface=fsl.ImageMaths(out_data_type='char',
                                               suffix='_thresh'),
                         name='threshold')
  preproc.connect(maskfunc, ('out_file', pickfirst), threshold, 'in_file')


Define a function to get 10% of the intensity

::

  def getthreshop(thresh):
      return '-thr %.10f -Tmin -bin'%(0.1*thresh[0][1])
  preproc.connect(getthresh, ('out_stat', getthreshop), threshold, 'op_string')


Determine the median value of the functional runs using the mask

::

  medianval = pe.MapNode(interface=fsl.ImageStats(op_string='-k %s -p 50'),
                         iterfield = ['in_file'],
                         name='medianval')
  preproc.connect(motion_correct, 'out_file', medianval, 'in_file')
  preproc.connect(threshold, 'out_file', medianval, 'mask_file')


Dilate the mask

::

  dilatemask = pe.Node(interface=fsl.ImageMaths(suffix='_dil',
                                                op_string='-dilF'),
                         name='dilatemask')
  preproc.connect(threshold, 'out_file', dilatemask, 'in_file')


Mask the motion corrected functional runs with the dilated mask

::

  maskfunc2 = pe.MapNode(interface=fsl.ImageMaths(suffix='_mask',
                                                  op_string='-mas'),
                        iterfield=['in_file'],
                        name='maskfunc2')
  preproc.connect(motion_correct, 'out_file', maskfunc2, 'in_file')
  preproc.connect(dilatemask, 'out_file', maskfunc2, 'in_file2')


Determine the mean image from each functional run

::

  meanfunc2 = pe.MapNode(interface=fsl.ImageMaths(op_string='-Tmean',
                                                  suffix='_mean'),
                         iterfield=['in_file'],
                         name='meanfunc2')
  preproc.connect(maskfunc2, 'out_file', meanfunc2, 'in_file')


Merge the median values with the mean functional images into a coupled list

::

  mergenode = pe.Node(interface=util.Merge(2, axis='hstack'),
                      name='merge')
  preproc.connect(meanfunc2,'out_file', mergenode, 'in1')
  preproc.connect(medianval,'out_stat', mergenode, 'in2')



Smooth each run using SUSAN with the brightness threshold set to 75% of the
median value for each run and a mask consituting the mean functional

::

  smooth = pe.MapNode(interface=fsl.SUSAN(),
                      iterfield=['in_file', 'brightness_threshold','usans'],
                      name='smooth')


Define a function to get the brightness threshold for SUSAN

::

  def getbtthresh(medianvals):
      return [0.75*val for val in medianvals]

  preproc.connect(maskfunc2, 'out_file', smooth, 'in_file')
  preproc.connect(medianval, ('out_stat', getbtthresh), smooth, 'brightness_threshold')
  preproc.connect(mergenode, ('out', lambda x: [[tuple([val[0],0.75*val[1]])] for val in x]), smooth, 'usans')


Mask the smoothed data with the dilated mask

::

  maskfunc3 = pe.MapNode(interface=fsl.ImageMaths(suffix='_mask',
                                                  op_string='-mas'),
                        iterfield=['in_file'],
                        name='maskfunc3')
  preproc.connect(smooth, 'smoothed_file', maskfunc3, 'in_file')
  preproc.connect(dilatemask, 'out_file', maskfunc3, 'in_file2')


Scale each volume of the run so that the median value of the run is set to 10000

::

  intnorm = pe.MapNode(interface=fsl.ImageMaths(suffix='_intnorm'),
                        iterfield=['in_file','op_string'],
                        name='intnorm')
  preproc.connect(maskfunc3, 'out_file', intnorm, 'in_file')


Define a function to get the scaling factor for intensity normalization

::

  def getinormscale(medianvals):
      return ['-mul %.10f'%(10000./val) for val in medianvals]
  preproc.connect(medianval, ('out_stat', getinormscale), intnorm, 'op_string')


Perform temporal highpass filtering on the data

::

  highpass = pe.MapNode(interface=fsl.ImageMaths(suffix='_tempfilt'),
                        iterfield=['in_file'],
                        name='highpass')
  preproc.connect(intnorm, 'out_file', highpass, 'in_file')


Generate a mean functional image from the first run

::

  meanfunc3 = pe.MapNode(interface=fsl.ImageMaths(op_string='-Tmean',
                                                  suffix='_mean'),
                         iterfield=['in_file'],
                        name='meanfunc3')
  preproc.connect(highpass, ('out_file', pickfirst), meanfunc3, 'in_file')


Strip the structural image a coregister the mean functional image to the
structural image

::

  nosestrip = pe.Node(interface=fsl.BET(frac=0.3),
                      name = 'nosestrip')
  skullstrip = pe.Node(interface=fsl.BET(mask = True),
                       name = 'stripstruct')

  coregister = pe.Node(interface=fsl.FLIRT(dof=6),
                       name = 'coregister')


Use :class:`nipype.algorithms.rapidart` to determine which of the
images in the functional series are outliers based on deviations in
intensity and/or movement.

::

  art = pe.Node(interface=ra.ArtifactDetect(use_differences = [False,True],
                                            use_norm = True,
                                            norm_threshold = 0.5,
                                            zintensity_threshold = 3,
                                            parameter_source = 'FSL',
                                            mask_type = 'file'),
                name="art")


  preproc.connect([(inputnode, nosestrip,[('struct','in_file')]),
                   (nosestrip, skullstrip, [('out_file','in_file')]),
                   (skullstrip, coregister,[('out_file','in_file')]),
                   (meanfunc2, coregister,[(('out_file',pickfirst),'reference')]),
                   (motion_correct, art, [('par_file','realignment_parameters')]),
                   (maskfunc2, art, [('out_file','realigned_files')]),
                   (dilatemask, art, [('out_file', 'mask_file')]),
                   ])


Set up model fitting workflow
-----------------------------


::

  modelfit = pe.Workflow(name='modelfit')


Use :class:`nipype.algorithms.modelgen.SpecifyModel` to generate design information.

::

  modelspec = pe.Node(interface=model.SpecifyModel(),  name="modelspec")
  modelspec.inputs.concatenate_runs = False


Use :class:`nipype.interfaces.fsl.Level1Design` to generate a run specific fsf
file for analysis

::

  level1design = pe.Node(interface=fsl.Level1Design(), name="level1design")


Use :class:`nipype.interfaces.fsl.FEATModel` to generate a run specific mat
file for use by FILMGLS

::

  modelgen = pe.MapNode(interface=fsl.FEATModel(), name='modelgen',
                        iterfield = ['fsf_file'])


Set the model generation to run everytime. Since the fsf file, which is the
input to modelgen only references the ev files, modelgen will not run if the ev
file contents are changed but the fsf file is untouched.

::

  modelgen.overwrite = True


Use :class:`nipype.interfaces.fsl.FILMGLS` to estimate a model specified by a
mat file and a functional run

::

  modelestimate = pe.MapNode(interface=fsl.FILMGLS(smooth_autocorr=True,
                                                   mask_size=5,
                                                   threshold=1000),
                             name='modelestimate',
                             iterfield = ['design_file','in_file'])


Use :class:`nipype.interfaces.fsl.ContrastMgr` to generate contrast estimates

::

  conestimate = pe.MapNode(interface=fsl.ContrastMgr(), name='conestimate',
                           iterfield = ['tcon_file','stats_dir'])

  modelfit.connect([
     (modelspec,level1design,[('session_info','session_info')]),
     (level1design,modelgen,[('fsf_files','fsf_file')]),
     (modelgen,modelestimate,[('design_file','design_file')]),
     (modelgen,conestimate,[('con_file','tcon_file')]),
     (modelestimate,conestimate,[('results_dir','stats_dir')]),
     ])


Set up fixed-effects workflow
-----------------------------


::

  fixed_fx = pe.Workflow(name='fixedfx')


Use :class:`nipype.interfaces.fsl.Merge` to merge the copes and
varcopes for each condition

::

  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")


Use :class:`nipype.interfaces.fsl.L2Model` to generate subject and condition
specific level 2 model design files

::

  level2model = pe.Node(interface=fsl.L2Model(),
                        name='l2model')


Use :class:`nipype.interfaces.fsl.FLAMEO` to estimate a second level model

::

  flameo = pe.MapNode(interface=fsl.FLAMEO(run_mode='fe'), name="flameo",
                      iterfield=['cope_file','var_cope_file'])

  fixed_fx.connect([(copemerge,flameo,[('merged_file','cope_file')]),
                    (varcopemerge,flameo,[('merged_file','var_cope_file')]),
                    (level2model,flameo, [('design_mat','design_file'),
                                          ('design_con','t_con_file'),
                                          ('design_grp','cov_split_file')]),
                    ])



Set up first-level workflow
---------------------------


::

  def sort_copes(files):
      numelements = len(files[0])
      outfiles = []
      for i in range(numelements):
          outfiles.insert(i,[])
          for j, elements in enumerate(files):
              outfiles[i].append(elements[i])
      return outfiles

  def num_copes(files):
      return len(files)

  firstlevel = pe.Workflow(name='firstlevel')
  firstlevel.connect([(preproc, modelfit, [('highpass.out_file', 'modelspec.functional_runs'),
                                           ('art.outlier_files', 'modelspec.outlier_files'),
                                           ('highpass.out_file','modelestimate.in_file')]),
                      (preproc, fixed_fx, [('coregister.out_file', 'flameo.mask_file')]),
                      (modelfit, fixed_fx,[(('conestimate.copes', sort_copes),'copemerge.in_files'),
                                           (('conestimate.varcopes', sort_copes),'varcopemerge.in_files'),
                                           (('conestimate.copes', num_copes),'l2model.num_copes'),
                                           ])
                      ])



Experiment specific components
------------------------------

The nipype tutorial contains data for two subjects.  Subject data
is in two subdirectories, ``s1`` and ``s2``.  Each subject directory
contains four functional volumes: f3.nii, f5.nii, f7.nii, f10.nii. And
one anatomical volume named struct.nii.

Below we set some variables to inform the ``datasource`` about the
layout of our data.  We specify the location of the data, the subject
sub-directories and a dictionary that maps each run to a mnemonic (or
field) for the run type (``struct`` or ``func``).  These fields become
the output fields of the ``datasource`` node in the pipeline.

In the example below, run 'f3' is of type 'func' and gets mapped to a
nifti filename through a template '%s.nii'. So 'f3' would become
'f3.nii'.


::

  # Specify the location of the data.
  data_dir = os.path.abspath('data')
  # Specify the subject directories
  subject_list = ['s1'] #, 's3']
  # Map field names to individual subject runs.
  info = dict(func=[['subject_id', ['f3','f5','f7','f10']]],
              struct=[['subject_id','struct']])

  infosource = pe.Node(interface=util.IdentityInterface(fields=['subject_id']),
                       name="infosource")


Here we set up iteration over all the subjects. The following line
is a particular example of the flexibility of the system.  The
``datasource`` attribute ``iterables`` tells the pipeline engine that
it should repeat the analysis on each of the items in the
``subject_list``. In the current example, the entire first level
preprocessing and estimation will be repeated for each subject
contained in subject_list.

::

  infosource.iterables = ('subject_id', subject_list)


Now we create a :class:`nipype.interfaces.io.DataSource` object and
fill in the information from above about the layout of our data.  The
:class:`nipype.pipeline.NodeWrapper` module wraps the interface object
and provides additional housekeeping and pipeline specific
functionality.

::

  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'
  datasource.inputs.template_args = info


Use the get_node function to retrieve an internal node by name. Then set the
iterables on this node to perform two different extents of smoothing.

::

  smoothnode = firstlevel.get_node('preproc.smooth')
  assert(str(smoothnode)=='preproc.smooth')
  smoothnode.iterables = ('fwhm', [5.,10.])

  hpcutoff = 120
  TR = 3.
  firstlevel.inputs.preproc.highpass.suffix = '_hpf'
  firstlevel.inputs.preproc.highpass.op_string = '-bptf %d -1'%(hpcutoff/TR)



Setup a function that returns subject-specific information about the
experimental paradigm. This is used by the
:class:`nipype.interfaces.spm.SpecifyModel` to create the information necessary
to generate an SPM design matrix. In this tutorial, the same paradigm was used
for every participant. Other examples of this function are available in the
`doc/examples` folder. Note: Python knowledge required here.

::

  from nipype.interfaces.base import Bunch
  from copy import deepcopy
  def subjectinfo(subject_id):
      print "Subject ID: %s\n"%str(subject_id)
      output = []
      names = ['Task-Odd','Task-Even']
      for r in range(4):
          onsets = [range(15,240,60),range(45,240,60)]
          output.insert(r,
                        Bunch(conditions=names,
                              onsets=deepcopy(onsets),
                              durations=[[15] for s in names],
                              amplitudes=None,
                              tmod=None,
                              pmod=None,
                              regressor_names=None,
                              regressors=None))
      return output


Setup the contrast structure that needs to be evaluated. This is a list of
lists. The inner list specifies the contrasts and has the following format -
[Name,Stat,[list of condition names],[weights on those conditions]. The
condition names must match the `names` listed in the `subjectinfo` function
described above.

::

  cont1 = ['Task>Baseline','T', ['Task-Odd','Task-Even'],[0.5,0.5]]
  cont2 = ['Task-Odd>Task-Even','T', ['Task-Odd','Task-Even'],[1,-1]]
  cont3 = ['Task','F', [cont1, cont2]]
  contrasts = [cont1,cont2]

  firstlevel.inputs.modelfit.modelspec.input_units = 'secs'
  firstlevel.inputs.modelfit.modelspec.output_units = 'secs'
  firstlevel.inputs.modelfit.modelspec.time_repetition = TR
  firstlevel.inputs.modelfit.modelspec.high_pass_filter_cutoff = hpcutoff

  firstlevel.inputs.modelfit.level1design.interscan_interval = TR
  firstlevel.inputs.modelfit.level1design.bases = {'dgamma':{'derivs': False}}
  firstlevel.inputs.modelfit.level1design.contrasts = contrasts


Set up complete workflow
========================

::

  l1pipeline = pe.Workflow(name= "level1")
  l1pipeline.base_dir = os.path.abspath('./fsl/workingdir')
  l1pipeline.config = dict(crashdump_dir=os.path.abspath('./fsl/crashdumps'))

  l1pipeline.connect([(infosource, datasource, [('subject_id', 'subject_id')]),
                      (infosource, firstlevel, [(('subject_id', subjectinfo), 'modelfit.modelspec.subject_info')]),
                      (datasource, firstlevel, [('struct','preproc.inputspec.struct'),
                                                ('func', 'preproc.inputspec.func'),
                                                ]),
                      ])


Execute the pipeline
--------------------

The code discussed above sets up all the necessary data structures with
appropriate parameters and the connectivity between the processes, but does not
generate any output. To actually run the analysis on the data the
``nipype.pipeline.engine.Pipeline.Run`` function needs to be called.

::

  if __name__ == '__main__':
      l1pipeline.run()
      l1pipeline.write_graph(graph2use='flat')



.. seealso::
  The full source code of this example is included in the Nipype source distribution (`../examples/fsl_tutorial2.py`).
