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# fMRI: FSL reuse workflows

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

    python fmri_fsl_reuse.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 nipype.workflows.fmri.fsl import (create_featreg_preproc,
                                      create_modelfit_workflow,
                                      create_fixed_effects_flow)

## Preliminaries

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')

    level1_workflow = pe.Workflow(name='level1flow')

    preproc = create_featreg_preproc(whichvol='first')

    modelfit = create_modelfit_workflow()

    fixed_fx = create_fixed_effects_flow()

Add artifact detection and model specification nodes between the preprocessing and modelfitting workflows.

    art = pe.MapNode(interface=ra.ArtifactDetect(use_differences = [True, False],
                                                 use_norm = True,
                                                 norm_threshold = 1,
                                                 zintensity_threshold = 3,
                                                 parameter_source = 'FSL',
                                                 mask_type = 'file'),
                     iterfield=['realigned_files', 'realignment_parameters', 'mask_file'],
                     name="art")

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

    level1_workflow.connect([(preproc, art, [('outputspec.motion_parameters',
                                              'realignment_parameters'),
                                              ('outputspec.realigned_files',
                                               'realigned_files'),
                                              ('outputspec.mask', 'mask_file')]),
                             (preproc, modelspec, [('outputspec.highpassed_files',
                                                    'functional_runs'),
                                                   ('outputspec.motion_parameters',
                                                    'realignment_parameters')]),
                             (art, modelspec, [('outlier_files', 'outlier_files')]),
                             (modelspec, modelfit, [('session_info', 'inputspec.session_info')]),
                             (preproc, modelfit, [('outputspec.highpassed_files', 'inputspec.functional_data')])
                            ])

## 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)

    pickfirst = lambda x : x[0]

    level1_workflow.connect([(preproc, fixed_fx, [(('outputspec.mask', pickfirst),
                                                   'flameo.mask_file')]),
                             (modelfit, fixed_fx, [(('outputspec.copes', sort_copes),
                                                    'inputspec.copes'),
                                                    ('outputspec.dof_file',
                                                     'inputspec.dof_files'),
                                                   (('outputspec.varcopes',
                                                     sort_copes),
                                                    'inputspec.varcopes'),
                                                   (('outputspec.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 `nipype.interfaces.io.DataSource` object and fill in the information from above about the layout of our data. The `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.

    inputnode = level1_workflow.get_node('featpreproc.inputspec')
    inputnode.iterables = ('fwhm', [5.,10.])

    hpcutoff = 120.
    TR = 3.
    inputnode.inputs.highpass = hpcutoff/(2*TR)

Setup a function that returns subject-specific information about the experimental paradigm. This is used by the `nipype.modelgen.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.

    def subjectinfo(subject_id):
        from nipype.interfaces.base import Bunch
        from copy import deepcopy
        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]))
        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]

    modelspec.inputs.input_units = 'secs'
    modelspec.inputs.time_repetition = TR
    modelspec.inputs.high_pass_filter_cutoff = hpcutoff

    modelfit.inputs.inputspec.interscan_interval = TR
    modelfit.inputs.inputspec.bases = {'dgamma':{'derivs': False}}
    modelfit.inputs.inputspec.contrasts = contrasts
    modelfit.inputs.inputspec.model_serial_correlations = True
    modelfit.inputs.inputspec.film_threshold = 1000

    level1_workflow.base_dir = os.path.abspath('./fsl/workingdir')
    level1_workflow.config['execution'] = dict(crashdump_dir=os.path.abspath('./fsl/crashdumps'))

    level1_workflow.connect([(infosource, datasource, [('subject_id', 'subject_id')]),
                             (infosource, modelspec, [(('subject_id', subjectinfo),
                                                       'subject_info')]),
                             (datasource, preproc, [('func', '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__':
        #level1_workflow.write_graph()
        level1_workflow.run()
        #level1_workflow.run(plugin='MultiProc', plugin_args={'n_procs':2})

Example source code

You can download `the full source code of this example`. This same script is also included in the Nipype source distribution under the `examples` directory.