Home · Quickstart · Documentation · Citation · NiPy

# fMRI: FEEDS - FSL

A pipeline example that data from the FSL FEEDS set. Single subject, two stimuli.

You can find it at http://www.fmrib.ox.ac.uk/fsl/feeds/doc/index.html

    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

## 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')

## 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):
        from nibabel import load
        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')

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]

    def convert_th(x):
        return [[tuple([val[0],0.75*val[1]])] for val in x]

    preproc.connect(maskfunc2, 'out_file', smooth, 'in_file')
    preproc.connect(medianval, ('out_stat', getbtthresh), smooth, 'brightness_threshold')
    preproc.connect(mergenode, ('out', convert_th), 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')

    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')]),
                     ])

## Set up model fitting workflow

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

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

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

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

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

Use `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', 'ev_files'])

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

    conestimate = pe.MapNode(interface=fsl.ContrastMgr(), name='conestimate',
                             iterfield = ['fcon_file', 'tcon_file','param_estimates',
                                          'sigmasquareds', 'corrections',
                                          'dof_file'])

    modelfit.connect([
       (modelspec,level1design,[('session_info','session_info')]),
       (level1design,modelgen,[('fsf_files','fsf_file'),
                               ('ev_files', 'ev_files')]),
       (modelgen,modelestimate,[('design_file','design_file')]),
       (modelgen,conestimate,[('con_file','tcon_file')]),
       (modelgen,conestimate,[('fcon_file','fcon_file')]),
       (modelestimate,conestimate,[('param_estimates','param_estimates'),
                                   ('sigmasquareds', 'sigmasquareds'),
                                   ('corrections','corrections'),
                                   ('dof_file','dof_file')]),
       ])

## Set up fixed-effects workflow

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

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

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

Use `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'),
                                             ('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

This tutorial does a single subject analysis so we are not using infosource and iterables

    # Specify the location of the FEEDS data. You can find it at http://www.fmrib.ox.ac.uk/fsl/feeds/doc/index.html
    feeds_data_dir = os.path.abspath('feeds_data')
    # Specify the subject directories
    # Map field names to individual subject runs.
    info = dict(func=[['fmri']],
                struct=[['structural']])

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.Node` module wraps the interface object and provides additional housekeeping and pipeline specific functionality.

    datasource = pe.Node(interface=nio.DataGrabber(outfields=['func', 'struct']),
                         name = 'datasource')
    datasource.inputs.base_directory = feeds_data_dir
    datasource.inputs.template = '%s.nii.gz'
    datasource.inputs.template_args = info

    firstlevel.inputs.preproc.smooth.fwhm = 5

    hpcutoff = 100
    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 `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

    firstlevel.inputs.modelfit.modelspec.subject_info = [Bunch(conditions=['Visual','Auditory'],
                            onsets=[range(0,int(180*TR),60),range(0,int(180*TR),90)],
                            durations=[[30], [45]],
                            amplitudes=None,
                            tmod=None,
                            pmod=None,
                            regressor_names=None,
                            regressors=None)]

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 = ['Visual>Baseline','T', ['Visual','Auditory'],[1,0]]
    cont2 = ['Auditory>Baseline','T', ['Visual','Auditory'],[0,1]]
    cont3 = ['Task','F', [cont1, cont2]]
    contrasts = [cont1,cont2,cont3]

    model_serial_correlations = True

    firstlevel.inputs.modelfit.modelspec.input_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': True}}
    firstlevel.inputs.modelfit.level1design.contrasts = contrasts
    firstlevel.inputs.modelfit.level1design.model_serial_correlations = model_serial_correlations

### Set up complete workflow

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

    l1pipeline.connect([(datasource, firstlevel, [('struct','preproc.inputspec.struct'),
                                                  ('func', 'preproc.inputspec.func'),
                                                  ]),
                        ])

Setup the datasink

    datasink = pe.Node(interface=nio.DataSink(parameterization=False), name="datasink")
    datasink.inputs.base_directory = os.path.abspath('./fsl_feeds/l1out')
    datasink.inputs.substitutions = [('dtype_mcf_mask_mean', 'meanfunc'),
                                     ('brain_brain_flirt','coregistered')]
    # store relevant outputs from various stages of the 1st level analysis
    l1pipeline.connect([(firstlevel, datasink,[('fixedfx.flameo.stats_dir',"fixedfx.@con"),
                                                ('preproc.coregister.out_file','coregstruct'),
                                                ('preproc.meanfunc2.out_file','meanfunc'),
                                                ('modelfit.conestimate.zstats', 'level1.@Z'),
                                                ])
                        ])

## 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()
    #    l2pipeline.run()

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.