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# fMRI: FSL

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

    python fmri_fsl.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

## 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')

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 constituting 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 getusans(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', getusans), 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 and 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 `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.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'],
                     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 `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 = ['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')]),
       (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'),
                                             ('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 `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.

    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 `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.

    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],
                                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.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
    firstlevel.inputs.modelfit.level1design.model_serial_correlations = True

### Set up complete workflow

    l1pipeline = pe.Workflow(name= "level1")
    l1pipeline.base_dir = os.path.abspath('./fsl/workingdir')
    l1pipeline.config = {"execution": {"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.write_graph()
        l1pipeline.run()
        #l1pipeline.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.