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# fMRI: OpenfMRI.org data, FSL

A growing number of datasets are available on OpenfMRI. This script demonstrates how to use nipype to analyze a data set.

> python fmri_openfmri.py –datasetdir ds107

    from glob import glob
    import os

    import nipype.pipeline.engine as pe
    import nipype.algorithms.modelgen as model
    import nipype.algorithms.rapidart as ra
    import nipype.interfaces.fsl as fsl
    import nipype.interfaces.io as nio
    import nipype.interfaces.utility as niu
    from nipype.workflows.fmri.fsl import (create_featreg_preproc,
                                      create_modelfit_workflow,
                                      create_fixed_effects_flow)

    fsl.FSLCommand.set_default_output_type('NIFTI_GZ')

    def get_subjectinfo(subject_id, base_dir, task_id, model_id):
        """Get info for a given subject

        Parameters
        ----------
        subject_id : string
            Subject identifier (e.g., sub001)
        base_dir : string
            Path to base directory of the dataset
        task_id : int
            Which task to process
        model_id : int
            Which model to process

        Returns
        -------
        run_ids : list of ints
            Run numbers
        conds : list of str
            Condition names
        TR : float
            Repetition time
        """
        from glob import glob
        import os
        import numpy as np
        condition_info = []
        cond_file = os.path.join(base_dir, 'models', 'model%03d' % model_id,
                                 'condition_key.txt')
        with open(cond_file, 'rt') as fp:
            for line in fp:
                info = line.strip().split()
                condition_info.append([info[0], info[1], ' '.join(info[2:])])
        if len(condition_info) == 0:
            raise ValueError('No condition info found in %s' % cond_file)
        taskinfo = np.array(condition_info)
        n_tasks = len(np.unique(taskinfo[:, 0]))
        conds = []
        run_ids = []
        if task_id > n_tasks:
            raise ValueError('Task id %d does not exist' % task_id)
        for idx in range(n_tasks):
            taskidx = np.where(taskinfo[:, 0] == 'task%03d' % (idx + 1))
            conds.append([condition.replace(' ', '_') for condition
                          in taskinfo[taskidx[0], 2]])
            files = glob(os.path.join(base_dir,
                                      subject_id,
                                      'BOLD',
                                      'task%03d_run*' % (idx + 1)))
            run_ids.insert(idx, range(1, len(files) + 1))
        TR = np.genfromtxt(os.path.join(base_dir, 'scan_key.txt'))[1]
        return run_ids[task_id - 1], conds[task_id - 1], TR

    def analyze_openfmri_dataset(data_dir, subject=None, model_id=None, work_dir=None):
        """Analyzes an open fmri dataset

        Parameters
        ----------

        data_dir : str
            Path to the base data directory

        work_dir : str
            Nipype working directory (defaults to cwd)
        """

Load nipype workflows

    preproc = create_featreg_preproc(whichvol='first')
    modelfit = create_modelfit_workflow()
    fixed_fx = create_fixed_effects_flow()

Remove the plotting connection so that plot iterables don’t propagate to the model stage

    preproc.disconnect(preproc.get_node('plot_motion'), 'out_file',
                       preproc.get_node('outputspec'), 'motion_plots')

Set up openfmri data specific components

    subjects = [path.split(os.path.sep)[-1] for path in
                glob(os.path.join(data_dir, 'sub*'))]

    infosource = pe.Node(niu.IdentityInterface(fields=['subject_id',
                                                       'model_id']),
                         name='infosource')
    if subject is None:
        infosource.iterables = [('subject_id', subjects),
            ('model_id', [model_id])]
    else:
        infosource.iterables = [('subject_id',
                                 [subjects[subjects.index(subject)]]),
                                ('model_id', [model_id])]

    subjinfo = pe.Node(niu.Function(input_names=['subject_id', 'base_dir',
                                                 'task_id', 'model_id'],
                                    output_names=['run_id', 'conds', 'TR'],
                                    function=get_subjectinfo),
                       name='subjectinfo')
    subjinfo.inputs.base_dir = data_dir

Return data components as anat, bold and behav

    datasource = pe.Node(nio.DataGrabber(infields=['subject_id', 'run_id',
                                                   'model_id'],
                                         outfields=['anat', 'bold', 'behav']),
                         name='datasource')
    datasource.inputs.base_directory = data_dir
    datasource.inputs.template = '*'
    datasource.inputs.field_template = {'anat': '%s/anatomy/highres001.nii.gz',
                                'bold': '%s/BOLD/task001_r*/bold.nii.gz',
                                'behav': ('%s/model/model%03d/onsets/task001_'
                                          'run%03d/cond*.txt')}
    datasource.inputs.template_args = {'anat': [['subject_id']],
                                       'bold': [['subject_id']],
                                       'behav': [['subject_id', 'model_id',
                                                  'run_id']]}
    datasource.inputs.sorted = True

Create meta workflow

    wf = pe.Workflow(name='openfmri')
    wf.connect(infosource, 'subject_id', subjinfo, 'subject_id')
    wf.connect(infosource, 'model_id', subjinfo, 'model_id')
    wf.connect(infosource, 'subject_id', datasource, 'subject_id')
    wf.connect(infosource, 'model_id', datasource, 'model_id')
    wf.connect(subjinfo, 'run_id', datasource, 'run_id')
    wf.connect([(datasource, preproc, [('bold', 'inputspec.func')]),
                ])

    def get_highpass(TR, hpcutoff):
        return hpcutoff / (2 * TR)
    gethighpass = pe.Node(niu.Function(input_names=['TR', 'hpcutoff'],
                                       output_names=['highpass'],
                                       function=get_highpass),
                          name='gethighpass')
    wf.connect(subjinfo, 'TR', gethighpass, 'TR')
    wf.connect(gethighpass, 'highpass', preproc, 'inputspec.highpass')

Setup a basic set of contrasts, a t-test per condition

    def get_contrasts(base_dir, model_id, conds):
        import numpy as np
        import os
        contrast_file = os.path.join(base_dir, 'models', 'model%03d' % model_id,
                                     'task_contrasts.txt')
        contrast_def = np.genfromtxt(contrast_file, dtype=object)
        contrasts = []
        for row in contrast_def:
            con = [row[0], 'T', ['cond%03d' % i  for i in range(len(conds))],
                   row[1:].astype(float).tolist()]
            contrasts.append(con)
        return contrasts

    contrastgen = pe.Node(niu.Function(input_names=['base_dir', 'model_id',
                                                    'conds'],
                                       output_names=['contrasts'],
                                       function=get_contrasts),
                          name='contrastgen')
    contrastgen.inputs.base_dir = data_dir

    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")
    modelspec.inputs.input_units = 'secs'

    wf.connect(subjinfo, 'TR', modelspec, 'time_repetition')
    wf.connect(datasource, 'behav', modelspec, 'event_files')
    wf.connect(subjinfo, 'TR', modelfit, 'inputspec.interscan_interval')
    wf.connect(subjinfo, 'conds', contrastgen, 'conds')
    wf.connect(infosource, 'model_id', contrastgen, 'model_id')
    wf.connect(contrastgen, 'contrasts', modelfit, 'inputspec.contrasts')

    wf.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')])
                ])

Reorder the copes so that now it combines across runs

    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]

    wf.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'),
                                       ])
                ])

Connect to a datasink

    def get_subs(subject_id, conds):
        subs = [('_subject_id_%s/' % subject_id, '')]
        for i in range(len(conds)):
            subs.append(('_flameo%d/cope1.' % i, 'cope%02d.' % (i + 1)))
            subs.append(('_flameo%d/varcope1.' % i, 'varcope%02d.' % (i + 1)))
            subs.append(('_flameo%d/zstat1.' % i, 'zstat%02d.' % (i + 1)))
            subs.append(('_flameo%d/tstat1.' % i, 'tstat%02d.' % (i + 1)))
            subs.append(('_flameo%d/res4d.' % i, 'res4d%02d.' % (i + 1)))
        return subs

    subsgen = pe.Node(niu.Function(input_names=['subject_id', 'conds'],
                                   output_names=['substitutions'],
                                   function=get_subs),
                      name='subsgen')

    datasink = pe.Node(interface=nio.DataSink(),
                       name="datasink")
    wf.connect(infosource, 'subject_id', datasink, 'container')
    wf.connect(infosource, 'subject_id', subsgen, 'subject_id')
    wf.connect(subjinfo, 'conds', subsgen, 'conds')
    wf.connect(subsgen, 'substitutions', datasink, 'substitutions')
    wf.connect([(fixed_fx.get_node('outputspec'), datasink,
                                 [('res4d', 'res4d'),
                                  ('copes', 'copes'),
                                  ('varcopes', 'varcopes'),
                                  ('zstats', 'zstats'),
                                  ('tstats', 'tstats')])
                                 ])

Set processing parameters

        hpcutoff = 120.
        subjinfo.inputs.task_id = 1
        preproc.inputs.inputspec.fwhm = 6.0
        gethighpass.inputs.hpcutoff = hpcutoff
        modelspec.inputs.high_pass_filter_cutoff = hpcutoff
        modelfit.inputs.inputspec.bases = {'dgamma': {'derivs': True}}
        modelfit.inputs.inputspec.model_serial_correlations = True
        modelfit.inputs.inputspec.film_threshold = 1000

        if work_dir is None:
            work_dir = os.path.join(os.getcwd(), 'working')
        wf.base_dir = work_dir
        datasink.inputs.base_directory = os.path.join(work_dir, 'output')
        wf.config['execution'] = dict(crashdump_dir=os.path.join(work_dir,
                                                                 'crashdumps'),
                                      stop_on_first_crash=True)
        wf.run('MultiProc', plugin_args={'n_procs': 2})

    if __name__ == '__main__':
        import argparse
        parser = argparse.ArgumentParser(prog='fmri_openfmri.py',
                                         description=__doc__)
        parser.add_argument('--datasetdir', required=True)
        parser.add_argument('--subject', default=None)
        parser.add_argument('--model', default=1)
        args = parser.parse_args()
        analyze_openfmri_dataset(data_dir=os.path.abspath(args.datasetdir),
                                 subject=args.subject,
                                 model_id=int(args.model))

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.