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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.