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fMRI: OpenfMRI.org data, FSL, ANTS, c3daffine

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

python fmri_ants_openfmri.py –datasetdir ds107
from nipype import config
config.enable_provenance()
from nipype.external import six


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.ants as ants
from nipype.interfaces.c3 import C3dAffineTool
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)

from nipype import LooseVersion

version = 0
if fsl.Info.version() and \
    LooseVersion(fsl.Info.version()) > LooseVersion('5.0.6'):
    version = 507

fsl.FSLCommand.set_default_output_type('NIFTI_GZ')


def create_reg_workflow(name='registration'):
    """Create a FEAT preprocessing workflow together with freesurfer

    Parameters
    ----------

    ::

        name : name of workflow (default: 'registration')

    Inputs::

        inputspec.source_files : files (filename or list of filenames to register)
        inputspec.mean_image : reference image to use
        inputspec.anatomical_image : anatomical image to coregister to
        inputspec.target_image : registration target

    Outputs::

        outputspec.func2anat_transform : FLIRT transform
        outputspec.anat2target_transform : FLIRT+FNIRT transform
        outputspec.transformed_files : transformed files in target space
        outputspec.transformed_mean : mean image in target space

    Example
    -------

register = pe.Workflow(name=name) inputnode = pe.Node(interface=niu.IdentityInterface(fields=[‘source_files’,

‘mean_image’, ‘anatomical_image’, ‘target_image’, ‘target_image_brain’, ‘config_file’]),

name=’inputspec’)

outputnode = pe.Node(interface=niu.IdentityInterface(fields=[‘func2anat_transform’,
‘anat2target_transform’, ‘transformed_files’, ‘transformed_mean’, ]),

name=’outputspec’)

::

Estimate the tissue classes from the anatomical image. But use spm’s segment as FSL appears to be breaking. “”“

stripper = pe.Node(fsl.BET(), name=’stripper’) register.connect(inputnode, ‘anatomical_image’, stripper, ‘in_file’) fast = pe.Node(fsl.FAST(), name=’fast’) register.connect(stripper, ‘out_file’, fast, ‘in_files’)

Binarize the segmentation

binarize = pe.Node(fsl.ImageMaths(op_string='-nan -thr 0.5 -bin'),
                   name='binarize')
pickindex = lambda x, i: x[i]
register.connect(fast, ('partial_volume_files', pickindex, 2),
                 binarize, 'in_file')

Calculate rigid transform from mean image to anatomical image

mean2anat = pe.Node(fsl.FLIRT(), name='mean2anat')
mean2anat.inputs.dof = 6
register.connect(inputnode, 'mean_image', mean2anat, 'in_file')
register.connect(stripper, 'out_file', mean2anat, 'reference')

Now use bbr cost function to improve the transform

mean2anatbbr = pe.Node(fsl.FLIRT(), name='mean2anatbbr')
mean2anatbbr.inputs.dof = 6
mean2anatbbr.inputs.cost = 'bbr'
mean2anatbbr.inputs.schedule = os.path.join(os.getenv('FSLDIR'),
                                            'etc/flirtsch/bbr.sch')
register.connect(inputnode, 'mean_image', mean2anatbbr, 'in_file')
register.connect(binarize, 'out_file', mean2anatbbr, 'wm_seg')
register.connect(inputnode, 'anatomical_image', mean2anatbbr, 'reference')
register.connect(mean2anat, 'out_matrix_file',
                 mean2anatbbr, 'in_matrix_file')
"""
Convert the BBRegister transformation to ANTS ITK format
"""

convert2itk = pe.Node(C3dAffineTool(),
                      name='convert2itk')
convert2itk.inputs.fsl2ras = True
convert2itk.inputs.itk_transform = True
register.connect(mean2anatbbr, 'out_matrix_file', convert2itk, 'transform_file')
register.connect(inputnode, 'mean_image',convert2itk, 'source_file')
register.connect(stripper, 'out_file', convert2itk, 'reference_file')

Compute registration between the subject’s structural and MNI template This is currently set to perform a very quick registration. However, the registration can be made significantly more accurate for cortical structures by increasing the number of iterations All parameters are set using the example from: #https://github.com/stnava/ANTs/blob/master/Scripts/newAntsExample.sh

reg = pe.Node(ants.Registration(), name='antsRegister')
reg.inputs.output_transform_prefix = "output_"
reg.inputs.transforms = ['Rigid', 'Affine', 'SyN']
reg.inputs.transform_parameters = [(0.1,), (0.1,), (0.2, 3.0, 0.0)]
#reg.inputs.number_of_iterations = ([[10000, 111110, 11110]] * 2 + [[100, 50, 30]])
reg.inputs.number_of_iterations = [[10000, 11110, 11110]] * 2 + [[100, 30, 20]]
reg.inputs.dimension = 3
reg.inputs.write_composite_transform = True
reg.inputs.collapse_output_transforms = True
reg.inputs.initial_moving_transform_com = True
reg.inputs.metric = ['Mattes'] * 2 + [['Mattes', 'CC']]
reg.inputs.metric_weight = [1] * 2 + [[0.5, 0.5]]
reg.inputs.radius_or_number_of_bins = [32] * 2 + [[32, 4]]
reg.inputs.sampling_strategy = ['Regular'] * 2 + [[None, None]]
reg.inputs.sampling_percentage = [0.3] * 2 + [[None, None]]
reg.inputs.convergence_threshold = [1.e-8] * 2 + [-0.01]
reg.inputs.convergence_window_size = [20] * 2 + [5]
reg.inputs.smoothing_sigmas = [[4, 2, 1]] * 2 + [[1, 0.5, 0]]
reg.inputs.sigma_units = ['vox'] * 3
reg.inputs.shrink_factors = [[3, 2, 1]]*2 + [[4, 2, 1]]
reg.inputs.use_estimate_learning_rate_once = [True] * 3
reg.inputs.use_histogram_matching = [False] * 2 + [True]
reg.inputs.winsorize_lower_quantile = 0.005
reg.inputs.winsorize_upper_quantile = 0.995
reg.inputs.args = '--float'
reg.inputs.output_warped_image = 'output_warped_image.nii.gz'
reg.inputs.num_threads = 4
reg.plugin_args = {'qsub_args': '-l nodes=1:ppn=4'}
register.connect(stripper, 'out_file', reg, 'moving_image')
register.connect(inputnode,'target_image_brain', reg,'fixed_image')

Concatenate the affine and ants transforms into a list

pickfirst = lambda x: x[0]

merge = pe.Node(niu.Merge(2), iterfield=['in2'], name='mergexfm')
register.connect(convert2itk, 'itk_transform', merge, 'in2')
register.connect(reg, ('composite_transform', pickfirst), merge, 'in1')

Transform the mean image. First to anatomical and then to target

::
warpmean = pe.Node(ants.ApplyTransforms(),
name=’warpmean’)

warpmean.inputs.input_image_type = 3 warpmean.inputs.interpolation = ‘BSpline’ warpmean.inputs.invert_transform_flags = [False, False] warpmean.inputs.terminal_output = ‘file’

register.connect(inputnode,’target_image_brain’, warpmean,’reference_image’) register.connect(inputnode, ‘mean_image’, warpmean, ‘input_image’) register.connect(merge, ‘out’, warpmean, ‘transforms’)

Transform the remaining images. First to anatomical and then to target

warpall = pe.MapNode(ants.ApplyTransforms(),
                     iterfield=['input_image'],
                     name='warpall')
warpall.inputs.input_image_type = 3
warpall.inputs.interpolation = 'BSpline'
warpall.inputs.invert_transform_flags = [False, False]
warpall.inputs.terminal_output = 'file'

register.connect(inputnode,'target_image_brain',warpall,'reference_image')
register.connect(inputnode,'source_files', warpall, 'input_image')
register.connect(merge, 'out', warpall, 'transforms')

Assign all the output files

    register.connect(warpmean, 'output_image', outputnode, 'transformed_mean')
    register.connect(warpall, 'output_image', outputnode, 'transformed_files')
    register.connect(mean2anatbbr, 'out_matrix_file',
                     outputnode, 'func2anat_transform')
    register.connect(reg, 'composite_transform',
                     outputnode, 'anat2target_transform')

    return register

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,
                             task_id=None, output_dir=None, subj_prefix='*',
                             hpcutoff=120., use_derivatives=True,
                             fwhm=6.0):
    """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()
registration = create_reg_workflow()

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 = sorted([path.split(os.path.sep)[-1] for path in
                   glob(os.path.join(data_dir, subj_prefix))])

infosource = pe.Node(niu.IdentityInterface(fields=['subject_id',
                                                   'model_id',
                                                   'task_id']),
                     name='infosource')
if len(subject) == 0:
    infosource.iterables = [('subject_id', subjects),
                            ('model_id', [model_id]),
                            ('task_id', task_id)]
else:
    infosource.iterables = [('subject_id',
                             [subjects[subjects.index(subj)] for subj in subject]),
                            ('model_id', [model_id]),
                            ('task_id', task_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',
                                               'task_id', 'model_id'],
                                     outfields=['anat', 'bold', 'behav',
                                                'contrasts']),
                     name='datasource')
datasource.inputs.base_directory = data_dir
datasource.inputs.template = '*'
datasource.inputs.field_template = {'anat': '%s/anatomy/highres001.nii.gz',
                            'bold': '%s/BOLD/task%03d_r*/bold.nii.gz',
                            'behav': ('%s/model/model%03d/onsets/task%03d_'
                                      'run%03d/cond*.txt'),
                            'contrasts': ('models/model%03d/'
                                          'task_contrasts.txt')}
datasource.inputs.template_args = {'anat': [['subject_id']],
                                   'bold': [['subject_id', 'task_id']],
                                   'behav': [['subject_id', 'model_id',
                                              'task_id', 'run_id']],
                                   'contrasts': [['model_id']]}
datasource.inputs.sort_filelist = 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, 'task_id', subjinfo, 'task_id')
wf.connect(infosource, 'subject_id', datasource, 'subject_id')
wf.connect(infosource, 'model_id', datasource, 'model_id')
wf.connect(infosource, 'task_id', datasource, 'task_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(contrast_file, task_id, conds):
    import numpy as np
    contrast_def = np.genfromtxt(contrast_file, dtype=object)
    if len(contrast_def.shape) == 1:
        contrast_def = contrast_def[None, :]
    contrasts = []
    for row in contrast_def:
        if row[0] != 'task%03d' % task_id:
            continue
        con = [row[1], 'T', ['cond%03d' % (i + 1)  for i in range(len(conds))],
               row[2:].astype(float).tolist()]
        contrasts.append(con)
    # add auto contrasts for each column
    for i, cond in enumerate(conds):
        con = [cond, 'T', ['cond%03d' % (i + 1)], [1]]
        contrasts.append(con)
    return contrasts

contrastgen = pe.Node(niu.Function(input_names=['contrast_file',
                                                'task_id', 'conds'],
                                   output_names=['contrasts'],
                                   function=get_contrasts),
                      name='contrastgen')

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'

def check_behav_list(behav):
    out_behav = []
    if isinstance(behav, six.string_types):
        behav = [behav]
    for val in behav:
        if not isinstance(val, list):
            out_behav.append([val])
        else:
            out_behav.append(val)
    return out_behav

wf.connect(subjinfo, 'TR', modelspec, 'time_repetition')
wf.connect(datasource, ('behav', check_behav_list), modelspec, 'event_files')
wf.connect(subjinfo, 'TR', modelfit, 'inputspec.interscan_interval')
wf.connect(subjinfo, 'conds', contrastgen, 'conds')
wf.connect(datasource, 'contrasts', contrastgen, 'contrast_file')
wf.connect(infosource, 'task_id', contrastgen, 'task_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'),
                                   ])
            ])

wf.connect(preproc, 'outputspec.mean', registration, 'inputspec.mean_image')
wf.connect(datasource, 'anat', registration, 'inputspec.anatomical_image')
registration.inputs.inputspec.target_image = fsl.Info.standard_image('MNI152_T1_2mm.nii.gz')
registration.inputs.inputspec.target_image_brain = fsl.Info.standard_image('MNI152_T1_2mm_brain.nii.gz')
registration.inputs.inputspec.config_file = 'T1_2_MNI152_2mm'

def merge_files(copes, varcopes, zstats):
    out_files = []
    splits = []
    out_files.extend(copes)
    splits.append(len(copes))
    out_files.extend(varcopes)
    splits.append(len(varcopes))
    out_files.extend(zstats)
    splits.append(len(zstats))
    return out_files, splits

mergefunc = pe.Node(niu.Function(input_names=['copes', 'varcopes',
                                              'zstats'],
                               output_names=['out_files', 'splits'],
                               function=merge_files),
                  name='merge_files')
wf.connect([(fixed_fx.get_node('outputspec'), mergefunc,
                             [('copes', 'copes'),
                              ('varcopes', 'varcopes'),
                              ('zstats', 'zstats'),
                              ])])
wf.connect(mergefunc, 'out_files', registration, 'inputspec.source_files')

def split_files(in_files, splits):
    copes = in_files[:splits[0]]
    varcopes = in_files[splits[0]:(splits[0] + splits[1])]
    zstats = in_files[(splits[0] + splits[1]):]
    return copes, varcopes, zstats

splitfunc = pe.Node(niu.Function(input_names=['in_files', 'splits'],
                                 output_names=['copes', 'varcopes',
                                               'zstats'],
                                 function=split_files),
                  name='split_files')
wf.connect(mergefunc, 'splits', splitfunc, 'splits')
wf.connect(registration, 'outputspec.transformed_files',
           splitfunc, 'in_files')

Connect to a datasink

def get_subs(subject_id, conds, model_id, task_id):
    subs = [('_subject_id_%s_' % subject_id, '')]
    subs.append(('_model_id_%d' % model_id, 'model%03d' %model_id))
    subs.append(('task_id_%d/' % task_id, '/task%03d_' % task_id))
    subs.append(('bold_dtype_mcf_mask_smooth_mask_gms_tempfilt_mean_warp',
    'mean'))
    subs.append(('bold_dtype_mcf_mask_smooth_mask_gms_tempfilt_mean_flirt',
    'affine'))

    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)))
        subs.append(('_warpall%d/cope1_warp.' % i,
                     'cope%02d.' % (i + 1)))
        subs.append(('_warpall%d/varcope1_warp.' % (len(conds) + i),
                     'varcope%02d.' % (i + 1)))
        subs.append(('_warpall%d/zstat1_warp.' % (2 * len(conds) + i),
                     'zstat%02d.' % (i + 1)))
        subs.append(('_warpall%d/cope1_trans.' % i,
                     'cope%02d.' % (i + 1)))
        subs.append(('_warpall%d/varcope1_trans.' % (len(conds) + i),
                     'varcope%02d.' % (i + 1)))
        subs.append(('_warpall%d/zstat1_trans.' % (2 * len(conds) + i),
                     'zstat%02d.' % (i + 1)))
    return subs

subsgen = pe.Node(niu.Function(input_names=['subject_id', 'conds',
                                            'model_id', 'task_id'],
                               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(infosource, 'model_id', subsgen, 'model_id')
wf.connect(infosource, 'task_id', subsgen, 'task_id')
wf.connect(contrastgen, 'contrasts', 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')])
                             ])
wf.connect([(splitfunc, datasink,
             [('copes', 'copes.mni'),
              ('varcopes', 'varcopes.mni'),
              ('zstats', 'zstats.mni'),
              ])])
wf.connect(registration, 'outputspec.transformed_mean', datasink, 'mean.mni')
wf.connect(registration, 'outputspec.func2anat_transform', datasink, 'xfm.mean2anat')
wf.connect(registration, 'outputspec.anat2target_transform', datasink, 'xfm.anat2target')

Set processing parameters

::

preproc.inputs.inputspec.fwhm = fwhm gethighpass.inputs.hpcutoff = hpcutoff modelspec.inputs.high_pass_filter_cutoff = hpcutoff modelfit.inputs.inputspec.bases = {‘dgamma’: {‘derivs’: use_derivatives}} modelfit.inputs.inputspec.model_serial_correlations = True if version < 507:

modelfit.inputs.inputspec.film_threshold = 1000
else:
modelfit.inputs.inputspec.film_threshold = -1000

datasink.inputs.base_directory = output_dir return wf

if __name__ == ‘__main__’:

import argparse defstr = ‘ (default %(default)s)’ parser = argparse.ArgumentParser(prog=’fmri_openfmri.py’,

description=__doc__)

parser.add_argument(‘-d’, ‘–datasetdir’, required=True) parser.add_argument(‘-s’, ‘–subject’, default=[],

nargs=’+’, type=str, help=”Subject name (e.g. ‘sub001’)”)
parser.add_argument(‘-m’, ‘–model’, default=1,
help=”Model index” + defstr)
parser.add_argument(‘-x’, ‘–subjectprefix’, default=’sub*’,
help=”Subject prefix” + defstr)
parser.add_argument(‘-t’, ‘–task’, default=1, #nargs=’+’,
type=int, help=”Task index” + defstr)
parser.add_argument(‘–hpfilter’, default=120.,
type=float, help=”High pass filter cutoff (in secs)” + defstr)
parser.add_argument(‘–fwhm’, default=6.,
type=float, help=”Spatial FWHM” + defstr)
parser.add_argument(‘–derivatives’, action=”store_true”,
help=”Use derivatives” + defstr)
parser.add_argument(“-o”, “–output_dir”, dest=”outdir”,
help=”Output directory base”)
parser.add_argument(“-w”, “–work_dir”, dest=”work_dir”,
help=”Output directory base”)
parser.add_argument(“-p”, “–plugin”, dest=”plugin”,
default=’Linear’, help=”Plugin to use”)
parser.add_argument(“–plugin_args”, dest=”plugin_args”,
help=”Plugin arguments”)

args = parser.parse_args() outdir = args.outdir work_dir = os.getcwd() if args.work_dir:

work_dir = os.path.abspath(args.work_dir)
if outdir:
outdir = os.path.abspath(outdir)
else:
outdir = os.path.join(work_dir, ‘output’)
outdir = os.path.join(outdir, ‘model%02d’ % int(args.model),
‘task%03d’ % int(args.task))

derivatives = args.derivatives if derivatives is None:

derivatives = False
wf = analyze_openfmri_dataset(data_dir=os.path.abspath(args.datasetdir),
subject=args.subject, model_id=int(args.model), task_id=[int(args.task)], subj_prefix=args.subjectprefix, output_dir=outdir, hpcutoff=args.hpfilter, use_derivatives=derivatives, fwhm=args.fwhm)

wf.base_dir = work_dir if args.plugin_args:

wf.run(args.plugin, plugin_args=eval(args.plugin_args))
else:
wf.run(args.plugin)

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.