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

from __future__ import division
from builtins import range

import os  # system functions
from nipype.interfaces import io as nio  # Data i/o
from nipype.interfaces import utility as niu  # Utilities
from nipype.interfaces import fsl  # fsl
from nipype.pipeline import engine as pe  # pypeline engine
from nipype.algorithms import modelgen as model  # model generation
from niflow.nipype1.workflows.fmri.fsl import (
    create_featreg_preproc, create_modelfit_workflow, create_reg_workflow)
from nipype.interfaces.base import Bunch

iminaries

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')

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

inputnode = pe.Node(
    niu.IdentityInterface(fields=['in_data']), name='inputnode')
# 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.template = 'feeds/data/%s.nii.gz'
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = True

preproc = create_featreg_preproc(whichvol='first')
TR = 3.
preproc.inputs.inputspec.fwhm = 5
preproc.inputs.inputspec.highpass = 100. / TR

modelspec = pe.Node(interface=model.SpecifyModel(), name="modelspec")
modelspec.inputs.input_units = 'secs'
modelspec.inputs.time_repetition = TR
modelspec.inputs.high_pass_filter_cutoff = 100
modelspec.inputs.subject_info = [
    Bunch(
        conditions=['Visual', 'Auditory'],
        onsets=[
            list(range(0, int(180 * TR), 60)),
            list(range(0, int(180 * TR), 90))
        ],
        durations=[[30], [45]],
        amplitudes=None,
        tmod=None,
        pmod=None,
        regressor_names=None,
        regressors=None)
]

modelfit = create_modelfit_workflow(f_contrasts=True)
modelfit.inputs.inputspec.interscan_interval = TR
modelfit.inputs.inputspec.model_serial_correlations = True
modelfit.inputs.inputspec.bases = {'dgamma': {'derivs': True}}
cont1 = ['Visual>Baseline', 'T', ['Visual', 'Auditory'], [1, 0]]
cont2 = ['Auditory>Baseline', 'T', ['Visual', 'Auditory'], [0, 1]]
cont3 = ['Task', 'F', [cont1, cont2]]
modelfit.inputs.inputspec.contrasts = [cont1, cont2, cont3]

registration = create_reg_workflow()
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'

Set up complete workflow

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

l1pipeline.connect(inputnode, 'in_data', datasource, 'base_directory')
l1pipeline.connect(datasource, 'func', preproc, 'inputspec.func')
l1pipeline.connect(preproc, 'outputspec.highpassed_files', modelspec,
                   'functional_runs')
l1pipeline.connect(preproc, 'outputspec.motion_parameters', modelspec,
                   'realignment_parameters')
l1pipeline.connect(modelspec, 'session_info', modelfit,
                   'inputspec.session_info')
l1pipeline.connect(preproc, 'outputspec.highpassed_files', modelfit,
                   'inputspec.functional_data')
l1pipeline.connect(preproc, 'outputspec.mean', registration,
                   'inputspec.mean_image')
l1pipeline.connect(datasource, 'struct', registration,
                   'inputspec.anatomical_image')
l1pipeline.connect(modelfit, 'outputspec.zfiles', registration,
                   'inputspec.source_files')

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 = [
    ('fmri_dtype_mcf_mask_smooth_mask_gms_mean_warp', 'meanfunc')
]
# store relevant outputs from various stages of the 1st level analysis
l1pipeline.connect(registration, 'outputspec.transformed_files', datasink,
                   '[email protected]')
l1pipeline.connect(registration, 'outputspec.transformed_mean', datasink,
                   'meanfunc')

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.inputs.inputnode.in_data = os.path.abspath('feeds/data')
    l1pipeline.run()

Example source code

You can download the full source code of this example. This same script is also included in Nipype1 Examples Niflow under the package/niflow/nipype1/examples directory.