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#!/usr/bin/env python
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
==============================
rsfMRI: FSL, Nipype, tCompCorr
==============================
Performs preprocessing for resting state data based on the tCompCorr method
described in Behzadi et al. (2007).
Tell python where to find the appropriate functions.
"""
import os # system functions
import nipype.interfaces.io as nio # Data i/o
import nipype.pipeline.engine as pe # pypeline engine
import nipype.interfaces.utility as util
#####################################################################
# Preliminaries
from nipype.workflows.fmri.fsl import create_resting_preproc
"""
Set up parameters for the resting state preprocessing workflow.
"""
TR = 3.0
restingflow = create_resting_preproc()
restingflow.inputs.inputspec.num_noise_components = 6
restingflow.inputs.inputspec.highpass_sigma = 100/(2*TR)
restingflow.inputs.inputspec.lowpass_sigma = 12.5/(2*TR)
# Specify the location of the data.
data_dir = os.path.abspath('data')
# Specify the subject directories
subject_list = ['s1']
infosource = pe.Node(interface=util.IdentityInterface(fields=['subject_id']),
name="infosource")
"""Here we set up iteration over all the subjects.
"""
infosource.iterables = ('subject_id', subject_list)
"""
Preprocessing pipeline nodes
----------------------------
Now we create a :class:`nipype.interfaces.io.DataSource` object and
fill in the information from above about the layout of our data.
"""
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'
# Map field names to individual subject runs.
info = dict(func=[['subject_id', ['f3',]]])
datasource.inputs.template_args = info
"""
Store significant result-files in a special directory
"""
datasink = pe.Node(interface=nio.DataSink(parameterization=False),
name='datasink')
datasink.inputs.base_directory = os.path.abspath('./fslresting/compcorred')
"""
Set up complete workflow
------------------------
"""
def get_substitutions(subject_id):
'''Replace output names of files with more meaningful ones
'''
return [('vol0000_warp_merged_detrended_regfilt_filt',
'%s_filtered'%subject_id),
('vol0000_warp_merged_tsnr_stddev_thresh',
'%s_noisyvoxels'%subject_id)]
l1pipeline = pe.Workflow(name= "resting")
l1pipeline.base_dir = os.path.abspath('./fslresting/workingdir')
l1pipeline.connect([(infosource, datasource, [('subject_id', 'subject_id')]),
(datasource, restingflow, [('func', 'inputspec.func')]),
(infosource, datasink, [('subject_id', 'container'),
(('subject_id', get_substitutions),
'substitutions')]),
(restingflow, datasink, [('outputspec.noise_mask_file',
'@noisefile'),
('outputspec.filtered_file',
'@filteredfile')])
])
if __name__ == '__main__':
l1pipeline.run()
l1pipeline.write_graph()
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