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dmri_group_connectivity_mrtrix.py
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"""
==================================================
dMRI: Group connectivity - MRtrix, FSL, FreeSurfer
==================================================
Introduction
============
This script, dmri_group_connectivity_mrtrix.py, runs group-based connectivity analysis using
the dmri.mrtrix.connectivity_mapping Nipype workflow. Further detail on the processing can be
found in :doc:`dmri_connectivity_advanced`. This tutorial can be run using::
python dmri_group_connectivity_mrtrix.py
We perform this analysis using one healthy subject and two subjects who suffer from Parkinson's disease.
The whole package (960 mb as .tar.gz / 1.3 gb uncompressed) including the Freesurfer directories for these subjects, can be acquired from here:
* http://db.tt/b6F1t0QV
A data package containing the outputs of this pipeline can be obtained from here:
* http://db.tt/elmMnIt1
Along with MRtrix, FSL, and Freesurfer, you must also have the Connectome File Format
library installed as well as the Connectome Mapper (cmp).
* MRtrix: http://www.brain.org.au/software/mrtrix/
* FSL: http://www.fmrib.ox.ac.uk/fsl/
* Freesurfer: http://surfer.nmr.mgh.harvard.edu/
* CTMK: http://www.cmtk.org/
* CFF: sudo apt-get install python-cfflib
Or on github at:
* CFFlib: https://github.com/LTS5/cfflib
* CMP: https://github.com/LTS5/cmp
Output data can be visualized in ConnectomeViewer, TrackVis, Gephi,
the MRtrix Viewer (mrview), and anything that can view Nifti files.
* ConnectomeViewer: https://github.com/LTS5/connectomeviewer
* TrackVis: http://trackvis.org/
* Gephi: http://gephi.org/
The fiber data is available in Numpy arrays, and the connectivity matrix
is also produced as a MATLAB matrix.
Import the workflows
--------------------
First, we import the necessary modules from nipype.
"""
import nipype.interfaces.fsl as fsl
import nipype.interfaces.freesurfer as fs # freesurfer
import os.path as op # system functions
import cmp
from niflow.nipype1.workflows.dmri.mrtrix.group_connectivity import create_group_connectivity_pipeline
from niflow.nipype1.workflows.dmri.connectivity.group_connectivity import (
create_merge_network_results_by_group_workflow,
create_merge_group_network_results_workflow,
create_average_networks_by_group_workflow)
"""
Set the proper directories
--------------------------
First, we import the necessary modules from nipype.
"""
subjects_dir = op.abspath('groupcondatapackage/subjects/')
data_dir = op.abspath('groupcondatapackage/data/')
fs.FSCommand.set_default_subjects_dir(subjects_dir)
fsl.FSLCommand.set_default_output_type('NIFTI')
"""
Define the groups
-----------------
Here we define the groups for this study. We would like to search for differences between the healthy subject and the two
vegetative patients. The group list is defined as a Python dictionary (see http://docs.python.org/tutorial/datastructures.html),
with group IDs ('controls', 'parkinsons') as keys, and subject/patient names as values. We set the main output directory as 'groupcon'.
"""
group_list = {}
group_list['controls'] = ['cont17']
group_list['parkinsons'] = ['pat10', 'pat20']
"""
The output directory must be named as well.
"""
global output_dir
output_dir = op.abspath('dmri_group_connectivity_mrtrix')
"""
Main processing loop
====================
The title for the final grouped-network connectome file is dependent on the group names. The resulting file for this example
is 'parkinsons-controls.cff'. The following code implements the format a-b-c-...x.cff for an arbitary number of groups.
.. warning::
The 'info' dictionary below is used to define the input files. In this case, the diffusion weighted image contains the string 'dti'.
The same applies to the b-values and b-vector files, and this must be changed to fit your naming scheme.
The workflow is created given the information input about the groups and subjects.
.. seealso::
* nipype/workflows/dmri/mrtrix/group_connectivity.py
* nipype/workflows/dmri/mrtrix/connectivity_mapping.py
* :doc:`dmri_connectivity_advanced`
We set values for absolute threshold used on the fractional anisotropy map. This is done
in order to identify single-fiber voxels. In brains with more damage, however, it may be necessary
to reduce the threshold, since their brains are have lower average fractional anisotropy values.
We invert the b-vectors in the encoding file, and set the maximum harmonic order
of the pre-tractography spherical deconvolution step. This is done to show
how to set inputs that will affect both groups.
Next we create and run the second-level pipeline. The purpose of this workflow is simple:
It is used to merge each subject's CFF file into one, so that there is a single file containing
all of the networks for each group. This can be useful for performing Network Brain Statistics
using the NBS plugin in ConnectomeViewer.
.. seealso::
http://www.connectomeviewer.org/documentation/users/tutorials/tut_nbs.html
"""
title = ''
for idx, group_id in enumerate(group_list.keys()):
title += group_id
if not idx == len(list(group_list.keys())) - 1:
title += '-'
info = dict(
dwi=[['subject_id', 'dti']],
bvecs=[['subject_id', 'bvecs']],
bvals=[['subject_id', 'bvals']])
l1pipeline = create_group_connectivity_pipeline(
group_list, group_id, data_dir, subjects_dir, output_dir, info)
# Here with invert the b-vectors in the Y direction and set the maximum harmonic order of the
# spherical deconvolution step
l1pipeline.inputs.connectivity.mapping.fsl2mrtrix.invert_y = True
l1pipeline.inputs.connectivity.mapping.csdeconv.maximum_harmonic_order = 6
# Here we define the parcellation scheme and the number of tracks to produce
parcellation_name = 'scale500'
l1pipeline.inputs.connectivity.mapping.Parcellate.parcellation_name = parcellation_name
cmp_config = cmp.configuration.PipelineConfiguration()
cmp_config.parcellation_scheme = "Lausanne2008"
l1pipeline.inputs.connectivity.mapping.inputnode_within.resolution_network_file = cmp_config._get_lausanne_parcellation(
'Lausanne2008')[parcellation_name]['node_information_graphml']
l1pipeline.inputs.connectivity.mapping.probCSDstreamtrack.desired_number_of_tracks = 100000
l1pipeline.run()
l1pipeline.write_graph(format='eps', graph2use='flat')
# The second-level pipeline is created here
l2pipeline = create_merge_network_results_by_group_workflow(
group_list, group_id, data_dir, subjects_dir, output_dir)
l2pipeline.inputs.l2inputnode.network_file = cmp_config._get_lausanne_parcellation(
'Lausanne2008')[parcellation_name]['node_information_graphml']
l2pipeline.run()
l2pipeline.write_graph(format='eps', graph2use='flat')
"""
Now that the for loop is complete there are two grouped CFF files each containing the appropriate subjects.
It is also convenient to have every subject in a single CFF file, so that is what the third-level pipeline does.
"""
l3pipeline = create_merge_group_network_results_workflow(
group_list, data_dir, subjects_dir, output_dir, title)
l3pipeline.run()
l3pipeline.write_graph(format='eps', graph2use='flat')
"""
The fourth and final workflow averages the networks and saves them in another CFF file
"""
l4pipeline = create_average_networks_by_group_workflow(
group_list, data_dir, subjects_dir, output_dir, title)
l4pipeline.run()
l4pipeline.write_graph(format='eps', graph2use='flat')