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# workflows.dmri.mrtrix.group_connectivity

## `create_group_connectivity_pipeline()`

Link to code

Creates a pipeline that performs MRtrix structural connectivity processing on groups of subjects. Given a diffusion-weighted image, and text files containing the associated b-values and b-vectors, the workflow will return each subjects’ connectomes in a Connectome File Format (CFF) file, for use in Connectome Viewer (http://www.cmtk.org).

### Example

    >>> import nipype.interfaces.freesurfer as fs
    >>> import nipype.workflows.dmri.mrtrix.group_connectivity as groupwork
    >>> import cmp
    >>> from nipype.testing import example_data
    >>> subjects_dir = '.'
    >>> data_dir = '.'
    >>> output_dir = '.'
    >>> fs.FSCommand.set_default_subjects_dir(subjects_dir)
    >>> group_list = {}
    >>> group_list['group1'] = ['subj1', 'subj2']
    >>> group_list['group2'] = ['subj3', 'subj4']
    >>> template_args = dict(dwi=[['subject_id', 'dwi']], bvecs=[['subject_id', 'bvecs']], bvals=[['subject_id', 'bvals']])
    >>> group_id = 'group1'
    >>> l1pipeline = groupwork.create_group_connectivity_pipeline(group_list, group_id, data_dir, subjects_dir, output_dir, template_args)
    >>> 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.run()

Inputs:

    group_list: Dictionary of subject lists, keyed by group name
    group_id: String containing the group name
    data_dir: Path to the data directory
    subjects_dir: Path to the Freesurfer 'subjects' directory
    output_dir: Path for the output files
    template_args_dict: Dictionary of template arguments for the connectivity pipeline datasource
                            e.g.    info = dict(dwi=[['subject_id', 'dwi']],
                                            bvecs=[['subject_id','bvecs']],
                                            bvals=[['subject_id','bvals']])