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# dMRI: TBSS on NKI RS data

A pipeline to do a TBSS analysis on the NKI rockland sample data

::
  from nipype.workflows.dmri.fsl.dti import create_eddy_correct_pipeline from nipype.workflows.dmri.fsl.tbss import create_tbss_non_FA, create_tbss_all

Tell python where to find the appropriate functions.

    import nipype.interfaces.io as nio           # Data i/o
    import nipype.interfaces.fsl as fsl          # fsl
    import nipype.interfaces.utility as util     # utility
    import nipype.pipeline.engine as pe          # pypeline engine
    import os                                    # system functions

    fsl.FSLCommand.set_default_output_type('NIFTI')

You can get the data from:

http://fcon_1000.projects.nitrc.org/indi/pro/eNKI_RS_TRT/FrontPage.html

    dataDir = os.path.abspath('nki_rs_data')
    workingdir = './tbss_example'
    subjects_list = ['2475376', '3313349', '3808535', '3893245', '8735778',
                     '9630905']

    gen_fa = pe.Workflow(name="gen_fa")
    gen_fa.base_dir = os.path.join(os.path.abspath(workingdir), 'l1')

    subject_id_infosource = pe.Node(util.IdentityInterface(fields=['subject_id']),
                                    name='subject_id_infosource')
    subject_id_infosource.iterables = ('subject_id', subjects_list)

    datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
                                                   outfields=['dwi', 'bvec',
                                                              'bval']),
                         name='datasource')
    datasource.inputs.base_directory = os.path.abspath(dataDir)
    datasource.inputs.template = '%s/session2/DTI_mx_137/dti.%s'
    datasource.inputs.template_args = dict(dwi=[['subject_id', 'nii.gz']],
                                           bvec=[['subject_id', 'bvec']],
                                           bval=[['subject_id', 'bval']])
    gen_fa.connect(subject_id_infosource, 'subject_id', datasource, 'subject_id')

    eddy_correct = create_eddy_correct_pipeline()
    eddy_correct.inputs.inputnode.ref_num = 0
    gen_fa.connect(datasource, 'dwi', eddy_correct, 'inputnode.in_file')

    bet = pe.Node(interface=fsl.BET(), name='bet')
    bet.inputs.mask = True
    bet.inputs.frac = 0.34
    gen_fa.connect(eddy_correct, 'pick_ref.out', bet, 'in_file')

    dtifit = pe.Node(interface=fsl.DTIFit(), name='dtifit')
    gen_fa.connect(eddy_correct, 'outputnode.eddy_corrected', dtifit, 'dwi')
    gen_fa.connect(subject_id_infosource, 'subject_id', dtifit, 'base_name')
    gen_fa.connect(bet, 'mask_file', dtifit, 'mask')
    gen_fa.connect(datasource, 'bvec', dtifit, 'bvecs')
    gen_fa.connect(datasource, 'bval', dtifit, 'bvals')

    datasink = pe.Node(interface=nio.DataSink(), name="datasink")
    datasink.inputs.base_directory = os.path.join(os.path.abspath(workingdir),
                                                  'l1_results')
    datasink.inputs.parameterization = False
    gen_fa.connect(dtifit, 'FA', datasink, 'FA')
    gen_fa.connect(dtifit, 'MD', datasink, 'MD')

    if __name__ == '__main__':
        gen_fa.write_graph()
        gen_fa.run()

Here we get the FA list including all the subjects.

    tbss_source = pe.Node(interface=nio.DataGrabber(outfiles=['fa_list',
                                                              'md_list']),
                          name='tbss_source')
    tbss_source.inputs.base_directory = datasink.inputs.base_directory
    tbss_source.inputs.template = '%s/%s_%s.nii'
    tbss_source.inputs.template_args = dict(fa_list=[['FA', subjects_list, 'FA']],
                                            md_list=[['MD', subjects_list, 'MD']])

TBSS analysis

    tbss_all = create_tbss_all()
    tbss_all.inputs.inputnode.skeleton_thresh = 0.2

    tbssproc = pe.Workflow(name="tbssproc")
    tbssproc.base_dir = os.path.join(os.path.abspath(workingdir), 'l2')
    tbssproc.connect(tbss_source, 'fa_list', tbss_all, 'inputnode.fa_list')

    tbss_MD = create_tbss_non_FA(name='tbss_MD')
    tbss_MD.inputs.inputnode.skeleton_thresh = tbss_all.inputs.inputnode.skeleton_thresh

    tbssproc.connect([(tbss_all, tbss_MD, [('tbss2.outputnode.field_list',
                                            'inputnode.field_list'),
                                           ('tbss3.outputnode.groupmask',
                                            'inputnode.groupmask'),
                                           ('tbss3.outputnode.meanfa_file',
                                            'inputnode.meanfa_file'),
                                           ('tbss4.outputnode.distance_map',
                                            'inputnode.distance_map')]),
                      (tbss_source, tbss_MD, [('md_list',
                                               'inputnode.file_list')]),
                ])

    if __name__ == '__main__':
        tbssproc.write_graph()
        tbssproc.run()

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.