csdeconv.inputs.maximum_harmonic_order = 6 """ Finally, we track probabilistically using the orientation distribution functions obtained earlier. The tracts are then used to generate a tract-density image, and they are also converted to TrackVis format. """ probCSDstreamtrack = pe.Node( interface=mrtrix.ProbabilisticSphericallyDeconvolutedStreamlineTrack(), name='probCSDstreamtrack') probCSDstreamtrack.inputs.inputmodel = 'SD_PROB' probCSDstreamtrack.inputs.desired_number_of_tracks = 150000 tracks2prob = pe.Node(interface=mrtrix.Tracks2Prob(), name='tracks2prob') tracks2prob.inputs.colour = True MRconvert_tracks2prob = MRconvert_fa.clone(name='MRconvert_tracks2prob') tck2trk = pe.Node(interface=mrtrix.MRTrix2TrackVis(), name='tck2trk') trk2tdi = pe.Node(interface=dipy.TrackDensityMap(), name='trk2tdi') """ Structural segmentation nodes ----------------------------- """ """ The following node identifies the transformation between the diffusion-weighted image and the structural image. This transformation is then applied to the tracts so that they are in the same space as the regions of interest. """ coregister = pe.Node(interface=fsl.FLIRT(dof=6), name='coregister') coregister.inputs.cost = ('normmi') """ Parcellation is performed given the aparc+aseg image from Freesurfer. The CMTK Parcellation step subdivides these regions to return a higher-resolution parcellation scheme.
def create_connectivity_pipeline(name="connectivity", parcellation_name='scale500'): """Creates a pipeline that does the same connectivity processing as in the :ref:`example_dmri_connectivity_advanced` example script. Given a subject id (and completed Freesurfer reconstruction) diffusion-weighted image, b-values, and b-vectors, the workflow will return the subject's connectome as a Connectome File Format (CFF) file for use in Connectome Viewer (http://www.cmtk.org). Example ------- >>> from nipype.workflows.dmri.mrtrix.connectivity_mapping import create_connectivity_pipeline >>> conmapper = create_connectivity_pipeline("nipype_conmap") >>> conmapper.inputs.inputnode.subjects_dir = '.' >>> conmapper.inputs.inputnode.subject_id = 'subj1' >>> conmapper.inputs.inputnode.dwi = 'data.nii.gz' >>> conmapper.inputs.inputnode.bvecs = 'bvecs' >>> conmapper.inputs.inputnode.bvals = 'bvals' >>> conmapper.run() # doctest: +SKIP Inputs:: inputnode.subject_id inputnode.subjects_dir inputnode.dwi inputnode.bvecs inputnode.bvals inputnode.resolution_network_file Outputs:: outputnode.connectome outputnode.cmatrix outputnode.networks outputnode.fa outputnode.struct outputnode.tracts outputnode.rois outputnode.odfs outputnode.filtered_tractography outputnode.tdi outputnode.nxstatscff outputnode.nxcsv outputnode.cmatrices_csv outputnode.mean_fiber_length outputnode.median_fiber_length outputnode.fiber_length_std """ inputnode_within = pe.Node(util.IdentityInterface(fields=[ "subject_id", "dwi", "bvecs", "bvals", "subjects_dir", "resolution_network_file" ]), name="inputnode_within") FreeSurferSource = pe.Node(interface=nio.FreeSurferSource(), name='fssource') FreeSurferSourceLH = pe.Node(interface=nio.FreeSurferSource(), name='fssourceLH') FreeSurferSourceLH.inputs.hemi = 'lh' FreeSurferSourceRH = pe.Node(interface=nio.FreeSurferSource(), name='fssourceRH') FreeSurferSourceRH.inputs.hemi = 'rh' """ Creating the workflow's nodes ============================= """ """ Conversion nodes ---------------- """ """ A number of conversion operations are required to obtain NIFTI files from the FreesurferSource for each subject. Nodes are used to convert the following: * Original structural image to NIFTI * Pial, white, inflated, and spherical surfaces for both the left and right hemispheres are converted to GIFTI for visualization in ConnectomeViewer * Parcellated annotation files for the left and right hemispheres are also converted to GIFTI """ mri_convert_Brain = pe.Node(interface=fs.MRIConvert(), name='mri_convert_Brain') mri_convert_Brain.inputs.out_type = 'nii' mri_convert_ROI_scale500 = mri_convert_Brain.clone( 'mri_convert_ROI_scale500') mris_convertLH = pe.Node(interface=fs.MRIsConvert(), name='mris_convertLH') mris_convertLH.inputs.out_datatype = 'gii' mris_convertRH = mris_convertLH.clone('mris_convertRH') mris_convertRHwhite = mris_convertLH.clone('mris_convertRHwhite') mris_convertLHwhite = mris_convertLH.clone('mris_convertLHwhite') mris_convertRHinflated = mris_convertLH.clone('mris_convertRHinflated') mris_convertLHinflated = mris_convertLH.clone('mris_convertLHinflated') mris_convertRHsphere = mris_convertLH.clone('mris_convertRHsphere') mris_convertLHsphere = mris_convertLH.clone('mris_convertLHsphere') mris_convertLHlabels = mris_convertLH.clone('mris_convertLHlabels') mris_convertRHlabels = mris_convertLH.clone('mris_convertRHlabels') """ Diffusion processing nodes -------------------------- .. seealso:: dmri_mrtrix_dti.py Tutorial that focuses solely on the MRtrix diffusion processing http://www.brain.org.au/software/mrtrix/index.html MRtrix's online documentation """ """ b-values and b-vectors stored in FSL's format are converted into a single encoding file for MRTrix. """ fsl2mrtrix = pe.Node(interface=mrtrix.FSL2MRTrix(), name='fsl2mrtrix') """ Distortions induced by eddy currents are corrected prior to fitting the tensors. The first image is used as a reference for which to warp the others. """ eddycorrect = create_eddy_correct_pipeline(name='eddycorrect') eddycorrect.inputs.inputnode.ref_num = 1 """ Tensors are fitted to each voxel in the diffusion-weighted image and from these three maps are created: * Major eigenvector in each voxel * Apparent diffusion coefficient * Fractional anisotropy """ dwi2tensor = pe.Node(interface=mrtrix.DWI2Tensor(), name='dwi2tensor') tensor2vector = pe.Node(interface=mrtrix.Tensor2Vector(), name='tensor2vector') tensor2adc = pe.Node(interface=mrtrix.Tensor2ApparentDiffusion(), name='tensor2adc') tensor2fa = pe.Node(interface=mrtrix.Tensor2FractionalAnisotropy(), name='tensor2fa') MRconvert_fa = pe.Node(interface=mrtrix.MRConvert(), name='MRconvert_fa') MRconvert_fa.inputs.extension = 'nii' """ These nodes are used to create a rough brain mask from the b0 image. The b0 image is extracted from the original diffusion-weighted image, put through a simple thresholding routine, and smoothed using a 3x3 median filter. """ MRconvert = pe.Node(interface=mrtrix.MRConvert(), name='MRconvert') MRconvert.inputs.extract_at_axis = 3 MRconvert.inputs.extract_at_coordinate = [0] threshold_b0 = pe.Node(interface=mrtrix.Threshold(), name='threshold_b0') median3d = pe.Node(interface=mrtrix.MedianFilter3D(), name='median3d') """ The brain mask is also used to help identify single-fiber voxels. This is done by passing the brain mask through two erosion steps, multiplying the remaining mask with the fractional anisotropy map, and thresholding the result to obtain some highly anisotropic within-brain voxels. """ erode_mask_firstpass = pe.Node(interface=mrtrix.Erode(), name='erode_mask_firstpass') erode_mask_secondpass = pe.Node(interface=mrtrix.Erode(), name='erode_mask_secondpass') MRmultiply = pe.Node(interface=mrtrix.MRMultiply(), name='MRmultiply') MRmult_merge = pe.Node(interface=util.Merge(2), name='MRmultiply_merge') threshold_FA = pe.Node(interface=mrtrix.Threshold(), name='threshold_FA') threshold_FA.inputs.absolute_threshold_value = 0.7 """ For whole-brain tracking we also require a broad white-matter seed mask. This is created by generating a white matter mask, given a brainmask, and thresholding it at a reasonably high level. """ bet = pe.Node(interface=fsl.BET(mask=True), name='bet_b0') gen_WM_mask = pe.Node(interface=mrtrix.GenerateWhiteMatterMask(), name='gen_WM_mask') threshold_wmmask = pe.Node(interface=mrtrix.Threshold(), name='threshold_wmmask') threshold_wmmask.inputs.absolute_threshold_value = 0.4 """ The spherical deconvolution step depends on the estimate of the response function in the highly anisotropic voxels we obtained above. .. warning:: For damaged or pathological brains one should take care to lower the maximum harmonic order of these steps. """ estimateresponse = pe.Node(interface=mrtrix.EstimateResponseForSH(), name='estimateresponse') estimateresponse.inputs.maximum_harmonic_order = 6 csdeconv = pe.Node(interface=mrtrix.ConstrainedSphericalDeconvolution(), name='csdeconv') csdeconv.inputs.maximum_harmonic_order = 6 """ Finally, we track probabilistically using the orientation distribution functions obtained earlier. The tracts are then used to generate a tract-density image, and they are also converted to TrackVis format. """ probCSDstreamtrack = pe.Node( interface=mrtrix.ProbabilisticSphericallyDeconvolutedStreamlineTrack(), name='probCSDstreamtrack') probCSDstreamtrack.inputs.inputmodel = 'SD_PROB' probCSDstreamtrack.inputs.desired_number_of_tracks = 150000 tracks2prob = pe.Node(interface=mrtrix.Tracks2Prob(), name='tracks2prob') tracks2prob.inputs.colour = True MRconvert_tracks2prob = MRconvert_fa.clone(name='MRconvert_tracks2prob') tck2trk = pe.Node(interface=mrtrix.MRTrix2TrackVis(), name='tck2trk') trk2tdi = pe.Node(interface=dipy.TrackDensityMap(), name='trk2tdi') """ Structural segmentation nodes ----------------------------- """ """ The following node identifies the transformation between the diffusion-weighted image and the structural image. This transformation is then applied to the tracts so that they are in the same space as the regions of interest. """ coregister = pe.Node(interface=fsl.FLIRT(dof=6), name='coregister') coregister.inputs.cost = ('normmi') """ Parcellation is performed given the aparc+aseg image from Freesurfer. The CMTK Parcellation step subdivides these regions to return a higher-resolution parcellation scheme. The parcellation used here is entitled "scale500" and returns 1015 regions. """ parcellate = pe.Node(interface=cmtk.Parcellate(), name="Parcellate") parcellate.inputs.parcellation_name = parcellation_name """ The CreateMatrix interface takes in the remapped aparc+aseg image as well as the label dictionary and fiber tracts and outputs a number of different files. The most important of which is the connectivity network itself, which is stored as a 'gpickle' and can be loaded using Python's NetworkX package (see CreateMatrix docstring). Also outputted are various NumPy arrays containing detailed tract information, such as the start and endpoint regions, and statistics on the mean and standard deviation for the fiber length of each connection. These matrices can be used in the ConnectomeViewer to plot the specific tracts that connect between user-selected regions. Here we choose the Lausanne2008 parcellation scheme, since we are incorporating the CMTK parcellation step. """ creatematrix = pe.Node(interface=cmtk.CreateMatrix(), name="CreateMatrix") creatematrix.inputs.count_region_intersections = True """ Next we define the endpoint of this tutorial, which is the CFFConverter node, as well as a few nodes which use the Nipype Merge utility. These are useful for passing lists of the files we want packaged in our CFF file. The inspect.getfile command is used to package this script into the resulting CFF file, so that it is easy to look back at the processing parameters that were used. """ CFFConverter = pe.Node(interface=cmtk.CFFConverter(), name="CFFConverter") CFFConverter.inputs.script_files = op.abspath( inspect.getfile(inspect.currentframe())) giftiSurfaces = pe.Node(interface=util.Merge(8), name="GiftiSurfaces") giftiLabels = pe.Node(interface=util.Merge(2), name="GiftiLabels") niftiVolumes = pe.Node(interface=util.Merge(3), name="NiftiVolumes") fiberDataArrays = pe.Node(interface=util.Merge(4), name="FiberDataArrays") """ We also create a node to calculate several network metrics on our resulting file, and another CFF converter which will be used to package these networks into a single file. """ networkx = create_networkx_pipeline(name='networkx') cmats_to_csv = create_cmats_to_csv_pipeline(name='cmats_to_csv') nfibs_to_csv = pe.Node(interface=misc.Matlab2CSV(), name='nfibs_to_csv') merge_nfib_csvs = pe.Node(interface=misc.MergeCSVFiles(), name='merge_nfib_csvs') merge_nfib_csvs.inputs.extra_column_heading = 'Subject' merge_nfib_csvs.inputs.out_file = 'fibers.csv' NxStatsCFFConverter = pe.Node(interface=cmtk.CFFConverter(), name="NxStatsCFFConverter") NxStatsCFFConverter.inputs.script_files = op.abspath( inspect.getfile(inspect.currentframe())) """ Connecting the workflow ======================= Here we connect our processing pipeline. """ """ Connecting the inputs, FreeSurfer nodes, and conversions -------------------------------------------------------- """ mapping = pe.Workflow(name='mapping') """ First, we connect the input node to the FreeSurfer input nodes. """ mapping.connect([(inputnode_within, FreeSurferSource, [("subjects_dir", "subjects_dir")])]) mapping.connect([(inputnode_within, FreeSurferSource, [("subject_id", "subject_id")])]) mapping.connect([(inputnode_within, FreeSurferSourceLH, [("subjects_dir", "subjects_dir")])]) mapping.connect([(inputnode_within, FreeSurferSourceLH, [("subject_id", "subject_id")])]) mapping.connect([(inputnode_within, FreeSurferSourceRH, [("subjects_dir", "subjects_dir")])]) mapping.connect([(inputnode_within, FreeSurferSourceRH, [("subject_id", "subject_id")])]) mapping.connect([(inputnode_within, parcellate, [("subjects_dir", "subjects_dir")])]) mapping.connect([(inputnode_within, parcellate, [("subject_id", "subject_id")])]) mapping.connect([(parcellate, mri_convert_ROI_scale500, [('roi_file', 'in_file')])]) """ Nifti conversion for subject's stripped brain image from Freesurfer: """ mapping.connect([(FreeSurferSource, mri_convert_Brain, [('brain', 'in_file')])]) """ Surface conversions to GIFTI (pial, white, inflated, and sphere for both hemispheres) """ mapping.connect([(FreeSurferSourceLH, mris_convertLH, [('pial', 'in_file') ])]) mapping.connect([(FreeSurferSourceRH, mris_convertRH, [('pial', 'in_file') ])]) mapping.connect([(FreeSurferSourceLH, mris_convertLHwhite, [('white', 'in_file')])]) mapping.connect([(FreeSurferSourceRH, mris_convertRHwhite, [('white', 'in_file')])]) mapping.connect([(FreeSurferSourceLH, mris_convertLHinflated, [('inflated', 'in_file')])]) mapping.connect([(FreeSurferSourceRH, mris_convertRHinflated, [('inflated', 'in_file')])]) mapping.connect([(FreeSurferSourceLH, mris_convertLHsphere, [('sphere', 'in_file')])]) mapping.connect([(FreeSurferSourceRH, mris_convertRHsphere, [('sphere', 'in_file')])]) """ The annotation files are converted using the pial surface as a map via the MRIsConvert interface. One of the functions defined earlier is used to select the lh.aparc.annot and rh.aparc.annot files specifically (rather than e.g. rh.aparc.a2009s.annot) from the output list given by the FreeSurferSource. """ mapping.connect([(FreeSurferSourceLH, mris_convertLHlabels, [('pial', 'in_file')])]) mapping.connect([(FreeSurferSourceRH, mris_convertRHlabels, [('pial', 'in_file')])]) mapping.connect([(FreeSurferSourceLH, mris_convertLHlabels, [(('annot', select_aparc_annot), 'annot_file')])]) mapping.connect([(FreeSurferSourceRH, mris_convertRHlabels, [(('annot', select_aparc_annot), 'annot_file')])]) """ Diffusion Processing -------------------- Now we connect the tensor computations: """ mapping.connect([(inputnode_within, fsl2mrtrix, [("bvecs", "bvec_file"), ("bvals", "bval_file")])]) mapping.connect([(inputnode_within, eddycorrect, [("dwi", "inputnode.in_file")])]) mapping.connect([(eddycorrect, dwi2tensor, [("outputnode.eddy_corrected", "in_file")])]) mapping.connect([(fsl2mrtrix, dwi2tensor, [("encoding_file", "encoding_file")])]) mapping.connect([ (dwi2tensor, tensor2vector, [['tensor', 'in_file']]), (dwi2tensor, tensor2adc, [['tensor', 'in_file']]), (dwi2tensor, tensor2fa, [['tensor', 'in_file']]), ]) mapping.connect([(tensor2fa, MRmult_merge, [("FA", "in1")])]) mapping.connect([(tensor2fa, MRconvert_fa, [("FA", "in_file")])]) """ This block creates the rough brain mask to be multiplied, mulitplies it with the fractional anisotropy image, and thresholds it to get the single-fiber voxels. """ mapping.connect([(eddycorrect, MRconvert, [("outputnode.eddy_corrected", "in_file")])]) mapping.connect([(MRconvert, threshold_b0, [("converted", "in_file")])]) mapping.connect([(threshold_b0, median3d, [("out_file", "in_file")])]) mapping.connect([(median3d, erode_mask_firstpass, [("out_file", "in_file") ])]) mapping.connect([(erode_mask_firstpass, erode_mask_secondpass, [("out_file", "in_file")])]) mapping.connect([(erode_mask_secondpass, MRmult_merge, [("out_file", "in2") ])]) mapping.connect([(MRmult_merge, MRmultiply, [("out", "in_files")])]) mapping.connect([(MRmultiply, threshold_FA, [("out_file", "in_file")])]) """ Here the thresholded white matter mask is created for seeding the tractography. """ mapping.connect([(eddycorrect, bet, [("outputnode.eddy_corrected", "in_file")])]) mapping.connect([(eddycorrect, gen_WM_mask, [("outputnode.eddy_corrected", "in_file")])]) mapping.connect([(bet, gen_WM_mask, [("mask_file", "binary_mask")])]) mapping.connect([(fsl2mrtrix, gen_WM_mask, [("encoding_file", "encoding_file")])]) mapping.connect([(gen_WM_mask, threshold_wmmask, [("WMprobabilitymap", "in_file")])]) """ Next we estimate the fiber response distribution. """ mapping.connect([(eddycorrect, estimateresponse, [("outputnode.eddy_corrected", "in_file")])]) mapping.connect([(fsl2mrtrix, estimateresponse, [("encoding_file", "encoding_file")])]) mapping.connect([(threshold_FA, estimateresponse, [("out_file", "mask_image")])]) """ Run constrained spherical deconvolution. """ mapping.connect([(eddycorrect, csdeconv, [("outputnode.eddy_corrected", "in_file")])]) mapping.connect([(gen_WM_mask, csdeconv, [("WMprobabilitymap", "mask_image")])]) mapping.connect([(estimateresponse, csdeconv, [("response", "response_file")])]) mapping.connect([(fsl2mrtrix, csdeconv, [("encoding_file", "encoding_file") ])]) """ Connect the tractography and compute the tract density image. """ mapping.connect([(threshold_wmmask, probCSDstreamtrack, [("out_file", "seed_file")])]) mapping.connect([(csdeconv, probCSDstreamtrack, [("spherical_harmonics_image", "in_file")])]) mapping.connect([(probCSDstreamtrack, tracks2prob, [("tracked", "in_file") ])]) mapping.connect([(eddycorrect, tracks2prob, [("outputnode.eddy_corrected", "template_file")])]) mapping.connect([(tracks2prob, MRconvert_tracks2prob, [("tract_image", "in_file")])]) """ Structural Processing --------------------- First, we coregister the diffusion image to the structural image """ mapping.connect([(eddycorrect, coregister, [("outputnode.eddy_corrected", "in_file")])]) mapping.connect([(mri_convert_Brain, coregister, [('out_file', 'reference') ])]) """ The MRtrix-tracked fibers are converted to TrackVis format (with voxel and data dimensions grabbed from the DWI). The connectivity matrix is created with the transformed .trk fibers and the parcellation file. """ mapping.connect([(eddycorrect, tck2trk, [("outputnode.eddy_corrected", "image_file")])]) mapping.connect([(mri_convert_Brain, tck2trk, [("out_file", "registration_image_file")])]) mapping.connect([(coregister, tck2trk, [("out_matrix_file", "matrix_file") ])]) mapping.connect([(probCSDstreamtrack, tck2trk, [("tracked", "in_file")])]) mapping.connect([(tck2trk, creatematrix, [("out_file", "tract_file")])]) mapping.connect([(tck2trk, trk2tdi, [("out_file", "in_file")])]) mapping.connect(inputnode_within, 'resolution_network_file', creatematrix, 'resolution_network_file') mapping.connect([(inputnode_within, creatematrix, [("subject_id", "out_matrix_file")])]) mapping.connect([(inputnode_within, creatematrix, [("subject_id", "out_matrix_mat_file")])]) mapping.connect([(parcellate, creatematrix, [("roi_file", "roi_file")])]) """ The merge nodes defined earlier are used here to create lists of the files which are destined for the CFFConverter. """ mapping.connect([(mris_convertLH, giftiSurfaces, [("converted", "in1")])]) mapping.connect([(mris_convertRH, giftiSurfaces, [("converted", "in2")])]) mapping.connect([(mris_convertLHwhite, giftiSurfaces, [("converted", "in3") ])]) mapping.connect([(mris_convertRHwhite, giftiSurfaces, [("converted", "in4") ])]) mapping.connect([(mris_convertLHinflated, giftiSurfaces, [("converted", "in5")])]) mapping.connect([(mris_convertRHinflated, giftiSurfaces, [("converted", "in6")])]) mapping.connect([(mris_convertLHsphere, giftiSurfaces, [("converted", "in7")])]) mapping.connect([(mris_convertRHsphere, giftiSurfaces, [("converted", "in8")])]) mapping.connect([(mris_convertLHlabels, giftiLabels, [("converted", "in1") ])]) mapping.connect([(mris_convertRHlabels, giftiLabels, [("converted", "in2") ])]) mapping.connect([(parcellate, niftiVolumes, [("roi_file", "in1")])]) mapping.connect([(eddycorrect, niftiVolumes, [("outputnode.eddy_corrected", "in2")])]) mapping.connect([(mri_convert_Brain, niftiVolumes, [("out_file", "in3")])]) mapping.connect([(creatematrix, fiberDataArrays, [("endpoint_file", "in1") ])]) mapping.connect([(creatematrix, fiberDataArrays, [("endpoint_file_mm", "in2")])]) mapping.connect([(creatematrix, fiberDataArrays, [("fiber_length_file", "in3")])]) mapping.connect([(creatematrix, fiberDataArrays, [("fiber_label_file", "in4")])]) """ This block actually connects the merged lists to the CFF converter. We pass the surfaces and volumes that are to be included, as well as the tracts and the network itself. The currently running pipeline (dmri_connectivity_advanced.py) is also scraped and included in the CFF file. This makes it easy for the user to examine the entire processing pathway used to generate the end product. """ mapping.connect([(giftiSurfaces, CFFConverter, [("out", "gifti_surfaces")]) ]) mapping.connect([(giftiLabels, CFFConverter, [("out", "gifti_labels")])]) mapping.connect([(creatematrix, CFFConverter, [("matrix_files", "gpickled_networks")])]) mapping.connect([(niftiVolumes, CFFConverter, [("out", "nifti_volumes")])]) mapping.connect([(fiberDataArrays, CFFConverter, [("out", "data_files")])]) mapping.connect([(creatematrix, CFFConverter, [("filtered_tractography", "tract_files")])]) mapping.connect([(inputnode_within, CFFConverter, [("subject_id", "title") ])]) """ The graph theoretical metrics which have been generated are placed into another CFF file. """ mapping.connect([(inputnode_within, networkx, [("subject_id", "inputnode.extra_field")])]) mapping.connect([(creatematrix, networkx, [("intersection_matrix_file", "inputnode.network_file")])]) mapping.connect([(networkx, NxStatsCFFConverter, [("outputnode.network_files", "gpickled_networks")])]) mapping.connect([(giftiSurfaces, NxStatsCFFConverter, [("out", "gifti_surfaces")])]) mapping.connect([(giftiLabels, NxStatsCFFConverter, [("out", "gifti_labels")])]) mapping.connect([(niftiVolumes, NxStatsCFFConverter, [("out", "nifti_volumes")])]) mapping.connect([(fiberDataArrays, NxStatsCFFConverter, [("out", "data_files")])]) mapping.connect([(inputnode_within, NxStatsCFFConverter, [("subject_id", "title")])]) mapping.connect([(inputnode_within, cmats_to_csv, [("subject_id", "inputnode.extra_field")])]) mapping.connect([(creatematrix, cmats_to_csv, [ ("matlab_matrix_files", "inputnode.matlab_matrix_files") ])]) mapping.connect([(creatematrix, nfibs_to_csv, [("stats_file", "in_file")]) ]) mapping.connect([(nfibs_to_csv, merge_nfib_csvs, [("csv_files", "in_files") ])]) mapping.connect([(inputnode_within, merge_nfib_csvs, [("subject_id", "extra_field")])]) """ Create a higher-level workflow -------------------------------------- Finally, we create another higher-level workflow to connect our mapping workflow with the info and datagrabbing nodes declared at the beginning. Our tutorial can is now extensible to any arbitrary number of subjects by simply adding their names to the subject list and their data to the proper folders. """ inputnode = pe.Node(interface=util.IdentityInterface( fields=["subject_id", "dwi", "bvecs", "bvals", "subjects_dir"]), name="inputnode") outputnode = pe.Node(interface=util.IdentityInterface(fields=[ "fa", "struct", "tracts", "tracks2prob", "connectome", "nxstatscff", "nxmatlab", "nxcsv", "fiber_csv", "cmatrices_csv", "nxmergedcsv", "cmatrix", "networks", "filtered_tracts", "rois", "odfs", "tdi", "mean_fiber_length", "median_fiber_length", "fiber_length_std" ]), name="outputnode") connectivity = pe.Workflow(name="connectivity") connectivity.base_output_dir = name connectivity.base_dir = name connectivity.connect([(inputnode, mapping, [ ("dwi", "inputnode_within.dwi"), ("bvals", "inputnode_within.bvals"), ("bvecs", "inputnode_within.bvecs"), ("subject_id", "inputnode_within.subject_id"), ("subjects_dir", "inputnode_within.subjects_dir") ])]) connectivity.connect([(mapping, outputnode, [ ("tck2trk.out_file", "tracts"), ("CFFConverter.connectome_file", "connectome"), ("NxStatsCFFConverter.connectome_file", "nxstatscff"), ("CreateMatrix.matrix_mat_file", "cmatrix"), ("CreateMatrix.mean_fiber_length_matrix_mat_file", "mean_fiber_length"), ("CreateMatrix.median_fiber_length_matrix_mat_file", "median_fiber_length"), ("CreateMatrix.fiber_length_std_matrix_mat_file", "fiber_length_std"), ("CreateMatrix.matrix_files", "networks"), ("CreateMatrix.filtered_tractographies", "filtered_tracts"), ("merge_nfib_csvs.csv_file", "fiber_csv"), ("mri_convert_ROI_scale500.out_file", "rois"), ("trk2tdi.out_file", "tdi"), ("csdeconv.spherical_harmonics_image", "odfs"), ("mri_convert_Brain.out_file", "struct"), ("MRconvert_fa.converted", "fa"), ("MRconvert_tracks2prob.converted", "tracks2prob") ])]) connectivity.connect([(cmats_to_csv, outputnode, [("outputnode.csv_file", "cmatrices_csv")])]) connectivity.connect([(networkx, outputnode, [("outputnode.csv_files", "nxcsv")])]) return connectivity