Single-subject connectivity tutorials run properly

Erik Ziegler · Erik Ziegler · commit cb0efcb14270 · 2012-03-06T00:43:49.000+01:00
diff --git a/examples/dmri_connectivity.py b/examples/dmri_connectivity.py
@@ -279,24 +279,6 @@ def select_aparc_annot(list_of_files):
 track = pe.Node(interface=camino.TrackPICo(), name="track")
 track.inputs.iterations = 1
 
-"""
-This tutorial demonstrates two methods of connectivity mapping. The first uses the conmap function of Camino.
-The problem with this method is that it can be very memory intensive. To deal with this, we add a "shredding" node
-which removes roughly half of the tracts in the file.
-"""
-
-tractshred = pe.Node(interface=camino.TractShredder(), name='tractshred')
-tractshred.inputs.offset = 0
-tractshred.inputs.bunchsize = 2
-tractshred.inputs.space = 1
-
-"""
-Here we create the Camino-based connectivity mapping node. For visualization, it can be beneficial to set a
-threshold for the minimum number of fiber connections that are required for an edge to be drawn on the graph.
-"""
-
-conmap = pe.Node(interface=camino.Conmap(), name='conmap')
-conmap.inputs.threshold = 100
 
 """
 Currently, the best program for visualizing tracts is TrackVis. For this reason, a node is included to
@@ -350,6 +332,7 @@ def select_aparc_annot(list_of_files):
 cmp_config = cmp.configuration.PipelineConfiguration(parcellation_scheme = "NativeFreesurfer")
 cmp_config.parcellation_scheme = "NativeFreesurfer"
 roigen.inputs.LUT_file = cmp_config.get_freeview_lut("NativeFreesurfer")['freesurferaparc']
+roigen_structspace = roigen.clone('ROIGen_structspace')
 
 """
 The CreateMatrix interface takes in the remapped aparc+aseg image as well as the label dictionary and fiber tracts
@@ -361,7 +344,9 @@ def select_aparc_annot(list_of_files):
 """
 
 creatematrix = pe.Node(interface=cmtk.CreateMatrix(), name="CreateMatrix")
-creatematrix.inputs.resolution_network_file = cmp_config.parcellation['freesurferaparc']['node_information_graphml']
+creatematrix.inputs.count_region_intersections = True
+createnodes = pe.Node(interface=cmtk.CreateNodes(), name="CreateNodes")
+createnodes.inputs.resolution_network_file = cmp_config.parcellation['freesurferaparc']['node_information_graphml']
 
 """
 Here we define the endpoint of this tutorial, which is the CFFConverter node, as well as a few nodes which use
@@ -447,7 +432,6 @@ def select_aparc_annot(list_of_files):
 mapping.connect([(b0Strip, inverse,[('out_file','reference')])])
 mapping.connect([(convertxfm, inverse,[('out_file','in_matrix_file')])])
 mapping.connect([(mri_convert_WMParc, inverse,[('out_file','in_file')])])
-#mapping.connect([(inverse, conmap,[('out_file','roi_file')])])
 
 """
 The tractography pipeline consists of the following nodes. Further information about the tractography
@@ -460,14 +444,6 @@ def select_aparc_annot(list_of_files):
 mapping.connect([(dtifit, picopdfs,[("tensor_fitted","in_file")])])
 mapping.connect([(picopdfs, track,[("pdfs","in_file")])])
 
-"""
-The tractography is passed to the shredder in preparation for connectivity mapping.
-Again, the required conmap connection is left commented out.
-"""
-
-mapping.connect([(track, tractshred,[("tracked","in_file")])])
-#mapping.connect([(tractshred, conmap,[("shredded","in_file")])])
-
 """
 Connecting the Fractional Anisotropy and Trace nodes is simple, as they obtain their input from the
 tensor fitting. This is also where our voxel- and data-grabbing functions come in. We pass these functions,
@@ -513,23 +489,28 @@ def select_aparc_annot(list_of_files):
 original tracts, and label file are then given to CreateMatrix.
 """
 
+mapping.connect(createnodes, 'node_network',
+                creatematrix, 'resolution_network_file')
 mapping.connect([(FreeSurferSource, mri_convert_AparcAseg, [(('aparc_aseg', select_aparc), 'in_file')])])
 
 mapping.connect([(b0Strip, inverse_AparcAseg,[('out_file','reference')])])
 mapping.connect([(convertxfm, inverse_AparcAseg,[('out_file','in_matrix_file')])])
 mapping.connect([(mri_convert_AparcAseg, inverse_AparcAseg,[('out_file','in_file')])])
+mapping.connect([(mri_convert_AparcAseg, roigen_structspace,[('out_file','aparc_aseg_file')])])
+mapping.connect([(roigen_structspace, createnodes,[("roi_file","roi_file")])])
 
 mapping.connect([(inverse_AparcAseg, roigen,[("out_file","aparc_aseg_file")])])
 mapping.connect([(roigen, creatematrix,[("roi_file","roi_file")])])
 mapping.connect([(camino2trackvis, creatematrix,[("trackvis","tract_file")])])
 mapping.connect([(inputnode, creatematrix,[("subject_id","out_matrix_file")])])
+mapping.connect([(inputnode, creatematrix,[("subject_id","out_matrix_mat_file")])])
 
 """
 The merge nodes defined earlier are used here to create lists of the files which are
 destined for the CFFConverter.
 """
 
-mapping.connect([(creatematrix, gpickledNetworks,[("matrix_file","in1")])])
+mapping.connect([(creatematrix, gpickledNetworks,[("matrix_files","in1")])])
 
 mapping.connect([(mris_convertLH, giftiSurfaces,[("converted","in1")])])
 mapping.connect([(mris_convertRH, giftiSurfaces,[("converted","in2")])])
@@ -576,7 +557,7 @@ def select_aparc_annot(list_of_files):
 """
 
 connectivity = pe.Workflow(name="connectivity")
-connectivity.base_dir = op.abspath('connectivity_tutorial')
+connectivity.base_dir = op.abspath('dmri_connectivity')
 connectivity.connect([
                     (infosource,datasource,[('subject_id', 'subject_id')]),
                     (datasource,mapping,[('dwi','inputnode.dwi'),
diff --git a/examples/dmri_connectivity_advanced.py b/examples/dmri_connectivity_advanced.py
@@ -58,7 +58,7 @@
 from nipype.workflows.dmri.camino.connectivity_mapping import select_aparc_annot
 from nipype.utils.misc import package_check
 import warnings
-from nipype.workflows.dmri.connectivity.group_connectivity import pullnodeIDs
+from nipype.workflows.dmri.connectivity.nx import create_networkx_pipeline, create_cmats_to_csv_pipeline
 
 try:
     package_check('cmp')
@@ -307,12 +307,15 @@
 Here we choose the Lausanne2008 parcellation scheme, since we are incorporating the CMTK parcellation step.
 """
 
-creatematrix = pe.Node(interface=cmtk.CreateMatrix(), name="CreateMatrix")
+parcellation_name = 'scale500'
 cmp_config = cmp.configuration.PipelineConfiguration()
 cmp_config.parcellation_scheme = "Lausanne2008"
 createnodes = pe.Node(interface=cmtk.CreateNodes(), name="CreateNodes")
 createnodes.inputs.resolution_network_file = cmp_config._get_lausanne_parcellation('Lausanne2008')[parcellation_name]['node_information_graphml']
 
+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.
@@ -329,29 +332,15 @@
 gpickledNetworks = pe.Node(interface=util.Merge(2), name="NetworkFiles")
 
 """
-We also create a node to calculate several network metrics on our resulting file, and another CFF converter
+We also create a workflow 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.
 """
 
-ntwkMetrics = pe.Node(interface=cmtk.NetworkXMetrics(), name="NetworkXMetrics")
+networkx = create_networkx_pipeline(name='networkx')
+cmats_to_csv = create_cmats_to_csv_pipeline(name='cmats_to_csv')
 NxStatsCFFConverter = pe.Node(interface=cmtk.CFFConverter(), name="NxStatsCFFConverter")
 NxStatsCFFConverter.inputs.script_files = op.abspath(inspect.getfile(inspect.currentframe()))
 
-Matlab2CSV_node = pe.Node(interface=misc.Matlab2CSV(), name="Matlab2CSV_node")
-Matlab2CSV_global = Matlab2CSV_node.clone(name="Matlab2CSV_global")
-Matlab2CSV_cmatrix = Matlab2CSV_node.clone(name="Matlab2CSV_cmatrix")
-Matlab2CSV_meanfib = Matlab2CSV_node.clone(name="Matlab2CSV_meanfib")
-Matlab2CSV_medianfib = Matlab2CSV_node.clone(name="Matlab2CSV_medianfib")
-Matlab2CSV_fibstd = Matlab2CSV_node.clone(name="Matlab2CSV_fibstd")
-
-MergeCSVFiles_node = pe.Node(interface=misc.MergeCSVFiles(), name="MergeCSVFiles_node")
-rowIDs = pullnodeIDs(op.abspath(cmp_config._get_lausanne_parcellation('Lausanne2008')[parcellation_name]['node_information_graphml']))
-MergeCSVFiles_node.inputs.row_headings = rowIDs
-MergeCSVFiles_node.inputs.extra_column_heading = 'subject'
-mergeCSVMatrices = pe.Node(interface=util.Merge(4), name="mergeCSVMatrices")
-MergeCSVFiles_cmatrices = pe.Node(interface=misc.MergeCSVFiles(), name="MergeCSVFiles_cmatrices")
-MergeCSVFiles_cmatrices.inputs.extra_column_heading = 'subject'
-
 """
 Connecting the workflow
 =======================
@@ -547,44 +536,28 @@
 
 mapping.connect([(giftiSurfaces, CFFConverter,[("out","gifti_surfaces")])])
 mapping.connect([(giftiLabels, CFFConverter,[("out","gifti_labels")])])
-mapping.connect([(creatematrix, CFFConverter,[("matrix_file","gpickled_networks")])])
+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, CFFConverter,[("subject_id","title")])])
 
 """
-The graph theoretical metrics which have been generated are placed into another CFF file.
+The graph theoretical metrics are computed using the networkx workflow and placed in another CFF file
 """
 
-mapping.connect([(creatematrix, ntwkMetrics,[("matrix_file","in_file")])])
-mapping.connect([(creatematrix, gpickledNetworks,[("matrix_file","in1")])])
-mapping.connect([(ntwkMetrics, gpickledNetworks,[("gpickled_network_files","in2")])])
-mapping.connect([(gpickledNetworks, NxStatsCFFConverter,[("out","gpickled_networks")])])
+mapping.connect([(inputnode, 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, NxStatsCFFConverter,[("subject_id","title")])])
 
-mapping.connect([(ntwkMetrics, Matlab2CSV_node,[("node_measures_matlab","in_file")])])
-mapping.connect([(ntwkMetrics, Matlab2CSV_global,[("global_measures_matlab","in_file")])])
-mapping.connect([(creatematrix, Matlab2CSV_cmatrix,[("matrix_mat_file","in_file")])])
-mapping.connect([(creatematrix, Matlab2CSV_meanfib,[("mean_fiber_length_matrix_mat_file","in_file")])])
-mapping.connect([(creatematrix, Matlab2CSV_medianfib,[("median_fiber_length_matrix_mat_file","in_file")])])
-mapping.connect([(creatematrix, Matlab2CSV_fibstd,[("fiber_length_std_matrix_mat_file","in_file")])])
-mapping.connect([(Matlab2CSV_node, MergeCSVFiles_node,[("csv_files","in_files")])])
-mapping.connect([(inputnode, MergeCSVFiles_node,[("subject_id","out_file")])])
-mapping.connect([(inputnode, MergeCSVFiles_node,[("subject_id","extra_field")])])
-
-mapping.connect([(Matlab2CSV_cmatrix, mergeCSVMatrices,[("csv_files","in1")])])
-mapping.connect([(Matlab2CSV_meanfib, mergeCSVMatrices,[("csv_files","in2")])])
-mapping.connect([(Matlab2CSV_medianfib, mergeCSVMatrices,[("csv_files","in3")])])
-mapping.connect([(Matlab2CSV_fibstd, mergeCSVMatrices,[("csv_files","in4")])])
-mapping.connect([(mergeCSVMatrices, MergeCSVFiles_cmatrices,[("out","in_files")])])
-mapping.connect([(inputnode, MergeCSVFiles_cmatrices,[("subject_id","out_file")])])
-mapping.connect([(inputnode, MergeCSVFiles_cmatrices,[("subject_id","extra_field")])])
+mapping.connect([(inputnode, cmats_to_csv,[("subject_id","inputnode.extra_field")])])
+mapping.connect([(creatematrix, cmats_to_csv,[("matlab_matrix_files","inputnode.matlab_matrix_files")])])
 
 """
 Create a higher-level workflow
@@ -596,7 +569,7 @@
 
 connectivity = pe.Workflow(name="connectivity")
 
-connectivity.base_dir = op.abspath('connectivity_tutorial_advanced')
+connectivity.base_dir = op.abspath('dmri_connectivity_advanced')
 connectivity.connect([
                     (infosource,datasource,[('subject_id', 'subject_id')]),
                     (datasource,mapping,[('dwi','inputnode.dwi'),
diff --git a/nipype/workflows/dmri/mrtrix/connectivity_mapping.py b/nipype/workflows/dmri/mrtrix/connectivity_mapping.py
@@ -46,23 +46,20 @@ def create_connectivity_pipeline(name="connectivity", parcellation_name='scale50
 
         outputnode.connectome
         outputnode.cmatrix
-        outputnode.gpickled_network
+        outputnode.networks
         outputnode.fa
         outputnode.struct
         outputnode.tracts
         outputnode.rois
         outputnode.odfs
         outputnode.filtered_tractography
+        outputnode.tdi
         outputnode.nxstatscff
-        outputnode.nxmatlab
         outputnode.nxcsv
-        outputnode.nxmergedcsv
-        outputnode.cmatrix_csv
         outputnode.cmatrices_csv
         outputnode.mean_fiber_length
-        outputnode.meanfib_csv
+        outputnode.median_fiber_length
         outputnode.fiber_length_std
-        outputnode.fibstd_csv
     """
 
     inputnode_within = pe.Node(util.IdentityInterface(fields=["subject_id",
