"""
probtrackx = pe.Node(interface=fsl.ProbTrackX(), name='probtrackx')
probtrackx.inputs.mode = 'seedmask'
probtrackx.inputs.c_thresh = 0.2
probtrackx.inputs.n_steps = 2000
probtrackx.inputs.step_length = 0.5
probtrackx.inputs.n_samples = 5000
probtrackx.inputs.opd = True
probtrackx.inputs.os2t = True
probtrackx.inputs.loop_check = True
"""
perform hard segmentation on the output of probtrackx
"""
findthebiggest = pe.Node(interface=fsl.FindTheBiggest(), name='findthebiggest')
"""
connect all the nodes for this workflow
"""
tractography.add_nodes([bedpostx, flirt])
tractography.connect([(bedpostx, probtrackx,
[('outputnode.thsamples',
'thsamples'), ('outputnode.phsamples', 'phsamples'),
('outputnode.fsamples', 'fsamples')]),
(probtrackx, findthebiggest, [('targets', 'in_files')]),
(flirt, probtrackx, [('out_matrix_file', 'xfm')])])
"""
Setup data storage area
"""
def pbX_wf(subject_id, sink_directory, name='hcp_pbX'):
hcp_pbX_wf = pe.Workflow(name='hcp_pbX_wf')
#making all the keys for the dictionary
info = dict(merged_thsamples=[['subject_id', 'merged_th']],
merged_phsamples=[['subject_id', 'merged_ph']],
merged_fsamples=[['subject_id', 'merged_f']],
dmri_brain=[['subject_id', 'T1w_acpc_dc_restore_1.25']],
fs_brain=[['subject_id', 'T1w_acpc_dc']],
aparcaseg=[['subject_id', 'aparc+aseg']],
mask=[['subject_id', 'nodif_brain_mask']])
# Create a datasource node to get the dwi, bvecs, and bvals
#This uses the dictionary created above and inputs the keys from the dictionary
datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
outfields=list(
info.keys())),
name='datasource')
datasource.inputs.template = '%s/%s'
datasource.inputs.subject_id = subject_id
datasource.inputs.base_directory = os.path.abspath('/home/data/hcp')
datasource.inputs.field_template = dict(
merged_thsamples=
'/home/data/madlab/data/mri/hcp/bedpostX/%s/hcpbpX/thsamples/%s*.nii.gz',
merged_phsamples=
'/home/data/madlab/data/mri/hcp/bedpostX/%s/hcpbpX/phsamples/%s*.nii.gz',
merged_fsamples=
'/home/data/madlab/data/mri/hcp/bedpostX/%s/hcpbpX/fsamples/%s*.nii.gz',
dmri_brain='/home/data/hcp/%s/T1w/%s.nii.gz',
fs_brain='/home/data/hcp/%s/T1w/%s.nii.gz',
aparcaseg='/home/data/hcp/%s/T1w/%s.nii.gz',
mask='/home/data/hcp/%s/T1w/Diffusion/%s.nii.gz')
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = True
# Create a flirt node to calculate the dmri_brain to fs_brain xfm
#Basically creating a conversion from DWI space to Freesurfer space
dmri2fs_xfm = pe.Node(fsl.FLIRT(), name='dmri2fs_xfm')
dmri2fs_xfm.inputs.out_matrix_file = 'dmri_2_fs_xfm.mat'
hcp_pbX_wf.connect(datasource, 'dmri_brain', dmri2fs_xfm, 'in_file')
hcp_pbX_wf.connect(datasource, 'fs_brain', dmri2fs_xfm, 'reference')
# Create a convertxfm node to create inverse xfm of dmri2fs affine
# Basicaaly creating a conversion from freesurfer space to DWI space
invt_dmri2fs = pe.Node(fsl.ConvertXFM(), name='invt_dmri2fs')
invt_dmri2fs.inputs.invert_xfm = True
invt_dmri2fs.inputs.out_file = 'fs_2_dmri_xfm.mat'
hcp_pbX_wf.connect(dmri2fs_xfm, 'out_matrix_file', invt_dmri2fs, 'in_file')
# Extract thalamus seed masks from aparc+aseg.nii.gz file
# Here 10 is the left thalamus, and 49 is the right thalamus
thal_seed_mask = pe.MapNode(fs.Binarize(),
iterfield=['match', 'binary_file'],
name='thal_seed_mask')
#thal_seed_mask.inputs.subject_dir = 'aparcaseg'
thal_seed_mask.inputs.match = [[10], [49]]
thal_seed_mask.inputs.binary_file = ['lft_thal.nii.gz', 'rt_thal.nii.gz']
hcp_pbX_wf.connect(datasource, 'aparcaseg', thal_seed_mask, 'in_file')
#Next we need to avoid the ventricles by creating an -avoid_mask
#There are no left and right 3rd and 4th ventricle, so we are making one mask
avoid_mask = pe.Node(
fs.Binarize(),
#out_type='nii.gz',
name='avoid_mask')
#avoid_mask.inputs.subject_dir = 'aparcaseg'
avoid_mask.inputs.match = [
4, 14, 15, 43, 72
] #lft_lat_ven, 3rd_ven, 4th_ven, rgt_lat_ven, 5th_ven
avoid_mask.inputs.binary_file = 'ventricles.nii.gz'
hcp_pbX_wf.connect(datasource, 'aparcaseg', avoid_mask, 'in_file')
# Extract cortical target masks from aparc+aseg.nii.gz file
# The ".match" is the freesurfer label and the binary_file is the label/name
ctx_targ_mask = pe.MapNode(fs.Binarize(),
iterfield=['match', 'binary_file'],
name='ctx_targ_mask')
#ctx_targ_mask.inputs.subject_dir = 'aparcaseg'
ctx_targ_mask.inputs.match = [[1024], [1022],
[1003, 1028, 1027, 1012, 1019, 1020, 1032],
[1031, 1029, 1008],
[1009, 1015, 1033, 1035, 1034, 1030], [1011],
[1017], [1002], [1014], [1026], [1028],
[1023, 1025, 1010], [1005, 1013,
1021], [1007], [1006],
[1016], [17], [18], [26], [2024], [2022],
[2003, 2028, 2027, 2012, 2019, 2020, 2032],
[2031, 2029, 2008],
[2009, 2015, 2033, 2035, 2034, 2030], [2011],
[2017], [2002], [2014], [2026], [2028],
[2023, 2025, 2010], [2005, 2013, 2021],
[2007], [2006], [2016], [53], [54], [58]]
ctx_targ_mask.inputs.binary_file = [
'ctx_lh_precentral.nii.gz', 'ctx_lh_postcentral.nii.gz',
'ctx_lh_latfront.nii.gz', 'ctx_lh_parietal.nii.gz',
'ctx_lh_temporal.nii.gz', 'ctx_lh_occipital.nii.gz',
'ctx_lh_paracentral.nii.gz', 'ctx_lh_caudantcing.nii.gz',
'ctx_lh_medorbfront.nii.gz', 'ctx_lh_rostantcing.nii.gz',
'ctx_lh_superfront.nii.gz', 'ctx_lh_medpost.nii.gz',
'ctx_lh_medoccipital.nii.gz', 'ctx_lh_fusiform.nii.gz',
'ctx_lh_entorhinal.nii.gz', 'ctx_lh_parahippocampal.nii.gz',
'lh_hpc.nii.gz', 'lh_amy.nii.gz', 'lh_nacc.nii.gz',
'ctx_rh_precentral.nii.gz', 'ctx_rh_postcentral.nii.gz',
'ctx_rh_latfront.nii.gz', 'ctx_rh_parietal.nii.gz',
'ctx_rh_temporal.nii.gz', 'ctx_rh_occipital.nii.gz',
'ctx_rh_paracentral.nii.gz', 'ctx_rh_caudantcing.nii.gz',
'ctx_rh_medorbfront.nii.gz', 'ctx_rh_rostantcing.nii.gz',
'ctx_rh_superfront.nii.gz', 'ctx_rh_medpost.nii.gz',
'ctx_rh_medoccipital.nii.gz', 'ctx_rh_fusiform.nii.gz',
'ctx_rh_entorhinal.nii.gz', 'ctx_rh_parahippocampal.nii.gz',
'rh_hpc.nii.gz', 'rh_amy.nii.gz', 'rh_nacc.nii.gz'
]
hcp_pbX_wf.connect(datasource, 'aparcaseg', ctx_targ_mask, 'in_file')
# Create a flirt node to apply inverse transform to seeds
# Basically you convert the masks (seeds) that were in freesurfer space to the DWI space
seedxfm_fs2dmri = pe.MapNode(fsl.FLIRT(),
iterfield=['in_file'],
name='seedxfm_fs2dmri')
seedxfm_fs2dmri.inputs.apply_xfm = True
seedxfm_fs2dmri.inputs.interp = 'nearestneighbour'
hcp_pbX_wf.connect(thal_seed_mask, 'binary_file', seedxfm_fs2dmri,
'in_file')
hcp_pbX_wf.connect(datasource, 'dmri_brain', seedxfm_fs2dmri, 'reference')
hcp_pbX_wf.connect(invt_dmri2fs, 'out_file', seedxfm_fs2dmri,
'in_matrix_file')
# Create a flirt node to apply inverse transform to targets
# You do the same as the previous node, but to the target masks
targxfm_fs2dmri = pe.MapNode(fsl.FLIRT(),
iterfield=['in_file'],
name='targxfm_fs2dmri')
targxfm_fs2dmri.inputs.apply_xfm = True
targxfm_fs2dmri.inputs.interp = 'nearestneighbour'
hcp_pbX_wf.connect(ctx_targ_mask, 'binary_file', targxfm_fs2dmri,
'in_file')
hcp_pbX_wf.connect(datasource, 'dmri_brain', targxfm_fs2dmri, 'reference')
hcp_pbX_wf.connect(invt_dmri2fs, 'out_file', targxfm_fs2dmri,
'in_matrix_file')
#Apply the inverse transform for the avoid masks from freesurfer to DWI space
avoidmaskxfm_fs2dmri = pe.Node(fsl.FLIRT(), name='avoidmaskxfm_fs2dmri')
avoidmaskxfm_fs2dmri.inputs.apply_xfm = True
avoidmaskxfm_fs2dmri.inputs.interp = 'nearestneighbour'
hcp_pbX_wf.connect(avoid_mask, 'binary_file', avoidmaskxfm_fs2dmri,
'in_file')
hcp_pbX_wf.connect(datasource, 'dmri_brain', avoidmaskxfm_fs2dmri,
'reference')
hcp_pbX_wf.connect(invt_dmri2fs, 'out_file', avoidmaskxfm_fs2dmri,
'in_matrix_file')
# Compute motion regressors (save file with 1st and 2nd derivatives)
#make_targ_lists = pe.Node(util.Function(input_names=['in_files'],
# output_names='out_list',
# function=create_two_lists),
# name='make_targ_lists')
#hcp_pbX_wf.connect(targxfm_fs2dmri, 'out_file', make_targ_lists, 'in_files')
#PROBTRACKX NODE
pbx2 = pe.MapNode(
fsl.ProbTrackX2(),
iterfield=['seed',
'target_masks'], #Should I have included avoid_mp here?
name='pbx2')
pbx2.inputs.c_thresh = 0.2
pbx2.inputs.n_steps = 2000
pbx2.inputs.step_length = 0.5
pbx2.inputs.n_samples = 25000
pbx2.inputs.opd = True
pbx2.inputs.os2t = True
pbx2.inputs.loop_check = True
#pbx2.plugin_args = {'bsub_args': '-q PQ_madlab'} #old way new way below
pbx2.plugin_args = {
'sbatch_args':
('-p IB_40C_1.5T --qos pq_madlab --account iacc_madlab -N 1 -n 6')
}
hcp_pbX_wf.connect(datasource, 'merged_thsamples', pbx2, 'thsamples')
hcp_pbX_wf.connect(datasource, 'merged_phsamples', pbx2, 'phsamples')
hcp_pbX_wf.connect(datasource, 'merged_fsamples', pbx2, 'fsamples')
hcp_pbX_wf.connect(seedxfm_fs2dmri, 'out_file', pbx2, 'seed')
hcp_pbX_wf.connect(targxfm_fs2dmri, ('out_file', hemispherize), pbx2,
'target_masks')
#hcp_pbX_wf.connect(make_targ_lists, 'out_list', pbx2, 'target_masks')
hcp_pbX_wf.connect(avoidmaskxfm_fs2dmri, 'out_file', pbx2, 'avoid_mp')
hcp_pbX_wf.connect(datasource, 'mask', pbx2, 'mask')
# Create a findthebiggest node to do hard segmentation between
# seeds and targets
#basically this segments the seed region on the basis of outputs of probtrackX when classification targets are being used.
findthebiggest = pe.MapNode(fsl.FindTheBiggest(),
iterfield=['in_files'],
name='findthebiggest')
hcp_pbX_wf.connect(pbx2, 'targets', findthebiggest, 'in_files')
# Create a datasink node to save outputs.
datasink = pe.Node(interface=nio.DataSink(), name='datasink')
datasink.inputs.base_directory = os.path.abspath(sink_directory)
datasink.inputs.container = subject_id + '/' + 'thal_seed'
hcp_pbX_wf.connect(pbx2, 'log', datasink, 'hcpprobX.log')
hcp_pbX_wf.connect(pbx2, 'fdt_paths', datasink, 'hcpprobX.fdt')
hcp_pbX_wf.connect(pbx2, 'way_total', datasink, 'hcpprobX.waytotal')
hcp_pbX_wf.connect(pbx2, 'targets', datasink, 'hcpprobX.targets')
hcp_pbX_wf.connect(findthebiggest, 'out_file', datasink,
'hcpprobX.fbiggest.@biggestsegmentation')
#hcp_pbX_wf.connect(thal_seed_mask, 'binary_file', datasink, 'hcpprobX.thal_mask')
hcp_pbX_wf.connect(seedxfm_fs2dmri, 'out_file', datasink,
'hcpprobX.seed_masks')
#from seed_xsfm(out_file) to datasink "seed_files"
#do we need this - > emu_pbX_wf.connect(datasource, 'ref_b0', datasink, 'emuprobX.b0')
#do we need this - > emu_pbX_wf.connect(thal_seed_mask, 'binary_file', datasink, 'emuprobX.thal_mask')
return hcp_pbX_wf
