def wbWorkflow():
# Nodes
warp = applyTransformNode(name='warpBraintoFMRI', transform='nac2fmri')
mask = maskNode(name='wholeBrainMask', fileName='wholeBrainMask.nii', low=0.5, high=1.0, flags=['largest'])
avg = afninodes.maskavenode('AFNI_1D', name='afni3DmaskAve_whole')
# Pipeline
wb = pipe.Workflow(name='wb')
wb.connect([(warp, mask, [('output_image', 'input_file')]),
(mask, avg, [('output_file', 'mask')]),
])
return wb
def gmWorkflow():
# Nodes
warp = applyTransformNode(name='warpGMtoFMRI', transform='t12fmri')
mask = maskNode(name='grmMask', fileName='grmMask.nii', low=0.99, high=1.0)
avg = afninodes.maskavenode('AFNI_1D', 'afni3DmaskAve_grm')
# Pipeline
gm = pipe.Workflow(name='gm')
gm.connect([(warp, mask, [('output_image', 'input_file')]),
(mask, avg, [('output_file', 'mask')]),
])
return gm
def wmWorkflow():
# Nodes
warp = applyTransformNode(name='warpWMtoFMRI', transform='t12fmri')
mask = maskNode(name='wmMask', fileName='whiteMatterMask.nii', low=0.99, high=1.0, flags=['erode'])
avg = afninodes.maskavenode('AFNI_1D', 'afni3DmaskAve_wm')
# Pipeline
wm = pipe.Workflow(name='wm')
wm.connect([(warp, mask, [('output_image', 'input_file')]),
(mask, avg, [('output_file', 'mask')]),
])
return wm
def csfWorkflow():
# Nodes
warp = applyTransformNode(name='warpCSFtoFMRI', transform='nac2fmri')
mask = maskNode(name='csfMask', fileName='csfMask.nii', low=3, high=42, flags=['binary'])
avg = afninodes.maskavenode('AFNI_1D', 'afni3DmaskAve_csf')
# Pipeline
csf = pipe.Workflow(name='csf')
csf.connect([(warp, mask, [('output_image', 'input_file')]),
(mask, avg, [('output_file', 'mask')]),
])
return csf
def gmWorkflow():
# Nodes
warp = applyTransformNode(name='warpGMtoFMRI', transform='t12fmri')
mask = maskNode(name='grmMask', fileName='grmMask.nii', low=0.99, high=1.0)
avg = afninodes.maskavenode('AFNI_1D', 'afni3DmaskAve_grm')
# Pipeline
gm = pipe.Workflow(name='gm')
gm.connect([
(warp, mask, [('output_image', 'input_file')]),
(mask, avg, [('output_file', 'mask')]),
])
return gm
def wbWorkflow():
# Nodes
warp = applyTransformNode(name='warpBraintoFMRI', transform='nac2fmri')
mask = maskNode(name='wholeBrainMask',
fileName='wholeBrainMask.nii',
low=0.5,
high=1.0,
flags=['largest'])
avg = afninodes.maskavenode('AFNI_1D', name='afni3DmaskAve_whole')
# Pipeline
wb = pipe.Workflow(name='wb')
wb.connect([
(warp, mask, [('output_image', 'input_file')]),
(mask, avg, [('output_file', 'mask')]),
])
return wb
def wmWorkflow():
# Nodes
warp = applyTransformNode(name='warpWMtoFMRI', transform='t12fmri')
mask = maskNode(name='wmMask',
fileName='whiteMatterMask.nii',
low=0.99,
high=1.0,
flags=['erode'])
avg = afninodes.maskavenode('AFNI_1D', 'afni3DmaskAve_wm')
# Pipeline
wm = pipe.Workflow(name='wm')
wm.connect([
(warp, mask, [('output_image', 'input_file')]),
(mask, avg, [('output_file', 'mask')]),
])
return wm
def csfWorkflow():
# Nodes
warp = applyTransformNode(name='warpCSFtoFMRI', transform='nac2fmri')
mask = maskNode(name='csfMask',
fileName='csfMask.nii',
low=3,
high=42,
flags=['binary'])
avg = afninodes.maskavenode('AFNI_1D', 'afni3DmaskAve_csf')
# Pipeline
csf = pipe.Workflow(name='csf')
csf.connect([
(warp, mask, [('output_image', 'input_file')]),
(mask, avg, [('output_file', 'mask')]),
])
return csf
def pipeline(args):
if args['debug']:
config.enable_debug_mode()
config.update_config({'logging': {'log_directory':makeSupportDir(args['name'], "logs")}})
logging.update_logging(config)
# CONSTANTS
sessionID = args['session']
outputType = args['format'].upper()
fOutputType = args['freesurfer']
preprocessOn = args['preprocess']
maskGM = args['maskgm']
maskWholeBrain = args['maskwb']
maskWhiteMatterFromSeeds = args['maskseeds']
# print args['name']
t1_experiment = "20141001_PREDICTHD_long_Results" #"20130729_PREDICT_Results"
atlasFile = os.path.abspath(os.path.join(os.path.dirname(__file__), "ReferenceAtlas", "template_t1.nii.gz"))
wholeBrainFile = os.path.abspath(os.path.join(os.path.dirname(__file__), "ReferenceAtlas", "template_brain.nii.gz"))
atlasLabel = os.path.abspath(os.path.join(os.path.dirname(__file__), "ReferenceAtlas", "template_nac_labels.nii.gz"))
resampleResolution = (2.0, 2.0, 2.0)
downsampledfilename = 'downsampled_atlas.nii.gz'
master = pipe.Workflow(name=args['name'] + "_CACHE")
master.base_dir = os.path.abspath("/Shared/sinapse/CACHE")
sessions = pipe.Node(interface=IdentityInterface(fields=['session_id']), name='sessionIDs')
sessions.iterables = ('session_id', sessionID)
downsampleAtlas = pipe.Node(interface=Function(function=resampleImage,
input_names=['inputVolume', 'outputVolume', 'resolution'],
output_names=['outputVolume']),
name="downsampleAtlas")
downsampleAtlas.inputs.inputVolume = atlasFile
downsampleAtlas.inputs.outputVolume = downsampledfilename
downsampleAtlas.inputs.resolution = [int(x) for x in resampleResolution]
# HACK: Remove node from pipeline until Nipype/AFNI file copy issue is resolved
# fmri_DataSink = pipe.Node(interface=DataSink(), name="fmri_DataSink")
# fmri_DataSink.overwrite = REWRITE_DATASINKS
# Output to: /Shared/paulsen/Experiments/YYYYMMDD_<experiment>_Results/fmri
# fmri_DataSink.inputs.base_directory = os.path.join(master.base_dir, RESULTS_DIR, 'fmri')
# fmri_DataSink.inputs.substitutions = [('to_3D_out+orig', 'to3D')]
# fmri_DataSink.inputs.parameterization = False
#
# master.connect([(sessions, fmri_DataSink, [('session_id', 'container')])])
# END HACK
registration = registrationWorkflow.workflow(t1_experiment, outputType, name="registration_wkfl")
master.connect([(sessions, registration, [('session_id', "inputs.session_id")])])
detrend = afninodes.detrendnode(outputType, 'afni3Ddetrend')
# define grabber
site = "*"
subject = "*"
if preprocessOn:
grabber = dataio.iowaGrabber(t1_experiment, site, subject, maskGM, maskWholeBrain)
master.connect([(sessions, grabber, [('session_id', 'session_id')]),
(grabber, registration, [('t1_File', 'inputs.t1')])])
# Why isn't preprocessWorkflow.workflow() used instead? It would avoid most of the nuisance connections here...
preprocessing = preprocessWorkflow.prepWorkflow(skipCount=6, outputType=outputType)
name = args.pop('name') # HACK: prevent name conflict with nuisance workflow
nuisance = nuisanceWorkflow.workflow(outputType=outputType, **args)
args['name'] = name # END HACK
master.connect([(grabber, preprocessing, [('fmri_dicom_dir', 'to_3D.infolder'),
('fmri_dicom_dir', 'formatFMRINode.dicomDirectory')]),
(grabber, nuisance, [('whmFile', 'wm.warpWMtoFMRI.input_image')]),
(preprocessing, registration, [('merge.out_file', 'inputs.fmri'), # 7
('automask.out_file', 'tstat.mask_file')]), # *optional*
(registration, nuisance, [('outputs.fmri_reference', 'csf.warpCSFtoFMRI.reference_image'), # CSF
('outputs.nac2fmri_list', 'csf.warpCSFtoFMRI.transforms'),
('outputs.fmri_reference', 'wm.warpWMtoFMRI.reference_image'), # WM
('outputs.t12fmri_list', 'wm.warpWMtoFMRI.transforms')]),
])
warpCSFtoFMRInode = nuisance.get_node('csf').get_node('warpCSFtoFMRI')
warpCSFtoFMRInode.inputs.input_image = atlasFile
if maskGM:
master.connect([(grabber, nuisance, [('gryFile', 'gm.warpGMtoFMRI.input_image')]),
(registration, nuisance, [('outputs.fmri_reference', 'gm.warpGMtoFMRI.reference_image'),
('outputs.t12fmri_list', 'gm.warpGMtoFMRI.transforms')]),
(preprocessing, nuisance, [('calc.out_file', 'gm.afni3DmaskAve_grm.in_file'),
('volreg.oned_file', 'afni3Ddeconvolve.stim_file_4')])])
elif maskWholeBrain:
master.connect([(registration, nuisance, [('outputs.fmri_reference', 'wb.warpBraintoFMRI.reference_image'),
('outputs.nac2fmri_list', 'wb.warpBraintoFMRI.transforms')]),
(preprocessing, nuisance, [('calc.out_file', 'wb.afni3DmaskAve_whole.in_file'),
('volreg.oned_file', 'afni3Ddeconvolve.stim_file_4')])])
warpBraintoFMRInode = nuisance.get_node('wb').get_node('warpBraintoFMRI')
warpBraintoFMRInode.inputs.input_image= wholeBrainFile
else:
master.connect([(preprocessing, nuisance, [('volreg.oned_file', 'afni3Ddeconvolve.stim_file_3')])])
master.connect([(preprocessing, nuisance, [('calc.out_file', 'wm.afni3DmaskAve_wm.in_file'),
('calc.out_file', 'csf.afni3DmaskAve_csf.in_file'),
('calc.out_file', 'afni3Ddeconvolve.in_file')]),
(nuisance, detrend, [('afni3Ddeconvolve.out_errts', 'in_file')])]) # 13
else:
cleveland_grabber = dataio.clevelandGrabber()
grabber = dataio.autoworkupGrabber(t1_experiment, site, subject)
converter = pipe.Node(interface=Copy(), name='converter') # Convert ANALYZE to AFNI
master.connect([(sessions, grabber, [('session_id', 'session_id')]),
(grabber, registration, [('t1_File', 'inputs.t1')]),
(sessions, cleveland_grabber, [('session_id', 'session_id')]),
(cleveland_grabber, converter, [('fmriHdr', 'in_file')]),
(converter, registration, [('out_file', 'inputs.fmri')]),
(converter, detrend, [('out_file', 'in_file')]), # in fMRI_space
])
t1_wf = registrationWorkflow.t1Workflow()
babc_wf = registrationWorkflow.babcWorkflow()
# HACK: No EPI
# epi_wf = registrationWorkflow.epiWorkflow()
lb_wf = registrationWorkflow.labelWorkflow()
seed_wf = registrationWorkflow.seedWorkflow()
bandpass = afninodes.fouriernode(outputType, 'fourier') # Fourier is the last NIFTI file format in the AFNI pipeline
master.connect([(detrend, bandpass, [('out_file', 'in_file')]), # Per Dawei, bandpass after running 3dDetrend
(grabber, t1_wf, [('t1_File', 'warpT1toFMRI.input_image')]),
(registration, t1_wf, [('outputs.fmri_reference', 'warpT1toFMRI.reference_image'), # T1
('outputs.t12fmri_list', 'warpT1toFMRI.transforms')]),
(grabber, babc_wf, [('csfFile', 'warpBABCtoFMRI.input_image')]),
(registration, babc_wf, [('outputs.fmri_reference', 'warpBABCtoFMRI.reference_image'), # Labels
('outputs.t12fmri_list', 'warpBABCtoFMRI.transforms')]),
# HACK: No EPI
# (downsampleAtlas, epi_wf, [('outputVolume', 'warpEPItoNAC.reference_image')]),
# (registration, epi_wf, [('outputs.fmri2nac_list', 'warpEPItoNAC.transforms')]),
# (bandpass, epi_wf, [('out_file', 'warpEPItoNAC.input_image')]),
# END HACK
(downsampleAtlas, lb_wf, [('outputVolume', 'warpLabeltoNAC.reference_image')]),
(registration, lb_wf, [('outputs.fmri2nac_list', 'warpLabeltoNAC.transforms')]),
(t1_wf, seed_wf, [('warpT1toFMRI.output_image', 'warpSeedtoFMRI.reference_image')]),
(registration, seed_wf, [('outputs.nac2fmri_list', 'warpSeedtoFMRI.transforms')]),
])
renameMasks = pipe.Node(interface=Rename(format_string='%(label)s_mask'), name='renameMasksAtlas')
renameMasks.inputs.keep_ext = True
atlas_DataSink = dataio.atlasSink(base_directory=master.base_dir, **args)
master.connect([(renameMasks, atlas_DataSink, [('out_file', 'Atlas')]),
(downsampleAtlas, atlas_DataSink, [('outputVolume', 'Atlas.@resampled')]),
])
renameMasks2 = pipe.Node(interface=Rename(format_string='%(session)s_%(label)s_mask'), name='renameMasksFMRI')
renameMasks2.inputs.keep_ext = True
master.connect(sessions, 'session_id', renameMasks2, 'session')
clipSeedWithVentriclesNode = pipe.Node(interface=Function(function=clipSeedWithVentricles,
input_names=['seed', 'label', 'outfile'],
output_names=['clipped_seed_fn']),
name='clipSeedWithVentriclesNode')
clipSeedWithVentriclesNode.inputs.outfile = "clipped_seed.nii.gz"
master.connect(seed_wf, 'warpSeedtoFMRI.output_image', clipSeedWithVentriclesNode, 'seed')
master.connect(babc_wf, 'warpBABCtoFMRI.output_image', clipSeedWithVentriclesNode, 'label')
if not maskWhiteMatterFromSeeds:
master.connect(clipSeedWithVentriclesNode, 'clipped_seed_fn', renameMasks2, 'in_file')
else:
clipSeedWithWhiteMatterNode = pipe.Node(interface=Function(function=clipSeedWithWhiteMatter,
input_names=['seed', 'mask', 'outfile'],
output_names=['outfile']),
name='clipSeedWithWhiteMatterNode')
clipSeedWithWhiteMatterNode.inputs.outfile = 'clipped_wm_seed.nii.gz'
master.connect(babc_wf, 'warpBABCtoFMRI.output_image', clipSeedWithWhiteMatterNode, 'mask')
master.connect(clipSeedWithVentriclesNode, 'clipped_seed_fn', clipSeedWithWhiteMatterNode, 'seed')
master.connect(clipSeedWithWhiteMatterNode, 'outfile', renameMasks2, 'in_file')
# Labels are iterated over, so we need a seperate datasink to avoid overwriting any preprocessing
# results when the labels are iterated (e.g. To3d output)
# Write out to: /Shared/sinapse/CACHE/YYYYMMDD_<experiment>_Results/<SESSION>
fmri_label_DataSink = dataio.fmriSink(master.base_dir, **args)
master.connect(sessions, 'session_id', fmri_label_DataSink, 'container')
master.connect(renameMasks2, 'out_file', fmri_label_DataSink, 'masks')
master.connect(bandpass,'out_file', fmri_label_DataSink, 'masks.@bandpass')
roiMedian = afninodes.maskavenode('AFNI_1D', 'afni_roiMedian', '-mrange 1 1')
master.connect(renameMasks2, 'out_file', roiMedian, 'mask')
master.connect(bandpass, 'out_file', roiMedian, 'in_file')
correlate = afninodes.fimnode('Correlation', 'afni_correlate')
master.connect(roiMedian, 'out_file', correlate, 'ideal_file')
master.connect(bandpass, 'out_file', correlate, 'in_file')
regionLogCalc = afninodes.logcalcnode(outputType, 'afni_regionLogCalc')
master.connect(correlate, 'out_file', regionLogCalc, 'in_file_a')
renameZscore = pipe.Node(interface=Rename(format_string="%(session)s_%(label)s_zscore"), name='renameZscore')
renameZscore.inputs.keep_ext = True
master.connect(sessions, 'session_id', renameZscore, 'session')
master.connect(regionLogCalc, 'out_file', renameZscore, 'in_file')
master.connect(renameZscore, 'out_file', fmri_label_DataSink, 'zscores')
master.connect(t1_wf, 'warpT1toFMRI.output_image', fmri_label_DataSink, 'zscores.@t1Underlay')
# Move z values back into NAC atlas space
# master.connect(downsampleAtlas, 'outputVolume', lb_wf, 'warpLabeltoNAC.reference_image')
master.connect(regionLogCalc, 'out_file', lb_wf, 'warpLabeltoNAC.input_image')
renameZscore2 = pipe.Node(interface=Rename(format_string="%(session)s_%(label)s_result"), name='renameZscore2')
renameZscore2.inputs.keep_ext = True
master.connect(sessions, 'session_id', renameZscore2, 'session')
master.connect(lb_wf, 'warpLabeltoNAC.output_image', renameZscore2, 'in_file')
master.connect(renameZscore2, 'out_file', atlas_DataSink, 'Atlas.@zscore')
# Connect seed subworkflow
seedSubflow = seedWorkflow.workflow(args['seeds'], outputType='NIFTI_GZ', name='seed_wkfl')
master.connect([(downsampleAtlas, seedSubflow, [('outputVolume', 'afni3Dcalc_seeds.in_file_a')]),
(seedSubflow, renameMasks, [('afni3Dcalc_seeds.out_file', 'in_file'),
('selectLabel.out', 'label')]),
(seedSubflow, renameMasks2, [('selectLabel.out', 'label')]),
(seedSubflow, renameZscore, [('selectLabel.out', 'label')]),
(seedSubflow, renameZscore2, [('selectLabel.out', 'label')]),
(seedSubflow, seed_wf, [('afni3Dcalc_seeds.out_file', 'warpSeedtoFMRI.input_image')])
])
imageDir = makeSupportDir(args['name'], "images")
if args['plot']:
registration.write_graph(dotfilename=os.path.join(imageDir, 'register.dot'), graph2use='orig', format='png',
simple_form=False)
if preprocessOn:
preprocessing.write_graph(dotfilename=os.path.join(imageDir, 'preprocess.dot'), graph2use='orig', format='png',
simple_form=False)
nuisance.write_graph(dotfilename=os.path.join(imageDir, 'nuisance.dot'), graph2use='orig', format='png',
simple_form=False)
seedSubflow.write_graph(dotfilename=os.path.join(imageDir, 'seed.dot'), graph2use='orig', format='png',
simple_form=False)
master.write_graph(dotfilename=os.path.join(imageDir, 'master.dot'), graph2use="orig", format='png', simple_form=False)
elif args['debug']:
try:
master.run(updatehash=True)
# Run restingState on the all threads
# Setup environment for CPU load balancing of ITK based programs.
# --------
# import multiprocessing
# total_CPUS = 10 # multiprocessing.cpu_count()
# master.run(plugin='MultiProc', plugin_args={'n_proc': total_CPUS}) #, updatehash=True)
# --------
# Run restingState on the local cluster
# master.run(plugin='SGE', plugin_args={'template': os.path.join(os.getcwd(), 'ENV/bin/activate'),
# 'qsub_args': '-S /bin/bash -cwd'}) #, updatehash=True)
except:
pass
master.name = "master" # HACK: Bug in Graphviz for nodes beginning with numbers
master.write_graph(dotfilename=os.path.join(imageDir, 'debug_hier.dot'), graph2use="colored", format='png')
master.write_graph(dotfilename=os.path.join(imageDir, 'debug_orig.dot'), graph2use="flat", format='png')
else:
import multiprocessing
total_CPUS = multiprocessing.cpu_count()
master.run(plugin='MultiProc', plugin_args={'n_proc': total_CPUS}) #, updatehash=True)
return 0
def pipeline(args):
if args['debug']:
config.enable_debug_mode()
config.update_config(
{'logging': {
'log_directory': makeSupportDir(args['name'], "logs")
}})
logging.update_logging(config)
# CONSTANTS
sessionID = args['session']
outputType = args['format'].upper()
fOutputType = args['freesurfer']
preprocessOn = args['preprocess']
maskGM = args['maskgm']
maskWholeBrain = args['maskwb']
maskWhiteMatterFromSeeds = args['maskseeds']
# print args['name']
t1_experiment = "20141001_PREDICTHD_long_Results" #"20130729_PREDICT_Results"
atlasFile = os.path.abspath(
os.path.join(os.path.dirname(__file__), "ReferenceAtlas",
"template_t1.nii.gz"))
wholeBrainFile = os.path.abspath(
os.path.join(os.path.dirname(__file__), "ReferenceAtlas",
"template_brain.nii.gz"))
atlasLabel = os.path.abspath(
os.path.join(os.path.dirname(__file__), "ReferenceAtlas",
"template_nac_labels.nii.gz"))
resampleResolution = (2.0, 2.0, 2.0)
downsampledfilename = 'downsampled_atlas.nii.gz'
master = pipe.Workflow(name=args['name'] + "_CACHE")
master.base_dir = os.path.abspath("/Shared/sinapse/CACHE")
sessions = pipe.Node(interface=IdentityInterface(fields=['session_id']),
name='sessionIDs')
sessions.iterables = ('session_id', sessionID)
downsampleAtlas = pipe.Node(interface=Function(
function=resampleImage,
input_names=['inputVolume', 'outputVolume', 'resolution'],
output_names=['outputVolume']),
name="downsampleAtlas")
downsampleAtlas.inputs.inputVolume = atlasFile
downsampleAtlas.inputs.outputVolume = downsampledfilename
downsampleAtlas.inputs.resolution = [int(x) for x in resampleResolution]
# HACK: Remove node from pipeline until Nipype/AFNI file copy issue is resolved
# fmri_DataSink = pipe.Node(interface=DataSink(), name="fmri_DataSink")
# fmri_DataSink.overwrite = REWRITE_DATASINKS
# Output to: /Shared/paulsen/Experiments/YYYYMMDD_<experiment>_Results/fmri
# fmri_DataSink.inputs.base_directory = os.path.join(master.base_dir, RESULTS_DIR, 'fmri')
# fmri_DataSink.inputs.substitutions = [('to_3D_out+orig', 'to3D')]
# fmri_DataSink.inputs.parameterization = False
#
# master.connect([(sessions, fmri_DataSink, [('session_id', 'container')])])
# END HACK
registration = registrationWorkflow.workflow(t1_experiment,
outputType,
name="registration_wkfl")
master.connect([(sessions, registration, [('session_id',
"inputs.session_id")])])
detrend = afninodes.detrendnode(outputType, 'afni3Ddetrend')
# define grabber
site = "*"
subject = "*"
if preprocessOn:
grabber = dataio.iowaGrabber(t1_experiment, site, subject, maskGM,
maskWholeBrain)
master.connect([(sessions, grabber, [('session_id', 'session_id')]),
(grabber, registration, [('t1_File', 'inputs.t1')])])
# Why isn't preprocessWorkflow.workflow() used instead? It would avoid most of the nuisance connections here...
preprocessing = preprocessWorkflow.prepWorkflow(skipCount=6,
outputType=outputType)
name = args.pop(
'name') # HACK: prevent name conflict with nuisance workflow
nuisance = nuisanceWorkflow.workflow(outputType=outputType, **args)
args['name'] = name # END HACK
master.connect([
(grabber, preprocessing, [('fmri_dicom_dir', 'to_3D.infolder'),
('fmri_dicom_dir',
'formatFMRINode.dicomDirectory')]),
(grabber, nuisance, [('whmFile', 'wm.warpWMtoFMRI.input_image')]),
(
preprocessing,
registration,
[
('merge.out_file', 'inputs.fmri'), # 7
('automask.out_file', 'tstat.mask_file')
]), # *optional*
(
registration,
nuisance,
[
('outputs.fmri_reference',
'csf.warpCSFtoFMRI.reference_image'), # CSF
('outputs.nac2fmri_list', 'csf.warpCSFtoFMRI.transforms'),
('outputs.fmri_reference',
'wm.warpWMtoFMRI.reference_image'), # WM
('outputs.t12fmri_list', 'wm.warpWMtoFMRI.transforms')
]),
])
warpCSFtoFMRInode = nuisance.get_node('csf').get_node('warpCSFtoFMRI')
warpCSFtoFMRInode.inputs.input_image = atlasFile
if maskGM:
master.connect([
(grabber, nuisance, [('gryFile', 'gm.warpGMtoFMRI.input_image')
]),
(registration, nuisance, [('outputs.fmri_reference',
'gm.warpGMtoFMRI.reference_image'),
('outputs.t12fmri_list',
'gm.warpGMtoFMRI.transforms')]),
(preprocessing, nuisance,
[('calc.out_file', 'gm.afni3DmaskAve_grm.in_file'),
('volreg.oned_file', 'afni3Ddeconvolve.stim_file_4')])
])
elif maskWholeBrain:
master.connect([
(registration, nuisance,
[('outputs.fmri_reference',
'wb.warpBraintoFMRI.reference_image'),
('outputs.nac2fmri_list', 'wb.warpBraintoFMRI.transforms')]),
(preprocessing, nuisance,
[('calc.out_file', 'wb.afni3DmaskAve_whole.in_file'),
('volreg.oned_file', 'afni3Ddeconvolve.stim_file_4')])
])
warpBraintoFMRInode = nuisance.get_node('wb').get_node(
'warpBraintoFMRI')
warpBraintoFMRInode.inputs.input_image = wholeBrainFile
else:
master.connect([(preprocessing, nuisance, [
('volreg.oned_file', 'afni3Ddeconvolve.stim_file_3')
])])
master.connect([(preprocessing, nuisance,
[('calc.out_file', 'wm.afni3DmaskAve_wm.in_file'),
('calc.out_file', 'csf.afni3DmaskAve_csf.in_file'),
('calc.out_file', 'afni3Ddeconvolve.in_file')]),
(nuisance, detrend, [('afni3Ddeconvolve.out_errts',
'in_file')])]) # 13
else:
cleveland_grabber = dataio.clevelandGrabber()
grabber = dataio.autoworkupGrabber(t1_experiment, site, subject)
converter = pipe.Node(interface=Copy(),
name='converter') # Convert ANALYZE to AFNI
master.connect([
(sessions, grabber, [('session_id', 'session_id')]),
(grabber, registration, [('t1_File', 'inputs.t1')]),
(sessions, cleveland_grabber, [('session_id', 'session_id')]),
(cleveland_grabber, converter, [('fmriHdr', 'in_file')]),
(converter, registration, [('out_file', 'inputs.fmri')]),
(converter, detrend, [('out_file', 'in_file')]), # in fMRI_space
])
t1_wf = registrationWorkflow.t1Workflow()
babc_wf = registrationWorkflow.babcWorkflow()
# HACK: No EPI
# epi_wf = registrationWorkflow.epiWorkflow()
lb_wf = registrationWorkflow.labelWorkflow()
seed_wf = registrationWorkflow.seedWorkflow()
bandpass = afninodes.fouriernode(
outputType, 'fourier'
) # Fourier is the last NIFTI file format in the AFNI pipeline
master.connect([
(detrend, bandpass, [('out_file', 'in_file')
]), # Per Dawei, bandpass after running 3dDetrend
(grabber, t1_wf, [('t1_File', 'warpT1toFMRI.input_image')]),
(
registration,
t1_wf,
[
('outputs.fmri_reference',
'warpT1toFMRI.reference_image'), # T1
('outputs.t12fmri_list', 'warpT1toFMRI.transforms')
]),
(grabber, babc_wf, [('csfFile', 'warpBABCtoFMRI.input_image')]),
(
registration,
babc_wf,
[
('outputs.fmri_reference',
'warpBABCtoFMRI.reference_image'), # Labels
('outputs.t12fmri_list', 'warpBABCtoFMRI.transforms')
]),
# HACK: No EPI
# (downsampleAtlas, epi_wf, [('outputVolume', 'warpEPItoNAC.reference_image')]),
# (registration, epi_wf, [('outputs.fmri2nac_list', 'warpEPItoNAC.transforms')]),
# (bandpass, epi_wf, [('out_file', 'warpEPItoNAC.input_image')]),
# END HACK
(downsampleAtlas, lb_wf, [('outputVolume',
'warpLabeltoNAC.reference_image')]),
(registration, lb_wf, [('outputs.fmri2nac_list',
'warpLabeltoNAC.transforms')]),
(t1_wf, seed_wf, [('warpT1toFMRI.output_image',
'warpSeedtoFMRI.reference_image')]),
(registration, seed_wf, [('outputs.nac2fmri_list',
'warpSeedtoFMRI.transforms')]),
])
renameMasks = pipe.Node(interface=Rename(format_string='%(label)s_mask'),
name='renameMasksAtlas')
renameMasks.inputs.keep_ext = True
atlas_DataSink = dataio.atlasSink(base_directory=master.base_dir, **args)
master.connect([
(renameMasks, atlas_DataSink, [('out_file', 'Atlas')]),
(downsampleAtlas, atlas_DataSink, [('outputVolume', 'Atlas.@resampled')
]),
])
renameMasks2 = pipe.Node(
interface=Rename(format_string='%(session)s_%(label)s_mask'),
name='renameMasksFMRI')
renameMasks2.inputs.keep_ext = True
master.connect(sessions, 'session_id', renameMasks2, 'session')
clipSeedWithVentriclesNode = pipe.Node(interface=Function(
function=clipSeedWithVentricles,
input_names=['seed', 'label', 'outfile'],
output_names=['clipped_seed_fn']),
name='clipSeedWithVentriclesNode')
clipSeedWithVentriclesNode.inputs.outfile = "clipped_seed.nii.gz"
master.connect(seed_wf, 'warpSeedtoFMRI.output_image',
clipSeedWithVentriclesNode, 'seed')
master.connect(babc_wf, 'warpBABCtoFMRI.output_image',
clipSeedWithVentriclesNode, 'label')
if not maskWhiteMatterFromSeeds:
master.connect(clipSeedWithVentriclesNode, 'clipped_seed_fn',
renameMasks2, 'in_file')
else:
clipSeedWithWhiteMatterNode = pipe.Node(
interface=Function(function=clipSeedWithWhiteMatter,
input_names=['seed', 'mask', 'outfile'],
output_names=['outfile']),
name='clipSeedWithWhiteMatterNode')
clipSeedWithWhiteMatterNode.inputs.outfile = 'clipped_wm_seed.nii.gz'
master.connect(babc_wf, 'warpBABCtoFMRI.output_image',
clipSeedWithWhiteMatterNode, 'mask')
master.connect(clipSeedWithVentriclesNode, 'clipped_seed_fn',
clipSeedWithWhiteMatterNode, 'seed')
master.connect(clipSeedWithWhiteMatterNode, 'outfile', renameMasks2,
'in_file')
# Labels are iterated over, so we need a seperate datasink to avoid overwriting any preprocessing
# results when the labels are iterated (e.g. To3d output)
# Write out to: /Shared/sinapse/CACHE/YYYYMMDD_<experiment>_Results/<SESSION>
fmri_label_DataSink = dataio.fmriSink(master.base_dir, **args)
master.connect(sessions, 'session_id', fmri_label_DataSink, 'container')
master.connect(renameMasks2, 'out_file', fmri_label_DataSink, 'masks')
master.connect(bandpass, 'out_file', fmri_label_DataSink,
'masks.@bandpass')
roiMedian = afninodes.maskavenode('AFNI_1D', 'afni_roiMedian',
'-mrange 1 1')
master.connect(renameMasks2, 'out_file', roiMedian, 'mask')
master.connect(bandpass, 'out_file', roiMedian, 'in_file')
correlate = afninodes.fimnode('Correlation', 'afni_correlate')
master.connect(roiMedian, 'out_file', correlate, 'ideal_file')
master.connect(bandpass, 'out_file', correlate, 'in_file')
regionLogCalc = afninodes.logcalcnode(outputType, 'afni_regionLogCalc')
master.connect(correlate, 'out_file', regionLogCalc, 'in_file_a')
renameZscore = pipe.Node(
interface=Rename(format_string="%(session)s_%(label)s_zscore"),
name='renameZscore')
renameZscore.inputs.keep_ext = True
master.connect(sessions, 'session_id', renameZscore, 'session')
master.connect(regionLogCalc, 'out_file', renameZscore, 'in_file')
master.connect(renameZscore, 'out_file', fmri_label_DataSink, 'zscores')
master.connect(t1_wf, 'warpT1toFMRI.output_image', fmri_label_DataSink,
'zscores.@t1Underlay')
# Move z values back into NAC atlas space
# master.connect(downsampleAtlas, 'outputVolume', lb_wf, 'warpLabeltoNAC.reference_image')
master.connect(regionLogCalc, 'out_file', lb_wf,
'warpLabeltoNAC.input_image')
renameZscore2 = pipe.Node(
interface=Rename(format_string="%(session)s_%(label)s_result"),
name='renameZscore2')
renameZscore2.inputs.keep_ext = True
master.connect(sessions, 'session_id', renameZscore2, 'session')
master.connect(lb_wf, 'warpLabeltoNAC.output_image', renameZscore2,
'in_file')
master.connect(renameZscore2, 'out_file', atlas_DataSink, 'Atlas.@zscore')
# Connect seed subworkflow
seedSubflow = seedWorkflow.workflow(args['seeds'],
outputType='NIFTI_GZ',
name='seed_wkfl')
master.connect([
(downsampleAtlas, seedSubflow, [('outputVolume',
'afni3Dcalc_seeds.in_file_a')]),
(seedSubflow, renameMasks, [('afni3Dcalc_seeds.out_file', 'in_file'),
('selectLabel.out', 'label')]),
(seedSubflow, renameMasks2, [('selectLabel.out', 'label')]),
(seedSubflow, renameZscore, [('selectLabel.out', 'label')]),
(seedSubflow, renameZscore2, [('selectLabel.out', 'label')]),
(seedSubflow, seed_wf, [('afni3Dcalc_seeds.out_file',
'warpSeedtoFMRI.input_image')])
])
imageDir = makeSupportDir(args['name'], "images")
if args['plot']:
registration.write_graph(dotfilename=os.path.join(
imageDir, 'register.dot'),
graph2use='orig',
format='png',
simple_form=False)
if preprocessOn:
preprocessing.write_graph(dotfilename=os.path.join(
imageDir, 'preprocess.dot'),
graph2use='orig',
format='png',
simple_form=False)
nuisance.write_graph(dotfilename=os.path.join(
imageDir, 'nuisance.dot'),
graph2use='orig',
format='png',
simple_form=False)
seedSubflow.write_graph(dotfilename=os.path.join(imageDir, 'seed.dot'),
graph2use='orig',
format='png',
simple_form=False)
master.write_graph(dotfilename=os.path.join(imageDir, 'master.dot'),
graph2use="orig",
format='png',
simple_form=False)
elif args['debug']:
try:
master.run(updatehash=True)
# Run restingState on the all threads
# Setup environment for CPU load balancing of ITK based programs.
# --------
# import multiprocessing
# total_CPUS = 10 # multiprocessing.cpu_count()
# master.run(plugin='MultiProc', plugin_args={'n_proc': total_CPUS}) #, updatehash=True)
# --------
# Run restingState on the local cluster
# master.run(plugin='SGE', plugin_args={'template': os.path.join(os.getcwd(), 'ENV/bin/activate'),
# 'qsub_args': '-S /bin/bash -cwd'}) #, updatehash=True)
except:
pass
master.name = "master" # HACK: Bug in Graphviz for nodes beginning with numbers
master.write_graph(dotfilename=os.path.join(imageDir,
'debug_hier.dot'),
graph2use="colored",
format='png')
master.write_graph(dotfilename=os.path.join(imageDir,
'debug_orig.dot'),
graph2use="flat",
format='png')
else:
import multiprocessing
total_CPUS = multiprocessing.cpu_count()
master.run(plugin='MultiProc',
plugin_args={'n_proc': total_CPUS}) #, updatehash=True)
return 0
