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