def ants_apply_warps_func_mni(workflow,
output_name,
func_key,
ref_key,
num_strat,
strat,
interpolation_method='LanczosWindowedSinc',
distcor=False,
map_node=False,
inverse=False,
symmetry='asymmetric',
input_image_type=0,
num_ants_cores=1,
registration_template='t1',
func_type='non-ica-aroma',
num_cpus=1):
"""
Applies previously calculated ANTS registration transforms to input
images. This workflow employs the antsApplyTransforms tool:
http://stnava.github.io/ANTs/
Parameters
----------
name : string, optional
Name of the workflow.
Returns
-------
apply_ants_warp_wf : nipype.pipeline.engine.Workflow
Notes
-----
Workflow Inputs::
workflow: Nipype workflow object
the workflow containing the resources involved
output_name: str
what the name of the warped functional should be when written to the
resource pool
func_key: string
resource pool key correspoding to the node containing the 3D or 4D
functional file to be written into MNI space, use 'leaf' for a
leaf node
ref_key: string
resource pool key correspoding to the file path to the template brain
used for functional-to-template registration
num_strat: int
the number of strategy objects
strat: C-PAC Strategy object
a strategy with one or more resource pools
interpolation_method: str
which interpolation to use when applying the warps, commonly used
options are 'Linear', 'Bspline', 'LanczosWindowedSinc' (default)
for derivatives and image data 'NearestNeighbor' for masks
distcor: boolean
indicates whether a distortion correction transformation should be
added to the transforms, this of course requires that a distortion
correction map exist in the resource pool
map_node: boolean
indicates whether a mapnode should be used, if TRUE func_key is
expected to correspond to a list of resources that should each
be written into standard space with the other parameters
inverse: boolean
writes the invrse of the transform, i.e. MNI->EPI instead of
EPI->MNI
input_image_type: int
argument taken by the ANTs apply warp tool; in this case, should be
0 for scalars (default) and 3 for 4D functional time-series
num_ants_cores: int
the number of CPU cores dedicated to ANTS anatomical-to-standard
registration
registration_template: str
which template to use as a target for the apply warps ('t1' or 'epi'),
should be the same as the target used in the warp calculation
(registration)
func_type: str
'non-ica-aroma' or 'ica-aroma' - how to handle the functional time series
based on the particular demands of ICA-AROMA processed time series
num_cpus: int
the number of CPUs dedicated to each participant workflow - this is
used to determine how to parallelize the warp application step
Workflow Outputs::
outputspec.output_image : string (nifti file)
Normalized output file
Workflow Graph:
.. image::
:width: 500
Detailed Workflow Graph:
.. image::
:width: 500
Apply the functional-to-structural and structural-to-template warps to
the 4D functional time-series to warp it to template space.
Parameters
----------
"""
# if the input is a string, assume that it is resource pool key,
# if it is a tuple, assume that it is a node, outfile pair,
# otherwise, something funky is going on
if isinstance(func_key, str):
if func_key == "leaf":
input_node, input_out = strat.get_leaf_properties()
else:
input_node, input_out = strat[func_key]
elif isinstance(func_key, tuple):
input_node, input_out = func_key
if isinstance(ref_key, str):
ref_node, ref_out = strat[ref_key]
elif isinstance(ref_key, tuple):
ref_node, ref_out = func_key
# when inverse is enabled, we want to update the name of various
# nodes so that we know they were inverted
inverse_string = ''
if inverse is True:
inverse_string = '_inverse'
# make sure that resource pool has some required resources before proceeding
if 'fsl_mat_as_itk' not in strat and registration_template == 't1':
fsl_reg_2_itk = pe.Node(c3.C3dAffineTool(),
name='fsl_reg_2_itk_{0}'.format(num_strat))
fsl_reg_2_itk.inputs.itk_transform = True
fsl_reg_2_itk.inputs.fsl2ras = True
# convert the .mat from linear Func->Anat to
# ANTS format
node, out_file = strat['functional_to_anat_linear_xfm']
workflow.connect(node, out_file, fsl_reg_2_itk, 'transform_file')
node, out_file = strat['anatomical_brain']
workflow.connect(node, out_file, fsl_reg_2_itk, 'reference_file')
ref_node, ref_out = strat['mean_functional']
workflow.connect(ref_node, ref_out, fsl_reg_2_itk, 'source_file')
itk_imports = ['import os']
change_transform = pe.Node(
util.Function(input_names=['input_affine_file'],
output_names=['updated_affine_file'],
function=change_itk_transform_type,
imports=itk_imports),
name='change_transform_type_{0}'.format(num_strat))
workflow.connect(fsl_reg_2_itk, 'itk_transform', change_transform,
'input_affine_file')
strat.update_resource_pool(
{'fsl_mat_as_itk': (change_transform, 'updated_affine_file')})
strat.append_name(fsl_reg_2_itk.name)
# stack of transforms to be combined to acheive the desired transformation
num_transforms = 5
collect_transforms_key = \
'collect_transforms{0}'.format(inverse_string)
if distcor is True and func_type not in 'ica-aroma':
num_transforms = 6
collect_transforms_key = \
'collect_transforms{0}{1}'.format('_distcor',
inverse_string)
if collect_transforms_key not in strat:
if registration_template == 't1':
# handle both symmetric and asymmetric transforms
ants_transformation_dict = {
'asymmetric': {
'anatomical_to_mni_nonlinear_xfm':
'anatomical_to_mni_nonlinear_xfm',
'mni_to_anatomical_nonlinear_xfm':
'mni_to_anatomical_nonlinear_xfm',
'ants_affine_xfm': 'ants_affine_xfm',
'ants_rigid_xfm': 'ants_rigid_xfm',
'ants_initial_xfm': 'ants_initial_xfm',
'blip_warp': 'blip_warp',
'blip_warp_inverse': 'blip_warp_inverse',
'fsl_mat_as_itk': 'fsl_mat_as_itk',
},
'symmetric': {
'anatomical_to_mni_nonlinear_xfm':
'anatomical_to_symmetric_mni_nonlinear_xfm',
'mni_to_anatomical_nonlinear_xfm':
'symmetric_mni_to_anatomical_nonlinear_xfm',
'ants_affine_xfm': 'ants_symmetric_affine_xfm',
'ants_rigid_xfm': 'ants_symmetric_rigid_xfm',
'ants_initial_xfm': 'ants_symmetric_initial_xfm',
'blip_warp': 'blip_warp',
'blip_warp_inverse': 'blip_warp_inverse',
'fsl_mat_as_itk': 'fsl_mat_as_itk',
}
}
# transforms to be concatenated, the first element of each tuple is
# the resource pool key related to the resource that should be
# connected in, and the second element is the input to which it
# should be connected
if inverse is True:
if distcor is True and func_type not in 'ica-aroma':
# Field file from anatomical nonlinear registration
transforms_to_combine = [\
('mni_to_anatomical_nonlinear_xfm', 'in6'),
('ants_affine_xfm', 'in5'),
('ants_rigid_xfm', 'in4'),
('ants_initial_xfm', 'in3'),
('fsl_mat_as_itk', 'in2'),
('blip_warp_inverse', 'in1')]
else:
transforms_to_combine = [\
('mni_to_anatomical_nonlinear_xfm', 'in5'),
('ants_affine_xfm', 'in4'),
('ants_rigid_xfm', 'in3'),
('ants_initial_xfm', 'in2'),
('fsl_mat_as_itk', 'in1')]
else:
transforms_to_combine = [\
('anatomical_to_mni_nonlinear_xfm', 'in1'),
('ants_affine_xfm', 'in2'),
('ants_rigid_xfm', 'in3'),
('ants_initial_xfm', 'in4'),
('fsl_mat_as_itk', 'in5')]
if distcor is True and func_type not in 'ica-aroma':
transforms_to_combine.append(('blip_warp', 'in6'))
if registration_template == 'epi':
# handle both symmetric and asymmetric transforms
ants_transformation_dict = {
'asymmetric': {
'func_to_epi_nonlinear_xfm': 'func_to_epi_nonlinear_xfm',
'epi_to_func_nonlinear_xfm': 'epi_to_func_nonlinear_xfm',
'func_to_epi_ants_affine_xfm':
'func_to_epi_ants_affine_xfm',
'func_to_epi_ants_rigid_xfm': 'func_to_epi_ants_rigid_xfm',
'func_to_epi_ants_initial_xfm':
'func_to_epi_ants_initial_xfm',
# 'blip_warp': 'blip_warp',
# 'blip_warp_inverse': 'blip_warp_inverse',
# 'fsl_mat_as_itk': 'fsl_mat_as_itk',
},
# 'symmetric': {
# 'func_to_epi_nonlinear_xfm': 'anatomical_to_mni_nonlinear_xfm',
# 'func_to_epi_ants_affine_xfm': 'func_to_epi_ants_affine_xfm',
# 'func_to_epi_ants_rigid_xfm': 'func_to_epi_ants_rigid_xfm',
# 'func_to_epi_ants_initial_xfm': 'ants_initial_xfm',
# 'blip_warp': 'blip_warp',
# 'blip_warp_inverse': 'blip_warp_inverse',
# 'fsl_mat_as_itk': 'fsl_mat_as_itk',
# }
}
# transforms to be concatenated, the first element of each tuple is
# the resource pool key related to the resource that should be
# connected in, and the second element is the input to which it
# should be connected
if inverse is True:
if distcor is True and func_type not in 'ica-aroma':
# Field file from anatomical nonlinear registration
transforms_to_combine = [\
('epi_to_func_nonlinear_xfm', 'in4'),
('func_to_epi_ants_affine_xfm', 'in3'),
('func_to_epi_ants_rigid_xfm', 'in2'),
('func_to_epi_ants_initial_xfm', 'in1')]
else:
transforms_to_combine = [\
('epi_to_func_nonlinear_xfm', 'in4'),
('func_to_epi_ants_affine_xfm', 'in3'),
('func_to_epi_ants_rigid_xfm', 'in2'),
('func_to_epi_ants_initial_xfm', 'in1')]
else:
transforms_to_combine = [\
('func_to_epi_nonlinear_xfm', 'in1'),
('func_to_epi_ants_affine_xfm', 'in2'),
('func_to_epi_ants_rigid_xfm', 'in3'),
('func_to_epi_ants_initial_xfm', 'in4')]
# define the node
collect_transforms = pe.Node(
util.Merge(num_transforms),
name='collect_transforms_{0}_{1}_{2}_{3}'.format(
output_name, inverse_string, registration_template, num_strat))
# wire in the various transformations
for transform_key, input_port in transforms_to_combine:
try:
node, out_file = strat[ants_transformation_dict[symmetry]
[transform_key]]
except KeyError:
raise Exception(locals())
workflow.connect(node, out_file, collect_transforms, input_port)
# check transform list (if missing any init/rig/affine) and exclude Nonetype
check_transform = pe.Node(
util.Function(
input_names=['transform_list'],
output_names=['checked_transform_list', 'list_length'],
function=check_transforms),
name='check_transforms{0}_{1}_{2}_{3}'.format(
output_name, inverse_string, registration_template, num_strat))
workflow.connect(collect_transforms, 'out', check_transform,
'transform_list')
# generate inverse transform flags, which depends on the number of transforms
inverse_transform_flags = pe.Node(
util.Function(input_names=['transform_list'],
output_names=['inverse_transform_flags'],
function=generate_inverse_transform_flags),
name='inverse_transform_flags_{0}_{1}_{2}_{3}'.format(
output_name, inverse_string, registration_template, num_strat))
workflow.connect(check_transform, 'checked_transform_list',
inverse_transform_flags, 'transform_list')
# set the output
strat.update_resource_pool({
collect_transforms_key: (check_transform, 'checked_transform_list')
})
strat.append_name(check_transform.name)
strat.append_name(inverse_transform_flags.name)
#### now we add in the apply ants warps node
if int(num_cpus) > 1 and input_image_type == 3:
# parallelize time series warp application
map_node = True
if map_node:
apply_ants_warp = pe.MapNode(
interface=ants.ApplyTransforms(),
name='apply_ants_warp_{0}_mapnode_{1}_{2}_{3}'.format(
output_name, inverse_string, registration_template, num_strat),
iterfield=['input_image'],
mem_gb=10.0)
else:
apply_ants_warp = pe.Node(
interface=ants.ApplyTransforms(),
name='apply_ants_warp_{0}_{1}_{2}_{3}'.format(
output_name, inverse_string, registration_template, num_strat),
mem_gb=10.0)
apply_ants_warp.inputs.out_postfix = '_antswarp'
apply_ants_warp.interface.num_threads = int(num_ants_cores)
if inverse is True:
workflow.connect(inverse_transform_flags, 'inverse_transform_flags',
apply_ants_warp, 'invert_transform_flags')
# input_image_type:
# (0 or 1 or 2 or 3)
# Option specifying the input image type of scalar
# (default), vector, tensor, or time series.
apply_ants_warp.inputs.input_image_type = input_image_type
apply_ants_warp.inputs.dimension = 3
apply_ants_warp.inputs.interpolation = interpolation_method
node, out_file = strat[ref_key]
workflow.connect(node, out_file, apply_ants_warp, 'reference_image')
collect_node, collect_out = strat[collect_transforms_key]
workflow.connect(collect_node, collect_out, apply_ants_warp, 'transforms')
if output_name == "functional_to_standard":
# write out the composite functional to standard transforms
write_composite_xfm = pe.Node(
interface=ants.ApplyTransforms(),
name='write_composite_xfm_{0}_{1}_{2}_{3}'.format(
output_name, inverse_string, registration_template, num_strat),
mem_gb=8.0)
write_composite_xfm.inputs.print_out_composite_warp_file = True
write_composite_xfm.inputs.output_image = "func_to_standard_xfm.nii.gz"
workflow.connect(input_node, input_out, write_composite_xfm,
'input_image')
write_composite_xfm.inputs.input_image_type = input_image_type
write_composite_xfm.inputs.dimension = 3
write_composite_xfm.inputs.interpolation = interpolation_method
node, out_file = strat[ref_key]
workflow.connect(node, out_file, write_composite_xfm,
'reference_image')
collect_node, collect_out = strat[collect_transforms_key]
workflow.connect(collect_node, collect_out, write_composite_xfm,
'transforms')
# write_composite_inv_xfm = pe.Node(
# interface=ants.ApplyTransforms(),
# name='write_composite_xfm_{0}_{1}_{2}_{3}'.format(output_name,
# '_inverse', registration_template, num_strat), mem_gb=1.5)
# write_composite_inv_xfm.inputs.print_out_composite_warp_file = True
# write_composite_inv_xfm.inputs.output_image = "func_to_standard_inverse-xfm.nii.gz"
#
# workflow.connect(input_node, input_out,
# write_composite_inv_xfm, 'input_image')
#
# workflow.connect(inverse_transform_flags, 'inverse_transform_flags',
# write_composite_inv_xfm, 'invert_transform_flags')
#
#
# write_composite_inv_xfm.inputs.input_image_type = input_image_type
# write_composite_inv_xfm.inputs.dimension = 3
# write_composite_inv_xfm.inputs.interpolation = interpolation_method
#
# node, out_file = strat[ref_key]
# workflow.connect(node, out_file,
# write_composite_inv_xfm, 'reference_image')
#
# collect_node, collect_out = strat[collect_transforms_key]
# workflow.connect(collect_node, collect_out,
# write_composite_inv_xfm, 'transforms')
strat.update_resource_pool({
"functional_to_standard_xfm": (write_composite_xfm, 'output_image')
})
#"functional_to_standard_inverse-xfm": (write_composite_inv_xfm, 'output_image')
#})
# parallelize the apply warp, if multiple CPUs, and it's a time series!
if int(num_cpus) > 1 and input_image_type == 3:
node_id = f'_{output_name}_{inverse_string}_{registration_template}_{num_strat}'
chunk_imports = ['import nibabel as nb']
chunk = pe.Node(Function(input_names=['func_file', 'n_cpus'],
output_names=['TR_ranges'],
function=chunk_ts,
imports=chunk_imports),
name=f'chunk_{node_id}')
chunk.inputs.n_cpus = int(num_cpus)
workflow.connect(input_node, input_out, chunk, 'func_file')
split_imports = ['import os', 'import subprocess']
split = pe.Node(Function(input_names=['func_file', 'tr_ranges'],
output_names=['split_funcs'],
function=split_ts_chunks,
imports=split_imports),
name=f'split_{node_id}')
workflow.connect(input_node, input_out, split, 'func_file')
workflow.connect(chunk, 'TR_ranges', split, 'tr_ranges')
workflow.connect(split, 'split_funcs', apply_ants_warp, 'input_image')
func_concat = pe.Node(interface=afni_utils.TCat(),
name=f'func_concat_{node_id}')
func_concat.inputs.outputtype = 'NIFTI_GZ'
workflow.connect(apply_ants_warp, 'output_image', func_concat,
'in_files')
strat.update_resource_pool({output_name: (func_concat, 'out_file')})
else:
workflow.connect(input_node, input_out, apply_ants_warp, 'input_image')
strat.update_resource_pool(
{output_name: (apply_ants_warp, 'output_image')})
strat.append_name(apply_ants_warp.name)
return workflow
def create_sca(name_sca='sca'):
"""
Map of the correlations of the Region of Interest(Seed in native or MNI space) with the rest of brain voxels.
The map is normalized to contain Z-scores, mapped in standard space and treated with spatial smoothing.
Parameters
----------
name_sca : a string
Name of the SCA workflow
Returns
-------
sca_workflow : workflow
Seed Based Correlation Analysis Workflow
Notes
-----
`Source <https://github.com/FCP-INDI/C-PAC/blob/master/CPAC/sca/sca.py>`_
Workflow Inputs::
inputspec.rest_res_filt : string (existing nifti file)
Band passed Image with Global Signal , white matter, csf and
motion regression. Recommended bandpass filter (0.001,0.1) )
inputspec.timeseries_one_d : string (existing nifti file)
1D 3dTcorr1D compatible timeseries file. 1D file can be timeseries
from a mask or from a parcellation containing ROIs
Workflow Outputs::
outputspec.correlation_file : string (nifti file)
Correlations of the functional file and the input time series
outputspec.Z_score : string (nifti file)
Fisher Z transformed correlations of the seed
SCA Workflow Procedure:
1. Compute pearson correlation between input timeseries 1D file and input functional file
Use 3dTcorr1D to compute that. Input timeseries can be a 1D file containing parcellation ROI's
or a 3D mask
2. Compute Fisher Z score of the correlation computed in step above. If a mask is provided then a
a single Z score file is returned, otherwise z-scores for all ROIs are returned as a list of
nifti files
.. exec::
from CPAC.sca import create_sca
wf = create_sca()
wf.write_graph(
graph2use='orig',
dotfilename='./images/generated/sca.dot'
)
Workflow:
.. image:: ../../images/generated/sca.png
:width: 500
Detailed Workflow:
.. image:: ../../images/generated/sca_detailed.png
:width: 500
Examples
--------
>>> sca_w = create_sca("sca_wf")
>>> sca_w.inputs.inputspec.functional_file = '/home/data/subject/func/rest_bandpassed.nii.gz'
>>> sca_w.inputs.inputspec.timeseries_one_d = '/home/data/subject/func/ts.1D'
>>> sca_w.run() # doctest: +SKIP
"""
from CPAC.utils.utils import get_roi_num_list
sca = pe.Workflow(name=name_sca)
inputNode = pe.Node(util.IdentityInterface(fields=[
'timeseries_one_d',
'functional_file',
]),
name='inputspec')
outputNode = pe.Node(util.IdentityInterface(fields=[
'correlation_stack',
'correlation_files',
'Z_score',
]),
name='outputspec')
# 2. Compute voxel-wise correlation with Seed Timeseries
corr = pe.Node(interface=preprocess.TCorr1D(),
name='3dTCorr1D',
mem_gb=3.0)
corr.inputs.pearson = True
corr.inputs.outputtype = 'NIFTI_GZ'
sca.connect(inputNode, 'timeseries_one_d', corr, 'y_1d')
sca.connect(inputNode, 'functional_file', corr, 'xset')
# Transform the sub-bricks into volumes
try:
concat = pe.Node(interface=preprocess.TCat(), name='3dTCat')
except AttributeError:
from nipype.interfaces.afni import utils as afni_utils
concat = pe.Node(interface=afni_utils.TCat(), name='3dTCat')
concat.inputs.outputtype = 'NIFTI_GZ'
# also write out volumes as individual files
#split = pe.Node(interface=fsl.Split(), name='split_raw_volumes_sca')
#split.inputs.dimension = 't'
#split.inputs.out_base_name = 'sca_'
#get_roi_num_list = pe.Node(util.Function(input_names=['timeseries_file',
# 'prefix'],
# output_names=['roi_list'],
# function=get_roi_num_list),
# name='get_roi_num_list')
#get_roi_num_list.inputs.prefix = "sca"
#sca.connect(inputNode, 'timeseries_one_d', get_roi_num_list,
# 'timeseries_file')
#rename_rois = pe.MapNode(interface=util.Rename(), name='output_rois',
# iterfield=['in_file', 'format_string'])
#rename_rois.inputs.keep_ext = True
#sca.connect(split, 'out_files', rename_rois, 'in_file')
#sca.connect(get_roi_num_list, 'roi_list', rename_rois, 'format_string')
sca.connect(corr, 'out_file', concat, 'in_files')
#sca.connect(concat, 'out_file', split, 'in_file')
sca.connect(concat, 'out_file', outputNode, 'correlation_stack')
#sca.connect(rename_rois, 'out_file', outputNode,
# 'correlation_files')
return sca
def fsl_apply_transform_func_to_mni(workflow,
output_name,
func_key,
ref_key,
num_strat,
strat,
interpolation_method,
distcor=False,
map_node=False,
func_ts=False,
num_cpus=1):
"""
Applies previously calculated FSL registration transforms to input
images. This workflow employs the FSL applywarp tool:
https://fsl.fmrib.ox.ac.uk/fslcourse/lectures/practicals/registration/index.html
Parameters
----------
workflow : Nipype workflow object
the workflow containing the resources involved
output_name : str
what the name of the warped functional should be when written to the
resource pool
func_key : string
resource pool key correspoding to the node containing the 3D or 4D
functional file to be written into MNI space, use 'leaf' for a
leaf node
ref_key : string
resource pool key correspoding to the file path to the template brain
used for functional-to-template registration
num_strat : int
the number of strategy objects
strat : C-PAC Strategy object
a strategy with one or more resource pools
interpolation_method : str
which interpolation to use when applying the warps
distcor : boolean
indicates whether a distortion correction transformation should be
added to the transforms, this of course requires that a distortion
correction map exist in the resource pool
map_node : boolean
indicates whether a mapnode should be used, if TRUE func_key is
expected to correspond to a list of resources that should each
be written into standard space with the other parameters
func_ts : boolean
indicates whether the input image is a 4D time series
num_cpus : int
the number of CPUs dedicated to each participant workflow - this
is used to determine how to parallelize the warp application step
Returns
-------
workflow : nipype.pipeline.engine.Workflow
"""
strat_nodes = strat.get_nodes_names()
# if the input is a string, assume that it is resource pool key,
# if it is a tuple, assume that it is a node, outfile pair,
# otherwise, something funky is going on
if isinstance(func_key, str):
if func_key == "leaf":
func_node, func_file = strat.get_leaf_properties()
else:
func_node, func_file = strat[func_key]
elif isinstance(func_key, tuple):
func_node, func_file = func_key
if isinstance(ref_key, str):
ref_node, ref_out_file = strat[ref_key]
elif isinstance(ref_key, tuple):
ref_node, ref_out_file = ref_key
if int(num_cpus) > 1 and func_ts:
# parallelize time series warp application
map_node = True
if map_node == True:
# func_mni_warp
func_mni_warp = pe.MapNode(interface=fsl.ApplyWarp(),
name='func_mni_fsl_warp_{0}_{1:d}'.format(
output_name, num_strat),
iterfield=['in_file'],
mem_gb=1.5)
else:
# func_mni_warp
func_mni_warp = pe.Node(interface=fsl.ApplyWarp(),
name='func_mni_fsl_warp_{0}_{1:d}'.format(
output_name, num_strat))
func_mni_warp.inputs.interp = interpolation_method
# parallelize the apply warp, if multiple CPUs, and it's a time series!
if int(num_cpus) > 1 and func_ts:
node_id = '{0}_{1:d}'.format(output_name, num_strat)
chunk_imports = ['import nibabel as nb']
chunk = pe.Node(Function(input_names=['func_file', 'n_cpus'],
output_names=['TR_ranges'],
function=chunk_ts,
imports=chunk_imports),
name=f'chunk_{node_id}')
chunk.inputs.n_cpus = int(num_cpus)
workflow.connect(func_node, func_file, chunk, 'func_file')
split_imports = ['import os', 'import subprocess']
split = pe.Node(Function(input_names=['func_file', 'tr_ranges'],
output_names=['split_funcs'],
function=split_ts_chunks,
imports=split_imports),
name=f'split_{node_id}')
workflow.connect(func_node, func_file, split, 'func_file')
workflow.connect(chunk, 'TR_ranges', split, 'tr_ranges')
workflow.connect(split, 'split_funcs', func_mni_warp, 'in_file')
func_concat = pe.Node(interface=afni_utils.TCat(),
name=f'func_concat{node_id}')
func_concat.inputs.outputtype = 'NIFTI_GZ'
workflow.connect(func_mni_warp, 'out_file', func_concat, 'in_files')
strat.update_resource_pool({output_name: (func_concat, 'out_file')})
else:
workflow.connect(func_node, func_file, func_mni_warp, 'in_file')
strat.update_resource_pool({output_name: (func_mni_warp, 'out_file')})
workflow.connect(ref_node, ref_out_file, func_mni_warp, 'ref_file')
if 'anat_mni_fnirt_register' in strat_nodes:
node, out_file = strat['functional_to_anat_linear_xfm']
workflow.connect(node, out_file, func_mni_warp, 'premat')
node, out_file = strat['anatomical_to_mni_nonlinear_xfm']
workflow.connect(node, out_file, func_mni_warp, 'field_file')
if output_name == "functional_to_standard":
write_composite_xfm = pe.Node(interface=fsl.ConvertWarp(),
name='combine_fsl_warps_{0}_{1:d}'.format(output_name,\
num_strat))
workflow.connect(ref_node, ref_out_file, write_composite_xfm,
'reference')
node, out_file = strat['functional_to_anat_linear_xfm']
workflow.connect(node, out_file, write_composite_xfm, 'premat')
node, out_file = strat['anatomical_to_mni_nonlinear_xfm']
workflow.connect(node, out_file, write_composite_xfm, 'warp1')
strat.update_resource_pool({
"functional_to_standard_xfm": (write_composite_xfm, 'out_file')
})
elif 'anat_mni_flirt_register' in strat_nodes:
if 'functional_to_mni_linear_xfm' not in strat:
combine_transforms = pe.Node(interface=fsl.ConvertXFM(),
name='combine_fsl_xforms_{0}_{1:d}'.format(output_name,\
num_strat))
combine_transforms.inputs.concat_xfm = True
node, out_file = strat['anatomical_to_mni_linear_xfm']
workflow.connect(node, out_file, combine_transforms, 'in_file2')
node, out_file = strat['functional_to_anat_linear_xfm']
workflow.connect(node, out_file, combine_transforms, 'in_file')
strat.update_resource_pool({
'functional_to_mni_linear_xfm':
(combine_transforms, 'out_file')
})
strat.append_name(combine_transforms.name)
combine_transforms, outfile = strat['functional_to_mni_linear_xfm']
workflow.connect(combine_transforms, outfile, func_mni_warp, 'premat')
else:
raise ValueError('Could not find flirt or fnirt registration in nodes')
strat.append_name(func_mni_warp.name)
return workflow
