def init_bold_surf_wf(mem_gb,
surface_spaces,
medial_surface_nan,
name='bold_surf_wf'):
"""
Sample functional images to FreeSurfer surfaces.
For each vertex, the cortical ribbon is sampled at six points (spaced 20% of thickness apart)
and averaged.
Outputs are in GIFTI format.
Workflow Graph
.. workflow::
:graph2use: colored
:simple_form: yes
from fmriprep_rodents.workflows.bold import init_bold_surf_wf
wf = init_bold_surf_wf(mem_gb=0.1,
surface_spaces=['fsnative', 'fsaverage5'],
medial_surface_nan=False)
Parameters
----------
surface_spaces : :obj:`list`
List of FreeSurfer surface-spaces (either ``fsaverage{3,4,5,6,}`` or ``fsnative``)
the functional images are to be resampled to.
For ``fsnative``, images will be resampled to the individual subject's
native surface.
medial_surface_nan : :obj:`bool`
Replace medial wall values with NaNs on functional GIFTI files
Inputs
------
source_file
Motion-corrected BOLD series in T1 space
t1w_preproc
Bias-corrected structural template image
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
t1w2fsnative_xfm
LTA-style affine matrix translating from T1w to FreeSurfer-conformed subject space
Outputs
-------
surfaces
BOLD series, resampled to FreeSurfer surfaces
"""
from nipype.interfaces.io import FreeSurferSource
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.surf import GiftiSetAnatomicalStructure
workflow = Workflow(name=name)
workflow.__desc__ = """\
The BOLD time-series were resampled onto the following surfaces
(FreeSurfer reconstruction nomenclature):
{out_spaces}.
""".format(out_spaces=', '.join(['*%s*' % s for s in surface_spaces]))
inputnode = pe.Node(niu.IdentityInterface(fields=[
'source_file', 'subject_id', 'subjects_dir', 't1w2fsnative_xfm'
]),
name='inputnode')
itersource = pe.Node(niu.IdentityInterface(fields=['target']),
name='itersource')
itersource.iterables = [('target', surface_spaces)]
get_fsnative = pe.Node(FreeSurferSource(),
name='get_fsnative',
run_without_submitting=True)
def select_target(subject_id, space):
"""Get the target subject ID, given a source subject ID and a target space."""
return subject_id if space == 'fsnative' else space
targets = pe.Node(niu.Function(function=select_target),
name='targets',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
# Rename the source file to the output space to simplify naming later
rename_src = pe.Node(niu.Rename(format_string='%(subject)s',
keep_ext=True),
name='rename_src',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
itk2lta = pe.Node(niu.Function(function=_itk2lta),
name="itk2lta",
run_without_submitting=True)
sampler = pe.MapNode(fs.SampleToSurface(
cortex_mask=True,
interp_method='trilinear',
out_type='gii',
override_reg_subj=True,
sampling_method='average',
sampling_range=(0, 1, 0.2),
sampling_units='frac',
),
iterfield=['hemi'],
name='sampler',
mem_gb=mem_gb * 3)
sampler.inputs.hemi = ['lh', 'rh']
update_metadata = pe.MapNode(GiftiSetAnatomicalStructure(),
iterfield=['in_file'],
name='update_metadata',
mem_gb=DEFAULT_MEMORY_MIN_GB)
outputnode = pe.JoinNode(
niu.IdentityInterface(fields=['surfaces', 'target']),
joinsource='itersource',
name='outputnode')
workflow.connect([
(inputnode, get_fsnative, [('subject_id', 'subject_id'),
('subjects_dir', 'subjects_dir')]),
(inputnode, targets, [('subject_id', 'subject_id')]),
(inputnode, rename_src, [('source_file', 'in_file')]),
(inputnode, itk2lta, [('source_file', 'src_file'),
('t1w2fsnative_xfm', 'in_file')]),
(get_fsnative, itk2lta, [('T1', 'dst_file')]),
(inputnode, sampler, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(itersource, targets, [('target', 'space')]),
(itersource, rename_src, [('target', 'subject')]),
(itk2lta, sampler, [('out', 'reg_file')]),
(targets, sampler, [('out', 'target_subject')]),
(rename_src, sampler, [('out_file', 'source_file')]),
(update_metadata, outputnode, [('out_file', 'surfaces')]),
(itersource, outputnode, [('target', 'target')]),
])
if not medial_surface_nan:
workflow.connect(sampler, 'out_file', update_metadata, 'in_file')
return workflow
from niworkflows.interfaces.freesurfer import MedialNaNs
# Refine if medial vertices should be NaNs
medial_nans = pe.MapNode(MedialNaNs(),
iterfield=['in_file'],
name='medial_nans',
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, medial_nans, [('subjects_dir', 'subjects_dir')]),
(sampler, medial_nans, [('out_file', 'in_file')]),
(medial_nans, update_metadata, [('out_file', 'in_file')]),
])
return workflow
def init_bold_surf_wf(mem_gb,
output_spaces,
medial_surface_nan,
name='bold_surf_wf'):
"""
This workflow samples functional images to FreeSurfer surfaces
For each vertex, the cortical ribbon is sampled at six points (spaced 20% of thickness apart)
and averaged.
Outputs are in GIFTI format.
.. workflow::
:graph2use: colored
:simple_form: yes
from fmriprep.workflows.bold import init_bold_surf_wf
wf = init_bold_surf_wf(mem_gb=0.1,
output_spaces=['T1w', 'fsnative',
'template', 'fsaverage5'],
medial_surface_nan=False)
**Parameters**
output_spaces : list
List of output spaces functional images are to be resampled to
Target spaces beginning with ``fs`` will be selected for resampling,
such as ``fsaverage`` or related template spaces
If the list contains ``fsnative``, images will be resampled to the
individual subject's native surface
medial_surface_nan : bool
Replace medial wall values with NaNs on functional GIFTI files
**Inputs**
source_file
Motion-corrected BOLD series in T1 space
t1_preproc
Bias-corrected structural template image
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
t1_2_fsnative_forward_transform
LTA-style affine matrix translating from T1w to FreeSurfer-conformed subject space
**Outputs**
surfaces
BOLD series, resampled to FreeSurfer surfaces
"""
# Ensure volumetric spaces do not sneak into this workflow
spaces = [space for space in output_spaces if space.startswith('fs')]
workflow = Workflow(name=name)
if spaces:
workflow.__desc__ = """\
The BOLD time-series, were resampled to surfaces on the following
spaces: {out_spaces}.
""".format(out_spaces=', '.join(['*%s*' % s for s in spaces]))
inputnode = pe.Node(niu.IdentityInterface(fields=[
'source_file', 't1_preproc', 'subject_id', 'subjects_dir',
't1_2_fsnative_forward_transform'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['surfaces']),
name='outputnode')
def select_target(subject_id, space):
""" Given a source subject ID and a target space, get the target subject ID """
return subject_id if space == 'fsnative' else space
targets = pe.MapNode(niu.Function(function=select_target),
iterfield=['space'],
name='targets',
mem_gb=DEFAULT_MEMORY_MIN_GB)
targets.inputs.space = spaces
# Rename the source file to the output space to simplify naming later
rename_src = pe.MapNode(niu.Rename(format_string='%(subject)s',
keep_ext=True),
iterfield='subject',
name='rename_src',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
rename_src.inputs.subject = spaces
resampling_xfm = pe.Node(LTAConvert(in_lta='identity.nofile',
out_lta=True),
name='resampling_xfm')
set_xfm_source = pe.Node(ConcatenateLTA(out_type='RAS2RAS'),
name='set_xfm_source')
sampler = pe.MapNode(fs.SampleToSurface(sampling_method='average',
sampling_range=(0, 1, 0.2),
sampling_units='frac',
interp_method='trilinear',
cortex_mask=True,
override_reg_subj=True,
out_type='gii'),
iterfield=['source_file', 'target_subject'],
iterables=('hemi', ['lh', 'rh']),
name='sampler',
mem_gb=mem_gb * 3)
medial_nans = pe.MapNode(MedialNaNs(),
iterfield=['in_file', 'target_subject'],
name='medial_nans',
mem_gb=DEFAULT_MEMORY_MIN_GB)
merger = pe.JoinNode(niu.Merge(1, ravel_inputs=True),
name='merger',
joinsource='sampler',
joinfield=['in1'],
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
update_metadata = pe.MapNode(GiftiSetAnatomicalStructure(),
iterfield='in_file',
name='update_metadata',
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, targets, [('subject_id', 'subject_id')]),
(inputnode, rename_src, [('source_file', 'in_file')]),
(inputnode, resampling_xfm, [('source_file', 'source_file'),
('t1_preproc', 'target_file')]),
(inputnode, set_xfm_source, [('t1_2_fsnative_forward_transform',
'in_lta2')]),
(resampling_xfm, set_xfm_source, [('out_lta', 'in_lta1')]),
(inputnode, sampler, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(set_xfm_source, sampler, [('out_file', 'reg_file')]),
(targets, sampler, [('out', 'target_subject')]),
(rename_src, sampler, [('out_file', 'source_file')]),
(merger, update_metadata, [('out', 'in_file')]),
(update_metadata, outputnode, [('out_file', 'surfaces')]),
])
if medial_surface_nan:
workflow.connect([
(inputnode, medial_nans, [('subjects_dir', 'subjects_dir')]),
(sampler, medial_nans, [('out_file', 'in_file')]),
(targets, medial_nans, [('out', 'target_subject')]),
(medial_nans, merger, [('out_file', 'in1')]),
])
else:
workflow.connect(sampler, 'out_file', merger, 'in1')
return workflow
def init_bold_surf_wf(mem_gb,
output_spaces,
medial_surface_nan,
fslr_density=None,
name='bold_surf_wf'):
"""
Sample functional images to FreeSurfer surfaces.
For each vertex, the cortical ribbon is sampled at six points (spaced 20% of thickness apart)
and averaged.
Outputs are in GIFTI format.
Workflow Graph
.. workflow::
:graph2use: colored
:simple_form: yes
from fmriprep.workflows.bold import init_bold_surf_wf
wf = init_bold_surf_wf(mem_gb=0.1,
output_spaces=['T1w', 'fsnative',
'template', 'fsaverage5'],
medial_surface_nan=False)
Parameters
----------
output_spaces : list
List of output spaces functional images are to be resampled to
Target spaces beginning with ``fs`` will be selected for resampling,
such as ``fsaverage`` or related template spaces
If the list contains ``fsnative``, images will be resampled to the
individual subject's native surface
If the list contains ``fsLR``, images will be resampled twice;
first to ``fsaverage`` and then to ``fsLR``.
medial_surface_nan : bool
Replace medial wall values with NaNs on functional GIFTI files
fslr_density : str, optional
Density of fsLR surface (32k or 59k)
Inputs
------
source_file
Motion-corrected BOLD series in T1 space
t1w_preproc
Bias-corrected structural template image
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
t1w2fsnative_xfm
LTA-style affine matrix translating from T1w to FreeSurfer-conformed subject space
Outputs
-------
surfaces
BOLD series, resampled to FreeSurfer surfaces
"""
# Ensure volumetric spaces do not sneak into this workflow
spaces = [space for space in output_spaces if space.startswith('fs')]
workflow = Workflow(name=name)
if spaces:
workflow.__desc__ = """\
The BOLD time-series, were resampled to surfaces on the following
spaces: {out_spaces}.
""".format(out_spaces=', '.join(['*%s*' % s for s in spaces]))
inputnode = pe.Node(niu.IdentityInterface(fields=[
'source_file', 't1w_preproc', 'subject_id', 'subjects_dir',
't1w2fsnative_xfm'
]),
name='inputnode')
to_fslr = False
if 'fsLR' in output_spaces:
to_fslr = 'fsaverage' in output_spaces and fslr_density
spaces.pop(spaces.index('fsLR'))
outputnode = pe.Node(niu.IdentityInterface(fields=['surfaces']),
name='outputnode')
def select_target(subject_id, space):
"""Get the target subject ID, given a source subject ID and a target space."""
return subject_id if space == 'fsnative' else space
targets = pe.MapNode(niu.Function(function=select_target),
iterfield=['space'],
name='targets',
mem_gb=DEFAULT_MEMORY_MIN_GB)
targets.inputs.space = spaces
# Rename the source file to the output space to simplify naming later
rename_src = pe.MapNode(niu.Rename(format_string='%(subject)s',
keep_ext=True),
iterfield='subject',
name='rename_src',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
rename_src.inputs.subject = spaces
resampling_xfm = pe.Node(LTAConvert(in_lta='identity.nofile',
out_lta=True),
name='resampling_xfm')
set_xfm_source = pe.Node(ConcatenateLTA(out_type='RAS2RAS'),
name='set_xfm_source')
sampler = pe.MapNode(fs.SampleToSurface(sampling_method='average',
sampling_range=(0, 1, 0.2),
sampling_units='frac',
interp_method='trilinear',
cortex_mask=True,
override_reg_subj=True,
out_type='gii'),
iterfield=['source_file', 'target_subject'],
iterables=('hemi', ['lh', 'rh']),
name='sampler',
mem_gb=mem_gb * 3)
if to_fslr:
filter_fsavg = pe.Node(niu.Function(
function=_select_fsaverage_hemi,
output_names=['fsaverage_bold', 'hemi']),
name='filter_fsavg',
mem_gb=DEFAULT_MEMORY_MIN_GB,
run_without_submitting=True)
rename_fslr = pe.Node(niu.Rename(format_string="%(hemi)s.fsLR",
keep_ext=True,
parse_string=r'^(?P<hemi>\w+)'),
name='rename_fslr',
mem_gb=DEFAULT_MEMORY_MIN_GB,
run_without_submitting=True)
fetch_fslr_tpls = pe.Node(niu.Function(function=_fetch_fslr_templates,
output_names=[
'fsaverage_sphere',
'fslr_sphere',
'fsaverage_midthick',
'fslr_midthick'
]),
name='fetch_fslr_tpls',
mem_gb=DEFAULT_MEMORY_MIN_GB,
overwrite=True)
fetch_fslr_tpls.inputs.den = fslr_density
resample_fslr = pe.Node(wb.MetricResample(method='ADAP_BARY_AREA',
area_metrics=True),
name='resample_fslr')
merge_fslr = pe.Node(niu.Merge(2),
name='merge_fslr',
mem_gb=DEFAULT_MEMORY_MIN_GB,
run_without_submitting=True)
def _basename(in_file):
import os
return os.path.basename(in_file)
workflow.connect([
(sampler, filter_fsavg, [('out_file', 'in_files')]),
(filter_fsavg, fetch_fslr_tpls, [('hemi', 'hemi')]),
(filter_fsavg, rename_fslr, [('fsaverage_bold', 'in_file')]),
(rename_fslr, resample_fslr, [('out_file', 'in_file')]),
(rename_fslr, resample_fslr, [(('out_file', _basename), 'out_file')
]),
(fetch_fslr_tpls, resample_fslr,
[('fsaverage_sphere', 'current_sphere'),
('fslr_sphere', 'new_sphere'),
('fsaverage_midthick', 'current_area'),
('fslr_midthick', 'new_area')]),
(sampler, merge_fslr, [('out_file', 'in1')]),
(resample_fslr, merge_fslr, [('out_file', 'in2')]),
])
merger = pe.JoinNode(niu.Merge(1, ravel_inputs=True),
name='merger',
joinsource='sampler',
joinfield=['in1'],
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
if medial_surface_nan:
medial_nans = pe.MapNode(MedialNaNs(),
iterfield=['in_file'],
name='medial_nans',
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, medial_nans, [('subjects_dir', 'subjects_dir')]),
(medial_nans, merger, [('out_file', 'in1')]),
])
update_metadata = pe.MapNode(GiftiSetAnatomicalStructure(),
iterfield='in_file',
name='update_metadata',
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, targets, [('subject_id', 'subject_id')]),
(inputnode, rename_src, [('source_file', 'in_file')]),
(inputnode, resampling_xfm, [('source_file', 'source_file'),
('t1w_preproc', 'target_file')]),
(inputnode, set_xfm_source, [('t1w2fsnative_xfm', 'in_lta2')]),
(resampling_xfm, set_xfm_source, [('out_lta', 'in_lta1')]),
(inputnode, sampler, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(set_xfm_source, sampler, [('out_file', 'reg_file')]),
(targets, sampler, [('out', 'target_subject')]),
(rename_src, sampler, [('out_file', 'source_file')]),
(merger, update_metadata, [('out', 'in_file')]),
(update_metadata, outputnode, [('out_file', 'surfaces')]),
])
if to_fslr and medial_surface_nan:
medial_nans.inputs.density = fslr_density
workflow.connect(merge_fslr, 'out', medial_nans, 'in_file')
elif to_fslr:
workflow.connect(merge_fslr, 'out', merger, 'in1')
elif medial_surface_nan:
workflow.connect(sampler, 'out_file', medial_nans, 'in_file')
else:
workflow.connect(sampler, 'out_file', merger, 'in1')
return workflow
