def test_masking(input_fname, expected_fname):
basename = Path(input_fname[0]).name
dsname = Path(expected_fname).parent.name
# Reconstruct base_fname from above
reports_dir = Path(os.getenv("FMRIPREP_REGRESSION_REPORTS", ""))
newpath = reports_dir / dsname
name = basename.rstrip("_bold.nii.gz").replace("-", "_")
bold_reference_wf = init_bold_reference_wf(omp_nthreads=1,
name=name,
multiecho=len(input_fname) > 1)
bold_reference_wf.inputs.inputnode.bold_file = input_fname[0] if len(input_fname) == 1 \
else input_fname
base_dir = os.getenv("CACHED_WORK_DIRECTORY")
if base_dir:
base_dir = Path(base_dir) / dsname
base_dir.mkdir(parents=True, exist_ok=True)
bold_reference_wf.base_dir = str(base_dir)
out_fname = fname_presuffix(Path(expected_fname).name,
suffix=".svg",
use_ext=False,
newpath=str(newpath))
newpath.mkdir(parents=True, exist_ok=True)
mask_diff_plot = pe.Node(ROIsPlot(colors=["limegreen"], levels=[0.5]),
name="mask_diff_plot")
mask_diff_plot.always_run = True
mask_diff_plot.inputs.in_mask = expected_fname
mask_diff_plot.inputs.out_report = out_fname
outputnode = bold_reference_wf.get_node("outputnode")
bold_reference_wf.connect([(
outputnode,
mask_diff_plot,
[("ref_image", "in_file"), ("bold_mask", "in_rois")],
)])
res = bold_reference_wf.run(plugin="MultiProc")
combine_masks = [
node for node in res.nodes if node.name.endswith("combine_masks")
][0]
overlap = symmetric_overlap(expected_fname,
combine_masks.result.outputs.out_file)
mask_dir = reports_dir / "fmriprep_bold_mask" / dsname
mask_dir.mkdir(parents=True, exist_ok=True)
copyfile(
combine_masks.result.outputs.out_file,
str(mask_dir / Path(expected_fname).name),
copy=True,
)
assert overlap > 0.95, input_fname
def test_masking(input_fname, expected_fname):
basename = Path(input_fname).name
dsname = Path(expected_fname).parent.name
# Reconstruct base_fname from above
reports_dir = Path(os.getenv('FMRIPREP_REGRESSION_REPORTS', ''))
newpath = reports_dir / dsname
name = basename.rstrip('_bold.nii.gz').replace('-', '_')
bold_reference_wf = init_bold_reference_wf(omp_nthreads=1, name=name)
bold_reference_wf.inputs.inputnode.bold_file = input_fname
base_dir = os.getenv('CACHED_WORK_DIRECTORY')
if base_dir:
base_dir = Path(base_dir) / dsname
base_dir.mkdir(parents=True, exist_ok=True)
bold_reference_wf.base_dir = str(base_dir)
out_fname = fname_presuffix(basename,
suffix='_masks.svg',
use_ext=False,
newpath=str(newpath))
newpath.mkdir(parents=True, exist_ok=True)
mask_diff_plot = pe.Node(ROIsPlot(colors=['limegreen'], levels=[0.5]),
name='mask_diff_plot')
mask_diff_plot.always_run = True
mask_diff_plot.inputs.in_mask = expected_fname
mask_diff_plot.inputs.out_report = out_fname
outputnode = bold_reference_wf.get_node('outputnode')
bold_reference_wf.connect([(outputnode, mask_diff_plot,
[('ref_image', 'in_file'),
('bold_mask', 'in_rois')])])
res = bold_reference_wf.run(plugin='MultiProc')
combine_masks = [
node for node in res.nodes if node.name.endswith('combine_masks')
][0]
overlap = symmetric_overlap(expected_fname,
combine_masks.result.outputs.out_file)
mask_dir = reports_dir / 'fmriprep_bold_mask' / dsname
mask_dir.mkdir(parents=True, exist_ok=True)
copyfile(combine_masks.result.outputs.out_file,
fname_presuffix(basename,
suffix='_mask',
use_ext=True,
newpath=str(mask_dir)),
copy=True)
assert overlap > 0.95, input_fname
def test_ROIsPlot2(tmp_path):
""" the BET report capable test """
import nibabel as nb
import numpy as np
im = nb.load(
str(
get_template(
"OASIS30ANTs",
resolution=1,
desc="4",
suffix="dseg",
extension=[".nii", ".nii.gz"],
)))
lookup = np.zeros(5, dtype=int)
lookup[1] = 1
lookup[2] = 4
lookup[3] = 2
lookup[4] = 3
newdata = lookup[np.round(im.get_fdata()).astype(int)]
hdr = im.header.copy()
hdr.set_data_dtype("int16")
hdr["scl_slope"] = 1
hdr["scl_inter"] = 0
out_files = []
for i in range(1, 5):
seg = np.zeros_like(newdata, dtype="uint8")
seg[(newdata > 0) & (newdata <= i)] = 1
out_file = str(tmp_path / ("segments%02d.nii.gz" % i))
nb.Nifti1Image(seg, im.affine, hdr).to_filename(out_file)
out_files.append(out_file)
roi_rpt = ROIsPlot(
generate_report=True,
in_file=str(
get_template("OASIS30ANTs", resolution=1, desc=None,
suffix="T1w")),
in_mask=str(
get_template("OASIS30ANTs",
resolution=1,
desc="brain",
suffix="mask")),
in_rois=out_files,
colors=["gold", "lightblue", "b", "g"],
)
_smoke_test_report(roi_rpt, "testROIsPlot2.svg")
def test_ROIsPlot2(tmp_path):
""" the BET report capable test """
import nibabel as nb
import numpy as np
im = nb.load(
str(
get_template('OASIS30ANTs',
resolution=1,
desc='4',
suffix='dseg',
extension=['.nii', '.nii.gz'])))
lookup = np.zeros(5, dtype=int)
lookup[1] = 1
lookup[2] = 4
lookup[3] = 2
lookup[4] = 3
newdata = lookup[np.round(im.get_data()).astype(int)]
hdr = im.header.copy()
hdr.set_data_dtype('int16')
hdr['scl_slope'] = 1
hdr['scl_inter'] = 0
out_files = []
for i in range(1, 5):
seg = np.zeros_like(newdata, dtype='uint8')
seg[(newdata > 0) & (newdata <= i)] = 1
out_file = str(tmp_path / ('segments%02d.nii.gz' % i))
nb.Nifti1Image(seg, im.affine, hdr).to_filename(out_file)
out_files.append(out_file)
roi_rpt = ROIsPlot(generate_report=True,
in_file=str(
get_template('OASIS30ANTs',
resolution=1,
desc=None,
suffix='T1w')),
in_mask=str(
get_template('OASIS30ANTs',
resolution=1,
desc='brain',
suffix='mask')),
in_rois=out_files,
colors=['gold', 'lightblue', 'b', 'g'])
_smoke_test_report(roi_rpt, 'testROIsPlot2.svg')
def test_ROIsPlot(tmp_path):
""" the BET report capable test """
import nibabel as nb
import numpy as np
im = nb.load(
str(
get_template(
"OASIS30ANTs",
resolution=1,
desc="4",
suffix="dseg",
extension=[".nii", ".nii.gz"],
)))
lookup = np.zeros(5, dtype=int)
lookup[1] = 1
lookup[2] = 4
lookup[3] = 2
lookup[4] = 3
newdata = lookup[np.round(im.get_fdata()).astype(int)]
hdr = im.header.copy()
hdr.set_data_dtype("int16")
hdr["scl_slope"] = 1
hdr["scl_inter"] = 0
out_file = str(tmp_path / "segments.nii.gz")
nb.Nifti1Image(newdata, im.affine, hdr).to_filename(out_file)
roi_rpt = ROIsPlot(
generate_report=True,
in_file=str(
get_template("OASIS30ANTs", resolution=1, desc=None,
suffix="T1w")),
in_mask=str(
get_template("OASIS30ANTs",
resolution=1,
desc="brain",
suffix="mask")),
in_rois=[out_file],
levels=[1.5, 2.5, 3.5],
colors=["gold", "magenta", "b"],
)
_smoke_test_report(roi_rpt, "testROIsPlot.svg")
def test_ROIsPlot(tmp_path):
""" the BET report capable test """
import nibabel as nb
import numpy as np
im = nb.load(
str(
get_template('OASIS30ANTs',
resolution=1,
desc='4',
suffix='dseg',
extension=['.nii', '.nii.gz'])))
lookup = np.zeros(5, dtype=int)
lookup[1] = 1
lookup[2] = 4
lookup[3] = 2
lookup[4] = 3
newdata = lookup[np.round(im.get_data()).astype(int)]
hdr = im.header.copy()
hdr.set_data_dtype('int16')
hdr['scl_slope'] = 1
hdr['scl_inter'] = 0
out_file = str(tmp_path / 'segments.nii.gz')
nb.Nifti1Image(newdata, im.affine, hdr).to_filename(out_file)
roi_rpt = ROIsPlot(
generate_report=True,
in_file=str(
get_template('OASIS30ANTs', resolution=1, desc=None,
suffix='T1w')),
in_mask=str(
get_template('OASIS30ANTs',
resolution=1,
desc='brain',
suffix='mask')),
in_rois=[out_file],
levels=[1.5, 2.5, 3.5],
colors=['gold', 'magenta', 'b'],
)
_smoke_test_report(roi_rpt, 'testROIsPlot.svg')
def test_ROIsPlot(oasis_dir):
""" the BET report capable test """
import nibabel as nb
import numpy as np
labels = nb.load(
os.path.join(oasis_dir, 'T_template0_glm_4labelsJointFusion.nii.gz'))
data = labels.get_data()
out_files = []
ldata = np.zeros_like(data)
for i, l in enumerate([1, 3, 4, 2]):
ldata[data == l] = 1
out_files.append(os.path.abspath('label%d.nii.gz' % i))
lfile = nb.Nifti1Image(ldata, labels.affine, labels.header)
lfile.to_filename(out_files[-1])
roi_rpt = ROIsPlot(generate_report=True,
in_file=os.path.join(oasis_dir, 'T_template0.nii.gz'),
in_mask=out_files[-1],
in_rois=out_files[:-1],
colors=['g', 'y'])
_smoke_test_report(roi_rpt, 'testROIsPlot.svg')
def test_masking(input_fname, expected_fname):
bold_reference_wf = init_bold_reference_wf(omp_nthreads=1)
bold_reference_wf.inputs.inputnode.bold_file = input_fname
# Reconstruct base_fname from above
dirname, basename = os.path.split(input_fname)
dsname = os.path.basename(dirname)
reports_dir = Path(os.getenv('FMRIPREP_REGRESSION_REPORTS', ''))
newpath = reports_dir / dsname
out_fname = fname_presuffix(basename, suffix='_masks.svg', use_ext=False,
newpath=str(newpath))
newpath.mkdir(parents=True, exist_ok=True)
mask_diff_plot = pe.Node(ROIsPlot(), name='mask_diff_plot')
mask_diff_plot.inputs.in_mask = expected_fname
mask_diff_plot.inputs.out_report = out_fname
outputnode = bold_reference_wf.get_node('outputnode')
bold_reference_wf.connect([
(outputnode, mask_diff_plot, [('ref_image', 'in_file'),
('bold_mask', 'in_rois')])
])
res = bold_reference_wf.run(plugin='MultiProc')
combine_masks = [node for node in res.nodes if node.name.endswith('combine_masks')][0]
overlap = symmetric_overlap(expected_fname,
combine_masks.result.outputs.out_file)
mask_dir = reports_dir / 'fmriprep_bold_mask' / dsname
mask_dir.mkdir(parents=True, exist_ok=True)
copyfile(combine_masks.result.outputs.out_file,
fname_presuffix(basename, suffix='_mask',
use_ext=True, newpath=str(mask_dir)),
copy=True)
assert overlap > 0.95, input_fname
def init_bold_confs_wf(
mem_gb,
metadata,
regressors_all_comps,
regressors_dvars_th,
regressors_fd_th,
name="bold_confs_wf",
):
"""
Build a workflow to generate and write out confounding signals.
This workflow calculates confounds for a BOLD series, and aggregates them
into a :abbr:`TSV (tab-separated value)` file, for use as nuisance
regressors in a :abbr:`GLM (general linear model)`.
The following confounds are calculated, with column headings in parentheses:
#. Region-wise average signal (``csf``, ``white_matter``, ``global_signal``)
#. DVARS - original and standardized variants (``dvars``, ``std_dvars``)
#. Framewise displacement, based on head-motion parameters
(``framewise_displacement``)
#. Temporal CompCor (``t_comp_cor_XX``)
#. Anatomical CompCor (``a_comp_cor_XX``)
#. Cosine basis set for high-pass filtering w/ 0.008 Hz cut-off
(``cosine_XX``)
#. Non-steady-state volumes (``non_steady_state_XX``)
#. Estimated head-motion parameters, in mm and rad
(``trans_x``, ``trans_y``, ``trans_z``, ``rot_x``, ``rot_y``, ``rot_z``)
Prior to estimating aCompCor and tCompCor, non-steady-state volumes are
censored and high-pass filtered using a :abbr:`DCT (discrete cosine
transform)` basis.
The cosine basis, as well as one regressor per censored volume, are included
for convenience.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold.confounds import init_bold_confs_wf
wf = init_bold_confs_wf(
mem_gb=1,
metadata={},
regressors_all_comps=False,
regressors_dvars_th=1.5,
regressors_fd_th=0.5,
)
Parameters
----------
mem_gb : :obj:`float`
Size of BOLD file in GB - please note that this size
should be calculated after resamplings that may extend
the FoV
metadata : :obj:`dict`
BIDS metadata for BOLD file
name : :obj:`str`
Name of workflow (default: ``bold_confs_wf``)
regressors_all_comps : :obj:`bool`
Indicates whether CompCor decompositions should return all
components instead of the minimal number of components necessary
to explain 50 percent of the variance in the decomposition mask.
regressors_dvars_th : :obj:`float`
Criterion for flagging DVARS outliers
regressors_fd_th : :obj:`float`
Criterion for flagging framewise displacement outliers
Inputs
------
bold
BOLD image, after the prescribed corrections (STC, HMC and SDC)
when available.
bold_mask
BOLD series mask
movpar_file
SPM-formatted motion parameters file
rmsd_file
Framewise displacement as measured by ``fsl_motion_outliers``.
skip_vols
number of non steady state volumes
t1w_mask
Mask of the skull-stripped template image
t1w_tpms
List of tissue probability maps in T1w space
t1_bold_xform
Affine matrix that maps the T1w space into alignment with
the native BOLD space
Outputs
-------
confounds_file
TSV of all aggregated confounds
rois_report
Reportlet visualizing white-matter/CSF mask used for aCompCor,
the ROI for tCompCor and the BOLD brain mask.
confounds_metadata
Confounds metadata dictionary.
"""
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.confounds import ExpandModel, SpikeRegressors
from niworkflows.interfaces.fixes import FixHeaderApplyTransforms as ApplyTransforms
from niworkflows.interfaces.images import SignalExtraction
from niworkflows.interfaces.masks import ROIsPlot
from niworkflows.interfaces.patches import (
RobustACompCor as ACompCor,
RobustTCompCor as TCompCor,
)
from niworkflows.interfaces.plotting import (CompCorVariancePlot,
ConfoundsCorrelationPlot)
from niworkflows.interfaces.utils import (TPM2ROI, AddTPMs, AddTSVHeader,
TSV2JSON, DictMerge)
workflow = Workflow(name=name)
workflow.__desc__ = """\
Several confounding time-series were calculated based on the
*preprocessed BOLD*: framewise displacement (FD), DVARS and
three region-wise global signals.
FD was computed using two formulations following Power (absolute sum of
relative motions, @power_fd_dvars) and Jenkinson (relative root mean square
displacement between affines, @mcflirt).
FD and DVARS are calculated for each functional run, both using their
implementations in *Nipype* [following the definitions by @power_fd_dvars].
The three global signals are extracted within the CSF, the WM, and
the whole-brain masks.
Additionally, a set of physiological regressors were extracted to
allow for component-based noise correction [*CompCor*, @compcor].
Principal components are estimated after high-pass filtering the
*preprocessed BOLD* time-series (using a discrete cosine filter with
128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
and anatomical (aCompCor).
tCompCor components are then calculated from the top 5% variable
voxels within a mask covering the subcortical regions.
This subcortical mask is obtained by heavily eroding the brain mask,
which ensures it does not include cortical GM regions.
For aCompCor, components are calculated within the intersection of
the aforementioned mask and the union of CSF and WM masks calculated
in T1w space, after their projection to the native space of each
functional run (using the inverse BOLD-to-T1w transformation). Components
are also calculated separately within the WM and CSF masks.
For each CompCor decomposition, the *k* components with the largest singular
values are retained, such that the retained components' time series are
sufficient to explain 50 percent of variance across the nuisance mask (CSF,
WM, combined, or temporal). The remaining components are dropped from
consideration.
The head-motion estimates calculated in the correction step were also
placed within the corresponding confounds file.
The confound time series derived from head motion estimates and global
signals were expanded with the inclusion of temporal derivatives and
quadratic terms for each [@confounds_satterthwaite_2013].
Frames that exceeded a threshold of {fd} mm FD or {dv} standardised DVARS
were annotated as motion outliers.
""".format(fd=regressors_fd_th, dv=regressors_dvars_th)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'bold', 'bold_mask', 'movpar_file', 'rmsd_file', 'skip_vols',
't1w_mask', 't1w_tpms', 't1_bold_xform'
]),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['confounds_file', 'confounds_metadata']),
name='outputnode')
# Get masks ready in T1w space
acc_tpm = pe.Node(
AddTPMs(indices=[1, 2]), # BIDS convention (WM=1, CSF=2)
name='acc_tpm') # acc stands for aCompCor
csf_roi = pe.Node(TPM2ROI(erode_mm=0, mask_erode_mm=30), name='csf_roi')
wm_roi = pe.Node(
TPM2ROI(erode_prop=0.6,
mask_erode_prop=0.6**3), # 0.6 = radius; 0.6^3 = volume
name='wm_roi')
acc_roi = pe.Node(
TPM2ROI(erode_prop=0.6,
mask_erode_prop=0.6**3), # 0.6 = radius; 0.6^3 = volume
name='acc_roi')
# Map ROIs in T1w space into BOLD space
csf_tfm = pe.Node(ApplyTransforms(interpolation='NearestNeighbor',
float=True),
name='csf_tfm',
mem_gb=0.1)
wm_tfm = pe.Node(ApplyTransforms(interpolation='NearestNeighbor',
float=True),
name='wm_tfm',
mem_gb=0.1)
acc_tfm = pe.Node(ApplyTransforms(interpolation='NearestNeighbor',
float=True),
name='acc_tfm',
mem_gb=0.1)
tcc_tfm = pe.Node(ApplyTransforms(interpolation='NearestNeighbor',
float=True),
name='tcc_tfm',
mem_gb=0.1)
# Ensure ROIs don't go off-limits (reduced FoV)
csf_msk = pe.Node(niu.Function(function=_maskroi), name='csf_msk')
wm_msk = pe.Node(niu.Function(function=_maskroi), name='wm_msk')
acc_msk = pe.Node(niu.Function(function=_maskroi), name='acc_msk')
tcc_msk = pe.Node(niu.Function(function=_maskroi), name='tcc_msk')
# DVARS
dvars = pe.Node(nac.ComputeDVARS(save_nstd=True,
save_std=True,
remove_zerovariance=True),
name="dvars",
mem_gb=mem_gb)
# Frame displacement
fdisp = pe.Node(nac.FramewiseDisplacement(parameter_source="SPM"),
name="fdisp",
mem_gb=mem_gb)
# a/t-CompCor
mrg_lbl_cc = pe.Node(niu.Merge(3),
name='merge_rois_cc',
run_without_submitting=True)
tcompcor = pe.Node(TCompCor(components_file='tcompcor.tsv',
header_prefix='t_comp_cor_',
pre_filter='cosine',
save_pre_filter=True,
save_metadata=True,
percentile_threshold=.05,
failure_mode='NaN'),
name="tcompcor",
mem_gb=mem_gb)
acompcor = pe.Node(ACompCor(components_file='acompcor.tsv',
header_prefix='a_comp_cor_',
pre_filter='cosine',
save_pre_filter=True,
save_metadata=True,
mask_names=['combined', 'CSF', 'WM'],
merge_method='none',
failure_mode='NaN'),
name="acompcor",
mem_gb=mem_gb)
# Set number of components
if regressors_all_comps:
acompcor.inputs.num_components = 'all'
tcompcor.inputs.num_components = 'all'
else:
acompcor.inputs.variance_threshold = 0.5
tcompcor.inputs.variance_threshold = 0.5
# Set TR if present
if 'RepetitionTime' in metadata:
tcompcor.inputs.repetition_time = metadata['RepetitionTime']
acompcor.inputs.repetition_time = metadata['RepetitionTime']
# Global and segment regressors
signals_class_labels = ["csf", "white_matter", "global_signal"]
mrg_lbl = pe.Node(niu.Merge(3),
name='merge_rois',
run_without_submitting=True)
signals = pe.Node(SignalExtraction(class_labels=signals_class_labels),
name="signals",
mem_gb=mem_gb)
# Arrange confounds
add_dvars_header = pe.Node(AddTSVHeader(columns=["dvars"]),
name="add_dvars_header",
mem_gb=0.01,
run_without_submitting=True)
add_std_dvars_header = pe.Node(AddTSVHeader(columns=["std_dvars"]),
name="add_std_dvars_header",
mem_gb=0.01,
run_without_submitting=True)
add_motion_headers = pe.Node(AddTSVHeader(
columns=["trans_x", "trans_y", "trans_z", "rot_x", "rot_y", "rot_z"]),
name="add_motion_headers",
mem_gb=0.01,
run_without_submitting=True)
add_rmsd_header = pe.Node(AddTSVHeader(columns=["rmsd"]),
name="add_rmsd_header",
mem_gb=0.01,
run_without_submitting=True)
concat = pe.Node(GatherConfounds(),
name="concat",
mem_gb=0.01,
run_without_submitting=True)
# CompCor metadata
tcc_metadata_fmt = pe.Node(TSV2JSON(
index_column='component',
drop_columns=['mask'],
output=None,
additional_metadata={'Method': 'tCompCor'},
enforce_case=True),
name='tcc_metadata_fmt')
acc_metadata_fmt = pe.Node(TSV2JSON(
index_column='component',
output=None,
additional_metadata={'Method': 'aCompCor'},
enforce_case=True),
name='acc_metadata_fmt')
mrg_conf_metadata = pe.Node(niu.Merge(3),
name='merge_confound_metadata',
run_without_submitting=True)
mrg_conf_metadata.inputs.in3 = {
label: {
'Method': 'Mean'
}
for label in signals_class_labels
}
mrg_conf_metadata2 = pe.Node(DictMerge(),
name='merge_confound_metadata2',
run_without_submitting=True)
# Expand model to include derivatives and quadratics
model_expand = pe.Node(
ExpandModel(model_formula='(dd1(rps + wm + csf + gsr))^^2 + others'),
name='model_expansion')
# Add spike regressors
spike_regress = pe.Node(SpikeRegressors(fd_thresh=regressors_fd_th,
dvars_thresh=regressors_dvars_th),
name='spike_regressors')
# Generate reportlet (ROIs)
mrg_compcor = pe.Node(niu.Merge(2),
name='merge_compcor',
run_without_submitting=True)
rois_plot = pe.Node(ROIsPlot(colors=['b', 'magenta'],
generate_report=True),
name='rois_plot',
mem_gb=mem_gb)
ds_report_bold_rois = pe.Node(DerivativesDataSink(
desc='rois', datatype="figures", dismiss_entities=("echo", )),
name='ds_report_bold_rois',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
# Generate reportlet (CompCor)
mrg_cc_metadata = pe.Node(niu.Merge(2),
name='merge_compcor_metadata',
run_without_submitting=True)
compcor_plot = pe.Node(CompCorVariancePlot(
variance_thresholds=(0.5, 0.7, 0.9),
metadata_sources=['tCompCor', 'aCompCor']),
name='compcor_plot')
ds_report_compcor = pe.Node(DerivativesDataSink(
desc='compcorvar', datatype="figures", dismiss_entities=("echo", )),
name='ds_report_compcor',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
# Generate reportlet (Confound correlation)
conf_corr_plot = pe.Node(ConfoundsCorrelationPlot(
reference_column='global_signal', max_dim=70),
name='conf_corr_plot')
ds_report_conf_corr = pe.Node(DerivativesDataSink(
desc='confoundcorr', datatype="figures", dismiss_entities=("echo", )),
name='ds_report_conf_corr',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
def _pick_csf(files):
return files[2] # after smriprep#189, this is BIDS-compliant.
def _pick_wm(files):
return files[1] # after smriprep#189, this is BIDS-compliant.
workflow.connect([
# Massage ROIs (in T1w space)
(inputnode, acc_tpm, [('t1w_tpms', 'in_files')]),
(inputnode, csf_roi, [(('t1w_tpms', _pick_csf), 'in_tpm'),
('t1w_mask', 'in_mask')]),
(inputnode, wm_roi, [(('t1w_tpms', _pick_wm), 'in_tpm'),
('t1w_mask', 'in_mask')]),
(inputnode, acc_roi, [('t1w_mask', 'in_mask')]),
(acc_tpm, acc_roi, [('out_file', 'in_tpm')]),
# Map ROIs to BOLD
(inputnode, csf_tfm, [('bold_mask', 'reference_image'),
('t1_bold_xform', 'transforms')]),
(csf_roi, csf_tfm, [('roi_file', 'input_image')]),
(inputnode, wm_tfm, [('bold_mask', 'reference_image'),
('t1_bold_xform', 'transforms')]),
(wm_roi, wm_tfm, [('roi_file', 'input_image')]),
(inputnode, acc_tfm, [('bold_mask', 'reference_image'),
('t1_bold_xform', 'transforms')]),
(acc_roi, acc_tfm, [('roi_file', 'input_image')]),
(inputnode, tcc_tfm, [('bold_mask', 'reference_image'),
('t1_bold_xform', 'transforms')]),
(csf_roi, tcc_tfm, [('eroded_mask', 'input_image')]),
# Mask ROIs with bold_mask
(inputnode, csf_msk, [('bold_mask', 'in_mask')]),
(inputnode, wm_msk, [('bold_mask', 'in_mask')]),
(inputnode, acc_msk, [('bold_mask', 'in_mask')]),
(inputnode, tcc_msk, [('bold_mask', 'in_mask')]),
# connect inputnode to each non-anatomical confound node
(inputnode, dvars, [('bold', 'in_file'), ('bold_mask', 'in_mask')]),
(inputnode, fdisp, [('movpar_file', 'in_file')]),
# tCompCor
(inputnode, tcompcor, [('bold', 'realigned_file')]),
(inputnode, tcompcor, [('skip_vols', 'ignore_initial_volumes')]),
(tcc_tfm, tcc_msk, [('output_image', 'roi_file')]),
(tcc_msk, tcompcor, [('out', 'mask_files')]),
# aCompCor
(inputnode, acompcor, [('bold', 'realigned_file')]),
(inputnode, acompcor, [('skip_vols', 'ignore_initial_volumes')]),
(acc_tfm, acc_msk, [('output_image', 'roi_file')]),
(acc_msk, mrg_lbl_cc, [('out', 'in1')]),
(csf_msk, mrg_lbl_cc, [('out', 'in2')]),
(wm_msk, mrg_lbl_cc, [('out', 'in3')]),
(mrg_lbl_cc, acompcor, [('out', 'mask_files')]),
# Global signals extraction (constrained by anatomy)
(inputnode, signals, [('bold', 'in_file')]),
(csf_tfm, csf_msk, [('output_image', 'roi_file')]),
(csf_msk, mrg_lbl, [('out', 'in1')]),
(wm_tfm, wm_msk, [('output_image', 'roi_file')]),
(wm_msk, mrg_lbl, [('out', 'in2')]),
(inputnode, mrg_lbl, [('bold_mask', 'in3')]),
(mrg_lbl, signals, [('out', 'label_files')]),
# Collate computed confounds together
(inputnode, add_motion_headers, [('movpar_file', 'in_file')]),
(inputnode, add_rmsd_header, [('rmsd_file', 'in_file')]),
(dvars, add_dvars_header, [('out_nstd', 'in_file')]),
(dvars, add_std_dvars_header, [('out_std', 'in_file')]),
(signals, concat, [('out_file', 'signals')]),
(fdisp, concat, [('out_file', 'fd')]),
(tcompcor, concat, [('components_file', 'tcompcor'),
('pre_filter_file', 'cos_basis')]),
(acompcor, concat, [('components_file', 'acompcor')]),
(add_motion_headers, concat, [('out_file', 'motion')]),
(add_rmsd_header, concat, [('out_file', 'rmsd')]),
(add_dvars_header, concat, [('out_file', 'dvars')]),
(add_std_dvars_header, concat, [('out_file', 'std_dvars')]),
# Confounds metadata
(tcompcor, tcc_metadata_fmt, [('metadata_file', 'in_file')]),
(acompcor, acc_metadata_fmt, [('metadata_file', 'in_file')]),
(tcc_metadata_fmt, mrg_conf_metadata, [('output', 'in1')]),
(acc_metadata_fmt, mrg_conf_metadata, [('output', 'in2')]),
(mrg_conf_metadata, mrg_conf_metadata2, [('out', 'in_dicts')]),
# Expand the model with derivatives, quadratics, and spikes
(concat, model_expand, [('confounds_file', 'confounds_file')]),
(model_expand, spike_regress, [('confounds_file', 'confounds_file')]),
# Set outputs
(spike_regress, outputnode, [('confounds_file', 'confounds_file')]),
(mrg_conf_metadata2, outputnode, [('out_dict', 'confounds_metadata')]),
(inputnode, rois_plot, [('bold', 'in_file'),
('bold_mask', 'in_mask')]),
(tcompcor, mrg_compcor, [('high_variance_masks', 'in1')]),
(acc_msk, mrg_compcor, [('out', 'in2')]),
(mrg_compcor, rois_plot, [('out', 'in_rois')]),
(rois_plot, ds_report_bold_rois, [('out_report', 'in_file')]),
(tcompcor, mrg_cc_metadata, [('metadata_file', 'in1')]),
(acompcor, mrg_cc_metadata, [('metadata_file', 'in2')]),
(mrg_cc_metadata, compcor_plot, [('out', 'metadata_files')]),
(compcor_plot, ds_report_compcor, [('out_file', 'in_file')]),
(concat, conf_corr_plot, [('confounds_file', 'confounds_file')]),
(conf_corr_plot, ds_report_conf_corr, [('out_file', 'in_file')]),
])
return workflow
def init_bold_confs_wf(
out_dir,
out_path_base,
source_file,
mem_gb,
regressors_all_comps,
regressors_dvars_th,
regressors_fd_th,
dt=None,
work_dir=None,
name="bold_confs_wf",
):
"""
This workflow calculates confounds for a BOLD series, and aggregates them
into a :abbr:`TSV (tab-separated value)` file, for use as nuisance
regressors in a :abbr:`GLM (general linear model)`.
The following confounds are calculated, with column headings in parentheses:
#. Region-wise average signal (``csf``, ``white_matter``, ``global_signal``)
#. DVARS - original and standardized variants (``dvars``, ``std_dvars``)
#. Framewise displacement, based on head-motion parameters
(``framewise_displacement``)
#. Temporal CompCor (``t_comp_cor_XX``)
#. Anatomical CompCor (``a_comp_cor_XX``)
#. Cosine basis set for high-pass filtering w/ 0.008 Hz cut-off
(``cosine_XX``)
#. Non-steady-state volumes (``non_steady_state_XX``)
#. Estimated head-motion parameters, in mm and rad
(``trans_x``, ``trans_y``, ``trans_z``, ``rot_x``, ``rot_y``, ``rot_z``)
Prior to estimating aCompCor and tCompCor, non-steady-state volumes are
censored and high-pass filtered using a :abbr:`DCT (discrete cosine
transform)` basis.
The cosine basis, as well as one regressor per censored volume, are included
for convenience.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold.confounds import init_bold_confs_wf
wf = init_bold_confs_wf(
mem_gb=1,
regressors_all_comps=False,
regressors_dvars_th=1.5,
regressors_fd_th=0.5,
dt=2.0,
)
**Parameters**
mem_gb : float
Size of BOLD file in GB - please note that this size
should be calculated after resamplings that may extend
the FoV
regressors_all_comps: bool
Indicates whether CompCor decompositions should return all
components instead of the minimal number of components necessary
to explain 50 percent of the variance in the decomposition mask.
regressors_dvars_th
Criterion for flagging DVARS outliers
regressors_fd_th
Criterion for flagging framewise displacement outliers
dt: float
repetition time
name : str
Name of workflow (default: ``bold_confs_wf``)
**Inputs**
bold
BOLD image, after the prescribed corrections (STC, HMC and SDC)
when available.
bold_mask
BOLD series mask
movpar_file
SPM-formatted motion parameters file
skip_vols
number of non steady state volumes
csf_mask
csk mask in MNI 2mm space
wm_mask
wm mask in MNI 2mm space
cortical_gm_mask
gm mask in MNI 2mm space
**Outputs**
confounds_file
TSV of all aggregated confounds
confounds_metadata
Confounds metadata dictionary.
"""
DerivativesDataSink.out_path_base = out_path_base
workflow = Workflow(name=name, base_dir=work_dir)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'bold', 'bold_mask', 'movpar_file', 'skip_vols', 'csf_mask', 'wm_mask',
'cortical_gm_mask'
]),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['confounds_file', 'confounds_metadata']),
name='outputnode')
# create tcc mask: fslmaths cortical_gm_mask -dilD -mul -1 -add bold_mask -bin
tcc_roi = pe.Node(fsl.utils.ImageMaths(op_string='-dilD -mul -1 -add',
args='-bin'),
name='tcc_roi')
# create acc mask fslmaths wm_mask -add csf_mask
acc_roi = pe.Node(fsl.utils.ImageMaths(op_string='-add'), name='acc_roi')
# Ensure ROIs don't go off-limits (reduced FoV)
csf_msk = pe.Node(niu.Function(function=_maskroi), name='csf_msk')
wm_msk = pe.Node(niu.Function(function=_maskroi), name='wm_msk')
acc_msk = pe.Node(niu.Function(function=_maskroi), name='acc_msk')
tcc_msk = pe.Node(niu.Function(function=_maskroi), name='tcc_msk')
# DVARS
dvars = pe.Node(nac.ComputeDVARS(save_nstd=True,
save_std=True,
remove_zerovariance=True),
name="dvars",
mem_gb=mem_gb)
# Frame displacement
fdisp = pe.Node(nac.FramewiseDisplacement(parameter_source="SPM"),
name="fdisp",
mem_gb=mem_gb)
# a/t-Compcor
mrg_lbl_cc = pe.Node(niu.Merge(3),
name='merge_rois_cc',
run_without_submitting=True)
tcompcor = pe.Node(TCompCor(components_file='tcompcor.tsv',
header_prefix='t_comp_cor_',
pre_filter='cosine',
save_pre_filter=True,
save_metadata=True,
percentile_threshold=.05,
failure_mode='NaN'),
name="tcompcor",
mem_gb=mem_gb)
acompcor = pe.Node(ACompCor(components_file='acompcor.tsv',
header_prefix='a_comp_cor_',
pre_filter='cosine',
save_pre_filter=True,
save_metadata=True,
mask_names=['combined', 'CSF', 'WM'],
merge_method='none',
failure_mode='NaN'),
name="acompcor",
mem_gb=mem_gb)
# Set number of components
if regressors_all_comps:
acompcor.inputs.num_components = 'all'
tcompcor.inputs.num_components = 'all'
else:
acompcor.inputs.variance_threshold = 0.5
tcompcor.inputs.variance_threshold = 0.5
# Set TR if present
if dt:
tcompcor.inputs.repetition_time = dt
acompcor.inputs.repetition_time = dt
# Global and segment regressors
mrg_lbl = pe.Node(niu.Merge(3),
name='merge_rois',
run_without_submitting=True)
signals = pe.Node(SignalExtraction(
class_labels=["csf", "white_matter", "global_signal"]),
name="signals",
mem_gb=mem_gb)
# Arrange confounds
add_dvars_header = pe.Node(AddTSVHeader(columns=["dvars"]),
name="add_dvars_header",
mem_gb=0.01,
run_without_submitting=True)
add_std_dvars_header = pe.Node(AddTSVHeader(columns=["std_dvars"]),
name="add_std_dvars_header",
mem_gb=0.01,
run_without_submitting=True)
add_motion_headers = pe.Node(AddTSVHeader(
columns=["trans_x", "trans_y", "trans_z", "rot_x", "rot_y", "rot_z"]),
name="add_motion_headers",
mem_gb=0.01,
run_without_submitting=True)
concat = pe.Node(GatherConfounds(),
name="concat",
mem_gb=0.01,
run_without_submitting=True)
# CompCor metadata
tcc_metadata_fmt = pe.Node(TSV2JSON(
index_column='component',
drop_columns=['mask'],
output=None,
additional_metadata={'Method': 'tCompCor'},
enforce_case=True),
name='tcc_metadata_fmt')
acc_metadata_fmt = pe.Node(TSV2JSON(
index_column='component',
output=None,
additional_metadata={'Method': 'aCompCor'},
enforce_case=True),
name='acc_metadata_fmt')
mrg_conf_metadata = pe.Node(niu.Merge(2),
name='merge_confound_metadata',
run_without_submitting=True)
mrg_conf_metadata2 = pe.Node(DictMerge(),
name='merge_confound_metadata2',
run_without_submitting=True)
# Expand model to include derivatives and quadratics
model_expand = pe.Node(
ExpandModel(model_formula='(dd1(rps + wm + csf + gsr))^^2 + others'),
name='model_expansion')
# Add spike regressors
spike_regress = pe.Node(SpikeRegressors(fd_thresh=regressors_fd_th,
dvars_thresh=regressors_dvars_th),
name='spike_regressors')
# Generate reportlet (ROIs)
mrg_compcor = pe.Node(niu.Merge(2),
name='merge_compcor',
run_without_submitting=True)
rois_plot = pe.Node(ROIsPlot(colors=['b', 'magenta'],
generate_report=True),
name='rois_plot',
mem_gb=mem_gb)
ds_report_bold_rois = pe.Node(DerivativesDataSink(base_directory=out_dir,
desc='rois',
source_file=source_file,
suffix='reportlet',
keep_dtype=True),
name='ds_report_bold_rois',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
# Generate reportlet (CompCor)
mrg_cc_metadata = pe.Node(niu.Merge(2),
name='merge_compcor_metadata',
run_without_submitting=True)
compcor_plot = pe.Node(
CompCorVariancePlot(metadata_sources=['tCompCor', 'aCompCor']),
name='compcor_plot')
ds_report_compcor = pe.Node(DerivativesDataSink(base_directory=out_dir,
desc='compcorvar',
source_file=source_file,
keep_dtype=True),
name='ds_report_compcor',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
# Generate reportlet (Confound correlation)
conf_corr_plot = pe.Node(ConfoundsCorrelationPlot(
reference_column='global_signal', max_dim=70),
name='conf_corr_plot')
ds_report_conf_corr = pe.Node(DerivativesDataSink(base_directory=out_dir,
desc='confoundcorr',
source_file=source_file,
keep_dtype=True),
name='ds_report_conf_corr',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
# generate tcc and acc rois
(inputnode, tcc_roi, [('cortical_gm_mask', 'in_file'),
('bold_mask', 'in_file2')]),
(inputnode, acc_roi, [('wm_mask', 'in_file'),
('csf_mask', 'in_file2')]),
# Mask ROIs with bold_mask
(inputnode, csf_msk, [('bold_mask', 'in_mask')]),
(inputnode, wm_msk, [('bold_mask', 'in_mask')]),
(inputnode, acc_msk, [('bold_mask', 'in_mask')]),
(inputnode, tcc_msk, [('bold_mask', 'in_mask')]),
# connect inputnode to each non-anatomical confound node
(inputnode, dvars, [('bold', 'in_file'), ('bold_mask', 'in_mask')]),
(inputnode, fdisp, [('movpar_file', 'in_file')]),
# tCompCor
(inputnode, tcompcor, [('bold', 'realigned_file')]),
(inputnode, tcompcor, [('skip_vols', 'ignore_initial_volumes')]),
(tcc_roi, tcc_msk, [('out_file', 'roi_file')]),
(tcc_msk, tcompcor, [('out', 'mask_files')]),
# aCompCor
(inputnode, acompcor, [('bold', 'realigned_file')]),
(inputnode, acompcor, [('skip_vols', 'ignore_initial_volumes')]),
(acc_roi, acc_msk, [('out_file', 'roi_file')]),
(acc_msk, mrg_lbl_cc, [('out', 'in1')]),
(inputnode, mrg_lbl_cc, [('csf_mask', 'in2')]),
(inputnode, mrg_lbl_cc, [('wm_mask', 'in3')]),
(mrg_lbl_cc, acompcor, [('out', 'mask_files')]),
# Global signals extraction (constrained by anatomy)
(inputnode, signals, [('bold', 'in_file')]),
(inputnode, csf_msk, [('csf_mask', 'roi_file')]),
(csf_msk, mrg_lbl, [('out', 'in1')]),
(inputnode, wm_msk, [('wm_mask', 'roi_file')]),
(wm_msk, mrg_lbl, [('out', 'in2')]),
(inputnode, mrg_lbl, [('bold_mask', 'in3')]),
(mrg_lbl, signals, [('out', 'label_files')]),
# Collate computed confounds together
(inputnode, add_motion_headers, [('movpar_file', 'in_file')]),
(dvars, add_dvars_header, [('out_nstd', 'in_file')]),
(dvars, add_std_dvars_header, [('out_std', 'in_file')]),
(signals, concat, [('out_file', 'signals')]),
(fdisp, concat, [('out_file', 'fd')]),
(tcompcor, concat, [('components_file', 'tcompcor'),
('pre_filter_file', 'cos_basis')]),
(acompcor, concat, [('components_file', 'acompcor')]),
(add_motion_headers, concat, [('out_file', 'motion')]),
(add_dvars_header, concat, [('out_file', 'dvars')]),
(add_std_dvars_header, concat, [('out_file', 'std_dvars')]),
# Confounds metadata
(tcompcor, tcc_metadata_fmt, [('metadata_file', 'in_file')]),
(acompcor, acc_metadata_fmt, [('metadata_file', 'in_file')]),
(tcc_metadata_fmt, mrg_conf_metadata, [('output', 'in1')]),
(acc_metadata_fmt, mrg_conf_metadata, [('output', 'in2')]),
(mrg_conf_metadata, mrg_conf_metadata2, [('out', 'in_dicts')]),
# Expand the model with derivatives, quadratics, and spikes
(concat, model_expand, [('confounds_file', 'confounds_file')]),
(model_expand, spike_regress, [('confounds_file', 'confounds_file')]),
# Set outputs
(spike_regress, outputnode, [('confounds_file', 'confounds_file')]),
(mrg_conf_metadata2, outputnode, [('out_dict', 'confounds_metadata')]),
(inputnode, rois_plot, [('bold', 'in_file'),
('bold_mask', 'in_mask')]),
(tcompcor, mrg_compcor, [('high_variance_masks', 'in1')]),
(acc_msk, mrg_compcor, [('out', 'in2')]),
(mrg_compcor, rois_plot, [('out', 'in_rois')]),
(rois_plot, ds_report_bold_rois, [('out_report', 'in_file')]),
(tcompcor, mrg_cc_metadata, [('metadata_file', 'in1')]),
(acompcor, mrg_cc_metadata, [('metadata_file', 'in2')]),
(mrg_cc_metadata, compcor_plot, [('out', 'metadata_files')]),
(compcor_plot, ds_report_compcor, [('out_file', 'in_file')]),
(concat, conf_corr_plot, [('confounds_file', 'confounds_file')]),
(conf_corr_plot, ds_report_conf_corr, [('out_file', 'in_file')]),
])
return workflow
def init_anat_reports_wf(*, freesurfer, output_dir, name='anat_reports_wf'):
"""
Set up a battery of datasinks to store reports in the right location.
Parameters
----------
freesurfer : :obj:`bool`
FreeSurfer was enabled
output_dir : :obj:`str`
Directory in which to save derivatives
name : :obj:`str`
Workflow name (default: anat_reports_wf)
Inputs
------
source_file
Input T1w image
std_t1w
T1w image resampled to standard space
std_mask
Mask of skull-stripped template
subject_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
t1w_conform_report
Conformation report
t1w_preproc
The T1w reference map, which is calculated as the average of bias-corrected
and preprocessed T1w images, defining the anatomical space.
t1w_dseg
Segmentation in T1w space
t1w_mask
Brain (binary) mask estimated by brain extraction.
template
Template space and specifications
"""
from niworkflows.interfaces import SimpleBeforeAfter
from niworkflows.interfaces.masks import ROIsPlot
from ..interfaces.templateflow import TemplateFlowSelect
workflow = Workflow(name=name)
inputfields = [
'source_file', 't1w_conform_report', 't1w_preproc', 't1w_dseg',
't1w_mask', 'template', 'std_t1w', 'std_mask', 'subject_id',
'subjects_dir'
]
inputnode = pe.Node(niu.IdentityInterface(fields=inputfields),
name='inputnode')
seg_rpt = pe.Node(ROIsPlot(colors=['b', 'magenta'], levels=[1.5, 2.5]),
name='seg_rpt')
t1w_conform_check = pe.Node(niu.Function(function=_empty_report),
name='t1w_conform_check',
run_without_submitting=True)
ds_t1w_conform_report = pe.Node(DerivativesDataSink(
base_directory=output_dir,
desc='conform',
datatype="figures",
dismiss_entities=("session", )),
name='ds_t1w_conform_report',
run_without_submitting=True)
ds_t1w_dseg_mask_report = pe.Node(DerivativesDataSink(
base_directory=output_dir,
suffix='dseg',
datatype="figures",
dismiss_entities=("session", )),
name='ds_t1w_dseg_mask_report',
run_without_submitting=True)
workflow.connect([
(inputnode, t1w_conform_check, [('t1w_conform_report', 'in_file')]),
(t1w_conform_check, ds_t1w_conform_report, [('out', 'in_file')]),
(inputnode, ds_t1w_conform_report, [('source_file', 'source_file')]),
(inputnode, ds_t1w_dseg_mask_report, [('source_file', 'source_file')]),
(inputnode, seg_rpt, [('t1w_preproc', 'in_file'),
('t1w_mask', 'in_mask'),
('t1w_dseg', 'in_rois')]),
(seg_rpt, ds_t1w_dseg_mask_report, [('out_report', 'in_file')]),
])
# Generate reportlets showing spatial normalization
tf_select = pe.Node(TemplateFlowSelect(resolution=1),
name='tf_select',
run_without_submitting=True)
norm_msk = pe.Node(niu.Function(
function=_rpt_masks,
output_names=['before', 'after'],
input_names=['mask_file', 'before', 'after', 'after_mask']),
name='norm_msk')
norm_rpt = pe.Node(SimpleBeforeAfter(), name='norm_rpt', mem_gb=0.1)
norm_rpt.inputs.after_label = 'Participant' # after
ds_std_t1w_report = pe.Node(DerivativesDataSink(
base_directory=output_dir,
suffix='T1w',
datatype="figures",
dismiss_entities=("session", )),
name='ds_std_t1w_report',
run_without_submitting=True)
workflow.connect([
(inputnode, tf_select, [('template', 'template')]),
(inputnode, norm_rpt, [('template', 'before_label')]),
(inputnode, norm_msk, [('std_t1w', 'after'),
('std_mask', 'after_mask')]),
(tf_select, norm_msk, [('t1w_file', 'before'),
('brain_mask', 'mask_file')]),
(norm_msk, norm_rpt, [('before', 'before'), ('after', 'after')]),
(inputnode, ds_std_t1w_report, [(('template', _fmt_cohort), 'space'),
('source_file', 'source_file')]),
(norm_rpt, ds_std_t1w_report, [('out_report', 'in_file')]),
])
if freesurfer:
from ..interfaces.reports import FSSurfaceReport
recon_report = pe.Node(FSSurfaceReport(), name='recon_report')
recon_report.interface._always_run = True
ds_recon_report = pe.Node(DerivativesDataSink(
base_directory=output_dir,
desc='reconall',
datatype="figures",
dismiss_entities=("session", )),
name='ds_recon_report',
run_without_submitting=True)
workflow.connect([
(inputnode, recon_report, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(recon_report, ds_recon_report, [('out_report', 'in_file')]),
(inputnode, ds_recon_report, [('source_file', 'source_file')])
])
return workflow
def init_anat_reports_wf(reportlets_dir, freesurfer, name='anat_reports_wf'):
"""Set up a battery of datasinks to store reports in the right location."""
from niworkflows.interfaces import SimpleBeforeAfter
from niworkflows.interfaces.masks import ROIsPlot
from ..interfaces.templateflow import TemplateFlowSelect
workflow = Workflow(name=name)
inputfields = [
'source_file', 't1w_conform_report', 't1w_preproc', 't1w_dseg',
't1w_mask', 'template', 'template_spec', 'std_t1w', 'std_mask',
'subject_id', 'subjects_dir'
]
inputnode = pe.Node(niu.IdentityInterface(fields=inputfields),
name='inputnode')
seg_rpt = pe.Node(ROIsPlot(colors=['magenta', 'b'], levels=[1.5, 2.5]),
name='seg_rpt')
ds_t1w_conform_report = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, desc='conform', keep_dtype=True),
name='ds_t1w_conform_report',
run_without_submitting=True)
ds_t1w_dseg_mask_report = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, suffix='dseg'),
name='ds_t1w_dseg_mask_report',
run_without_submitting=True)
workflow.connect([
(inputnode, ds_t1w_conform_report, [('source_file', 'source_file'),
('t1w_conform_report', 'in_file')
]),
(inputnode, ds_t1w_dseg_mask_report, [('source_file', 'source_file')]),
(inputnode, seg_rpt, [('t1w_preproc', 'in_file'),
('t1w_mask', 'in_mask'),
('t1w_dseg', 'in_rois')]),
(seg_rpt, ds_t1w_dseg_mask_report, [('out_report', 'in_file')]),
])
# Generate reportlets showing spatial normalization
tf_select = pe.Node(TemplateFlowSelect(resolution=1),
name='tf_select',
run_without_submitting=True)
norm_msk = pe.Node(niu.Function(
function=_rpt_masks,
output_names=['before', 'after'],
input_names=['mask_file', 'before', 'after', 'after_mask']),
name='norm_msk')
norm_rpt = pe.Node(SimpleBeforeAfter(), name='norm_rpt', mem_gb=0.1)
norm_rpt.inputs.after_label = 'Participant' # after
ds_std_t1w_report = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, suffix='T1w'),
name='ds_std_t1w_report',
run_without_submitting=True)
workflow.connect([
(inputnode, tf_select, [('template', 'template'),
('template_spec', 'template_spec')]),
(inputnode, norm_rpt, [('template', 'before_label')]),
(inputnode, norm_msk, [('std_t1w', 'after'),
('std_mask', 'after_mask')]),
(tf_select, norm_msk, [('t1w_file', 'before'),
('brain_mask', 'mask_file')]),
(norm_msk, norm_rpt, [('before', 'before'), ('after', 'after')]),
(inputnode, ds_std_t1w_report, [('template', 'space'),
('source_file', 'source_file')]),
(norm_rpt, ds_std_t1w_report, [('out_report', 'in_file')]),
])
if freesurfer:
from ..interfaces.reports import FSSurfaceReport
recon_report = pe.Node(FSSurfaceReport(), name='recon_report')
recon_report.interface._always_run = True
ds_recon_report = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, desc='reconall', keep_dtype=True),
name='ds_recon_report',
run_without_submitting=True)
workflow.connect([
(inputnode, recon_report, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(recon_report, ds_recon_report, [('out_report', 'in_file')]),
(inputnode, ds_recon_report, [('source_file', 'source_file')])
])
return workflow
def init_anat_preproc_wf(skull_strip_template, output_spaces, template, debug,
freesurfer, longitudinal, omp_nthreads, hires, reportlets_dir,
output_dir, num_t1w,
name='anat_preproc_wf'):
r"""
This workflow controls the anatomical preprocessing stages of FMRIPREP.
This includes:
- Creation of a structural template
- Skull-stripping and bias correction
- Tissue segmentation
- Normalization
- Surface reconstruction with FreeSurfer
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.anatomical import init_anat_preproc_wf
wf = init_anat_preproc_wf(omp_nthreads=1,
reportlets_dir='.',
output_dir='.',
template='MNI152NLin2009cAsym',
output_spaces=['T1w', 'fsnative',
'template', 'fsaverage5'],
skull_strip_template='OASIS',
freesurfer=True,
longitudinal=False,
debug=False,
hires=True,
num_t1w=1)
**Parameters**
skull_strip_template : str
Name of ANTs skull-stripping template ('OASIS' or 'NKI')
output_spaces : list
List of output spaces functional images are to be resampled to.
Some pipeline components will only be instantiated for some output spaces.
Valid spaces:
- T1w
- template
- fsnative
- fsaverage (or other pre-existing FreeSurfer templates)
template : str
Name of template targeted by `'template'` output space
debug : bool
Enable debugging outputs
freesurfer : bool
Enable FreeSurfer surface reconstruction (may increase runtime)
longitudinal : bool
Create unbiased structural template, regardless of number of inputs
(may increase runtime)
omp_nthreads : int
Maximum number of threads an individual process may use
hires : bool
Enable sub-millimeter preprocessing in FreeSurfer
reportlets_dir : str
Directory in which to save reportlets
output_dir : str
Directory in which to save derivatives
name : str, optional
Workflow name (default: anat_preproc_wf)
**Inputs**
t1w
List of T1-weighted structural images
t2w
List of T2-weighted structural images
subjects_dir
FreeSurfer SUBJECTS_DIR
**Outputs**
t1_preproc
Bias-corrected structural template, defining T1w space
t1_brain
Skull-stripped ``t1_preproc``
t1_mask
Mask of the skull-stripped template image
t1_seg
Segmentation of preprocessed structural image, including
gray-matter (GM), white-matter (WM) and cerebrospinal fluid (CSF)
t1_tpms
List of tissue probability maps in T1w space
t1_2_mni
T1w template, normalized to MNI space
t1_2_mni_forward_transform
ANTs-compatible affine-and-warp transform file
t1_2_mni_reverse_transform
ANTs-compatible affine-and-warp transform file (inverse)
mni_mask
Mask of skull-stripped template, in MNI space
mni_seg
Segmentation, resampled into MNI space
mni_tpms
List of tissue probability maps in MNI space
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
t1_2_fsnative_reverse_transform
LTA-style affine matrix translating from FreeSurfer-conformed subject space to T1w
surfaces
GIFTI surfaces (gray/white boundary, midthickness, pial, inflated)
**Subworkflows**
* :py:func:`~fmriprep.workflows.anatomical.init_skullstrip_ants_wf`
* :py:func:`~fmriprep.workflows.anatomical.init_surface_recon_wf`
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=['t1w', 't2w', 'subjects_dir', 'subject_id']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['t1_preproc', 't1_brain', 't1_mask', 't1_seg', 't1_tpms',
't1_2_mni', 't1_2_mni_forward_transform', 't1_2_mni_reverse_transform',
'mni_mask', 'mni_seg', 'mni_tpms',
'template_transforms',
'subjects_dir', 'subject_id', 't1_2_fsnative_forward_transform',
't1_2_fsnative_reverse_transform', 'surfaces']),
name='outputnode')
buffernode = pe.Node(niu.IdentityInterface(
fields=['t1_brain', 't1_mask']), name='buffernode')
anat_template_wf = init_anat_template_wf(longitudinal=longitudinal, omp_nthreads=omp_nthreads,
num_t1w=num_t1w)
# 3. Skull-stripping
# Bias field correction is handled in skull strip workflows.
skullstrip_ants_wf = init_skullstrip_ants_wf(name='skullstrip_ants_wf',
skull_strip_template=skull_strip_template,
debug=debug,
omp_nthreads=omp_nthreads)
workflow.connect([
(inputnode, anat_template_wf, [('t1w', 'inputnode.t1w')]),
(anat_template_wf, skullstrip_ants_wf, [('outputnode.t1_template', 'inputnode.in_file')]),
(skullstrip_ants_wf, outputnode, [('outputnode.bias_corrected', 't1_preproc')]),
(anat_template_wf, outputnode, [
('outputnode.template_transforms', 't1_template_transforms')]),
(buffernode, outputnode, [('t1_brain', 't1_brain'),
('t1_mask', 't1_mask')]),
])
# 4. Surface reconstruction
if freesurfer:
surface_recon_wf = init_surface_recon_wf(name='surface_recon_wf',
omp_nthreads=omp_nthreads, hires=hires)
applyrefined = pe.Node(fsl.ApplyMask(), name='applyrefined')
workflow.connect([
(inputnode, surface_recon_wf, [
('t2w', 'inputnode.t2w'),
('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id')]),
(anat_template_wf, surface_recon_wf, [('outputnode.t1_template', 'inputnode.t1w')]),
(skullstrip_ants_wf, surface_recon_wf, [
('outputnode.out_file', 'inputnode.skullstripped_t1'),
('outputnode.out_segs', 'inputnode.ants_segs'),
('outputnode.bias_corrected', 'inputnode.corrected_t1')]),
(skullstrip_ants_wf, applyrefined, [
('outputnode.bias_corrected', 'in_file')]),
(surface_recon_wf, applyrefined, [
('outputnode.out_brainmask', 'mask_file')]),
(surface_recon_wf, outputnode, [
('outputnode.subjects_dir', 'subjects_dir'),
('outputnode.subject_id', 'subject_id'),
('outputnode.t1_2_fsnative_forward_transform', 't1_2_fsnative_forward_transform'),
('outputnode.t1_2_fsnative_reverse_transform', 't1_2_fsnative_reverse_transform'),
('outputnode.surfaces', 'surfaces')]),
(applyrefined, buffernode, [('out_file', 't1_brain')]),
(surface_recon_wf, buffernode, [
('outputnode.out_brainmask', 't1_mask')]),
])
else:
workflow.connect([
(skullstrip_ants_wf, buffernode, [
('outputnode.out_file', 't1_brain'),
('outputnode.out_mask', 't1_mask')]),
])
# 5. Segmentation
t1_seg = pe.Node(fsl.FAST(segments=True, no_bias=True, probability_maps=True),
name='t1_seg', mem_gb=3)
workflow.connect([
(buffernode, t1_seg, [('t1_brain', 'in_files')]),
(t1_seg, outputnode, [('tissue_class_map', 't1_seg'),
('probability_maps', 't1_tpms')]),
])
# 6. Spatial normalization (T1w to MNI registration)
t1_2_mni = pe.Node(
RobustMNINormalizationRPT(
float=True,
generate_report=True,
flavor='testing' if debug else 'precise',
),
name='t1_2_mni',
n_procs=omp_nthreads,
mem_gb=2
)
# Resample the brain mask and the tissue probability maps into mni space
mni_mask = pe.Node(
ApplyTransforms(dimension=3, default_value=0, float=True,
interpolation='NearestNeighbor'),
name='mni_mask'
)
mni_seg = pe.Node(
ApplyTransforms(dimension=3, default_value=0, float=True,
interpolation='NearestNeighbor'),
name='mni_seg'
)
mni_tpms = pe.MapNode(
ApplyTransforms(dimension=3, default_value=0, float=True,
interpolation='Linear'),
iterfield=['input_image'],
name='mni_tpms'
)
if 'template' in output_spaces:
template_str = nid.TEMPLATE_MAP[template]
ref_img = op.join(nid.get_dataset(template_str), '1mm_T1.nii.gz')
t1_2_mni.inputs.template = template_str
mni_mask.inputs.reference_image = ref_img
mni_seg.inputs.reference_image = ref_img
mni_tpms.inputs.reference_image = ref_img
workflow.connect([
(skullstrip_ants_wf, t1_2_mni, [('outputnode.bias_corrected', 'moving_image')]),
(buffernode, t1_2_mni, [('t1_mask', 'moving_mask')]),
(buffernode, mni_mask, [('t1_mask', 'input_image')]),
(t1_2_mni, mni_mask, [('composite_transform', 'transforms')]),
(t1_seg, mni_seg, [('tissue_class_map', 'input_image')]),
(t1_2_mni, mni_seg, [('composite_transform', 'transforms')]),
(t1_seg, mni_tpms, [('probability_maps', 'input_image')]),
(t1_2_mni, mni_tpms, [('composite_transform', 'transforms')]),
(t1_2_mni, outputnode, [
('warped_image', 't1_2_mni'),
('composite_transform', 't1_2_mni_forward_transform'),
('inverse_composite_transform', 't1_2_mni_reverse_transform')]),
(mni_mask, outputnode, [('output_image', 'mni_mask')]),
(mni_seg, outputnode, [('output_image', 'mni_seg')]),
(mni_tpms, outputnode, [('output_image', 'mni_tpms')]),
])
seg2msks = pe.Node(niu.Function(function=_seg2msks), name='seg2msks')
seg_rpt = pe.Node(ROIsPlot(colors=['r', 'magenta', 'b', 'g']), name='seg_rpt')
anat_reports_wf = init_anat_reports_wf(
reportlets_dir=reportlets_dir, output_spaces=output_spaces, template=template,
freesurfer=freesurfer)
workflow.connect([
(inputnode, anat_reports_wf, [
(('t1w', fix_multi_T1w_source_name), 'inputnode.source_file')]),
(anat_template_wf, anat_reports_wf, [
('outputnode.out_report', 'inputnode.t1_conform_report')]),
(anat_template_wf, seg_rpt, [
('outputnode.t1_template', 'in_file')]),
(t1_seg, seg2msks, [('tissue_class_map', 'in_file')]),
(seg2msks, seg_rpt, [('out', 'in_rois')]),
(outputnode, seg_rpt, [('t1_mask', 'in_mask')]),
(seg_rpt, anat_reports_wf, [('out_report', 'inputnode.seg_report')]),
])
if freesurfer:
workflow.connect([
(surface_recon_wf, anat_reports_wf, [
('outputnode.out_report', 'inputnode.recon_report')])
])
if 'template' in output_spaces:
workflow.connect([
(t1_2_mni, anat_reports_wf, [('out_report', 'inputnode.t1_2_mni_report')]),
])
anat_derivatives_wf = init_anat_derivatives_wf(output_dir=output_dir,
output_spaces=output_spaces,
template=template,
freesurfer=freesurfer)
workflow.connect([
(anat_template_wf, anat_derivatives_wf, [
('outputnode.t1w_valid_list', 'inputnode.source_files')]),
(outputnode, anat_derivatives_wf, [
('t1_template_transforms', 'inputnode.t1_template_transforms'),
('t1_preproc', 'inputnode.t1_preproc'),
('t1_mask', 'inputnode.t1_mask'),
('t1_seg', 'inputnode.t1_seg'),
('t1_tpms', 'inputnode.t1_tpms'),
('t1_2_mni_forward_transform', 'inputnode.t1_2_mni_forward_transform'),
('t1_2_mni_reverse_transform', 'inputnode.t1_2_mni_reverse_transform'),
('t1_2_mni', 'inputnode.t1_2_mni'),
('mni_mask', 'inputnode.mni_mask'),
('mni_seg', 'inputnode.mni_seg'),
('mni_tpms', 'inputnode.mni_tpms'),
('t1_2_fsnative_forward_transform', 'inputnode.t1_2_fsnative_forward_transform'),
('surfaces', 'inputnode.surfaces'),
]),
])
return workflow
def init_bold_confs_wf(mem_gb, metadata, name="bold_confs_wf"):
"""
This workflow calculates confounds for a BOLD series, and aggregates them
into a :abbr:`TSV (tab-separated value)` file, for use as nuisance
regressors in a :abbr:`GLM (general linear model)`.
The following confounds are calculated, with column headings in parentheses:
#. Region-wise average signal (``csf``, ``white_matter``, ``global_signal``)
#. DVARS - original and standardized variants (``dvars``, ``std_dvars``)
#. Framewise displacement, based on head-motion parameters
(``framewise_displacement``)
#. Temporal CompCor (``t_comp_cor_XX``)
#. Anatomical CompCor (``a_comp_cor_XX``)
#. Cosine basis set for high-pass filtering w/ 0.008 Hz cut-off
(``cosine_XX``)
#. Non-steady-state volumes (``non_steady_state_XX``)
#. Estimated head-motion parameters, in mm and rad
(``trans_x``, ``trans_y``, ``trans_z``, ``rot_x``, ``rot_y``, ``rot_z``)
Prior to estimating aCompCor and tCompCor, non-steady-state volumes are
censored and high-pass filtered using a :abbr:`DCT (discrete cosine
transform)` basis.
The cosine basis, as well as one regressor per censored volume, are included
for convenience.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold.confounds import init_bold_confs_wf
wf = init_bold_confs_wf(
mem_gb=1,
metadata={})
**Parameters**
mem_gb : float
Size of BOLD file in GB - please note that this size
should be calculated after resamplings that may extend
the FoV
metadata : dict
BIDS metadata for BOLD file
name : str
Name of workflow (default: ``bold_confs_wf``)
**Inputs**
bold
BOLD image, after the prescribed corrections (STC, HMC and SDC)
when available.
bold_mask
BOLD series mask
movpar_file
SPM-formatted motion parameters file
skip_vols
number of non steady state volumes
t1_mask
Mask of the skull-stripped template image
t1_tpms
List of tissue probability maps in T1w space
t1_bold_xform
Affine matrix that maps the T1w space into alignment with
the native BOLD space
**Outputs**
confounds_file
TSV of all aggregated confounds
rois_report
Reportlet visualizing white-matter/CSF mask used for aCompCor,
the ROI for tCompCor and the BOLD brain mask.
"""
workflow = Workflow(name=name)
workflow.__desc__ = """\
Several confounding time-series were calculated based on the
*preprocessed BOLD*: framewise displacement (FD), DVARS and
three region-wise global signals.
FD and DVARS are calculated for each functional run, both using their
implementations in *Nipype* [following the definitions by @power_fd_dvars].
The three global signals are extracted within the CSF, the WM, and
the whole-brain masks.
Additionally, a set of physiological regressors were extracted to
allow for component-based noise correction [*CompCor*, @compcor].
Principal components are estimated after high-pass filtering the
*preprocessed BOLD* time-series (using a discrete cosine filter with
128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
and anatomical (aCompCor).
Six tCompCor components are then calculated from the top 5% variable
voxels within a mask covering the subcortical regions.
This subcortical mask is obtained by heavily eroding the brain mask,
which ensures it does not include cortical GM regions.
For aCompCor, six components are calculated within the intersection of
the aforementioned mask and the union of CSF and WM masks calculated
in T1w space, after their projection to the native space of each
functional run (using the inverse BOLD-to-T1w transformation).
The head-motion estimates calculated in the correction step were also
placed within the corresponding confounds file.
"""
inputnode = pe.Node(niu.IdentityInterface(fields=[
'bold', 'bold_mask', 'movpar_file', 'skip_vols', 't1_mask', 't1_tpms',
't1_bold_xform'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['confounds_file']),
name='outputnode')
# Get masks ready in T1w space
acc_tpm = pe.Node(AddTPMs(indices=[0, 2]),
name='tpms_add_csf_wm') # acc stands for aCompCor
csf_roi = pe.Node(TPM2ROI(erode_mm=0, mask_erode_mm=30), name='csf_roi')
wm_roi = pe.Node(
TPM2ROI(erode_prop=0.6,
mask_erode_prop=0.6**3), # 0.6 = radius; 0.6^3 = volume
name='wm_roi')
acc_roi = pe.Node(
TPM2ROI(erode_prop=0.6,
mask_erode_prop=0.6**3), # 0.6 = radius; 0.6^3 = volume
name='acc_roi')
# Map ROIs in T1w space into BOLD space
csf_tfm = pe.Node(ApplyTransforms(interpolation='NearestNeighbor',
float=True),
name='csf_tfm',
mem_gb=0.1)
wm_tfm = pe.Node(ApplyTransforms(interpolation='NearestNeighbor',
float=True),
name='wm_tfm',
mem_gb=0.1)
acc_tfm = pe.Node(ApplyTransforms(interpolation='NearestNeighbor',
float=True),
name='acc_tfm',
mem_gb=0.1)
tcc_tfm = pe.Node(ApplyTransforms(interpolation='NearestNeighbor',
float=True),
name='tcc_tfm',
mem_gb=0.1)
# Ensure ROIs don't go off-limits (reduced FoV)
csf_msk = pe.Node(niu.Function(function=_maskroi), name='csf_msk')
wm_msk = pe.Node(niu.Function(function=_maskroi), name='wm_msk')
acc_msk = pe.Node(niu.Function(function=_maskroi), name='acc_msk')
tcc_msk = pe.Node(niu.Function(function=_maskroi), name='tcc_msk')
# DVARS
dvars = pe.Node(nac.ComputeDVARS(save_nstd=True,
save_std=True,
remove_zerovariance=True),
name="dvars",
mem_gb=mem_gb)
# Frame displacement
fdisp = pe.Node(nac.FramewiseDisplacement(parameter_source="SPM"),
name="fdisp",
mem_gb=mem_gb)
# a/t-CompCor
tcompcor = pe.Node(TCompCor(components_file='tcompcor.tsv',
header_prefix='t_comp_cor_',
pre_filter='cosine',
save_pre_filter=True,
percentile_threshold=.05),
name="tcompcor",
mem_gb=mem_gb)
acompcor = pe.Node(ACompCor(components_file='acompcor.tsv',
header_prefix='a_comp_cor_',
pre_filter='cosine',
save_pre_filter=True),
name="acompcor",
mem_gb=mem_gb)
# Set TR if present
if 'RepetitionTime' in metadata:
tcompcor.inputs.repetition_time = metadata['RepetitionTime']
acompcor.inputs.repetition_time = metadata['RepetitionTime']
# Global and segment regressors
mrg_lbl = pe.Node(niu.Merge(3),
name='merge_rois',
run_without_submitting=True)
signals = pe.Node(SignalExtraction(
class_labels=["csf", "white_matter", "global_signal"]),
name="signals",
mem_gb=mem_gb)
# Arrange confounds
add_dvars_header = pe.Node(AddTSVHeader(columns=["dvars"]),
name="add_dvars_header",
mem_gb=0.01,
run_without_submitting=True)
add_std_dvars_header = pe.Node(AddTSVHeader(columns=["std_dvars"]),
name="add_std_dvars_header",
mem_gb=0.01,
run_without_submitting=True)
add_motion_headers = pe.Node(AddTSVHeader(
columns=["trans_x", "trans_y", "trans_z", "rot_x", "rot_y", "rot_z"]),
name="add_motion_headers",
mem_gb=0.01,
run_without_submitting=True)
concat = pe.Node(GatherConfounds(),
name="concat",
mem_gb=0.01,
run_without_submitting=True)
# Generate reportlet
mrg_compcor = pe.Node(niu.Merge(2),
name='merge_compcor',
run_without_submitting=True)
rois_plot = pe.Node(ROIsPlot(colors=['r', 'b', 'magenta'],
generate_report=True),
name='rois_plot',
mem_gb=mem_gb)
ds_report_bold_rois = pe.Node(DerivativesDataSink(suffix='rois'),
name='ds_report_bold_rois',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
def _pick_csf(files):
return files[0]
def _pick_wm(files):
return files[-1]
workflow.connect([
# Massage ROIs (in T1w space)
(inputnode, acc_tpm, [('t1_tpms', 'in_files')]),
(inputnode, csf_roi, [(('t1_tpms', _pick_csf), 'in_tpm'),
('t1_mask', 'in_mask')]),
(inputnode, wm_roi, [(('t1_tpms', _pick_wm), 'in_tpm'),
('t1_mask', 'in_mask')]),
(inputnode, acc_roi, [('t1_mask', 'in_mask')]),
(acc_tpm, acc_roi, [('out_file', 'in_tpm')]),
# Map ROIs to BOLD
(inputnode, csf_tfm, [('bold_mask', 'reference_image'),
('t1_bold_xform', 'transforms')]),
(csf_roi, csf_tfm, [('roi_file', 'input_image')]),
(inputnode, wm_tfm, [('bold_mask', 'reference_image'),
('t1_bold_xform', 'transforms')]),
(wm_roi, wm_tfm, [('roi_file', 'input_image')]),
(inputnode, acc_tfm, [('bold_mask', 'reference_image'),
('t1_bold_xform', 'transforms')]),
(acc_roi, acc_tfm, [('roi_file', 'input_image')]),
(inputnode, tcc_tfm, [('bold_mask', 'reference_image'),
('t1_bold_xform', 'transforms')]),
(csf_roi, tcc_tfm, [('eroded_mask', 'input_image')]),
# Mask ROIs with bold_mask
(inputnode, csf_msk, [('bold_mask', 'in_mask')]),
(inputnode, wm_msk, [('bold_mask', 'in_mask')]),
(inputnode, acc_msk, [('bold_mask', 'in_mask')]),
(inputnode, tcc_msk, [('bold_mask', 'in_mask')]),
# connect inputnode to each non-anatomical confound node
(inputnode, dvars, [('bold', 'in_file'), ('bold_mask', 'in_mask')]),
(inputnode, fdisp, [('movpar_file', 'in_file')]),
# tCompCor
(inputnode, tcompcor, [('bold', 'realigned_file')]),
(inputnode, tcompcor, [('skip_vols', 'ignore_initial_volumes')]),
(tcc_tfm, tcc_msk, [('output_image', 'roi_file')]),
(tcc_msk, tcompcor, [('out', 'mask_files')]),
# aCompCor
(inputnode, acompcor, [('bold', 'realigned_file')]),
(inputnode, acompcor, [('skip_vols', 'ignore_initial_volumes')]),
(acc_tfm, acc_msk, [('output_image', 'roi_file')]),
(acc_msk, acompcor, [('out', 'mask_files')]),
# Global signals extraction (constrained by anatomy)
(inputnode, signals, [('bold', 'in_file')]),
(csf_tfm, csf_msk, [('output_image', 'roi_file')]),
(csf_msk, mrg_lbl, [('out', 'in1')]),
(wm_tfm, wm_msk, [('output_image', 'roi_file')]),
(wm_msk, mrg_lbl, [('out', 'in2')]),
(inputnode, mrg_lbl, [('bold_mask', 'in3')]),
(mrg_lbl, signals, [('out', 'label_files')]),
# Collate computed confounds together
(inputnode, add_motion_headers, [('movpar_file', 'in_file')]),
(dvars, add_dvars_header, [('out_nstd', 'in_file')]),
(dvars, add_std_dvars_header, [('out_std', 'in_file')]),
(signals, concat, [('out_file', 'signals')]),
(fdisp, concat, [('out_file', 'fd')]),
(tcompcor, concat, [('components_file', 'tcompcor'),
('pre_filter_file', 'cos_basis')]),
(acompcor, concat, [('components_file', 'acompcor')]),
(add_motion_headers, concat, [('out_file', 'motion')]),
(add_dvars_header, concat, [('out_file', 'dvars')]),
(add_std_dvars_header, concat, [('out_file', 'std_dvars')]),
# Set outputs
(concat, outputnode, [('confounds_file', 'confounds_file')]),
(inputnode, rois_plot, [('bold', 'in_file'),
('bold_mask', 'in_mask')]),
(tcompcor, mrg_compcor, [('high_variance_masks', 'in1')]),
(acc_msk, mrg_compcor, [('out', 'in2')]),
(mrg_compcor, rois_plot, [('out', 'in_rois')]),
(rois_plot, ds_report_bold_rois, [('out_report', 'in_file')]),
])
return workflow
def init_bold_confs_wf(mem_gb, use_aroma, ignore_aroma_err, metadata,
name="bold_confs_wf"):
"""
This workflow calculates confounds for a BOLD series, and aggregates them
into a :abbr:`TSV (tab-separated value)` file, for use as nuisance
regressors in a :abbr:`GLM (general linear model)`.
The following confounds are calculated, with column headings in parentheses:
#. Region-wise average signal (``CSF``, ``WhiteMatter``, ``GlobalSignal``)
#. DVARS - standard, nonstandard, and voxel-wise standard variants
(``stdDVARS``, ``non-stdDVARS``, ``vx-wisestdDVARS``)
#. Framewise displacement, based on MCFLIRT motion parameters
(``FramewiseDisplacement``)
#. Temporal CompCor (``tCompCorXX``)
#. Anatomical CompCor (``aCompCorXX``)
#. Cosine basis set for high-pass filtering w/ 0.008 Hz cut-off
(``CosineXX``)
#. Non-steady-state volumes (``NonSteadyStateXX``)
#. Estimated head-motion parameters, in mm and rad
(``X``, ``Y``, ``Z``, ``RotX``, ``RotY``, ``RotZ``)
#. ICA-AROMA-identified noise components, if enabled
(``AROMAAggrCompXX``)
Prior to estimating aCompCor and tCompCor, non-steady-state volumes are
censored and high-pass filtered using a :abbr:`DCT (discrete cosine
transform)` basis.
The cosine basis, as well as one regressor per censored volume, are included
for convenience.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold.confounds import init_bold_confs_wf
wf = init_bold_confs_wf(
mem_gb=1,
use_aroma=True,
ignore_aroma_err=True,
metadata={})
**Parameters**
mem_gb : float
Size of BOLD file in GB - please note that this size
should be calculated after resamplings that may extend
the FoV
use_aroma : bool
Perform ICA-AROMA on MNI-resampled functional series
ignore_aroma_err : bool
Do not fail on ICA-AROMA errors
metadata : dict
BIDS metadata for BOLD file
**Inputs**
bold
BOLD image, after the prescribed corrections (STC, HMC and SDC)
when available.
bold_mask
BOLD series mask
movpar_file
SPM-formatted motion parameters file
t1_mask
Mask of the skull-stripped template image
t1_tpms
List of tissue probability maps in T1w space
t1_bold_xform
Affine matrix that maps the T1w space into alignment with
the native BOLD space
bold_mni
BOLD image resampled in MNI space (only if ``use_aroma`` enabled)
bold_mask_mni
Brain mask corresponding to the BOLD image resampled in MNI space
(only if ``use_aroma`` enabled)
**Outputs**
confounds_file
TSV of all aggregated confounds
confounds_list
List of calculated confounds for reporting
acompcor_report
Reportlet visualizing white-matter/CSF mask used for aCompCor
tcompcor_report
Reportlet visualizing ROI identified in tCompCor
ica_aroma_report
Reportlet visualizing MELODIC ICs, with ICA-AROMA signal/noise labels
aroma_noise_ics
CSV of noise components identified by ICA-AROMA
melodic_mix
FSL MELODIC mixing matrix
nonaggr_denoised_file
BOLD series with non-aggressive ICA-AROMA denoising applied
**Subworkflows**
* :py:func:`~fmriprep.workflows.bold.confounds.init_ica_aroma_wf`
"""
inputnode = pe.Node(niu.IdentityInterface(
fields=['bold', 'bold_mask', 'movpar_file', 't1_mask', 't1_tpms',
't1_bold_xform', 'bold_mni', 'bold_mask_mni']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['confounds_file', 'confounds_list', 'rois_report', 'ica_aroma_report',
'aroma_noise_ics', 'melodic_mix', 'nonaggr_denoised_file']),
name='outputnode')
# Get masks ready in T1w space
acc_tpm = pe.Node(AddTPMs(indices=[0, 2]), name='tpms_add_csf_wm') # acc stands for aCompCor
csf_roi = pe.Node(TPM2ROI(erode_mm=0, mask_erode_mm=30), name='csf_roi')
wm_roi = pe.Node(TPM2ROI(
erode_prop=0.6, mask_erode_prop=0.6**3), # 0.6 = radius; 0.6^3 = volume
name='wm_roi')
acc_roi = pe.Node(TPM2ROI(
erode_prop=0.6, mask_erode_prop=0.6**3), # 0.6 = radius; 0.6^3 = volume
name='acc_roi')
# Map ROIs in T1w space into BOLD space
csf_tfm = pe.Node(ApplyTransforms(interpolation='NearestNeighbor', float=True),
name='csf_tfm', mem_gb=0.1)
wm_tfm = pe.Node(ApplyTransforms(interpolation='NearestNeighbor', float=True),
name='wm_tfm', mem_gb=0.1)
acc_tfm = pe.Node(ApplyTransforms(interpolation='NearestNeighbor', float=True),
name='acc_tfm', mem_gb=0.1)
tcc_tfm = pe.Node(ApplyTransforms(interpolation='NearestNeighbor', float=True),
name='tcc_tfm', mem_gb=0.1)
# Ensure ROIs don't go off-limits (reduced FoV)
csf_msk = pe.Node(niu.Function(function=_maskroi), name='csf_msk')
wm_msk = pe.Node(niu.Function(function=_maskroi), name='wm_msk')
acc_msk = pe.Node(niu.Function(function=_maskroi), name='acc_msk')
tcc_msk = pe.Node(niu.Function(function=_maskroi), name='tcc_msk')
# DVARS
dvars = pe.Node(nac.ComputeDVARS(save_all=True, remove_zerovariance=True),
name="dvars", mem_gb=mem_gb)
# Frame displacement
fdisp = pe.Node(nac.FramewiseDisplacement(parameter_source="SPM"),
name="fdisp", mem_gb=mem_gb)
# a/t-CompCor
non_steady_state = pe.Node(nac.NonSteadyStateDetector(), name='non_steady_state')
tcompcor = pe.Node(nac.TCompCor(
components_file='tcompcor.tsv', pre_filter='cosine', save_pre_filter=True,
percentile_threshold=.05), name="tcompcor", mem_gb=mem_gb)
acompcor = pe.Node(nac.ACompCor(
components_file='acompcor.tsv', pre_filter='cosine', save_pre_filter=True),
name="acompcor", mem_gb=mem_gb)
# Set TR if present
if 'RepetitionTime' in metadata:
tcompcor.inputs.repetition_time = metadata['RepetitionTime']
acompcor.inputs.repetition_time = metadata['RepetitionTime']
# Global and segment regressors
mrg_lbl = pe.Node(niu.Merge(3), name='merge_rois', run_without_submitting=True)
signals = pe.Node(SignalExtraction(
detrend=True, class_labels=["CSF", "WhiteMatter", "GlobalSignal"]),
name="signals", mem_gb=mem_gb)
# Arrange confounds
add_header = pe.Node(AddTSVHeader(columns=["X", "Y", "Z", "RotX", "RotY", "RotZ"]),
name="add_header", mem_gb=0.01, run_without_submitting=True)
concat = pe.Node(GatherConfounds(), name="concat", mem_gb=0.01, run_without_submitting=True)
# Generate reportlet
mrg_compcor = pe.Node(niu.Merge(2), name='merge_compcor', run_without_submitting=True)
rois_plot = pe.Node(ROIsPlot(compress_report=True, colors=['r', 'b', 'magenta'],
generate_report=True), name='rois_plot')
def _pick_csf(files):
return files[0]
def _pick_wm(files):
return files[-1]
workflow = pe.Workflow(name=name)
workflow.connect([
# Massage ROIs (in T1w space)
(inputnode, acc_tpm, [('t1_tpms', 'in_files')]),
(inputnode, csf_roi, [(('t1_tpms', _pick_csf), 'in_tpm'),
('t1_mask', 'in_mask')]),
(inputnode, wm_roi, [(('t1_tpms', _pick_wm), 'in_tpm'),
('t1_mask', 'in_mask')]),
(inputnode, acc_roi, [('t1_mask', 'in_mask')]),
(acc_tpm, acc_roi, [('out_file', 'in_tpm')]),
# Map ROIs to BOLD
(inputnode, csf_tfm, [('bold_mask', 'reference_image'),
('t1_bold_xform', 'transforms')]),
(csf_roi, csf_tfm, [('roi_file', 'input_image')]),
(inputnode, wm_tfm, [('bold_mask', 'reference_image'),
('t1_bold_xform', 'transforms')]),
(wm_roi, wm_tfm, [('roi_file', 'input_image')]),
(inputnode, acc_tfm, [('bold_mask', 'reference_image'),
('t1_bold_xform', 'transforms')]),
(acc_roi, acc_tfm, [('roi_file', 'input_image')]),
(inputnode, tcc_tfm, [('bold_mask', 'reference_image'),
('t1_bold_xform', 'transforms')]),
(csf_roi, tcc_tfm, [('eroded_mask', 'input_image')]),
# Mask ROIs with bold_mask
(inputnode, csf_msk, [('bold_mask', 'in_mask')]),
(inputnode, wm_msk, [('bold_mask', 'in_mask')]),
(inputnode, acc_msk, [('bold_mask', 'in_mask')]),
(inputnode, tcc_msk, [('bold_mask', 'in_mask')]),
# connect inputnode to each non-anatomical confound node
(inputnode, dvars, [('bold', 'in_file'),
('bold_mask', 'in_mask')]),
(inputnode, fdisp, [('movpar_file', 'in_file')]),
# Calculate nonsteady state
(inputnode, non_steady_state, [('bold', 'in_file')]),
# tCompCor
(inputnode, tcompcor, [('bold', 'realigned_file')]),
(non_steady_state, tcompcor, [('n_volumes_to_discard', 'ignore_initial_volumes')]),
(tcc_tfm, tcc_msk, [('output_image', 'roi_file')]),
(tcc_msk, tcompcor, [('out', 'mask_files')]),
# aCompCor
(inputnode, acompcor, [('bold', 'realigned_file')]),
(non_steady_state, acompcor, [('n_volumes_to_discard', 'ignore_initial_volumes')]),
(acc_tfm, acc_msk, [('output_image', 'roi_file')]),
(acc_msk, acompcor, [('out', 'mask_files')]),
# Global signals extraction (constrained by anatomy)
(inputnode, signals, [('bold', 'in_file')]),
(csf_tfm, csf_msk, [('output_image', 'roi_file')]),
(csf_msk, mrg_lbl, [('out', 'in1')]),
(wm_tfm, wm_msk, [('output_image', 'roi_file')]),
(wm_msk, mrg_lbl, [('out', 'in2')]),
(inputnode, mrg_lbl, [('bold_mask', 'in3')]),
(mrg_lbl, signals, [('out', 'label_files')]),
# Collate computed confounds together
(inputnode, add_header, [('movpar_file', 'in_file')]),
(signals, concat, [('out_file', 'signals')]),
(dvars, concat, [('out_all', 'dvars')]),
(fdisp, concat, [('out_file', 'fd')]),
(tcompcor, concat, [('components_file', 'tcompcor'),
('pre_filter_file', 'cos_basis')]),
(acompcor, concat, [('components_file', 'acompcor')]),
(add_header, concat, [('out_file', 'motion')]),
# Set outputs
(concat, outputnode, [('confounds_file', 'confounds_file'),
('confounds_list', 'confounds_list')]),
(inputnode, rois_plot, [('bold', 'in_file'),
('bold_mask', 'in_mask')]),
(tcompcor, mrg_compcor, [('high_variance_masks', 'in1')]),
(acc_msk, mrg_compcor, [('out', 'in2')]),
(mrg_compcor, rois_plot, [('out', 'in_rois')]),
(rois_plot, outputnode, [('out_report', 'rois_report')]),
])
if use_aroma:
# ICA-AROMA
ica_aroma_wf = init_ica_aroma_wf(name='ica_aroma_wf',
ignore_aroma_err=ignore_aroma_err)
workflow.connect([
(inputnode, ica_aroma_wf, [('bold_mni', 'inputnode.bold_mni'),
('bold_mask_mni', 'inputnode.bold_mask_mni'),
('movpar_file', 'inputnode.movpar_file')]),
(ica_aroma_wf, concat,
[('outputnode.aroma_confounds', 'aroma')]),
(ica_aroma_wf, outputnode,
[('outputnode.out_report', 'ica_aroma_report'),
('outputnode.aroma_noise_ics', 'aroma_noise_ics'),
('outputnode.melodic_mix', 'melodic_mix'),
('outputnode.nonaggr_denoised_file', 'nonaggr_denoised_file')])
])
return workflow
def init_bold_confs_wf(
mem_gb,
metadata,
regressors_all_comps,
regressors_dvars_th,
regressors_fd_th,
freesurfer=False,
name="bold_confs_wf",
):
"""
Build a workflow to generate and write out confounding signals.
This workflow calculates confounds for a BOLD series, and aggregates them
into a :abbr:`TSV (tab-separated value)` file, for use as nuisance
regressors in a :abbr:`GLM (general linear model)`.
The following confounds are calculated, with column headings in parentheses:
#. Region-wise average signal (``csf``, ``white_matter``, ``global_signal``)
#. DVARS - original and standardized variants (``dvars``, ``std_dvars``)
#. Framewise displacement, based on head-motion parameters
(``framewise_displacement``)
#. Temporal CompCor (``t_comp_cor_XX``)
#. Anatomical CompCor (``a_comp_cor_XX``)
#. Cosine basis set for high-pass filtering w/ 0.008 Hz cut-off
(``cosine_XX``)
#. Non-steady-state volumes (``non_steady_state_XX``)
#. Estimated head-motion parameters, in mm and rad
(``trans_x``, ``trans_y``, ``trans_z``, ``rot_x``, ``rot_y``, ``rot_z``)
Prior to estimating aCompCor and tCompCor, non-steady-state volumes are
censored and high-pass filtered using a :abbr:`DCT (discrete cosine
transform)` basis.
The cosine basis, as well as one regressor per censored volume, are included
for convenience.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold.confounds import init_bold_confs_wf
wf = init_bold_confs_wf(
mem_gb=1,
metadata={},
regressors_all_comps=False,
regressors_dvars_th=1.5,
regressors_fd_th=0.5,
)
Parameters
----------
mem_gb : :obj:`float`
Size of BOLD file in GB - please note that this size
should be calculated after resamplings that may extend
the FoV
metadata : :obj:`dict`
BIDS metadata for BOLD file
name : :obj:`str`
Name of workflow (default: ``bold_confs_wf``)
regressors_all_comps : :obj:`bool`
Indicates whether CompCor decompositions should return all
components instead of the minimal number of components necessary
to explain 50 percent of the variance in the decomposition mask.
regressors_dvars_th : :obj:`float`
Criterion for flagging DVARS outliers
regressors_fd_th : :obj:`float`
Criterion for flagging framewise displacement outliers
Inputs
------
bold
BOLD image, after the prescribed corrections (STC, HMC and SDC)
when available.
bold_mask
BOLD series mask
movpar_file
SPM-formatted motion parameters file
rmsd_file
Framewise displacement as measured by ``fsl_motion_outliers``.
skip_vols
number of non steady state volumes
t1w_mask
Mask of the skull-stripped template image
t1w_tpms
List of tissue probability maps in T1w space
t1_bold_xform
Affine matrix that maps the T1w space into alignment with
the native BOLD space
Outputs
-------
confounds_file
TSV of all aggregated confounds
rois_report
Reportlet visualizing white-matter/CSF mask used for aCompCor,
the ROI for tCompCor and the BOLD brain mask.
confounds_metadata
Confounds metadata dictionary.
"""
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.confounds import ExpandModel, SpikeRegressors
from niworkflows.interfaces.fixes import FixHeaderApplyTransforms as ApplyTransforms
from niworkflows.interfaces.images import SignalExtraction
from niworkflows.interfaces.masks import ROIsPlot
from niworkflows.interfaces.nibabel import ApplyMask, Binarize
from niworkflows.interfaces.patches import (
RobustACompCor as ACompCor,
RobustTCompCor as TCompCor,
)
from niworkflows.interfaces.plotting import (CompCorVariancePlot,
ConfoundsCorrelationPlot)
from niworkflows.interfaces.utils import (AddTSVHeader, TSV2JSON,
DictMerge)
from ...interfaces.confounds import aCompCorMasks
gm_desc = (
"dilating a GM mask extracted from the FreeSurfer's *aseg* segmentation"
if freesurfer else
"thresholding the corresponding partial volume map at 0.05")
workflow = Workflow(name=name)
workflow.__desc__ = f"""\
Several confounding time-series were calculated based on the
*preprocessed BOLD*: framewise displacement (FD), DVARS and
three region-wise global signals.
FD was computed using two formulations following Power (absolute sum of
relative motions, @power_fd_dvars) and Jenkinson (relative root mean square
displacement between affines, @mcflirt).
FD and DVARS are calculated for each functional run, both using their
implementations in *Nipype* [following the definitions by @power_fd_dvars].
The three global signals are extracted within the CSF, the WM, and
the whole-brain masks.
Additionally, a set of physiological regressors were extracted to
allow for component-based noise correction [*CompCor*, @compcor].
Principal components are estimated after high-pass filtering the
*preprocessed BOLD* time-series (using a discrete cosine filter with
128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
and anatomical (aCompCor).
tCompCor components are then calculated from the top 2% variable
voxels within the brain mask.
For aCompCor, three probabilistic masks (CSF, WM and combined CSF+WM)
are generated in anatomical space.
The implementation differs from that of Behzadi et al. in that instead
of eroding the masks by 2 pixels on BOLD space, the aCompCor masks are
subtracted a mask of pixels that likely contain a volume fraction of GM.
This mask is obtained by {gm_desc}, and it ensures components are not extracted
from voxels containing a minimal fraction of GM.
Finally, these masks are resampled into BOLD space and binarized by
thresholding at 0.99 (as in the original implementation).
Components are also calculated separately within the WM and CSF masks.
For each CompCor decomposition, the *k* components with the largest singular
values are retained, such that the retained components' time series are
sufficient to explain 50 percent of variance across the nuisance mask (CSF,
WM, combined, or temporal). The remaining components are dropped from
consideration.
The head-motion estimates calculated in the correction step were also
placed within the corresponding confounds file.
The confound time series derived from head motion estimates and global
signals were expanded with the inclusion of temporal derivatives and
quadratic terms for each [@confounds_satterthwaite_2013].
Frames that exceeded a threshold of {regressors_fd_th} mm FD or
{regressors_dvars_th} standardised DVARS were annotated as motion outliers.
"""
inputnode = pe.Node(niu.IdentityInterface(fields=[
'bold', 'bold_mask', 'movpar_file', 'rmsd_file', 'skip_vols',
't1w_mask', 't1w_tpms', 't1_bold_xform'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'confounds_file', 'confounds_metadata', 'acompcor_masks',
'tcompcor_mask'
]),
name='outputnode')
# DVARS
dvars = pe.Node(nac.ComputeDVARS(save_nstd=True,
save_std=True,
remove_zerovariance=True),
name="dvars",
mem_gb=mem_gb)
# Frame displacement
fdisp = pe.Node(nac.FramewiseDisplacement(parameter_source="SPM"),
name="fdisp",
mem_gb=mem_gb)
# Generate aCompCor probseg maps
acc_masks = pe.Node(aCompCorMasks(is_aseg=freesurfer), name="acc_masks")
# Resample probseg maps in BOLD space via T1w-to-BOLD transform
acc_msk_tfm = pe.MapNode(ApplyTransforms(interpolation='Gaussian',
float=False),
iterfield=["input_image"],
name='acc_msk_tfm',
mem_gb=0.1)
acc_msk_brain = pe.MapNode(ApplyMask(),
name="acc_msk_brain",
iterfield=["in_file"])
acc_msk_bin = pe.MapNode(Binarize(thresh_low=0.99),
name='acc_msk_bin',
iterfield=["in_file"])
acompcor = pe.Node(ACompCor(components_file='acompcor.tsv',
header_prefix='a_comp_cor_',
pre_filter='cosine',
save_pre_filter=True,
save_metadata=True,
mask_names=['CSF', 'WM', 'combined'],
merge_method='none',
failure_mode='NaN'),
name="acompcor",
mem_gb=mem_gb)
tcompcor = pe.Node(TCompCor(components_file='tcompcor.tsv',
header_prefix='t_comp_cor_',
pre_filter='cosine',
save_pre_filter=True,
save_metadata=True,
percentile_threshold=.02,
failure_mode='NaN'),
name="tcompcor",
mem_gb=mem_gb)
# Set number of components
if regressors_all_comps:
acompcor.inputs.num_components = 'all'
tcompcor.inputs.num_components = 'all'
else:
acompcor.inputs.variance_threshold = 0.5
tcompcor.inputs.variance_threshold = 0.5
# Set TR if present
if 'RepetitionTime' in metadata:
tcompcor.inputs.repetition_time = metadata['RepetitionTime']
acompcor.inputs.repetition_time = metadata['RepetitionTime']
# Global and segment regressors
signals_class_labels = [
"global_signal",
"csf",
"white_matter",
"csf_wm",
"tcompcor",
]
merge_rois = pe.Node(niu.Merge(3, ravel_inputs=True),
name='merge_rois',
run_without_submitting=True)
signals = pe.Node(SignalExtraction(class_labels=signals_class_labels),
name="signals",
mem_gb=mem_gb)
# Arrange confounds
add_dvars_header = pe.Node(AddTSVHeader(columns=["dvars"]),
name="add_dvars_header",
mem_gb=0.01,
run_without_submitting=True)
add_std_dvars_header = pe.Node(AddTSVHeader(columns=["std_dvars"]),
name="add_std_dvars_header",
mem_gb=0.01,
run_without_submitting=True)
add_motion_headers = pe.Node(AddTSVHeader(
columns=["trans_x", "trans_y", "trans_z", "rot_x", "rot_y", "rot_z"]),
name="add_motion_headers",
mem_gb=0.01,
run_without_submitting=True)
add_rmsd_header = pe.Node(AddTSVHeader(columns=["rmsd"]),
name="add_rmsd_header",
mem_gb=0.01,
run_without_submitting=True)
concat = pe.Node(GatherConfounds(),
name="concat",
mem_gb=0.01,
run_without_submitting=True)
# CompCor metadata
tcc_metadata_fmt = pe.Node(TSV2JSON(
index_column='component',
drop_columns=['mask'],
output=None,
additional_metadata={'Method': 'tCompCor'},
enforce_case=True),
name='tcc_metadata_fmt')
acc_metadata_fmt = pe.Node(TSV2JSON(
index_column='component',
output=None,
additional_metadata={'Method': 'aCompCor'},
enforce_case=True),
name='acc_metadata_fmt')
mrg_conf_metadata = pe.Node(niu.Merge(3),
name='merge_confound_metadata',
run_without_submitting=True)
mrg_conf_metadata.inputs.in3 = {
label: {
'Method': 'Mean'
}
for label in signals_class_labels
}
mrg_conf_metadata2 = pe.Node(DictMerge(),
name='merge_confound_metadata2',
run_without_submitting=True)
# Expand model to include derivatives and quadratics
model_expand = pe.Node(
ExpandModel(model_formula='(dd1(rps + wm + csf + gsr))^^2 + others'),
name='model_expansion')
# Add spike regressors
spike_regress = pe.Node(SpikeRegressors(fd_thresh=regressors_fd_th,
dvars_thresh=regressors_dvars_th),
name='spike_regressors')
# Generate reportlet (ROIs)
mrg_compcor = pe.Node(niu.Merge(2, ravel_inputs=True),
name='mrg_compcor',
run_without_submitting=True)
rois_plot = pe.Node(ROIsPlot(colors=['b', 'magenta'],
generate_report=True),
name='rois_plot',
mem_gb=mem_gb)
ds_report_bold_rois = pe.Node(DerivativesDataSink(
desc='rois', datatype="figures", dismiss_entities=("echo", )),
name='ds_report_bold_rois',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
# Generate reportlet (CompCor)
mrg_cc_metadata = pe.Node(niu.Merge(2),
name='merge_compcor_metadata',
run_without_submitting=True)
compcor_plot = pe.Node(CompCorVariancePlot(
variance_thresholds=(0.5, 0.7, 0.9),
metadata_sources=['tCompCor', 'aCompCor']),
name='compcor_plot')
ds_report_compcor = pe.Node(DerivativesDataSink(
desc='compcorvar', datatype="figures", dismiss_entities=("echo", )),
name='ds_report_compcor',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
# Generate reportlet (Confound correlation)
conf_corr_plot = pe.Node(ConfoundsCorrelationPlot(
reference_column='global_signal', max_dim=20),
name='conf_corr_plot')
ds_report_conf_corr = pe.Node(DerivativesDataSink(
desc='confoundcorr', datatype="figures", dismiss_entities=("echo", )),
name='ds_report_conf_corr',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
def _last(inlist):
return inlist[-1]
def _select_cols(table):
import pandas as pd
return [
col for col in pd.read_table(table, nrows=2).columns
if not col.startswith(("a_comp_cor_", "t_comp_cor_", "std_dvars"))
]
workflow.connect([
# connect inputnode to each non-anatomical confound node
(inputnode, dvars, [('bold', 'in_file'), ('bold_mask', 'in_mask')]),
(inputnode, fdisp, [('movpar_file', 'in_file')]),
# aCompCor
(inputnode, acompcor, [("bold", "realigned_file"),
("skip_vols", "ignore_initial_volumes")]),
(inputnode, acc_masks, [("t1w_tpms", "in_vfs"),
(("bold", _get_zooms), "bold_zooms")]),
(inputnode, acc_msk_tfm, [("t1_bold_xform", "transforms"),
("bold_mask", "reference_image")]),
(inputnode, acc_msk_brain, [("bold_mask", "in_mask")]),
(acc_masks, acc_msk_tfm, [("out_masks", "input_image")]),
(acc_msk_tfm, acc_msk_brain, [("output_image", "in_file")]),
(acc_msk_brain, acc_msk_bin, [("out_file", "in_file")]),
(acc_msk_bin, acompcor, [("out_file", "mask_files")]),
# tCompCor
(inputnode, tcompcor, [("bold", "realigned_file"),
("skip_vols", "ignore_initial_volumes"),
("bold_mask", "mask_files")]),
# Global signals extraction (constrained by anatomy)
(inputnode, signals, [('bold', 'in_file')]),
(inputnode, merge_rois, [('bold_mask', 'in1')]),
(acc_msk_bin, merge_rois, [('out_file', 'in2')]),
(tcompcor, merge_rois, [('high_variance_masks', 'in3')]),
(merge_rois, signals, [('out', 'label_files')]),
# Collate computed confounds together
(inputnode, add_motion_headers, [('movpar_file', 'in_file')]),
(inputnode, add_rmsd_header, [('rmsd_file', 'in_file')]),
(dvars, add_dvars_header, [('out_nstd', 'in_file')]),
(dvars, add_std_dvars_header, [('out_std', 'in_file')]),
(signals, concat, [('out_file', 'signals')]),
(fdisp, concat, [('out_file', 'fd')]),
(tcompcor, concat, [('components_file', 'tcompcor'),
('pre_filter_file', 'cos_basis')]),
(acompcor, concat, [('components_file', 'acompcor')]),
(add_motion_headers, concat, [('out_file', 'motion')]),
(add_rmsd_header, concat, [('out_file', 'rmsd')]),
(add_dvars_header, concat, [('out_file', 'dvars')]),
(add_std_dvars_header, concat, [('out_file', 'std_dvars')]),
# Confounds metadata
(tcompcor, tcc_metadata_fmt, [('metadata_file', 'in_file')]),
(acompcor, acc_metadata_fmt, [('metadata_file', 'in_file')]),
(tcc_metadata_fmt, mrg_conf_metadata, [('output', 'in1')]),
(acc_metadata_fmt, mrg_conf_metadata, [('output', 'in2')]),
(mrg_conf_metadata, mrg_conf_metadata2, [('out', 'in_dicts')]),
# Expand the model with derivatives, quadratics, and spikes
(concat, model_expand, [('confounds_file', 'confounds_file')]),
(model_expand, spike_regress, [('confounds_file', 'confounds_file')]),
# Set outputs
(spike_regress, outputnode, [('confounds_file', 'confounds_file')]),
(mrg_conf_metadata2, outputnode, [('out_dict', 'confounds_metadata')]),
(tcompcor, outputnode, [("high_variance_masks", "tcompcor_mask")]),
(acc_msk_bin, outputnode, [("out_file", "acompcor_masks")]),
(inputnode, rois_plot, [('bold', 'in_file'),
('bold_mask', 'in_mask')]),
(tcompcor, mrg_compcor, [('high_variance_masks', 'in1')]),
(acc_msk_bin, mrg_compcor, [(('out_file', _last), 'in2')]),
(mrg_compcor, rois_plot, [('out', 'in_rois')]),
(rois_plot, ds_report_bold_rois, [('out_report', 'in_file')]),
(tcompcor, mrg_cc_metadata, [('metadata_file', 'in1')]),
(acompcor, mrg_cc_metadata, [('metadata_file', 'in2')]),
(mrg_cc_metadata, compcor_plot, [('out', 'metadata_files')]),
(compcor_plot, ds_report_compcor, [('out_file', 'in_file')]),
(concat, conf_corr_plot, [('confounds_file', 'confounds_file'),
(('confounds_file', _select_cols), 'columns')
]),
(conf_corr_plot, ds_report_conf_corr, [('out_file', 'in_file')]),
])
return workflow
def init_anat_preproc_wf(bids_root,
freesurfer,
hires,
longitudinal,
omp_nthreads,
output_dir,
output_spaces,
num_t1w,
reportlets_dir,
skull_strip_template,
debug=False,
name='anat_preproc_wf',
skull_strip_fixed_seed=False):
"""
This workflow controls the anatomical preprocessing stages of smriprep.
This includes:
- Creation of a structural template
- Skull-stripping and bias correction
- Tissue segmentation
- Normalization
- Surface reconstruction with FreeSurfer
.. workflow::
:graph2use: orig
:simple_form: yes
from collections import OrderedDict
from smriprep.workflows.anatomical import init_anat_preproc_wf
wf = init_anat_preproc_wf(
bids_root='.',
freesurfer=True,
hires=True,
longitudinal=False,
num_t1w=1,
omp_nthreads=1,
output_dir='.',
output_spaces=OrderedDict([
('MNI152NLin2009cAsym', {}), ('fsaverage5', {})]),
reportlets_dir='.',
skull_strip_template=('MNI152NLin2009cAsym', {}),
)
**Parameters**
bids_root : str
Path of the input BIDS dataset root
debug : bool
Enable debugging outputs
freesurfer : bool
Enable FreeSurfer surface reconstruction (increases runtime by 6h,
at the very least)
output_spaces : list
List of spatial normalization targets. Some parts of pipeline will
only be instantiated for some output spaces. Valid spaces:
- Any template identifier from TemplateFlow
- Path to a template folder organized following TemplateFlow's
conventions
hires : bool
Enable sub-millimeter preprocessing in FreeSurfer
longitudinal : bool
Create unbiased structural template, regardless of number of inputs
(may increase runtime)
name : str, optional
Workflow name (default: anat_preproc_wf)
omp_nthreads : int
Maximum number of threads an individual process may use
output_dir : str
Directory in which to save derivatives
reportlets_dir : str
Directory in which to save reportlets
skull_strip_fixed_seed : bool
Do not use a random seed for skull-stripping - will ensure
run-to-run replicability when used with --omp-nthreads 1
(default: ``False``).
skull_strip_template : tuple
Name of ANTs skull-stripping template and specifications.
**Inputs**
t1w
List of T1-weighted structural images
t2w
List of T2-weighted structural images
flair
List of FLAIR images
subjects_dir
FreeSurfer SUBJECTS_DIR
**Outputs**
t1_preproc
Bias-corrected structural template, defining T1w space
t1_brain
Skull-stripped ``t1_preproc``
t1_mask
Mask of the skull-stripped template image
t1_seg
Segmentation of preprocessed structural image, including
gray-matter (GM), white-matter (WM) and cerebrospinal fluid (CSF)
t1_tpms
List of tissue probability maps in T1w space
t1_2_tpl
T1w template, normalized to MNI space
t1_2_tpl_forward_transform
ANTs-compatible affine-and-warp transform file
t1_2_tpl_reverse_transform
ANTs-compatible affine-and-warp transform file (inverse)
tpl_mask
Mask of skull-stripped template, in MNI space
tpl_seg
Segmentation, resampled into MNI space
tpl_tpms
List of tissue probability maps in MNI space
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
t1_2_fsnative_reverse_transform
LTA-style affine matrix translating from FreeSurfer-conformed
subject space to T1w
surfaces
GIFTI surfaces (gray/white boundary, midthickness, pial, inflated)
**Subworkflows**
* :py:func:`~niworkflows.anat.ants.init_brain_extraction_wf`
* :py:func:`~smriprep.workflows.surfaces.init_surface_recon_wf`
"""
workflow = Workflow(name=name)
desc = """Anatomical data preprocessing
: """
desc += """\
A total of {num_t1w} T1-weighted (T1w) images were found within the input
BIDS dataset.
All of them were corrected for intensity non-uniformity (INU)
""" if num_t1w > 1 else """\
The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
"""
desc += """\
with `N4BiasFieldCorrection` [@n4], distributed with ANTs {ants_ver} \
[@ants, RRID:SCR_004757]"""
desc += '.\n' if num_t1w > 1 else ", and used as T1w-reference throughout the workflow.\n"
desc += """\
The T1w-reference was then skull-stripped with a *Nipype* implementation of
the `antsBrainExtraction.sh` workflow (from ANTs), using {skullstrip_tpl}
as target template.
Brain tissue segmentation of cerebrospinal fluid (CSF),
white-matter (WM) and gray-matter (GM) was performed on
the brain-extracted T1w using `fast` [FSL {fsl_ver}, RRID:SCR_002823,
@fsl_fast].
"""
workflow.__desc__ = desc.format(
ants_ver=ANTsInfo.version() or '(version unknown)',
fsl_ver=fsl.FAST().version or '(version unknown)',
num_t1w=num_t1w,
skullstrip_tpl=skull_strip_template[0],
)
inputnode = pe.Node(niu.IdentityInterface(
fields=['t1w', 't2w', 'roi', 'flair', 'subjects_dir', 'subject_id']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
't1_preproc', 't1_brain', 't1_mask', 't1_seg', 't1_tpms', 'template',
'warped', 'forward_transform', 'reverse_transform', 'joint_template',
'joint_forward_transform', 'joint_reverse_transform', 'tpl_mask',
'tpl_seg', 'tpl_tpms', 'template_transforms', 'subjects_dir',
'subject_id', 't1_2_fsnative_forward_transform',
't1_2_fsnative_reverse_transform', 'surfaces', 't1_aseg', 't1_aparc'
]),
name='outputnode')
buffernode = pe.Node(niu.IdentityInterface(fields=['t1_brain', 't1_mask']),
name='buffernode')
anat_template_wf = init_anat_template_wf(longitudinal=longitudinal,
omp_nthreads=omp_nthreads,
num_t1w=num_t1w)
anat_validate = pe.Node(ValidateImage(),
name='anat_validate',
run_without_submitting=True)
# 3. Skull-stripping
# Bias field correction is handled in skull strip workflows.
brain_extraction_wf = init_brain_extraction_wf(
in_template=skull_strip_template[0],
template_spec=skull_strip_template[1],
atropos_use_random_seed=not skull_strip_fixed_seed,
omp_nthreads=omp_nthreads,
normalization_quality='precise' if not debug else 'testing')
workflow.connect([
(inputnode, anat_template_wf, [('t1w', 'inputnode.t1w')]),
(anat_template_wf, anat_validate, [('outputnode.t1_template',
'in_file')]),
(anat_validate, brain_extraction_wf, [('out_file',
'inputnode.in_files')]),
(brain_extraction_wf, outputnode, [('outputnode.bias_corrected',
't1_preproc')]),
(anat_template_wf, outputnode, [('outputnode.template_transforms',
't1_template_transforms')]),
(buffernode, outputnode, [('t1_brain', 't1_brain'),
('t1_mask', 't1_mask')]),
])
# 4. Surface reconstruction
if freesurfer:
surface_recon_wf = init_surface_recon_wf(name='surface_recon_wf',
omp_nthreads=omp_nthreads,
hires=hires)
applyrefined = pe.Node(fsl.ApplyMask(), name='applyrefined')
workflow.connect([
(inputnode, surface_recon_wf,
[('t2w', 'inputnode.t2w'), ('flair', 'inputnode.flair'),
('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id')]),
(anat_validate, surface_recon_wf, [('out_file', 'inputnode.t1w')]),
(brain_extraction_wf, surface_recon_wf, [
(('outputnode.out_file', _pop), 'inputnode.skullstripped_t1'),
('outputnode.out_segm', 'inputnode.ants_segs'),
(('outputnode.bias_corrected', _pop), 'inputnode.corrected_t1')
]),
(brain_extraction_wf, applyrefined, [(('outputnode.bias_corrected',
_pop), 'in_file')]),
(surface_recon_wf, applyrefined, [('outputnode.out_brainmask',
'mask_file')]),
(surface_recon_wf, outputnode,
[('outputnode.subjects_dir', 'subjects_dir'),
('outputnode.subject_id', 'subject_id'),
('outputnode.t1_2_fsnative_forward_transform',
't1_2_fsnative_forward_transform'),
('outputnode.t1_2_fsnative_reverse_transform',
't1_2_fsnative_reverse_transform'),
('outputnode.surfaces', 'surfaces'),
('outputnode.out_aseg', 't1_aseg'),
('outputnode.out_aparc', 't1_aparc')]),
(applyrefined, buffernode, [('out_file', 't1_brain')]),
(surface_recon_wf, buffernode, [('outputnode.out_brainmask',
't1_mask')]),
])
else:
workflow.connect([
(brain_extraction_wf, buffernode,
[(('outputnode.out_file', _pop), 't1_brain'),
('outputnode.out_mask', 't1_mask')]),
])
# 5. Segmentation
t1_seg = pe.Node(fsl.FAST(segments=True,
no_bias=True,
probability_maps=True),
name='t1_seg',
mem_gb=3)
workflow.connect([
(buffernode, t1_seg, [('t1_brain', 'in_files')]),
(t1_seg, outputnode, [('tissue_class_map', 't1_seg'),
('probability_maps', 't1_tpms')]),
])
seg_rpt = pe.Node(ROIsPlot(colors=['magenta', 'b'], levels=[1.5, 2.5]),
name='seg_rpt')
vol_spaces = [k for k in output_spaces.keys() if not k.startswith('fs')]
# 6. Spatial normalization
anat_norm_wf = init_anat_norm_wf(
debug=debug,
omp_nthreads=omp_nthreads,
reportlets_dir=reportlets_dir,
template_list=vol_spaces,
template_specs=[output_spaces[k] for k in vol_spaces])
workflow.connect([
(inputnode, anat_norm_wf, [(('t1w', fix_multi_T1w_source_name),
'inputnode.orig_t1w'),
('roi', 'inputnode.lesion_mask')]),
(brain_extraction_wf, anat_norm_wf,
[(('outputnode.bias_corrected', _pop), 'inputnode.moving_image')]),
(buffernode, anat_norm_wf, [('t1_mask', 'inputnode.moving_mask')]),
(t1_seg, anat_norm_wf, [('tissue_class_map',
'inputnode.moving_segmentation')]),
(t1_seg, anat_norm_wf, [('probability_maps', 'inputnode.moving_tpms')
]),
(anat_norm_wf, outputnode, [
('poutputnode.warped', 'warped'),
('poutputnode.template', 'template'),
('poutputnode.forward_transform', 'forward_transform'),
('poutputnode.reverse_transform', 'reverse_transform'),
('poutputnode.tpl_mask', 'tpl_mask'),
('poutputnode.tpl_seg', 'tpl_seg'),
('poutputnode.tpl_tpms', 'tpl_tpms'),
('outputnode.template', 'joint_template'),
('outputnode.forward_transform', 'joint_forward_transform'),
('outputnode.reverse_transform', 'joint_reverse_transform'),
]),
])
anat_reports_wf = init_anat_reports_wf(reportlets_dir=reportlets_dir,
freesurfer=freesurfer)
workflow.connect([
(inputnode, anat_reports_wf, [(('t1w', fix_multi_T1w_source_name),
'inputnode.source_file')]),
(anat_template_wf, anat_reports_wf, [('outputnode.out_report',
'inputnode.t1_conform_report')]),
(anat_template_wf, seg_rpt, [('outputnode.t1_template', 'in_file')]),
(t1_seg, seg_rpt, [('tissue_class_map', 'in_rois')]),
(outputnode, seg_rpt, [('t1_mask', 'in_mask')]),
(seg_rpt, anat_reports_wf, [('out_report', 'inputnode.seg_report')]),
])
if freesurfer:
workflow.connect([
(surface_recon_wf, anat_reports_wf, [('outputnode.out_report',
'inputnode.recon_report')]),
])
anat_derivatives_wf = init_anat_derivatives_wf(
bids_root=bids_root,
freesurfer=freesurfer,
output_dir=output_dir,
)
workflow.connect([
(anat_template_wf, anat_derivatives_wf, [('outputnode.t1w_valid_list',
'inputnode.source_files')]),
(anat_norm_wf, anat_derivatives_wf, [('poutputnode.template',
'inputnode.template')]),
(outputnode, anat_derivatives_wf, [
('warped', 'inputnode.t1_2_tpl'),
('forward_transform', 'inputnode.t1_2_tpl_forward_transform'),
('reverse_transform', 'inputnode.t1_2_tpl_reverse_transform'),
('t1_template_transforms', 'inputnode.t1_template_transforms'),
('t1_preproc', 'inputnode.t1_preproc'),
('t1_mask', 'inputnode.t1_mask'),
('t1_seg', 'inputnode.t1_seg'),
('t1_tpms', 'inputnode.t1_tpms'),
('tpl_mask', 'inputnode.tpl_mask'),
('tpl_seg', 'inputnode.tpl_seg'),
('tpl_tpms', 'inputnode.tpl_tpms'),
('t1_2_fsnative_forward_transform',
'inputnode.t1_2_fsnative_forward_transform'),
('surfaces', 'inputnode.surfaces'),
]),
])
if freesurfer:
workflow.connect([
(surface_recon_wf, anat_derivatives_wf, [
('outputnode.out_aseg', 'inputnode.t1_fs_aseg'),
('outputnode.out_aparc', 'inputnode.t1_fs_aparc'),
]),
])
return workflow
def init_anat_preproc_wf(
bids_root, freesurfer, fs_spaces, hires, longitudinal,
omp_nthreads, output_dir, num_t1w, reportlets_dir,
skull_strip_template, template,
debug=False, name='anat_preproc_wf', skull_strip_fixed_seed=False):
r"""
This workflow controls the anatomical preprocessing stages of smriprep.
This includes:
- Creation of a structural template
- Skull-stripping and bias correction
- Tissue segmentation
- Normalization
- Surface reconstruction with FreeSurfer
.. workflow::
:graph2use: orig
:simple_form: yes
from smriprep.workflows.anatomical import init_anat_preproc_wf
wf = init_anat_preproc_wf(
bids_root='.',
freesurfer=True,
fs_spaces=['T1w', 'fsnative',
'template', 'fsaverage5'],
hires=True,
longitudinal=False,
omp_nthreads=1,
output_dir='.',
num_t1w=1,
reportlets_dir='.',
skull_strip_template='MNI152NLin2009cAsym',
template='MNI152NLin2009cAsym',
)
**Parameters**
bids_root : str
Path of the input BIDS dataset root
debug : bool
Enable debugging outputs
freesurfer : bool
Enable FreeSurfer surface reconstruction (increases runtime by 6h, at the very least)
fs_spaces : list
List of output spaces functional images are to be resampled to.
Some pipeline components will only be instantiated for some output spaces.
Valid spaces:
- T1w
- template
- fsnative
- fsaverage (or other pre-existing FreeSurfer templates)
hires : bool
Enable sub-millimeter preprocessing in FreeSurfer
longitudinal : bool
Create unbiased structural template, regardless of number of inputs
(may increase runtime)
name : str, optional
Workflow name (default: anat_preproc_wf)
omp_nthreads : int
Maximum number of threads an individual process may use
output_dir : str
Directory in which to save derivatives
reportlets_dir : str
Directory in which to save reportlets
skull_strip_fixed_seed : bool
Do not use a random seed for skull-stripping - will ensure
run-to-run replicability when used with --omp-nthreads 1 (default: ``False``)
skull_strip_template : str
Name of ANTs skull-stripping template ('MNI152NLin2009cAsym', 'OASIS30ANTs' or 'NKI')
template : str
Name of template targeted by ``template`` output space
**Inputs**
t1w
List of T1-weighted structural images
t2w
List of T2-weighted structural images
flair
List of FLAIR images
subjects_dir
FreeSurfer SUBJECTS_DIR
**Outputs**
t1_preproc
Bias-corrected structural template, defining T1w space
t1_brain
Skull-stripped ``t1_preproc``
t1_mask
Mask of the skull-stripped template image
t1_seg
Segmentation of preprocessed structural image, including
gray-matter (GM), white-matter (WM) and cerebrospinal fluid (CSF)
t1_tpms
List of tissue probability maps in T1w space
t1_2_mni
T1w template, normalized to MNI space
t1_2_mni_forward_transform
ANTs-compatible affine-and-warp transform file
t1_2_mni_reverse_transform
ANTs-compatible affine-and-warp transform file (inverse)
mni_mask
Mask of skull-stripped template, in MNI space
mni_seg
Segmentation, resampled into MNI space
mni_tpms
List of tissue probability maps in MNI space
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
t1_2_fsnative_reverse_transform
LTA-style affine matrix translating from FreeSurfer-conformed subject space to T1w
surfaces
GIFTI surfaces (gray/white boundary, midthickness, pial, inflated)
**Subworkflows**
* :py:func:`~niworkflows.anat.ants.init_brain_extraction_wf`
* :py:func:`~smriprep.workflows.surfaces.init_surface_recon_wf`
"""
if isinstance(template, list): # THIS SHOULD BE DELETED
template = template[0]
template_meta = get_metadata(template)
template_refs = ['@%s' % template.lower()]
if template_meta.get('RRID', None):
template_refs += ['RRID:%s' % template_meta['RRID']]
workflow = Workflow(name=name)
workflow.__postdesc__ = """\
Spatial normalization to the
*{template_name}* [{template_refs}]
was performed through nonlinear registration with `antsRegistration`
(ANTs {ants_ver}), using brain-extracted versions of both T1w volume
and template.
Brain tissue segmentation of cerebrospinal fluid (CSF),
white-matter (WM) and gray-matter (GM) was performed on
the brain-extracted T1w using `fast` [FSL {fsl_ver}, RRID:SCR_002823,
@fsl_fast].
""".format(
ants_ver=ANTsInfo.version() or '<ver>',
fsl_ver=fsl.FAST().version or '<ver>',
template_name=template_meta['Name'],
template_refs=', '.join(template_refs),
)
desc = """Anatomical data preprocessing
: """
desc += """\
A total of {num_t1w} T1-weighted (T1w) images were found within the input
BIDS dataset.
All of them were corrected for intensity non-uniformity (INU)
""" if num_t1w > 1 else """\
The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
"""
desc += """\
with `N4BiasFieldCorrection` [@n4], distributed with ANTs {ants_ver} \
[@ants, RRID:SCR_004757]"""
desc += '.\n' if num_t1w > 1 else ", and used as T1w-reference throughout the workflow.\n"
desc += """\
The T1w-reference was then skull-stripped with a *Nipype* implementation of
the `antsBrainExtraction.sh` workflow (from ANTs), using {skullstrip_tpl}
as target template.
""".format(skullstrip_tpl=skull_strip_template)
workflow.__desc__ = desc.format(
num_t1w=num_t1w,
ants_ver=ANTsInfo.version() or '<ver>'
)
inputnode = pe.Node(
niu.IdentityInterface(fields=['t1w', 't2w', 'roi', 'flair', 'subjects_dir', 'subject_id']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['t1_preproc', 't1_brain', 't1_mask', 't1_seg', 't1_tpms',
't1_2_mni', 't1_2_mni_forward_transform', 't1_2_mni_reverse_transform',
'mni_mask', 'mni_seg', 'mni_tpms',
'template_transforms',
'subjects_dir', 'subject_id', 't1_2_fsnative_forward_transform',
't1_2_fsnative_reverse_transform', 'surfaces', 't1_aseg', 't1_aparc']),
name='outputnode')
buffernode = pe.Node(niu.IdentityInterface(
fields=['t1_brain', 't1_mask']), name='buffernode')
anat_template_wf = init_anat_template_wf(longitudinal=longitudinal, omp_nthreads=omp_nthreads,
num_t1w=num_t1w)
# 3. Skull-stripping
# Bias field correction is handled in skull strip workflows.
brain_extraction_wf = init_brain_extraction_wf(
in_template=skull_strip_template,
atropos_use_random_seed=not skull_strip_fixed_seed,
omp_nthreads=omp_nthreads,
normalization_quality='precise' if not debug else 'testing')
workflow.connect([
(inputnode, anat_template_wf, [('t1w', 'inputnode.t1w')]),
(anat_template_wf, brain_extraction_wf, [
('outputnode.t1_template', 'inputnode.in_files')]),
(brain_extraction_wf, outputnode, [
('outputnode.bias_corrected', 't1_preproc')]),
(anat_template_wf, outputnode, [
('outputnode.template_transforms', 't1_template_transforms')]),
(buffernode, outputnode, [('t1_brain', 't1_brain'),
('t1_mask', 't1_mask')]),
])
# 4. Surface reconstruction
if freesurfer:
surface_recon_wf = init_surface_recon_wf(name='surface_recon_wf',
omp_nthreads=omp_nthreads, hires=hires)
applyrefined = pe.Node(fsl.ApplyMask(), name='applyrefined')
workflow.connect([
(inputnode, surface_recon_wf, [
('t2w', 'inputnode.t2w'),
('flair', 'inputnode.flair'),
('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id')]),
(anat_template_wf, surface_recon_wf, [('outputnode.t1_template', 'inputnode.t1w')]),
(brain_extraction_wf, surface_recon_wf, [
(('outputnode.out_file', _pop), 'inputnode.skullstripped_t1'),
('outputnode.out_segm', 'inputnode.ants_segs'),
(('outputnode.bias_corrected', _pop), 'inputnode.corrected_t1')]),
(brain_extraction_wf, applyrefined, [
(('outputnode.bias_corrected', _pop), 'in_file')]),
(surface_recon_wf, applyrefined, [
('outputnode.out_brainmask', 'mask_file')]),
(surface_recon_wf, outputnode, [
('outputnode.subjects_dir', 'subjects_dir'),
('outputnode.subject_id', 'subject_id'),
('outputnode.t1_2_fsnative_forward_transform', 't1_2_fsnative_forward_transform'),
('outputnode.t1_2_fsnative_reverse_transform', 't1_2_fsnative_reverse_transform'),
('outputnode.surfaces', 'surfaces'),
('outputnode.out_aseg', 't1_aseg'),
('outputnode.out_aparc', 't1_aparc')]),
(applyrefined, buffernode, [('out_file', 't1_brain')]),
(surface_recon_wf, buffernode, [
('outputnode.out_brainmask', 't1_mask')]),
])
else:
workflow.connect([
(brain_extraction_wf, buffernode, [
(('outputnode.out_file', _pop), 't1_brain'),
('outputnode.out_mask', 't1_mask')]),
])
# 5. Segmentation
t1_seg = pe.Node(fsl.FAST(segments=True, no_bias=True, probability_maps=True),
name='t1_seg', mem_gb=3)
workflow.connect([
(buffernode, t1_seg, [('t1_brain', 'in_files')]),
(t1_seg, outputnode, [('tissue_class_map', 't1_seg'),
('probability_maps', 't1_tpms')]),
])
# 6. Spatial normalization (T1w to MNI registration)
t1_2_mni = pe.Node(
RobustMNINormalizationRPT(
float=True,
generate_report=True,
flavor='testing' if debug else 'precise',
),
name='t1_2_mni',
n_procs=omp_nthreads,
mem_gb=2
)
# Resample the brain mask and the tissue probability maps into mni space
mni_mask = pe.Node(
ApplyTransforms(dimension=3, default_value=0, float=True,
interpolation='MultiLabel'),
name='mni_mask'
)
mni_seg = pe.Node(
ApplyTransforms(dimension=3, default_value=0, float=True,
interpolation='MultiLabel'),
name='mni_seg'
)
mni_tpms = pe.MapNode(
ApplyTransforms(dimension=3, default_value=0, float=True,
interpolation='Linear'),
iterfield=['input_image'],
name='mni_tpms'
)
# TODO isolate the spatial normalization workflow #############
ref_img = str(get_template(template, resolution=1, desc=None, suffix='T1w',
extensions=['.nii', '.nii.gz']))
t1_2_mni.inputs.template = template
mni_mask.inputs.reference_image = ref_img
mni_seg.inputs.reference_image = ref_img
mni_tpms.inputs.reference_image = ref_img
workflow.connect([
(inputnode, t1_2_mni, [('roi', 'lesion_mask')]),
(brain_extraction_wf, t1_2_mni, [
(('outputnode.bias_corrected', _pop), 'moving_image')]),
(buffernode, t1_2_mni, [('t1_mask', 'moving_mask')]),
(buffernode, mni_mask, [('t1_mask', 'input_image')]),
(t1_2_mni, mni_mask, [('composite_transform', 'transforms')]),
(t1_seg, mni_seg, [('tissue_class_map', 'input_image')]),
(t1_2_mni, mni_seg, [('composite_transform', 'transforms')]),
(t1_seg, mni_tpms, [('probability_maps', 'input_image')]),
(t1_2_mni, mni_tpms, [('composite_transform', 'transforms')]),
(t1_2_mni, outputnode, [
('warped_image', 't1_2_mni'),
('composite_transform', 't1_2_mni_forward_transform'),
('inverse_composite_transform', 't1_2_mni_reverse_transform')]),
(mni_mask, outputnode, [('output_image', 'mni_mask')]),
(mni_seg, outputnode, [('output_image', 'mni_seg')]),
(mni_tpms, outputnode, [('output_image', 'mni_tpms')]),
])
# spatial normalization ends here ###############################
seg_rpt = pe.Node(ROIsPlot(colors=['magenta', 'b'], levels=[1.5, 2.5]),
name='seg_rpt')
anat_reports_wf = init_anat_reports_wf(
reportlets_dir=reportlets_dir, template=template,
freesurfer=freesurfer)
workflow.connect([
(inputnode, anat_reports_wf, [
(('t1w', fix_multi_T1w_source_name), 'inputnode.source_file')]),
(anat_template_wf, anat_reports_wf, [
('outputnode.out_report', 'inputnode.t1_conform_report')]),
(anat_template_wf, seg_rpt, [
('outputnode.t1_template', 'in_file')]),
(t1_seg, seg_rpt, [('tissue_class_map', 'in_rois')]),
(outputnode, seg_rpt, [('t1_mask', 'in_mask')]),
(seg_rpt, anat_reports_wf, [('out_report', 'inputnode.seg_report')]),
(t1_2_mni, anat_reports_wf, [('out_report', 'inputnode.t1_2_mni_report')]),
])
if freesurfer:
workflow.connect([
(surface_recon_wf, anat_reports_wf, [
('outputnode.out_report', 'inputnode.recon_report')]),
])
anat_derivatives_wf = init_anat_derivatives_wf(
bids_root=bids_root,
freesurfer=freesurfer,
output_dir=output_dir,
template=template,
)
workflow.connect([
(anat_template_wf, anat_derivatives_wf, [
('outputnode.t1w_valid_list', 'inputnode.source_files')]),
(outputnode, anat_derivatives_wf, [
('t1_template_transforms', 'inputnode.t1_template_transforms'),
('t1_preproc', 'inputnode.t1_preproc'),
('t1_mask', 'inputnode.t1_mask'),
('t1_seg', 'inputnode.t1_seg'),
('t1_tpms', 'inputnode.t1_tpms'),
('t1_2_mni_forward_transform', 'inputnode.t1_2_mni_forward_transform'),
('t1_2_mni_reverse_transform', 'inputnode.t1_2_mni_reverse_transform'),
('t1_2_mni', 'inputnode.t1_2_mni'),
('mni_mask', 'inputnode.mni_mask'),
('mni_seg', 'inputnode.mni_seg'),
('mni_tpms', 'inputnode.mni_tpms'),
('t1_2_fsnative_forward_transform', 'inputnode.t1_2_fsnative_forward_transform'),
('surfaces', 'inputnode.surfaces'),
]),
])
if freesurfer:
workflow.connect([
(surface_recon_wf, anat_derivatives_wf, [
('outputnode.out_aseg', 'inputnode.t1_fs_aseg'),
('outputnode.out_aparc', 'inputnode.t1_fs_aparc'),
]),
])
return workflow
