def init_single_subject_wf(
opts: ArgumentParser,
layout: BIDSLayout,
run_uuid: str,
work_dir: str,
output_dir: str,
name: str,
subject_id: str,
reportlets_dir: str,
):
import nilearn
if name in ('single_subject_wf', 'single_subject_test_wf'):
# for documentation purposes
subject_data = {
'bold': ['/completely/made/up/path/sub-01_task-nback_bold.nii.gz']
}
else:
subject_data = collect_data(layout, subject_id, None)[0]
workflow = Workflow(name=name)
workflow.base_dir = work_dir
workflow.__desc__ = """
Results included in this manuscript come from processing
performed using *atlasTransform* {atlasTransform_ver}
(@atlasTransform1; @atlasTransform; RRID:some.id),
which is based on *Nipype* {nipype_ver}
(@nipype1; @nipype2; RRID:SCR_002502).
""".format(atlasTransform_ver=__version__, nipype_ver=nipype_ver)
workflow.__postdesc__ = """
Many internal operations of *atlasTransform* use
*Nilearn* {nilearn_ver} [@nilearn, RRID:SCR_001362].
### Copyright Waiver
The above boilerplate text was automatically generated by fMRIPrep
with the express intention that users should copy and paste this
text into their manuscripts *unchanged*.
It is released under the [CC0]\
(https://creativecommons.org/publicdomain/zero/1.0/) license.
### References
""".format(nilearn_ver=nilearn.version.__version__)
inputnode = pe.Node(niu.IdentityInterface(fields=['subjects_dir']),
name='inputnode')
#
# # require_masks = opts.source_format == 'bold'
# bidssrc = pe.Node(BIDSPlusDataGrabber(subject_data=subject_data, require_masks=False),
# name='bidssrc')
#
# # bids_info = pe.Node(BIDSInfo(
# # bids_dir=layout.root, bids_validate=False), name='bids_info')
#
# summary = pe.Node(SubjectSummary(),
# name='summary', run_without_submitting=True)
#
# about = pe.Node(AboutSummary(version=__version__,
# command=' '.join(sys.argv)),
# name='about', run_without_submitting=True)
#
# ds_report_summary = pe.Node(
# DerivativesDataSink(base_directory=reportlets_dir,
# desc='summary', keep_dtype=True),
# name='ds_report_summary', run_without_submitting=True)
#
# ds_report_about = pe.Node(
# DerivativesDataSink(base_directory=reportlets_dir,
# desc='about', keep_dtype=True),
# name='ds_report_about', run_without_submitting=True)
# Preprocessing of T1w (includes registration to MNI)
# workflow.connect([
# # (bidssrc, bids_info, [('bold', 'in_file')]),
# (inputnode, summary, [('subjects_dir', 'subjects_dir')]),
# (bidssrc, summary, [('t1w', 't1w'),
# ('t2w', 't2w'),
# ('bold', 'bold')]),
# # (bids_info, summary, [('subject', 'subject_id')]),
# (bidssrc, ds_report_summary, [('bold', 'source_file')]),
# # (summary, ds_report_summary, [('out_report', 'in_file')]),
# (bidssrc, ds_report_about, [('bold', 'source_file')]),
# (about, ds_report_about, [('out_report', 'in_file')]),
# ])
# Overwrite ``out_path_base`` of smriprep's DataSinks
for node in workflow.list_node_names():
if node.split('.')[-1].startswith('ds_'):
workflow.get_node(node).interface.out_path_base = 'atlasTransform'
for i in range(len(subject_data[opts.source])):
transform_wf = init_atlas_transform_workflow(
nifti=subject_data[opts.source][i],
atlas_name=opts.atlas_name,
options=opts,
bids_dir=str(layout.root),
name='atlas_transform_%d_wf' % i)
workflow.connect([
(inputnode, transform_wf, [('subjects_dir',
'inputnode.subjects_dir')]),
])
# outputs_wf = init_datasink_wf(bids_root=str(layout.root), output_dir=str(opts.output_dir), atlas_name=opts.atlas_name, name='ds_%d_wf' % i)
#
# workflow.connect([(transform_wf,outputs_wf,[('outputnode.transformed','inputnode.source_file')])])
# # outputs_wf.inputs.source_file = subject_data['csv'][i] if subject_data.__contains__('mask') else subject_data['bold'][i]
#
# workflow.connect([(transform_wf, outputs_wf, [
# ('outputnode.transformed', 'inputnode.transformed')
# ])])
return workflow
def init_ica_aroma_wf(
dt,
aroma_melodic_dim=-200,
err_on_aroma_warn=False,
susan_fwhm=6.0,
name='ica_aroma_wf',
):
"""
Build a workflow that runs `ICA-AROMA`_.
This workflow wraps `ICA-AROMA`_ to identify and remove motion-related
independent components from a BOLD time series.
The following steps are performed:
#. Remove non-steady state volumes from the bold series.
#. Smooth data using FSL `susan`, with a kernel width FWHM=6.0mm.
#. Run FSL `melodic` outside of ICA-AROMA to generate the report
#. Run ICA-AROMA
#. Aggregate identified motion components (aggressive) to TSV
#. Return ``classified_motion_ICs`` and ``melodic_mix`` for user to complete
non-aggressive denoising in T1w space
#. Calculate ICA-AROMA-identified noise components
(columns named ``AROMAAggrCompXX``)
There is a current discussion on whether other confounds should be extracted
before or after denoising `here
<http://nbviewer.jupyter.org/github/nipreps/fmriprep-notebooks/blob/922e436429b879271fa13e76767a6e73443e74d9/issue-817_aroma_confounds.ipynb>`__.
.. _ICA-AROMA: https://github.com/maartenmennes/ICA-AROMA
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from ecp.workflows.confounds import init_ica_aroma_wf
wf = init_ica_aroma_wf(
dt=1.0)
Parameters
----------
dt : :obj:`float`
bold repetition time
aroma_melodic_dim : :obj:`int`
Set the dimensionality of the MELODIC ICA decomposition.
Negative numbers set a maximum on automatic dimensionality estimation.
Positive numbers set an exact number of components to extract.
(default: -200, i.e., estimate <=200 components)
err_on_aroma_warn : :obj:`bool`
Do not fail on ICA-AROMA errors
susan_fwhm : :obj:`float`
Kernel width (FWHM in mm) for the smoothing step with
FSL ``susan`` (default: 6.0mm)
name : :obj:`str`
Name of workflow (default: ``ica_aroma_wf``)
Inputs
------
bold_std
BOLD series NIfTI file in MNI152NLin6Asym space
bold_mask_std
BOLD mask for MNI152NLin6Asym space
movpar_file
movement parameter file
skip_vols
number of non steady state volumes
Outputs
-------
aroma_confounds
TSV of confounds identified as noise by ICA-AROMA
aroma_noise_ics
CSV of noise components identified by ICA-AROMA
melodic_mix
FSL MELODIC mixing matrix
aroma_metatdata
metadata
out_report
aroma out report
"""
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.segmentation import ICA_AROMARPT
from niworkflows.interfaces.utility import KeySelect
from niworkflows.interfaces.utils import TSV2JSON
workflow = Workflow(name=name)
workflow.__postdesc__ = """\
Automatic removal of motion artifacts using independent component analysis
[ICA-AROMA, @aroma] was performed on the *preprocessed BOLD on MNI space*
time-series after removal of non-steady state volumes and spatial smoothing
with an isotropic, Gaussian kernel of 6mm FWHM (full-width half-maximum).
The "aggressive" noise-regressors were collected and placed
in the corresponding confounds file.
"""
inputnode = pe.Node(niu.IdentityInterface(fields=[
'bold_std',
'bold_mask_std',
'movpar_file',
'skip_vols',
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'aroma_confounds', 'aroma_noise_ics', 'melodic_mix', 'aroma_metadata',
'out_report'
]),
name='outputnode')
# extract out to BOLD base
rm_non_steady_state = pe.Node(Trim(), name='rm_nonsteady')
trim_movement = pe.Node(TrimMovement(), name='trim_movement')
calc_median_val = pe.Node(fsl.ImageStats(op_string='-k %s -p 50'),
name='calc_median_val')
calc_bold_mean = pe.Node(fsl.MeanImage(), name='calc_bold_mean')
def _getusans_func(image, thresh):
return [tuple([image, thresh])]
getusans = pe.Node(niu.Function(function=_getusans_func,
output_names=['usans']),
name='getusans',
mem_gb=0.01)
smooth = pe.Node(fsl.SUSAN(fwhm=susan_fwhm), name='smooth')
# melodic node
melodic = pe.Node(fsl.MELODIC(no_bet=True,
tr_sec=dt,
mm_thresh=0.5,
out_stats=True,
dim=aroma_melodic_dim),
name="melodic")
# ica_aroma node
ica_aroma = pe.Node(ICA_AROMARPT(denoise_type='no',
generate_report=True,
TR=dt,
args='-np'),
name='ica_aroma')
# extract the confound ICs from the results
ica_aroma_confound_extraction = pe.Node(
ICAConfounds(err_on_aroma_warn=err_on_aroma_warn),
name='ica_aroma_confound_extraction')
ica_aroma_metadata_fmt = pe.Node(TSV2JSON(index_column='IC',
output=None,
enforce_case=True,
additional_metadata={
'Method': {
'Name':
'ICA-AROMA',
'Version':
getenv(
'AROMA_VERSION',
'n/a')
}
}),
name='ica_aroma_metadata_fmt')
def _getbtthresh(medianval):
return 0.75 * medianval
# connect the nodes
workflow.connect([
(inputnode, ica_aroma, [('movpar_file', 'motion_parameters')]),
(inputnode, rm_non_steady_state, [('skip_vols', 'begin_index')]),
(inputnode, rm_non_steady_state, [('bold_std', 'in_file')]),
(inputnode, calc_median_val, [('bold_mask_std', 'mask_file')]),
(inputnode, trim_movement, [('movpar_file', 'movpar_file')]),
(inputnode, trim_movement, [('skip_vols', 'skip_vols')]),
(rm_non_steady_state, calc_median_val, [('out_file', 'in_file')]),
(rm_non_steady_state, calc_bold_mean, [('out_file', 'in_file')]),
(calc_bold_mean, getusans, [('out_file', 'image')]),
(calc_median_val, getusans, [('out_stat', 'thresh')]),
# Connect input nodes to complete smoothing
(rm_non_steady_state, smooth, [('out_file', 'in_file')]),
(getusans, smooth, [('usans', 'usans')]),
(calc_median_val, smooth, [(('out_stat', _getbtthresh),
'brightness_threshold')]),
# connect smooth to melodic
(smooth, melodic, [('smoothed_file', 'in_files')]),
(inputnode, melodic, [('bold_mask_std', 'mask')]),
# connect nodes to ICA-AROMA
(smooth, ica_aroma, [('smoothed_file', 'in_file')]),
(inputnode, ica_aroma, [('bold_mask_std', 'report_mask'),
('bold_mask_std', 'mask')]),
(melodic, ica_aroma, [('out_dir', 'melodic_dir')]),
# generate tsvs from ICA-AROMA
(ica_aroma, ica_aroma_confound_extraction, [('out_dir', 'in_directory')
]),
(inputnode, ica_aroma_confound_extraction, [('skip_vols', 'skip_vols')
]),
(ica_aroma_confound_extraction, ica_aroma_metadata_fmt,
[('aroma_metadata', 'in_file')]),
# output for processing and reporting
(ica_aroma_confound_extraction,
outputnode, [('aroma_confounds', 'aroma_confounds'),
('aroma_noise_ics', 'aroma_noise_ics'),
('melodic_mix', 'melodic_mix')]),
(ica_aroma_metadata_fmt, outputnode, [('output', 'aroma_metadata')]),
(ica_aroma, outputnode, [('out_report', 'out_report')]),
])
return workflow