def init_ica_aroma_wf(template,
metadata,
mem_gb,
omp_nthreads,
name='ica_aroma_wf',
susan_fwhm=6.0,
ignore_aroma_err=False,
aroma_melodic_dim=-200,
use_fieldwarp=True):
"""
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``)
Additionally, non-aggressive denoising is performed on the BOLD series
resampled into MNI space.
There is a current discussion on whether other confounds should be extracted
before or after denoising `here <http://nbviewer.jupyter.org/github/poldracklab/\
fmriprep-notebooks/blob/922e436429b879271fa13e76767a6e73443e74d9/issue-817_\
aroma_confounds.ipynb>`__.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold.confounds import init_ica_aroma_wf
wf = init_ica_aroma_wf(template='MNI152NLin2009cAsym',
metadata={'RepetitionTime': 1.0},
mem_gb=3,
omp_nthreads=1)
**Parameters**
template : str
Spatial normalization template used as target when that
registration step was previously calculated with
:py:func:`~fmriprep.workflows.bold.registration.init_bold_reg_wf`.
The template must be one of the MNI templates (fMRIPrep uses
``MNI152NLin2009cAsym`` by default).
metadata : dict
BIDS metadata for BOLD file
mem_gb : float
Size of BOLD file in GB
omp_nthreads : int
Maximum number of threads an individual process may use
name : str
Name of workflow (default: ``bold_mni_trans_wf``)
susan_fwhm : float
Kernel width (FWHM in mm) for the smoothing step with
FSL ``susan`` (default: 6.0mm)
use_fieldwarp : bool
Include SDC warp in single-shot transform from BOLD to MNI
ignore_aroma_err : bool
Do not fail on ICA-AROMA errors
aroma_melodic_dim: 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)
**Inputs**
itk_bold_to_t1
Affine transform from ``ref_bold_brain`` to T1 space (ITK format)
t1_2_mni_forward_transform
ANTs-compatible affine-and-warp transform file
name_source
BOLD series NIfTI file
Used to recover original information lost during processing
skip_vols
number of non steady state volumes
bold_split
Individual 3D BOLD volumes, not motion corrected
bold_mask
BOLD series mask in template space
hmc_xforms
List of affine transforms aligning each volume to ``ref_image`` in ITK format
fieldwarp
a :abbr:`DFM (displacements field map)` in ITK format
movpar_file
SPM-formatted motion parameters file
**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
nonaggr_denoised_file
BOLD series with non-aggressive ICA-AROMA denoising applied
.. _ICA-AROMA: https://github.com/maartenmennes/ICA-AROMA
"""
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).
Corresponding "non-aggresively" denoised runs were produced after such
smoothing.
Additionally, the "aggressive" noise-regressors were collected and placed
in the corresponding confounds file.
"""
inputnode = pe.Node(niu.IdentityInterface(fields=[
'itk_bold_to_t1', 't1_2_mni_forward_transform', 'name_source',
'skip_vols', 'bold_split', 'bold_mask', 'hmc_xforms', 'fieldwarp',
'movpar_file'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'aroma_confounds', 'aroma_noise_ics', 'melodic_mix',
'nonaggr_denoised_file'
]),
name='outputnode')
bold_mni_trans_wf = init_bold_mni_trans_wf(
template=template,
mem_gb=mem_gb,
omp_nthreads=omp_nthreads,
template_out_grid=str(
get_template('MNI152Lin') /
'tpl-MNI152Lin_space-MNI_res-02_T1w.nii.gz'),
use_compression=False,
use_fieldwarp=use_fieldwarp,
name='bold_mni_trans_wf')
bold_mni_trans_wf.__desc__ = None
rm_non_steady_state = pe.Node(niu.Function(function=_remove_volumes,
output_names=['bold_cut']),
name='rm_nonsteady')
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=float(metadata['RepetitionTime']),
mm_thresh=0.5,
out_stats=True,
dim=aroma_melodic_dim),
name="melodic")
# ica_aroma node
ica_aroma = pe.Node(ICA_AROMARPT(denoise_type='nonaggr',
generate_report=True,
TR=metadata['RepetitionTime']),
name='ica_aroma')
add_non_steady_state = pe.Node(niu.Function(function=_add_volumes,
output_names=['bold_add']),
name='add_nonsteady')
# extract the confound ICs from the results
ica_aroma_confound_extraction = pe.Node(
ICAConfounds(ignore_aroma_err=ignore_aroma_err),
name='ica_aroma_confound_extraction')
ds_report_ica_aroma = pe.Node(DerivativesDataSink(suffix='ica_aroma'),
name='ds_report_ica_aroma',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
def _getbtthresh(medianval):
return 0.75 * medianval
# connect the nodes
workflow.connect([
(inputnode, bold_mni_trans_wf,
[('name_source', 'inputnode.name_source'),
('bold_split', 'inputnode.bold_split'),
('bold_mask', 'inputnode.bold_mask'),
('hmc_xforms', 'inputnode.hmc_xforms'),
('itk_bold_to_t1', 'inputnode.itk_bold_to_t1'),
('t1_2_mni_forward_transform',
'inputnode.t1_2_mni_forward_transform'),
('fieldwarp', 'inputnode.fieldwarp')]),
(inputnode, ica_aroma, [('movpar_file', 'motion_parameters')]),
(inputnode, rm_non_steady_state, [('skip_vols', 'skip_vols')]),
(bold_mni_trans_wf, rm_non_steady_state, [('outputnode.bold_mni',
'bold_file')]),
(bold_mni_trans_wf, calc_median_val, [('outputnode.bold_mask_mni',
'mask_file')]),
(rm_non_steady_state, calc_median_val, [('bold_cut', 'in_file')]),
(rm_non_steady_state, calc_bold_mean, [('bold_cut', '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, [('bold_cut', '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')]),
(bold_mni_trans_wf, melodic, [('outputnode.bold_mask_mni', 'mask')]),
# connect nodes to ICA-AROMA
(smooth, ica_aroma, [('smoothed_file', 'in_file')]),
(bold_mni_trans_wf, ica_aroma, [('outputnode.bold_mask_mni',
'report_mask'),
('outputnode.bold_mask_mni', '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')
]),
# output for processing and reporting
(ica_aroma_confound_extraction,
outputnode, [('aroma_confounds', 'aroma_confounds'),
('aroma_noise_ics', 'aroma_noise_ics'),
('melodic_mix', 'melodic_mix')]),
# TODO change melodic report to reflect noise and non-noise components
(ica_aroma, add_non_steady_state, [('nonaggr_denoised_file',
'bold_cut_file')]),
(bold_mni_trans_wf, add_non_steady_state, [('outputnode.bold_mni',
'bold_file')]),
(inputnode, add_non_steady_state, [('skip_vols', 'skip_vols')]),
(add_non_steady_state, outputnode, [('bold_add',
'nonaggr_denoised_file')]),
(ica_aroma, ds_report_ica_aroma, [('out_report', 'in_file')]),
])
return workflow
def init_ica_aroma_wf(name='ica_aroma_wf', ignore_aroma_err=False):
'''
This workflow wraps `ICA-AROMA`_ to identify and remove motion-related
independent components from a BOLD time series.
The following steps are performed:
#. Smooth data using SUSAN
#. Run 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
Additionally, non-aggressive denoising is performed on the BOLD series
resampled into MNI space.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold.confounds import init_ica_aroma_wf
wf = init_ica_aroma_wf()
**Parameters**
ignore_aroma_err : bool
Do not fail on ICA-AROMA errors
**Inputs**
bold_mni
BOLD series, resampled to template space
movpar_file
SPM-formatted motion parameters file
bold_mask_mni
BOLD series mask in template space
**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
nonaggr_denoised_file
BOLD series with non-aggressive ICA-AROMA denoising applied
out_report
Reportlet visualizing MELODIC ICs, with ICA-AROMA signal/noise labels
.. _ICA-AROMA: https://github.com/rhr-pruim/ICA-AROMA
'''
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(
fields=['bold_mni', 'movpar_file', 'bold_mask_mni']), name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['aroma_confounds', 'out_report',
'aroma_noise_ics', 'melodic_mix',
'nonaggr_denoised_file']), name='outputnode')
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=6.0), name='smooth')
# melodic node
melodic = pe.Node(fsl.MELODIC(no_bet=True, no_mm=True), name="melodic")
# ica_aroma node
ica_aroma = pe.Node(ICA_AROMARPT(denoise_type='nonaggr', generate_report=True),
name='ica_aroma')
# extract the confound ICs from the results
ica_aroma_confound_extraction = pe.Node(ICAConfounds(ignore_aroma_err=ignore_aroma_err),
name='ica_aroma_confound_extraction')
def _getbtthresh(medianval):
return 0.75 * medianval
# connect the nodes
workflow.connect([
# Connect input nodes to complete smoothing
(inputnode, calc_median_val, [('bold_mni', 'in_file'),
('bold_mask_mni', 'mask_file')]),
(inputnode, calc_bold_mean, [('bold_mni', 'in_file')]),
(calc_bold_mean, getusans, [('out_file', 'image')]),
(calc_median_val, getusans, [('out_stat', 'thresh')]),
(inputnode, smooth, [('bold_mni', '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_mni', 'mask')]),
# connect nodes to ICA-AROMA
(smooth, ica_aroma, [('smoothed_file', 'in_file')]),
(inputnode, ica_aroma, [('bold_mask_mni', 'report_mask'),
('movpar_file', 'motion_parameters')]),
(melodic, ica_aroma, [('out_dir', 'melodic_dir')]),
# generate tsvs from ICA-AROMA
(ica_aroma, ica_aroma_confound_extraction, [('out_dir', 'in_directory')]),
# output for processing and reporting
(ica_aroma_confound_extraction, outputnode, [('aroma_confounds', 'aroma_confounds'),
('aroma_noise_ics', 'aroma_noise_ics'),
('melodic_mix', 'melodic_mix')]),
# TODO change melodic report to reflect noise and non-noise components
(ica_aroma, outputnode, [('out_report', 'out_report'),
('nonaggr_denoised_file', 'nonaggr_denoised_file')]),
])
return workflow
def init_ica_aroma_wf(template, metadata, mem_gb, omp_nthreads,
name='ica_aroma_wf',
susan_fwhm=6.0,
ignore_aroma_err=False,
aroma_melodic_dim=None,
use_fieldwarp=True):
"""
This workflow wraps `ICA-AROMA`_ to identify and remove motion-related
independent components from a BOLD time series.
The following steps are performed:
#. 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``)
Additionally, non-aggressive denoising is performed on the BOLD series
resampled into MNI space.
There is a current discussion on whether other confounds should be extracted
before or after denoising `here <http://nbviewer.jupyter.org/github/poldracklab/\
fmriprep-notebooks/blob/922e436429b879271fa13e76767a6e73443e74d9/issue-817_\
aroma_confounds.ipynb>`__.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold.confounds import init_ica_aroma_wf
wf = init_ica_aroma_wf(template='MNI152NLin2009cAsym',
metadata={'RepetitionTime': 1.0},
mem_gb=3,
omp_nthreads=1)
**Parameters**
template : str
Spatial normalization template used as target when that
registration step was previously calculated with
:py:func:`~fmriprep.workflows.bold.registration.init_bold_reg_wf`.
The template must be one of the MNI templates (fMRIPrep uses
``MNI152NLin2009cAsym`` by default).
metadata : dict
BIDS metadata for BOLD file
mem_gb : float
Size of BOLD file in GB
omp_nthreads : int
Maximum number of threads an individual process may use
name : str
Name of workflow (default: ``bold_mni_trans_wf``)
susan_fwhm : float
Kernel width (FWHM in mm) for the smoothing step with
FSL ``susan`` (default: 6.0mm)
use_fieldwarp : bool
Include SDC warp in single-shot transform from BOLD to MNI
ignore_aroma_err : bool
Do not fail on ICA-AROMA errors
aroma_melodic_dim: int or None
Set the dimensionality of the Melodic ICA decomposition
If None, MELODIC automatically estimates dimensionality.
**Inputs**
bold_mni
BOLD series, resampled to template space
movpar_file
SPM-formatted motion parameters file
bold_mask_mni
BOLD series mask in template space
**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
nonaggr_denoised_file
BOLD series with non-aggressive ICA-AROMA denoising applied
.. _ICA-AROMA: https://github.com/rhr-pruim/ICA-AROMA
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(
fields=[
'itk_bold_to_t1',
't1_2_mni_forward_transform',
'name_source',
'bold_split',
'bold_mask',
'hmc_xforms',
'fieldwarp',
'movpar_file']), name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['aroma_confounds', 'aroma_noise_ics', 'melodic_mix',
'nonaggr_denoised_file']), name='outputnode')
bold_mni_trans_wf = init_bold_mni_trans_wf(
template=template,
mem_gb=mem_gb,
omp_nthreads=omp_nthreads,
template_out_grid=os.path.join(get_mni_icbm152_linear(),
'2mm_T1.nii.gz'),
use_compression=False,
use_fieldwarp=use_fieldwarp,
name='bold_mni_trans_wf'
)
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=float(metadata['RepetitionTime']), mm_thresh=0.5, out_stats=True),
name="melodic")
if aroma_melodic_dim is not None:
melodic.inputs.dim = aroma_melodic_dim
# ica_aroma node
ica_aroma = pe.Node(ICA_AROMARPT(
denoise_type='nonaggr', generate_report=True, TR=metadata['RepetitionTime']),
name='ica_aroma')
# extract the confound ICs from the results
ica_aroma_confound_extraction = pe.Node(ICAConfounds(ignore_aroma_err=ignore_aroma_err),
name='ica_aroma_confound_extraction')
ds_report_ica_aroma = pe.Node(
DerivativesDataSink(suffix='ica_aroma'),
name='ds_report_ica_aroma', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
def _getbtthresh(medianval):
return 0.75 * medianval
# connect the nodes
workflow.connect([
(inputnode, bold_mni_trans_wf, [
('name_source', 'inputnode.name_source'),
('bold_split', 'inputnode.bold_split'),
('bold_mask', 'inputnode.bold_mask'),
('hmc_xforms', 'inputnode.hmc_xforms'),
('itk_bold_to_t1', 'inputnode.itk_bold_to_t1'),
('t1_2_mni_forward_transform', 'inputnode.t1_2_mni_forward_transform'),
('fieldwarp', 'inputnode.fieldwarp')]),
(inputnode, ica_aroma, [('movpar_file', 'motion_parameters')]),
(bold_mni_trans_wf, calc_median_val, [
('outputnode.bold_mni', 'in_file'),
('outputnode.bold_mask_mni', 'mask_file')]),
(bold_mni_trans_wf, calc_bold_mean, [
('outputnode.bold_mni', 'in_file')]),
(calc_bold_mean, getusans, [('out_file', 'image')]),
(calc_median_val, getusans, [('out_stat', 'thresh')]),
# Connect input nodes to complete smoothing
(bold_mni_trans_wf, smooth, [
('outputnode.bold_mni', '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')]),
(bold_mni_trans_wf, melodic, [
('outputnode.bold_mask_mni', 'mask')]),
# connect nodes to ICA-AROMA
(smooth, ica_aroma, [('smoothed_file', 'in_file')]),
(bold_mni_trans_wf, ica_aroma, [
('outputnode.bold_mask_mni', 'report_mask')]),
(melodic, ica_aroma, [('out_dir', 'melodic_dir')]),
# generate tsvs from ICA-AROMA
(ica_aroma, ica_aroma_confound_extraction, [('out_dir', 'in_directory')]),
# output for processing and reporting
(ica_aroma_confound_extraction, outputnode, [('aroma_confounds', 'aroma_confounds'),
('aroma_noise_ics', 'aroma_noise_ics'),
('melodic_mix', 'melodic_mix')]),
# TODO change melodic report to reflect noise and non-noise components
(ica_aroma, outputnode, [('nonaggr_denoised_file', 'nonaggr_denoised_file')]),
(ica_aroma, ds_report_ica_aroma, [('out_report', 'in_file')]),
])
return workflow
def init_ica_aroma_wf(
mem_gb,
metadata,
omp_nthreads,
aroma_melodic_dim=-200,
err_on_aroma_warn=False,
name='ica_aroma_wf',
susan_fwhm=6.0,
):
"""
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``)
Additionally, non-aggressive denoising is performed on the BOLD series
resampled into MNI space.
There is a current discussion on whether other confounds should be extracted
before or after denoising `here
<http://nbviewer.jupyter.org/github/poldracklab/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 fmriprep.workflows.bold.confounds import init_ica_aroma_wf
wf = init_ica_aroma_wf(
mem_gb=3,
metadata={'RepetitionTime': 1.0},
omp_nthreads=1)
Parameters
----------
metadata : :obj:`dict`
BIDS metadata for BOLD file
mem_gb : :obj:`float`
Size of BOLD file in GB
omp_nthreads : :obj:`int`
Maximum number of threads an individual process may use
name : :obj:`str`
Name of workflow (default: ``bold_tpl_trans_wf``)
susan_fwhm : :obj:`float`
Kernel width (FWHM in mm) for the smoothing step with
FSL ``susan`` (default: 6.0mm)
err_on_aroma_warn : :obj:`bool`
Do not fail on ICA-AROMA errors
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)
Inputs
------
itk_bold_to_t1
Affine transform from ``ref_bold_brain`` to T1 space (ITK format)
anat2std_xfm
ANTs-compatible affine-and-warp transform file
name_source
BOLD series NIfTI file
Used to recover original information lost during processing
skip_vols
number of non steady state volumes
bold_split
Individual 3D BOLD volumes, not motion corrected
bold_mask
BOLD series mask in template space
hmc_xforms
List of affine transforms aligning each volume to ``ref_image`` in ITK format
movpar_file
SPM-formatted motion parameters file
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
nonaggr_denoised_file
BOLD series with non-aggressive ICA-AROMA denoising applied
"""
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).
Corresponding "non-aggresively" denoised runs were produced after such
smoothing.
Additionally, 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',
'name_source',
'skip_vols',
'spatial_reference',
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'aroma_confounds', 'aroma_noise_ics', 'melodic_mix',
'nonaggr_denoised_file', 'aroma_metadata'
]),
name='outputnode')
# extract out to BOLD base
select_std = pe.Node(KeySelect(fields=['bold_mask_std', 'bold_std']),
name='select_std',
run_without_submitting=True)
select_std.inputs.key = 'MNI152NLin6Asym_res-2'
rm_non_steady_state = pe.Node(niu.Function(function=_remove_volumes,
output_names=['bold_cut']),
name='rm_nonsteady')
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=float(metadata['RepetitionTime']),
mm_thresh=0.5,
out_stats=True,
dim=aroma_melodic_dim),
name="melodic")
# ica_aroma node
ica_aroma = pe.Node(ICA_AROMARPT(denoise_type='nonaggr',
generate_report=True,
TR=metadata['RepetitionTime'],
args='-np'),
name='ica_aroma')
add_non_steady_state = pe.Node(niu.Function(function=_add_volumes,
output_names=['bold_add']),
name='add_nonsteady')
# 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')
ds_report_ica_aroma = pe.Node(DerivativesDataSink(
desc='aroma', datatype="figures", dismiss_entities=("echo", )),
name='ds_report_ica_aroma',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
def _getbtthresh(medianval):
return 0.75 * medianval
# connect the nodes
workflow.connect([
(inputnode, select_std, [('spatial_reference', 'keys'),
('bold_std', 'bold_std'),
('bold_mask_std', 'bold_mask_std')]),
(inputnode, ica_aroma, [('movpar_file', 'motion_parameters')]),
(inputnode, rm_non_steady_state, [('skip_vols', 'skip_vols')]),
(select_std, rm_non_steady_state, [('bold_std', 'bold_file')]),
(select_std, calc_median_val, [('bold_mask_std', 'mask_file')]),
(rm_non_steady_state, calc_median_val, [('bold_cut', 'in_file')]),
(rm_non_steady_state, calc_bold_mean, [('bold_cut', '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, [('bold_cut', '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')]),
(select_std, melodic, [('bold_mask_std', 'mask')]),
# connect nodes to ICA-AROMA
(smooth, ica_aroma, [('smoothed_file', 'in_file')]),
(select_std, 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, add_non_steady_state, [('nonaggr_denoised_file',
'bold_cut_file')]),
(select_std, add_non_steady_state, [('bold_std', 'bold_file')]),
(inputnode, add_non_steady_state, [('skip_vols', 'skip_vols')]),
(add_non_steady_state, outputnode, [('bold_add',
'nonaggr_denoised_file')]),
(ica_aroma, ds_report_ica_aroma, [('out_report', 'in_file')]),
])
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