def init_ica_aroma_wf(name='ica_aroma_wf', ignore_aroma_err=False):
'''
From: https://github.com/rhr-pruim/ICA-AROMA
Description:
ICA-AROMA (i.e. ‘ICA-based Automatic Removal Of Motion Artifacts’) concerns
a data-driven method to identify and remove motion-related independent
components from fMRI data.
Preconditions/Assumptions:
The input fmri bold file is in standard space
(for ease of interfacing with the original ICA-AROMA code)
Steps:
1) smooth data using SUSAN
2) run melodic outside of ICA_AROMA to generate the report
3) run ICA_AROMA
4) print identified motion components (aggressive) to tsv
5) pass classified_motion_ICs and melodic_mix for user to complete nonaggr denoising
'''
workflow = pe.Workflow(name=name)
inputnode = pe.Node(utility.IdentityInterface(
fields=['epi_mni', 'movpar_file', 'epi_mask_mni']),
name='inputnode')
outputnode = pe.Node(utility.IdentityInterface(fields=[
'aroma_confounds', 'out_report', 'aroma_noise_ics', 'melodic_mix'
]),
name='outputnode')
# helper function to get
# smoothing node (SUSAN)
# functions to help set SUSAN
def getbtthresh(medianval):
return 0.75 * medianval
def getusans_func(image, thresh):
return [tuple([image, thresh])]
calc_median_val = pe.Node(fsl.ImageStats(op_string='-k %s -p 50'),
name='calc_median_val')
calc_epi_mean = pe.Node(fsl.MeanImage(), name='calc_epi_mean')
brightness_threshold = pe.Node(utility.Function(function=getbtthresh,
input_names=['medianval'],
output_names=['thresh']),
name='brightness_threshold')
getusans = pe.Node(utility.Function(function=getusans_func,
input_names=['image', 'thresh'],
output_names=['usans']),
name='getusans')
smooth = pe.Node(fsl.SUSAN(fwhm=6.0), name='smooth')
# melodic node
melodic = pe.Node(nws.MELODICRPT(no_bet=True,
no_mm=True,
generate_report=True),
name="melodic")
# ica_aroma node
ica_aroma = pe.Node(aroma.ICA_AROMA(denoise_type='no'), name='ica_aroma')
# extract the confound ICs from the results
ica_aroma_confound_extraction = pe.Node(
utility.Function(
function=get_ica_confounds,
input_names=['ica_out_dir', 'ignore_aroma_err'],
output_names=['aroma_confounds', 'aroma_noise_ics',
'melodic_mix']),
name='ica_aroma_confound_extraction')
ica_aroma_confound_extraction.inputs.ignore_aroma_err = ignore_aroma_err
# connect the nodes
workflow.connect([
# Connect input nodes to complete smoothing
(inputnode, calc_median_val, [('epi_mni', 'in_file'),
('epi_mask_mni', 'mask_file')]),
(calc_median_val, brightness_threshold, [('out_stat', 'medianval')]),
(inputnode, calc_epi_mean, [('epi_mni', 'in_file')]),
(calc_epi_mean, getusans, [('out_file', 'image')]),
(calc_median_val, getusans, [('out_stat', 'thresh')]),
(inputnode, smooth, [('epi_mni', 'in_file')]),
(getusans, smooth, [('usans', 'usans')]),
(brightness_threshold, smooth, [('thresh', 'brightness_threshold')]),
# connect smooth to melodic
(smooth, melodic, [('smoothed_file', 'in_files')]),
(inputnode, melodic, [('epi_mask_mni', 'report_mask'),
('epi_mask_mni', 'mask')]),
# connect nodes to ICA-AROMA
(smooth, ica_aroma, [('smoothed_file', 'in_file')]),
(inputnode, ica_aroma, [('movpar_file', 'motion_parameters')]),
(melodic, ica_aroma, [('out_dir', 'melodic_dir')]),
# geneerate tsvs from ICA_AROMA
(ica_aroma, ica_aroma_confound_extraction, [('out_dir', 'ica_out_dir')]
),
# 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
(melodic, outputnode, [('out_report', 'out_report')]),
])
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',
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 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
#. Calculate ICA-AROMA-identified noise components
(columns named ``AROMAAggrCompXX``)
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(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``)
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=6.0), 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