def create_connectome(name='connectome'):
wf = pe.Workflow(name=name)
inputspec = pe.Node(
util.IdentityInterface(fields=[
'time_series',
'method'
]),
name='inputspec'
)
outputspec = pe.Node(
util.IdentityInterface(fields=[
'connectome',
]),
name='outputspec'
)
node = pe.Node(Function(input_names=['time_series', 'method'],
output_names=['connectome'],
function=compute_correlation,
as_module=True),
name='connectome')
wf.connect([
(inputspec, node, [('time_series', 'time_series')]),
(inputspec, node, [('method', 'method')]),
(node, outputspec, [('connectome', 'connectome')]),
])
return wf
def create_qc_skullstrip(wf_name='qc_skullstrip'):
wf = pe.Workflow(name=wf_name)
input_node = pe.Node(util.IdentityInterface(
fields=['anatomical_brain', 'anatomical_reorient']),
name='inputspec')
output_node = pe.Node(
util.IdentityInterface(fields=['axial_image', 'sagittal_image']),
name='outputspec')
skull_edge = pe.Node(Function(input_names=['in_file'],
output_names=['out_file'],
function=afni_Edge3,
as_module=True),
name='skull_edge')
montage_skull = create_montage('montage_skull', 'red', 'skull_vis')
wf.connect(input_node, 'anatomical_reorient', skull_edge, 'in_file')
wf.connect(input_node, 'anatomical_brain', montage_skull,
'inputspec.underlay')
wf.connect(skull_edge, 'out_file', montage_skull, 'inputspec.overlay')
wf.connect(montage_skull, 'outputspec.axial_png', output_node,
'axial_image')
wf.connect(montage_skull, 'outputspec.sagittal_png', output_node,
'sagittal_image')
return wf
def create_qc_motion(wf_name='qc_motion'):
wf = pe.Workflow(name=wf_name)
input_node = pe.Node(util.IdentityInterface(fields=['motion_parameters']),
name='inputspec')
output_node = pe.Node(util.IdentityInterface(
fields=['motion_translation_plot', 'motion_rotation_plot']),
name='outputspec')
mov_plot = pe.Node(Function(
input_names=['motion_parameters'],
output_names=['translation_plot', 'rotation_plot'],
function=gen_motion_plt,
as_module=True),
name='motion_plot')
wf.connect(input_node, 'motion_parameters', mov_plot, 'motion_parameters')
wf.connect(mov_plot, 'translation_plot', output_node,
'motion_translation_plot')
wf.connect(mov_plot, 'rotation_plot', output_node, 'motion_rotation_plot')
return wf
def test_function_str():
f = pe.Node(Function(input_names=['scan', 'rest_dict', 'resource'],
output_names=['file_path'],
function=get_rest),
name='get_rest')
f.inputs.set(resource=resource, rest_dict=rest_dict, scan=scan)
results = f.run()
assert rest_dict['rest_acq-1_run-1']['scan'] == results.outputs.file_path
def test_iterable_selector():
selector_test = yaml.load(selector)['Regressors']
nuisance_wf = pe.Workflow(name='iterable_selector')
nuisance_wf.base_dir = '/tmp/iter_working_dir'
try:
import shutil
shutil.rmtree(nuisance_wf.base_dir)
except:
pass
inputspec = pe.Node(util.IdentityInterface(fields=['selector']),
name='inputspec')
summarize_timeseries_node = pe.Node(Function(
input_names=['selector'],
output_names=['residual_file_path', 'regressors_file_path'],
function=summarize_timeseries,
as_module=True,
),
name='summarize_timeseries')
outputspec = pe.Node(util.IdentityInterface(
fields=['residual_file_path', 'regressors_file_path']),
name='outputspec')
nuisance_wf.connect(inputspec, 'selector', summarize_timeseries_node,
'selector')
nuisance_wf.connect(summarize_timeseries_node, 'residual_file_path',
outputspec, 'residual_file_path')
nuisance_wf.connect(summarize_timeseries_node, 'regressors_file_path',
outputspec, 'regressors_file_path')
nuisance_wf.get_node('inputspec').iterables = (('selector', [
NuisanceRegressor(s) for s in selector_test
]))
nuisance_wf.run()
def create_qc_fd(wf_name='qc_fd'):
wf = pe.Workflow(name=wf_name)
input_node = pe.Node(
util.IdentityInterface(fields=['fd', 'excluded_volumes']),
name='inputspec')
output_node = pe.Node(util.IdentityInterface(fields=['fd_histogram_plot']),
name='outputspec')
fd_plot = pe.Node(Function(input_names=['arr', 'measure', 'ex_vol'],
output_names=['hist_path'],
function=gen_plot_png,
as_module=True),
name='fd_plot')
fd_plot.inputs.measure = 'FD'
wf.connect(input_node, 'fd', fd_plot, 'arr')
wf.connect(input_node, 'excluded_volumes', fd_plot, 'ex_vol')
wf.connect(fd_plot, 'hist_path', output_node, 'fd_histogram_plot')
return wf
def create_montage_gm_wm_csf(wf_name, png_name):
wf = pe.Workflow(name=wf_name)
inputNode = pe.Node(util.IdentityInterface(
fields=['underlay', 'overlay_csf', 'overlay_wm', 'overlay_gm']),
name='inputspec')
outputNode = pe.Node(util.IdentityInterface(fields=[
'axial_png', 'sagittal_png', 'resampled_underlay',
'resampled_overlay_csf', 'resampled_overlay_wm', 'resampled_overlay_gm'
]),
name='outputspec')
resample_u = pe.Node(Function(input_names=['file_'],
output_names=['new_fname'],
function=resample_1mm,
as_module=True),
name='resample_u')
resample_o_csf = resample_u.clone('resample_o_csf')
resample_o_wm = resample_u.clone('resample_o_wm')
resample_o_gm = resample_u.clone('resample_o_gm')
wf.connect(inputNode, 'underlay', resample_u, 'file_')
wf.connect(inputNode, 'overlay_csf', resample_o_csf, 'file_')
wf.connect(inputNode, 'overlay_gm', resample_o_gm, 'file_')
wf.connect(inputNode, 'overlay_wm', resample_o_wm, 'file_')
montage_a = pe.Node(Function(input_names=[
'overlay_csf', 'overlay_wm', 'overlay_gm', 'underlay', 'png_name'
],
output_names=['png_name'],
function=montage_gm_wm_csf_axial,
as_module=True),
name='montage_a')
wf.connect(resample_u, 'new_fname', montage_a, 'underlay')
wf.connect(resample_o_csf, 'new_fname', montage_a, 'overlay_csf')
wf.connect(resample_o_gm, 'new_fname', montage_a, 'overlay_gm')
wf.connect(resample_o_wm, 'new_fname', montage_a, 'overlay_wm')
montage_a.inputs.png_name = png_name + '_a.png'
montage_s = pe.Node(Function(input_names=[
'overlay_csf', 'overlay_wm', 'overlay_gm', 'underlay', 'png_name'
],
output_names=['png_name'],
function=montage_gm_wm_csf_sagittal,
as_module=True),
name='montage_s')
montage_s.inputs.png_name = png_name + '_s.png'
wf.connect(resample_u, 'new_fname', montage_s, 'underlay')
wf.connect(resample_o_csf, 'new_fname', montage_s, 'overlay_csf')
wf.connect(resample_o_gm, 'new_fname', montage_s, 'overlay_gm')
wf.connect(resample_o_wm, 'new_fname', montage_s, 'overlay_wm')
wf.connect(resample_u, 'new_fname', outputNode, 'resampled_underlay')
wf.connect(resample_o_csf, 'new_fname', outputNode,
'resampled_overlay_csf')
wf.connect(resample_o_wm, 'new_fname', outputNode, 'resampled_overlay_wm')
wf.connect(resample_o_gm, 'new_fname', outputNode, 'resampled_overlay_gm')
wf.connect(montage_a, 'png_name', outputNode, 'axial_png')
wf.connect(montage_s, 'png_name', outputNode, 'sagittal_png')
return wf
def create_cwas(name='cwas', working_dir=None, crash_dir=None):
"""
Connectome Wide Association Studies
This workflow performs CWAS on a group of subjects.
Parameters
----------
name : string, optional
Name of the workflow.
Returns
-------
cwas : nipype.pipeline.engine.Workflow
CWAS workflow.
Notes
-----
Workflow Inputs::
inputspec.subjects : dict (subject id: nifti files)
4-D timeseries of a group of subjects normalized to MNI space
inputspec.roi : string (nifti file)
Mask of region(s) of interest
inputspec.regressor : list (float)
Corresponding list of the regressor variable of shape (`N`) or (`N`,`1`), `N` subjects
inputspec.cols : list (int)
todo
inputspec.f_samples : int
Number of permutation samples to draw from the pseudo F distribution
inputspec.parallel_nodes : integer
Number of nodes to create and potentially parallelize over
Workflow Outputs::
outputspec.F_map : string (nifti file)
Pseudo F values of CWAS
outputspec.p_map : string (nifti file)
Significance p values calculated from permutation tests
CWAS Procedure:
1. Calculate spatial correlation of a voxel
2. Correlate spatial z-score maps for every subject pair
3. Convert matrix to distance matrix, `1-r`
4. Calculate MDMR statistics for the voxel
5. Determine significance of MDMR statistics with permutation tests
Workflow Graph:
.. image:: ../images/cwas.dot.png
:width: 500
Detailed Workflow Graph:
.. image:: ../images/cwas_detailed.dot.png
:width: 500
References
----------
.. [1] Shehzad Z, Kelly C, Reiss PT, Emerson JW, McMahon K, Copland DA, Castellanos FX, Milham MP. An Analytic Framework for Connectome-Wide Association Studies. Under Review.
"""
if not working_dir:
working_dir = os.path.join(os.getcwd(), 'MDMR_work_dir')
if not crash_dir:
crash_dir = os.path.join(os.getcwd(), 'MDMR_crash_dir')
workflow = pe.Workflow(name=name)
workflow.base_dir = working_dir
workflow.config['execution'] = {
'hash_method': 'timestamp',
'crashdump_dir': os.path.abspath(crash_dir)
}
inputspec = pe.Node(util.IdentityInterface(fields=[
'roi', 'subjects', 'regressor', 'participant_column', 'columns',
'permutations', 'parallel_nodes'
]),
name='inputspec')
outputspec = pe.Node(
util.IdentityInterface(fields=['F_map', 'p_map', 'neglog_p_map']),
name='outputspec')
ccb = pe.Node(Function(input_names=['mask_file', 'batches'],
output_names='batch_list',
function=create_cwas_batches,
as_module=True),
name='cwas_batches')
ncwas = pe.MapNode(Function(input_names=[
'subjects', 'mask_file', 'regressor_file', 'participant_column',
'columns_string', 'permutations', 'voxel_range'
],
output_names=['result_batch'],
function=nifti_cwas,
as_module=True),
name='cwas_batch',
iterfield='voxel_range')
jmask = pe.Node(Function(input_names=['subjects', 'mask_file'],
output_names=['joint_mask'],
function=joint_mask,
as_module=True),
name='joint_mask')
mcwasb = pe.Node(Function(
input_names=['cwas_batches', 'mask_file'],
output_names=['F_file', 'p_file', 'neglog_p_file'],
function=merge_cwas_batches,
as_module=True),
name='cwas_volumes')
#Compute the joint mask
workflow.connect(inputspec, 'subjects', jmask, 'subjects')
workflow.connect(inputspec, 'roi', jmask, 'mask_file')
#Create batches based on the joint mask
workflow.connect(jmask, 'joint_mask', ccb, 'mask_file')
workflow.connect(inputspec, 'parallel_nodes', ccb, 'batches')
#Compute CWAS over batches of voxels
workflow.connect(jmask, 'joint_mask', ncwas, 'mask_file')
workflow.connect(inputspec, 'subjects', ncwas, 'subjects')
workflow.connect(inputspec, 'regressor', ncwas, 'regressor_file')
workflow.connect(inputspec, 'permutations', ncwas, 'permutations')
workflow.connect(inputspec, 'participant_column', ncwas,
'participant_column')
workflow.connect(inputspec, 'columns', ncwas, 'columns_string')
workflow.connect(ccb, 'batch_list', ncwas, 'voxel_range')
#Merge the computed CWAS data
workflow.connect(ncwas, 'result_batch', mcwasb, 'cwas_batches')
workflow.connect(jmask, 'joint_mask', mcwasb, 'mask_file')
workflow.connect(mcwasb, 'F_file', outputspec, 'F_map')
workflow.connect(mcwasb, 'p_file', outputspec, 'p_map')
workflow.connect(mcwasb, 'neglog_p_file', outputspec, 'neglog_p_map')
return workflow
def create_montage(wf_name, cbar_name, png_name):
wf = pe.Workflow(name=wf_name)
inputnode = pe.Node(util.IdentityInterface(fields=['underlay', 'overlay']),
name='inputspec')
outputnode = pe.Node(util.IdentityInterface(fields=[
'axial_png', 'sagittal_png', 'resampled_underlay', 'resampled_overlay'
]),
name='outputspec')
# node for resampling create_montage images to 1mm for QC pages
resample_u = pe.Node(Function(input_names=['file_'],
output_names=['new_fname'],
function=resample_1mm,
as_module=True),
name='resample_u')
wf.connect(inputnode, 'underlay', resample_u, 'file_')
wf.connect(resample_u, 'new_fname', outputnode, 'resampled_underlay')
# same for overlays (resampling to 1mm)
resample_o = pe.Node(Function(input_names=['file_'],
output_names=['new_fname'],
function=resample_1mm,
as_module=True),
name='resample_o')
wf.connect(inputnode, 'overlay', resample_o, 'file_')
wf.connect(resample_o, 'new_fname', outputnode, 'resampled_overlay')
# node for axial montages
montage_a = pe.MapNode(Function(
input_names=['overlay', 'underlay', 'png_name', 'cbar_name'],
output_names=['png_name'],
function=montage_axial,
as_module=True),
name='montage_a',
iterfield=['overlay'])
montage_a.inputs.cbar_name = cbar_name
montage_a.inputs.png_name = png_name + '_a.png'
wf.connect(resample_u, 'new_fname', montage_a, 'underlay')
wf.connect(resample_o, 'new_fname', montage_a, 'overlay')
# node for sagittal montages
montage_s = pe.MapNode(Function(
input_names=['overlay', 'underlay', 'png_name', 'cbar_name'],
output_names=['png_name'],
function=montage_sagittal,
as_module=True),
name='montage_s',
iterfield=['overlay'])
montage_s.inputs.cbar_name = cbar_name
montage_s.inputs.png_name = png_name + '_s.png'
wf.connect(resample_u, 'new_fname', montage_s, 'underlay')
wf.connect(resample_o, 'new_fname', montage_s, 'overlay')
wf.connect(montage_a, 'png_name', outputnode, 'axial_png')
wf.connect(montage_s, 'png_name', outputnode, 'sagittal_png')
return wf
def qa_montages(workflow, c, strat, num_strat, qc_montage_id_a,
qc_montage_id_s, qc_hist_id, measure, idx):
try:
overlay, out_file = strat[measure]
overlay_drop_percent = pe.MapNode(
Function(input_names=['measure_file', 'percent'],
output_names=['modified_measure_file'],
function=drop_percent,
as_module=True),
name='dp_%s_%d' % (measure, num_strat),
iterfield=['measure_file'])
overlay_drop_percent.inputs.percent = 99.999
workflow.connect(overlay, out_file, overlay_drop_percent,
'measure_file')
montage = create_montage('montage_%s_%d' % (measure, num_strat),
'cyan_to_yellow', measure)
montage.inputs.inputspec.underlay = c.template_brain_only_for_func
workflow.connect(overlay_drop_percent, 'modified_measure_file',
montage, 'inputspec.overlay')
if 'centrality' in measure:
histogram = pe.MapNode(
Function(input_names=['measure_file', 'measure'],
output_names=['hist_path'],
function=gen_histogram,
as_module=True),
name='hist_{0}_{1}'.format(measure, num_strat),
iterfield=['measure_file'])
else:
histogram = pe.Node(Function(
input_names=['measure_file', 'measure'],
output_names=['hist_path'],
function=gen_histogram,
as_module=True),
name='hist_{0}_{1}'.format(measure, num_strat))
histogram.inputs.measure = measure
workflow.connect(overlay, out_file, histogram, 'measure_file')
strat.update_resource_pool({
'qc___%s_a' % measure: (montage, 'outputspec.axial_png'),
'qc___%s_s' % measure: (montage, 'outputspec.sagittal_png'),
'qc___%s_hist' % measure: (histogram, 'hist_path')
})
if not idx in qc_montage_id_a:
qc_montage_id_a[idx] = '%s_a' % measure
qc_montage_id_s[idx] = '%s_s' % measure
qc_hist_id[idx] = '%s_hist' % measure
except Exception as e:
print "[!] Connection of QA montages workflow for %s " \
"has failed.\n" % measure
print "Error: %s" % e
pass
def create_isfc(name='isfc', working_dir=None, crash_dir=None):
"""
Inter-Subject Functional Correlation
Parameters
----------
name : string, optional
Name of the workflow.
Returns
-------
workflow : nipype.pipeline.engine.Workflow
ISFC workflow.
Notes
-----
Workflow Inputs::
Workflow Outputs::
References
----------
.. [1] Simony, E., Honey, C. J., Chen, J., Lositsky, O., Yeshurun,
Y., Wiesel, A., & Hasson, U. (2016). Dynamic reconfiguration of the
default mode network during narrative comprehension.
Nature Communications, 7(May 2015), 1-13.
https://doi.org/10.1038/ncomms12141
"""
if not working_dir:
working_dir = os.path.join(os.getcwd(), 'ISC_work_dir')
if not crash_dir:
crash_dir = os.path.join(os.getcwd(), 'ISC_crash_dir')
wf = pe.Workflow(name=name)
wf.base_dir = working_dir
wf.config['execution'] = {'hash_method': 'timestamp',
'crashdump_dir': os.path.abspath(crash_dir)}
inputspec = pe.Node(
util.IdentityInterface(fields=[
'subjects',
'permutations',
'collapse_subj',
'std',
'two_sided',
'random_state'
]),
name='inputspec'
)
data_node = pe.Node(Function(input_names=['subjects'],
output_names=['subject_ids', 'D', 'voxel_masker'],
function=load_data,
as_module=True),
name='data')
save_node = pe.Node(Function(input_names=['subject_ids', 'ISFC', 'p', 'collapse_subj'],
output_names=['subject_ids', 'correlations', 'significance'],
function=save_data_isfc,
as_module=True),
name='save')
outputspec = pe.Node(
util.IdentityInterface(fields=['correlations', 'significance']),
name='outputspec'
)
isfc_node = pe.Node(Function(input_names=['D',
'std',
'collapse_subj'],
output_names=['ISFC', 'masked'],
function=node_isfc,
as_module=True),
name='ISFC')
permutations_node = pe.MapNode(Function(input_names=['permutation',
'D',
'masked',
'collapse_subj',
'random_state'],
output_names=['permutation',
'min_null',
'max_null'],
function=node_isfc_permutation,
as_module=True),
name='ISFC_permutation', iterfield='permutation')
significance_node = pe.Node(Function(input_names=['ISFC',
'min_null',
'max_null',
'two_sided'],
output_names=['p'],
function=node_isfc_significance,
as_module=True),
name='ISFC_p')
wf.connect([
(inputspec, data_node, [('subjects', 'subjects')]),
(inputspec, isfc_node, [('collapse_subj', 'collapse_subj')]),
(inputspec, isfc_node, [('std', 'std')]),
(data_node, isfc_node, [('D', 'D')]),
(isfc_node, significance_node, [('ISFC', 'ISFC')]),
(data_node, permutations_node, [('D', 'D')]),
(isfc_node, permutations_node, [('masked', 'masked')]),
(inputspec, permutations_node, [('collapse_subj', 'collapse_subj')]),
(inputspec, permutations_node, [(('permutations', _permutations), 'permutation')]),
(inputspec, permutations_node, [('random_state', 'random_state')]),
(permutations_node, significance_node, [('min_null', 'min_null')]),
(permutations_node, significance_node, [('max_null', 'max_null')]),
(inputspec, significance_node, [('two_sided', 'two_sided')]),
(data_node, save_node, [('subject_ids', 'subject_ids')]),
(inputspec, save_node, [('collapse_subj', 'collapse_subj')]),
(isfc_node, save_node, [('ISFC', 'ISFC')]),
(significance_node, save_node, [('p', 'p')]),
(save_node, outputspec, [('subject_ids', 'subject_ids')]),
(save_node, outputspec, [('correlations', 'correlations')]),
(save_node, outputspec, [('significance', 'significance')]),
])
return wf
def create_qc_carpet(wf_name='qc_carpet', output_image='qc_carpet'):
wf = pe.Workflow(name=wf_name)
input_node = pe.Node(util.IdentityInterface(fields=[
'functional_to_standard', 'mean_functional_to_standard',
'anatomical_gm_mask', 'anatomical_wm_mask', 'anatomical_csf_mask'
]),
name='inputspec')
output_node = pe.Node(util.IdentityInterface(fields=['carpet_plot']),
name='outputspec')
gm_resample = pe.Node(afni.Resample(), name='gm_resample')
gm_resample.inputs.outputtype = 'NIFTI'
wf.connect(input_node, 'anatomical_gm_mask', gm_resample, 'in_file')
wf.connect(input_node, 'mean_functional_to_standard', gm_resample,
'master')
gm_mask = pe.Node(afni.Calc(), name="gm_mask")
gm_mask.inputs.expr = 'astep(a, 0.5)'
gm_mask.inputs.outputtype = 'NIFTI'
wf.connect(gm_resample, 'out_file', gm_mask, 'in_file_a')
wm_resample = pe.Node(afni.Resample(), name='wm_resample')
wm_resample.inputs.outputtype = 'NIFTI'
wf.connect(input_node, 'anatomical_wm_mask', wm_resample, 'in_file')
wf.connect(input_node, 'mean_functional_to_standard', wm_resample,
'master')
wm_mask = pe.Node(afni.Calc(), name="wm_mask")
wm_mask.inputs.expr = 'astep(a, 0.5)'
wm_mask.inputs.outputtype = 'NIFTI'
wf.connect(wm_resample, 'out_file', wm_mask, 'in_file_a')
csf_resample = pe.Node(afni.Resample(), name='csf_resample')
csf_resample.inputs.outputtype = 'NIFTI'
wf.connect(input_node, 'anatomical_csf_mask', csf_resample, 'in_file')
wf.connect(input_node, 'mean_functional_to_standard', csf_resample,
'master')
csf_mask = pe.Node(afni.Calc(), name="csf_mask")
csf_mask.inputs.expr = 'astep(a, 0.5)'
csf_mask.inputs.outputtype = 'NIFTI'
wf.connect(csf_resample, 'out_file', csf_mask, 'in_file_a')
carpet_plot = pe.Node(Function(input_names=[
'gm_mask', 'wm_mask', 'csf_mask', 'functional_to_standard', 'output'
],
output_names=['carpet_plot'],
function=gen_carpet_plt,
as_module=True),
name='carpet_plot')
carpet_plot.inputs.output = output_image
wf.connect(gm_mask, 'out_file', carpet_plot, 'gm_mask')
wf.connect(wm_mask, 'out_file', carpet_plot, 'wm_mask')
wf.connect(csf_mask, 'out_file', carpet_plot, 'csf_mask')
wf.connect(input_node, 'functional_to_standard', carpet_plot,
'functional_to_standard')
wf.connect(carpet_plot, 'carpet_plot', output_node, 'carpet_plot')
return wf
def generate_summarize_tissue_mask(nuisance_wf,
pipeline_resource_pool,
regressor_descriptor,
regressor_selector,
use_ants=True):
"""
Add tissue mask generation into pipeline according to the selector.
:param nuisance_wf: Nuisance regressor workflow.
:param pipeline_resource_pool: dictionary of available resources.
:param regressor_descriptor: dictionary of steps to build, including keys:
'tissue', 'resolution', 'erosion'
:param regressor_selector: dictionary with the original selector
:return: the full path of the 3D nifti file containing the mask created by
this operation.
"""
steps = [
key
for key in ['tissue', 'resolution', 'erosion']
if key in regressor_descriptor
]
full_mask_key = "_".join(
regressor_descriptor[s]
for s in steps
)
for step_i, step in enumerate(steps):
mask_key = "_".join(
regressor_descriptor[s]
for s in steps[:step_i+1]
)
if mask_key in pipeline_resource_pool:
continue
node_mask_key = re.sub(r"[^\w]", "_", mask_key)
prev_mask_key = "_".join(
regressor_descriptor[s]
for s in steps[:step_i]
)
if step == 'tissue':
if mask_key.startswith('FunctionalVariance'):
create_variance_mask_node = pe.Node(
Function(
input_names=[
'functional_file_path',
'mask_file_path',
'threshold',
'by_slice'
],
output_names=['mask_file_path'],
function=create_temporal_variance_mask,
as_module=True,
),
name='create_temporal_variance_mask_{}'
.format(node_mask_key)
)
nuisance_wf.connect(*(
pipeline_resource_pool['Functional'] +
(create_variance_mask_node, 'functional_file_path')
))
nuisance_wf.connect(*(
pipeline_resource_pool['GlobalSignal'] +
(create_variance_mask_node, 'mask_file_path')
))
create_variance_mask_node.inputs.threshold = \
regressor_selector['threshold']
create_variance_mask_node.inputs.by_slice = \
regressor_selector['by_slice']
pipeline_resource_pool[mask_key] = \
(create_variance_mask_node, 'mask_file_path')
elif step == 'resolution':
mask_to_epi = pe.Node(interface=fsl.FLIRT(),
name='{}_flirt'
.format(node_mask_key))
mask_to_epi.inputs.interp = 'nearestneighbour'
if regressor_selector['extraction_resolution'] == "Functional":
nuisance_wf.connect(*(
pipeline_resource_pool['Functional'] +
(mask_to_epi, 'reference')
))
else:
resolution = regressor_selector['extraction_resolution']
mask_to_epi.inputs.apply_isoxfm = \
resolution
nuisance_wf.connect(*(
pipeline_resource_pool['Anatomical_{}mm'
.format(resolution)] +
(mask_to_epi, 'reference')
))
nuisance_wf.connect(*(
pipeline_resource_pool[prev_mask_key] +
(mask_to_epi, 'in_file')
))
pipeline_resource_pool[mask_key] = \
(mask_to_epi, 'out_file')
elif step == 'erosion':
erode_mask_node = pe.Node(interface=afni.Calc(),
name='{}'.format(node_mask_key))
erode_mask_node.inputs.args = "-b a+i -c a-i -d a+j " + \
"-e a-j -f a+k -g a-k"
erode_mask_node.inputs.expr = 'a*(1-amongst(0,b,c,d,e,f,g))'
erode_mask_node.inputs.outputtype = 'NIFTI_GZ'
erode_mask_node.inputs.out_file = 'erode_mask_node.nii.gz'
nuisance_wf.connect(*(
pipeline_resource_pool[prev_mask_key] +
(erode_mask_node, 'in_file_a')
))
pipeline_resource_pool[mask_key] = \
(erode_mask_node, 'out_file')
# Mask CSF with Ventricles
if full_mask_key.startswith('CerebrospinalFluid'):
if '{}_Unmasked'.format(full_mask_key) not in pipeline_resource_pool:
# reduce CSF mask to the lateral ventricles
mask_csf_with_lat_ven = pe.Node(interface=afni.Calc(), name='{}_Ventricles'.format(full_mask_key))
mask_csf_with_lat_ven.inputs.expr = 'a*b'
mask_csf_with_lat_ven.inputs.outputtype = 'NIFTI_GZ'
mask_csf_with_lat_ven.inputs.out_file = 'csf_lat_ven_mask.nii.gz'
ventricles_key = 'VentriclesToAnat'
if 'resolution' in regressor_descriptor:
ventricles_key += '_{}'.format(regressor_descriptor['resolution'])
if ventricles_key not in pipeline_resource_pool:
transforms = pipeline_resource_pool['Transformations']
if use_ants is True:
# perform the transform using ANTS
collect_linear_transforms = pe.Node(util.Merge(3), name='{}_ants_transforms'.format(ventricles_key))
nuisance_wf.connect(*(transforms['anat_to_mni_initial_xfm'] + (collect_linear_transforms, 'in1')))
nuisance_wf.connect(*(transforms['anat_to_mni_rigid_xfm'] + (collect_linear_transforms, 'in2')))
nuisance_wf.connect(*(transforms['anat_to_mni_affine_xfm'] + (collect_linear_transforms, 'in3')))
lat_ven_mni_to_anat = pe.Node(interface=ants.ApplyTransforms(), name='{}_ants'.format(ventricles_key))
lat_ven_mni_to_anat.inputs.invert_transform_flags = [True, True, True]
lat_ven_mni_to_anat.inputs.interpolation = 'NearestNeighbor'
lat_ven_mni_to_anat.inputs.dimension = 3
nuisance_wf.connect(collect_linear_transforms, 'out', lat_ven_mni_to_anat, 'transforms')
nuisance_wf.connect(*(pipeline_resource_pool['Ventricles'] + (lat_ven_mni_to_anat, 'input_image')))
nuisance_wf.connect(*(pipeline_resource_pool[full_mask_key] + (lat_ven_mni_to_anat, 'reference_image')))
pipeline_resource_pool[ventricles_key] = (lat_ven_mni_to_anat, 'output_image')
else:
# perform the transform using FLIRT
lat_ven_mni_to_anat = pe.Node(interface=fsl.FLIRT(), name='{}_flirt'.format(ventricles_key))
lat_ven_mni_to_anat.inputs.interp = 'nearestneighbour'
resolution = regressor_selector['extraction_resolution']
lat_ven_mni_to_anat.inputs.apply_isoxfm = \
resolution
nuisance_wf.connect(*(transforms['mni_to_anat_linear_xfm'] + (lat_ven_mni_to_anat, 'in_matrix_file')))
nuisance_wf.connect(*(pipeline_resource_pool['Ventricles'] + (lat_ven_mni_to_anat, 'in_file')))
nuisance_wf.connect(*(pipeline_resource_pool[full_mask_key] + (lat_ven_mni_to_anat, 'reference')))
pipeline_resource_pool[ventricles_key] = (lat_ven_mni_to_anat, 'out_file')
nuisance_wf.connect(*(pipeline_resource_pool[ventricles_key] + (mask_csf_with_lat_ven, 'in_file_a')))
nuisance_wf.connect(*(pipeline_resource_pool[full_mask_key] + (mask_csf_with_lat_ven, 'in_file_b')))
pipeline_resource_pool['{}_Unmasked'.format(full_mask_key)] = pipeline_resource_pool[full_mask_key]
pipeline_resource_pool[full_mask_key] = (mask_csf_with_lat_ven, 'out_file')
return pipeline_resource_pool, full_mask_key
def create_randomise(name='randomise', working_dir=None, crash_dir=None):
"""
Parameters
----------
Returns
-------
workflow : nipype.pipeline.engine.Workflow
Randomise workflow.
Notes
-----
Workflow Inputs::
Workflow Outputs::
References
----------
"""
if not working_dir:
working_dir = os.path.join(os.getcwd(), 'Randomise_work_dir')
if not crash_dir:
crash_dir = os.path.join(os.getcwd(), 'Randomise_crash_dir')
wf = pe.Workflow(name=name)
wf.base_dir = working_dir
wf.config['execution'] = {
'hash_method': 'timestamp',
'crashdump_dir': os.path.abspath(crash_dir)
}
inputspec = pe.Node(util.IdentityInterface(fields=[
'subjects_list', 'pipeline_output_folder', 'permutations',
'mask_boolean', 'demean', 'c_thresh'
]),
name='inputspec')
outputspec = pe.Node(util.IdentityInterface(fields=[
'tstat_files', 't_corrected_p_files', 'index_file', 'threshold_file',
'localmax_txt_file', 'localmax_vol_file', 'max_file', 'mean_file',
'pval_file', 'size_file'
]),
name='outputspec')
#merge = pe.Node(interface=fsl.Merge(), name='fsl_merge')
#merge.inputs.dimension = 't'
#merge.inputs.merged_file = "randomise_merged.nii.gz"
#wf.connect(inputspec, 'subjects', merge, 'in_files')
#mask = pe.Node(interface=fsl.maths.MathsCommand(), name='fsl_maths')
#mask.inputs.args = '-abs -Tmin -bin'
#mask.inputs.out_file = "randomise_mask.nii.gz"
#wf.connect(inputspec, 'subjects', mask, 'in_file')
randomise = pe.Node(interface=fsl.Randomise(), name='randomise')
randomise.inputs.base_name = "randomise"
randomise.inputs.demean = True
randomise.inputs.tfce = True
wf.connect([(inputspec, randomise, [
('subjects', 'in_file'),
('design_matrix_file', 'design_mat'),
('constrast_file', 'tcon'),
('permutations', 'num_perm'),
])])
wf.connect(randomise, 'tstat_files', outputspec, 'tstat_files')
wf.connect(randomise, 't_corrected_p_files', outputspec,
't_corrected_p_files')
#------------- issue here arises while using tfce. By not using tfce, you don't get t_corrected_p files. R V in a conundrum? --------------------#
select_tcorrp_files = pe.Node(Function(input_names=['input_list'],
output_names=['out_file'],
function=select),
name='select_t_corrp')
wf.connect(randomise, 't_corrected_p_files', select_tcorrp_files,
'input_list')
wf.connect(select_tcorrp_files, 'out_file', outputspec,
'out_tcorr_corrected')
select_tstat_files = pe.Node(Function(input_names=['input_list'],
output_names=['out_file'],
function=select),
name='select_t_stat')
wf.connect(randomise, 'tstat_files', select_tstat_files, 'input_list')
wf.connect(select_tstat_files, 'out_file', outputspec,
'out_tstat_corrected')
thresh = pe.Node(interface=fsl.Threshold(), name='fsl_threshold_contrast')
thresh.inputs.thresh = 0.95
thresh.inputs.out_file = 'rando_pipe_thresh_tstat.nii.gz'
wf.connect(select_tstat_files, 'out_file', thresh, 'in_file')
wf.connect(thresh, 'out_file', outputspec,
'rando_pipe_thresh_tstat.nii.gz')
thresh_bin = pe.Node(interface=fsl.UnaryMaths(),
name='fsl_threshold_bin_contrast')
thresh_bin.inputs.operation = 'bin'
wf.connect(thresh, 'out_file', thresh_bin, 'in_file')
wf.connect(thresh_bin, 'out_file', outputspec, 'thresh_bin_out')
apply_mask = pe.Node(interface=fsl.ApplyMask(),
name='fsl_applymask_contrast')
wf.connect(select_tstat_files, 'out_file', apply_mask, 'in_file')
wf.connect(thresh_bin, 'out_file', apply_mask, 'mask_file')
cluster = pe.Node(interface=fsl.Cluster(), name='cluster_contrast')
cluster.inputs.threshold = 0.0001
cluster.inputs.out_index_file = "index_file"
cluster.inputs.out_localmax_txt_file = "lmax_contrast.txt"
cluster.inputs.out_size_file = "cluster_size_contrast"
cluster.inputs.out_threshold_file = True
cluster.inputs.out_max_file = True
cluster.inputs.out_mean_file = True
cluster.inputs.out_pval_file = True
cluster.inputs.out_size_file = True
wf.connect(apply_mask, 'out_file', cluster, 'in_file')
wf.connect(cluster, 'index_file', outputspec, 'index_file')
wf.connect(cluster, 'threshold_file', outputspec, 'threshold_file')
wf.connect(cluster, 'localmax_txt_file', outputspec, 'localmax_txt_file')
wf.connect(cluster, 'localmax_vol_file', outputspec, 'localmax_vol_file')
wf.connect(cluster, 'max_file', outputspec, 'max_file')
wf.connect(cluster, 'mean_file', outputspec, 'meal_file')
wf.connect(cluster, 'pval_file', outputspec, 'pval_file')
wf.connect(cluster, 'size_file', outputspec, 'size_file')
return wf
def motion_power_statistics(name='motion_stats'):
"""
The main purpose of this workflow is to get various statistical measures
from the movement/motion parameters obtained in functional preprocessing.
Parameters
----------
:param str name: Name of the workflow, defaults to 'motion_stats'
:return: Nuisance workflow.
:rtype: nipype.pipeline.engine.Workflow
Notes
-----
Workflow Inputs::
inputspec.subject_id : string
Subject name or id
inputspec.scan_id : string
Functional Scan id or name
inputspec.motion_correct : string (func/rest file or a list of func/rest nifti file)
Path to motion corrected functional data
inputspec.mask : string (nifti file)
Path to field containing brain-only mask for the functional data
inputspec.max_displacement : string (Mat file)
maximum displacement (in mm) vector for brain voxels in each volume.
This file is obtained in functional preprocessing step
inputspec.movement_parameters : string (Mat file)
1D file containing six movement/motion parameters(3 Translation, 3 Rotations)
in different columns (roll pitch yaw dS dL dP), obtained in functional preprocessing step
Workflow Outputs::
outputspec.FDP_1D : 1D file
mean Framewise Displacement (FD)
outputspec.power_params : txt file
Text file containing various power parameters for scrubbing
outputspec.motion_params : txt file
Text file containing various movement parameters
Order of commands:
- Calculate Framewise Displacement FD as per power et al., 2012
Differentiating head realignment parameters across frames yields a six dimensional timeseries that represents instantaneous head motion.
Rotational displacements are converted from degrees to millimeters by calculating displacement on the surface of a sphere of radius 50 mm.[R5]
- Calculate Framewise Displacement FD as per jenkinson et al., 2002
- Calculate DVARS
DVARS (D temporal derivative of timecourses, VARS referring to RMS variance over voxels) indexes
the rate of change of BOLD signal across the entire brain at each frame of data.To calculate
DVARS, the volumetric timeseries is differentiated (by backwards differences) and RMS signal
change is calculated over the whole brain.DVARS is thus a measure of how much the intensity
of a brain image changes in comparison to the previous timepoint (as opposed to the global
signal, which is the average value of a brain image at a timepoint).[R5]
- Calculate Power parameters::
MeanFD : Mean (across time/frames) of the absolute values for Framewise Displacement (FD),
computed as described in Power et al., Neuroimage, 2012)
rootMeanSquareFD : Root mean square (RMS; across time/frames) of the absolute values for FD
rmsFD : Root mean square (RMS; across time/frames) of the absolute values for FD
FDquartile(top 1/4th FD) : Mean of the top 25% highest FD values
MeanDVARS : Mean of voxel DVARS
- Calculate Motion Parameters
Following motion parameters are calculated::
Subject
Scan
Mean Relative RMS Displacement
Max Relative RMS Displacement
Movements > threshold
Mean Relative Mean Rotation
Mean Relative Maxdisp
Max Relative Maxdisp
Max Abs Maxdisp
Max Relative Roll
Max Relative Pitch
Max Relative Yaw
Max Relative dS-I
Max Relative dL-R
Max Relative dP-A
Mean Relative Roll
Mean Relative Pitch
Mean Relative Yaw
Mean Relative dS-I
Mean Relative dL-R
Mean Relative dP-A
Max Abs Roll
Max Abs Pitch
Max Abs Yaw
Max Abs dS-I
Max Abs dL-R
Max Abs dP-A
Mean Abs Roll
Mean Abs Pitch
Mean Abs Yaw
Mean Abs dS-I
Mean Abs dL-R
Mean Abs dP-A
High Level Workflow Graph:
.. exec::
from CPAC.generate_motion_statistics import motion_power_statistics
wf = motion_power_statistics()
wf.write_graph(
graph2use='orig',
dotfilename='./images/parameters.dot'
)
.. image:: ../images/parameters.png
:width: 1000
Detailed Workflow Graph:
.. image:: ../images/parameters_detailed.png
:width: 1000
Examples
--------
>>> import generate_motion_statistics
>>> wf = generate_motion_statistics.motion_power_statistics("generate_statistics")
>>> wf.inputs.inputspec.movement_parameters = 'CPAC_outupts/sub01/func/movement_parameteres/rest_mc.1D'
>>> wf.inputs.inputspec.max_displacement = 'CPAC_outputs/sub01/func/max_dispalcement/max_disp.1D'
>>> wf.inputs.inputspec.motion_correct = 'CPAC_outputs/sub01/func/motion_correct/rest_mc.nii.gz'
>>> wf.inputs.inputspec.mask = 'CPAC_outputs/sub01/func/func_mask/rest_mask.nii.gz'
>>> wf.inputs.inputspec.transformations = 'CPAC_outputs/sub01/func/coordinate_transformation/rest_mc.aff12.1D'
>>> wf.inputs.inputspec.subject_id = 'sub01'
>>> wf.inputs.inputspec.scan_id = 'rest_1'
>>> wf.base_dir = './working_dir'
>>> wf.run()
References
----------
.. [1] Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious
but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage, 59(3),
2142-2154. doi:10.1016/j.neuroimage.2011.10.018
.. [2] Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Steps
toward optimizing motion artifact removal in functional connectivity MRI; a reply to Carp.
NeuroImage. doi:10.1016/j.neuroimage.2012.03.017
.. [3] Jenkinson, M., Bannister, P., Brady, M., Smith, S., 2002. Improved optimization for the robust
and accurate linear registration and motion correction of brain images. Neuroimage 17, 825-841.
"""
wf = pe.Workflow(name=name)
input_node = pe.Node(util.IdentityInterface(fields=[
'subject_id', 'scan_id', 'movement_parameters', 'max_displacement',
'motion_correct', 'mask', 'transformations'
]),
name='inputspec')
output_node = pe.Node(util.IdentityInterface(fields=[
'FDP_1D', 'FDJ_1D', 'DVARS_1D', 'power_params', 'motion_params'
]),
name='outputspec')
cal_DVARS = pe.Node(Function(input_names=['rest', 'mask'],
output_names=['out_file'],
function=calculate_DVARS,
as_module=True),
name='cal_DVARS')
# calculate mean DVARS
wf.connect(input_node, 'motion_correct', cal_DVARS, 'rest')
wf.connect(input_node, 'mask', cal_DVARS, 'mask')
wf.connect(cal_DVARS, 'out_file', output_node, 'DVARS_1D')
# Calculating mean Framewise Displacement as per power et al., 2012
calculate_FDP = pe.Node(Function(input_names=['in_file'],
output_names=['out_file'],
function=calculate_FD_P,
as_module=True),
name='calculate_FD')
wf.connect(input_node, 'movement_parameters', calculate_FDP, 'in_file')
wf.connect(calculate_FDP, 'out_file', output_node, 'FDP_1D')
# Calculating mean Framewise Displacement as per jenkinson et al., 2002
calculate_FDJ = pe.Node(Function(input_names=['in_file'],
output_names=['out_file'],
function=calculate_FD_J,
as_module=True),
name='calculate_FDJ')
wf.connect(input_node, 'transformations', calculate_FDJ, 'in_file')
wf.connect(calculate_FDJ, 'out_file', output_node, 'FDJ_1D')
calc_motion_parameters = pe.Node(Function(input_names=[
"subject_id", "scan_id", "movement_parameters", "max_displacement"
],
output_names=['out_file'],
function=gen_motion_parameters,
as_module=True),
name='calc_motion_parameters')
wf.connect(input_node, 'subject_id', calc_motion_parameters, 'subject_id')
wf.connect(input_node, 'scan_id', calc_motion_parameters, 'scan_id')
wf.connect(input_node, 'movement_parameters', calc_motion_parameters,
'movement_parameters')
wf.connect(input_node, 'max_displacement', calc_motion_parameters,
'max_displacement')
wf.connect(calc_motion_parameters, 'out_file', output_node,
'motion_params')
calc_power_parameters = pe.Node(Function(
input_names=["subject_id", "scan_id", "fdp", "fdj", "dvars"],
output_names=['out_file'],
function=gen_power_parameters,
as_module=True),
name='calc_power_parameters')
wf.connect(input_node, 'subject_id', calc_power_parameters, 'subject_id')
wf.connect(input_node, 'scan_id', calc_power_parameters, 'scan_id')
wf.connect(cal_DVARS, 'out_file', calc_power_parameters, 'dvars')
wf.connect(calculate_FDP, 'out_file', calc_power_parameters, 'fdp')
wf.connect(calculate_FDJ, 'out_file', calc_power_parameters, 'fdj')
wf.connect(calc_power_parameters, 'out_file', output_node, 'power_params')
return wf
def create_nuisance_workflow(nuisance_selectors,
use_ants,
name='nuisance'):
"""
Workflow for the removal of various signals considered to be noise from resting state
fMRI data. The residual signals for linear regression denoising is performed in a single
model. Therefore the residual time-series will be orthogonal to all signals.
Parameters
----------
:param nuisance_selectors: dictionary describing nuisance regression to be performed
:param use_ants: flag indicating whether FNIRT or ANTS is used
:param name: Name of the workflow, defaults to 'nuisance'
:return: nuisance : nipype.pipeline.engine.Workflow
Nuisance workflow.
Notes
-----
Workflow Inputs
---------------
Workflow Inputs::
inputspec.functional_file_path : string (nifti file)
Path to realigned and motion corrected functional image (nifti) file.
inputspec.functional_brain_mask_file_path : string (nifti file)
Whole brain mask corresponding to the functional data.
inputspec.anatomical_file_path : string (nifti file)
Corresponding preprocessed anatomical.
inputspec.wm_mask_file_path : string (nifti file)
Corresponding white matter mask.
inputspec.csf_mask_file_path : string (nifti file)
Corresponding cerebral spinal fluid mask.
inputspec.gm_mask_file_path : string (nifti file)
Corresponding grey matter mask.
inputspec.lat_ventricles_mask_file_path : string (nifti file)
Mask of lateral ventricles calculated from the Harvard Oxford Atlas.
inputspec.mni_to_anat_linear_xfm_file_path: string (nifti file)
FLIRT Linear MNI to Anat transform
inputspec.anat_to_mni_initial_xfm_file_path: string (nifti file)
ANTS initial transform from anat to MNI
inputspec.anat_to_mni_rigid_xfm_file_path: string (nifti file)
ANTS rigid (6 parameter, no scaling) transform from anat to MNI
inputspec.anat_to_mni_affine_xfm_file_path: string (nifti file)
ANTS affine (13 parameter, scales and shears) transform from anat to MNI
inputspec.func_to_anat_linear_xfm_file_path: string (nifti file)
FLIRT Linear Transform between functional and anatomical spaces
inputspec.motion_parameter_file_path : string (text file)
Corresponding rigid-body motion parameters. Matrix in the file should be of shape
(`T`, `R`), `T` time points and `R` motion parameters.
inputspec.fd_j_file_path : string (text file)
Framewise displacement calculated from the volume alignment.
inputspec.fd_p_file_path : string (text file)
Framewise displacement calculated from the motion parameters.
inputspec.dvars_file_path : string (text file)
DVARS calculated from the functional data.
inputspec.selector : Dictionary containing configuration parameters for nuisance regression.
To not run a type of nuisance regression, it may be ommited from the dictionary.
selector = {
aCompCor: {
symmary: {
method: 'DetrendPC', aCompCor will always extract the principal components from
detrended tissues signal,
components: number of components to retain,
},
tissues: list of tissues to extract regressors.
Valid values are: 'WhiteMatter', 'CerebrospinalFluid',
extraction_resolution: None | floating point value indicating isotropic
resolution (ex. 2 for 2mm x 2mm x 2mm that data should be extracted at,
the corresponding tissue mask will be resampled to this resolution. The
functional data will also be resampled to this resolution, and the
extraction will occur at this new resolution. The goal is to avoid
contamination from undesired tissue components when extracting nuisance
regressors,
erode_mask: True | False, whether or not the mask should be eroded to
further avoid a mask overlapping with a different tissue class,
include_delayed: True | False, whether or not to include a one-frame delay regressor,
default to False,
include_squared: True | False, whether or not to include a squared regressor,
default to False,
include_delayed_squared: True | False, whether or not to include a squared one-frame
delay regressor, default to False,
},
tCompCor: {
symmary: {
method: 'PC', tCompCor will always extract the principal components from
BOLD signal,
components: number of components to retain,
},
threshold:
floating point number = cutoff as raw variance value,
floating point number followed by SD (ex. 1.5SD) = mean + a multiple of the SD,
floating point number followed by PCT (ex. 2PCT) = percentile from the top (ex is top 2%),
by_slice: True | False, whether or not the threshold criterion should be applied
by slice or across the entire volume, makes most sense for thresholds
using SD or PCT,
include_delayed: True | False,
include_squared: True | False,
include_delayed_squared: True | False,
},
WhiteMatter: {
symmary: {
method: 'PC', 'DetrendPC', 'Mean', 'NormMean' or 'DetrendNormMean',
components: number of components to retain, if PC,
},
extraction_resolution: None | floating point value (same as for aCompCor),
erode_mask: True | False (same as for aCompCor),
include_delayed: True | False (same as for aCompCor),
include_squared: True | False (same as for aCompCor),
include_delayed_squared: True | False (same as for aCompCor),
},
CerebrospinalFluid: {
symmary: {
method: 'PC', 'DetrendPC', 'Mean', 'NormMean' or 'DetrendNormMean',
components: number of components to retain, if PC,
},
extraction_resolution: None | floating point value (same as for aCompCor),
erode_mask: True | False (same as for aCompCor),
include_delayed: True | False (same as for aCompCor),
include_squared: True | False (same as for aCompCor),
include_delayed_squared: True | False (same as for aCompCor),
},
GreyMatter: {
symmary: {
method: 'PC', 'DetrendPC', 'Mean', 'NormMean' or 'DetrendNormMean',
components: number of components to retain, if PC,
},
extraction_resolution: None | floating point value (same as for aCompCor),
erode_mask: True | False (same as for aCompCor),
include_delayed: True | False (same as for aCompCor),
include_squared: True | False (same as for aCompCor),
include_delayed_squared: True | False (same as for aCompCor),
},
GlobalSignal: {
symmary: {
method: 'PC', 'DetrendPC', 'Mean', 'NormMean' or 'DetrendNormMean',
components: number of components to retain, if PC,
},
include_delayed: True | False (same as for aCompCor),
include_squared: True | False (same as for aCompCor),
include_delayed_squared: True | False (same as for aCompCor),
},
Motion: None | {
include_delayed: True | False (same as for aCompCor),
include_squared: True | False (same as for aCompCor),
include_delayed_squared: True | False (same as for aCompCor),
},
Censor: {
method: 'Kill', 'Zero', 'Interpolate', 'SpikeRegression',
thresholds: list of dictionary, {
type: 'FD_J', 'FD_P', 'DVARS',
value: threshold value to be applied to metric
},
number_of_previous_trs_to_censor: integer, number of previous
TRs to censor (remove or regress, if spike regression)
number_of_subsequent_trs_to_censor: integer, number of
subsequent TRs to censor (remove or regress, if spike
regression)
},
PolyOrt: {
degree: integer, polynomial degree up to which will be removed,
e.g. 2 means constant + linear + quadratic, practically
that is probably, the most that will be need especially
if band pass filtering
},
Bandpass: {
bottom_frequency: floating point value, frequency in hertz of
the highpass part of the pass band, frequencies below this
will be removed,
top_frequency: floating point value, frequency in hertz of the
lowpass part of the pass band, frequencies above this
will be removed
}
}
Workflow Outputs::
outputspec.residual_file_path : string (nifti file)
Path of residual file in nifti format
outputspec.regressors_file_path : string (TSV file)
Path of TSV file of regressors used. Column name indicates the regressors included .
Nuisance Procedure:
1. Compute nuisance regressors based on input selections.
2. Calculate residuals with respect to these nuisance regressors in a
single model for every voxel.
High Level Workflow Graph:
.. exec::
from CPAC.nuisance import create_nuisance_workflow
wf = create_nuisance_workflow({
'PolyOrt': {'degree': 2},
'tCompCor': {'summary': {'method': 'PC', 'components': 5}, 'threshold': '1.5SD', 'by_slice': True},
'aCompCor': {'summary': {'method': 'PC', 'components': 5}, 'tissues': ['WhiteMatter', 'CerebrospinalFluid'], 'extraction_resolution': 2},
'WhiteMatter': {'summary': {'method': 'PC', 'components': 5}, 'extraction_resolution': 2},
'CerebrospinalFluid': {'summary': {'method': 'PC', 'components': 5}, 'extraction_resolution': 2, 'erode_mask': True},
'GreyMatter': {'summary': {'method': 'PC', 'components': 5}, 'extraction_resolution': 2, 'erode_mask': True},
'GlobalSignal': {'summary': 'Mean', 'include_delayed': True, 'include_squared': True, 'include_delayed_squared': True},
'Motion': {'include_delayed': True, 'include_squared': True, 'include_delayed_squared': True},
'Censor': {'method': 'Interpolate', 'thresholds': [{'type': 'FD_J', 'value': 0.5}, {'type': 'DVARS', 'value': 0.7}]}
}, use_ants=False)
wf.write_graph(
graph2use='orig',
dotfilename='./images/nuisance.dot'
)
.. image:: ../images/nuisance.png
:width: 1000
Detailed Workflow Graph:
.. image:: ../images/nuisance_detailed.png
:width: 1000
"""
nuisance_wf = pe.Workflow(name=name)
inputspec = pe.Node(util.IdentityInterface(fields=[
'selector',
'functional_file_path',
'anatomical_file_path',
'gm_mask_file_path',
'wm_mask_file_path',
'csf_mask_file_path',
'lat_ventricles_mask_file_path',
'functional_brain_mask_file_path',
'func_to_anat_linear_xfm_file_path',
'mni_to_anat_linear_xfm_file_path',
'anat_to_mni_initial_xfm_file_path',
'anat_to_mni_rigid_xfm_file_path',
'anat_to_mni_affine_xfm_file_path',
'motion_parameters_file_path',
'fd_j_file_path',
'fd_p_file_path',
'dvars_file_path',
]), name='inputspec')
outputspec = pe.Node(util.IdentityInterface(fields=['residual_file_path',
'regressors_file_path']),
name='outputspec')
# Resources to create regressors
pipeline_resource_pool = {
"Anatomical": (inputspec, 'anatomical_file_path'),
"Functional": (inputspec, 'functional_file_path'),
"GlobalSignal": (inputspec, 'functional_brain_mask_file_path'),
"WhiteMatter": (inputspec, 'wm_mask_file_path'),
"CerebrospinalFluid": (inputspec, 'csf_mask_file_path'),
"GreyMatter": (inputspec, 'gm_mask_file_path'),
"Ventricles": (inputspec, 'lat_ventricles_mask_file_path'),
"Transformations": {
"func_to_anat_linear_xfm": (inputspec, "func_to_anat_linear_xfm_file_path"),
"mni_to_anat_linear_xfm": (inputspec, "mni_to_anat_linear_xfm_file_path"),
"anat_to_mni_initial_xfm": (inputspec, "anat_to_mni_initial_xfm_file_path"),
"anat_to_mni_rigid_xfm": (inputspec, "anat_to_mni_rigid_xfm_file_path"),
"anat_to_mni_affine_xfm": (inputspec, "anat_to_mni_affine_xfm_file_path"),
}
}
# Regressor map to simplify construction of the needed regressors
regressors = {
'GreyMatter': ['grey_matter_summary_file_path', ()],
'WhiteMatter': ['white_matter_summary_file_path', ()],
'CerebrospinalFluid': ['csf_summary_file_path', ()],
'aCompCor': ['acompcor_file_path', ()],
'tCompCor': ['tcompcor_file_path', ()],
'GlobalSignal': ['global_summary_file_path', ()],
'DVARS': ['dvars_file_path', (inputspec, 'dvars_file_path')],
'FD_J': ['framewise_displacement_j_file_path', (inputspec, 'framewise_displacement_j_file_path')],
'FD_P': ['framewise_displacement_p_file_path', (inputspec, 'framewise_displacement_p_file_path')],
'Motion': ['motion_parameters_file_path', (inputspec, 'motion_parameters_file_path')]
}
derived = ['tCompCor', 'aCompCor']
tissues = ['GreyMatter', 'WhiteMatter', 'CerebrospinalFluid']
for regressor_type, regressor_resource in regressors.items():
if regressor_type not in nuisance_selectors:
continue
regressor_selector = nuisance_selectors[regressor_type]
# Set summary method for tCompCor and aCompCor
if regressor_type in derived:
if 'summary' not in regressor_selector:
regressor_selector['summary'] = {}
if type(regressor_selector['summary']) is not dict:
raise ValueError("Regressor {0} requires PC summary method, "
"but {1} specified"
.format(regressor_type,
regressor_selector['summary']))
regressor_selector['summary']['method'] = \
'DetrendPC' if regressor_type == 'aCompCor' else 'PC'
if not regressor_selector['summary'].get('components'):
regressor_selector['summary']['components'] = 1
# If regressor is not present, build up the regressor
if not regressor_resource[1]:
# We don't have the regressor, look for it in the resource pool,
# build a corresponding key, this is seperated in to a mask key
# and an extraction key, which when concatenated provide the
# resource key for the regressor
regressor_descriptor = {'tissue': regressor_type}
if regressor_type == 'aCompCor':
if not regressor_selector.get('tissues'):
raise ValueError("Tissue type required for aCompCor, "
"but none specified")
regressor_descriptor = {
'tissue': regressor_selector['tissues']
}
if regressor_type == 'tCompCor':
if not regressor_selector.get('threshold'):
raise ValueError("Threshold required for tCompCor, "
"but none specified.")
regressor_descriptor = {
'tissue': 'FunctionalVariance-{}'
.format(regressor_selector['threshold'])
}
if regressor_selector.get('by_slice'):
regressor_descriptor['tissue'] += '-BySlice'
else:
regressor_selector['by_slice'] = False
# Add selector into regressor description
if regressor_selector.get('extraction_resolution'):
regressor_descriptor['resolution'] = \
str(regressor_selector['extraction_resolution']) + "mm"
elif regressor_type in tissues:
regressor_selector['extraction_resolution'] = "Functional"
regressor_descriptor['resolution'] = "Functional"
if regressor_selector.get('erode_mask'):
regressor_descriptor['erosion'] = 'Eroded'
if not regressor_selector.get('summary'):
raise ValueError("Summary method required for {0}, "
"but none specified".format(regressor_type))
if type(regressor_selector['summary']) is dict:
regressor_descriptor['extraction'] = \
regressor_selector['summary']['method']
else:
regressor_descriptor['extraction'] = \
regressor_selector['summary']
if regressor_descriptor['extraction'] in ['DetrendPC', 'PC']:
if not regressor_selector['summary'].get('components'):
raise ValueError("Summary method PC requires components, "
"but received none.")
regressor_descriptor['extraction'] += \
'_{0}'.format(regressor_selector['summary']['components'])
if type(regressor_descriptor['tissue']) is not list:
regressor_descriptor['tissue'] = \
[regressor_descriptor['tissue']]
if regressor_selector.get('extraction_resolution') and \
regressor_selector["extraction_resolution"] != "Functional":
functional_at_resolution_key = "Functional_{0}mm".format(
regressor_selector["extraction_resolution"]
)
anatomical_at_resolution_key = "Anatomical_{0}mm".format(
regressor_selector["extraction_resolution"]
)
if anatomical_at_resolution_key not in pipeline_resource_pool:
anat_resample = pe.Node(
interface=fsl.FLIRT(),
name='{}_flirt'
.format(anatomical_at_resolution_key)
)
anat_resample.inputs.apply_isoxfm = regressor_selector["extraction_resolution"]
nuisance_wf.connect(*(
pipeline_resource_pool['Anatomical'] +
(anat_resample, 'in_file')
))
nuisance_wf.connect(*(
pipeline_resource_pool['Anatomical'] +
(anat_resample, 'reference')
))
pipeline_resource_pool[anatomical_at_resolution_key] = \
(anat_resample, 'out_file')
if functional_at_resolution_key not in pipeline_resource_pool:
func_resample = pe.Node(
interface=fsl.FLIRT(),
name='{}_flirt'
.format(functional_at_resolution_key)
)
func_resample.inputs.apply_xfm = True
nuisance_wf.connect(*(
pipeline_resource_pool['Transformations']['func_to_anat_linear_xfm'] +
(func_resample, 'in_matrix_file')
))
nuisance_wf.connect(*(
pipeline_resource_pool['Functional'] +
(func_resample, 'in_file')
))
nuisance_wf.connect(*(
pipeline_resource_pool[anatomical_at_resolution_key] +
(func_resample, 'reference')
))
pipeline_resource_pool[functional_at_resolution_key] = \
(func_resample, 'out_file')
# Create merger to summarize the functional timeseries
regressor_mask_file_resource_keys = []
for tissue in regressor_descriptor['tissue']:
# Ignore non tissue masks
if tissue not in tissues and \
not tissue.startswith('FunctionalVariance'):
regressor_mask_file_resource_keys += [tissue]
continue
tissue_regressor_descriptor = regressor_descriptor.copy()
tissue_regressor_descriptor['tissue'] = tissue
# Generate resource masks
(pipeline_resource_pool,
regressor_mask_file_resource_key) = \
generate_summarize_tissue_mask(
nuisance_wf,
pipeline_resource_pool,
tissue_regressor_descriptor,
regressor_selector,
use_ants=use_ants
)
regressor_mask_file_resource_keys += \
[regressor_mask_file_resource_key]
# Keep tissus ordered, to avoid duplicates
regressor_mask_file_resource_keys = \
list(sorted(regressor_mask_file_resource_keys))
# Create key for the final regressors
regressor_file_resource_key = "_".join([
"-".join(regressor_descriptor[key])
if type(regressor_descriptor[key]) == list
else regressor_descriptor[key]
for key in ['tissue', 'resolution', 'erosion', 'extraction']
if key in regressor_descriptor
])
if regressor_file_resource_key not in pipeline_resource_pool:
# Retrieve summary from voxels at provided mask
summarize_timeseries_node = pe.Node(
Function(
input_names=[
'functional_path',
'masks_path',
'summary'
],
output_names=['components_path'],
function=summarize_timeseries,
as_module=True,
),
name='{}_summarization'.format(regressor_type)
)
summarize_timeseries_node.inputs.summary = \
regressor_selector['summary']
# Merge mask paths to extract voxel timeseries
merge_masks_paths = pe.Node(
util.Merge(len(regressor_mask_file_resource_keys)),
name='{}_marge_masks'.format(regressor_type)
)
for i, regressor_mask_file_resource_key in \
enumerate(regressor_mask_file_resource_keys):
node, node_output = \
pipeline_resource_pool[regressor_mask_file_resource_key]
nuisance_wf.connect(
node, node_output,
merge_masks_paths, "in{}".format(i + 1)
)
nuisance_wf.connect(
merge_masks_paths, 'out',
summarize_timeseries_node, 'masks_path'
)
functional_key = 'Functional'
if regressor_selector.get('extraction_resolution') and \
regressor_selector["extraction_resolution"] != "Functional":
functional_key = 'Functional_{}mm'.format(
regressor_selector['extraction_resolution']
)
nuisance_wf.connect(*(
pipeline_resource_pool[functional_key] +
(summarize_timeseries_node, 'functional_path')
))
pipeline_resource_pool[regressor_file_resource_key] = \
(summarize_timeseries_node, 'components_path')
# Add it to internal resource pool
regressor_resource[1] = \
pipeline_resource_pool[regressor_file_resource_key]
# Build regressors and combine them into a single file
build_nuisance_regressors = pe.Node(Function(
input_names=['functional_file_path',
'selector',
'grey_matter_summary_file_path',
'white_matter_summary_file_path',
'csf_summary_file_path',
'acompcor_file_path',
'tcompcor_file_path',
'global_summary_file_path',
'motion_parameters_file_path',
'censor_file_path'],
output_names=['out_file'],
function=gather_nuisance,
as_module=True
), name="build_nuisance_regressors")
nuisance_wf.connect(
inputspec, 'functional_file_path',
build_nuisance_regressors, 'functional_file_path'
)
nuisance_wf.connect(
inputspec, 'selector',
build_nuisance_regressors, 'selector'
)
# Check for any regressors to combine into files
has_nuisance_regressors = any(
regressor_resource[1]
for regressor_key, regressor_resource
in regressors.items()
)
if has_nuisance_regressors:
for regressor_key, (regressor_arg, regressor_node) in regressors.items():
if regressor_key in nuisance_selectors:
nuisance_wf.connect(
regressor_node[0], regressor_node[1],
build_nuisance_regressors, regressor_arg
)
if nuisance_selectors.get('Censor'):
censor_methods = ['Kill', 'Zero', 'Interpolate', 'SpikeRegression']
censor_selector = nuisance_selectors.get('Censor')
if censor_selector.get('method') not in censor_methods:
raise ValueError("Improper censoring method specified ({0}), "
"should be one of {1}."
.format(censor_selector.get('method'),
censor_methods))
find_censors = pe.Node(Function(
input_names=['fd_j_file_path',
'fd_j_threshold',
'fd_p_file_path',
'fd_p_threshold',
'dvars_file_path',
'dvars_threshold',
'number_of_previous_trs_to_censor',
'number_of_subsequent_trs_to_censor'],
output_names=['out_file'],
function=find_offending_time_points,
as_module=True
), name="find_offending_time_points")
if not censor_selector.get('thresholds'):
raise ValueError(
'Censoring requested, but thresh_metric not provided.'
)
for threshold in censor_selector['thresholds']:
if 'type' not in threshold or threshold['type'] not in ['DVARS', 'FD_J', 'FD_P']:
raise ValueError(
'Censoring requested, but with invalid threshold type.'
)
if 'value' not in threshold:
raise ValueError(
'Censoring requested, but threshold not provided.'
)
if threshold['type'] == 'FD_J':
find_censors.inputs.fd_j_threshold = threshold['value']
nuisance_wf.connect(inputspec, "fd_j_file_path",
find_censors, "fd_j_file_path")
if threshold['type'] == 'FD_P':
find_censors.inputs.fd_p_threshold = threshold['value']
nuisance_wf.connect(inputspec, "fd_p_file_path",
find_censors, "fd_p_file_path")
if threshold['type'] == 'DVARS':
find_censors.inputs.dvars_threshold = threshold['value']
nuisance_wf.connect(inputspec, "dvars_file_path",
find_censors, "dvars_file_path")
if censor_selector.get('number_of_previous_trs_to_censor') and \
censor_selector['method'] != 'SpikeRegression':
find_censors.inputs.number_of_previous_trs_to_censor = \
censor_selector['number_of_previous_trs_to_censor']
else:
find_censors.inputs.number_of_previous_trs_to_censor = 0
if censor_selector.get('number_of_subsequent_trs_to_censor') and \
censor_selector['method'] != 'SpikeRegression':
find_censors.inputs.number_of_subsequent_trs_to_censor = \
censor_selector['number_of_subsequent_trs_to_censor']
else:
find_censors.inputs.number_of_subsequent_trs_to_censor = 0
# Use 3dTproject to perform nuisance variable regression
nuisance_regression = pe.Node(interface=afni.TProject(),
name='nuisance_regression')
nuisance_regression.inputs.out_file = 'residuals.nii.gz'
nuisance_regression.inputs.outputtype = 'NIFTI_GZ'
nuisance_regression.inputs.norm = False
if nuisance_selectors.get('Censor'):
if nuisance_selectors['Censor']['method'] == 'SpikeRegression':
nuisance_wf.connect(find_censors, 'out_file',
build_nuisance_regressors, 'censor_file_path')
else:
if nuisance_selectors['Censor']['method'] == 'Interpolate':
nuisance_regression.inputs.cenmode = 'NTRP'
else:
nuisance_regression.inputs.cenmode = \
nuisance_selectors['Censor']['method'].upper()
nuisance_wf.connect(find_censors, 'out_file',
nuisance_regression, 'censor')
if nuisance_selectors.get('PolyOrt'):
if not nuisance_selectors['PolyOrt'].get('degree'):
raise ValueError("Polynomial orthogonalization requested, "
"but degree not provided.")
nuisance_regression.inputs.polort = \
nuisance_selectors['PolyOrt']['degree']
else:
nuisance_regression.inputs.polort = 0
nuisance_wf.connect([
(inputspec, nuisance_regression, [
('functional_file_path', 'in_file'),
('functional_brain_mask_file_path', 'mask'),
]),
])
if has_nuisance_regressors:
nuisance_wf.connect(build_nuisance_regressors, 'out_file',
nuisance_regression, 'ort')
nuisance_wf.connect(nuisance_regression, 'out_file',
outputspec, 'residual_file_path')
nuisance_wf.connect(build_nuisance_regressors, 'out_file',
outputspec, 'regressors_file_path')
return nuisance_wf
def create_qc_workflow(workflow, c, strategies, qc_outputs):
qc_montage_id_a = {}
qc_montage_id_s = {}
qc_plot_id = {}
qc_hist_id = {}
for num_strat, strat in enumerate(strategies):
nodes = strat.get_nodes_names()
preproc, out_file = strat['functional_preprocessed']
brain_mask, mask_file = strat['functional_brain_mask']
func_to_anat_xfm, xfm_file = strat['functional_to_anat_linear_xfm']
anat_ref, ref_file = strat['anatomical_brain']
mfa, mfa_file = strat['mean_functional_in_anat']
# make SNR plot
qc_workflow = create_qc_snr('qc_snr_{0}'.format(num_strat))
workflow.connect(preproc, out_file, qc_workflow,
'inputspec.functional_preprocessed')
workflow.connect(brain_mask, mask_file, qc_workflow,
'inputspec.functional_brain_mask')
workflow.connect(func_to_anat_xfm, xfm_file, qc_workflow,
'inputspec.functional_to_anat_linear_xfm')
workflow.connect(anat_ref, ref_file, qc_workflow,
'inputspec.anatomical_brain')
workflow.connect(mfa, mfa_file, qc_workflow,
'inputspec.mean_functional_in_anat')
strat.update_resource_pool({
'qc___snr_a': (qc_workflow, 'outputspec.snr_axial_image'),
'qc___snr_s': (qc_workflow, 'outputspec.snr_sagittal_image'),
'qc___snr_hist': (qc_workflow, 'outputspec.snr_histogram_image'),
'qc___snr_val': (qc_workflow, 'outputspec.snr_mean')
})
if not 3 in qc_montage_id_a:
qc_montage_id_a[3] = 'snr_a'
qc_montage_id_s[3] = 'snr_s'
qc_hist_id[3] = 'snr_hist'
# make motion parameters plot
mov_param, out_file = strat['movement_parameters']
qc_workflow = create_qc_motion('qc_motion_{0}'.format(num_strat))
workflow.connect(mov_param, out_file, qc_workflow,
'inputspec.motion_parameters')
strat.update_resource_pool({
'qc___movement_trans_plot':
(qc_workflow, 'outputspec.motion_translation_plot'),
'qc___movement_rot_plot':
(qc_workflow, 'outputspec.motion_rotation_plot')
})
if not 6 in qc_plot_id:
qc_plot_id[6] = 'movement_trans_plot'
if not 7 in qc_plot_id:
qc_plot_id[7] = 'movement_rot_plot'
# make FD plot and volumes removed
if 'gen_motion_stats' in nodes and 1 in c.runNuisance:
if c.fdCalc == 'Power':
fd, out_file = strat['frame_wise_displacement_power']
else:
fd, out_file = strat['frame_wise_displacement_jenkinson']
qc_workflow = create_qc_fd('qc_fd_{0}'.format(num_strat))
workflow.connect(fd, out_file, qc_workflow, 'inputspec.fd')
if "De-Spiking" in c.runMotionSpike:
excluded, out_file_ex = strat['despiking_frames_excluded']
workflow.connect(excluded, out_file_ex, qc_workflow,
'inputspec.excluded_volumes')
elif "Scrubbing" in c.runMotionSpike:
excluded, out_file_ex = strat['scrubbing_frames_excluded']
workflow.connect(excluded, out_file_ex, qc_workflow,
'inputspec.excluded_volumes')
strat.update_resource_pool({
'qc___fd_plot': (qc_workflow, 'outputspec.fd_histogram_plot')
})
if not 8 in qc_plot_id:
qc_plot_id[8] = 'fd_plot'
# make QC montages for Skull Stripping Visualization
anat_underlay, out_file = strat['anatomical_brain']
skull, out_file_s = strat['anatomical_reorient']
qc_workflow = create_qc_skullstrip(
'qc_skullstrip_{0}'.format(num_strat))
workflow.connect(anat_underlay, out_file, qc_workflow,
'inputspec.anatomical_brain')
workflow.connect(skull, out_file_s, qc_workflow,
'inputspec.anatomical_reorient')
strat.update_resource_pool({
'qc___skullstrip_vis_a': (qc_workflow, 'outputspec.axial_image'),
'qc___skullstrip_vis_s': (qc_workflow, 'outputspec.sagittal_image')
})
if not 1 in qc_montage_id_a:
qc_montage_id_a[1] = 'skullstrip_vis_a'
qc_montage_id_s[1] = 'skullstrip_vis_s'
# make QC montages for mni normalized anatomical image
mni_anat_underlay, out_file = strat['mean_functional_in_anat']
montage_mni_anat = create_montage(
'montage_mni_anat_{0}'.format(num_strat), 'red', 'mni_anat')
workflow.connect(mni_anat_underlay, out_file, montage_mni_anat,
'inputspec.underlay')
montage_mni_anat.inputs.inputspec.overlay = os.path.abspath(
p.resource_filename(
'CPAC', 'resources/templates/MNI152_Edge_AllTissues.nii.gz'))
strat.update_resource_pool({
'qc___mni_normalized_anatomical_a':
(montage_mni_anat, 'outputspec.axial_png'),
'qc___mni_normalized_anatomical_s':
(montage_mni_anat, 'outputspec.sagittal_png')
})
if not 6 in qc_montage_id_a:
qc_montage_id_a[6] = 'mni_normalized_anatomical_a'
qc_montage_id_s[6] = 'mni_normalized_anatomical_s'
# make QC montages for CSF WM GM
if 'seg_preproc' in nodes:
anat_underlay, out_file = strat['anatomical_brain']
csf_overlay, out_file_csf = strat['anatomical_csf_mask']
wm_overlay, out_file_wm = strat['anatomical_wm_mask']
gm_overlay, out_file_gm = strat['anatomical_gm_mask']
montage_csf_gm_wm = create_montage_gm_wm_csf(
'montage_csf_gm_wm_%d' % num_strat, 'montage_csf_gm_wm')
workflow.connect(anat_underlay, out_file, montage_csf_gm_wm,
'inputspec.underlay')
workflow.connect(csf_overlay, out_file_csf, montage_csf_gm_wm,
'inputspec.overlay_csf')
workflow.connect(wm_overlay, out_file_wm, montage_csf_gm_wm,
'inputspec.overlay_wm')
workflow.connect(gm_overlay, out_file_gm, montage_csf_gm_wm,
'inputspec.overlay_gm')
strat.update_resource_pool({
'qc___csf_gm_wm_a':
(montage_csf_gm_wm, 'outputspec.axial_png'),
'qc___csf_gm_wm_s':
(montage_csf_gm_wm, 'outputspec.sagittal_png')
})
if not 2 in qc_montage_id_a:
qc_montage_id_a[2] = 'csf_gm_wm_a'
qc_montage_id_s[2] = 'csf_gm_wm_s'
# make QC montage for Mean Functional in T1 with T1 edge
anat, out_file = strat['anatomical_brain']
m_f_a, out_file_mfa = strat['mean_functional_in_anat']
anat_edge = pe.Node(Function(input_names=['in_file'],
output_names=['out_file'],
function=afni_Edge3,
as_module=True),
name='anat_edge_%d' % num_strat)
montage_anat = create_montage('montage_anat_%d' % num_strat, 'red',
't1_edge_on_mean_func_in_t1')
workflow.connect(anat, out_file, anat_edge, 'in_file')
workflow.connect(anat_edge, 'out_file', montage_anat,
'inputspec.overlay')
workflow.connect(m_f_a, out_file_mfa, montage_anat,
'inputspec.underlay')
strat.update_resource_pool({
'qc___mean_func_with_t1_edge_a':
(montage_anat, 'outputspec.axial_png'),
'qc___mean_func_with_t1_edge_s':
(montage_anat, 'outputspec.sagittal_png')
})
if not 4 in qc_montage_id_a:
qc_montage_id_a[4] = 'mean_func_with_t1_edge_a'
qc_montage_id_s[4] = 'mean_func_with_t1_edge_s'
# make QC montage for Mean Functional in MNI with MNI edge
m_f_i, out_file = strat['mean_functional_to_standard']
montage_mfi = create_montage('montage_mfi_%d' % num_strat, 'red',
'MNI_edge_on_mean_func_mni')
workflow.connect(m_f_i, out_file, montage_mfi, 'inputspec.underlay')
montage_mfi.inputs.inputspec.overlay = os.path.abspath(
p.resource_filename(
'CPAC', 'resources/templates/MNI152_Edge_AllTissues.nii.gz'))
strat.update_resource_pool({
'qc___mean_func_with_mni_edge_a':
(montage_mfi, 'outputspec.axial_png'),
'qc___mean_func_with_mni_edge_s':
(montage_mfi, 'outputspec.sagittal_png')
})
if not 5 in qc_montage_id_a:
qc_montage_id_a[5] = 'mean_func_with_mni_edge_a'
qc_montage_id_s[5] = 'mean_func_with_mni_edge_s'
# Link all the derivatives to the QC pages
idx = 7
rp = strat.get_resource_pool()
for key in sorted(rp.keys()):
# qc_outputs is from the outputs CSV
if key in qc_outputs:
qa_montages(workflow, c, strat, num_strat, qc_montage_id_a,
qc_montage_id_s, qc_hist_id, key, idx)
idx += 1
return qc_montage_id_a, qc_montage_id_s, qc_hist_id, qc_plot_id
def create_qc_snr(wf_name='qc_snr'):
wf = pe.Workflow(name=wf_name)
input_node = pe.Node(util.IdentityInterface(fields=[
'functional_preprocessed', 'functional_brain_mask',
'functional_to_anat_linear_xfm', 'anatomical_brain',
'mean_functional_in_anat'
]),
name='inputspec')
output_node = pe.Node(util.IdentityInterface(fields=[
'snr_axial_image', 'snr_sagittal_image', 'snr_histogram_image',
'snr_mean'
]),
name='outputspec')
std_dev = pe.Node(afni.TStat(args='-stdev'), name='std_dev')
std_dev.inputs.outputtype = 'NIFTI_GZ'
wf.connect(input_node, 'functional_preprocessed', std_dev, 'in_file')
wf.connect(input_node, 'functional_brain_mask', std_dev, 'mask')
std_dev_anat = pe.Node(fsl.ApplyWarp(interp='trilinear'),
name='std_dev_anat')
wf.connect(input_node, 'functional_to_anat_linear_xfm', std_dev_anat,
'premat')
wf.connect(std_dev, 'out_file', std_dev_anat, 'in_file')
snr = pe.Node(afni.Calc(expr='b/a'), name='snr')
snr.inputs.outputtype = 'NIFTI_GZ'
wf.connect(input_node, 'anatomical_brain', std_dev_anat, 'ref_file')
wf.connect(input_node, 'mean_functional_in_anat', snr, 'in_file_b')
wf.connect(std_dev_anat, 'out_file', snr, 'in_file_a')
snr_val = pe.Node(Function(input_names=['measure_file'],
output_names=['snr_storefl'],
function=cal_snr_val,
as_module=True),
name='snr_val')
wf.connect(snr, 'out_file', snr_val, 'measure_file')
hist_snr = pe.Node(Function(input_names=['measure_file', 'measure'],
output_names=['hist_path'],
function=gen_histogram,
as_module=True),
name='hist_snr')
hist_snr.inputs.measure = 'snr'
wf.connect(snr, 'out_file', hist_snr, 'measure_file')
snr_drop_percent = pe.Node(Function(
input_names=['measure_file', 'percent'],
output_names=['modified_measure_file'],
function=drop_percent,
as_module=True),
name='dp_snr')
snr_drop_percent.inputs.percent = 99
wf.connect(snr, 'out_file', snr_drop_percent, 'measure_file')
montage_snr = create_montage('montage_snr', 'red_to_blue', 'snr')
wf.connect(snr_drop_percent, 'modified_measure_file', montage_snr,
'inputspec.overlay')
wf.connect(input_node, 'anatomical_brain', montage_snr,
'inputspec.underlay')
wf.connect(montage_snr, 'outputspec.axial_png', output_node,
'snr_axial_image')
wf.connect(montage_snr, 'outputspec.sagittal_png', output_node,
'snr_sagittal_image')
wf.connect(hist_snr, 'hist_path', output_node, 'snr_histogram_image')
wf.connect(snr_val, 'snr_storefl', output_node, 'snr_mean')
return wf