def group_melodic_pipeline(self, **kwargs):
pipeline = self.create_pipeline(
name='group_melodic',
inputs=[
DatasetSpec('smoothed_ts', nifti_gz_format),
FieldSpec('tr', float)
],
outputs=[DatasetSpec('group_melodic', directory_format)],
desc=("Group ICA"),
version=1,
citations=[fsl_cite],
**kwargs)
gica = pipeline.create_join_subjects_node(MELODIC(),
joinfield=['in_files'],
name='gica',
requirements=[fsl510_req],
wall_time=7200)
gica.inputs.no_bet = True
gica.inputs.bg_threshold = self.parameter('brain_thresh_percent')
gica.inputs.bg_image = self.parameter('MNI_template')
gica.inputs.dim = self.parameter('group_ica_components')
gica.inputs.report = True
gica.inputs.out_stats = True
gica.inputs.mm_thresh = 0.5
gica.inputs.sep_vn = True
gica.inputs.mask = self.parameter('MNI_template_mask')
gica.inputs.out_dir = 'group_melodic.ica'
pipeline.connect_input('smoothed_ts', gica, 'in_files')
pipeline.connect_input('tr', gica, 'tr_sec')
pipeline.connect_output('group_melodic', gica, 'out_dir')
return pipeline
def group_melodic_pipeline(self, **name_maps):
pipeline = self.new_pipeline(name='group_melodic',
desc=("Group ICA"),
citations=[fsl_cite],
name_maps=name_maps)
pipeline.add(MELODIC(
no_bet=True,
bg_threshold=self.parameter('brain_thresh_percent'),
dim=self.parameter('group_ica_components'),
report=True,
out_stats=True,
mm_thresh=0.5,
sep_vn=True,
out_dir='group_melodic.ica',
output_type='NIFTI_GZ'),
inputs={
'bg_image': ('template_brain', nifti_gz_format),
'mask': ('template_mask', nifti_gz_format),
'in_files': ('smoothed_ts', nifti_gz_format),
'tr_sec': ('tr', float)
},
outputs={'group_melodic': ('out_dir', directory_format)},
joinsource=self.SUBJECT_ID,
joinfield=['in_files'],
name='gica',
requirements=[fsl_req.v('5.0.10')],
wall_time=7200)
return pipeline
def single_subject_melodic_pipeline(self, **kwargs):
pipeline = self.create_pipeline(
name='MelodicL1',
inputs=[
DatasetSpec('filtered_data', nifti_gz_format),
FieldSpec('tr', float),
DatasetSpec('brain_mask', nifti_gz_format)
],
outputs=[DatasetSpec('melodic_ica', directory_format)],
desc=("Single subject ICA analysis using FSL MELODIC."),
version=1,
citations=[fsl_cite],
**kwargs)
mel = pipeline.create_node(MELODIC(),
name='melodic_L1',
wall_time=15,
requirements=[fsl5_req])
mel.inputs.no_bet = True
pipeline.connect_input('brain_mask', mel, 'mask')
mel.inputs.bg_threshold = self.parameter('brain_thresh_percent')
mel.inputs.report = True
mel.inputs.out_stats = True
mel.inputs.mm_thresh = 0.5
mel.inputs.out_dir = 'melodic_ica'
pipeline.connect_input('tr', mel, 'tr_sec')
pipeline.connect_input('filtered_data', mel, 'in_files')
pipeline.connect_output('melodic_ica', mel, 'out_dir')
return pipeline
def single_subject_melodic_pipeline(self, **name_maps):
pipeline = self.new_pipeline(
name='MelodicL1',
desc=("Single subject ICA analysis using FSL MELODIC."),
citations=[fsl_cite],
name_maps=name_maps)
pipeline.add('melodic_L1',
MELODIC(
no_bet=True,
bg_threshold=self.parameter('brain_thresh_percent'),
report=True,
out_stats=True,
mm_thresh=0.5,
out_dir='melodic_ica',
output_type='NIFTI_GZ'),
inputs={
'mask': (self.brain_mask_spec_name, nifti_gz_format),
'tr_sec': ('tr', float),
'in_files': ('filtered_data', nifti_gz_format)
},
outputs={'melodic_ica': ('out_dir', directory_format)},
wall_time=15,
requirements=[fsl_req.v('5.0.10')])
return pipeline
