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Starting group level [skip ci@
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@bclenet
bclenet committed Apr 15, 2024
1 parent 7dcd88f commit 6c3d996
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Showing 2 changed files with 10 additions and 126 deletions.
2 changes: 1 addition & 1 deletion narps_open/pipelines/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@
'3TR7': 'PipelineTeam3TR7',
'43FJ': None,
'46CD': None,
'4SZ2': None,
'4SZ2': 'PipelineTeam4SZ2',
'4TQ6': None,
'50GV': None,
'51PW': 'PipelineTeam51PW',
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134 changes: 9 additions & 125 deletions narps_open/pipelines/team_4SZ2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -137,7 +137,7 @@ def get_run_level_analysis(self):

# Level1Design Node - Generate files for run level computation
model_design = Node(Level1Design(), name = 'model_design')
model_design.inputs.bases = {'dgamma' : {'derivs' : False }}
model_design.inputs.bases = {'dgamma' : {'derivs' : True }}
model_design.inputs.interscan_interval = TaskInformation()['RepetitionTime']
model_design.inputs.model_serial_correlations = True
model_design.inputs.contrasts = self.run_level_contrasts
Expand Down Expand Up @@ -194,123 +194,8 @@ def get_run_level_outputs(self):
for parameter_values in parameter_sets for template in templates]

def get_subject_level_analysis(self):
"""
Create the subject level analysis workflow.
Returns:
- subject_level_analysis : nipype.WorkFlow
"""
# Second level (single-subject, mean of all four scans) analysis workflow.
subject_level = Workflow(
base_dir = self.directories.working_dir,
name = 'subject_level_analysis')

# Infosource Node - To iterate on subject and runs
information_source = Node(IdentityInterface(
fields = ['subject_id', 'contrast_id']),
name = 'information_source')
information_source.iterables = [
('subject_id', self.subject_list),
('contrast_id', self.contrast_list)
]

# SelectFiles node - to select necessary files
templates = {
'cope' : join(self.directories.output_dir,
'run_level_analysis', '_run_id_*_subject_id_{subject_id}', 'results',
'cope{contrast_id}.nii.gz'),
'varcope' : join(self.directories.output_dir,
'run_level_analysis', '_run_id_*_subject_id_{subject_id}', 'results',
'varcope{contrast_id}.nii.gz'),
'masks' : join('derivatives', 'fmriprep', 'sub-{subject_id}', 'func',
'sub-{subject_id}_task-MGT_run-{run_id}_bold_space-MNI152NLin2009cAsym_brainmask.nii.gz')
}
select_files = Node(SelectFiles(templates), name = 'select_files')
select_files.inputs.base_directory= self.directories.results_dir
subject_level.connect(information_source, 'subject_id', select_files, 'subject_id')
subject_level.connect(information_source, 'contrast_id', select_files, 'contrast_id')

# Merge Node - Merge copes files for each subject
merge_copes = Node(Merge(), name = 'merge_copes')
merge_copes.inputs.dimension = 't'
subject_level.connect(select_files, 'cope', merge_copes, 'in_files')

# Merge Node - Merge varcopes files for each subject
merge_varcopes = Node(Merge(), name = 'merge_varcopes')
merge_varcopes.inputs.dimension = 't'
subject_level.connect(select_files, 'varcope', merge_varcopes, 'in_files')

# Split Node - Split mask list to serve them as inputs of the MultiImageMaths node.
split_masks = Node(Split(), name = 'split_masks')
split_masks.inputs.splits = [1, len(self.run_list) - 1]
split_masks.inputs.squeeze = True # Unfold one-element splits removing the list
subject_level.connect(select_files, 'masks', split_masks, 'inlist')

# MultiImageMaths Node - Create a subject mask by
# computing the intersection of all run masks.
mask_intersection = Node(MultiImageMaths(), name = 'mask_intersection')
mask_intersection.inputs.op_string = '-mul %s ' * (len(self.run_list) - 1)
subject_level.connect(split_masks, 'out1', mask_intersection, 'in_file')
subject_level.connect(split_masks, 'out2', mask_intersection, 'operand_files')

# L2Model Node - Generate subject specific second level model
generate_model = Node(L2Model(), name = 'generate_model')
generate_model.inputs.num_copes = len(self.run_list)

# FLAMEO Node - Estimate model
estimate_model = Node(FLAMEO(), name = 'estimate_model')
estimate_model.inputs.run_mode = 'flame1'
subject_level.connect(mask_intersection, 'out_file', estimate_model, 'mask_file')
subject_level.connect(merge_copes, 'merged_file', estimate_model, 'cope_file')
subject_level.connect(merge_varcopes, 'merged_file', estimate_model, 'var_cope_file')
subject_level.connect(generate_model, 'design_mat', estimate_model, 'design_file')
subject_level.connect(generate_model, 'design_con', estimate_model, 't_con_file')
subject_level.connect(generate_model, 'design_grp', estimate_model, 'cov_split_file')

# DataSink Node - store the wanted results in the wanted directory
data_sink = Node(DataSink(), name = 'data_sink')
data_sink.inputs.base_directory = self.directories.output_dir
subject_level.connect(
mask_intersection, 'out_file', data_sink, 'subject_level_analysis.@mask')
subject_level.connect(estimate_model, 'zstats', data_sink, 'subject_level_analysis.@stats')
subject_level.connect(
estimate_model, 'tstats', data_sink, 'subject_level_analysis.@tstats')
subject_level.connect(estimate_model, 'copes', data_sink, 'subject_level_analysis.@copes')
subject_level.connect(
estimate_model, 'var_copes', data_sink, 'subject_level_analysis.@varcopes')

return subject_level

def get_subject_level_outputs(self):
""" Return the names of the files the subject level analysis is supposed to generate. """

parameters = {
'contrast_id' : self.contrast_list,
'subject_id' : self.subject_list,
'file' : ['cope1.nii.gz', 'tstat1.nii.gz', 'varcope1.nii.gz', 'zstat1.nii.gz']
}
parameter_sets = product(*parameters.values())
template = join(
self.directories.output_dir,
'subject_level_analysis', '_contrast_id_{contrast_id}_subject_id_{subject_id}','{file}'
)
return_list = [template.format(**dict(zip(parameters.keys(), parameter_values)))\
for parameter_values in parameter_sets]

parameters = {
'contrast_id' : self.contrast_list,
'subject_id' : self.subject_list,
}
parameter_sets = product(*parameters.values())
template = join(
self.directories.output_dir,
'subject_level_analysis', '_contrast_id_{contrast_id}_subject_id_{subject_id}',
'sub-{subject_id}_task-MGT_run-01_bold_space-MNI152NLin2009cAsym_preproc_brain_mask_maths.nii.gz'
)
return_list += [template.format(**dict(zip(parameters.keys(), parameter_values)))\
for parameter_values in parameter_sets]

return return_list
""" No subject level analysis has been done by team 4SZ2 """
return None

def get_one_sample_t_test_regressors(subject_list: list) -> dict:
"""
Expand Down Expand Up @@ -400,14 +285,13 @@ def get_group_level_analysis_sub_workflow(self, method):
# SelectFiles Node - select necessary files
templates = {
'cope' : join(self.directories.output_dir,
'subject_level_analysis', '_contrast_id_{contrast_id}_subject_id_*',
'cope1.nii.gz'),
'run_level_analysis', '_run_id_*_subject_id_*', 'results',
'cope{contrast_id}.nii.gz'),
'varcope' : join(self.directories.output_dir,
'subject_level_analysis', '_contrast_id_{contrast_id}_subject_id_*',
'varcope1.nii.gz'),
'masks': join(self.directories.output_dir,
'subject_level_analysis', '_contrast_id_1_subject_id_*',
'sub-*_task-MGT_run-*_bold_space-MNI152NLin2009cAsym_preproc_brain_mask_maths.nii.gz')
'run_level_analysis', '_run_id_*_subject_id_*', 'results',
'varcope{contrast_id}.nii.gz'),
'masks': join('derivatives', 'fmriprep', 'sub-*', 'func',
'sub-*_task-MGT_run-*_bold_space-MNI152NLin2009cAsym_brainmask.nii.gz')
}
select_files = Node(SelectFiles(templates), name = 'select_files')
select_files.inputs.base_directory = self.directories.results_dir
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