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preprocessing is tested and working
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ATAY Melvin Selim committed Dec 13, 2024
1 parent b963c45 commit 86f8157
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2 changes: 1 addition & 1 deletion narps_open/pipelines/__init__.py
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Expand Up @@ -54,7 +54,7 @@
'J7F9': 'PipelineTeamJ7F9',
'K9P0': None,
'L1A8': None,
'L3V8': None,
'L3V8': 'PipelineTeamL3V8',
'L7J7': 'PipelineTeamL7J7',
'L9G5': None,
'O03M': None,
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213 changes: 85 additions & 128 deletions narps_open/pipelines/team_L3V8.py
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Expand Up @@ -25,41 +25,40 @@
)
from narps_open.utils.configuration import Configuration

class L3V8(Pipeline):
class PipelineTeamL3V8(Pipeline):
""" NARPS team L3V8 pipeline class definition and description of pipeline steps
Participants:101"""

def __init__(self):
super().__init__()
self.fwhm = 6.0
self.team_id = 'L3V8'
self.contrast_list = ['0001', '0002', '0003', '0004', '0005', '0006']
self.contrast_list = ['0001', '0002', '0003', '0004', '0005', '0006'] # why is this selected?

# Define contrasts
gain_conditions = [f'gamble_run{r}xgain_run{r}^1' for r in range(1,len(self.run_list) + 1)]
loss_conditions = [f'gamble_run{r}xloss_run{r}^1' for r in range(1,len(self.run_list) + 1)]
gain_conditions = [f'gamble_run{i}xgain_run{i}^1' for i in range(1,len(self.run_list) + 1)] # to name runs
loss_conditions = [f'gamble_run{i}xloss_run{i}^1' for i in range(1,len(self.run_list) + 1)]
self.subject_level_contrasts = [
('pos_gain', 'T', gain_conditions, [1, 1, 1, 1]), # average postivite activation -gain value
('pos_gain', 'T', gain_conditions, [1, 1, 1, 1]), # average postivite activation -gain value
('pos_loss', 'T', loss_conditions, [1, 1, 1, 1]), # average positive effect -loss value
('pos_loss', 'T', loss_conditions, [1, 1, 1, 1]), # average positive effect -loss value
('neg_gain', 'T', gain_conditions, [-1, -1, -1, -1]), # average negative activation - gain value
('neg_loss', 'T', loss_conditions, [-1, -1, -1, -1]) # average negative activation - loss value
# figure out group contrast settings # TODO
('contrast1', 'T', gain_conditions, [-1, 1, -1, 1, -1, 1, -1, 1]) # group differences between EqI and EqR

('neg_loss', 'T', loss_conditions, [-1, -1, -1, -1]), # average negative activation - loss value
]
def get_preprocessing_sub_workflow(self):
# figure out group contrast settings # TODO - make sure why?
#('contrast1', 'T', gain_conditions, [-1, 1, -1, 1, -1, 1, -1, 1]) # group differences between EqI and EqR

def get_preprocessing(self):
"""
Create the second part of the preprocessing workflow.
Returns:
- preprocessing : nipype.WorkFlow
"""
# Infosource Node - To iterate on subjects
information_source = Node(IdentityInterface(
info_source = Node(IdentityInterface(
fields = ['subject_id', 'run_id']),
name = 'information_source')
information_source.iterables = [
name = 'info_source')
info_source.iterables = [
('subject_id', self.subject_list), ('run_id', self.run_list)
]

Expand All @@ -69,14 +68,35 @@ def get_preprocessing_sub_workflow(self):
'func' : join('sub-{subject_id}', 'func',
'sub-{subject_id}_task-MGT_run-{run_id}_bold.nii.gz'),
}

preprocessing = Workflow(
base_dir = self.directories.working_dir,
name = 'preprocessing'
)

# select files
select_files = Node(SelectFiles(templates), name = 'select_files')
select_files.inputs.base_directory = self.directories.dataset_dir

# DataSink Node - store the wanted results in the wanted repository save output
data_sink = Node(DataSink(), name = 'data_sink')
data_sink.inputs.base_directory = self.directories.output_dir

# selectfiles node connection
preprocessing.connect(info_source, 'subject_id', select_files, 'subject_id')
preprocessing.connect(info_source, 'run_id', select_files, 'run_id')

# Order of operations:
# Realignment, co-registration, segmentation, normalization, smooth (6 mm).

# Gunzip nodes - gunzip files because SPM do not use .nii.gz files
gunzip_anat = Node(Gunzip(), name = 'gunzip_anat')
gunzip_func = Node(Gunzip(), name = 'gunzip_func')

# gunzip node connection
preprocessing.connect(select_files, 'anat', gunzip_anat, 'in_file')
preprocessing.connect(select_files, 'func', gunzip_func, 'in_file')

# motion correction - default realignment node in SPM

motion_correction = Node(interface=Realign(), name='realign')
Expand All @@ -91,17 +111,27 @@ def get_preprocessing_sub_workflow(self):
motion_correction.inputs.write_wrap = [0, 0, 0]
motion_correction.inputs.write_mask = True
motion_correction.inputs.jobtype = 'estwrite'




# connection node for motion correction
preprocessing.connect(gunzip_func, 'out_file', motion_correction, 'in_files')
preprocessing.connect( motion_correction, 'realigned_files', data_sink, 'preprocessing.@realigned_files' )
preprocessing.connect( motion_correction, 'realignment_parameters', data_sink, 'preprocessing.@realignment_parameters' )
preprocessing.connect( motion_correction, 'mean_image', data_sink, 'preprocessing.@mean_image' )

# coregistration node

coregister = Node(Coregister(), name="coregister")
coregister.inputs.jobtype = 'estimate'
coregister.inputs.cost_function = 'nmi'
coregister.inputs.fwhm = [6.0, 6.0]
coregister.inputs.separation = [4.0, 2.0]
coregister.inputs.tolerance = [0.02, 0.02, 0.02, 0.001, 0.001, 0.001, 0.01, 0.01, 0.01, 0.001, 0.001, 0.001]

coregisteration = Node(Coregister(), name="coregister")
coregisteration.inputs.jobtype = 'estimate'
coregisteration.inputs.cost_function = 'nmi'
coregisteration.inputs.fwhm = [6.0, 6.0]
coregisteration.inputs.separation = [4.0, 2.0]
coregisteration.inputs.tolerance = [0.02, 0.02, 0.02, 0.001, 0.001, 0.001, 0.01, 0.01, 0.01, 0.001, 0.001, 0.001]

# connect coreg
preprocessing.connect( motion_correction, 'mean_image', coregisteration, 'target' ) # target=mean
preprocessing.connect( gunzip_anat, 'out_file', coregisteration, 'source' ) # T1w=source anat

# Get SPM Tissue Probability Maps file
spm_tissues_file = join(SPMInfo.getinfo()['path'], 'tpm', 'TPM.nii')

Expand All @@ -120,120 +150,47 @@ def get_preprocessing_sub_workflow(self):
[(spm_tissues_file, 5), 4, (True,False), (False, False)],
[(spm_tissues_file, 6), 2, (False,False), (False, False)]
]

# segmentation connection
preprocessing.connect(coregisteration, 'coregistered_files', segmentation, 'channel_files' )
preprocessing.connect(segmentation, 'bias_corrected_images', data_sink, 'preprocessing.@bias_corrected_images')
preprocessing.connect(segmentation, 'native_class_images', data_sink, 'preprocessing.@native_class_images')
preprocessing.connect(segmentation, 'forward_deformation_field', data_sink, 'preprocessing.@forward_deformation_field')


# normalization node

normalize = Node(Normalize12(), name="normalize") #old normalize now
normalize.inputs.jobtype = 'write'
normalize.inputs.write_voxel_sizes = [3, 3, 3]
normalize.inputs.write_interp = 4
normalize.inputs.warping_regularization = [0, 0.001, 0.5, 0.05, 0.2]
normalization = Node(Normalize12(), name="normalize")
normalization.inputs.jobtype = 'write'
normalization.inputs.write_voxel_sizes = [3, 3, 3]
normalization.inputs.write_interp = 4
normalization.inputs.warping_regularization = [0, 0.001, 0.5, 0.05, 0.2]

# normalization connection
preprocessing.connect(segmentation, 'forward_deformation_field', normalization, 'deformation_file')
preprocessing.connect(normalization, 'normalized_files', data_sink, 'preprocessing.@normalized_files')


# smoothing node

smooth = Node(Smooth(), name="smooth")
smooth.inputs.fwhm = [6, 6, 6]
smooth.inputs.implicit_masking = False

# DataSink Node - store the wanted results in the wanted repository
data_sink = Node(DataSink(), name = 'data_sink')
data_sink.inputs.base_directory = self.directories.output_dir
preprocessing.connect(normalization, 'normalized_files', smooth, 'in_files')
preprocessing.connect(smooth, 'smoothed_files', data_sink, 'preprocessing.@smoothed_files')


# Create preprocessing workflow and connect its nodes
preprocessing = Workflow(base_dir = self.directories.working_dir, name = 'preprocessing')
preprocessing.config['execution']['stop_on_first_crash'] = 'true'
preprocessing.connect([
(information_source, select_files, [
('subject_id', 'subject_id'),
('run_id', 'run_id')]),
(select_files, gunzip_anat, [('anat', 'in_file')]),
(select_files, gunzip_func, [('func', 'in_file')]),
(gunzip_func, motion_correction, [('out_file', 'in_files')]),
(motion_correction, data_sink, [('realigned_files', 'preprocessing.@realigned_files'),
('realignment_parameters', 'preprocessing.@realignment_parameters'),
('mean_image', 'preprocessing.@mean_image')]),
(motion_correction, coregister, [('mean_image', 'source')]),
(gunzip_anat, coregister, [('out_file', 'source')]),
(coregister, segmentation, [('coregistered_files', 'channel_files')]),
(gunzip_anat, segmentation, [('out_file', 'channel_files')]),
(segmentation, data_sink, [('bias_corrected_images', 'preprocessing.@bias_corrected_images'),
('forward_deformation_field', 'preprocessing.@forward_deformation_field'),
('backward_deformation_field', 'preprocessing.@backward_deformation_field')]),
(segmentation, normalize, [('forward_deformation_field', 'deformation_file')]),
(normalize, smooth, [('normalized_files', 'in_files')]), # check normalization anat
(smooth, data_sink, [('smoothed_files', 'preprocessing.@smoothed_files')]),
])

# Remove large files, if requested
if Configuration()['pipelines']['remove_unused_data']:

# Merge Node - Merge file names to be removed after datasink node is performed
merge_removable_files = Node(Merge(5), name = 'merge_removable_files')
merge_removable_files.inputs.ravel_inputs = True

# Function Nodes remove_files - Remove sizeable files once they aren't needed
remove_after_datasink = MapNode(Function(
function = remove_parent_directory,
input_names = ['_', 'file_name'],
output_names = []
), name = 'remove_after_datasink', iterfield = 'file_name')

# Add connections
preprocessing.connect([
(gunzip_func, merge_removable_files, [('out_file', 'in1')]),
(gunzip_anat, merge_removable_files, [('out_file', 'in2')]),
(select_files, merge_removable_files, [('out_file', 'in3')]),
(coregister, merge_removable_files, [('coregistered_files', 'in4')]),
(segmentation, merge_removable_files, [('native_class_images', 'in5')]),
(merge_removable_files, remove_after_datasink, [('out', 'file_name')]),
(data_sink, remove_after_datasink, [('out_file', '_')])
])
# all nodes connected and returns preprocessing

return preprocessing

def get_preprocessing(self):
"""
Create the full preprocessing workflow.
Returns: a list of nipype.WorkFlow
"""
return [
self.get_preprocessing_sub_workflow()
]

# TODO edit below code.
def get_preprocessing_outputs(self):
""" Return the names of the files the preprocessing is supposed to generate. """

# Outputs from dartel workflow
return_list = [join(self.directories.output_dir, 'dartel_template', 'template_6.nii')]
return_list += [join(self.directories.output_dir, 'dartel_template',
f'u_rc1subject_id_{subject_id}_struct_template.nii')\
for subject_id in self.subject_list]

# Outputs from preprocessing
parameters = {
'subject_id': self.subject_list,
'run_id': self.run_list,
}
parameter_sets = product(*parameters.values())
output_dir = join(
self.directories.output_dir,
'preprocessing',
'_run_id_{run_id}_subject_id_{subject_id}'
)
templates = [
# Realignment parameters
join(output_dir, 'rp_asub-{subject_id}_task-MGT_run-{run_id}_bold.txt'),
# Realigned unwarped files
join(output_dir, 'asub-{subject_id}_task-MGT_run-{run_id}_bold.nii'),
# Normalized_files
join(output_dir, 'swasub-{subject_id}_task-MGT_run-{run_id}_bold.nii'),
# Normalized class images
join(output_dir, 'wc2sub-{subject_id}_T1w.nii'),
join(output_dir, 'wc1sub-{subject_id}_T1w.nii')
]
return_list += [template.format(**dict(zip(parameters.keys(), parameter_values)))\
for parameter_values in parameter_sets for template in templates]

return return_list


def get_run_level_analysis(self):

return
def get_subject_level_analysis(self):
return
def get_group_level_analysis(self):
return
def get_hypotheses_outputs(self):
return

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