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4TQ6 reproduction - improvement #204

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4TQ6 reproduction - improvement #204

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769272a
Repro of 4TQ6 from Elodie's work
bclenet Apr 12, 2024
cb5e2be
4TQ8 marked as implemented
bclenet Apr 15, 2024
e7efbca
Merge branch 'main' into 4TQ6_repro
bclenet Apr 17, 2024
9394b53
Last checks on 4TQ6
bclenet Apr 18, 2024
01c7ebd
[TEST] 4TQ6
bclenet Apr 18, 2024
c88ae6c
New masking strategy
bclenet Jun 13, 2024
74b432c
Merge branch 'main' into 4TQ6_repro
bclenet Jun 13, 2024
38ba3bb
Select masks from subjects in the analysis
bclenet Jun 13, 2024
5b8edec
FSL's MathsCommand for mask intersection
bclenet Jun 14, 2024
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37 changes: 25 additions & 12 deletions narps_open/pipelines/team_4TQ6.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@
FSLCommand, Randomise
)
from nipype.algorithms.modelgen import SpecifyModel
from nipype.interfaces.fsl.maths import MultiImageMaths
from nipype.interfaces.fsl.maths import MultiImageMaths, MathsCommand

from narps_open.utils.configuration import Configuration
from narps_open.pipelines import Pipeline
Expand Down Expand Up @@ -429,6 +429,19 @@ def get_group_level_analysis_sub_workflow(self, method):
)
group_level.connect(select_files, 'cope', get_copes, 'input_str')

# Function Node elements_in_string
# Get masks for these subjects
# Note : using a MapNode with elements_in_string requires using clean_list to remove
# None values from the out_list
get_masks = MapNode(Function(
function = elements_in_string,
input_names = ['input_str', 'elements'],
output_names = ['out_list']
),
name = 'get_masks', iterfield = 'input_str'
)
group_level.connect(select_files, 'masks', get_masks, 'input_str')

# Function Node elements_in_string
# Get variance of the estimated copes (varcope) for these subjects
# Note : using a MapNode with elements_in_string requires using clean_list to remove
Expand All @@ -452,18 +465,16 @@ def get_group_level_analysis_sub_workflow(self, method):
merge_varcopes.inputs.dimension = 't'
group_level.connect(get_varcopes, ('out_list', clean_list), 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.subject_list) - 1]
split_masks.inputs.squeeze = True # Unfold one-element splits removing the list
group_level.connect(select_files, 'masks', split_masks, 'inlist')
# Merge Node - Merge masks for further intersection
merge_masks = Node(Merge(), name = 'merge_masks')
merge_masks.inputs.dimension = 't'
group_level.connect(get_masks, ('out_list', clean_list), merge_masks, 'in_files')

# 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.subject_list) - 1)
group_level.connect(split_masks, 'out1', mask_intersection, 'in_file')
group_level.connect(split_masks, 'out2', mask_intersection, 'operand_files')
# MathsCommand Node - Create a group mask by
# computing the intersection of all subject masks.
mask_intersection = Node(MathsCommand(), name = 'mask_intersection')
mask_intersection.inputs.args = '-Tmin -thr 0.9'
group_level.connect(merge_masks, 'merged_file', mask_intersection, 'in_file')

# MultipleRegressDesign Node - Specify model
specify_model = Node(MultipleRegressDesign(), name = 'specify_model')
Expand Down Expand Up @@ -519,6 +530,7 @@ def get_group_level_analysis_sub_workflow(self, method):
get_group_subjects.inputs.list_2 = self.subject_list
group_level.connect(get_group_subjects, 'out_list', get_copes, 'elements')
group_level.connect(get_group_subjects, 'out_list', get_varcopes, 'elements')
group_level.connect(get_group_subjects, 'out_list', get_masks, 'elements')

# Function Node get_one_sample_t_test_regressors
# Get regressors in the equalRange and equalIndifference method case
Expand All @@ -539,6 +551,7 @@ def get_group_level_analysis_sub_workflow(self, method):
# Indeed the SelectFiles node asks for all (*) subjects available
get_copes.inputs.elements = self.subject_list
get_varcopes.inputs.elements = self.subject_list
get_masks.inputs.elements = self.subject_list

# Setup a two sample t-test
specify_model.inputs.contrasts = [
Expand Down
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