6FH5 repro-initial attempt

narps_open/pipelines/__init__.py

@@ -31,7 +31,7 @@
     '50GV': None,
     '51PW': 'PipelineTeam51PW',
     '5G9K': None,
-    '6FH5': None,
+    '6FH5': 'PipelineTeam6FH5',
     '6VV2': None,
     '80GC': None,
     '94GU': None,
@@ -54,7 +54,7 @@
     'J7F9': 'PipelineTeamJ7F9',
     'K9P0': None,
     'L1A8': None,
-    'L3V8': None,
+    'L3V8': 'PipelineTeamL3V8',
     'L7J7': 'PipelineTeamL7J7',
     'L9G5': None,
     'O03M': None,

narps_open/pipelines/team_6FH5.py

@@ -0,0 +1,164 @@
+#!/usr/bin/python
+# coding: utf-8
+
+"""
+NARPS team 6FH5 rewritten using Nipype-SPM
+
+"""
+
+
+from os.path import join
+
+# [INFO] The import of base objects from Nipype, to create Workflows
+from nipype import Node, Workflow # , JoinNode, MapNode
+
+# [INFO] a list of interfaces used to manpulate data
+from nipype.interfaces.utility import IdentityInterface, Function
+from nipype.interfaces.io import SelectFiles, DataSink
+from os.path import join
+from itertools import product
+
+from nipype import Workflow, Node, MapNode
+from nipype.interfaces.utility import IdentityInterface, Function, Rename, Merge
+from nipype.interfaces.io import SelectFiles, DataSink
+from nipype.algorithms.misc import Gunzip
+from nipype.interfaces.spm import (RealignUnwarp, Coregister, NewSegment, Segment,
+    Normalize12, Smooth, OneSampleTTestDesign, EstimateModel, FieldMap,
+    EstimateContrast, Level1Design, TwoSampleTTestDesign, Threshold)
+from nipype.interfaces.spm.base import Info as SPMInfo
+from nipype.interfaces.fsl import ExtractROI
+from nipype.algorithms.modelgen import SpecifySPMModel
+
+from narps_open.pipelines import Pipeline
+from narps_open.data.task import TaskInformation
+from narps_open.data.participants import get_group
+from narps_open.core.common import (
+    remove_parent_directory, list_intersection, elements_in_string, clean_list
+    )
+from narps_open.utils.configuration import Configuration
+
+
+
+
+# [INFO] In order to inherit from Pipeline
+from narps_open.pipelines import Pipeline
+
+class PipelineTeam6FH5(Pipeline):
+    """ A class that defines the pipeline of team 6FH5 """
+
+    def __init__(self):
+        super().__init__()
+        self.fwhm = 6.0
+        self.team_id = '6FH5'
+        #self.contrast_list = ['0001', '0002', '0003', '0004', '0005', '0006'] # why is this selected?
+
+        # Define contrasts they onl give group contrasts
+
+
+    def get_preprocessing(self):
+        """ Return a Nipype workflow describing the prerpocessing part of the pipeline """
+
+        # [INFO] The following part stays the same for all preprocessing pipelines
+
+        # IdentityInterface node - allows to iterate over subjects and runs
+        info_source = Node(
+            IdentityInterface(fields=['subject_id', 'run_id']),
+            name='info_source'
+        )
+        info_source.iterables = [
+            ('subject_id', self.subject_list),
+            ('run_id', self.run_list),
+        ]
+
+        # Templates to select files node
+        templates = {
+            'anat': join(
+                'sub-{subject_id}', 'anat', 'sub-{subject_id}_T1w.nii.gz'
+                ),
+            'func': join(
+                'sub-{subject_id}', 'func', 'sub-{subject_id}_task-MGT_run-{run_id}_bold.nii.gz'
+                ),
+            'magnitude': join(
+                'sub-{subject_id}', 'fmap', 'sub-{subject_id}_magnitude1.nii.gz'
+                ),
+            'phasediff': join(
+                'sub-{subject_id}', 'fmap', 'sub-{subject_id}_phasediff.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')
+
+
+        # 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_magnitude = Node(Gunzip(), name = 'gunzip_magnitude')
+        gunzip_phase = Node(Gunzip(), name = 'gunzip_phase')
+        # gunzip node connection
+        preprocessing.connect(select_files, 'anat', gunzip_anat, 'in_file')
+        preprocessing.connect(select_files, 'func', gunzip_func, 'in_file')
+        preprocessing.connect(select_files, 'magnitude', gunzip_magnitude, 'in_file')
+        preprocessing.connect(select_files, 'phase', gunzip_phase, 'in_file')
+
+
+        # ORDER 
+        # VBM-fieldmap, realign-unwrap, slicetiming, coregistration, segmentation-spm-dartel, normalization-dartel, smoothing 
+
+        # FIELD MAP - Compute a voxel displacement map from magnitude and phase data
+        # values are from https://github.com/Inria-Empenn/narps_open_pipelines/blob/ff9f46922a4aef5946f094bbff6cbdfc6ab93917/narps_open/pipelines/team_R5K7.py
+        fieldmap = Node(FieldMap(), name = 'fieldmap')
+        fieldmap.inputs.blip_direction = -1
+        fieldmap.inputs.echo_times = (4.92, 7.38)
+        fieldmap.inputs.total_readout_time = 29.15
+        fieldmap.inputs.template = join(SPMInfo.getinfo()['path'], 'toolbox', 'FieldMap', 'T1.nii')
+
+        # fieldmap node connection
+        preprocessing.connect(gunzip_magnitude, 'out_file', fieldmap, 'magnitude_file')
+        preprocessing.connect(gunzip_phase, 'out_file', fieldmap, 'phase_file')
+        preprocessing.connect(gunzip_anat, 'out_file', fieldmap, 'anat_file')
+
+
+        # REALIGN UNWARP
+        # 
+
+
+
+        realign_unwarp = MapNode(RealignUnwarp(), name = 'realign_unwarp', iterfield = 'in_files')
+        realign_unwarp.inputs.quality = 0.95
+        realign_unwarp.inputs.separation = 3
+        realign_unwarp.inputs.register_to_mean = False
+        realign_unwarp.inputs.interp = 7
+        realign_unwarp.inputs.reslice_interp = 7
+        # realign unwarp node connection 
+        preprocessing.connect(fieldmap, 'vdm', realign_unwarp, 'phase_map')
+        preprocessing.connect(gunzip_func, 'out_file', realign_unwarp, 'in_files')
+
+
+        return preprocessing
+
+    # [INFO] There was no run level analysis for the pipelines using SPM
+    def get_run_level_analysis(self):
+        """ Return a Nipype workflow describing the run level analysis part of the pipeline """
+        return None
+    def get_subject_level_analysis(self):
+        return
+    def get_group_level_analysis(self):
+        return
+    def get_hypotheses_outputs(self):
+        return 

narps_open/pipelines/team_L3V8.py

@@ -0,0 +1,188 @@
+#!/usr/bin/python
+# coding: utf-8
+
+""" Write the work of NARPS team L3V8 using Nipype """
+
+from os.path import join
+from itertools import product
+
+from nipype import Workflow, Node, MapNode
+from nipype.interfaces.utility import IdentityInterface, Function, Rename, Merge
+from nipype.interfaces.io import SelectFiles, DataSink
+from nipype.algorithms.misc import Gunzip
+from nipype.interfaces.spm import (Realign, Coregister, NewSegment,
+    Normalize12, Smooth, OneSampleTTestDesign, EstimateModel, 
+    EstimateContrast, Level1Design, TwoSampleTTestDesign, Threshold)
+from nipype.interfaces.spm.base import Info as SPMInfo
+from nipype.interfaces.fsl import ExtractROI
+from nipype.algorithms.modelgen import SpecifySPMModel
+
+from narps_open.pipelines import Pipeline
+from narps_open.data.task import TaskInformation
+from narps_open.data.participants import get_group
+from narps_open.core.common import (
+    remove_parent_directory, list_intersection, elements_in_string, clean_list
+    )
+from narps_open.utils.configuration import Configuration
+
+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'] # why is this selected?
+
+        # Define contrasts
+        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_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 - 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
+        info_source = Node(IdentityInterface(
+            fields = ['subject_id', 'run_id']),
+            name = 'info_source')
+        info_source.iterables = [
+            ('subject_id', self.subject_list), ('run_id', self.run_list)
+            ]
+
+        # SelectFiles Node - Select necessary files
+        templates = {
+            'anat' : join('sub-{subject_id}', 'anat', 'sub-{subject_id}_T1w.nii.gz'),
+            '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')
+        motion_correction.inputs.register_to_mean = False # they registered to 1st slice
+        motion_correction.inputs.interp = 4
+        motion_correction.inputs.quality = 0.9
+        motion_correction.inputs.write_interp = 4 
+        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
+
+        coregisteration = Node(Coregister(), name="coregister")
+        coregisteration.inputs.jobtype = 'estimate'
+        coregisteration.inputs.cost_function = 'nmi'
+
+
+        # connect coreg
+        preprocessing.connect( motion_correction, 'mean_image', coregisteration, 'target') # target=mean
+        preprocessing.connect( gunzip_anat, 'out_file', coregisteration, 'source') # T1w=source anat
+        preprocessing.connect( coregisteration, 'coregistered_source', data_sink, 'preprocessing.@coregistered_source') # T1w=source anat
+
+        # Get SPM Tissue Probability Maps file
+        spm_tissues_file = join(SPMInfo.getinfo()['path'], 'tpm', 'TPM.nii')
+
+        # Segmentation Node - SPM Segment function via custom scripts (defaults left in place)
+        segmentation = Node(NewSegment(), name = 'segmentation')
+        segmentation.inputs.write_deformation_fields = [True, True]
+        segmentation.inputs.channel_info = (0.0001, 60, (True, True))
+        segmentation.inputs.affine_regularization = 'mni'
+        segmentation.inputs.tissues = [
+            [(spm_tissues_file, 1), 1, (True,False), (True, False)],
+            [(spm_tissues_file, 2), 1, (True,False), (True, False)],
+            [(spm_tissues_file, 3), 2, (True,False), (False, False)],
+            [(spm_tissues_file, 4), 3, (True,False), (False, False)],
+            [(spm_tissues_file, 5), 4, (True,False), (False, False)],
+            [(spm_tissues_file, 6), 2, (False,False), (False, False)]
+        ]
+        # segmentation connection
+        preprocessing.connect(coregisteration, 'coregistered_source', 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
+
+        normalization = Node(Normalize12(), name="normalize") 
+        normalization.inputs.jobtype = 'write'
+        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(motion_correction, 'realigned_files', normalization, 'apply_to_files')
+
+        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
+
+        preprocessing.connect(normalization, 'normalized_files', smooth, 'in_files') 
+        preprocessing.connect(smooth, 'smoothed_files', data_sink, 'preprocessing.@smoothed_files') 
+
+
+        # all nodes connected and returns preprocessing
+
+        return preprocessing
+
+
+    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