def fix_preparation_pipeline(self, **kwargs): pipeline = self.create_pipeline( name='prepare_fix', inputs=[ DatasetSpec('melodic_ica', directory_format), DatasetSpec('filtered_data', nifti_gz_format), DatasetSpec('coreg_to_atlas_mat', text_matrix_format), DatasetSpec('coreg_matrix', text_matrix_format), DatasetSpec('preproc', nifti_gz_format), DatasetSpec('brain', nifti_gz_format), DatasetSpec('coreg_ref_brain', nifti_gz_format), DatasetSpec('mc_par', par_format), DatasetSpec('brain_mask', nifti_gz_format) ], outputs=[ DatasetSpec('fix_dir', directory_format), DatasetSpec('hand_label_noise', text_format) ], desc=("Pipeline to create the right folder structure before " "running FIX"), version=1, citations=[fsl_cite], **kwargs) struct_ants2fsl = pipeline.create_node(ANTs2FSLMatrixConversion(), name='struct_ants2fsl', requirements=[c3d_req]) struct_ants2fsl.inputs.ras2fsl = True struct_ants2fsl.inputs.reference_file = self.parameter('MNI_template') pipeline.connect_input('coreg_to_atlas_mat', struct_ants2fsl, 'itk_file') pipeline.connect_input('coreg_ref_brain', struct_ants2fsl, 'source_file') epi_ants2fsl = pipeline.create_node(ANTs2FSLMatrixConversion(), name='epi_ants2fsl', requirements=[c3d_req]) epi_ants2fsl.inputs.ras2fsl = True pipeline.connect_input('brain', epi_ants2fsl, 'source_file') pipeline.connect_input('coreg_matrix', epi_ants2fsl, 'itk_file') pipeline.connect_input('coreg_ref_brain', epi_ants2fsl, 'reference_file') MNI2t1 = pipeline.create_node(ConvertXFM(), name='MNI2t1', wall_time=5, requirements=[fsl509_req]) MNI2t1.inputs.invert_xfm = True pipeline.connect(struct_ants2fsl, 'fsl_matrix', MNI2t1, 'in_file') struct2epi = pipeline.create_node(ConvertXFM(), name='struct2epi', wall_time=5, requirements=[fsl509_req]) struct2epi.inputs.invert_xfm = True pipeline.connect(epi_ants2fsl, 'fsl_matrix', struct2epi, 'in_file') meanfunc = pipeline.create_node(ImageMaths(op_string='-Tmean', suffix='_mean'), name='meanfunc', wall_time=5, requirements=[fsl509_req]) pipeline.connect_input('preproc', meanfunc, 'in_file') prep_fix = pipeline.create_node(PrepareFIX(), name='prep_fix') pipeline.connect_input('melodic_ica', prep_fix, 'melodic_dir') pipeline.connect_input('coreg_ref_brain', prep_fix, 't1_brain') pipeline.connect_input('mc_par', prep_fix, 'mc_par') pipeline.connect_input('brain_mask', prep_fix, 'epi_brain_mask') pipeline.connect_input('preproc', prep_fix, 'epi_preproc') pipeline.connect_input('filtered_data', prep_fix, 'filtered_epi') pipeline.connect(epi_ants2fsl, 'fsl_matrix', prep_fix, 'epi2t1_mat') pipeline.connect(struct_ants2fsl, 'fsl_matrix', prep_fix, 't12MNI_mat') pipeline.connect(MNI2t1, 'out_file', prep_fix, 'MNI2t1_mat') pipeline.connect(struct2epi, 'out_file', prep_fix, 't12epi_mat') pipeline.connect(meanfunc, 'out_file', prep_fix, 'epi_mean') pipeline.connect_output('fix_dir', prep_fix, 'fix_dir') pipeline.connect_output('hand_label_noise', prep_fix, 'hand_label_file') return pipeline
def motion_correction_pipeline(self, **name_maps): if 'struct2align' in self.input_names: StructAlignment = True else: StructAlignment = False pipeline = self.new_pipeline( name='pet_mc', desc=("Given a folder with reconstructed PET data, this " "pipeline will generate a motion corrected PET" "image using information extracted from the MR-based " "motion detection pipeline"), citations=[fsl_cite], name_maps=name_maps) check_pet = pipeline.add( 'check_pet_data', CheckPetMCInputs(), inputs={ 'pet_data': ('pet_data_prepared', directory_format), 'reference': ('ref_brain', nifti_gz_format) }, requirements=[fsl_req.v('5.0.9'), mrtrix_req.v('3.0rc3')]) if self.branch('dynamic_pet_mc'): pipeline.connect_input('fixed_binning_mats', check_pet, 'motion_mats') else: pipeline.connect_input('average_mats', check_pet, 'motion_mats') pipeline.connect_input('correction_factors', check_pet, 'corr_factors') if StructAlignment: struct_reg = pipeline.add('ref2structural_reg', FLIRT(dof=6, cost_func='normmi', cost='normmi', output_type='NIFTI_GZ'), inputs={ 'reference': ('ref_brain', nifti_gz_format), 'in_file': ('struct2align', nifti_gz_format) }, requirements=[fsl_req.v('5.0.9')]) if self.branch('dynamic_pet_mc'): pet_mc = pipeline.add('pet_mc', PetImageMotionCorrection(), inputs={ 'pet_image': (check_pet, 'pet_images'), 'motion_mat': (check_pet, 'motion_mats'), 'pet2ref_mat': (check_pet, 'pet2ref_mat') }, requirements=[fsl_req.v('5.0.9')], iterfield=['pet_image', 'motion_mat']) else: pet_mc = pipeline.add( 'pet_mc', PetImageMotionCorrection(), inputs={'corr_factor': (check_pet, 'corr_factors')}, requirements=[fsl_req.v('5.0.9')], iterfield=['corr_factor', 'pet_image', 'motion_mat']) if StructAlignment: pipeline.connect(struct_reg, 'out_matrix_file', pet_mc, 'structural2ref_regmat') pipeline.connect_input('struct2align', pet_mc, 'structural_image') if self.parameter('PET2MNI_reg'): mni_reg = True else: mni_reg = False if self.branch('dynamic_pet_mc'): merge_mc = pipeline.add( 'merge_pet_mc', fsl.Merge(dimension='t'), inputs={'in_files': (pet_mc, 'pet_mc_image')}, requirements=[fsl_req.v('5.0.9')]) merge_no_mc = pipeline.add( 'merge_pet_no_mc', fsl.Merge(dimension='t'), inputs={'in_files': (pet_mc, 'pet_no_mc_image')}, requirements=[fsl_req.v('5.0.9')]) else: static_mc = pipeline.add('static_mc_generation', StaticPETImageGeneration(), inputs={ 'pet_mc_images': (pet_mc, 'pet_mc_image'), 'pet_no_mc_images': (pet_mc, 'pet_no_mc_image') }, requirements=[fsl_req.v('5.0.9')]) merge_outputs = pipeline.add( 'merge_outputs', Merge(3), inputs={'in1': ('mean_displacement_plot', png_format)}) if not StructAlignment: cropping = pipeline.add( 'pet_cropping', PETFovCropping(x_min=self.parameter('crop_xmin'), x_size=self.parameter('crop_xsize'), y_min=self.parameter('crop_ymin'), y_size=self.parameter('crop_ysize'), z_min=self.parameter('crop_zmin'), z_size=self.parameter('crop_zsize'))) if self.branch('dynamic_pet_mc'): pipeline.connect(merge_mc, 'merged_file', cropping, 'pet_image') else: pipeline.connect(static_mc, 'static_mc', cropping, 'pet_image') cropping_no_mc = pipeline.add( 'pet_no_mc_cropping', PETFovCropping(x_min=self.parameter('crop_xmin'), x_size=self.parameter('crop_xsize'), y_min=self.parameter('crop_ymin'), y_size=self.parameter('crop_ysize'), z_min=self.parameter('crop_zmin'), z_size=self.parameter('crop_zsize'))) if self.branch('dynamic_pet_mc'): pipeline.connect(merge_no_mc, 'merged_file', cropping_no_mc, 'pet_image') else: pipeline.connect(static_mc, 'static_no_mc', cropping_no_mc, 'pet_image') if mni_reg: if self.branch('dynamic_pet_mc'): t_mean = pipeline.add( 'PET_temporal_mean', ImageMaths(op_string='-Tmean'), inputs={'in_file': (cropping, 'pet_cropped')}, requirements=[fsl_req.v('5.0.9')]) reg_tmean2MNI = pipeline.add( 'reg2MNI', AntsRegSyn(num_dimensions=3, transformation='s', out_prefix='reg2MNI', num_threads=4, ref_file=self.parameter('PET_template_MNI')), wall_time=25, requirements=[ants_req.v('2')]) if self.branch('dynamic_pet_mc'): pipeline.connect(t_mean, 'out_file', reg_tmean2MNI, 'input_file') merge_trans = pipeline.add('merge_transforms', Merge(2), inputs={ 'in1': (reg_tmean2MNI, 'warp_file'), 'in2': (reg_tmean2MNI, 'regmat') }, wall_time=1) apply_trans = pipeline.add( 'apply_trans', ApplyTransforms( reference_image=self.parameter('PET_template_MNI'), interpolation='Linear', input_image_type=3), inputs={ 'input_image': (cropping, 'pet_cropped'), 'transforms': (merge_trans, 'out') }, wall_time=7, mem_gb=24, requirements=[ants_req.v('2')]) pipeline.connect(apply_trans, 'output_image', merge_outputs, 'in2'), else: pipeline.connect(cropping, 'pet_cropped', reg_tmean2MNI, 'input_file') pipeline.connect(reg_tmean2MNI, 'reg_file', merge_outputs, 'in2') else: pipeline.connect(cropping, 'pet_cropped', merge_outputs, 'in2') pipeline.connect(cropping_no_mc, 'pet_cropped', merge_outputs, 'in3') else: if self.branch('dynamic_pet_mc'): pipeline.connect(merge_mc, 'merged_file', merge_outputs, 'in2') pipeline.connect(merge_no_mc, 'merged_file', merge_outputs, 'in3') else: pipeline.connect(static_mc, 'static_mc', merge_outputs, 'in2') pipeline.connect(static_mc, 'static_no_mc', merge_outputs, 'in3') # mcflirt = pipeline.add('mcflirt', MCFLIRT()) # 'in_file': (merge_mc_ps, 'merged_file'), # cost='normmi', copy2dir = pipeline.add('copy2dir', CopyToDir(), inputs={'in_files': (merge_outputs, 'out')}) if self.branch('dynamic_pet_mc'): pipeline.connect_output('dynamic_motion_correction_results', copy2dir, 'out_dir') else: pipeline.connect_output('static_motion_correction_results', copy2dir, 'out_dir') return pipeline
def fix_preparation_pipeline(self, **name_maps): pipeline = self.new_pipeline( name='prepare_fix', desc=("Pipeline to create the right folder structure before " "running FIX"), citations=[fsl_cite], name_maps=name_maps) if self.branch('coreg_to_tmpl_method', 'ants'): struct_ants2fsl = pipeline.add( 'struct_ants2fsl', ANTs2FSLMatrixConversion(ras2fsl=True), inputs={ 'reference_file': ('template_brain', nifti_gz_format), 'itk_file': ('coreg_to_tmpl_ants_mat', text_matrix_format), 'source_file': ('coreg_ref_brain', nifti_gz_format) }, requirements=[c3d_req.v('1.0.0')]) struct_matrix = (struct_ants2fsl, 'fsl_matrix') else: struct_matrix = ('coreg_to_tmpl_fsl_mat', text_matrix_format) # if self.branch('coreg_method', 'ants'): # epi_ants2fsl = pipeline.add( # 'epi_ants2fsl', # ANTs2FSLMatrixConversion( # ras2fsl=True), # inputs={ # 'source_file': ('brain', nifti_gz_format), # 'itk_file': ('coreg_ants_mat', text_matrix_format), # 'reference_file': ('coreg_ref_brain', nifti_gz_format)}, # requirements=[c3d_req.v('1.0.0')]) MNI2t1 = pipeline.add('MNI2t1', ConvertXFM(invert_xfm=True), inputs={'in_file': struct_matrix}, wall_time=5, requirements=[fsl_req.v('5.0.9')]) struct2epi = pipeline.add( 'struct2epi', ConvertXFM(invert_xfm=True), inputs={'in_file': ('coreg_fsl_mat', text_matrix_format)}, wall_time=5, requirements=[fsl_req.v('5.0.9')]) meanfunc = pipeline.add( 'meanfunc', ImageMaths(op_string='-Tmean', suffix='_mean', output_type='NIFTI_GZ'), inputs={'in_file': ('series_preproc', nifti_gz_format)}, wall_time=5, requirements=[fsl_req.v('5.0.9')]) pipeline.add('prep_fix', PrepareFIX(), inputs={ 'melodic_dir': ('melodic_ica', directory_format), 't1_brain': ('coreg_ref_brain', nifti_gz_format), 'mc_par': ('mc_par', par_format), 'epi_brain_mask': ('brain_mask', nifti_gz_format), 'epi_preproc': ('series_preproc', nifti_gz_format), 'filtered_epi': ('filtered_data', nifti_gz_format), 'epi2t1_mat': ('coreg_fsl_mat', text_matrix_format), 't12MNI_mat': (struct_ants2fsl, 'fsl_matrix'), 'MNI2t1_mat': (MNI2t1, 'out_file'), 't12epi_mat': (struct2epi, 'out_file'), 'epi_mean': (meanfunc, 'out_file') }, outputs={ 'fix_dir': ('fix_dir', directory_format), 'hand_label_noise': ('hand_label_file', text_format) }) return pipeline
def create_t1_based_unwarp(name='unwarp'): """ Unwarp an fMRI time series based on non-linear registration to T1. NOTE: AS IT STANDS THIS METHOD DID NOT PRODUCE ACCEPTABLE RESULTS IF BRAIN COVERAGE IS NOT COMPLETE ON THE EPI IMAGE. ALSO: NEED TO ADD AUTOMATIC READING OF EPI RESOLUTION TO GET """ unwarpflow = pe.Workflow(name=name) inputnode = pe.Node( interface=util.IdentityInterface(fields=['epi', 'T1W']), name='inputspec') outputnode = pe.Node(interface=util.IdentityInterface( fields=['unwarped_func', 'warp_files']), name='outputspec') tmedian = pe.Node(interface=ImageMaths(), name='tmedian') tmedian.inputs.op_string = '-Tmedian' epi_brain_ext = pe.Node(interface=util.Function( function=epi_brain_extract, input_names=['in_file'], output_names=['out_vol', 'out_mask']), name='epi_brain_ext') fast_debias = pe.Node(interface=FAST(), name='FAST_debias') fast_debias.inputs.output_biascorrected = True robex = pe.Node(interface=util.Function( function=my_robex, input_names=['in_file'], output_names=['out_file', 'out_mask']), name='robex') downsample_T1 = pe.Node(MRIConvert(), name='downsample_dti') downsample_T1.inputs.vox_size = (3.438, 3.438, 3.000) downsample_T1.inputs.out_type = 'niigz' contrast_invert = pe.Node(interface=util.Function( function=invert_contrast, input_names=['in_t1_brain', 'in_b0_brain'], output_names=['out_fn']), name='contrast_invert') ants_syn = pe.Node(interface=util.Function( function=my_ants_registration_syn, input_names=['in_T1W', 'in_epi'], output_names=['out_transforms']), name='ants_syn') ants_warp = pe.Node(interface=WarpTimeSeriesImageMultiTransform(), name='ants_warp') '''connections''' # unwarpflow.connect(inputnode, 'T1W', robex, 'in_file') unwarpflow.connect(inputnode, 'T1W', fast_debias, 'in_files') # unwarpflow.connect(robex, 'out_file', fast_debias, 'in_files') unwarpflow.connect(fast_debias, 'restored_image', robex, 'in_file') # unwarpflow.connect(fast_debias, 'restored_image', downsample_T1, 'in_file') unwarpflow.connect(robex, 'out_file', downsample_T1, 'in_file') unwarpflow.connect(downsample_T1, 'out_file', contrast_invert, 'in_t1_brain') unwarpflow.connect(inputnode, 'epi', tmedian, 'in_file') unwarpflow.connect(tmedian, 'out_file', epi_brain_ext, 'in_file') unwarpflow.connect(epi_brain_ext, 'out_vol', contrast_invert, 'in_b0_brain') unwarpflow.connect(contrast_invert, 'out_fn', ants_syn, 'in_T1W') unwarpflow.connect(epi_brain_ext, 'out_vol', ants_syn, 'in_epi') unwarpflow.connect(ants_syn, 'out_transforms', outputnode, 'out_transforms') unwarpflow.connect(inputnode, 'epi', ants_warp, 'input_image') unwarpflow.connect(contrast_invert, 'out_fn', ants_warp, 'reference_image') unwarpflow.connect(ants_syn, 'out_transforms', ants_warp, 'transformation_series') unwarpflow.connect(ants_syn, 'out_transforms', outputnode, 'warp_files') unwarpflow.connect(ants_warp, 'output_image', outputnode, 'unwarped_func') return unwarpflow
def firstlevel_wf(subject_id, sink_directory, name='wmaze_frstlvl_wf'): frstlvl_wf = Workflow(name='frstlvl_wf') info = dict(task_mri_files=[['subject_id', 'wmaze']], motion_noise_files=[['subject_id', 'filter_regressor']]) subject_info = Node(Function(input_names=['subject_id'], output_names=['output'], function=subjectinfo), name='subject_info') subject_info.inputs.ignore_exception = False subject_info.inputs.subject_id = subject_id getcontrasts = Node(Function(input_names=['subject_id', 'info'], output_names=['contrasts'], function=get_contrasts), name='getcontrasts') getcontrasts.inputs.ignore_exception = False getcontrasts.inputs.subject_id = subject_id frstlvl_wf.connect(subject_info, 'output', getcontrasts, 'info') #fFunction node to substitute names of folders and files created during pipeline getsubs = Node(Function(input_names=['cons'], output_names=['subs'], function=get_subs), name='getsubs') getsubs.inputs.ignore_exception = False getsubs.inputs.subject_id = subject_id frstlvl_wf.connect(subject_info, 'output', getsubs, 'info') frstlvl_wf.connect(getcontrasts, 'contrasts', getsubs, 'cons') #datasource node to get the task_mri and motion-noise files datasource = Node(DataGrabber(infields=['subject_id'], outfields=info.keys()), name='datasource') datasource.inputs.template = '*' datasource.inputs.subject_id = subject_id datasource.inputs.base_directory = os.path.abspath( '/home/data/madlab/data/mri/wmaze/preproc/') datasource.inputs.field_template = dict( task_mri_files='%s/func/realigned/*%s*.nii.gz', motion_noise_files='%s/noise/%s*.txt') datasource.inputs.template_args = info datasource.inputs.sort_filelist = True datasource.inputs.ignore_exception = False datasource.inputs.raise_on_empty = True #function node to remove last three volumes from functional data fslroi_epi = MapNode( ExtractROI(t_min=0, t_size=197), #start from first volume and end on -3 volume iterfield=['in_file'], name='fslroi_epi') fslroi_epi.output_type = 'NIFTI_GZ' fslroi_epi.terminal_output = 'stream' frstlvl_wf.connect(datasource, 'task_mri_files', fslroi_epi, 'in_file') #function node to modify motion and noise files to be single regressors motionnoise = Node(Function(input_names=['subjinfo', 'files'], output_names=['subjinfo'], function=motion_noise), name='motionnoise') motionnoise.inputs.ignore_exception = False frstlvl_wf.connect(subject_info, 'output', motionnoise, 'subjinfo') frstlvl_wf.connect(datasource, 'motion_noise_files', motionnoise, 'files') # Makes a model specification compatible with spm/fsl designers # Requires subjectinfo to be received in the form of a Bunch of a list of Bunch specify_model = Node(SpecifyModel(), name='specify_model') specify_model.inputs.high_pass_filter_cutoff = -1.0 specify_model.inputs.ignore_exception = False specify_model.inputs.input_units = 'secs' specify_model.inputs.time_repetition = 2.0 frstlvl_wf.connect(fslroi_epi, 'roi_file', specify_model, 'functional_runs') frstlvl_wf.connect(motionnoise, 'subjinfo', specify_model, 'subject_info') #basic interface class generates identity mappings modelfit_inputspec = Node(IdentityInterface(fields=[ 'session_info', 'interscan_interval', 'contrasts', 'film_threshold', 'functional_data', 'bases', 'model_serial_correlations' ], mandatory_inputs=True), name='modelfit_inputspec') modelfit_inputspec.inputs.bases = {'dgamma': {'derivs': False}} modelfit_inputspec.inputs.film_threshold = 0.0 modelfit_inputspec.inputs.interscan_interval = 2.0 modelfit_inputspec.inputs.model_serial_correlations = True frstlvl_wf.connect(fslroi_epi, 'roi_file', modelfit_inputspec, 'functional_data') frstlvl_wf.connect(getcontrasts, 'contrasts', modelfit_inputspec, 'contrasts') frstlvl_wf.connect(specify_model, 'session_info', modelfit_inputspec, 'session_info') #MapNode for first level SPM design matrix to demonstrate contrasts and motion/noise regressors level1_design = MapNode(Level1Design(), iterfield=['contrasts', 'session_info'], name='level1_design') level1_design.inputs.ignore_exception = False frstlvl_wf.connect(modelfit_inputspec, 'interscan_interval', level1_design, 'interscan_interval') frstlvl_wf.connect(modelfit_inputspec, 'session_info', level1_design, 'session_info') frstlvl_wf.connect(modelfit_inputspec, 'contrasts', level1_design, 'contrasts') frstlvl_wf.connect(modelfit_inputspec, 'bases', level1_design, 'bases') frstlvl_wf.connect(modelfit_inputspec, 'model_serial_correlations', level1_design, 'model_serial_correlations') #MapNode to generate a design.mat file for each run generate_model = MapNode(FEATModel(), iterfield=['fsf_file', 'ev_files'], name='generate_model') generate_model.inputs.environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'} generate_model.inputs.ignore_exception = False generate_model.inputs.output_type = 'NIFTI_GZ' generate_model.inputs.terminal_output = 'stream' frstlvl_wf.connect(level1_design, 'fsf_files', generate_model, 'fsf_file') frstlvl_wf.connect(level1_design, 'ev_files', generate_model, 'ev_files') #MapNode to estimate the model using FILMGLS -- fits the design matrix to the voxel timeseries estimate_model = MapNode(FILMGLS(), iterfield=['design_file', 'in_file', 'tcon_file'], name='estimate_model') estimate_model.inputs.environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'} estimate_model.inputs.ignore_exception = False estimate_model.inputs.mask_size = 5 estimate_model.inputs.output_type = 'NIFTI_GZ' estimate_model.inputs.results_dir = 'results' estimate_model.inputs.smooth_autocorr = True estimate_model.inputs.terminal_output = 'stream' frstlvl_wf.connect(modelfit_inputspec, 'film_threshold', estimate_model, 'threshold') frstlvl_wf.connect(modelfit_inputspec, 'functional_data', estimate_model, 'in_file') frstlvl_wf.connect(generate_model, 'design_file', estimate_model, 'design_file') frstlvl_wf.connect(generate_model, 'con_file', estimate_model, 'tcon_file') #merge node to merge the contrasts - necessary for fsl 5.0.7 and greater merge_contrasts = MapNode(Merge(2), iterfield=['in1'], name='merge_contrasts') frstlvl_wf.connect(estimate_model, 'zstats', merge_contrasts, 'in1') #MapNode to transform the z2pval z2pval = MapNode(ImageMaths(), iterfield=['in_file'], name='z2pval') z2pval.inputs.environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'} z2pval.inputs.ignore_exception = False z2pval.inputs.op_string = '-ztop' z2pval.inputs.output_type = 'NIFTI_GZ' z2pval.inputs.suffix = '_pval' z2pval.inputs.terminal_output = 'stream' frstlvl_wf.connect(merge_contrasts, ('out', pop_lambda), z2pval, 'in_file') #outputspec node using IdentityInterface() to receive information from estimate_model, merge_contrasts, z2pval, generate_model, and estimate_model modelfit_outputspec = Node(IdentityInterface(fields=[ 'copes', 'varcopes', 'dof_file', 'pfiles', 'parameter_estimates', 'zstats', 'design_image', 'design_file', 'design_cov', 'sigmasquareds' ], mandatory_inputs=True), name='modelfit_outputspec') frstlvl_wf.connect(estimate_model, 'copes', modelfit_outputspec, 'copes') frstlvl_wf.connect(estimate_model, 'varcopes', modelfit_outputspec, 'varcopes') frstlvl_wf.connect(merge_contrasts, 'out', modelfit_outputspec, 'zstats') frstlvl_wf.connect(z2pval, 'out_file', modelfit_outputspec, 'pfiles') frstlvl_wf.connect(generate_model, 'design_image', modelfit_outputspec, 'design_image') frstlvl_wf.connect(generate_model, 'design_file', modelfit_outputspec, 'design_file') frstlvl_wf.connect(generate_model, 'design_cov', modelfit_outputspec, 'design_cov') frstlvl_wf.connect(estimate_model, 'param_estimates', modelfit_outputspec, 'parameter_estimates') frstlvl_wf.connect(estimate_model, 'dof_file', modelfit_outputspec, 'dof_file') frstlvl_wf.connect(estimate_model, 'sigmasquareds', modelfit_outputspec, 'sigmasquareds') #datasink node to save output from multiple points in the pipeline sinkd = MapNode(DataSink(), iterfield=[ 'substitutions', 'modelfit.contrasts.@copes', 'modelfit.contrasts.@varcopes', 'modelfit.estimates', 'modelfit.contrasts.@zstats' ], name='sinkd') sinkd.inputs.base_directory = sink_directory sinkd.inputs.container = subject_id frstlvl_wf.connect(getsubs, 'subs', sinkd, 'substitutions') frstlvl_wf.connect(modelfit_outputspec, 'parameter_estimates', sinkd, 'modelfit.estimates') frstlvl_wf.connect(modelfit_outputspec, 'sigmasquareds', sinkd, 'modelfit.estimates.@sigsq') frstlvl_wf.connect(modelfit_outputspec, 'dof_file', sinkd, 'modelfit.dofs') frstlvl_wf.connect(modelfit_outputspec, 'copes', sinkd, 'modelfit.contrasts.@copes') frstlvl_wf.connect(modelfit_outputspec, 'varcopes', sinkd, 'modelfit.contrasts.@varcopes') frstlvl_wf.connect(modelfit_outputspec, 'zstats', sinkd, 'modelfit.contrasts.@zstats') frstlvl_wf.connect(modelfit_outputspec, 'design_image', sinkd, 'modelfit.design') frstlvl_wf.connect(modelfit_outputspec, 'design_cov', sinkd, 'modelfit.design.@cov') frstlvl_wf.connect(modelfit_outputspec, 'design_file', sinkd, 'modelfit.design.@matrix') frstlvl_wf.connect(modelfit_outputspec, 'pfiles', sinkd, 'modelfit.contrasts.@pstats') return frstlvl_wf
def firstlevel_wf(subject_id, sink_directory, name='wmaze_frstlvl_wf'): # Create the frstlvl workflow frstlvl_wf = Workflow(name='frstlvl_wf') # Dictionary holding the wildcard used in datasource info = dict(task_mri_files=[['subject_id', 'wmaze']], motion_noise_files=[['subject_id']]) # Calls the subjectinfo function with the name, onset, duration, and amplitude info subject_info = Node(Function(input_names=['subject_id'], output_names=['output'], function=subjectinfo), name='subject_info') subject_info.inputs.ignore_exception = False subject_info.inputs.subject_id = subject_id # Create another Function node to define the contrasts for the experiment getcontrasts = Node( Function( input_names=['subject_id', 'info'], output_names=['contrasts'], # Calls the function 'get_contrasts' function=get_contrasts), name='getcontrasts') getcontrasts.inputs.ignore_exception = False # Receives subject_id as input getcontrasts.inputs.subject_id = subject_id frstlvl_wf.connect(subject_info, 'output', getcontrasts, 'info') #### subject_info (output) ----> getcontrasts (info) # Create a Function node to substitute names of folders and files created during pipeline getsubs = Node( Function( input_names=['cons'], output_names=['subs'], # Calls the function 'get_subs' function=get_subs), name='getsubs') getsubs.inputs.ignore_exception = False # Receives subject_id as input getsubs.inputs.subject_id = subject_id frstlvl_wf.connect(subject_info, 'output', getsubs, 'info') frstlvl_wf.connect(getcontrasts, 'contrasts', getsubs, 'cons') # Create a datasource node to get the task_mri and motion-noise files datasource = Node(DataGrabber(infields=['subject_id'], outfields=info.keys()), name='datasource') # Indicates the string template to match (in this case, any that match the field template) datasource.inputs.template = '*' # Receives subject_id as an input datasource.inputs.subject_id = subject_id # Base directory to allow branching pathways datasource.inputs.base_directory = os.path.abspath( '/home/data/madlab/data/mri/wmaze/preproc/') datasource.inputs.field_template = dict( task_mri_files='%s/func/smoothed_fullspectrum/_maskfunc2*/*%s*.nii.gz', # Filter regressor noise files motion_noise_files='%s/noise/filter_regressor*.txt') # Inputs from the infields argument ('subject_id') that satisfy the template datasource.inputs.template_args = info # Forces DataGrabber to return data in sorted order when using wildcards datasource.inputs.sort_filelist = True # Do not ignore exceptions datasource.inputs.ignore_exception = False datasource.inputs.raise_on_empty = True # Function to remove last three volumes from functional data # Start from the first volume and end on the -3 volume fslroi_epi = MapNode(ExtractROI(t_min=0, t_size=197), iterfield=['in_file'], name='fslroi_epi') fslroi_epi.output_type = 'NIFTI_GZ' fslroi_epi.terminal_output = 'stream' frstlvl_wf.connect(datasource, 'task_mri_files', fslroi_epi, 'in_file') # Function node to modify the motion and noise files to be single regressors motionnoise = Node( Function( input_names=['subjinfo', 'files'], output_names=['subjinfo'], # Calls the function 'motion_noise' function=motion_noise), name='motionnoise') motionnoise.inputs.ignore_exception = False # The bunch from subject_info function containing regressor names, onsets, durations, and amplitudes frstlvl_wf.connect(subject_info, 'output', motionnoise, 'subjinfo') frstlvl_wf.connect(datasource, 'motion_noise_files', motionnoise, 'files') # Makes a model specification compatible with spm/fsl designers # Requires subjectinfo to be received in the form of a Bunch of a list of Bunch specify_model = Node(SpecifyModel(), name='specify_model') # High-pass filter cutoff in seconds specify_model.inputs.high_pass_filter_cutoff = -1.0 specify_model.inputs.ignore_exception = False # input units in either 'secs' or 'scans' specify_model.inputs.input_units = 'secs' # Time between start of one volume and the start of following volume specify_model.inputs.time_repetition = 2.0 # Editted data files for model -- list of 4D files frstlvl_wf.connect(fslroi_epi, 'roi_file', specify_model, 'functional_runs') # List of event description files in 3 column format corresponding to onsets, durations, and amplitudes frstlvl_wf.connect(motionnoise, 'subjinfo', specify_model, 'subject_info') # Basic interface class generates identity mappings modelfit_inputspec = Node(IdentityInterface(fields=[ 'session_info', 'interscan_interval', 'contrasts', 'film_threshold', 'functional_data', 'bases', 'model_serial_correlations' ], mandatory_inputs=True), name='modelfit_inputspec') # Set bases to a dictionary with a second dictionary setting the value of dgamma derivatives as 'False' modelfit_inputspec.inputs.bases = {'dgamma': {'derivs': False}} # Film threshold modelfit_inputspec.inputs.film_threshold = 0.0 # Interscan_interval modelfit_inputspec.inputs.interscan_interval = 2.0 # Create model serial correlations for Level1Design modelfit_inputspec.inputs.model_serial_correlations = True frstlvl_wf.connect(fslroi_epi, 'roi_file', modelfit_inputspec, 'functional_data') frstlvl_wf.connect(getcontrasts, 'contrasts', modelfit_inputspec, 'contrasts') frstlvl_wf.connect(specify_model, 'session_info', modelfit_inputspec, 'session_info') # Creates a first level SPM design matrix to demonstrate contrasts and motion/noise regressors level1_design = MapNode(Level1Design(), iterfield=['contrasts', 'session_info'], name='level1_design') level1_design.inputs.ignore_exception = False # Inputs the interscan interval (in secs) frstlvl_wf.connect(modelfit_inputspec, 'interscan_interval', level1_design, 'interscan_interval') # Session specific information generated by ``modelgen.SpecifyModel`` frstlvl_wf.connect(modelfit_inputspec, 'session_info', level1_design, 'session_info') # List of contrasts with each contrast being a list of the form -[('name', 'stat', [condition list], [weight list], [session list])]. # If session list is None or not provided, all sessions are used. frstlvl_wf.connect(modelfit_inputspec, 'contrasts', level1_design, 'contrasts') # Name of basis function and options e.g., {'dgamma': {'derivs': True}} frstlvl_wf.connect(modelfit_inputspec, 'bases', level1_design, 'bases') # Option to model serial correlations using an autoregressive estimator (order 1) # Setting this option is only useful in the context of the fsf file frstlvl_wf.connect(modelfit_inputspec, 'model_serial_correlations', level1_design, 'model_serial_correlations') # Create a MapNode to generate a design.mat file for each run generate_model = MapNode(FEATModel(), iterfield=['fsf_file', 'ev_files'], name='generate_model') generate_model.inputs.environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'} generate_model.inputs.ignore_exception = False generate_model.inputs.output_type = 'NIFTI_GZ' generate_model.inputs.terminal_output = 'stream' # File specifying the feat design spec file frstlvl_wf.connect(level1_design, 'fsf_files', generate_model, 'fsf_file') # Event spec files generated by level1design (condition information files) frstlvl_wf.connect(level1_design, 'ev_files', generate_model, 'ev_files') # Create a MapNode to estimate the model using FILMGLS -- fits the design matrix to the voxel timeseries estimate_model = MapNode(FILMGLS(), iterfield=['design_file', 'in_file', 'tcon_file'], name='estimate_model') estimate_model.inputs.environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'} estimate_model.inputs.ignore_exception = False # Susan-smooth mask size estimate_model.inputs.mask_size = 5 estimate_model.inputs.output_type = 'NIFTI_GZ' estimate_model.inputs.results_dir = 'results' # Smooth auto-correlation estimates estimate_model.inputs.smooth_autocorr = True estimate_model.inputs.terminal_output = 'stream' frstlvl_wf.connect(modelfit_inputspec, 'film_threshold', estimate_model, 'threshold') frstlvl_wf.connect(modelfit_inputspec, 'functional_data', estimate_model, 'in_file') # Mat file containing ascii matrix for design frstlvl_wf.connect(generate_model, 'design_file', estimate_model, 'design_file') # Contrast file containing contrast vectors frstlvl_wf.connect(generate_model, 'con_file', estimate_model, 'tcon_file') # Create a merge node to merge the contrasts - necessary for fsl 5.0.7 and greater merge_contrasts = MapNode(Merge(2), iterfield=['in1'], name='merge_contrasts') frstlvl_wf.connect(estimate_model, 'zstats', merge_contrasts, 'in1') # Create a MapNode to transform the z2pval z2pval = MapNode(ImageMaths(), iterfield=['in_file'], name='z2pval') z2pval.inputs.environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'} # Do not ignore exceptions z2pval.inputs.ignore_exception = False # Defines the operation used z2pval.inputs.op_string = '-ztop' # Set the outfile type to nii.gz z2pval.inputs.output_type = 'NIFTI_GZ' # Out-file suffix z2pval.inputs.suffix = '_pval' # Set output to stream in terminal z2pval.inputs.terminal_output = 'stream' frstlvl_wf.connect(merge_contrasts, ('out', pop_lambda), z2pval, 'in_file') # Create an outputspec node using IdentityInterface() to receive information from estimate_model, # merge_contrasts, z2pval, generate_model, and estimate_model modelfit_outputspec = Node(IdentityInterface(fields=[ 'copes', 'varcopes', 'dof_file', 'pfiles', 'parameter_estimates', 'zstats', 'design_image', 'design_file', 'design_cov', 'sigmasquareds' ], mandatory_inputs=True), name='modelfit_outputspec') # All lvl1 cope files frstlvl_wf.connect(estimate_model, 'copes', modelfit_outputspec, 'copes') # All lvl1 varcope files frstlvl_wf.connect(estimate_model, 'varcopes', modelfit_outputspec, 'varcopes') # All zstats across runs frstlvl_wf.connect(merge_contrasts, 'out', modelfit_outputspec, 'zstats') # frstlvl_wf.connect(z2pval, 'out_file', modelfit_outputspec, 'pfiles') # Graphical representation of design matrix frstlvl_wf.connect(generate_model, 'design_image', modelfit_outputspec, 'design_image') # Mat file containing ascii matrix for design frstlvl_wf.connect(generate_model, 'design_file', modelfit_outputspec, 'design_file') # Graphical representation of design covariance frstlvl_wf.connect(generate_model, 'design_cov', modelfit_outputspec, 'design_cov') # Parameter estimates for each column of the design matrix frstlvl_wf.connect(estimate_model, 'param_estimates', modelfit_outputspec, 'parameter_estimates') # Degrees of freedom frstlvl_wf.connect(estimate_model, 'dof_file', modelfit_outputspec, 'dof_file') # Summary of residuals frstlvl_wf.connect(estimate_model, 'sigmasquareds', modelfit_outputspec, 'sigmasquareds') # Create a datasink node to save output from multiple points in the pipeline sinkd = MapNode(DataSink(), iterfield=[ 'substitutions', 'modelfit.contrasts.@copes', 'modelfit.contrasts.@varcopes', 'modelfit.estimates', 'modelfit.contrasts.@zstats' ], name='sinkd') sinkd.inputs.base_directory = sink_directory sinkd.inputs.container = subject_id frstlvl_wf.connect(getsubs, 'subs', sinkd, 'substitutions') frstlvl_wf.connect(modelfit_outputspec, 'parameter_estimates', sinkd, 'modelfit.estimates') frstlvl_wf.connect(modelfit_outputspec, 'sigmasquareds', sinkd, 'modelfit.estimates.@sigsq') frstlvl_wf.connect(modelfit_outputspec, 'dof_file', sinkd, 'modelfit.dofs') frstlvl_wf.connect(modelfit_outputspec, 'copes', sinkd, 'modelfit.contrasts.@copes') frstlvl_wf.connect(modelfit_outputspec, 'varcopes', sinkd, 'modelfit.contrasts.@varcopes') frstlvl_wf.connect(modelfit_outputspec, 'zstats', sinkd, 'modelfit.contrasts.@zstats') frstlvl_wf.connect(modelfit_outputspec, 'design_image', sinkd, 'modelfit.design') frstlvl_wf.connect(modelfit_outputspec, 'design_cov', sinkd, 'modelfit.design.@cov') frstlvl_wf.connect(modelfit_outputspec, 'design_file', sinkd, 'modelfit.design.@matrix') frstlvl_wf.connect(modelfit_outputspec, 'pfiles', sinkd, 'modelfit.contrasts.@pstats') return frstlvl_wf
def firstlevel_wf(subject_id, sink_directory, name='ds008_R2_frstlvl_wf'): frstlvl_wf = Workflow(name='frstlvl_wf') info = dict(task_mri_files=[['subject_id', 'stopsignal']], motion_noise_files=[['subject_id', 'filter_regressor']]) # Create a Function node to define stimulus onsets, etc... for each subject subject_info = Node(Function(input_names=['subject_id'], output_names=['output'], function=subjectinfo), name='subject_info') subject_info.inputs.ignore_exception = False subject_info.inputs.subject_id = subject_id # Create another Function node to define the contrasts for the experiment getcontrasts = Node(Function(input_names=['subject_id'], output_names=['contrasts'], function=get_contrasts), name='getcontrasts') getcontrasts.inputs.ignore_exception = False getcontrasts.inputs.subject_id = subject_id # Create a Function node to substitute names of files created during pipeline getsubs = Node(Function(input_names=['subject_id', 'cons', 'info'], output_names=['subs'], function=get_subs), name='getsubs') getsubs.inputs.ignore_exception = False getsubs.inputs.subject_id = subject_id frstlvl_wf.connect(subject_info, 'output', getsubs, 'info') frstlvl_wf.connect(getcontrasts, 'contrasts', getsubs, 'cons') # Create a datasource node to get the task_mri and motion-noise files datasource = Node(DataGrabber(infields=['subject_id'], outfields=info.keys()), name='datasource') datasource.inputs.template = '*' datasource.inputs.subject_id = subject_id #datasource.inputs.base_directory = os.path.abspath('/scratch/PSB6351_2017/ds008_R2.0.0/preproc/') #datasource.inputs.field_template = dict(task_mri_files='%s/func/realigned/*%s*.nii.gz', # motion_noise_files='%s/noise/%s*.txt') datasource.inputs.base_directory = os.path.abspath( '/scratch/PSB6351_2017/students/salo/data/preproc/') datasource.inputs.field_template = dict( task_mri_files= '%s/preproc/func/smoothed/corr_*_task-%s_*_bold_bet_smooth_mask.nii.gz', motion_noise_files='%s/preproc/noise/%s*.txt') datasource.inputs.template_args = info datasource.inputs.sort_filelist = True datasource.inputs.ignore_exception = False datasource.inputs.raise_on_empty = True # Create a Function node to modify the motion and noise files to be single regressors motionnoise = Node(Function(input_names=['subjinfo', 'files'], output_names=['subjinfo'], function=motion_noise), name='motionnoise') motionnoise.inputs.ignore_exception = False frstlvl_wf.connect(subject_info, 'output', motionnoise, 'subjinfo') frstlvl_wf.connect(datasource, 'motion_noise_files', motionnoise, 'files') # Create a specify model node specify_model = Node(SpecifyModel(), name='specify_model') specify_model.inputs.high_pass_filter_cutoff = 128. specify_model.inputs.ignore_exception = False specify_model.inputs.input_units = 'secs' specify_model.inputs.time_repetition = 2. frstlvl_wf.connect(datasource, 'task_mri_files', specify_model, 'functional_runs') frstlvl_wf.connect(motionnoise, 'subjinfo', specify_model, 'subject_info') # Create an InputSpec node for the modelfit node modelfit_inputspec = Node(IdentityInterface(fields=[ 'session_info', 'interscan_interval', 'contrasts', 'film_threshold', 'functional_data', 'bases', 'model_serial_correlations' ], mandatory_inputs=True), name='modelfit_inputspec') modelfit_inputspec.inputs.bases = {'dgamma': {'derivs': False}} modelfit_inputspec.inputs.film_threshold = 0.0 modelfit_inputspec.inputs.interscan_interval = 2.0 modelfit_inputspec.inputs.model_serial_correlations = True frstlvl_wf.connect(datasource, 'task_mri_files', modelfit_inputspec, 'functional_data') frstlvl_wf.connect(getcontrasts, 'contrasts', modelfit_inputspec, 'contrasts') frstlvl_wf.connect(specify_model, 'session_info', modelfit_inputspec, 'session_info') # Create a level1 design node level1_design = Node(Level1Design(), name='level1_design') level1_design.inputs.ignore_exception = False frstlvl_wf.connect(modelfit_inputspec, 'interscan_interval', level1_design, 'interscan_interval') frstlvl_wf.connect(modelfit_inputspec, 'session_info', level1_design, 'session_info') frstlvl_wf.connect(modelfit_inputspec, 'contrasts', level1_design, 'contrasts') frstlvl_wf.connect(modelfit_inputspec, 'bases', level1_design, 'bases') frstlvl_wf.connect(modelfit_inputspec, 'model_serial_correlations', level1_design, 'model_serial_correlations') # Create a MapNode to generate a model for each run generate_model = MapNode(FEATModel(), iterfield=['fsf_file', 'ev_files'], name='generate_model') generate_model.inputs.environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'} generate_model.inputs.ignore_exception = False generate_model.inputs.output_type = 'NIFTI_GZ' generate_model.inputs.terminal_output = 'stream' frstlvl_wf.connect(level1_design, 'fsf_files', generate_model, 'fsf_file') frstlvl_wf.connect(level1_design, 'ev_files', generate_model, 'ev_files') # Create a MapNode to estimate the model using FILMGLS estimate_model = MapNode(FILMGLS(), iterfield=['design_file', 'in_file', 'tcon_file'], name='estimate_model') frstlvl_wf.connect(generate_model, 'design_file', estimate_model, 'design_file') frstlvl_wf.connect(generate_model, 'con_file', estimate_model, 'tcon_file') frstlvl_wf.connect(modelfit_inputspec, 'functional_data', estimate_model, 'in_file') # Create a merge node to merge the contrasts - necessary for fsl 5.0.7 and greater merge_contrasts = MapNode(Merge(2), iterfield=['in1'], name='merge_contrasts') frstlvl_wf.connect(estimate_model, 'zstats', merge_contrasts, 'in1') # Create a MapNode to transform the z2pval z2pval = MapNode(ImageMaths(), iterfield=['in_file'], name='z2pval') z2pval.inputs.environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'} z2pval.inputs.ignore_exception = False z2pval.inputs.op_string = '-ztop' z2pval.inputs.output_type = 'NIFTI_GZ' z2pval.inputs.suffix = '_pval' z2pval.inputs.terminal_output = 'stream' frstlvl_wf.connect(merge_contrasts, ('out', pop_lambda), z2pval, 'in_file') # Create an outputspec node modelfit_outputspec = Node(IdentityInterface(fields=[ 'copes', 'varcopes', 'dof_file', 'pfiles', 'parameter_estimates', 'zstats', 'design_image', 'design_file', 'design_cov', 'sigmasquareds' ], mandatory_inputs=True), name='modelfit_outputspec') frstlvl_wf.connect(estimate_model, 'copes', modelfit_outputspec, 'copes') frstlvl_wf.connect(estimate_model, 'varcopes', modelfit_outputspec, 'varcopes') frstlvl_wf.connect(merge_contrasts, 'out', modelfit_outputspec, 'zstats') frstlvl_wf.connect(z2pval, 'out_file', modelfit_outputspec, 'pfiles') frstlvl_wf.connect(generate_model, 'design_image', modelfit_outputspec, 'design_image') frstlvl_wf.connect(generate_model, 'design_file', modelfit_outputspec, 'design_file') frstlvl_wf.connect(generate_model, 'design_cov', modelfit_outputspec, 'design_cov') frstlvl_wf.connect(estimate_model, 'param_estimates', modelfit_outputspec, 'parameter_estimates') frstlvl_wf.connect(estimate_model, 'dof_file', modelfit_outputspec, 'dof_file') frstlvl_wf.connect(estimate_model, 'sigmasquareds', modelfit_outputspec, 'sigmasquareds') # Create a datasink node sinkd = Node(DataSink(), name='sinkd') sinkd.inputs.base_directory = sink_directory sinkd.inputs.container = subject_id frstlvl_wf.connect(getsubs, 'subs', sinkd, 'substitutions') frstlvl_wf.connect(modelfit_outputspec, 'parameter_estimates', sinkd, 'modelfit.estimates') frstlvl_wf.connect(modelfit_outputspec, 'sigmasquareds', sinkd, 'modelfit.estimates.@sigsq') frstlvl_wf.connect(modelfit_outputspec, 'dof_file', sinkd, 'modelfit.dofs') frstlvl_wf.connect(modelfit_outputspec, 'copes', sinkd, 'modelfit.contrasts.@copes') frstlvl_wf.connect(modelfit_outputspec, 'varcopes', sinkd, 'modelfit.contrasts.@varcopes') frstlvl_wf.connect(modelfit_outputspec, 'zstats', sinkd, 'modelfit.contrasts.@zstats') frstlvl_wf.connect(modelfit_outputspec, 'design_image', sinkd, 'modelfit.design') frstlvl_wf.connect(modelfit_outputspec, 'design_cov', sinkd, 'modelfit.design.@cov') frstlvl_wf.connect(modelfit_outputspec, 'design_file', sinkd, 'modelfit.design.@matrix') frstlvl_wf.connect(modelfit_outputspec, 'pfiles', sinkd, 'modelfit.contrasts.@pstats') return frstlvl_wf