def create_resting(subject, working_dir, data_dir, freesurfer_dir, out_dir, vol_to_remove, TR, epi_resolution, highpass, lowpass, echo_space, pe_dir, standard_brain, standard_brain_resampled, standard_brain_mask, standard_brain_mask_resampled, fwhm_smoothing): # set fsl output type to nii.gz fsl.FSLCommand.set_default_output_type('NIFTI_GZ') # main workflow func_preproc = Workflow(name='resting_postscrub') func_preproc.base_dir = working_dir func_preproc.config['execution'][ 'crashdump_dir'] = func_preproc.base_dir + "/crash_files" # select files templates = { 'epi_scrubbed_interp': 'preprocessing/preprocessed/{subject}/scrubbed_interpolated/rest2anat_denoised_scrubbed_intep.nii.gz', 'anat_head': 'preprocessing/preprocessed/{subject}/structural/T1.nii.gz', 'anat_brain': 'preprocessing/preprocessed/{subject}/structural/brain.nii.gz', 'brain_mask': 'preprocessing/preprocessed/{subject}/structural/T1_brain_mask.nii.gz', 'ants_affine': 'preprocessing/preprocessed/{subject}/structural/transforms2mni/transform0GenericAffine.mat', 'ants_warp': 'preprocessing/preprocessed/{subject}/structural/transforms2mni/transform1Warp.nii.gz' } selectfiles = Node(nio.SelectFiles(templates, base_directory=data_dir), name="selectfiles") selectfiles.inputs.subject = subject # node to remove first volumes # remove_vol = Node(util.Function(input_names=['in_file', 't_min'], # output_names=["out_file"], # function=strip_rois_func), # name='remove_vol') # remove_vol.inputs.t_min = vol_to_remove # # workflow for motion correction # moco = create_moco_pipeline() # # # workflow for fieldmap correction and coregistration # topup_coreg = create_topup_coreg_pipeline() # topup_coreg.inputs.inputnode.fs_subjects_dir = freesurfer_dir # topup_coreg.inputs.inputnode.fs_subject_id = subject # topup_coreg.inputs.inputnode.echo_space = echo_space # topup_coreg.inputs.inputnode.pe_dir = pe_dir # # # workflow for applying transformations to timeseries # transform_ts = create_transform_pipeline() # transform_ts.inputs.inputnode.resolution = epi_resolution # # # # workflow to denoise timeseries denoise = create_denoise_pipeline() denoise.inputs.inputnode.highpass_sigma = 1. / (2 * TR * highpass) denoise.inputs.inputnode.lowpass_sigma = 1. / (2 * TR * lowpass) # https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind1205&L=FSL&P=R57592&1=FSL&9=A&I=-3&J=on&d=No+Match%3BMatch%3BMatches&z=4 denoise.inputs.inputnode.tr = TR # workflow to transform timeseries to MNI ants_registration = create_ants_registration_pipeline() ants_registration.inputs.inputnode.ref = standard_brain ants_registration.inputs.inputnode.tr_sec = TR # FL added fullspectrum # workflow to transform fullspectrum timeseries to MNI #ants_registration_full = create_ants_registration_pipeline('ants_registration_full') #ants_registration_full.inputs.inputnode.ref = standard_brain #ants_registration_full.inputs.inputnode.tr_sec = TR # workflow to smooth smoothing = create_smoothing_pipeline() smoothing.inputs.inputnode.fwhm = fwhm_smoothing # visualize registration results visualize = create_visualize_pipeline() visualize.inputs.inputnode.mni_template = standard_brain # sink to store files sink = Node(nio.DataSink( parameterization=False, base_directory=out_dir, substitutions=[('rest_denoised_bandpassed_norm_trans.nii.gz', 'rest_scrubbed_int_mni_unsmoothed.nii.gz'), ('rest_denoised_bandpassed_norm_trans_smooth.nii.gz', 'rest_scrubbed_int_mni_smoothed.nii.gz'), ('rest_denoised_bandpassed_norm.nii.gz', 'rest_denoised_scrubbed_int_bp.nii.gz')]), name='sink') # connections func_preproc.connect([ # remove the first volumes # (selectfiles, remove_vol, [('func', 'in_file')]), # # # align volumes and motion correction # (remove_vol, moco, [('out_file', 'inputnode.epi')]), # # # prepare field map # (selectfiles, topup_coreg, [('ap', 'inputnode.ap'), # ('pa', 'inputnode.pa'), # ('anat_head', 'inputnode.anat_head'), # ('anat_brain', 'inputnode.anat_brain') # ]), # (moco, topup_coreg, [('outputnode.epi_mean', 'inputnode.epi_mean')]), # # # transform timeseries # (remove_vol, transform_ts, [('out_file', 'inputnode.orig_ts')]), # (selectfiles, transform_ts, [('anat_head', 'inputnode.anat_head')]), # (selectfiles, transform_ts, [('brain_mask', 'inputnode.brain_mask')]), # (moco, transform_ts, [('outputnode.mat_moco', 'inputnode.mat_moco')]), # (topup_coreg, transform_ts, [('outputnode.fmap_fullwarp', 'inputnode.fullwarp')]), # # # correct slicetiming # # FIXME slice timing? # # (transform_ts, slicetiming, [('outputnode.trans_ts_masked', 'inputnode.ts')]), # # (slicetiming, denoise, [('outputnode.ts_slicetcorrected', 'inputnode.epi_coreg')]), # (transform_ts, denoise, [('outputnode.trans_ts_masked', 'inputnode.epi_coreg')]), # denoise data (selectfiles, denoise, [('brain_mask', 'inputnode.brain_mask'), ('anat_brain', 'inputnode.anat_brain'), ('epi_scrubbed_interp', 'inputnode.epi_denoised')]), (denoise, ants_registration, [('outputnode.normalized_file', 'inputnode.denoised_ts')]), # registration to MNI space (selectfiles, ants_registration, [('ants_affine', 'inputnode.ants_affine')]), (selectfiles, ants_registration, [('ants_warp', 'inputnode.ants_warp') ]), # FL added fullspectrum #(selectfiles, ants_registration_full, [('epi_scrubbed_interp', 'inputnode.denoised_ts')]), #(selectfiles, ants_registration_full, [('ants_affine', 'inputnode.ants_affine')]), #(selectfiles, ants_registration_full, [('ants_warp', 'inputnode.ants_warp')]), (ants_registration, smoothing, [('outputnode.ants_reg_ts', 'inputnode.ts_transformed')]), (smoothing, visualize, [('outputnode.ts_smoothed', 'inputnode.ts_transformed')]), ##all the output # (moco, sink, [ # ('outputnode.epi_moco', 'realign.@realigned_ts'), # ('outputnode.par_moco', 'realign.@par'), # ('outputnode.rms_moco', 'realign.@rms'), # ('outputnode.mat_moco', 'realign.MAT.@mat'), # ('outputnode.epi_mean', 'realign.@mean'), # ('outputnode.rotplot', 'realign.plots.@rotplot'), # ('outputnode.transplot', 'realign.plots.@transplot'), # ('outputnode.dispplots', 'realign.plots.@dispplots'), # ('outputnode.tsnr_file', 'realign.@tsnr')]), # (topup_coreg, sink, [('outputnode.fmap', 'coregister.transforms2anat.@fmap'), # # ('outputnode.unwarpfield_epi2fmap', 'coregister.@unwarpfield_epi2fmap'), # ('outputnode.unwarped_mean_epi2fmap', 'coregister.@unwarped_mean_epi2fmap'), # ('outputnode.epi2fmap', 'coregister.@epi2fmap'), # # ('outputnode.shiftmap', 'coregister.@shiftmap'), # ('outputnode.fmap_fullwarp', 'coregister.transforms2anat.@fmap_fullwarp'), # ('outputnode.epi2anat', 'coregister.@epi2anat'), # ('outputnode.epi2anat_mat', 'coregister.transforms2anat.@epi2anat_mat'), # ('outputnode.epi2anat_dat', 'coregister.transforms2anat.@epi2anat_dat'), # ('outputnode.epi2anat_mincost', 'coregister.@epi2anat_mincost') # ]), # # (transform_ts, sink, [('outputnode.trans_ts_masked', 'coregister.@full_transform_ts'), # ('outputnode.trans_ts_mean', 'coregister.@full_transform_mean'), # ('outputnode.resamp_brain', 'coregister.@resamp_brain')]), ( denoise, sink, [ ('outputnode.normalized_file', 'denoise.@normalized'), # FL added fullspectrum ]), (ants_registration, sink, [('outputnode.ants_reg_ts', 'ants.@antsnormalized')]), #(ants_registration_full, sink, [('outputnode.ants_reg_ts', 'ants.@antsnormalized_fullspectrum')]), (smoothing, sink, [('outputnode.ts_smoothed', '@smoothed.FWHM6')]), ]) func_preproc.write_graph(dotfilename='func_preproc.dot', graph2use='colored', format='pdf', simple_form=True) func_preproc.run()
def create_lemon_resting(subject, working_dir, data_dir, freesurfer_dir, out_dir, vol_to_remove, TR, epi_resolution, highpass, lowpass, echo_space, te_diff, pe_dir, standard_brain, standard_brain_resampled, standard_brain_mask, standard_brain_mask_resampled, fwhm_smoothing): # set fsl output type to nii.gz fsl.FSLCommand.set_default_output_type('NIFTI_GZ') # main workflow func_preproc = Workflow(name='lemon_resting') func_preproc.base_dir = working_dir func_preproc.config['execution'][ 'crashdump_dir'] = func_preproc.base_dir + "/crash_files" # select files templates = { 'func': 'func/EPI_t2.nii', 'fmap_phase': 'unwarp/B0_ph.nii', 'fmap_mag': 'unwarp/B0_mag.nii', 'anat_head': 'preprocessed/mod/anat/T1.nii.gz', #either with mod or without 'anat_brain': 'preprocessed/mod/anat/brain.nii.gz', #new version with brain_extraction from freesurfer #T1_brain_brain.nii.gz', 'brain_mask': 'preprocessed/mod/anat/T1_brain_mask.nii.gz', #T1_brain_brain_mask.nii.gz', 'ants_affine': 'preprocessed/mod/anat/transforms2mni/transform0GenericAffine.mat', 'ants_warp': 'preprocessed/mod/anat/transforms2mni/transform1Warp.nii.gz' } selectfiles = Node(nio.SelectFiles(templates, base_directory=data_dir), name="selectfiles") # node to remove first volumes remove_vol = Node(util.Function(input_names=['in_file', 't_min'], output_names=["out_file"], function=strip_rois_func), name='remove_vol') remove_vol.inputs.t_min = vol_to_remove # workflow for motion correction moco = create_moco_pipeline() # workflow for fieldmap correction and coregistration fmap_coreg = create_fmap_coreg_pipeline() fmap_coreg.inputs.inputnode.fs_subjects_dir = freesurfer_dir fmap_coreg.inputs.inputnode.fs_subject_id = subject fmap_coreg.inputs.inputnode.echo_space = echo_space fmap_coreg.inputs.inputnode.te_diff = te_diff fmap_coreg.inputs.inputnode.pe_dir = pe_dir # workflow for applying transformations to timeseries transform_ts = create_transform_pipeline() transform_ts.inputs.inputnode.resolution = epi_resolution #workflow to convert signal into percent signal change normalize = create_normalize_pipeline() normalize.inputs.inputnode.tr = TR #workflow to transform timeseries to MNI ants_registration = create_ants_registration_pipeline() ants_registration.inputs.inputnode.ref = standard_brain_resampled #workflow to smooth smoothing = create_smoothing_pipeline() smoothing.inputs.inputnode.fwhm = fwhm_smoothing #workflow to correct slice timing slicetiming = create_slice_timing_pipeline() #visualize registration results visualize = create_visualize_pipeline() visualize.inputs.inputnode.mni_template = standard_brain_resampled #sink to store files sink = Node(nio.DataSink( parameterization=False, base_directory=out_dir, substitutions=[ ('fmap_phase_fslprepared', 'fieldmap'), ('fieldmap_fslprepared_fieldmap_unmasked_vsm', 'shiftmap'), ('plot.rest_coregistered', 'outlier_plot'), ('filter_motion_comp_norm_compcor_art_dmotion', 'nuissance_matrix'), ('rest_realigned.nii.gz_abs.rms', 'rest_realigned_abs.rms'), ('rest_realigned.nii.gz.par', 'rest_realigned.par'), ('rest_realigned.nii.gz_rel.rms', 'rest_realigned_rel.rms'), ('rest_realigned.nii.gz_abs_disp', 'abs_displacement_plot'), ('rest_realigned.nii.gz_rel_disp', 'rel_displacment_plot'), ('art.rest_coregistered_outliers', 'outliers'), ('global_intensity.rest_coregistered', 'global_intensity'), ('norm.rest_coregistered', 'composite_norm'), ('stats.rest_coregistered', 'stats'), ('rest_denoised_bandpassed_norm.nii.gz', 'rest_preprocessed.nii.gz'), ('rest_denoised_bandpassed_norm_trans.nii.gz', 'rest_mni.nii.gz'), ('rest2anat_masked_st_norm_trans_smooth.nii', 'rest_mni_smoothed.nii') ]), name='sink') # connections func_preproc.connect([ #remove the first volumes (selectfiles, remove_vol, [('func', 'in_file')]), #align volumes and motion correction (remove_vol, moco, [('out_file', 'inputnode.epi')]), #prepare field map (selectfiles, fmap_coreg, [('fmap_phase', 'inputnode.phase'), ('fmap_mag', 'inputnode.mag'), ('anat_head', 'inputnode.anat_head'), ('anat_brain', 'inputnode.anat_brain')]), (moco, fmap_coreg, [('outputnode.epi_mean', 'inputnode.epi_mean')]), (remove_vol, transform_ts, [('out_file', 'inputnode.orig_ts')]), (selectfiles, transform_ts, [('anat_head', 'inputnode.anat_head')]), (selectfiles, transform_ts, [('brain_mask', 'inputnode.brain_mask')]), (moco, transform_ts, [('outputnode.mat_moco', 'inputnode.mat_moco')]), (fmap_coreg, transform_ts, [('outputnode.fmap_fullwarp', 'inputnode.fullwarp')]), ##add slice time correction after applying motion realignement + unwarping (transform_ts, slicetiming, [('outputnode.trans_ts_masked', 'inputnode.ts')]), (slicetiming, normalize, [('outputnode.ts_slicetcorrected', 'inputnode.epi_coreg')]), (normalize, ants_registration, [('outputnode.normalized_file', 'inputnode.denoised_ts')]), #registration to MNI space (selectfiles, ants_registration, [('ants_affine', 'inputnode.ants_affine')]), (selectfiles, ants_registration, [('ants_warp', 'inputnode.ants_warp') ]), (ants_registration, smoothing, [('outputnode.ants_reg_ts', 'inputnode.ts_transformed')]), (smoothing, visualize, [('outputnode.ts_smoothed', 'inputnode.ts_transformed')]), ##all the output ( moco, sink, [ #('outputnode.epi_moco', 'realign.@realigned_ts'), ('outputnode.par_moco', 'realign.@par'), ('outputnode.rms_moco', 'realign.@rms'), ('outputnode.mat_moco', 'realign.MAT.@mat'), ('outputnode.epi_mean', 'realign.@mean'), ('outputnode.rotplot', 'realign.plots.@rotplot'), ('outputnode.transplot', 'realign.plots.@transplot'), ('outputnode.dispplots', 'realign.plots.@dispplots'), ('outputnode.tsnr_file', 'realign.@tsnr') ]), ( fmap_coreg, sink, [ ('outputnode.fmap', 'coregister.transforms2anat.@fmap'), #('outputnode.unwarpfield_epi2fmap', 'coregister.@unwarpfield_epi2fmap'), ('outputnode.unwarped_mean_epi2fmap', 'coregister.@unwarped_mean_epi2fmap'), ('outputnode.epi2fmap', 'coregister.@epi2fmap'), #('outputnode.shiftmap', 'coregister.@shiftmap'), ('outputnode.fmap_fullwarp', 'coregister.transforms2anat.@fmap_fullwarp'), ('outputnode.epi2anat', 'coregister.@epi2anat'), ('outputnode.epi2anat_mat', 'coregister.transforms2anat.@epi2anat_mat'), ('outputnode.epi2anat_dat', 'coregister.transforms2anat.@epi2anat_dat'), ('outputnode.epi2anat_mincost', 'coregister.@epi2anat_mincost') ]), ( transform_ts, sink, [ #('outputnode.trans_ts', 'coregister.@full_transform_ts'), ('outputnode.trans_ts_mean', 'coregister.@full_transform_mean'), ('outputnode.resamp_brain', 'coregister.@resamp_brain') ]), (ants_registration, sink, [('outputnode.ants_reg_ts', 'ants.@antsnormalized')]), (smoothing, sink, [('outputnode.ts_smoothed', '@smoothed.FWHM6')]), ]) #func_preproc.write_graph(dotfilename='func_preproc.dot', graph2use='colored', format='pdf', simple_form=True) func_preproc.run()
def create_hc_connec(subject, working_dir, data_dir, freesurfer_dir, out_dir,epi_resolution, standard_brain, standard_brain_resampled, standard_brain_mask, standard_brain_mask_resampled, fwhm_smoothing, side, half): # set fsl output type to nii.gz fsl.FSLCommand.set_default_output_type('NIFTI_GZ') # main workflow hc_connec = Workflow(name='transform_hc_mni') hc_connec.base_dir = working_dir hc_connec.config['execution']['crashdump_dir'] = hc_connec.base_dir + "/crash_files" # select files templates={ 'input': 'hc_connectivity_thr099/'+side+'/hc_masks/'+half+'/hc_mask_thr0.99.nii.gz', #'hc_connectivity/left/hc_masks/anterior_hc/hc_mask.nii.gz', # 'ants_affine': 'structural/transforms2mni/transform0GenericAffine.mat', 'ants_warp': 'structural/transforms2mni/transform1Warp.nii.gz', } selectfiles = Node(nio.SelectFiles(templates, base_directory=data_dir), name="selectfiles") #workflow to tranform correlations to MNI space ants_registration=create_ants_registration_pipeline() ants_registration.inputs.inputnode.ref=standard_brain #_resampled: 2x2x2mm brain for RSV #sink to store files sink = Node(nio.DataSink (parameterization=True, base_directory=out_dir), # substitutions=[('_binarize', 'binarize'), -> don't really seem to work and I don't know why. # #('_binarize', 'anterior_hc'), # ('_ants_reg1', 'posterior_hc'), # #('_ants_reg', 'anterior_hc'), # ('_smooth1', 'posterior_hc'), # ('_smooth0', 'anterior_hc'), # ('corr_Z_trans', 'corr_Z_MNI')], name='sink') sink.inputs.substitutions=[('hc_mask_trans', 'hc_mask2mni'), ('_ants_reg0', 'MNI')] # connections hc_connec.connect([ (selectfiles, ants_registration, [('input', 'inputnode.corr_Z')]), (selectfiles, ants_registration, [('ants_affine', 'inputnode.ants_affine')] ), (selectfiles, ants_registration, [('ants_warp', 'inputnode.ants_warp')] ), (ants_registration, sink, [('outputnode.ants_reg_corr_Z', 'hc_connectivity_thr099.'+side+'.hc_masks.' + half)]) ]) hc_connec.run() #it can't run in multiproc as in one moment one file is hardcoded and saved to the disk which is
def create_hc_connec(subject, working_dir, data_dir, freesurfer_dir, out_dir, epi_resolution, standard_brain, standard_brain_resampled, standard_brain_mask, standard_brain_mask_resampled, fwhm_smoothing, side, TR, highpass, lowpass): # set fsl output type to nii.gz fsl.FSLCommand.set_default_output_type('NIFTI_GZ') # main workflow hc_connec = Workflow(name='hc_connec_thr099_scrubbed') hc_connec.base_dir = working_dir hc_connec.config['execution'][ 'crashdump_dir'] = hc_connec.base_dir + "/crash_files" # select files templates = { #'rest_head': 'resting_state/denoise/rest_preprocessed_nativespace.nii.gz', #denoised and bandpass-filtered native space (2x2x2mm) image 'rest2anat_scrubbed': 'preprocessing/preprocessed/{subject}/scrubbed_interpolated/rest2anat_denoised_scrubbed_intep.nii.gz', #denoised, scrubbed, interp, bp-filtered native space 'ants_affine': 'preprocessing/preprocessed/{subject}/structural/transforms2mni/transform0GenericAffine.mat', 'ants_warp': 'preprocessing/preprocessed/{subject}/structural/transforms2mni/transform1Warp.nii.gz', 'scrubvols': 'quality_reports/poldrack_reports/{subject}/scrubvols.txt' } selectfiles = Node(nio.SelectFiles(templates, base_directory=data_dir), name="selectfiles") selectfiles.inputs.subject = subject denoise = create_denoise_pipeline() denoise.inputs.inputnode.highpass_sigma = 1. / (2 * TR * highpass) denoise.inputs.inputnode.lowpass_sigma = 1. / (2 * TR * lowpass) # https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind1205&L=FSL&P=R57592&1=FSL&9=A&I=-3&J=on&d=No+Match%3BMatch%3BMatches&z=4 denoise.inputs.inputnode.tr = TR #drop scrubbed volumes scrub_volumes = Node(util.Function( input_names=['scrubvols', 'in_file', 'working_dir'], output_names=['filename_scrubbed_img'], function=scrub_timepoints), name='scrub_volumes') scrub_volumes.inputs.working_dir = working_dir #get T1 brainmask get_T1_brainmask = create_get_T1_brainmask() get_T1_brainmask.inputs.inputnode.fs_subjects_dir = freesurfer_dir get_T1_brainmask.inputs.inputnode.fs_subject_id = subject #workflow to extract HC and transform into individual space transform_hc = create_transform_hc() transform_hc.inputs.inputnode.fs_subjects_dir = freesurfer_dir transform_hc.inputs.inputnode.fs_subject_id = subject transform_hc.inputs.inputnode.resolution = 2 transform_hc.inputs.inputnode.working_dir = working_dir #workflow to extract timeseries and correlate corr_ts = create_corr_ts() #workflow to tranform correlations to MNI space ants_registration = create_ants_registration_pipeline() ants_registration.inputs.inputnode.ref = standard_brain #_resampled: 2x2x2mm brain for RSV # smoothing = create_smoothing_pipeline() smoothing.inputs.inputnode.fwhm = fwhm_smoothing #sink to store files sink = Node( nio.DataSink(parameterization=True, base_directory=out_dir), # substitutions=[('_binarize', 'binarize'), -> don't really seem to work and I don't know why. # #('_binarize', 'anterior_hc'), # ('_ants_reg1', 'posterior_hc'), # #('_ants_reg', 'anterior_hc'), # ('_smooth1', 'posterior_hc'), # ('_smooth0', 'anterior_hc'), # ('corr_Z_trans', 'corr_Z_MNI')], name='sink') sink.inputs.substitutions = [('_binarize0', 'posterior_hc'), ('_binarize1', 'anterior_hc'), ('_ants_reg0', 'posterior_hc'), ('_ants_reg1', 'anterior_hc'), ('_smooth0', 'posterior_hc'), ('_smooth1', 'anterior_hc'), ('_apply_FisherZ0', 'posterior_hc'), ('_apply_FisherZ1', 'anterior_hc')] # connections hc_connec.connect([ #bandpass-filtering implemented after scrubbing and replacement! (selectfiles, scrub_volumes, [('scrubvols', 'scrubvols')]), (get_T1_brainmask, transform_hc, [('outputnode.T1', 'inputnode.anat_head')]), (transform_hc, corr_ts, [('outputnode.hc_transformed_bin', 'inputnode.hc_mask')]), (selectfiles, denoise, [('rest2anat_scrubbed', 'inputnode.epi_denoised')]), (denoise, scrub_volumes, [('outputnode.normalized_file', 'in_file')]), (scrub_volumes, corr_ts, [('filename_scrubbed_img', 'inputnode.ts')]), (corr_ts, sink, [('outputnode.corrmap_z', 'hc_connectivity_thr099.scrubbed.' + side + '.corr.nativespace.@transformed')]), (corr_ts, ants_registration, [('outputnode.corrmap_z', 'inputnode.corr_Z')]), (selectfiles, ants_registration, [('ants_affine', 'inputnode.ants_affine')]), (selectfiles, ants_registration, [('ants_warp', 'inputnode.ants_warp') ]), (ants_registration, sink, [('outputnode.ants_reg_corr_Z', 'hc_connectivity_thr099.scrubbed.' + side + '.corr.ants')]), (ants_registration, smoothing, [('outputnode.ants_reg_corr_Z', 'inputnode.ts_transformed')]), (smoothing, sink, [('outputnode.ts_smoothed', 'hc_connectivity_thr099.scrubbed.' + side + '.corr.smoothed')]), ]) hc_connec.run( ) #it can't run in multiproc as in one moment one file is hardcoded and saved to the disk which is
def create_lemon_resting(subject, working_dir, data_dir, data_dir_WDR, freesurfer_dir, out_dir, vol_to_remove, TR, epi_resolution, highpass, lowpass, echo_space, te_diff, pe_dir, standard_brain, standard_brain_resampled, standard_brain_mask, standard_brain_mask_resampled, fwhm_smoothing): # set fsl output type to nii.gz fsl.FSLCommand.set_default_output_type('NIFTI_GZ') # main workflow func_preproc = Workflow(name='lemon_resting') func_preproc.base_dir = working_dir func_preproc.config['execution']['crashdump_dir'] = func_preproc.base_dir + "/crash_files" # select files # select files templates={ 'anat_brain' : 'preprocessed/mod/anat/brain.nii.gz', 'brain_mask' : 'preprocessed/mod/anat/T1_brain_mask.nii.gz', 'ants_affine': 'preprocessed/mod/anat/transforms2mni/transform0GenericAffine.mat', 'ants_warp': 'preprocessed/mod/anat/transforms2mni/transform1Warp.nii.gz', } templates_WDR={ 'par_moco': 'lemon_resting/motion_correction/mcflirt/rest_realigned.nii.gz.par', 'trans_ts': 'lemon_resting/transform_timeseries/merge/rest2anat.nii.gz', 'epi2anat_dat': 'lemon_resting/fmap_coreg/bbregister/rest2anat.dat', 'unwarped_mean_epi2fmap': 'lemon_resting/fmap_coreg/applywarp0/rest_mean2fmap_unwarped.nii.gz', } selectfiles = Node(nio.SelectFiles(templates, base_directory=data_dir), name="selectfiles") selectfiles_WDR = Node(nio.SelectFiles(templates_WDR, base_directory=data_dir_WDR), name="selectfiles_WDR") # workflow to denoise timeseries denoise = create_denoise_pipeline() denoise.inputs.inputnode.highpass_sigma= 1./(2*TR*highpass) denoise.inputs.inputnode.lowpass_sigma= 1./(2*TR*lowpass) #https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind1205&L=FSL&P=R57592&1=FSL&9=A&I=-3&J=on&d=No+Match%3BMatch%3BMatches&z=4 denoise.inputs.inputnode.tr = TR #workflow to transform timeseries to MNI ants_registration=create_ants_registration_pipeline() ants_registration.inputs.inputnode.ref=standard_brain_resampled #workflow to smooth smoothing = create_smoothing_pipeline() smoothing.inputs.inputnode.fwhm=fwhm_smoothing #workflow to slice time in the end as a try slicetiming = create_slice_timing_pipeline() #visualize registration results visualize = create_visualize_pipeline() visualize.inputs.inputnode.mni_template=standard_brain_resampled #sink to store files sink = Node(nio.DataSink(parameterization=False, base_directory=out_dir, substitutions=[('fmap_phase_fslprepared', 'fieldmap'), ('fieldmap_fslprepared_fieldmap_unmasked_vsm', 'shiftmap'), ('plot.rest_coregistered', 'outlier_plot'), ('filter_motion_comp_norm_compcor_art_dmotion', 'nuissance_matrix'), ('rest_realigned.nii.gz_abs.rms', 'rest_realigned_abs.rms'), ('rest_realigned.nii.gz.par','rest_realigned.par'), ('rest_realigned.nii.gz_rel.rms', 'rest_realigned_rel.rms'), ('rest_realigned.nii.gz_abs_disp', 'abs_displacement_plot'), ('rest_realigned.nii.gz_rel_disp', 'rel_displacment_plot'), ('art.rest_coregistered_outliers', 'outliers'), ('global_intensity.rest_coregistered', 'global_intensity'), ('norm.rest_coregistered', 'composite_norm'), ('stats.rest_coregistered', 'stats'), ('rest_denoised_bandpassed_norm.nii.gz', 'rest_preprocessed_nativespace.nii.gz'), ('rest_denoised_bandpassed_norm_trans.nii.gz', 'rest_mni_unsmoothed.nii.gz'), ('rest_denoised_bandpassed_norm_trans_smooth.nii', 'rest_mni_smoothed.nii')]), name='sink') # connections func_preproc.connect([ #correct slicetiming (selectfiles_WDR, slicetiming, [('trans_ts', 'inputnode.ts')]), (slicetiming, denoise, [('outputnode.ts_slicetcorrected','inputnode.epi_coreg')]), #denoise data (selectfiles, denoise, [('brain_mask', 'inputnode.brain_mask'), ('anat_brain', 'inputnode.anat_brain')]), (selectfiles_WDR, denoise, [('par_moco', 'inputnode.moco_par')]), (selectfiles_WDR, denoise, [('epi2anat_dat', 'inputnode.epi2anat_dat'), ('unwarped_mean_epi2fmap', 'inputnode.unwarped_mean')]), (denoise, ants_registration, [('outputnode.normalized_file', 'inputnode.denoised_ts')]), #registration to MNI space (selectfiles, ants_registration, [('ants_affine', 'inputnode.ants_affine')] ), (selectfiles, ants_registration, [('ants_warp', 'inputnode.ants_warp')] ), (ants_registration, smoothing, [('outputnode.ants_reg_ts', 'inputnode.ts_transformed')]), (smoothing, visualize, [('outputnode.ts_smoothed', 'inputnode.ts_transformed')]), ##all the output (denoise, sink, [ ('outputnode.wmcsf_mask', 'denoise.mask.@wmcsf_masks'), ('outputnode.combined_motion','denoise.artefact.@combined_motion'), ('outputnode.outlier_files','denoise.artefact.@outlier'), ('outputnode.intensity_files','denoise.artefact.@intensity'), ('outputnode.outlier_stats','denoise.artefact.@outlierstats'), ('outputnode.outlier_plots','denoise.artefact.@outlierplots'), ('outputnode.mc_regressor', 'denoise.regress.@mc_regressor'), ('outputnode.comp_regressor', 'denoise.regress.@comp_regressor'), ('outputnode.mc_F', 'denoise.regress.@mc_F'), ('outputnode.mc_pF', 'denoise.regress.@mc_pF'), ('outputnode.comp_F', 'denoise.regress.@comp_F'), ('outputnode.comp_pF', 'denoise.regress.@comp_pF'), ('outputnode.brain_mask_resamp', 'denoise.mask.@brain_resamp'), ('outputnode.brain_mask2epi', 'denoise.mask.@brain_mask2epi'), ('outputnode.normalized_file', 'denoise.@normalized') ]), (ants_registration, sink, [('outputnode.ants_reg_ts', 'ants.@antsnormalized') ]), (smoothing, sink, [('outputnode.ts_smoothed', '@smoothed.FWHM6')]), ]) #func_preproc.write_graph(dotfilename='func_preproc.dot', graph2use='colored', format='pdf', simple_form=True) func_preproc.run() # plugin='MultiProc'plugin='MultiProc'plugin='CondorDAGMan') #func_preproc.run()plugin='CondorDAGMan'plugin='CondorDAGMan'plugin='CondorDAGMan'plugin='CondorDAGMan' #plugin='CondorDAGMan'
def normalization_fullspectrum(subject, working_dir, data_dir, freesurfer_dir, out_dir, vol_to_remove, TR, epi_resolution, highpass, lowpass, echo_space, te_diff, pe_dir, standard_brain, standard_brain_resampled, standard_brain_mask, standard_brain_mask_resampled, fwhm_smoothing): from nipype.pipeline.engine import Node, Workflow import nipype.interfaces.io as nio import nipype.interfaces.fsl as fsl from ants_registration import create_ants_registration_pipeline fsl.FSLCommand.set_default_output_type('NIFTI_GZ') # main workflow func_preproc = Workflow(name='lemon_resting') func_preproc.base_dir = working_dir func_preproc.config['execution']['crashdump_dir'] = func_preproc.base_dir + "/crash_files" # select files templates = { 'ts_fullspectrum': 'Lemon_mod/{subject_id}/lemon_resting/denoise/filternoise/rest2anat_denoised.nii.gz', # 'func': 'func/EPI_t2.nii', # 'fmap_phase': 'unwarp/B0_ph.nii', # 'fmap_mag': 'unwarp/B0_mag.nii', # 'anat_head': 'preprocessed/mod/anat/T1.nii.gz', # either with mod or without # 'anat_brain': 'preprocessed/mod/anat/brain.nii.gz', # # new version with brain_extraction from freesurfer #T1_brain_brain.nii.gz', # 'brain_mask': 'preprocessed/mod/anat/T1_brain_mask.nii.gz', # T1_brain_brain_mask.nii.gz', 'ants_affine': 'subjects/{subject_id}/preprocessed/mod/anat/transforms2mni/transform0GenericAffine.mat', 'ants_warp': 'subjects/{subject_id}/preprocessed/mod/anat/transforms2mni/transform1Warp.nii.gz' } # base = /scr/kennedy2/data_fbeyer/genetics/ selectfiles = Node(nio.SelectFiles(templates, base_directory=data_dir), name="selectfiles") selectfiles.inputs.subject_id = subject # FL added fullspectrum # workflow to transform fullspectrum timeseries to MNI ants_registration_full = create_ants_registration_pipeline('ants_registration_full') ants_registration_full.inputs.inputnode.ref = standard_brain_resampled # FL added fullspectrum func_preproc.connect(selectfiles, 'ts_fullspectrum', ants_registration_full, 'inputnode.denoised_ts') func_preproc.connect(selectfiles, 'ants_affine', ants_registration_full, 'inputnode.ants_affine') func_preproc.connect(selectfiles, 'ants_warp', ants_registration_full, 'inputnode.ants_warp') # sink to store files sink = Node(nio.DataSink(parameterization=False, base_directory=out_dir, substitutions=[ # ('fmap_phase_fslprepared', 'fieldmap'), # ('fieldmap_fslprepared_fieldmap_unmasked_vsm', 'shiftmap'), # ('plot.rest_coregistered', 'outlier_plot'), # ('filter_motion_comp_norm_compcor_art_dmotion', 'nuissance_matrix'), # ('rest_realigned.nii.gz_abs.rms', 'rest_realigned_abs.rms'), # ('rest_realigned.nii.gz.par', 'rest_realigned.par'), # ('rest_realigned.nii.gz_rel.rms', 'rest_realigned_rel.rms'), # ('rest_realigned.nii.gz_abs_disp', 'abs_displacement_plot'), # ('rest_realigned.nii.gz_rel_disp', 'rel_displacment_plot'), # ('art.rest_coregistered_outliers', 'outliers'), # ('global_intensity.rest_coregistered', 'global_intensity'), # ('norm.rest_coregistered', 'composite_norm'), # ('stats.rest_coregistered', 'stats'), # ('rest_denoised_bandpassed_norm.nii.gz', # 'rest_preprocessed_nativespace.nii.gz'), # ('rest_denoised_bandpassed_norm_trans.nii.gz', # 'rest_mni_unsmoothed.nii.gz'), # ('rest_denoised_bandpassed_norm_trans_smooth.nii', # 'rest_mni_smoothed.nii'), # FL added ('rest2anat_denoised.nii.gz', 'rest_preprocessed_nativespace_fullspectrum.nii.gz'), ('rest2anat_denoised_trans.nii.gz', 'rest_mni_unsmoothed_fullspectrum.nii.gz') ]), name='sink') func_preproc.connect(ants_registration_full, 'outputnode.ants_reg_ts', sink, 'ants.@antsnormalized_fullspectrum') func_preproc.write_graph(dotfilename='func_preproc.dot', graph2use='colored', format='pdf', simple_form=True) func_preproc.run(plugin='CondorDAGMan')
def create_hc_connec(subject, working_dir, data_dir, freesurfer_dir, out_dir, epi_resolution, standard_brain, standard_brain_resampled, standard_brain_mask, standard_brain_mask_resampled, fwhm_smoothing, side): # set fsl output type to nii.gz fsl.FSLCommand.set_default_output_type('NIFTI_GZ') # main workflow hc_connec = Workflow(name='hc_connec_thr099') hc_connec.base_dir = working_dir hc_connec.config['execution'][ 'crashdump_dir'] = hc_connec.base_dir + "/crash_files" # select files templates = { 'rest_head': 'resting_state/denoise/rest_preprocessed_nativespace.nii.gz', #denoised and bandpass-filtered native space (2x2x2mm) image #'rest_head': 'scrubbed_interpolated/denoise/rest_denoised_scrubbed_int_bp.nii.gz', #denoised, scrubbed, interp, bp-filtered native space 'ants_affine': 'structural/transforms2mni/transform0GenericAffine.mat', 'ants_warp': 'structural/transforms2mni/transform1Warp.nii.gz', 'ts_smoothed_nativ': 'resting_state/denoise/rest_preprocessed_nativespace.nii.gz' } selectfiles = Node(nio.SelectFiles(templates, base_directory=data_dir), name="selectfiles") #get T1 brainmask get_T1_brainmask = create_get_T1_brainmask() get_T1_brainmask.inputs.inputnode.fs_subjects_dir = freesurfer_dir get_T1_brainmask.inputs.inputnode.fs_subject_id = subject #workflow to extract HC and transform into individual space transform_hc = create_transform_hc() transform_hc.inputs.inputnode.fs_subjects_dir = freesurfer_dir transform_hc.inputs.inputnode.fs_subject_id = subject transform_hc.inputs.inputnode.resolution = 2 transform_hc.inputs.inputnode.working_dir = working_dir #workflow to extract timeseries and correlate corr_ts = create_corr_ts() #workflow to tranform correlations to MNI space ants_registration = create_ants_registration_pipeline() ants_registration.inputs.inputnode.ref = standard_brain #_resampled: 2x2x2mm brain for RSV # smoothing = create_smoothing_pipeline() smoothing.inputs.inputnode.fwhm = fwhm_smoothing #sink to store files sink = Node( nio.DataSink(parameterization=True, base_directory=out_dir), # substitutions=[('_binarize', 'binarize'), -> don't really seem to work and I don't know why. # #('_binarize', 'anterior_hc'), # ('_ants_reg1', 'posterior_hc'), # #('_ants_reg', 'anterior_hc'), # ('_smooth1', 'posterior_hc'), # ('_smooth0', 'anterior_hc'), # ('corr_Z_trans', 'corr_Z_MNI')], name='sink') sink.inputs.substitutions = [('_binarize0', 'posterior_hc'), ('_binarize1', 'anterior_hc'), ('_ants_reg0', 'posterior_hc'), ('_ants_reg1', 'anterior_hc'), ('_smooth0', 'posterior_hc'), ('_smooth1', 'anterior_hc'), ('_apply_FisherZ0', 'posterior_hc'), ('_apply_FisherZ1', 'anterior_hc')] # connections hc_connec.connect([ (get_T1_brainmask, transform_hc, [('outputnode.T1', 'inputnode.anat_head')]), #(selectfiles, transform_hc, [('rest_head', 'inputnode.anat_head')]), (selectfiles, corr_ts, [('ts_smoothed_nativ', 'inputnode.ts')]), (transform_hc, corr_ts, [('outputnode.hc_transformed_bin', 'inputnode.hc_mask')]), (transform_hc, sink, [('outputnode.hc_transformed_bin', 'hc_connectivity_thr099.' + side + '.hc_masks') ]), (corr_ts, sink, [('outputnode.corrmap_z', 'hc_connectivity_thr099.' + side + '.corr.nativespace.@transformed')]), (corr_ts, ants_registration, [('outputnode.corrmap_z', 'inputnode.corr_Z')]), (selectfiles, ants_registration, [('ants_affine', 'inputnode.ants_affine')]), (selectfiles, ants_registration, [('ants_warp', 'inputnode.ants_warp') ]), (ants_registration, sink, [('outputnode.ants_reg_corr_Z', 'hc_connectivity_thr099.' + side + '.corr.ants')]), (ants_registration, smoothing, [('outputnode.ants_reg_corr_Z', 'inputnode.ts_transformed')]), (smoothing, sink, [('outputnode.ts_smoothed', 'hc_connectivity_thr099.' + side + '.corr.smoothed')]), ]) hc_connec.run( ) #it can't run in multiproc as in one moment one file is hardcoded and saved to the disk which is
def create_lemon_resting(subject, working_dir, data_dir, freesurfer_dir, out_dir, vol_to_remove, TR, epi_resolution, highpass, lowpass, echo_space, te_diff, pe_dir, standard_brain, standard_brain_resampled, standard_brain_mask, standard_brain_mask_resampled, fwhm_smoothing): # set fsl output type to nii.gz fsl.FSLCommand.set_default_output_type('NIFTI_GZ') # main workflow func_preproc = Workflow(name='lemon_resting') func_preproc.base_dir = working_dir func_preproc.config['execution']['crashdump_dir'] = func_preproc.base_dir + "/crash_files" # select files templates = {'func': 'raw_data/{subject}/func/EPI_t2.nii', 'fmap_phase': 'raw_data/{subject}/unwarp/B0_ph.nii', 'fmap_mag': 'raw_data/{subject}/unwarp/B0_mag.nii', 'anat_head': 'preprocessed/{subject}/structural/T1.nii.gz', # either with mod or without 'anat_brain': 'preprocessed/{subject}/structural/brain.nii.gz', # new version with brain_extraction from freesurfer #T1_brain_brain.nii.gz', 'brain_mask': 'preprocessed/{subject}/structural/T1_brain_mask.nii.gz', # T1_brain_brain_mask.nii.gz', 'ants_affine': 'preprocessed/{subject}/structural/transforms2mni/transform0GenericAffine.mat', 'ants_warp': 'preprocessed/{subject}/structural/transforms2mni/transform1Warp.nii.gz' } selectfiles = Node(nio.SelectFiles(templates, base_directory=data_dir), name="selectfiles") selectfiles.inputs.subject = subject # node to remove first volumes remove_vol = Node(util.Function(input_names=['in_file', 't_min'], output_names=["out_file"], function=strip_rois_func), name='remove_vol') remove_vol.inputs.t_min = vol_to_remove # workflow for motion correction moco = create_moco_pipeline() # workflow for fieldmap correction and coregistration fmap_coreg = create_fmap_coreg_pipeline() fmap_coreg.inputs.inputnode.fs_subjects_dir = freesurfer_dir fmap_coreg.inputs.inputnode.fs_subject_id = subject fmap_coreg.inputs.inputnode.echo_space = echo_space fmap_coreg.inputs.inputnode.te_diff = te_diff fmap_coreg.inputs.inputnode.pe_dir = pe_dir # workflow for applying transformations to timeseries transform_ts = create_transform_pipeline() transform_ts.inputs.inputnode.resolution = epi_resolution # workflow to denoise timeseries denoise = create_denoise_pipeline() denoise.inputs.inputnode.highpass_sigma = 1. / (2 * TR * highpass) denoise.inputs.inputnode.lowpass_sigma = 1. / (2 * TR * lowpass) # https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind1205&L=FSL&P=R57592&1=FSL&9=A&I=-3&J=on&d=No+Match%3BMatch%3BMatches&z=4 denoise.inputs.inputnode.tr = TR # workflow to transform timeseries to MNI ants_registration = create_ants_registration_pipeline() ants_registration.inputs.inputnode.ref = standard_brain_resampled ants_registration.inputs.inputnode.tr_sec = TR # FL added fullspectrum # workflow to transform fullspectrum timeseries to MNI ants_registration_full = create_ants_registration_pipeline('ants_registration_full') ants_registration_full.inputs.inputnode.ref = standard_brain_resampled ants_registration_full.inputs.inputnode.tr_sec = TR # workflow to smooth smoothing = create_smoothing_pipeline() smoothing.inputs.inputnode.fwhm = fwhm_smoothing # visualize registration results visualize = create_visualize_pipeline() visualize.inputs.inputnode.mni_template = standard_brain_resampled # sink to store files sink = Node(nio.DataSink(parameterization=False, base_directory=out_dir, substitutions=[('fmap_phase_fslprepared', 'fieldmap'), ('fieldmap_fslprepared_fieldmap_unmasked_vsm', 'shiftmap'), ('plot.rest_coregistered', 'outlier_plot'), ('filter_motion_comp_norm_compcor_art_dmotion', 'nuissance_matrix'), ('rest_realigned.nii.gz_abs.rms', 'rest_realigned_abs.rms'), ('rest_realigned.nii.gz.par', 'rest_realigned.par'), ('rest_realigned.nii.gz_rel.rms', 'rest_realigned_rel.rms'), ('rest_realigned.nii.gz_abs_disp', 'abs_displacement_plot'), ('rest_realigned.nii.gz_rel_disp', 'rel_displacment_plot'), ('art.rest_coregistered_outliers', 'outliers'), ('global_intensity.rest_coregistered', 'global_intensity'), ('norm.rest_coregistered', 'composite_norm'), ('stats.rest_coregistered', 'stats'), ('rest_denoised_bandpassed_norm.nii.gz', 'rest_preprocessed_nativespace.nii.gz'), ('rest_denoised_bandpassed_norm_trans.nii.gz', 'rest_mni_unsmoothed.nii.gz'), ('rest_denoised_bandpassed_norm_trans_smooth.nii', 'rest_mni_smoothed.nii'), # FL added ('rest2anat_masked.nii.gz', 'rest_coregistered_nativespace.nii.gz'), ('rest2anat_denoised.nii.gz', 'rest_preprocessed_nativespace_fullspectrum.nii.gz'), ('rest2anat_denoised_trans.nii.gz', 'rest_mni_unsmoothed_fullspectrum.nii.gz') ]), name='sink') # connections func_preproc.connect([ # remove the first volumes (selectfiles, remove_vol, [('func', 'in_file')]), # align volumes and motion correction (remove_vol, moco, [('out_file', 'inputnode.epi')]), # prepare field map (selectfiles, fmap_coreg, [('fmap_phase', 'inputnode.phase'), ('fmap_mag', 'inputnode.mag'), ('anat_head', 'inputnode.anat_head'), ('anat_brain', 'inputnode.anat_brain') ]), (moco, fmap_coreg, [('outputnode.epi_mean', 'inputnode.epi_mean')]), # transform timeseries (remove_vol, transform_ts, [('out_file', 'inputnode.orig_ts')]), (selectfiles, transform_ts, [('anat_head', 'inputnode.anat_head')]), (selectfiles, transform_ts, [('brain_mask', 'inputnode.brain_mask')]), (moco, transform_ts, [('outputnode.mat_moco', 'inputnode.mat_moco')]), (fmap_coreg, transform_ts, [('outputnode.fmap_fullwarp', 'inputnode.fullwarp')]), # correct slicetiming # FIXME slice timing? # (transform_ts, slicetiming, [('outputnode.trans_ts_masked', 'inputnode.ts')]), # (slicetiming, denoise, [('outputnode.ts_slicetcorrected', 'inputnode.epi_coreg')]), (transform_ts, denoise, [('outputnode.trans_ts_masked', 'inputnode.epi_coreg')]), # denoise data (selectfiles, denoise, [('brain_mask', 'inputnode.brain_mask'), ('anat_brain', 'inputnode.anat_brain')]), (moco, denoise, [('outputnode.par_moco', 'inputnode.moco_par')]), (fmap_coreg, denoise, [('outputnode.epi2anat_dat', 'inputnode.epi2anat_dat'), ('outputnode.unwarped_mean_epi2fmap', 'inputnode.unwarped_mean')]), (denoise, ants_registration, [('outputnode.normalized_file', 'inputnode.denoised_ts')]), # registration to MNI space (selectfiles, ants_registration, [('ants_affine', 'inputnode.ants_affine')]), (selectfiles, ants_registration, [('ants_warp', 'inputnode.ants_warp')]), # FL added fullspectrum (denoise, ants_registration_full, [('outputnode.ts_fullspectrum', 'inputnode.denoised_ts')]), (selectfiles, ants_registration_full, [('ants_affine', 'inputnode.ants_affine')]), (selectfiles, ants_registration_full, [('ants_warp', 'inputnode.ants_warp')]), (ants_registration, smoothing, [('outputnode.ants_reg_ts', 'inputnode.ts_transformed')]), (smoothing, visualize, [('outputnode.ts_smoothed', 'inputnode.ts_transformed')]), ##all the output (moco, sink, [ # ('outputnode.epi_moco', 'realign.@realigned_ts'), ('outputnode.par_moco', 'realign.@par'), ('outputnode.rms_moco', 'realign.@rms'), ('outputnode.mat_moco', 'realign.MAT.@mat'), ('outputnode.epi_mean', 'realign.@mean'), ('outputnode.rotplot', 'realign.plots.@rotplot'), ('outputnode.transplot', 'realign.plots.@transplot'), ('outputnode.dispplots', 'realign.plots.@dispplots'), ('outputnode.tsnr_file', 'realign.@tsnr')]), (fmap_coreg, sink, [('outputnode.fmap', 'coregister.transforms2anat.@fmap'), # ('outputnode.unwarpfield_epi2fmap', 'coregister.@unwarpfield_epi2fmap'), ('outputnode.unwarped_mean_epi2fmap', 'coregister.@unwarped_mean_epi2fmap'), ('outputnode.epi2fmap', 'coregister.@epi2fmap'), # ('outputnode.shiftmap', 'coregister.@shiftmap'), ('outputnode.fmap_fullwarp', 'coregister.transforms2anat.@fmap_fullwarp'), ('outputnode.epi2anat', 'coregister.@epi2anat'), ('outputnode.epi2anat_mat', 'coregister.transforms2anat.@epi2anat_mat'), ('outputnode.epi2anat_dat', 'coregister.transforms2anat.@epi2anat_dat'), ('outputnode.epi2anat_mincost', 'coregister.@epi2anat_mincost') ]), (transform_ts, sink, [('outputnode.trans_ts_masked', 'coregister.@full_transform_ts'), ('outputnode.trans_ts_mean', 'coregister.@full_transform_mean'), ('outputnode.resamp_brain', 'coregister.@resamp_brain')]), (denoise, sink, [ ('outputnode.wmcsf_mask', 'denoise.mask.@wmcsf_masks'), ('outputnode.combined_motion', 'denoise.artefact.@combined_motion'), ('outputnode.outlier_files', 'denoise.artefact.@outlier'), ('outputnode.intensity_files', 'denoise.artefact.@intensity'), ('outputnode.outlier_stats', 'denoise.artefact.@outlierstats'), ('outputnode.outlier_plots', 'denoise.artefact.@outlierplots'), ('outputnode.mc_regressor', 'denoise.regress.@mc_regressor'), ('outputnode.comp_regressor', 'denoise.regress.@comp_regressor'), ('outputnode.mc_F', 'denoise.regress.@mc_F'), ('outputnode.mc_pF', 'denoise.regress.@mc_pF'), ('outputnode.comp_F', 'denoise.regress.@comp_F'), ('outputnode.comp_pF', 'denoise.regress.@comp_pF'), ('outputnode.brain_mask_resamp', 'denoise.mask.@brain_resamp'), ('outputnode.brain_mask2epi', 'denoise.mask.@brain_mask2epi'), ('outputnode.normalized_file', 'denoise.@normalized'), # FL added fullspectrum ('outputnode.ts_fullspectrum', 'denoise.@ts_fullspectrum') ]), (ants_registration, sink, [('outputnode.ants_reg_ts', 'ants.@antsnormalized')]), (ants_registration_full, sink, [('outputnode.ants_reg_ts', 'ants.@antsnormalized_fullspectrum')]), (smoothing, sink, [('outputnode.ts_smoothed', '@smoothed.FWHM6')]), ]) func_preproc.write_graph(dotfilename='func_preproc.dot', graph2use='colored', format='pdf', simple_form=True) func_preproc.run(plugin='CondorDAGMan', plugin_args={'initial_specs': 'request_memory = 1500'})
def create_lemon_resting( subject, working_dir, data_dir, freesurfer_dir, out_dir, vol_to_remove, TR, epi_resolution, highpass, lowpass, echo_space, te_diff, pe_dir, standard_brain, standard_brain_resampled, standard_brain_mask, standard_brain_mask_resampled, fwhm_smoothing, ): # set fsl output type to nii.gz fsl.FSLCommand.set_default_output_type("NIFTI_GZ") # main workflow func_preproc = Workflow(name="lemon_resting") func_preproc.base_dir = working_dir func_preproc.config["execution"]["crashdump_dir"] = func_preproc.base_dir + "/crash_files" # select files templates = { "func": "func/EPI_t2.nii", "fmap_phase": "unwarp/B0_ph.nii", "fmap_mag": "unwarp/B0_mag.nii", "anat_head": "preprocessed/mod/anat/T1.nii.gz", # either with mod or without "anat_brain": "preprocessed/mod/anat/brain.nii.gz", # new version with brain_extraction from freesurfer #T1_brain_brain.nii.gz', "brain_mask": "preprocessed/mod/anat/T1_brain_mask.nii.gz", # T1_brain_brain_mask.nii.gz', "ants_affine": "preprocessed/mod/anat/transforms2mni/transform0GenericAffine.mat", "ants_warp": "preprocessed/mod/anat/transforms2mni/transform1Warp.nii.gz", } selectfiles = Node(nio.SelectFiles(templates, base_directory=data_dir), name="selectfiles") # node to remove first volumes remove_vol = Node( util.Function(input_names=["in_file", "t_min"], output_names=["out_file"], function=strip_rois_func), name="remove_vol", ) remove_vol.inputs.t_min = vol_to_remove # workflow for motion correction moco = create_moco_pipeline() # workflow for fieldmap correction and coregistration fmap_coreg = create_fmap_coreg_pipeline() fmap_coreg.inputs.inputnode.fs_subjects_dir = freesurfer_dir fmap_coreg.inputs.inputnode.fs_subject_id = subject fmap_coreg.inputs.inputnode.echo_space = echo_space fmap_coreg.inputs.inputnode.te_diff = te_diff fmap_coreg.inputs.inputnode.pe_dir = pe_dir # workflow for applying transformations to timeseries transform_ts = create_transform_pipeline() transform_ts.inputs.inputnode.resolution = epi_resolution # workflow to convert signal into percent signal change normalize = create_normalize_pipeline() normalize.inputs.inputnode.tr = TR # workflow to transform timeseries to MNI ants_registration = create_ants_registration_pipeline() ants_registration.inputs.inputnode.ref = standard_brain_resampled # workflow to smooth smoothing = create_smoothing_pipeline() smoothing.inputs.inputnode.fwhm = fwhm_smoothing # workflow to correct slice timing slicetiming = create_slice_timing_pipeline() # visualize registration results visualize = create_visualize_pipeline() visualize.inputs.inputnode.mni_template = standard_brain_resampled # sink to store files sink = Node( nio.DataSink( parameterization=False, base_directory=out_dir, substitutions=[ ("fmap_phase_fslprepared", "fieldmap"), ("fieldmap_fslprepared_fieldmap_unmasked_vsm", "shiftmap"), ("plot.rest_coregistered", "outlier_plot"), ("filter_motion_comp_norm_compcor_art_dmotion", "nuissance_matrix"), ("rest_realigned.nii.gz_abs.rms", "rest_realigned_abs.rms"), ("rest_realigned.nii.gz.par", "rest_realigned.par"), ("rest_realigned.nii.gz_rel.rms", "rest_realigned_rel.rms"), ("rest_realigned.nii.gz_abs_disp", "abs_displacement_plot"), ("rest_realigned.nii.gz_rel_disp", "rel_displacment_plot"), ("art.rest_coregistered_outliers", "outliers"), ("global_intensity.rest_coregistered", "global_intensity"), ("norm.rest_coregistered", "composite_norm"), ("stats.rest_coregistered", "stats"), ("rest_denoised_bandpassed_norm.nii.gz", "rest_preprocessed.nii.gz"), ("rest_denoised_bandpassed_norm_trans.nii.gz", "rest_mni.nii.gz"), ("rest2anat_masked_st_norm_trans_smooth.nii", "rest_mni_smoothed.nii"), ], ), name="sink", ) # connections func_preproc.connect( [ # remove the first volumes (selectfiles, remove_vol, [("func", "in_file")]), # align volumes and motion correction (remove_vol, moco, [("out_file", "inputnode.epi")]), # prepare field map ( selectfiles, fmap_coreg, [ ("fmap_phase", "inputnode.phase"), ("fmap_mag", "inputnode.mag"), ("anat_head", "inputnode.anat_head"), ("anat_brain", "inputnode.anat_brain"), ], ), (moco, fmap_coreg, [("outputnode.epi_mean", "inputnode.epi_mean")]), (remove_vol, transform_ts, [("out_file", "inputnode.orig_ts")]), (selectfiles, transform_ts, [("anat_head", "inputnode.anat_head")]), (selectfiles, transform_ts, [("brain_mask", "inputnode.brain_mask")]), (moco, transform_ts, [("outputnode.mat_moco", "inputnode.mat_moco")]), (fmap_coreg, transform_ts, [("outputnode.fmap_fullwarp", "inputnode.fullwarp")]), ##add slice time correction after applying motion realignement + unwarping (transform_ts, slicetiming, [("outputnode.trans_ts_masked", "inputnode.ts")]), (slicetiming, normalize, [("outputnode.ts_slicetcorrected", "inputnode.epi_coreg")]), (normalize, ants_registration, [("outputnode.normalized_file", "inputnode.denoised_ts")]), # registration to MNI space (selectfiles, ants_registration, [("ants_affine", "inputnode.ants_affine")]), (selectfiles, ants_registration, [("ants_warp", "inputnode.ants_warp")]), (ants_registration, smoothing, [("outputnode.ants_reg_ts", "inputnode.ts_transformed")]), (smoothing, visualize, [("outputnode.ts_smoothed", "inputnode.ts_transformed")]), ##all the output ( moco, sink, [ # ('outputnode.epi_moco', 'realign.@realigned_ts'), ("outputnode.par_moco", "realign.@par"), ("outputnode.rms_moco", "realign.@rms"), ("outputnode.mat_moco", "realign.MAT.@mat"), ("outputnode.epi_mean", "realign.@mean"), ("outputnode.rotplot", "realign.plots.@rotplot"), ("outputnode.transplot", "realign.plots.@transplot"), ("outputnode.dispplots", "realign.plots.@dispplots"), ("outputnode.tsnr_file", "realign.@tsnr"), ], ), ( fmap_coreg, sink, [ ("outputnode.fmap", "coregister.transforms2anat.@fmap"), # ('outputnode.unwarpfield_epi2fmap', 'coregister.@unwarpfield_epi2fmap'), ("outputnode.unwarped_mean_epi2fmap", "coregister.@unwarped_mean_epi2fmap"), ("outputnode.epi2fmap", "coregister.@epi2fmap"), # ('outputnode.shiftmap', 'coregister.@shiftmap'), ("outputnode.fmap_fullwarp", "coregister.transforms2anat.@fmap_fullwarp"), ("outputnode.epi2anat", "coregister.@epi2anat"), ("outputnode.epi2anat_mat", "coregister.transforms2anat.@epi2anat_mat"), ("outputnode.epi2anat_dat", "coregister.transforms2anat.@epi2anat_dat"), ("outputnode.epi2anat_mincost", "coregister.@epi2anat_mincost"), ], ), ( transform_ts, sink, [ # ('outputnode.trans_ts', 'coregister.@full_transform_ts'), ("outputnode.trans_ts_mean", "coregister.@full_transform_mean"), ("outputnode.resamp_brain", "coregister.@resamp_brain"), ], ), (ants_registration, sink, [("outputnode.ants_reg_ts", "ants.@antsnormalized")]), (smoothing, sink, [("outputnode.ts_smoothed", "@smoothed.FWHM6")]), ] ) # func_preproc.write_graph(dotfilename='func_preproc.dot', graph2use='colored', format='pdf', simple_form=True) func_preproc.run()
def create_lemon_resting(subject, working_dir, data_dir, data_dir_WDR, freesurfer_dir, out_dir, vol_to_remove, TR, epi_resolution, highpass, lowpass, echo_space, te_diff, pe_dir, standard_brain, standard_brain_resampled, standard_brain_mask, standard_brain_mask_resampled, fwhm_smoothing): # set fsl output type to nii.gz fsl.FSLCommand.set_default_output_type('NIFTI_GZ') # main workflow func_preproc = Workflow(name='lemon_resting') func_preproc.base_dir = working_dir func_preproc.config['execution'][ 'crashdump_dir'] = func_preproc.base_dir + "/crash_files" # select files # select files templates = { 'anat_brain': 'preprocessed/mod/anat/brain.nii.gz', 'brain_mask': 'preprocessed/mod/anat/T1_brain_mask.nii.gz', 'ants_affine': 'preprocessed/mod/anat/transforms2mni/transform0GenericAffine.mat', 'ants_warp': 'preprocessed/mod/anat/transforms2mni/transform1Warp.nii.gz', } templates_WDR = { 'par_moco': 'lemon_resting/motion_correction/mcflirt/rest_realigned.nii.gz.par', 'trans_ts': 'lemon_resting/transform_timeseries/merge/rest2anat.nii.gz', 'epi2anat_dat': 'lemon_resting/fmap_coreg/bbregister/rest2anat.dat', 'unwarped_mean_epi2fmap': 'lemon_resting/fmap_coreg/applywarp0/rest_mean2fmap_unwarped.nii.gz', } selectfiles = Node(nio.SelectFiles(templates, base_directory=data_dir), name="selectfiles") selectfiles_WDR = Node(nio.SelectFiles(templates_WDR, base_directory=data_dir_WDR), name="selectfiles_WDR") # workflow to denoise timeseries denoise = create_denoise_pipeline() denoise.inputs.inputnode.highpass_sigma = 1. / (2 * TR * highpass) denoise.inputs.inputnode.lowpass_sigma = 1. / (2 * TR * lowpass) #https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind1205&L=FSL&P=R57592&1=FSL&9=A&I=-3&J=on&d=No+Match%3BMatch%3BMatches&z=4 denoise.inputs.inputnode.tr = TR #workflow to transform timeseries to MNI ants_registration = create_ants_registration_pipeline() ants_registration.inputs.inputnode.ref = standard_brain_resampled #workflow to smooth smoothing = create_smoothing_pipeline() smoothing.inputs.inputnode.fwhm = fwhm_smoothing #workflow to slice time in the end as a try slicetiming = create_slice_timing_pipeline() #visualize registration results visualize = create_visualize_pipeline() visualize.inputs.inputnode.mni_template = standard_brain_resampled #sink to store files sink = Node(nio.DataSink( parameterization=False, base_directory=out_dir, substitutions=[ ('fmap_phase_fslprepared', 'fieldmap'), ('fieldmap_fslprepared_fieldmap_unmasked_vsm', 'shiftmap'), ('plot.rest_coregistered', 'outlier_plot'), ('filter_motion_comp_norm_compcor_art_dmotion', 'nuissance_matrix'), ('rest_realigned.nii.gz_abs.rms', 'rest_realigned_abs.rms'), ('rest_realigned.nii.gz.par', 'rest_realigned.par'), ('rest_realigned.nii.gz_rel.rms', 'rest_realigned_rel.rms'), ('rest_realigned.nii.gz_abs_disp', 'abs_displacement_plot'), ('rest_realigned.nii.gz_rel_disp', 'rel_displacment_plot'), ('art.rest_coregistered_outliers', 'outliers'), ('global_intensity.rest_coregistered', 'global_intensity'), ('norm.rest_coregistered', 'composite_norm'), ('stats.rest_coregistered', 'stats'), ('rest_denoised_bandpassed_norm.nii.gz', 'rest_preprocessed_nativespace.nii.gz'), ('rest_denoised_bandpassed_norm_trans.nii.gz', 'rest_mni_unsmoothed.nii.gz'), ('rest_denoised_bandpassed_norm_trans_smooth.nii', 'rest_mni_smoothed.nii') ]), name='sink') # connections func_preproc.connect([ #correct slicetiming (selectfiles_WDR, slicetiming, [('trans_ts', 'inputnode.ts')]), (slicetiming, denoise, [('outputnode.ts_slicetcorrected', 'inputnode.epi_coreg')]), #denoise data (selectfiles, denoise, [('brain_mask', 'inputnode.brain_mask'), ('anat_brain', 'inputnode.anat_brain')]), (selectfiles_WDR, denoise, [('par_moco', 'inputnode.moco_par')]), (selectfiles_WDR, denoise, [('epi2anat_dat', 'inputnode.epi2anat_dat'), ('unwarped_mean_epi2fmap', 'inputnode.unwarped_mean')]), (denoise, ants_registration, [('outputnode.normalized_file', 'inputnode.denoised_ts')]), #registration to MNI space (selectfiles, ants_registration, [('ants_affine', 'inputnode.ants_affine')]), (selectfiles, ants_registration, [('ants_warp', 'inputnode.ants_warp') ]), (ants_registration, smoothing, [('outputnode.ants_reg_ts', 'inputnode.ts_transformed')]), (smoothing, visualize, [('outputnode.ts_smoothed', 'inputnode.ts_transformed')]), ##all the output (denoise, sink, [('outputnode.wmcsf_mask', 'denoise.mask.@wmcsf_masks'), ('outputnode.combined_motion', 'denoise.artefact.@combined_motion'), ('outputnode.outlier_files', 'denoise.artefact.@outlier'), ('outputnode.intensity_files', 'denoise.artefact.@intensity'), ('outputnode.outlier_stats', 'denoise.artefact.@outlierstats'), ('outputnode.outlier_plots', 'denoise.artefact.@outlierplots'), ('outputnode.mc_regressor', 'denoise.regress.@mc_regressor'), ('outputnode.comp_regressor', 'denoise.regress.@comp_regressor'), ('outputnode.mc_F', 'denoise.regress.@mc_F'), ('outputnode.mc_pF', 'denoise.regress.@mc_pF'), ('outputnode.comp_F', 'denoise.regress.@comp_F'), ('outputnode.comp_pF', 'denoise.regress.@comp_pF'), ('outputnode.brain_mask_resamp', 'denoise.mask.@brain_resamp'), ('outputnode.brain_mask2epi', 'denoise.mask.@brain_mask2epi'), ('outputnode.normalized_file', 'denoise.@normalized')]), (ants_registration, sink, [('outputnode.ants_reg_ts', 'ants.@antsnormalized')]), (smoothing, sink, [('outputnode.ts_smoothed', '@smoothed.FWHM6')]), ]) #func_preproc.write_graph(dotfilename='func_preproc.dot', graph2use='colored', format='pdf', simple_form=True) func_preproc.run() # plugin='MultiProc'plugin='MultiProc'plugin='CondorDAGMan') #func_preproc.run()plugin='CondorDAGMan'plugin='CondorDAGMan'plugin='CondorDAGMan'plugin='CondorDAGMan' #plugin='CondorDAGMan'
def create_resting(subject, working_dir, data_dir, freesurfer_dir, out_dir, vol_to_remove, TR, epi_resolution, highpass, lowpass, echo_space, pe_dir, standard_brain, standard_brain_resampled, standard_brain_mask, standard_brain_mask_resampled, fwhm_smoothing): # set fsl output type to nii.gz fsl.FSLCommand.set_default_output_type('NIFTI_GZ') # main workflow func_preproc = Workflow(name='lemon_resting') func_preproc.base_dir = working_dir func_preproc.config['execution']['crashdump_dir'] = func_preproc.base_dir + "/crash_files" # select files templates = {'func': 'raw/{subject}/func/EPI_t2.nii', 'ap': 'raw/{subject}/topup/se.nii', 'pa': 'raw/{subject}/topup/seinv_ph.nii', 'anat_head': 'preprocessing/preprocessed/{subject}/structural/T1.nii.gz', 'anat_brain': 'preprocessing/preprocessed/{subject}/structural/brain.nii.gz', 'brain_mask': 'preprocessing/preprocessed/{subject}/structural/T1_brain_mask.nii.gz', 'ants_affine': 'preprocessing/preprocessed/{subject}/structural/transforms2mni/transform0GenericAffine.mat', 'ants_warp': 'preprocessing/preprocessed/{subject}/structural/transforms2mni/transform1Warp.nii.gz' } selectfiles = Node(nio.SelectFiles(templates, base_directory=data_dir), name="selectfiles") selectfiles.inputs.subject = subject # node to remove first volumes remove_vol = Node(util.Function(input_names=['in_file', 't_min'], output_names=["out_file"], function=strip_rois_func), name='remove_vol') remove_vol.inputs.t_min = vol_to_remove # workflow for motion correction moco = create_moco_pipeline() # workflow for fieldmap correction and coregistration topup_coreg = create_topup_coreg_pipeline() topup_coreg.inputs.inputnode.fs_subjects_dir = freesurfer_dir topup_coreg.inputs.inputnode.fs_subject_id = subject topup_coreg.inputs.inputnode.echo_space = echo_space topup_coreg.inputs.inputnode.pe_dir = pe_dir # workflow for applying transformations to timeseries transform_ts = create_transform_pipeline() transform_ts.inputs.inputnode.resolution = epi_resolution # workflow to denoise timeseries denoise = create_denoise_pipeline() denoise.inputs.inputnode.highpass_sigma = 1. / (2 * TR * highpass) denoise.inputs.inputnode.lowpass_sigma = 1. / (2 * TR * lowpass) # https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind1205&L=FSL&P=R57592&1=FSL&9=A&I=-3&J=on&d=No+Match%3BMatch%3BMatches&z=4 denoise.inputs.inputnode.tr = TR # workflow to transform timeseries to MNI ants_registration = create_ants_registration_pipeline() ants_registration.inputs.inputnode.ref = standard_brain ants_registration.inputs.inputnode.tr_sec = TR # FL added fullspectrum # workflow to transform fullspectrum timeseries to MNI ants_registration_full = create_ants_registration_pipeline('ants_registration_full') ants_registration_full.inputs.inputnode.ref = standard_brain ants_registration_full.inputs.inputnode.tr_sec = TR # workflow to smooth smoothing = create_smoothing_pipeline() smoothing.inputs.inputnode.fwhm = fwhm_smoothing # visualize registration results visualize = create_visualize_pipeline() visualize.inputs.inputnode.mni_template = standard_brain # sink to store files sink = Node(nio.DataSink(parameterization=False, base_directory=out_dir, substitutions=[('fmap_phase_fslprepared', 'fieldmap'), ('fieldmap_fslprepared_fieldmap_unmasked_vsm', 'shiftmap'), ('plot.rest_coregistered', 'outlier_plot'), ('filter_motion_comp_norm_compcor_art_dmotion', 'nuissance_matrix'), ('rest_realigned.nii.gz_abs.rms', 'rest_realigned_abs.rms'), ('rest_realigned.nii.gz.par', 'rest_realigned.par'), ('rest_realigned.nii.gz_rel.rms', 'rest_realigned_rel.rms'), ('rest_realigned.nii.gz_abs_disp', 'abs_displacement_plot'), ('rest_realigned.nii.gz_rel_disp', 'rel_displacment_plot'), ('art.rest_coregistered_outliers', 'outliers'), ('global_intensity.rest_coregistered', 'global_intensity'), ('norm.rest_coregistered', 'composite_norm'), ('stats.rest_coregistered', 'stats'), ('rest_denoised_bandpassed_norm.nii.gz', 'rest_preprocessed_nativespace.nii.gz'), ('rest_denoised_bandpassed_norm_trans.nii.gz', 'rest_mni_unsmoothed.nii.gz'), ('rest_denoised_bandpassed_norm_trans_smooth.nii', 'rest_mni_smoothed.nii'), # FL added ('rest2anat_masked.nii.gz', 'rest_coregistered_nativespace.nii.gz'), ('rest2anat_denoised.nii.gz', 'rest_preprocessed_nativespace_fullspectrum.nii.gz'), ('rest2anat_denoised_trans.nii.gz', 'rest_mni_unsmoothed_fullspectrum.nii.gz') ]), name='sink') # connections func_preproc.connect([ # remove the first volumes (selectfiles, remove_vol, [('func', 'in_file')]), # align volumes and motion correction (remove_vol, moco, [('out_file', 'inputnode.epi')]), # prepare field map (selectfiles, topup_coreg, [('ap', 'inputnode.ap'), ('pa', 'inputnode.pa'), ('anat_head', 'inputnode.anat_head'), ('anat_brain', 'inputnode.anat_brain') ]), (moco, topup_coreg, [('outputnode.epi_mean', 'inputnode.epi_mean')]), # transform timeseries (remove_vol, transform_ts, [('out_file', 'inputnode.orig_ts')]), (selectfiles, transform_ts, [('anat_head', 'inputnode.anat_head')]), (selectfiles, transform_ts, [('brain_mask', 'inputnode.brain_mask')]), (moco, transform_ts, [('outputnode.mat_moco', 'inputnode.mat_moco')]), (topup_coreg, transform_ts, [('outputnode.fmap_fullwarp', 'inputnode.fullwarp')]), # correct slicetiming # FIXME slice timing? # (transform_ts, slicetiming, [('outputnode.trans_ts_masked', 'inputnode.ts')]), # (slicetiming, denoise, [('outputnode.ts_slicetcorrected', 'inputnode.epi_coreg')]), (transform_ts, denoise, [('outputnode.trans_ts_masked', 'inputnode.epi_coreg')]), # denoise data (selectfiles, denoise, [('brain_mask', 'inputnode.brain_mask'), ('anat_brain', 'inputnode.anat_brain')]), (moco, denoise, [('outputnode.par_moco', 'inputnode.moco_par')]), (topup_coreg, denoise, [('outputnode.epi2anat_dat', 'inputnode.epi2anat_dat'), ('outputnode.unwarped_mean_epi2fmap', 'inputnode.unwarped_mean')]), (denoise, ants_registration, [('outputnode.normalized_file', 'inputnode.denoised_ts')]), # registration to MNI space (selectfiles, ants_registration, [('ants_affine', 'inputnode.ants_affine')]), (selectfiles, ants_registration, [('ants_warp', 'inputnode.ants_warp')]), # FL added fullspectrum (denoise, ants_registration_full, [('outputnode.ts_fullspectrum', 'inputnode.denoised_ts')]), (selectfiles, ants_registration_full, [('ants_affine', 'inputnode.ants_affine')]), (selectfiles, ants_registration_full, [('ants_warp', 'inputnode.ants_warp')]), (ants_registration, smoothing, [('outputnode.ants_reg_ts', 'inputnode.ts_transformed')]), (smoothing, visualize, [('outputnode.ts_smoothed', 'inputnode.ts_transformed')]), ##all the output (moco, sink, [ # ('outputnode.epi_moco', 'realign.@realigned_ts'), ('outputnode.par_moco', 'realign.@par'), ('outputnode.rms_moco', 'realign.@rms'), ('outputnode.mat_moco', 'realign.MAT.@mat'), ('outputnode.epi_mean', 'realign.@mean'), ('outputnode.rotplot', 'realign.plots.@rotplot'), ('outputnode.transplot', 'realign.plots.@transplot'), ('outputnode.dispplots', 'realign.plots.@dispplots'), ('outputnode.tsnr_file', 'realign.@tsnr')]), (topup_coreg, sink, [('outputnode.fmap', 'coregister.transforms2anat.@fmap'), # ('outputnode.unwarpfield_epi2fmap', 'coregister.@unwarpfield_epi2fmap'), ('outputnode.unwarped_mean_epi2fmap', 'coregister.@unwarped_mean_epi2fmap'), ('outputnode.epi2fmap', 'coregister.@epi2fmap'), # ('outputnode.shiftmap', 'coregister.@shiftmap'), ('outputnode.fmap_fullwarp', 'coregister.transforms2anat.@fmap_fullwarp'), ('outputnode.epi2anat', 'coregister.@epi2anat'), ('outputnode.epi2anat_mat', 'coregister.transforms2anat.@epi2anat_mat'), ('outputnode.epi2anat_dat', 'coregister.transforms2anat.@epi2anat_dat'), ('outputnode.epi2anat_mincost', 'coregister.@epi2anat_mincost') ]), (transform_ts, sink, [('outputnode.trans_ts_masked', 'coregister.@full_transform_ts'), ('outputnode.trans_ts_mean', 'coregister.@full_transform_mean'), ('outputnode.resamp_brain', 'coregister.@resamp_brain')]), (denoise, sink, [ ('outputnode.wmcsf_mask', 'denoise.mask.@wmcsf_masks'), ('outputnode.combined_motion', 'denoise.artefact.@combined_motion'), ('outputnode.outlier_files', 'denoise.artefact.@outlier'), ('outputnode.intensity_files', 'denoise.artefact.@intensity'), ('outputnode.outlier_stats', 'denoise.artefact.@outlierstats'), ('outputnode.outlier_plots', 'denoise.artefact.@outlierplots'), ('outputnode.mc_regressor', 'denoise.regress.@mc_regressor'), ('outputnode.comp_regressor', 'denoise.regress.@comp_regressor'), ('outputnode.mc_F', 'denoise.regress.@mc_F'), ('outputnode.mc_pF', 'denoise.regress.@mc_pF'), ('outputnode.comp_F', 'denoise.regress.@comp_F'), ('outputnode.comp_pF', 'denoise.regress.@comp_pF'), ('outputnode.brain_mask_resamp', 'denoise.mask.@brain_resamp'), ('outputnode.brain_mask2epi', 'denoise.mask.@brain_mask2epi'), ('outputnode.normalized_file', 'denoise.@normalized'), # FL added fullspectrum ('outputnode.ts_fullspectrum', 'denoise.@ts_fullspectrum') ]), (ants_registration, sink, [('outputnode.ants_reg_ts', 'ants.@antsnormalized')]), (ants_registration_full, sink, [('outputnode.ants_reg_ts', 'ants.@antsnormalized_fullspectrum')]), (smoothing, sink, [('outputnode.ts_smoothed', '@smoothed.FWHM6')]), ]) func_preproc.write_graph(dotfilename='func_preproc.dot', graph2use='colored', format='pdf', simple_form=True) func_preproc.run(plugin='MultiProc')