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_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_rsfMRI_preproc_pipeline(working_dir, freesurfer_dir, ds_dir, use_fs_brainmask, name='rsfMRI_preprocessing'):
# initiate workflow
rsfMRI_preproc_wf = Workflow(name=name)
rsfMRI_preproc_wf.base_dir = os.path.join(working_dir, 'LeiCA_resting')
ds_dir = os.path.join(ds_dir, name)
# set fsl output
fsl.FSLCommand.set_default_output_type('NIFTI_GZ')
# inputnode
inputnode = Node(util.IdentityInterface(fields=['epi',
't1w',
'subject_id',
'TR_ms',
'vols_to_drop',
'lat_ventricle_mask_MNI',
'lp_cutoff_freq',
'hp_cutoff_freq']),
name='inputnode')
# outputnode
outputnode = Node(util.IdentityInterface(fields=['epi_moco',
'rs_preprocessed',
'epi_2_MNI_warp']),
name='outputnode')
# MOCO
moco = create_moco_pipeline(working_dir, ds_dir, 'motion_correction')
rsfMRI_preproc_wf.connect(inputnode, 'epi', moco, 'inputnode.epi')
rsfMRI_preproc_wf.connect(inputnode, 'vols_to_drop', moco, 'inputnode.vols_to_drop')
# STRUCT PREPROCESSING
struct_preproc = create_struct_preproc_pipeline(working_dir, freesurfer_dir, ds_dir, use_fs_brainmask, 'struct_preproc')
rsfMRI_preproc_wf.connect(inputnode, 't1w', struct_preproc, 'inputnode.t1w')
rsfMRI_preproc_wf.connect(inputnode, 'subject_id', struct_preproc, 'inputnode.subject_id')
# REGISTRATIONS
reg = create_registration_pipeline(working_dir, freesurfer_dir, ds_dir, 'registration')
rsfMRI_preproc_wf.connect(moco, 'outputnode.initial_mean_epi_moco', reg, 'inputnode.initial_mean_epi_moco')
rsfMRI_preproc_wf.connect(inputnode, 't1w', reg, 'inputnode.t1w')
rsfMRI_preproc_wf.connect(struct_preproc, 'outputnode.t1w_brain', reg, 'inputnode.t1w_brain')
rsfMRI_preproc_wf.connect(struct_preproc, 'outputnode.wm_mask_4_bbr', reg, 'inputnode.wm_mask_4_bbr')
rsfMRI_preproc_wf.connect(struct_preproc, 'outputnode.struct_brain_mask', reg, 'inputnode.struct_brain_mask')
rsfMRI_preproc_wf.connect(inputnode, 'subject_id', reg, 'inputnode.subject_id')
rsfMRI_preproc_wf.connect(reg, 'outputnode.epi_2_MNI_warp', outputnode, 'epi_2_MNI_warp')
# DESKULL EPI
deskull = create_deskull_pipeline(working_dir, ds_dir, 'deskull')
rsfMRI_preproc_wf.connect(moco, 'outputnode.epi_moco', deskull, 'inputnode.epi_moco')
rsfMRI_preproc_wf.connect(struct_preproc, 'outputnode.struct_brain_mask', deskull, 'inputnode.struct_brain_mask')
rsfMRI_preproc_wf.connect(reg, 'outputnode.struct_2_epi_mat', deskull, 'inputnode.struct_2_epi_mat')
# DENOISE
denoise = create_denoise_pipeline(working_dir, ds_dir, 'denoise')
rsfMRI_preproc_wf.connect(inputnode, 'TR_ms', denoise, 'inputnode.TR_ms')
rsfMRI_preproc_wf.connect(inputnode, 'subject_id', denoise, 'inputnode.subject_id')
rsfMRI_preproc_wf.connect(inputnode, 'lat_ventricle_mask_MNI', denoise, 'inputnode.lat_ventricle_mask_MNI')
rsfMRI_preproc_wf.connect(moco, 'outputnode.par_moco', denoise, 'inputnode.par_moco')
rsfMRI_preproc_wf.connect(deskull, 'outputnode.epi_deskulled', denoise, 'inputnode.epi')
rsfMRI_preproc_wf.connect(deskull, 'outputnode.mean_epi', denoise, 'inputnode.mean_epi')
rsfMRI_preproc_wf.connect(deskull, 'outputnode.brain_mask_epiSpace', denoise, 'inputnode.brain_mask_epiSpace')
rsfMRI_preproc_wf.connect(reg, 'outputnode.struct_2_epi_mat', denoise, 'inputnode.struct_2_epi_mat')
rsfMRI_preproc_wf.connect(reg, 'outputnode.MNI_2_epi_warp', denoise, 'inputnode.MNI_2_epi_warp')
rsfMRI_preproc_wf.connect(struct_preproc, 'outputnode.wm_mask', denoise, 'inputnode.wm_mask')
rsfMRI_preproc_wf.connect(struct_preproc, 'outputnode.csf_mask', denoise, 'inputnode.csf_mask')
rsfMRI_preproc_wf.connect(inputnode, 'lp_cutoff_freq', denoise, 'inputnode.lp_cutoff_freq')
rsfMRI_preproc_wf.connect(inputnode, 'hp_cutoff_freq', denoise, 'inputnode.hp_cutoff_freq')
rsfMRI_preproc_wf.connect(denoise, 'outputnode.rs_preprocessed', outputnode, 'rs_preprocessed')
# QC
qc = create_qc_pipeline(working_dir, ds_dir, 'qc')
rsfMRI_preproc_wf.connect(inputnode, 'subject_id', qc, 'inputnode.subject_id')
rsfMRI_preproc_wf.connect(moco, 'outputnode.par_moco', qc, 'inputnode.par_moco')
rsfMRI_preproc_wf.connect(deskull, 'outputnode.epi_deskulled', qc, 'inputnode.epi_deskulled')
rsfMRI_preproc_wf.connect(deskull, 'outputnode.brain_mask_epiSpace', qc, 'inputnode.brain_mask_epiSpace')
rsfMRI_preproc_wf.connect([(struct_preproc, qc, [('outputnode.t1w_brain', 'inputnode.t1w_brain'),
('outputnode.struct_brain_mask', 'inputnode.struct_brain_mask')])])
rsfMRI_preproc_wf.connect([(reg, qc, [('outputnode.mean_epi_structSpace', 'inputnode.mean_epi_structSpace'),
('outputnode.mean_epi_MNIspace', 'inputnode.mean_epi_MNIspace'),
('outputnode.struct_MNIspace', 'inputnode.struct_MNIspace'),
('outputnode.struct_2_MNI_warp', 'inputnode.struct_2_MNI_warp')])])
rsfMRI_preproc_wf.connect(denoise, 'outputnode.outlier_files', qc, 'inputnode.outlier_files')
rsfMRI_preproc_wf.connect(denoise, 'outputnode.rs_preprocessed', qc, 'inputnode.rs_preprocessed')
rsfMRI_preproc_wf.write_graph(dotfilename=rsfMRI_preproc_wf.name, graph2use='orig', format='pdf')
rsfMRI_preproc_wf.write_graph(dotfilename=rsfMRI_preproc_wf.name, graph2use='colored', format='pdf')
return rsfMRI_preproc_wf
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_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_rsfMRI_preproc_pipeline(working_dir,
freesurfer_dir,
ds_dir,
use_fs_brainmask,
name='rsfMRI_preprocessing'):
# initiate workflow
rsfMRI_preproc_wf = Workflow(name=name)
rsfMRI_preproc_wf.base_dir = os.path.join(working_dir, 'LeiCA_resting')
ds_dir = os.path.join(ds_dir, name)
# set fsl output
fsl.FSLCommand.set_default_output_type('NIFTI_GZ')
# inputnode
inputnode = Node(util.IdentityInterface(fields=[
'epi', 't1w', 'subject_id', 'TR_ms', 'vols_to_drop',
'lat_ventricle_mask_MNI', 'lp_cutoff_freq', 'hp_cutoff_freq'
]),
name='inputnode')
# outputnode
outputnode = Node(util.IdentityInterface(
fields=['epi_moco', 'rs_preprocessed', 'epi_2_MNI_warp']),
name='outputnode')
# MOCO
moco = create_moco_pipeline(working_dir, ds_dir, 'motion_correction')
rsfMRI_preproc_wf.connect(inputnode, 'epi', moco, 'inputnode.epi')
rsfMRI_preproc_wf.connect(inputnode, 'vols_to_drop', moco,
'inputnode.vols_to_drop')
# STRUCT PREPROCESSING
struct_preproc = create_struct_preproc_pipeline(working_dir,
freesurfer_dir, ds_dir,
use_fs_brainmask,
'struct_preproc')
rsfMRI_preproc_wf.connect(inputnode, 't1w', struct_preproc,
'inputnode.t1w')
rsfMRI_preproc_wf.connect(inputnode, 'subject_id', struct_preproc,
'inputnode.subject_id')
# REGISTRATIONS
reg = create_registration_pipeline(working_dir, freesurfer_dir, ds_dir,
'registration')
rsfMRI_preproc_wf.connect(moco, 'outputnode.initial_mean_epi_moco', reg,
'inputnode.initial_mean_epi_moco')
rsfMRI_preproc_wf.connect(inputnode, 't1w', reg, 'inputnode.t1w')
rsfMRI_preproc_wf.connect(struct_preproc, 'outputnode.t1w_brain', reg,
'inputnode.t1w_brain')
rsfMRI_preproc_wf.connect(struct_preproc, 'outputnode.wm_mask_4_bbr', reg,
'inputnode.wm_mask_4_bbr')
rsfMRI_preproc_wf.connect(struct_preproc, 'outputnode.struct_brain_mask',
reg, 'inputnode.struct_brain_mask')
rsfMRI_preproc_wf.connect(inputnode, 'subject_id', reg,
'inputnode.subject_id')
rsfMRI_preproc_wf.connect(reg, 'outputnode.epi_2_MNI_warp', outputnode,
'epi_2_MNI_warp')
# DESKULL EPI
deskull = create_deskull_pipeline(working_dir, ds_dir, 'deskull')
rsfMRI_preproc_wf.connect(moco, 'outputnode.epi_moco', deskull,
'inputnode.epi_moco')
rsfMRI_preproc_wf.connect(struct_preproc, 'outputnode.struct_brain_mask',
deskull, 'inputnode.struct_brain_mask')
rsfMRI_preproc_wf.connect(reg, 'outputnode.struct_2_epi_mat', deskull,
'inputnode.struct_2_epi_mat')
# DENOISE
denoise = create_denoise_pipeline(working_dir, ds_dir, 'denoise')
rsfMRI_preproc_wf.connect(inputnode, 'TR_ms', denoise, 'inputnode.TR_ms')
rsfMRI_preproc_wf.connect(inputnode, 'subject_id', denoise,
'inputnode.subject_id')
rsfMRI_preproc_wf.connect(inputnode, 'lat_ventricle_mask_MNI', denoise,
'inputnode.lat_ventricle_mask_MNI')
rsfMRI_preproc_wf.connect(moco, 'outputnode.par_moco', denoise,
'inputnode.par_moco')
rsfMRI_preproc_wf.connect(deskull, 'outputnode.epi_deskulled', denoise,
'inputnode.epi')
rsfMRI_preproc_wf.connect(deskull, 'outputnode.mean_epi', denoise,
'inputnode.mean_epi')
rsfMRI_preproc_wf.connect(deskull, 'outputnode.brain_mask_epiSpace',
denoise, 'inputnode.brain_mask_epiSpace')
rsfMRI_preproc_wf.connect(reg, 'outputnode.struct_2_epi_mat', denoise,
'inputnode.struct_2_epi_mat')
rsfMRI_preproc_wf.connect(reg, 'outputnode.MNI_2_epi_warp', denoise,
'inputnode.MNI_2_epi_warp')
rsfMRI_preproc_wf.connect(struct_preproc, 'outputnode.wm_mask', denoise,
'inputnode.wm_mask')
rsfMRI_preproc_wf.connect(struct_preproc, 'outputnode.csf_mask', denoise,
'inputnode.csf_mask')
rsfMRI_preproc_wf.connect(inputnode, 'lp_cutoff_freq', denoise,
'inputnode.lp_cutoff_freq')
rsfMRI_preproc_wf.connect(inputnode, 'hp_cutoff_freq', denoise,
'inputnode.hp_cutoff_freq')
rsfMRI_preproc_wf.connect(denoise, 'outputnode.rs_preprocessed',
outputnode, 'rs_preprocessed')
# QC
qc = create_qc_pipeline(working_dir, ds_dir, 'qc')
rsfMRI_preproc_wf.connect(inputnode, 'subject_id', qc,
'inputnode.subject_id')
rsfMRI_preproc_wf.connect(moco, 'outputnode.par_moco', qc,
'inputnode.par_moco')
rsfMRI_preproc_wf.connect(deskull, 'outputnode.epi_deskulled', qc,
'inputnode.epi_deskulled')
rsfMRI_preproc_wf.connect(deskull, 'outputnode.brain_mask_epiSpace', qc,
'inputnode.brain_mask_epiSpace')
rsfMRI_preproc_wf.connect([(struct_preproc, qc, [
('outputnode.t1w_brain', 'inputnode.t1w_brain'),
('outputnode.struct_brain_mask', 'inputnode.struct_brain_mask')
])])
rsfMRI_preproc_wf.connect([(reg, qc, [
('outputnode.mean_epi_structSpace', 'inputnode.mean_epi_structSpace'),
('outputnode.mean_epi_MNIspace', 'inputnode.mean_epi_MNIspace'),
('outputnode.struct_MNIspace', 'inputnode.struct_MNIspace'),
('outputnode.struct_2_MNI_warp', 'inputnode.struct_2_MNI_warp')
])])
rsfMRI_preproc_wf.connect(denoise, 'outputnode.outlier_files', qc,
'inputnode.outlier_files')
rsfMRI_preproc_wf.connect(denoise, 'outputnode.rs_preprocessed', qc,
'inputnode.rs_preprocessed')
rsfMRI_preproc_wf.write_graph(dotfilename=rsfMRI_preproc_wf.name,
graph2use='orig',
format='pdf')
rsfMRI_preproc_wf.write_graph(dotfilename=rsfMRI_preproc_wf.name,
graph2use='colored',
format='pdf')
return rsfMRI_preproc_wf
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')