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,
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_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'
