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, 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_resting():
# main workflow
func_preproc = Workflow(name='resting')
inputnode = Node(util.IdentityInterface(fields=[
'subject_id', 'out_dir', 'freesurfer_dir', 'func', 'rs_mag', 'rs_ph',
'anat_head', 'anat_brain', 'anat_brain_mask', 'wmseg', 'csfseg',
'vol_to_remove', 'TR', 'highpass_freq', 'epi_resolution', 'echo_space',
'te_diff', 'fwhm', 'pe_dir', 'composite_transform', 'standard_brain',
'standard_downsampled'
]),
name='inputnode')
#Use correct subject ID from long timepoint for bbregister
def change_subject_id(subject):
import re
[subj, ses] = re.split("_", subject)
new_subject_id = subject + '.long.' + subj
return new_subject_id
change_subject_id = Node(util.Function(input_names=["subject"],
output_names=["new_subject_id"],
function=change_subject_id),
name="change_subject_id")
outputnode = Node(util.IdentityInterface(fields=[
'brain', 'brainmask', 'anat2std_transforms', 'std2anat_transforms',
'anat2std', 'anat_head', 'wmseg', 'csfseg', 'wmedge', 'subject_id'
]),
name='outputnode')
##PREPROCESSING FOR AROMA (Steps 1 - 7)
def merge_if_list(in_file):
if type(in_file) == list:
import numpy as np
import nibabel as nb
import os
from nipype.utils.filemanip import split_filename
nii1 = nb.load(in_file[0])
nii1d = nii1.get_data()
nii2 = nb.load(in_file[1])
nii2d = nii2.get_data()
x = np.concatenate((nii1d, nii2d), axis=3)
new_nii = nb.Nifti1Image(x, nii1.get_affine(), nii1.get_header())
new_nii.set_data_dtype(np.float32)
_, base, _ = split_filename(in_file[0])
nb.save(new_nii, base + "_merged.nii.gz")
return os.path.abspath(base + "_merged.nii.gz")
else:
return in_file
#if rsfmri is a list -> merge files, otherwise return single list.
merge_rs = Node(util.Function(input_names=['in_file'],
output_names=["out_file"],
function=merge_if_list),
name='merge_rs')
# 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')
# workflow for motion correction
moco = create_moco_pipeline()
# workflow for fieldmap correction and coregistration
fmap_coreg = create_fmap_coreg_pipeline()
# workflow for applying transformations to timeseries
transform_ts = create_transform_pipeline()
#mean intensity normalization
meanintensnorm = Node(fsl.ImageMaths(op_string='-ing 10000'),
name='meanintensnorm')
smoothing = create_smoothing_pipeline()
# connections
func_preproc.connect([
(inputnode, merge_rs, [('func', 'in_file')]),
(merge_rs, remove_vol, [('out_file', 'in_file')]),
(inputnode, remove_vol, [('vol_to_remove', 't_min')]),
(inputnode, moco, [('anat_brain_mask', 'inputnode.brainmask')]),
(remove_vol, moco, [('out_file', 'inputnode.epi')]),
(inputnode, change_subject_id, [('subject_id', 'subject')]),
(change_subject_id, fmap_coreg, [('new_subject_id',
'inputnode.fs_subject_id')]),
(inputnode, fmap_coreg, [('rs_mag', 'inputnode.mag'),
('rs_ph', 'inputnode.phase'),
('freesurfer_dir',
'inputnode.fs_subjects_dir'),
('echo_space', 'inputnode.echo_space'),
('te_diff', 'inputnode.te_diff'),
('pe_dir', 'inputnode.pe_dir'),
('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')]),
(inputnode, transform_ts, [('anat_head', 'inputnode.anat_head')]),
(inputnode, transform_ts, [('anat_brain_mask', 'inputnode.brain_mask')
]),
(inputnode, transform_ts, [('epi_resolution', 'inputnode.resolution')
]),
(moco, transform_ts, [('outputnode.mat_moco', 'inputnode.mat_moco')]),
(fmap_coreg, transform_ts, [('outputnode.fmap_fullwarp',
'inputnode.fullwarp')]),
(transform_ts, meanintensnorm, [('outputnode.trans_ts', 'in_file')]),
(meanintensnorm, smoothing, [('out_file', 'inputnode.ts_transformed')
]),
(inputnode, smoothing, [('fwhm', 'inputnode.fwhm')])
])
##CALCULATE TRANSFORM from anatomical to standard space with FSL tools
# Anat > Standard
# register high-resolution to standard template with non-linear transform
# flirt serves as preparation for fnirt)
#reorient brain to standard (because Freesurfer space can cause problems)
reorient2std = Node(fsl.Reorient2Std(), name="reorient2std")
reorient2std_rs = Node(fsl.Reorient2Std(), name="reorient2std_rs")
reorient2std_mask = Node(fsl.Reorient2Std(), name="reorient2std_mask")
flirt_prep = Node(fsl.FLIRT(cost_func='mutualinfo', interp='trilinear'),
name='flirt_prep')
flirt_prep.inputs.interp = 'trilinear'
flirt_prep.inputs.dof = 12
fnirt = Node(fsl.FNIRT(), name='fnirt')
fnirt.inputs.field_file = True
fnirt.inputs.fieldcoeff_file = True
func_preproc.connect([
(inputnode, reorient2std, [('anat_brain', 'in_file')]),
(reorient2std, flirt_prep, [('out_file', 'in_file')]),
#(inputnode, flirt_prep, [('anat_brain', 'in_file')]),
(inputnode, flirt_prep, [('standard_brain', 'reference')]),
(flirt_prep, fnirt, [('out_matrix_file', 'affine_file')]),
(reorient2std, fnirt, [('out_file', 'in_file')]),
(inputnode, fnirt, [('standard_brain', 'ref_file')]),
])
def getcwd(subject_id):
import os
tmp = os.getcwd()
tmp = tmp[:-6]
tmp = tmp + 'ica_aroma/out' #%(subject_id)
return tmp
get_wd = Node(util.Function(input_names=['subject_id'],
output_names=["d"],
function=getcwd),
name='get_wd')
ica_aroma = Node(ICA_AROMA(), name="ica_aroma")
ica_aroma.inputs.denoise_type = 'both'
#ica_aroma.inputs.out_dir = os.getcwd()
func_preproc.connect([
(moco, ica_aroma, [('outputnode.par_moco', 'motion_parameters')]),
(smoothing, reorient2std_rs, [('outputnode.ts_smoothed', 'in_file')]),
(reorient2std_rs, ica_aroma, [('out_file', 'in_file')]),
(fnirt, ica_aroma, [('field_file', 'fnirt_warp_file')]),
(transform_ts, reorient2std_mask, [('outputnode.comb_mask_resamp',
'in_file')]),
(reorient2std_mask, ica_aroma, [('out_file', 'mask')]),
(inputnode, get_wd, [('subject_id', 'subject_id')]),
(get_wd, ica_aroma, [('d', 'out_dir')])
])
##POSTPROCESSING
postprocess = create_denoise_pipeline()
func_preproc.connect([
(reorient2std_mask, postprocess, [
('out_file', 'inputnode.brain_mask')
]), #use the correctly oriented mask
(ica_aroma, postprocess, [
('nonaggr_denoised_file', 'inputnode.epi_coreg')
]), #use the nonaggr_denoised_file
(inputnode, postprocess, [('TR', 'inputnode.tr')]),
(inputnode, postprocess, [('highpass_freq', 'inputnode.highpass_freq')
]),
(inputnode, postprocess, [('wmseg', 'inputnode.wmseg')]),
(inputnode, postprocess, [('csfseg', 'inputnode.csfseg')]),
])
#outputnode
outputnode = Node(util.IdentityInterface(fields=[
'par', 'rms', 'mean_epi', 'tsnr', 'stddev_file', 'realigned_ts',
'fmap', 'unwarped_mean_epi2fmap', 'coregistered_epi2fmap',
'fmap_fullwarp', 'epi2anat', 'epi2anat_mat', 'epi2anat_dat',
'epi2anat_mincost', 'full_transform_ts', 'full_transform_mean',
'resamp_t1', 'comb_mask_resamp', 'dvars_file', 'out_flirt_prep',
'out_matrix_flirt_prep', 'out_warped', 'out_warp_field',
'aggr_denoised_file', 'nonaggr_denoised_file', 'out_dir', 'wmcsf_mask',
'combined_motion', 'comp_regressor', 'comp_F', 'comp_pF', 'out_betas',
'ts_fullspectrum', 'ts_filtered'
]),
name='outputnode')
# connections
func_preproc.connect([
(
moco,
outputnode,
[ #('outputnode.epi_moco', 'realign.@realigned_ts'),
('outputnode.par_moco', 'par'),
('outputnode.rms_moco', 'rms'),
('outputnode.epi_moco', 'realigned_ts'),
('outputnode.epi_mean', 'mean_epi'),
('outputnode.tsnr_file', 'tsnr'),
('outputnode.stddev_file', 'stddev'),
]),
(fmap_coreg, outputnode,
[('outputnode.fmap', 'fmap'),
('outputnode.unwarped_mean_epi2fmap', 'unwarped_mean_epi2fmap'),
('outputnode.epi2fmap', 'coregistered_epi2fmap'),
('outputnode.fmap_fullwarp', 'fmap_fullwarp'),
('outputnode.epi2anat', 'epi2anat'),
('outputnode.epi2anat_mat', 'epi2anat_mat'),
('outputnode.epi2anat_dat', 'epi2anat_dat'),
('outputnode.epi2anat_mincost', 'epi2anat_mincost')]),
(transform_ts, outputnode,
[('outputnode.trans_ts', 'full_transform_ts'),
('outputnode.trans_ts_mean', 'full_transform_mean'),
('outputnode.resamp_t1', 'resamp_t1'),
('outputnode.comb_mask_resamp', 'comb_mask_resamp'),
('outputnode.out_dvars', 'dvars_file')]),
(flirt_prep, outputnode, [('out_file', 'out_flirt_prep'),
('out_matrix_file', 'out_matrix_flirt_prep')
]),
(fnirt, outputnode, [('warped_file', 'out_warped'),
('field_file', 'out_warp_field')]),
(ica_aroma, outputnode, [('aggr_denoised_file', 'aggr_denoised_file'),
('nonaggr_denoised_file',
'nonaggr_denoised_file'),
('out_dir', 'out_dir')]),
(postprocess, outputnode,
[('outputnode.wmcsf_mask', 'wmcsf_mask'),
('outputnode.combined_motion', 'combined_motion'),
('outputnode.comp_regressor', 'comp_regressor'),
('outputnode.comp_F', 'comp_F'), ('outputnode.comp_pF', 'comp_pF'),
('outputnode.out_betas', 'out_betas'),
('outputnode.ts_fullspectrum', 'ts_fullspectrum'),
('outputnode.ts_filtered', 'ts_filtered')])
])
return func_preproc
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, 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_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_aroma_prep(subject, working_dir, data_dir, freesurfer_dir, out_dir,
vol_to_remove, TR, epi_resolution, highpass, 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
aroma_prep = Workflow(name='aroma_prep')
aroma_prep.base_dir = working_dir
aroma_prep.config['execution'][
'crashdump_dir'] = aroma_prep.base_dir + "/crash_files"
#helper function to save array output of AROMA functions
def small_save(filename, in_data):
import numpy as np
np.save(filename, in_data)
return filename
# select files
templates = {
#'anat_head' : subject+'/preprocessed/mod/anat/T1.nii.gz',
'anat_brain': subject + '/preprocessed/mod/anat/brain.nii.gz',
'brain_mask': subject + '/preprocessed/mod/anat/T1_brain_mask.nii.gz',
'func': subject + '/func/EPI_t2.nii',
'mag': subject + '/unwarp/B0_mag.nii',
'phase': subject + '/unwarp/B0_ph.nii'
}
selectfiles = Node(nio.SelectFiles(templates, base_directory=data_dir),
name="selectfiles")
##################preprocessing############################################
# 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
aroma_prep.connect([(selectfiles, remove_vol, [('func', 'in_file')])])
#motion correction
moco = create_moco_pipeline()
#creating and applying the fieldmap
fmap_coreg = create_fmap_pipeline()
fmap_coreg.inputs.inputnode.echo_space = echo_space
fmap_coreg.inputs.inputnode.te_diff = te_diff
fmap_coreg.inputs.inputnode.pe_dir = pe_dir
aroma_prep.connect([
(selectfiles, fmap_coreg, [('mag', 'inputnode.mag')]),
(selectfiles, fmap_coreg, [('phase', 'inputnode.phase')]),
(moco, fmap_coreg, [('outputnode.epi_moco', 'inputnode.epi_coreg')]),
(moco, fmap_coreg, [('outputnode.epi_mean', 'inputnode.epi_mean')])
])
#reorient to std
reorient2std = Node(fsl.Reorient2Std(), name="reorient2std")
#mean intensity normalization
meanintensnorm = Node(fsl.ImageMaths(op_string='-ing 10000'),
name='meanintensnorm')
aroma_prep.connect([
#(unwarp, moco, [('warped_file', 'inputnode.epi')]),
(remove_vol, moco, [('out_file', 'inputnode.epi')]),
(selectfiles, reorient2std, [
('anat_brain', 'in_file')
]), #reorient to standard to avoid registration issues seen previously
(fmap_coreg, meanintensnorm, [('outputnode.unwarped_epi', 'in_file')])
])
#mask functional image
betfunctional = Node(fsl.BET(frac=0.3), name='betfunctional')
binmask = Node(fsl.ImageMaths(op_string='-bin'), name='binmask')
#smoothing (@6mm FWHM)
smoothing = create_smoothing_pipeline()
smoothing.inputs.inputnode.fwhm = fwhm_smoothing
aroma_prep.connect([(meanintensnorm, smoothing,
[('out_file', 'inputnode.ts_transformed')]),
(fmap_coreg, betfunctional,
[('outputnode.unwarped_mean_epi2fmap', 'in_file')]),
(betfunctional, binmask, [('out_file', 'in_file')])])
# Func > Anat
# register example func to high-resolution (use linear registration with 7 degrees of freedom and output
#matrix example_func2highres.mat
flirt = Node(fsl.FLIRT(cost_func='mutualinfo', interp='trilinear'),
name='flirt')
flirt.inputs.dof = 7
#
# Anat > Standard
# register high-resolution to standard template - ###flirt## (as preparation for fnirt)
flirt_prep = Node(fsl.FLIRT(cost_func='mutualinfo', interp='trilinear'),
name='flirt_prep')
flirt_prep.inputs.reference = standard_brain
flirt_prep.inputs.interp = 'trilinear'
flirt_prep.inputs.dof = 12
fnirt = Node(fsl.FNIRT(), name='fnirt')
fnirt.inputs.ref_file = standard_brain
fnirt.inputs.field_file = True
fnirt.inputs.fieldcoeff_file = True
aroma_prep.connect([
(reorient2std, flirt, [('out_file', 'reference')]),
(betfunctional, flirt, [('out_file', 'in_file')]),
(reorient2std, flirt_prep, [('out_file', 'in_file')]),
(flirt_prep, fnirt, [('out_matrix_file', 'affine_file')]),
(reorient2std, fnirt, [('out_file', 'in_file')]),
])
##################ICA-AROMA###############################################
#Step 1) MELODIC
runICA = Node(
name="runICA",
interface=Function(input_names=[
"fslDir", "outDir", "inFile", "melDirIn", "mask", "dim", "TR"
],
output_names=["mdir", 'melICmix', 'melodic_FTmix'],
function=aromafunc.runICA))
runICA.inputs.fslDir = os.path.join(os.environ["FSLDIR"], 'bin', '')
runICA.inputs.dim = 0 #automatically estimates network number using MDL
runICA.inputs.TR = 2.0
runICA.inputs.melDirIn = ""
runICA.inputs.outDir = out_dir
#Step 2) Automatic classification of the components
# - registering the spatial maps to MNI
regMNI = Node(name="regMNI",
interface=Function(input_names=[
"fslDir", "inFile", "outFile", "affmat", "warp"
],
output_names=['melodic_IC_MNI2mm'],
function=aromafunc.register2MNI))
regMNI.inputs.fslDir = os.path.join(os.environ["FSLDIR"], 'bin', '')
regMNI.inputs.outFile = out_dir + 'melodic.ica/melodic_IC_thr_MNI2mm.nii.gz'
#connect inputs to Melodic-ICA
aroma_prep.connect([
(binmask, runICA, [('out_file', 'mask')]),
(smoothing, runICA, [('outputnode.ts_smoothed', 'inFile')]),
#connect inputs to registration node
(runICA, regMNI, [('mdir', 'inFile')]),
(flirt, regMNI, [('out_matrix_file', 'affmat')]),
(fnirt, regMNI, [('fieldcoeff_file', 'warp')])
])
#extracting the Maximum RP correlation feature
feature_time_series = Node(name="feature_time_series",
interface=Function(
input_names=["melmix", "mc"],
output_names=["maxRPcorr"],
function=aromafunc.feature_time_series))
save_featts = Node(name="save_featts",
interface=Function(input_names=["filename", "in_data"],
output_names=["filename"],
function=small_save))
save_featts.inputs.filename = working_dir + '/aroma_prep/save_featts/maxRPcorr.npy'
aroma_prep.connect([(runICA, feature_time_series, [('melICmix', 'melmix')])
])
#connect moco to time_series features
aroma_prep.connect([
(moco, feature_time_series, [('outputnode.par_moco', 'mc')]),
(feature_time_series, save_featts, [("maxRPcorr", "in_data")])
])
#extracting the High-frequency content feature
feature_freq = Node(name="feature_freq",
interface=Function(
input_names=["melFTmix", "TR"],
output_names=["HFC"],
function=aromafunc.feature_frequency))
feature_freq.inputs.TR = 2.0
save_featfreq = Node(name="save_featfreq",
interface=Function(
input_names=["filename", "in_data"],
output_names=["filename"],
function=small_save))
save_featfreq.inputs.filename = working_dir + '/aroma_prep/feature_freq/HFC.npy'
aroma_prep.connect([(runICA, feature_freq, [('melodic_FTmix', 'melFTmix')
]),
(feature_freq, save_featfreq, [('HFC', 'in_data')])])
#extracting the CSF & Edge fraction features
feature_spatial = Node(
name="feature_spatial",
interface=Function(
input_names=["fslDir", "tempDir", "aromaDir", "melIC"],
output_names=["edgeFract", "csfFract"],
function=aromafunc.feature_spatial))
feature_spatial.inputs.fslDir = os.path.join(os.environ["FSLDIR"], 'bin',
'')
feature_spatial.inputs.tempDir = working_dir + "/aroma_prep/feature_spatial/"
feature_spatial.inputs.aromaDir = "/home/raid1/fbeyer/Documents/Scripts/ICA-AROMA/"
save_featsp_edge = Node(name="save_featsp_edge",
interface=Function(
input_names=["filename", "in_data"],
output_names=["filename"],
function=small_save))
save_featsp_edge.inputs.filename = working_dir + '/aroma_prep/feature_spatial/edge.npy'
save_featsp_csf = Node(name="save_featsp_csf",
interface=Function(
input_names=["filename", "in_data"],
output_names=["filename"],
function=small_save))
save_featsp_csf.inputs.filename = working_dir + '/aroma_prep/feature_spatial/csf.npy'
aroma_prep.connect([
(regMNI, feature_spatial, [('melodic_IC_MNI2mm', 'melIC')]),
(feature_spatial, save_featsp_edge, [('edgeFract', 'in_data')]),
(feature_spatial, save_featsp_csf, [('csfFract', 'in_data')])
])
#classification of features using predefined feature space
classification = Node(
name="classification",
interface=Function(input_names=[
"outDir", "maxRPcorr", "edgeFract", "HFC", "csfFract"
],
output_names=["motionICs"],
function=aromafunc.classification))
classification.inputs.outDir = out_dir + '/melodic.ica/'
save_class_mic = Node(name="save_class_mic",
interface=Function(
input_names=["filename", "in_data"],
output_names=["filename"],
function=small_save))
save_class_mic.inputs.filename = working_dir + '/aroma_prep/classification/motionICs.npy'
aroma_prep.connect([(classification, save_class_mic, [('motionICs',
'in_data')])])
#connections of classification with all features
aroma_prep.connect([
(feature_time_series, classification, [('maxRPcorr', 'maxRPcorr')]),
(feature_spatial, classification, [('edgeFract', 'edgeFract')]),
(feature_spatial, classification, [('csfFract', 'csfFract')]),
(feature_freq, classification, [('HFC', 'HFC')])
])
#Step 3) Data denoising' (using input data (=intensity normalized, motion corrected, smoothed fMRI in subject space))
denoising_ICA = Node(name="denoising_ICA",
interface=Function(input_names=[
"fslDir", "inFile", "outDir", "melmix", "denType",
"denIdx"
],
output_names=["denoised_func"],
function=aromafunc.denoising))
denoising_ICA.inputs.fslDir = os.path.join(os.environ["FSLDIR"], 'bin', '')
denoising_ICA.inputs.outDir = out_dir
denoising_ICA.inputs.melmix = out_dir + '/melodic.ica/melodic_mix'
denoising_ICA.inputs.denType = 2 #1=aggr, 2=non-aggr, 3=both
aroma_prep.connect([
(classification, denoising_ICA, [('motionICs', 'denIdx')]),
(smoothing, denoising_ICA, [('outputnode.ts_smoothed', 'inFile')])
])
##################post-processing###########################################
#WM/CSF/linear trend removal using regression of compcor-components
#highpass-filtering here!!
#registration of denoised image to MNI space
postprocess = create_denoise_pipeline()
postprocess.inputs.inputnode.highpass_sigma = 1. / (2 * TR * highpass)
# 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
postprocess.inputs.inputnode.tr = TR
aroma_prep.connect([
(binmask, postprocess, [('out_file', 'inputnode.brain_mask')]),
(denoising_ICA, postprocess, [('denoised_func', 'inputnode.epi_coreg')
]),
(moco, postprocess, [('outputnode.epi_mean', 'inputnode.unwarped_mean')
]),
(flirt, postprocess, [('out_matrix_file', 'inputnode.flirt_mat')]),
(reorient2std, postprocess, [('out_file', 'inputnode.anat_brain')])
])
#register only-AROMA denoised file into MNI space (without CSF/WM removal + highpass-filtering)
apply_TF = Node(fsl.ApplyWarp(), name="apply_TF")
apply_TF.inputs.ref_file = standard_brain
#register AROMA and CSF/WM denoised file into MNI space
apply_TF_denoised = Node(fsl.ApplyWarp(), name="apply_TF_denoised")
apply_TF_denoised.inputs.ref_file = standard_brain
aroma_prep.connect([
(postprocess, apply_TF_denoised, [
('outputnode.normalized_file', 'in_file')
]), #with bandpass-filtering
(flirt, apply_TF_denoised, [('out_matrix_file', 'premat')]),
(fnirt, apply_TF_denoised, [('fieldcoeff_file', 'field_file')]),
(denoising_ICA, apply_TF, [('denoised_func', 'in_file')]),
(flirt, apply_TF, [('out_matrix_file', 'premat')]),
(fnirt, apply_TF, [('fieldcoeff_file', 'field_file')]),
])
#sink to store files
sink = Node(nio.DataSink(
parameterization=False,
base_directory=out_dir,
substitutions=[
('brain_reoriented_warped.nii', 'brain2mni_warp_fnirt.nii'),
('brain_reoriented_flirt.nii', 'brain2mni_aff_flirt.nii'),
('brain_reoriented_fieldwarp.nii', 'brain2mni_warpcoeff_cout.nii'),
('brain_reoriented_field.nii', 'brain2mni_warpfield_fout.nii'),
('rest2anat_maths_smooth', 'func_preproc_smoothed'),
('rest_mean2fmap_unwarped_brain_maths', 'func_brain_mask'),
('denoised_func_data_nonaggr_warp', 'aroma_denoised_MNI'),
('rest_denoised_highpassed_norm_warp.nii',
'aroma_csfwm_denoised_MNI.nii'),
('rest2anat_denoised', 'rest_denoised_fullspectrum'),
('rest_denoised_highpassed_norm', 'rest_denoised_highpass'),
('wmcsf_mask_lowres_flirt', 'wmcsf_mask2epi'),
('brain2mni_aff_flirt', 'brain2epi')
]),
name='sink')
##all the output
aroma_prep.connect([
(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', 'fmap.transforms2anat.@fmap'),
('outputnode.unwarped_mean_epi2fmap',
'fmap.@unwarped_mean_epi2fmap'),
('outputnode.epi2fmap', 'fmap.@epi2fmap'),
('outputnode.unwarpfield_epi2fmap', 'fmap.@fmap_fullwarp'),
('outputnode.mean_from_unwarped_epi', 'fmap.@mean_from_unwarped')]),
(binmask, sink, [('out_file', 'aroma_inputs.@brainmask')]),
(smoothing, sink, [('outputnode.ts_smoothed', 'aroma_inputs.@FWHM6')]),
(flirt, sink, [('out_file', 'aroma_inputs.reg.affine.@result')]),
(flirt, sink, [('out_matrix_file', 'aroma_inputs.reg.affine.@matrix')
]),
(fnirt, sink, [('warped_file', 'aroma_inputs.reg.fnirt.@image')]),
(fnirt, sink, [('fieldcoeff_file',
'aroma_inputs.reg.fnirt.@fieldcoeff')]),
(fnirt, sink, [('field_file', 'aroma_inputs.reg.fnirt.@field')]),
(flirt_prep, sink, [('out_matrix_file', 'aroma_inputs.reg.fnirt.@mat')
]),
(flirt_prep, sink, [('out_file', 'aroma_inputs.reg.fnirt.@resultflirt')
]),
(save_featts, sink, [('filename', 'aroma_res.features.@rescorr')]),
(save_featsp_edge, sink, [('filename', 'aroma_res.features.@edge')]),
(save_featsp_csf, sink, [('filename', 'aroma_res.features.@csf')]),
(save_featfreq, sink, [('filename', 'aroma_res.features.@HFC')]),
(denoising_ICA, sink, [('denoised_func', 'aroma_res.@denoised_func')]),
(
postprocess,
sink,
[
('outputnode.wmcsf_mask', 'denoise.mask.@wmcsf_masks'),
('outputnode.combined_motion',
'denoise.artefact.@combined_motion'),
('outputnode.comp_regressor',
'denoise.regress.@comp_regressor'),
('outputnode.comp_F', 'denoise.regress.@comp_F'),
('outputnode.comp_pF', 'denoise.regress.@comp_pF'),
('outputnode.brain2epi', 'denoise.mask.@brain2epi'),
('outputnode.wmcsf_mask2epi', 'denoise.mask.@wmcsf_mask2epi'),
('outputnode.normalized_file', 'denoise.@normalized'),
# FL added fullspectrum
('outputnode.ts_fullspectrum', 'denoise.@ts_fullspectrum')
]),
(apply_TF, sink, [
('out_file', 'transforms.withoutCSFWM.@denoised_result_MNI')
]), #with only motion
(apply_TF_denoised, sink, [
('out_file', 'transforms.withCSFWM.@denoised_result_MNI')
]) #with motion, compcor CSF/WM + highpass
])
aroma_prep.write_graph(dotfilename='aroma_prep.dot',
graph2use='colored',
format='pdf',
simple_form=True)
aroma_prep.run(
plugin='MultiProc'
) #plugin='CondorDAGMan', plugin_args={'initial_specs': 'request_memory = 1500'}
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')
