Exemplo n.º 1
0
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()
Exemplo n.º 2
0
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
Exemplo n.º 3
0
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'})
Exemplo n.º 4
0
def create_resting():

    # main workflow
    func_preproc = Workflow(name='resting')

    inputnode = Node(util.IdentityInterface(fields=[
        'subject', 'out_dir', 'freesurfer_dir', 'func', 'fmap_mag',
        'fmap_phase', 'anat_head', 'anat_brain', 'anat_brain_mask',
        'vol_to_remove', 'TR', 'epi_resolution', 'echo_space', 'te_diff',
        'pe_dir'
    ]),
                     name='inputnode')

    # 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
    #have to rename subject for fu
    def rename_subject_for_fu(input_id):
        output_id = input_id + "_fu"
        return output_id

    #modify subject name so it can be saved in the same folder as other LIFE- freesurfer data
    rename = Node(util.Function(input_names=['input_id'],
                                output_names=['output_id'],
                                function=rename_subject_for_fu),
                  name="rename")

    fmap_coreg = create_fmap_coreg_pipeline()

    # workflow for applying transformations to timeseries
    transform_ts = create_transform_pipeline()

    #detrending
    detrend = Node(afni.Detrend(), name="detrend")
    detrend.inputs.args = '-polort 2'
    detrend.inputs.outputtype = "NIFTI"

    #outputnode
    outputnode = Node(util.IdentityInterface(fields=[
        'par', 'rms', 'mean_epi', 'tsnr', 'fmap', 'unwarped_mean_epi2fmap',
        'coregistered_epi2fmap', 'fmap_fullwarp', 'epi2anat', 'epi2anat_mat',
        'epi2anat_dat', 'epi2anat_mincost', 'full_transform_ts',
        'full_transform_mean', 'resamp_brain', 'detrended_epi'
    ]),
                      name='outputnode')

    # connections
    func_preproc.connect([

        #remove the first volumes
        (inputnode, remove_vol, [('func', 'in_file')]),
        (inputnode, remove_vol, [('vol_to_remove', 't_min')]),
        #align volumes and motion correction
        (remove_vol, moco, [('out_file', 'inputnode.epi')]),

        #prepare field map
        (inputnode, rename, [('subject', 'input_id')]),
        (rename, fmap_coreg, [('output_id', 'inputnode.fs_subject_id')]),
        (inputnode, fmap_coreg, [('fmap_phase', 'inputnode.phase'),
                                 ('freesurfer_dir',
                                  'inputnode.fs_subjects_dir'),
                                 ('echo_space', 'inputnode.echo_space'),
                                 ('te_diff', 'inputnode.te_diff'),
                                 ('pe_dir', 'inputnode.pe_dir'),
                                 ('fmap_mag', 'inputnode.mag'),
                                 ('anat_head', 'inputnode.anat_head'),
                                 ('anat_brain', 'inputnode.anat_brain')]),
        (moco, fmap_coreg, [('outputnode.epi_mean', 'inputnode.epi_mean')]),
        #transform ts
        (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, detrend, [('outputnode.trans_ts', 'in_file')]),
        ##all the output
        (
            moco,
            outputnode,
            [  #('outputnode.epi_moco', 'realign.@realigned_ts'),
                ('outputnode.par_moco', 'par'), ('outputnode.rms_moco', 'rms'),
                ('outputnode.epi_mean', 'mean_epi'),
                ('outputnode.tsnr_file', 'tsnr')
            ]),
        (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_brain', 'resamp_brain')]),
        (detrend, outputnode, [('out_file', 'detrended_epi')])
    ])

    return func_preproc
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()
Exemplo n.º 6
0
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')