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# rsfMRI: ANTS, FS, FSL, SPM, aCompCor

A preprocessing workflow for Siemens resting state data.

This workflow makes use of:

- ANTS
- FreeSurfer
- FSL
- SPM
- CompCor

For example:

    python rsfmri_preprocessing.py -d /data/12345-34-1.dcm -f /data/Resting.nii
        -s subj001 -o output -p PBS --plugin_args "dict(qsub_args='-q many')"

    or

    python rsfmri_vol_surface_preprocessing.py -f SUB_1024011/E?/func/rest.nii
        -t OASIS-30_Atropos_template_in_MNI152_2mm.nii.gz --TR 2 -s SUB_1024011
        --subjects_dir fsdata --slice_times 0 17 1 18 2 19 3 20 4 21 5 22 6 23
        7 24 8 25 9 26 10 27 11 28 12 29 13 30 14 31 15 32 16 -o .

This workflow takes resting timeseries and a Siemens dicom file corresponding to it and preprocesses it to produce timeseries coordinates or grayordinates.

This workflow also requires 2mm subcortical atlas and templates that are available from:

http://mindboggle.info/data.html

specifically the 2mm versions of:

- Joint Fusion Atlas
- MNI template

    import os

    from nipype.interfaces.base import CommandLine
    CommandLine.set_default_terminal_output('allatonce')

    from dicom import read_file

    from nipype.interfaces import (spm, fsl, Function, ants, freesurfer)
    from nipype.interfaces.c3 import C3dAffineTool

    fsl.FSLCommand.set_default_output_type('NIFTI')

    from nipype import Workflow, Node, MapNode
    from nipype.interfaces import matlab as mlab

    mlab.MatlabCommand.set_default_matlab_cmd("matlab -nodisplay")
    # If SPM is not in your MATLAB path you should add it here
    # mlab.MatlabCommand.set_default_paths('/software/matlab/spm12')

    from nipype.algorithms.rapidart import ArtifactDetect
    from nipype.algorithms.misc import TSNR
    from nipype.interfaces.utility import Rename, Merge, IdentityInterface
    from nipype.utils.filemanip import filename_to_list
    from nipype.interfaces.io import DataSink, FreeSurferSource

    import numpy as np
    import scipy as sp
    import nibabel as nb

    imports = ['import os',
               'import nibabel as nb',
               'import numpy as np',
               'import scipy as sp',
               'from nipype.utils.filemanip import filename_to_list, list_to_filename, split_filename',
               'from scipy.special import legendre'
               ]

    def get_info(dicom_files):
        from dcmstack.extract import default_extractor
        """Given a Siemens dicom file return metadata

        Returns
        -------
        RepetitionTime
        Slice Acquisition Times
        Spacing between slices
        """
        meta = default_extractor(read_file(filename_to_list(dicom_files)[0],
                                           stop_before_pixels=True,
                                           force=True))
        return (meta['RepetitionTime']/1000., meta['CsaImage.MosaicRefAcqTimes'],
                meta['SpacingBetweenSlices'])

    def median(in_files):
        """Computes an average of the median of each realigned timeseries

        Parameters
        ----------

        in_files: one or more realigned Nifti 4D time series

        Returns
        -------

        out_file: a 3D Nifti file
        """
        import numpy as np
        import nibabel as nb
        average = None
        for idx, filename in enumerate(filename_to_list(in_files)):
            img = nb.load(filename)
            data = np.median(img.get_data(), axis=3)
            if average is None:
                average = data
            else:
                average = average + data
        median_img = nb.Nifti1Image(average/float(idx + 1),
                                    img.get_affine(), img.get_header())
        filename = os.path.join(os.getcwd(), 'median.nii.gz')
        median_img.to_filename(filename)
        return filename

    def bandpass_filter(files, lowpass_freq, highpass_freq, fs):
        """Bandpass filter the input files

        Parameters
        ----------
        files: list of 4d nifti files
        lowpass_freq: cutoff frequency for the low pass filter (in Hz)
        highpass_freq: cutoff frequency for the high pass filter (in Hz)
        fs: sampling rate (in Hz)
        """
        from nipype.utils.filemanip import split_filename, list_to_filename
        import numpy as np
        import nibabel as nb
        out_files = []
        for filename in filename_to_list(files):
            path, name, ext = split_filename(filename)
            out_file = os.path.join(os.getcwd(), name + '_bp' + ext)
            img = nb.load(filename)
            timepoints = img.shape[-1]
            F = np.zeros((timepoints))
            lowidx = timepoints/2 + 1
            if lowpass_freq > 0:
                lowidx = np.round(lowpass_freq / fs * timepoints)
            highidx = 0
            if highpass_freq > 0:
                highidx = np.round(highpass_freq / fs * timepoints)
            F[highidx:lowidx] = 1
            F = ((F + F[::-1]) > 0).astype(int)
            data = img.get_data()
            if np.all(F == 1):
                filtered_data = data
            else:
                filtered_data = np.real(np.fft.ifftn(np.fft.fftn(data) * F))
            img_out = nb.Nifti1Image(filtered_data, img.get_affine(),
                                     img.get_header())
            img_out.to_filename(out_file)
            out_files.append(out_file)
        return list_to_filename(out_files)

    def motion_regressors(motion_params, order=0, derivatives=1):
        """Compute motion regressors upto given order and derivative

        motion + d(motion)/dt + d2(motion)/dt2 (linear + quadratic)
        """
        import numpy as np
        out_files = []
        for idx, filename in enumerate(filename_to_list(motion_params)):
            params = np.genfromtxt(filename)
            out_params = params
            for d in range(1, derivatives + 1):
                cparams = np.vstack((np.repeat(params[0, :][None, :], d, axis=0),
                                     params))
                out_params = np.hstack((out_params, np.diff(cparams, d, axis=0)))
            out_params2 = out_params
            for i in range(2, order + 1):
                out_params2 = np.hstack((out_params2, np.power(out_params, i)))
            filename = os.path.join(os.getcwd(), "motion_regressor%02d.txt" % idx)
            np.savetxt(filename, out_params2, fmt="%.10f")
            out_files.append(filename)
        return out_files

    def build_filter1(motion_params, comp_norm, outliers, detrend_poly=None):
        """Builds a regressor set comprisong motion parameters, composite norm and
        outliers

        The outliers are added as a single time point column for each outlier

        Parameters
        ----------

        motion_params: a text file containing motion parameters and its derivatives
        comp_norm: a text file containing the composite norm
        outliers: a text file containing 0-based outlier indices
        detrend_poly: number of polynomials to add to detrend

        Returns
        -------
        components_file: a text file containing all the regressors
        """
        import numpy as np
        import nibabel as nb
        from scipy.special import legendre
        out_files = []
        for idx, filename in enumerate(filename_to_list(motion_params)):
            params = np.genfromtxt(filename)
            norm_val = np.genfromtxt(filename_to_list(comp_norm)[idx])
            out_params = np.hstack((params, norm_val[:, None]))
            try:
                outlier_val = np.genfromtxt(filename_to_list(outliers)[idx])
            except IOError:
                outlier_val = np.empty((0))
            for index in np.atleast_1d(outlier_val):
                outlier_vector = np.zeros((out_params.shape[0], 1))
                outlier_vector[index] = 1
                out_params = np.hstack((out_params, outlier_vector))
            if detrend_poly:
                timepoints = out_params.shape[0]
                X = np.ones((timepoints, 1))
                for i in range(detrend_poly):
                    X = np.hstack((X, legendre(
                        i + 1)(np.linspace(-1, 1, timepoints))[:, None]))
                out_params = np.hstack((out_params, X))
            filename = os.path.join(os.getcwd(), "filter_regressor%02d.txt" % idx)
            np.savetxt(filename, out_params, fmt="%.10f")
            out_files.append(filename)
        return out_files

    def extract_noise_components(realigned_file, mask_file, num_components=5,
                                 extra_regressors=None):
        """Derive components most reflective of physiological noise

        Parameters
        ----------
        realigned_file: a 4D Nifti file containing realigned volumes
        mask_file: a 3D Nifti file containing white matter + ventricular masks
        num_components: number of components to use for noise decomposition
        extra_regressors: additional regressors to add

        Returns
        -------
        components_file: a text file containing the noise components
        """
        from scipy.linalg.decomp_svd import svd
        import numpy as np
        import nibabel as nb
        import os
        imgseries = nb.load(realigned_file)
        components = None
        for filename in filename_to_list(mask_file):
            mask = nb.load(filename).get_data()
            if len(np.nonzero(mask > 0)[0]) == 0:
                continue
            voxel_timecourses = imgseries.get_data()[mask > 0]
            voxel_timecourses[np.isnan(np.sum(voxel_timecourses, axis=1)), :] = 0
            # remove mean and normalize by variance
            # voxel_timecourses.shape == [nvoxels, time]
            X = voxel_timecourses.T
            stdX = np.std(X, axis=0)
            stdX[stdX == 0] = 1.
            stdX[np.isnan(stdX)] = 1.
            stdX[np.isinf(stdX)] = 1.
            X = (X - np.mean(X, axis=0))/stdX
            u, _, _ = svd(X, full_matrices=False)
            if components is None:
                components = u[:, :num_components]
            else:
                components = np.hstack((components, u[:, :num_components]))
        if extra_regressors:
            regressors = np.genfromtxt(extra_regressors)
            components = np.hstack((components, regressors))
        components_file = os.path.join(os.getcwd(), 'noise_components.txt')
        np.savetxt(components_file, components, fmt="%.10f")
        return components_file

    def rename(in_files, suffix=None):
        from nipype.utils.filemanip import (filename_to_list, split_filename,
                                            list_to_filename)
        out_files = []
        for idx, filename in enumerate(filename_to_list(in_files)):
            _, name, ext = split_filename(filename)
            if suffix is None:
                out_files.append(name + ('_%03d' % idx) + ext)
            else:
                out_files.append(name + suffix + ext)
        return list_to_filename(out_files)

    def get_aparc_aseg(files):
        """Return the aparc+aseg.mgz file"""
        for name in files:
            if 'aparc+aseg.mgz' in name:
                return name
        raise ValueError('aparc+aseg.mgz not found')

    def extract_subrois(timeseries_file, label_file, indices):
        """Extract voxel time courses for each subcortical roi index

        Parameters
        ----------

        timeseries_file: a 4D Nifti file
        label_file: a 3D file containing rois in the same space/size of the 4D file
        indices: a list of indices for ROIs to extract.

        Returns
        -------
        out_file: a text file containing time courses for each voxel of each roi
            The first four columns are: freesurfer index, i, j, k positions in the
            label file
        """
        from nipype.utils.filemanip import split_filename
        import nibabel as nb
        import os
        img = nb.load(timeseries_file)
        data = img.get_data()
        roiimg = nb.load(label_file)
        rois = roiimg.get_data()
        prefix = split_filename(timeseries_file)[1]
        out_ts_file = os.path.join(os.getcwd(), '%s_subcortical_ts.txt' % prefix)
        with open(out_ts_file, 'wt') as fp:
            for fsindex in indices:
                ijk = np.nonzero(rois == fsindex)
                ts = data[ijk]
                for i0, row in enumerate(ts):
                    fp.write('%d,%d,%d,%d,' % (fsindex, ijk[0][i0],
                                               ijk[1][i0], ijk[2][i0]) +
                             ','.join(['%.10f' % val for val in row]) + '\n')
        return out_ts_file

    def combine_hemi(left, right):
        """Combine left and right hemisphere time series into a single text file
        """
        import os
        import numpy as np
        lh_data = nb.load(left).get_data()
        rh_data = nb.load(right).get_data()

        indices = np.vstack((1000000 + np.arange(0, lh_data.shape[0])[:, None],
                             2000000 + np.arange(0, rh_data.shape[0])[:, None]))
        all_data = np.hstack((indices, np.vstack((lh_data.squeeze(),
                                                  rh_data.squeeze()))))
        filename = left.split('.')[1] + '_combined.txt'
        np.savetxt(filename, all_data,
                   fmt=','.join(['%d'] + ['%.10f'] * (all_data.shape[1] - 1)))
        return os.path.abspath(filename)

    def create_reg_workflow(name='registration'):
        """Create a FEAT preprocessing workflow together with freesurfer

        Parameters
        ----------

        ::

            name : name of workflow (default: 'registration')

        Inputs::

            inputspec.source_files : files (filename or list of filenames to register)
            inputspec.mean_image : reference image to use
            inputspec.anatomical_image : anatomical image to coregister to
            inputspec.target_image : registration target

        Outputs::

            outputspec.func2anat_transform : FLIRT transform
            outputspec.anat2target_transform : FLIRT+FNIRT transform
            outputspec.transformed_files : transformed files in target space
            outputspec.transformed_mean : mean image in target space
        """

        register = Workflow(name=name)

        inputnode = Node(interface=IdentityInterface(fields=['source_files',
                                                             'mean_image',
                                                             'subject_id',
                                                             'subjects_dir',
                                                             'target_image']),
                         name='inputspec')

        outputnode = Node(interface=IdentityInterface(fields=['func2anat_transform',
                                                              'out_reg_file',
                                                              'anat2target_transform',
                                                              'transforms',
                                                              'transformed_mean',
                                                              'segmentation_files',
                                                              'anat2target',
                                                              'aparc'
                                                              ]),
                          name='outputspec')

        # Get the subject's freesurfer source directory
        fssource = Node(FreeSurferSource(),
                        name='fssource')
        fssource.run_without_submitting = True
        register.connect(inputnode, 'subject_id', fssource, 'subject_id')
        register.connect(inputnode, 'subjects_dir', fssource, 'subjects_dir')

        convert = Node(freesurfer.MRIConvert(out_type='nii'),
                       name="convert")
        register.connect(fssource, 'T1', convert, 'in_file')

        # Coregister the median to the surface
        bbregister = Node(freesurfer.BBRegister(),
                        name='bbregister')
        bbregister.inputs.init = 'fsl'
        bbregister.inputs.contrast_type = 't2'
        bbregister.inputs.out_fsl_file = True
        bbregister.inputs.epi_mask = True
        register.connect(inputnode, 'subject_id', bbregister, 'subject_id')
        register.connect(inputnode, 'mean_image', bbregister, 'source_file')
        register.connect(inputnode, 'subjects_dir', bbregister, 'subjects_dir')

Estimate the tissue classes from the anatomical image. But use spm’s segment as FSL appears to be breaking.

    stripper = Node(fsl.BET(), name='stripper')
    register.connect(convert, 'out_file', stripper, 'in_file')
    fast = Node(fsl.FAST(), name='fast')
    register.connect(stripper, 'out_file', fast, 'in_files')

Binarize the segmentation

    binarize = MapNode(fsl.ImageMaths(op_string='-nan -thr 0.9 -ero -bin'),
                       iterfield=['in_file'],
                       name='binarize')
    register.connect(fast, 'partial_volume_files', binarize, 'in_file')

Apply inverse transform to take segmentations to functional space

    applyxfm = MapNode(freesurfer.ApplyVolTransform(inverse=True,
                                                    interp='nearest'),
                       iterfield=['target_file'],
                       name='inverse_transform')
    register.connect(inputnode, 'subjects_dir', applyxfm, 'subjects_dir')
    register.connect(bbregister, 'out_reg_file', applyxfm, 'reg_file')
    register.connect(binarize, 'out_file', applyxfm, 'target_file')
    register.connect(inputnode, 'mean_image', applyxfm, 'source_file')

Apply inverse transform to aparc file

    aparcxfm = Node(freesurfer.ApplyVolTransform(inverse=True,
                                                 interp='nearest'),
                    name='aparc_inverse_transform')
    register.connect(inputnode, 'subjects_dir', aparcxfm, 'subjects_dir')
    register.connect(bbregister, 'out_reg_file', aparcxfm, 'reg_file')
    register.connect(fssource, ('aparc_aseg', get_aparc_aseg),
                     aparcxfm, 'target_file')
    register.connect(inputnode, 'mean_image', aparcxfm, 'source_file')

Convert the BBRegister transformation to ANTS ITK format

    convert2itk = Node(C3dAffineTool(), name='convert2itk')
    convert2itk.inputs.fsl2ras = True
    convert2itk.inputs.itk_transform = True
    register.connect(bbregister, 'out_fsl_file', convert2itk, 'transform_file')
    register.connect(inputnode, 'mean_image',convert2itk, 'source_file')
    register.connect(stripper, 'out_file', convert2itk, 'reference_file')

Compute registration between the subject’s structural and MNI template This is currently set to perform a very quick registration. However, the registration can be made significantly more accurate for cortical structures by increasing the number of iterations All parameters are set using the example from: #https://github.com/stnava/ANTs/blob/master/Scripts/newAntsExample.sh

    reg = Node(ants.Registration(), name='antsRegister')
    reg.inputs.output_transform_prefix = "output_"
    reg.inputs.transforms = ['Rigid', 'Affine', 'SyN']
    reg.inputs.transform_parameters = [(0.1,), (0.1,), (0.2, 3.0, 0.0)]
    reg.inputs.number_of_iterations = [[10000, 11110, 11110]] * 2 + [[100, 30, 20]]
    reg.inputs.dimension = 3
    reg.inputs.write_composite_transform = True
    reg.inputs.collapse_output_transforms = True
    reg.inputs.initial_moving_transform_com = True
    reg.inputs.metric = ['Mattes'] * 2 + [['Mattes', 'CC']]
    reg.inputs.metric_weight = [1] * 2 + [[0.5, 0.5]]
    reg.inputs.radius_or_number_of_bins = [32] * 2 + [[32, 4]]
    reg.inputs.sampling_strategy = ['Regular'] * 2 + [[None, None]]
    reg.inputs.sampling_percentage = [0.3] * 2 + [[None, None]]
    reg.inputs.convergence_threshold = [1.e-8] * 2 + [-0.01]
    reg.inputs.convergence_window_size = [20] * 2 + [5]
    reg.inputs.smoothing_sigmas = [[4, 2, 1]] * 2 + [[1, 0.5, 0]]
    reg.inputs.sigma_units = ['vox'] * 3
    reg.inputs.shrink_factors = [[3, 2, 1]]*2 + [[4, 2, 1]]
    reg.inputs.use_estimate_learning_rate_once = [True] * 3
    reg.inputs.use_histogram_matching = [False] * 2 + [True]
    reg.inputs.winsorize_lower_quantile = 0.005
    reg.inputs.winsorize_upper_quantile = 0.995
    reg.inputs.args = '--float'
    reg.inputs.output_warped_image = 'output_warped_image.nii.gz'
    reg.inputs.num_threads = 4
    reg.plugin_args = {'qsub_args': '-l nodes=1:ppn=4'}
    register.connect(stripper, 'out_file', reg, 'moving_image')
    register.connect(inputnode,'target_image', reg,'fixed_image')

Concatenate the affine and ants transforms into a list

    pickfirst = lambda x: x[0]

    merge = Node(Merge(2), iterfield=['in2'], name='mergexfm')
    register.connect(convert2itk, 'itk_transform', merge, 'in2')
    register.connect(reg, ('composite_transform', pickfirst), merge, 'in1')

Transform the mean image. First to anatomical and then to target

    warpmean = Node(ants.ApplyTransforms(), name='warpmean')
    warpmean.inputs.input_image_type = 3
    warpmean.inputs.interpolation = 'BSpline'
    warpmean.inputs.invert_transform_flags = [False, False]
    warpmean.inputs.terminal_output = 'file'
    warpmean.inputs.args = '--float'
    warpmean.inputs.num_threads = 4

    register.connect(inputnode,'target_image', warpmean,'reference_image')
    register.connect(inputnode, 'mean_image', warpmean, 'input_image')
    register.connect(merge, 'out', warpmean, 'transforms')

Assign all the output files

    register.connect(reg, 'warped_image', outputnode, 'anat2target')
    register.connect(warpmean, 'output_image', outputnode, 'transformed_mean')
    register.connect(applyxfm, 'transformed_file',
                     outputnode, 'segmentation_files')
    register.connect(aparcxfm, 'transformed_file',
                     outputnode, 'aparc')
    register.connect(bbregister, 'out_fsl_file',
                     outputnode, 'func2anat_transform')
    register.connect(bbregister, 'out_reg_file',
                     outputnode, 'out_reg_file')
    register.connect(reg, 'composite_transform',
                     outputnode, 'anat2target_transform')
    register.connect(merge, 'out', outputnode, 'transforms')

    return register

Creates the main preprocessing workflow

    def create_workflow(files,
                        target_file,
                        subject_id,
                        TR,
                        slice_times,
                        norm_threshold=1,
                        num_components=5,
                        vol_fwhm=None,
                        surf_fwhm=None,
                        lowpass_freq=-1,
                        highpass_freq=-1,
                        subjects_dir=None,
                        sink_directory=os.getcwd(),
                        target_subject=['fsaverage3', 'fsaverage4'],
                        name='resting'):

        wf = Workflow(name=name)

        # Rename files in case they are named identically
        name_unique = MapNode(Rename(format_string='rest_%(run)02d'),
                              iterfield=['in_file', 'run'],
                              name='rename')
        name_unique.inputs.keep_ext = True
        name_unique.inputs.run = range(1, len(files) + 1)
        name_unique.inputs.in_file = files

        realign = Node(interface=spm.Realign(), name="realign")
        realign.inputs.jobtype = 'estwrite'

        num_slices = len(slice_times)
        slice_timing = Node(interface=spm.SliceTiming(), name="slice_timing")
        slice_timing.inputs.num_slices = num_slices
        slice_timing.inputs.time_repetition = TR
        slice_timing.inputs.time_acquisition = TR - TR/float(num_slices)
        slice_timing.inputs.slice_order = (np.argsort(slice_times) + 1).tolist()
        slice_timing.inputs.ref_slice = int(num_slices/2)

        # Comute TSNR on realigned data regressing polynomials upto order 2
        tsnr = MapNode(TSNR(regress_poly=2), iterfield=['in_file'], name='tsnr')
        wf.connect(slice_timing, 'timecorrected_files', tsnr, 'in_file')

        # Compute the median image across runs
        calc_median = Node(Function(input_names=['in_files'],
                                    output_names=['median_file'],
                                    function=median,
                                    imports=imports),
                           name='median')
        wf.connect(tsnr, 'detrended_file', calc_median, 'in_files')

Segment and Register

    registration = create_reg_workflow(name='registration')
    wf.connect(calc_median, 'median_file', registration, 'inputspec.mean_image')
    registration.inputs.inputspec.subject_id = subject_id
    registration.inputs.inputspec.subjects_dir = subjects_dir
    registration.inputs.inputspec.target_image = target_file

Use `nipype.algorithms.rapidart` to determine which of the images in the functional series are outliers based on deviations in intensity or movement.

    art = Node(interface=ArtifactDetect(), name="art")
    art.inputs.use_differences = [True, True]
    art.inputs.use_norm = True
    art.inputs.norm_threshold = norm_threshold
    art.inputs.zintensity_threshold = 9
    art.inputs.mask_type = 'spm_global'
    art.inputs.parameter_source = 'SPM'

Here we are connecting all the nodes together. Notice that we add the merge node only if you choose to use 4D. Also get_vox_dims function is passed along the input volume of normalise to set the optimal voxel sizes.

    wf.connect([(name_unique, realign, [('out_file', 'in_files')]),
                (realign, slice_timing, [('realigned_files', 'in_files')]),
                (slice_timing, art, [('timecorrected_files', 'realigned_files')]),
                (realign, art, [('realignment_parameters', 'realignment_parameters')]),
                ])

    def selectindex(files, idx):
        import numpy as np
        from nipype.utils.filemanip import filename_to_list, list_to_filename
        return list_to_filename(np.array(filename_to_list(files))[idx].tolist())

    mask = Node(fsl.BET(), name='getmask')
    mask.inputs.mask = True
    wf.connect(calc_median, 'median_file', mask, 'in_file')
    # get segmentation in normalized functional space

    def merge_files(in1, in2):
        out_files = filename_to_list(in1)
        out_files.extend(filename_to_list(in2))
        return out_files

    # filter some noise

    # Compute motion regressors
    motreg = Node(Function(input_names=['motion_params', 'order',
                                        'derivatives'],
                           output_names=['out_files'],
                           function=motion_regressors,
                           imports=imports),
                  name='getmotionregress')
    wf.connect(realign, 'realignment_parameters', motreg, 'motion_params')

    # Create a filter to remove motion and art confounds
    createfilter1 = Node(Function(input_names=['motion_params', 'comp_norm',
                                               'outliers', 'detrend_poly'],
                                  output_names=['out_files'],
                                  function=build_filter1,
                                  imports=imports),
                         name='makemotionbasedfilter')
    createfilter1.inputs.detrend_poly = 2
    wf.connect(motreg, 'out_files', createfilter1, 'motion_params')
    wf.connect(art, 'norm_files', createfilter1, 'comp_norm')
    wf.connect(art, 'outlier_files', createfilter1, 'outliers')

    filter1 = MapNode(fsl.GLM(out_f_name='F_mcart.nii',
                              out_pf_name='pF_mcart.nii',
                              demean=True),
                      iterfield=['in_file', 'design', 'out_res_name'],
                      name='filtermotion')

    wf.connect(slice_timing, 'timecorrected_files', filter1, 'in_file')
    wf.connect(slice_timing, ('timecorrected_files', rename, '_filtermotart'),
               filter1, 'out_res_name')
    wf.connect(createfilter1, 'out_files', filter1, 'design')

    createfilter2 = MapNode(Function(input_names=['realigned_file', 'mask_file',
                                                  'num_components',
                                                  'extra_regressors'],
                                     output_names=['out_files'],
                                     function=extract_noise_components,
                                     imports=imports),
                            iterfield=['realigned_file', 'extra_regressors'],
                            name='makecompcorrfilter')
    createfilter2.inputs.num_components = num_components

    wf.connect(createfilter1, 'out_files', createfilter2, 'extra_regressors')
    wf.connect(filter1, 'out_res', createfilter2, 'realigned_file')
    wf.connect(registration, ('outputspec.segmentation_files', selectindex, [0, 2]),
               createfilter2, 'mask_file')

    filter2 = MapNode(fsl.GLM(out_f_name='F.nii',
                              out_pf_name='pF.nii',
                              demean=True),
                      iterfield=['in_file', 'design', 'out_res_name'],
                      name='filter_noise_nosmooth')
    wf.connect(filter1, 'out_res', filter2, 'in_file')
    wf.connect(filter1, ('out_res', rename, '_cleaned'),
               filter2, 'out_res_name')
    wf.connect(createfilter2, 'out_files', filter2, 'design')
    wf.connect(mask, 'mask_file', filter2, 'mask')

    bandpass = Node(Function(input_names=['files', 'lowpass_freq',
                                           'highpass_freq', 'fs'],
                              output_names=['out_files'],
                              function=bandpass_filter,
                              imports=imports),
                     name='bandpass_unsmooth')
    bandpass.inputs.fs = 1./TR
    bandpass.inputs.highpass_freq = highpass_freq
    bandpass.inputs.lowpass_freq = lowpass_freq
    wf.connect(filter2, 'out_res', bandpass, 'files')

Smooth the functional data using `nipype.interfaces.spm.Smooth`.

    smooth = Node(interface=spm.Smooth(), name="smooth")
    smooth.inputs.fwhm = vol_fwhm

    wf.connect(bandpass, 'out_files', smooth, 'in_files')

    collector = Node(Merge(2), name='collect_streams')
    wf.connect(smooth, 'smoothed_files', collector, 'in1')
    wf.connect(bandpass, 'out_files', collector, 'in2')

Transform the remaining images. First to anatomical and then to target

    warpall = MapNode(ants.ApplyTransforms(), iterfield=['input_image'],
                      name='warpall')
    warpall.inputs.input_image_type = 3
    warpall.inputs.interpolation = 'BSpline'
    warpall.inputs.invert_transform_flags = [False, False]
    warpall.inputs.terminal_output = 'file'
    warpall.inputs.reference_image = target_file
    warpall.inputs.args = '--float'
    warpall.inputs.num_threads = 1

    # transform to target
    wf.connect(collector, 'out', warpall, 'input_image')
    wf.connect(registration, 'outputspec.transforms', warpall, 'transforms')

    mask_target = Node(fsl.ImageMaths(op_string='-bin'), name='target_mask')

    wf.connect(registration, 'outputspec.anat2target', mask_target, 'in_file')

    maskts = MapNode(fsl.ApplyMask(), iterfield=['in_file'], name='ts_masker')
    wf.connect(warpall, 'output_image', maskts, 'in_file')
    wf.connect(mask_target, 'out_file', maskts, 'mask_file')

    # map to surface
    # extract aparc+aseg ROIs
    # extract subcortical ROIs
    # extract target space ROIs
    # combine subcortical and cortical rois into a single cifti file

    #######
    # Convert aparc to subject functional space

    # Sample the average time series in aparc ROIs
    sampleaparc = MapNode(freesurfer.SegStats(default_color_table=True),
                          iterfield=['in_file', 'summary_file',
                                     'avgwf_txt_file'],
                          name='aparc_ts')
    sampleaparc.inputs.segment_id = ([8] + range(10, 14) + [17, 18, 26, 47] +
                                     range(49, 55) + [58] + range(1001, 1036) +
                                     range(2001, 2036))

    wf.connect(registration, 'outputspec.aparc',
               sampleaparc, 'segmentation_file')
    wf.connect(collector, 'out', sampleaparc, 'in_file')

    def get_names(files, suffix):
        """Generate appropriate names for output files
        """
        from nipype.utils.filemanip import (split_filename, filename_to_list,
                                            list_to_filename)
        out_names = []
        for filename in files:
            _, name, _ = split_filename(filename)
            out_names.append(name + suffix)
        return list_to_filename(out_names)

    wf.connect(collector, ('out', get_names, '_avgwf.txt'),
               sampleaparc, 'avgwf_txt_file')
    wf.connect(collector, ('out', get_names, '_summary.stats'),
               sampleaparc, 'summary_file')

    # Sample the time series onto the surface of the target surface. Performs
    # sampling into left and right hemisphere
    target = Node(IdentityInterface(fields=['target_subject']), name='target')
    target.iterables = ('target_subject', filename_to_list(target_subject))

    samplerlh = MapNode(freesurfer.SampleToSurface(),
                        iterfield=['source_file'],
                        name='sampler_lh')
    samplerlh.inputs.sampling_method = "average"
    samplerlh.inputs.sampling_range = (0.1, 0.9, 0.1)
    samplerlh.inputs.sampling_units = "frac"
    samplerlh.inputs.interp_method = "trilinear"
    samplerlh.inputs.smooth_surf = surf_fwhm
    #samplerlh.inputs.cortex_mask = True
    samplerlh.inputs.out_type = 'niigz'
    samplerlh.inputs.subjects_dir = subjects_dir

    samplerrh = samplerlh.clone('sampler_rh')

    samplerlh.inputs.hemi = 'lh'
    wf.connect(collector, 'out', samplerlh, 'source_file')
    wf.connect(registration, 'outputspec.out_reg_file', samplerlh, 'reg_file')
    wf.connect(target, 'target_subject', samplerlh, 'target_subject')

    samplerrh.set_input('hemi', 'rh')
    wf.connect(collector, 'out', samplerrh, 'source_file')
    wf.connect(registration, 'outputspec.out_reg_file', samplerrh, 'reg_file')
    wf.connect(target, 'target_subject', samplerrh, 'target_subject')

    # Combine left and right hemisphere to text file
    combiner = MapNode(Function(input_names=['left', 'right'],
                                output_names=['out_file'],
                                function=combine_hemi,
                                imports=imports),
                       iterfield=['left', 'right'],
                       name="combiner")
    wf.connect(samplerlh, 'out_file', combiner, 'left')
    wf.connect(samplerrh, 'out_file', combiner, 'right')

    # Sample the time series file for each subcortical roi
    ts2txt = MapNode(Function(input_names=['timeseries_file', 'label_file',
                                           'indices'],
                              output_names=['out_file'],
                              function=extract_subrois,
                              imports=imports),
                     iterfield=['timeseries_file'],
                     name='getsubcortts')
    ts2txt.inputs.indices = [8] + range(10, 14) + [17, 18, 26, 47] +\
                            range(49, 55) + [58]
    ts2txt.inputs.label_file = \
        os.path.abspath(('OASIS-TRT-20_jointfusion_DKT31_CMA_labels_in_MNI152_'
                         '2mm_v2.nii.gz'))
    wf.connect(maskts, 'out_file', ts2txt, 'timeseries_file')

    ######

    substitutions = [('_target_subject_', ''),
                     ('_filtermotart_cleaned_bp_trans_masked', ''),
                     ('_filtermotart_cleaned_bp', '')
                     ]
    regex_subs = [('_ts_masker.*/sar', '/smooth/'),
                  ('_ts_masker.*/ar', '/unsmooth/'),
                  ('_combiner.*/sar', '/smooth/'),
                  ('_combiner.*/ar', '/unsmooth/'),
                  ('_aparc_ts.*/sar', '/smooth/'),
                  ('_aparc_ts.*/ar', '/unsmooth/'),
                  ('_getsubcortts.*/sar', '/smooth/'),
                  ('_getsubcortts.*/ar', '/unsmooth/'),
                  ('series/sar', 'series/smooth/'),
                  ('series/ar', 'series/unsmooth/'),
                  ('_inverse_transform./', ''),
                  ]
    # Save the relevant data into an output directory
    datasink = Node(interface=DataSink(), name="datasink")
    datasink.inputs.base_directory = sink_directory
    datasink.inputs.container = subject_id
    datasink.inputs.substitutions = substitutions
    datasink.inputs.regexp_substitutions = regex_subs #(r'(/_.*(\d+/))', r'/run\2')
    wf.connect(realign, 'realignment_parameters', datasink, 'resting.qa.motion')
    wf.connect(art, 'norm_files', datasink, 'resting.qa.art.@norm')
    wf.connect(art, 'intensity_files', datasink, 'resting.qa.art.@intensity')
    wf.connect(art, 'outlier_files', datasink, 'resting.qa.art.@outlier_files')
    wf.connect(registration, 'outputspec.segmentation_files', datasink, 'resting.mask_files')
    wf.connect(registration, 'outputspec.anat2target', datasink, 'resting.qa.ants')
    wf.connect(mask, 'mask_file', datasink, 'resting.mask_files.@brainmask')
    wf.connect(mask_target, 'out_file', datasink, 'resting.mask_files.target')
    wf.connect(filter1, 'out_f', datasink, 'resting.qa.compmaps.@mc_F')
    wf.connect(filter1, 'out_pf', datasink, 'resting.qa.compmaps.@mc_pF')
    wf.connect(filter2, 'out_f', datasink, 'resting.qa.compmaps')
    wf.connect(filter2, 'out_pf', datasink, 'resting.qa.compmaps.@p')
    wf.connect(bandpass, 'out_files', datasink, 'resting.timeseries.@bandpassed')
    wf.connect(smooth, 'smoothed_files', datasink, 'resting.timeseries.@smoothed')
    wf.connect(createfilter1, 'out_files',
               datasink, 'resting.regress.@regressors')
    wf.connect(createfilter2, 'out_files',
               datasink, 'resting.regress.@compcorr')
    wf.connect(maskts, 'out_file', datasink, 'resting.timeseries.target')
    wf.connect(sampleaparc, 'summary_file',
               datasink, 'resting.parcellations.aparc')
    wf.connect(sampleaparc, 'avgwf_txt_file',
               datasink, 'resting.parcellations.aparc.@avgwf')
    wf.connect(ts2txt, 'out_file',
               datasink, 'resting.parcellations.grayo.@subcortical')

    datasink2 = Node(interface=DataSink(), name="datasink2")
    datasink2.inputs.base_directory = sink_directory
    datasink2.inputs.container = subject_id
    datasink2.inputs.substitutions = substitutions
    datasink2.inputs.regexp_substitutions = regex_subs #(r'(/_.*(\d+/))', r'/run\2')
    wf.connect(combiner, 'out_file',
               datasink2, 'resting.parcellations.grayo.@surface')
    return wf

Creates the full workflow including getting information from dicom files

    def create_resting_workflow(args, name=None):
        TR = args.TR
        slice_times = args.slice_times
        if args.dicom_file:
            TR, slice_times, slice_thickness = get_info(args.dicom_file)
            slice_times = (np.array(slice_times)/1000.).tolist()
        if name is None:
            name = 'resting_' + args.subject_id
        kwargs = dict(files=[os.path.abspath(filename) for filename in args.files],
                      target_file=os.path.abspath(args.target_file),
                      subject_id=args.subject_id,
                      TR=TR,
                      slice_times=slice_times,
                      vol_fwhm=args.vol_fwhm,
                      surf_fwhm=args.surf_fwhm,
                      norm_threshold=2.,
                      subjects_dir=os.path.abspath(args.fsdir),
                      target_subject=args.target_surfs,
                      lowpass_freq=args.lowpass_freq,
                      highpass_freq=args.highpass_freq,
                      sink_directory=os.path.abspath(args.sink),
                      name=name)
        wf = create_workflow(**kwargs)
        return wf

    if __name__ == "__main__":
        from argparse import ArgumentParser, RawTextHelpFormatter
        defstr = ' (default %(default)s)'
        parser = ArgumentParser(description=__doc__,
                                formatter_class=RawTextHelpFormatter)
        parser.add_argument("-d", "--dicom_file", dest="dicom_file",
                            help="an example dicom file from the resting series")
        parser.add_argument("-f", "--files", dest="files", nargs="+",
                            help="4d nifti files for resting state",
                            required=True)
        parser.add_argument("-t", "--target", dest="target_file",
                            help=("Target in MNI space. Best to use the MindBoggle "
                                  "template - "
                                  "OASIS-30_Atropos_template_in_MNI152_2mm.nii.gz"),
                            required=True)
        parser.add_argument("-s", "--subject_id", dest="subject_id",
                            help="FreeSurfer subject id", required=True)
        parser.add_argument("--subjects_dir", dest="fsdir",
                            help="FreeSurfer subject directory", required=True)
        parser.add_argument("--target_surfaces", dest="target_surfs", nargs="+",
                            default=['fsaverage5'],
                            help="FreeSurfer target surfaces" + defstr)
        parser.add_argument("--TR", dest="TR", default=None, type=float,
                            help="TR if dicom not provided in seconds")
        parser.add_argument("--slice_times", dest="slice_times", nargs="+",
                            type=float, help="Slice onset times in seconds")
        parser.add_argument('--vol_fwhm', default=6., dest='vol_fwhm',
                            type=float, help="Spatial FWHM" + defstr)
        parser.add_argument('--surf_fwhm', default=15., dest='surf_fwhm',
                            type=float, help="Spatial FWHM" + defstr)
        parser.add_argument("-l", "--lowpass_freq", dest="lowpass_freq",
                            default=0.1, help="Low pass frequency (Hz)" + defstr)
        parser.add_argument("-u", "--highpass_freq", dest="highpass_freq",
                            default=0.01, help="High pass frequency (Hz)" + defstr)
        parser.add_argument("-o", "--output_dir", dest="sink",
                            help="Output directory base", required=True)
        parser.add_argument("-w", "--work_dir", dest="work_dir",
                            help="Output directory base")
        parser.add_argument("-p", "--plugin", dest="plugin",
                            default='Linear',
                            help="Plugin to use")
        parser.add_argument("--plugin_args", dest="plugin_args",
                            help="Plugin arguments")
        args = parser.parse_args()

        wf = create_resting_workflow(args)

        if args.work_dir:
            work_dir = os.path.abspath(args.work_dir)
        else:
            work_dir = os.getcwd()

        wf.base_dir = work_dir
        if args.plugin_args:
            wf.run(args.plugin, plugin_args=eval(args.plugin_args))
        else:
            wf.run(args.plugin)

Example source code

You can download `the full source code of this example`. This same script is also included in the Nipype source distribution under the `examples` directory.