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:
from __future__ import division, unicode_literals
from builtins import open, range, str
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.affine,
img.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 = int(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.affine, img.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=b"%.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.empty((timepoints, 0))
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=b"%.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=b"%.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.float = True
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
merge = Node(Merge(2), iterfield=['in2'], name='mergexfm')
register.connect(convert2itk, 'itk_transform', merge, 'in2')
register.connect(reg, 'composite_transform', 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 = 'Linear'
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 = list(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 = 'Linear'
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] + list(range(10, 14)) + [17, 18, 26, 47] +
list(range(49, 55)) + [58] + list(range(1001, 1036)) +
list(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] + list(range(10, 14)) + [17, 18, 26, 47] +\
list(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, type=float,
help="Low pass frequency (Hz)" + defstr)
parser.add_argument("-u", "--highpass_freq", dest="highpass_freq",
default=0.01, type=float,
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.