name='artifact_remotion')
# BET - Skullstrip anatomical anf funtional images
bet_t1 = Node(BET(frac=0.55, robust=True, mask=True, output_type='NIFTI_GZ'),
name="bet_t1")
bet_fmri = Node(BET(frac=0.6, functional=True, output_type='NIFTI_GZ'),
name="bet_fmri")
# FAST - Image Segmentation
segmentation = Node(FAST(output_type='NIFTI'), name="segmentation")
# Normalize - normalizes functional and structural images to the MNI template
normalize_fmri = Node(Normalize12(jobtype='estwrite',
tpm=template,
write_voxel_sizes=[2, 2, 2],
write_bounding_box=[[-90, -126, -72],
[90, 90, 108]]),
name="normalize_fmri")
gunzip = Node(Gunzip(), name="gunzip")
normalize_t1 = Node(Normalize12(
jobtype='estwrite',
tpm=template,
write_voxel_sizes=[iso_size, iso_size, iso_size],
write_bounding_box=[[-90, -126, -72], [90, 90, 108]]),
name="normalize_t1")
normalize_masks = Node(Normalize12(
jobtype='estwrite',
def Couple_Preproc_Pipeline(base_dir=None,
output_dir=None,
subject_id=None,
spm_path=None):
""" Create a preprocessing workflow for the Couples Conflict Study using nipype
Args:
base_dir: path to data folder where raw subject folder is located
output_dir: path to where key output files should be saved
subject_id: subject_id (str)
spm_path: path to spm folder
Returns:
workflow: a nipype workflow that can be run
"""
from nipype.interfaces.dcm2nii import Dcm2nii
from nipype.interfaces.fsl import Merge, TOPUP, ApplyTOPUP
import nipype.interfaces.io as nio
import nipype.interfaces.utility as util
from nipype.interfaces.utility import Merge as Merge_List
from nipype.pipeline.engine import Node, Workflow
from nipype.interfaces.fsl.maths import UnaryMaths
from nipype.interfaces.nipy.preprocess import Trim
from nipype.algorithms.rapidart import ArtifactDetect
from nipype.interfaces import spm
from nipype.interfaces.spm import Normalize12
from nipype.algorithms.misc import Gunzip
from nipype.interfaces.nipy.preprocess import ComputeMask
import nipype.interfaces.matlab as mlab
from nltools.utils import get_resource_path, get_vox_dims, get_n_volumes
from nltools.interfaces import Plot_Coregistration_Montage, PlotRealignmentParameters, Create_Covariates
import os
import glob
########################################
## Setup Paths and Nodes
########################################
# Specify Paths
canonical_file = os.path.join(spm_path, 'canonical', 'single_subj_T1.nii')
template_file = os.path.join(spm_path, 'tpm', 'TPM.nii')
# Set the way matlab should be called
mlab.MatlabCommand.set_default_matlab_cmd("matlab -nodesktop -nosplash")
mlab.MatlabCommand.set_default_paths(spm_path)
# Get File Names for different types of scans. Parse into separate processing streams
datasource = Node(interface=nio.DataGrabber(
infields=['subject_id'], outfields=['struct', 'ap', 'pa']),
name='datasource')
datasource.inputs.base_directory = base_dir
datasource.inputs.template = '*'
datasource.inputs.field_template = {
'struct': '%s/Study*/t1w_32ch_mpr_08mm*',
'ap': '%s/Study*/distortion_corr_32ch_ap*',
'pa': '%s/Study*/distortion_corr_32ch_pa*'
}
datasource.inputs.template_args = {
'struct': [['subject_id']],
'ap': [['subject_id']],
'pa': [['subject_id']]
}
datasource.inputs.subject_id = subject_id
datasource.inputs.sort_filelist = True
# iterate over functional scans to define paths
scan_file_list = glob.glob(
os.path.join(base_dir, subject_id, 'Study*', '*'))
func_list = [s for s in scan_file_list if "romcon_ap_32ch_mb8" in s]
func_list = [s for s in func_list
if "SBRef" not in s] # Exclude sbref for now.
func_source = Node(interface=util.IdentityInterface(fields=['scan']),
name="func_source")
func_source.iterables = ('scan', func_list)
# Create Separate Converter Nodes for each different type of file. (dist corr scans need to be done before functional)
ap_dcm2nii = Node(interface=Dcm2nii(), name='ap_dcm2nii')
ap_dcm2nii.inputs.gzip_output = True
ap_dcm2nii.inputs.output_dir = '.'
ap_dcm2nii.inputs.date_in_filename = False
pa_dcm2nii = Node(interface=Dcm2nii(), name='pa_dcm2nii')
pa_dcm2nii.inputs.gzip_output = True
pa_dcm2nii.inputs.output_dir = '.'
pa_dcm2nii.inputs.date_in_filename = False
f_dcm2nii = Node(interface=Dcm2nii(), name='f_dcm2nii')
f_dcm2nii.inputs.gzip_output = True
f_dcm2nii.inputs.output_dir = '.'
f_dcm2nii.inputs.date_in_filename = False
s_dcm2nii = Node(interface=Dcm2nii(), name='s_dcm2nii')
s_dcm2nii.inputs.gzip_output = True
s_dcm2nii.inputs.output_dir = '.'
s_dcm2nii.inputs.date_in_filename = False
########################################
## Setup Nodes for distortion correction
########################################
# merge output files into list
merge_to_file_list = Node(interface=Merge_List(2),
infields=['in1', 'in2'],
name='merge_to_file_list')
# fsl merge AP + PA files (depends on direction)
merger = Node(interface=Merge(dimension='t'), name='merger')
merger.inputs.output_type = 'NIFTI_GZ'
# use topup to create distortion correction map
topup = Node(interface=TOPUP(), name='topup')
topup.inputs.encoding_file = os.path.join(get_resource_path(),
'epi_params_APPA_MB8.txt')
topup.inputs.output_type = "NIFTI_GZ"
topup.inputs.config = 'b02b0.cnf'
# apply topup to all functional images
apply_topup = Node(interface=ApplyTOPUP(), name='apply_topup')
apply_topup.inputs.in_index = [1]
apply_topup.inputs.encoding_file = os.path.join(get_resource_path(),
'epi_params_APPA_MB8.txt')
apply_topup.inputs.output_type = "NIFTI_GZ"
apply_topup.inputs.method = 'jac'
apply_topup.inputs.interp = 'spline'
# Clear out Zeros from spline interpolation using absolute value.
abs_maths = Node(interface=UnaryMaths(), name='abs_maths')
abs_maths.inputs.operation = 'abs'
########################################
## Preprocessing
########################################
# Trim - remove first 10 TRs
n_vols = 10
trim = Node(interface=Trim(), name='trim')
trim.inputs.begin_index = n_vols
#Realignment - 6 parameters - realign to first image of very first series.
realign = Node(interface=spm.Realign(), name="realign")
realign.inputs.register_to_mean = True
#Coregister - 12 parameters
coregister = Node(interface=spm.Coregister(), name="coregister")
coregister.inputs.jobtype = 'estwrite'
#Plot Realignment
plot_realign = Node(interface=PlotRealignmentParameters(),
name="plot_realign")
#Artifact Detection
art = Node(interface=ArtifactDetect(), name="art")
art.inputs.use_differences = [True, False]
art.inputs.use_norm = True
art.inputs.norm_threshold = 1
art.inputs.zintensity_threshold = 3
art.inputs.mask_type = 'file'
art.inputs.parameter_source = 'SPM'
# Gunzip - unzip the functional and structural images
gunzip_struc = Node(Gunzip(), name="gunzip_struc")
gunzip_func = Node(Gunzip(), name="gunzip_func")
# Normalize - normalizes functional and structural images to the MNI template
normalize = Node(interface=Normalize12(jobtype='estwrite',
tpm=template_file),
name="normalize")
#Plot normalization Check
plot_normalization_check = Node(interface=Plot_Coregistration_Montage(),
name="plot_normalization_check")
plot_normalization_check.inputs.canonical_img = canonical_file
#Create Mask
compute_mask = Node(interface=ComputeMask(), name="compute_mask")
#remove lower 5% of histogram of mean image
compute_mask.inputs.m = .05
#Smooth
#implicit masking (.im) = 0, dtype = 0
smooth = Node(interface=spm.Smooth(), name="smooth")
smooth.inputs.fwhm = 6
#Create Covariate matrix
make_cov = Node(interface=Create_Covariates(), name="make_cov")
# Create a datasink to clean up output files
datasink = Node(interface=nio.DataSink(), name='datasink')
datasink.inputs.base_directory = output_dir
datasink.inputs.container = subject_id
########################################
# Create Workflow
########################################
workflow = Workflow(name='Preprocessed')
workflow.base_dir = os.path.join(base_dir, subject_id)
workflow.connect([
(datasource, ap_dcm2nii, [('ap', 'source_dir')]),
(datasource, pa_dcm2nii, [('pa', 'source_dir')]),
(datasource, s_dcm2nii, [('struct', 'source_dir')]),
(func_source, f_dcm2nii, [('scan', 'source_dir')]),
(ap_dcm2nii, merge_to_file_list, [('converted_files', 'in1')]),
(pa_dcm2nii, merge_to_file_list, [('converted_files', 'in2')]),
(merge_to_file_list, merger, [('out', 'in_files')]),
(merger, topup, [('merged_file', 'in_file')]),
(topup, apply_topup, [('out_fieldcoef', 'in_topup_fieldcoef'),
('out_movpar', 'in_topup_movpar')]),
(f_dcm2nii, trim, [('converted_files', 'in_file')]),
(trim, apply_topup, [('out_file', 'in_files')]),
(apply_topup, abs_maths, [('out_corrected', 'in_file')]),
(abs_maths, gunzip_func, [('out_file', 'in_file')]),
(gunzip_func, realign, [('out_file', 'in_files')]),
(s_dcm2nii, gunzip_struc, [('converted_files', 'in_file')]),
(gunzip_struc, coregister, [('out_file', 'source')]),
(coregister, normalize, [('coregistered_source', 'image_to_align')]),
(realign, coregister, [('mean_image', 'target'),
('realigned_files', 'apply_to_files')]),
(realign, normalize, [(('mean_image', get_vox_dims),
'write_voxel_sizes')]),
(coregister, normalize, [('coregistered_files', 'apply_to_files')]),
(normalize, smooth, [('normalized_files', 'in_files')]),
(realign, compute_mask, [('mean_image', 'mean_volume')]),
(compute_mask, art, [('brain_mask', 'mask_file')]),
(realign, art, [('realignment_parameters', 'realignment_parameters'),
('realigned_files', 'realigned_files')]),
(realign, plot_realign, [('realignment_parameters',
'realignment_parameters')]),
(normalize, plot_normalization_check, [('normalized_files', 'wra_img')
]),
(realign, make_cov, [('realignment_parameters',
'realignment_parameters')]),
(art, make_cov, [('outlier_files', 'spike_id')]),
(normalize, datasink, [('normalized_files', 'structural.@normalize')]),
(coregister, datasink, [('coregistered_source', 'structural.@struct')
]),
(topup, datasink, [('out_fieldcoef', 'distortion.@fieldcoef')]),
(topup, datasink, [('out_movpar', 'distortion.@movpar')]),
(smooth, datasink, [('smoothed_files', 'functional.@smooth')]),
(plot_realign, datasink, [('plot', 'functional.@plot_realign')]),
(plot_normalization_check, datasink,
[('plot', 'functional.@plot_normalization')]),
(make_cov, datasink, [('covariates', 'functional.@covariates')])
])
return workflow
def run(base_dir):
template = '/home/brainlab/Desktop/Rudas/Data/Parcellation/TPM.nii'
matlab_cmd = '/home/brainlab/Desktop/Rudas/Tools/spm12_r7487/spm12/run_spm12.sh /home/brainlab/Desktop/Rudas/Tools/MCR/v713/ script'
spm.SPMCommand.set_mlab_paths(matlab_cmd=matlab_cmd, use_mcr=True)
print('SPM version: ' + str(spm.SPMCommand().version))
structural_dir = '/home/brainlab/Desktop/Rudas/Data/Propofol/Structurals/'
experiment_dir = opj(base_dir, 'output/')
output_dir = 'datasink'
working_dir = 'workingdir'
'''
subject_list = ['2014_05_02_02CB',
'2014_05_16_16RA',
'2014_05_30_30AQ',
'2014_07_04_04HD']
'''
subject_list = [
'2014_05_02_02CB', '2014_05_16_16RA', '2014_05_30_30AQ',
'2014_07_04_04HD', '2014_07_04_04SG', '2014_08_13_13CA',
'2014_10_08_08BC', '2014_10_08_08VR', '2014_10_22_22CY',
'2014_10_22_22TK', '2014_11_17_17EK', '2014_11_17_17NA',
'2014_11_19_19SA', '2014_11_19_AK', '2014_11_25.25JK',
'2014_11_27_27HF', '2014_12_10_10JR'
]
# list of subject identifiers
fwhm = 8 # Smoothing widths to apply (Gaussian kernel size)
TR = 2 # Repetition time
init_volume = 0 # Firts volumen identification which will use in the pipeline
iso_size = 2 # Isometric resample of functional images to voxel size (in mm)
# ExtractROI - skip dummy scans
extract = Node(ExtractROI(t_min=init_volume,
t_size=-1,
output_type='NIFTI'),
name="extract")
# MCFLIRT - motion correction
mcflirt = Node(MCFLIRT(mean_vol=True, save_plots=True,
output_type='NIFTI'),
name="motion_correction")
# SliceTimer - correct for slice wise acquisition
slicetimer = Node(SliceTimer(index_dir=False,
interleaved=True,
output_type='NIFTI',
time_repetition=TR),
name="slice_timing_correction")
# Smooth - image smoothing
smooth = Node(spm.Smooth(fwhm=fwhm), name="smooth")
n4bias = Node(N4Bias(out_file='t1_n4bias.nii.gz'), name='n4bias')
descomposition = Node(Descomposition(n_components=20,
low_pass=0.1,
high_pass=0.01,
tr=TR),
name='descomposition')
# Artifact Detection - determines outliers in functional images
art = Node(ArtifactDetect(norm_threshold=2,
zintensity_threshold=3,
mask_type='spm_global',
parameter_source='FSL',
use_differences=[True, False],
plot_type='svg'),
name="artifact_detection")
extract_confounds_ws_csf = Node(
ExtractConfounds(out_file='ev_without_gs.csv'),
name='extract_confounds_ws_csf')
extract_confounds_gs = Node(ExtractConfounds(out_file='ev_with_gs.csv',
delimiter=','),
name='extract_confounds_global_signal')
signal_extraction = Node(SignalExtraction(
time_series_out_file='time_series.csv',
correlation_matrix_out_file='correlation_matrix.png',
atlas_identifier='cort-maxprob-thr25-2mm',
tr=TR,
plot=True),
name='signal_extraction')
art_remotion = Node(ArtifacRemotion(out_file='fmri_art_removed.nii'),
name='artifact_remotion')
# BET - Skullstrip anatomical anf funtional images
bet_t1 = Node(BET(frac=0.5, robust=True, mask=True,
output_type='NIFTI_GZ'),
name="bet_t1")
# FAST - Image Segmentation
segmentation = Node(FAST(output_type='NIFTI'), name="segmentation")
# Normalize - normalizes functional and structural images to the MNI template
normalize_fmri = Node(Normalize12(jobtype='estwrite',
tpm=template,
write_voxel_sizes=[2, 2, 2],
write_bounding_box=[[-90, -126, -72],
[90, 90, 108]]),
name="normalize_fmri")
gunzip = Node(Gunzip(), name="gunzip")
normalize_t1 = Node(Normalize12(
jobtype='estwrite',
tpm=template,
write_voxel_sizes=[iso_size, iso_size, iso_size],
write_bounding_box=[[-90, -126, -72], [90, 90, 108]]),
name="normalize_t1")
normalize_masks = Node(Normalize12(
jobtype='estwrite',
tpm=template,
write_voxel_sizes=[iso_size, iso_size, iso_size],
write_bounding_box=[[-90, -126, -72], [90, 90, 108]]),
name="normalize_masks")
# Threshold - Threshold WM probability image
threshold = Node(Threshold(thresh=0.5, args='-bin',
output_type='NIFTI_GZ'),
name="wm_mask_threshold")
# FLIRT - pre-alignment of functional images to anatomical images
coreg_pre = Node(FLIRT(dof=6, output_type='NIFTI_GZ'),
name="linear_warp_estimation")
# FLIRT - coregistration of functional images to anatomical images with BBR
coreg_bbr = Node(FLIRT(dof=6,
cost='bbr',
schedule=opj(os.getenv('FSLDIR'),
'etc/flirtsch/bbr.sch'),
output_type='NIFTI_GZ'),
name="nonlinear_warp_estimation")
# Apply coregistration warp to functional images
applywarp = Node(FLIRT(interp='spline',
apply_isoxfm=iso_size,
output_type='NIFTI'),
name="registration_fmri")
# Apply coregistration warp to mean file
applywarp_mean = Node(FLIRT(interp='spline',
apply_isoxfm=iso_size,
output_type='NIFTI_GZ'),
name="registration_mean_fmri")
# Infosource - a function free node to iterate over the list of subject names
infosource = Node(IdentityInterface(fields=['subject_id']),
name="infosource")
infosource.iterables = [('subject_id', subject_list)]
# SelectFiles - to grab the data (alternativ to DataGrabber)
anat_file = opj(structural_dir, '{subject_id}', 't1.nii')
func_file = opj('{subject_id}', 'fmri.nii')
templates = {'anat': anat_file, 'func': func_file}
selectfiles = Node(SelectFiles(templates, base_directory=base_dir),
name="selectfiles")
# Datasink - creates output folder for important outputs
datasink = Node(DataSink(base_directory=experiment_dir,
container=output_dir),
name="datasink")
# Create a coregistration workflow
coregwf = Workflow(name='coreg_fmri_to_t1')
coregwf.base_dir = opj(experiment_dir, working_dir)
# Create a preprocessing workflow
preproc = Workflow(name='preproc')
preproc.base_dir = opj(experiment_dir, working_dir)
# Connect all components of the coregistration workflow
coregwf.connect([
(bet_t1, n4bias, [('out_file', 'in_file')]),
(n4bias, segmentation, [('out_file', 'in_files')]),
(segmentation, threshold, [(('partial_volume_files', get_latest),
'in_file')]),
(n4bias, coreg_pre, [('out_file', 'reference')]),
(threshold, coreg_bbr, [('out_file', 'wm_seg')]),
(coreg_pre, coreg_bbr, [('out_matrix_file', 'in_matrix_file')]),
(coreg_bbr, applywarp, [('out_matrix_file', 'in_matrix_file')]),
(n4bias, applywarp, [('out_file', 'reference')]),
(coreg_bbr, applywarp_mean, [('out_matrix_file', 'in_matrix_file')]),
(n4bias, applywarp_mean, [('out_file', 'reference')]),
])
## Use the following DataSink output substitutions
substitutions = [('_subject_id_', 'sub-')]
# ('_fwhm_', 'fwhm-'),
# ('_roi', ''),
# ('_mcf', ''),
# ('_st', ''),
# ('_flirt', ''),
# ('.nii_mean_reg', '_mean'),
# ('.nii.par', '.par'),
# ]
#subjFolders = [('fwhm-%s/' % f, 'fwhm-%s_' % f) for f in fwhm]
#substitutions.extend(subjFolders)
datasink.inputs.substitutions = substitutions
# Connect all components of the preprocessing workflow
preproc.connect([
(infosource, selectfiles, [('subject_id', 'subject_id')]),
(selectfiles, extract, [('func', 'in_file')]),
(extract, mcflirt, [('roi_file', 'in_file')]),
(mcflirt, slicetimer, [('out_file', 'in_file')]),
(selectfiles, coregwf, [('anat', 'bet_t1.in_file'),
('anat', 'nonlinear_warp_estimation.reference')
]),
(mcflirt, coregwf, [('mean_img', 'linear_warp_estimation.in_file'),
('mean_img', 'nonlinear_warp_estimation.in_file'),
('mean_img', 'registration_mean_fmri.in_file')]),
(slicetimer, coregwf, [('slice_time_corrected_file',
'registration_fmri.in_file')]),
(coregwf, art, [('registration_fmri.out_file', 'realigned_files')]),
(mcflirt, art, [('par_file', 'realignment_parameters')]),
(art, art_remotion, [('outlier_files', 'outlier_files')]),
(coregwf, art_remotion, [('registration_fmri.out_file', 'in_file')]),
(coregwf, gunzip, [('n4bias.out_file', 'in_file')]),
(selectfiles, normalize_fmri, [('anat', 'image_to_align')]),
(art_remotion, normalize_fmri, [('out_file', 'apply_to_files')]),
(selectfiles, normalize_t1, [('anat', 'image_to_align')]),
(gunzip, normalize_t1, [('out_file', 'apply_to_files')]),
(selectfiles, normalize_masks, [('anat', 'image_to_align')]),
(coregwf, normalize_masks, [(('segmentation.partial_volume_files',
get_wm_csf), 'apply_to_files')]),
(normalize_fmri, smooth, [('normalized_files', 'in_files')]),
(smooth, extract_confounds_ws_csf, [('smoothed_files', 'in_file')]),
(normalize_masks, extract_confounds_ws_csf, [('normalized_files',
'list_mask')]),
(mcflirt, extract_confounds_ws_csf, [('par_file', 'file_concat')]),
#(smooth, extract_confounds_gs, [('smoothed_files', 'in_file')]),
#(normalize_t1, extract_confounds_gs, [(('normalized_files',change_to_list), 'list_mask')]),
#(extract_confounds_ws_csf, extract_confounds_gs, [('out_file', 'file_concat')]),
(smooth, signal_extraction, [('smoothed_files', 'in_file')]),
#(extract_confounds_gs, signal_extraction, [('out_file', 'confounds_file')]),
(extract_confounds_ws_csf, signal_extraction, [('out_file',
'confounds_file')]),
#(smooth, descomposition, [('smoothed_files', 'in_file')]),
#(extract_confounds_ws_csf, descomposition, [('out_file', 'confounds_file')]),
#(extract_confounds_gs, datasink, [('out_file', 'preprocessing.@confounds_with_gs')]),
(extract_confounds_ws_csf, datasink,
[('out_file', 'preprocessing.@confounds_without_gs')]),
(smooth, datasink, [('smoothed_files', 'preprocessing.@smoothed')]),
(normalize_fmri, datasink, [('normalized_files',
'preprocessing.@fmri_normalized')]),
(normalize_t1, datasink, [('normalized_files',
'preprocessing.@t1_normalized')]),
(normalize_masks, datasink, [('normalized_files',
'preprocessing.@masks_normalized')]),
(signal_extraction, datasink, [('time_series_out_file',
'preprocessing.@time_serie')]),
(signal_extraction, datasink, [('correlation_matrix_out_file',
'preprocessing.@correlation_matrix')]),
(signal_extraction, datasink,
[('fmri_cleaned_out_file', 'preprocessing.@fmri_cleaned_out_file')]),
#(descomposition, datasink, [('out_file', 'preprocessing.@descomposition')]),
#(descomposition, datasink, [('plot_files', 'preprocessing.@descomposition_plot_files')])
])
preproc.write_graph(graph2use='colored', format='png', simple_form=True)
preproc.run()
segment.inputs.write_deformation_fields = [True, True]
tissue1 = ((tpm, 1), 1, (True,False), (True, False))
tissue2 = ((tpm, 2), 1, (True,False), (True, False))
tissue3 = ((tpm, 3), 2, (True,False), (True, False))
tissue4 = ((tpm, 4), 3, (False,False), (False, False))
tissue5 = ((tpm, 5), 4, (False,False), (False, False))
tissue6 = ((tpm, 6), 2, (False,False), (False, False))
segment.inputs.tissues = [tissue1, tissue2, tissue3, tissue4, tissue5, tissue6]
segment.inputs.affine_regularization = 'mni'
segment.inputs.warping_regularization = [0, 0.001, 0.5, 0.05, 0.2]
segment.inputs.cleanup = 1
segment.inputs.warp_fwhm = 0
segment.inputs.sampling_distance = 3
# Normalise - second step in normalisation
normalize_func = Node(Normalize12(jobtype='write',
write_voxel_sizes= [4, 4, 4]),
name='normalize_func')
normalize_mask = Node(Normalize12(jobtype='write',
write_voxel_sizes= [4, 4, 4]),
name='normalize_mask')
normalize_struct = Node(Normalize12(jobtype='write',
write_voxel_sizes= [1,1,1]),
name='normalize_struct')
# fsl bet
bet_struct = Node(fsl.BET(),name='bet_struct')
bet_struct.inputs.frac = 0.5
bet_struct.inputs.mask = True
def TV_Preproc_Pipeline(base_dir=None,
output_dir=None,
subject_id=None,
spm_path=None):
""" Create a preprocessing workflow for the Couples Conflict Study using nipype
Args:
base_dir: path to data folder where raw subject folder is located
output_dir: path to where key output files should be saved
subject_id: subject_id (str)
spm_path: path to spm folder
Returns:
workflow: a nipype workflow that can be run
"""
import nipype.interfaces.io as nio
import nipype.interfaces.utility as util
from nipype.interfaces.utility import Merge as Merge_List
from nipype.pipeline.engine import Node, Workflow
from nipype.interfaces.fsl.maths import UnaryMaths
from nipype.interfaces.nipy.preprocess import Trim
from nipype.algorithms.rapidart import ArtifactDetect
from nipype.interfaces import spm
from nipype.interfaces.spm import Normalize12
from nipype.algorithms.misc import Gunzip
from nipype.interfaces.nipy.preprocess import ComputeMask
import nipype.interfaces.matlab as mlab
from nltools.utils import get_resource_path, get_vox_dims, get_n_volumes
from nltools.interfaces import Plot_Coregistration_Montage, PlotRealignmentParameters, Create_Covariates, Plot_Quality_Control
import os
import glob
########################################
## Setup Paths and Nodes
########################################
# Specify Paths
canonical_file = os.path.join(spm_path, 'canonical', 'single_subj_T1.nii')
template_file = os.path.join(spm_path, 'tpm', 'TPM.nii')
# Set the way matlab should be called
mlab.MatlabCommand.set_default_matlab_cmd("matlab -nodesktop -nosplash")
mlab.MatlabCommand.set_default_paths(spm_path)
# Get File Names for different types of scans. Parse into separate processing streams
datasource = Node(interface=nio.DataGrabber(infields=['subject_id'],
outfields=['struct', 'func']),
name='datasource')
datasource.inputs.base_directory = base_dir
datasource.inputs.template = '*'
datasource.inputs.field_template = {
'struct': '%s/T1.nii.gz',
'func': '%s/*ep*.nii.gz'
}
datasource.inputs.template_args = {
'struct': [['subject_id']],
'func': [['subject_id']]
}
datasource.inputs.subject_id = subject_id
datasource.inputs.sort_filelist = True
# iterate over functional scans to define paths
func_source = Node(interface=util.IdentityInterface(fields=['scan']),
name="func_source")
func_source.iterables = ('scan',
glob.glob(
os.path.join(base_dir, subject_id,
'*ep*nii.gz')))
########################################
## Preprocessing
########################################
# Trim - remove first 5 TRs
n_vols = 5
trim = Node(interface=Trim(), name='trim')
trim.inputs.begin_index = n_vols
#Realignment - 6 parameters - realign to first image of very first series.
realign = Node(interface=spm.Realign(), name="realign")
realign.inputs.register_to_mean = True
#Coregister - 12 parameters
coregister = Node(interface=spm.Coregister(), name="coregister")
coregister.inputs.jobtype = 'estwrite'
#Plot Realignment
plot_realign = Node(interface=PlotRealignmentParameters(),
name="plot_realign")
#Artifact Detection
art = Node(interface=ArtifactDetect(), name="art")
art.inputs.use_differences = [True, False]
art.inputs.use_norm = True
art.inputs.norm_threshold = 1
art.inputs.zintensity_threshold = 3
art.inputs.mask_type = 'file'
art.inputs.parameter_source = 'SPM'
# Gunzip - unzip the functional and structural images
gunzip_struc = Node(Gunzip(), name="gunzip_struc")
gunzip_func = Node(Gunzip(), name="gunzip_func")
# Normalize - normalizes functional and structural images to the MNI template
normalize = Node(interface=Normalize12(jobtype='estwrite',
tpm=template_file),
name="normalize")
#Plot normalization Check
plot_normalization_check = Node(interface=Plot_Coregistration_Montage(),
name="plot_normalization_check")
plot_normalization_check.inputs.canonical_img = canonical_file
#Create Mask
compute_mask = Node(interface=ComputeMask(), name="compute_mask")
#remove lower 5% of histogram of mean image
compute_mask.inputs.m = .05
#Smooth
#implicit masking (.im) = 0, dtype = 0
smooth = Node(interface=spm.Smooth(), name="smooth")
smooth.inputs.fwhm = 6
#Create Covariate matrix
make_cov = Node(interface=Create_Covariates(), name="make_cov")
#Plot Quality Control Check
quality_control = Node(interface=Plot_Quality_Control(),
name='quality_control')
# Create a datasink to clean up output files
datasink = Node(interface=nio.DataSink(), name='datasink')
datasink.inputs.base_directory = output_dir
datasink.inputs.container = subject_id
########################################
# Create Workflow
########################################
workflow = Workflow(name='Preprocessed')
workflow.base_dir = os.path.join(base_dir, subject_id)
workflow.connect([
(datasource, gunzip_struc, [('struct', 'in_file')]),
(func_source, trim, [('scan', 'in_file')]),
(trim, gunzip_func, [('out_file', 'in_file')]),
(gunzip_func, realign, [('out_file', 'in_files')]),
(realign, quality_control, [('realigned_files', 'dat_img')]),
(gunzip_struc, coregister, [('out_file', 'source')]),
(coregister, normalize, [('coregistered_source', 'image_to_align')]),
(realign, coregister, [('mean_image', 'target'),
('realigned_files', 'apply_to_files')]),
(realign, normalize, [(('mean_image', get_vox_dims),
'write_voxel_sizes')]),
(coregister, normalize, [('coregistered_files', 'apply_to_files')]),
(normalize, smooth, [('normalized_files', 'in_files')]),
(realign, compute_mask, [('mean_image', 'mean_volume')]),
(compute_mask, art, [('brain_mask', 'mask_file')]),
(realign, art, [('realignment_parameters', 'realignment_parameters'),
('realigned_files', 'realigned_files')]),
(realign, plot_realign, [('realignment_parameters',
'realignment_parameters')]),
(normalize, plot_normalization_check, [('normalized_files', 'wra_img')
]),
(realign, make_cov, [('realignment_parameters',
'realignment_parameters')]),
(art, make_cov, [('outlier_files', 'spike_id')]),
(normalize, datasink, [('normalized_files', 'structural.@normalize')]),
(coregister, datasink, [('coregistered_source', 'structural.@struct')
]),
(smooth, datasink, [('smoothed_files', 'functional.@smooth')]),
(plot_realign, datasink, [('plot', 'functional.@plot_realign')]),
(plot_normalization_check, datasink,
[('plot', 'functional.@plot_normalization')]),
(make_cov, datasink, [('covariates', 'functional.@covariates')]),
(quality_control, datasink, [('plot', 'functional.@quality_control')])
])
return workflow
template = '/usr/local/MATLAB/R2014a/toolbox/spm12/tpm/TPM.nii'
###
# Specify Normalization Nodes
# Gunzip - unzip the structural image
gunzip_struct = Node(Gunzip(), name="gunzip_struct")
# Gunzip - unzip the contrast image
gunzip_con = MapNode(Gunzip(), name="gunzip_con",
iterfield=['in_file'])
# Normalize - normalizes functional and structural images to the MNI template
normalize = Node(Normalize12(jobtype='estwrite',
tpm=template,
write_voxel_sizes=[1, 1, 1]),
name="normalize")
###
# Specify Normalization-Workflow & Connect Nodes
normflow = Workflow(name='normflow')
normflow.base_dir = opj(experiment_dir, working_dir)
# Connect up ANTS normalization components
normflow.connect([(gunzip_struct, normalize, [('out_file', 'image_to_align')]),
(gunzip_con, normalize, [('out_file', 'apply_to_files')]),
])
###
def run(self):
matlab_cmd = self.paths['spm_path'] + ' ' + self.paths[
'mcr_path'] + '/ script'
spm.SPMCommand.set_mlab_paths(matlab_cmd=matlab_cmd, use_mcr=True)
print(matlab_cmd)
print('SPM version: ' + str(spm.SPMCommand().version))
experiment_dir = opj(self.paths['input_path'], 'output/')
output_dir = 'datasink'
working_dir = 'workingdir'
subject_list = self.subject_list
# list of subject identifiers
fwhm = self.parameters[
'fwhm'] # Smoothing widths to apply (Gaussian kernel size)
tr = self.parameters['tr'] # Repetition time
init_volume = self.parameters[
'init_volume'] # Firts volumen identification which will use in the pipeline
iso_size = self.parameters[
'iso_size'] # Isometric resample of functional images to voxel size (in mm)
low_pass = self.parameters['low_pass']
high_pass = self.parameters['high_pass']
t1_relative_path = self.paths['t1_relative_path']
fmri_relative_path = self.paths['fmri_relative_path']
# ExtractROI - skip dummy scans
extract = Node(ExtractROI(t_min=init_volume,
t_size=-1,
output_type='NIFTI'),
name="extract") #FSL
# MCFLIRT - motion correction
mcflirt = Node(MCFLIRT(mean_vol=True,
save_plots=True,
output_type='NIFTI'),
name="motion_correction") #FSL
# SliceTimer - correct for slice wise acquisition
slicetimer = Node(SliceTimer(index_dir=False,
interleaved=True,
output_type='NIFTI',
time_repetition=tr),
name="slice_timing_correction") #FSL
# Smooth - image smoothing
denoise = Node(Denoise(), name="denoising") #Interfaces with dipy
smooth = Node(spm.Smooth(fwhm=fwhm), name="smooth") #SPM
n4bias = Node(N4Bias(out_file='t1_n4bias.nii.gz'),
name='n4bias') #Interface with SimpleITK
descomposition = Node(Descomposition(n_components=20,
low_pass=0.1,
high_pass=0.01,
tr=tr),
name='descomposition') #Interface with nilearn
# Artifact Detection - determines outliers in functional images
art = Node(ArtifactDetect(norm_threshold=2,
zintensity_threshold=3,
mask_type='spm_global',
parameter_source='FSL',
use_differences=[True, False],
plot_type='svg'),
name="artifact_detection") #Rapidart
extract_confounds_ws_csf = Node(
ExtractConfounds(out_file='ev_without_gs.csv'),
name='extract_confounds_ws_csf') #Interfece
extract_confounds_gs = Node(ExtractConfounds(out_file='ev_with_gs.csv',
delimiter=','),
name='extract_confounds_global_signal')
signal_extraction = Node(SignalExtraction(
time_series_out_file='time_series.csv',
correlation_matrix_out_file='correlation_matrix.png',
labels_parcellation_path=self.paths['labels_parcellation_path'],
mask_mni_path=self.paths['mask_mni_path'],
tr=tr,
low_pass=low_pass,
high_pass=high_pass,
plot=False),
name='signal_extraction')
signal_extraction.iterables = [('image_parcellation_path',
self.paths['image_parcellation_path'])]
art_remotion = Node(
ArtifacRemotion(out_file='fmri_art_removed.nii'),
name='artifact_remotion') #This interface requires implementation
# BET - Skullstrip anatomical anf funtional images
bet_t1 = Node(BET(frac=0.5,
robust=True,
mask=True,
output_type='NIFTI_GZ'),
name="bet_t1") #FSL
# FAST - Image Segmentation
segmentation = Node(FAST(output_type='NIFTI'),
name="segmentation") #FSL
# Normalize - normalizes functional and structural images to the MNI template
normalize_fmri = Node(Normalize12(
jobtype='estwrite',
tpm=self.paths['template_spm_path'],
write_voxel_sizes=[iso_size, iso_size, iso_size],
write_bounding_box=[[-90, -126, -72], [90, 90, 108]]),
name="normalize_fmri") #SPM
gunzip = Node(Gunzip(), name="gunzip")
normalize_t1 = Node(Normalize12(
jobtype='estwrite',
tpm=self.paths['template_spm_path'],
write_voxel_sizes=[iso_size, iso_size, iso_size],
write_bounding_box=[[-90, -126, -72], [90, 90, 108]]),
name="normalize_t1")
normalize_masks = Node(Normalize12(
jobtype='estwrite',
tpm=self.paths['template_spm_path'],
write_voxel_sizes=[iso_size, iso_size, iso_size],
write_bounding_box=[[-90, -126, -72], [90, 90, 108]]),
name="normalize_masks")
# Threshold - Threshold WM probability image
threshold = Node(Threshold(thresh=0.5,
args='-bin',
output_type='NIFTI_GZ'),
name="wm_mask_threshold")
# FLIRT - pre-alignment of functional images to anatomical images
coreg_pre = Node(FLIRT(dof=6, output_type='NIFTI_GZ'),
name="linear_warp_estimation")
# FLIRT - coregistration of functional images to anatomical images with BBR
coreg_bbr = Node(FLIRT(dof=6,
cost='bbr',
schedule=opj(os.getenv('FSLDIR'),
'etc/flirtsch/bbr.sch'),
output_type='NIFTI_GZ'),
name="nonlinear_warp_estimation")
# Apply coregistration warp to functional images
applywarp = Node(FLIRT(interp='spline',
apply_isoxfm=iso_size,
output_type='NIFTI'),
name="registration_fmri")
# Apply coregistration warp to mean file
applywarp_mean = Node(FLIRT(interp='spline',
apply_isoxfm=iso_size,
output_type='NIFTI_GZ'),
name="registration_mean_fmri")
# Infosource - a function free node to iterate over the list of subject names
infosource = Node(IdentityInterface(fields=['subject_id']),
name="infosource")
infosource.iterables = [('subject_id', subject_list)]
# SelectFiles - to grab the data (alternativ to DataGrabber)
anat_file = opj('{subject_id}', t1_relative_path)
func_file = opj('{subject_id}', fmri_relative_path)
#anat_file = opj('{subject_id}/anat/', 'data.nii')
#func_file = opj('{subject_id}/func/', 'data.nii')
templates = {'anat': anat_file, 'func': func_file}
selectfiles = Node(SelectFiles(
templates, base_directory=self.paths['input_path']),
name="selectfiles")
# Datasink - creates output folder for important outputs
datasink = Node(DataSink(base_directory=experiment_dir,
container=output_dir),
name="datasink")
# Create a coregistration workflow
coregwf = Workflow(name='coreg_fmri_to_t1')
coregwf.base_dir = opj(experiment_dir, working_dir)
# Create a preprocessing workflow
preproc = Workflow(name='preproc')
preproc.base_dir = opj(experiment_dir, working_dir)
# Connect all components of the coregistration workflow
coregwf.connect([
(bet_t1, n4bias, [('out_file', 'in_file')]),
(n4bias, segmentation, [('out_file', 'in_files')]),
(segmentation, threshold, [(('partial_volume_files', get_latest),
'in_file')]),
(n4bias, coreg_pre, [('out_file', 'reference')]),
(threshold, coreg_bbr, [('out_file', 'wm_seg')]),
(coreg_pre, coreg_bbr, [('out_matrix_file', 'in_matrix_file')]),
(coreg_bbr, applywarp, [('out_matrix_file', 'in_matrix_file')]),
(n4bias, applywarp, [('out_file', 'reference')]),
(coreg_bbr, applywarp_mean, [('out_matrix_file', 'in_matrix_file')
]),
(n4bias, applywarp_mean, [('out_file', 'reference')]),
])
## Use the following DataSink output substitutions
substitutions = [('_subject_id_', 'sub-')]
# ('_fwhm_', 'fwhm-'),
# ('_roi', ''),
# ('_mcf', ''),
# ('_st', ''),
# ('_flirt', ''),
# ('.nii_mean_reg', '_mean'),
# ('.nii.par', '.par'),
# ]
# subjFolders = [('fwhm-%s/' % f, 'fwhm-%s_' % f) for f in fwhm]
# substitutions.extend(subjFolders)
datasink.inputs.substitutions = substitutions
# Connect all components of the preprocessing workflow
preproc.connect([
(infosource, selectfiles, [('subject_id', 'subject_id')]),
(selectfiles, extract, [('func', 'in_file')]),
(extract, mcflirt, [('roi_file', 'in_file')]),
(mcflirt, slicetimer, [('out_file', 'in_file')]),
(selectfiles, denoise, [('anat', 'in_file')]),
(denoise, coregwf, [('out_file', 'bet_t1.in_file'),
('out_file',
'nonlinear_warp_estimation.reference')]),
(mcflirt, coregwf,
[('mean_img', 'linear_warp_estimation.in_file'),
('mean_img', 'nonlinear_warp_estimation.in_file'),
('mean_img', 'registration_mean_fmri.in_file')]),
(slicetimer, coregwf, [('slice_time_corrected_file',
'registration_fmri.in_file')]),
(coregwf, art, [('registration_fmri.out_file', 'realigned_files')
]),
(mcflirt, art, [('par_file', 'realignment_parameters')]),
(art, art_remotion, [('outlier_files', 'outlier_files')]),
(coregwf, art_remotion, [('registration_fmri.out_file', 'in_file')
]),
(coregwf, gunzip, [('n4bias.out_file', 'in_file')]),
(selectfiles, normalize_fmri, [('anat', 'image_to_align')]),
(art_remotion, normalize_fmri, [('out_file', 'apply_to_files')]),
(selectfiles, normalize_t1, [('anat', 'image_to_align')]),
(gunzip, normalize_t1, [('out_file', 'apply_to_files')]),
(selectfiles, normalize_masks, [('anat', 'image_to_align')]),
(coregwf, normalize_masks, [(('segmentation.partial_volume_files',
get_wm_csf), 'apply_to_files')]),
(normalize_fmri, smooth, [('normalized_files', 'in_files')]),
(smooth, extract_confounds_ws_csf, [('smoothed_files', 'in_file')
]),
(normalize_masks, extract_confounds_ws_csf, [('normalized_files',
'list_mask')]),
(mcflirt, extract_confounds_ws_csf, [('par_file', 'file_concat')]),
(art, extract_confounds_ws_csf, [('outlier_files', 'outlier_files')
]),
# (smooth, extract_confounds_gs, [('smoothed_files', 'in_file')]),
# (normalize_t1, extract_confounds_gs, [(('normalized_files',change_to_list), 'list_mask')]),
# (extract_confounds_ws_csf, extract_confounds_gs, [('out_file', 'file_concat')]),
(smooth, signal_extraction, [('smoothed_files', 'in_file')]),
# (extract_confounds_gs, signal_extraction, [('out_file', 'confounds_file')]),
(extract_confounds_ws_csf, signal_extraction,
[('out_file', 'confounds_file')]),
#(smooth, descomposition, [('smoothed_files', 'in_file')]),
#(extract_confounds_ws_csf, descomposition, [('out_file', 'confounds_file')]),
# (extract_confounds_gs, datasink, [('out_file', 'preprocessing.@confounds_with_gs')]),
(denoise, datasink, [('out_file', 'preprocessing.@t1_denoised')]),
(extract_confounds_ws_csf, datasink,
[('out_file', 'preprocessing.@confounds_without_gs')]),
(smooth, datasink, [('smoothed_files', 'preprocessing.@smoothed')
]),
(normalize_fmri, datasink, [('normalized_files',
'preprocessing.@fmri_normalized')]),
(normalize_t1, datasink, [('normalized_files',
'preprocessing.@t1_normalized')]),
(normalize_masks, datasink, [('normalized_files',
'preprocessing.@masks_normalized')]),
(signal_extraction, datasink, [('time_series_out_file',
'preprocessing.@time_serie')]),
(signal_extraction, datasink,
[('correlation_matrix_out_file',
'preprocessing.@correlation_matrix')])
])
#(signal_extraction, datasink,
# [('fmri_cleaned_out_file', 'preprocessing.@fmri_cleaned_out_file')])])
#,
#(descomposition, datasink, [('out_file', 'preprocessing.@descomposition')]),
#(descomposition, datasink, [('plot_files', 'preprocessing.@descomposition_plot_files')])
#])
preproc.write_graph(graph2use='colored',
format='png',
simple_form=True)
preproc.run()