def create_workflow(subject_id, outdir, file_url):
"""Create a workflow for a single participant"""
sink_directory = os.path.join(outdir, subject_id)
wf = Workflow(name=subject_id)
getter = Node(Function(input_names=['url'],
output_names=['localfile'],
function=download_file),
name="download_url")
getter.inputs.url = file_url
orienter = Node(Reorient2Std(), name='reorient_brain')
wf.connect(getter, 'localfile', orienter, 'in_file')
better = Node(BET(), name='extract_brain')
wf.connect(orienter, 'out_file', better, 'in_file')
faster = Node(FAST(), name='segment_brain')
wf.connect(better, 'out_file', faster, 'in_files')
firster = Node(FIRST(), name='parcellate_brain')
structures = [
'L_Hipp', 'R_Hipp', 'L_Accu', 'R_Accu', 'L_Amyg', 'R_Amyg', 'L_Caud',
'R_Caud', 'L_Pall', 'R_Pall', 'L_Puta', 'R_Puta', 'L_Thal', 'R_Thal'
]
firster.inputs.list_of_specific_structures = structures
wf.connect(orienter, 'out_file', firster, 'in_file')
fslstatser = MapNode(ImageStats(),
iterfield=['op_string'],
name="compute_segment_stats")
fslstatser.inputs.op_string = [
'-l {thr1} -u {thr2} -v'.format(thr1=val + 0.5, thr2=val + 1.5)
for val in range(3)
]
wf.connect(faster, 'partial_volume_map', fslstatser, 'in_file')
jsonfiler = Node(Function(
input_names=['stats', 'seg_file', 'structure_map', 'struct_file'],
output_names=['out_file'],
function=toJSON),
name='save_json')
structure_map = [('Background', 0), ('Left-Thalamus-Proper', 10),
('Left-Caudate', 11), ('Left-Putamen', 12),
('Left-Pallidum', 13), ('Left-Hippocampus', 17),
('Left-Amygdala', 18), ('Left-Accumbens-area', 26),
('Right-Thalamus-Proper', 49), ('Right-Caudate', 50),
('Right-Putamen', 51), ('Right-Pallidum', 52),
('Right-Hippocampus', 53), ('Right-Amygdala', 54),
('Right-Accumbens-area', 58)]
jsonfiler.inputs.structure_map = structure_map
wf.connect(fslstatser, 'out_stat', jsonfiler, 'stats')
wf.connect(firster, 'segmentation_file', jsonfiler, 'seg_file')
sinker = Node(DataSink(), name='store_results')
sinker.inputs.base_directory = sink_directory
wf.connect(better, 'out_file', sinker, 'brain')
wf.connect(faster, 'bias_field', sinker, 'segs.@bias_field')
wf.connect(faster, 'partial_volume_files', sinker, 'segs.@partial_files')
wf.connect(faster, 'partial_volume_map', sinker, 'segs.@partial_map')
wf.connect(faster, 'probability_maps', sinker, 'segs.@prob_maps')
wf.connect(faster, 'restored_image', sinker, 'segs.@restored')
wf.connect(faster, 'tissue_class_files', sinker, 'segs.@tissue_files')
wf.connect(faster, 'tissue_class_map', sinker, 'segs.@tissue_map')
wf.connect(firster, 'bvars', sinker, 'parcels.@bvars')
wf.connect(firster, 'original_segmentations', sinker, 'parcels.@origsegs')
wf.connect(firster, 'segmentation_file', sinker, 'parcels.@segfile')
wf.connect(firster, 'vtk_surfaces', sinker, 'parcels.@vtk')
wf.connect(jsonfiler, 'out_file', sinker, '@stats')
return wf
def preproc_workflow(input_dir,
output_dir,
subject_list,
ses_list,
anat_file,
func_file,
scan_size=477,
bet_frac=0.37):
"""
The preprocessing workflow used in the preparation of the psilocybin vs escitalopram rsFMRI scans.
Workflows and notes are defined throughout. Inputs are designed to be general and masks/default MNI space is provided
:param input_dir: The input file directory containing all scans in BIDS format
:param output_dir: The output file directory
:param subject_list: a list of subject numbers
:param ses_list: a list of scan numbers (session numbers)
:param anat_file: The format of the anatomical scan within the input directory
:param func_file: The format of the functional scan within the input directory
:param scan_size: The length of the scan by number of images, most 10 minutes scans are around 400-500 depending
upon scanner defaults and parameters - confirm by looking at your data
:param bet_frac: brain extraction fractional intensity threshold
:return: the preprocessing workflow
"""
preproc = Workflow(name='preproc')
preproc.base_dir = output_dir
# Infosource - a function free node to iterate over the list of subject names
infosource = Node(IdentityInterface(fields=['subject_id', 'ses']),
name="infosource")
infosource.iterables = [('subject_id', subject_list), ('ses', ses_list)]
# SelectFiles - to grab the data (alternative to DataGrabber)
templates = {
'anat': anat_file,
'func': func_file
} # define the template of each file input
selectfiles = Node(SelectFiles(templates, base_directory=input_dir),
name="selectfiles")
# Datasink - creates output folder for important outputs
datasink = Node(DataSink(base_directory=output_dir, container=output_dir),
name="datasink")
preproc.connect([(infosource, selectfiles, [('subject_id', 'subject_id'),
('ses', 'ses')])])
'''
This is your functional processing workflow, used to trim scans, despike the signal, slice-time correct,
and motion correct your data
'''
fproc = Workflow(name='fproc') # the functional processing workflow
# ExtractROI - skip dummy scans at the beginning of the recording by removing the first three
trim = Node(ExtractROI(t_min=3, t_size=scan_size, output_type='NIFTI_GZ'),
name="trim")
# 3dDespike - despike
despike = Node(Despike(outputtype='NIFTI_GZ', args='-NEW'), name="despike")
fproc.connect([(trim, despike, [('roi_file', 'in_file')])])
preproc.connect([(selectfiles, fproc, [('func', 'trim.in_file')])])
# 3dTshift - slice time correction
slicetime = Node(TShift(outputtype='NIFTI_GZ', tpattern='alt+z2'),
name="slicetime")
fproc.connect([(despike, slicetime, [('out_file', 'in_file')])])
# 3dVolreg - correct motion and output 1d matrix
moco = Node(Volreg(outputtype='NIFTI_GZ',
interp='Fourier',
zpad=4,
args='-twopass'),
name="moco")
fproc.connect([(slicetime, moco, [('out_file', 'in_file')])])
moco_bpfdt = Node(
MOCObpfdt(), name='moco_bpfdt'
) # use the matlab function to correct the motion regressor
fproc.connect([(moco, moco_bpfdt, [('oned_file', 'in_file')])])
'''
This is the co-registration workflow using FSL and ANTs
'''
coreg = Workflow(name='coreg')
# BET - structural data brain extraction
bet_anat = Node(BET(output_type='NIFTI_GZ', frac=bet_frac, robust=True),
name="bet_anat")
# FSL segmentation process to get WM map
seg = Node(FAST(bias_iters=6,
img_type=1,
output_biascorrected=True,
output_type='NIFTI_GZ'),
name="seg")
coreg.connect([(bet_anat, seg, [('out_file', 'in_files')])])
# functional to structural registration
mean = Node(MCFLIRT(mean_vol=True, output_type='NIFTI_GZ'), name="mean")
# BBR using linear methods for initial transform fit
func2struc = Node(FLIRT(cost='bbr', dof=6, output_type='NIFTI_GZ'),
name='func2struc')
coreg.connect([(seg, func2struc, [('restored_image', 'reference')])])
coreg.connect([(mean, func2struc, [('mean_img', 'in_file')])])
coreg.connect([(seg, func2struc, [(('tissue_class_files', pickindex, 2),
'wm_seg')])])
# convert the FSL linear transform into a C3d format for AFNI
f2s_c3d = Node(C3dAffineTool(itk_transform=True, fsl2ras=True),
name='f2s_c3d')
coreg.connect([(func2struc, f2s_c3d, [('out_matrix_file', 'transform_file')
])])
coreg.connect([(mean, f2s_c3d, [('mean_img', 'source_file')])])
coreg.connect([(seg, f2s_c3d, [('restored_image', 'reference_file')])])
# Functional to structural registration via ANTs non-linear registration
reg = Node(Registration(
fixed_image='default_images/MNI152_T1_2mm_brain.nii.gz',
transforms=['Affine', 'SyN'],
transform_parameters=[(0.1, ), (0.1, 3.0, 0.0)],
number_of_iterations=[[1500, 1000, 1000], [100, 70, 50, 20]],
dimension=3,
write_composite_transform=True,
collapse_output_transforms=True,
metric=['MI'] + ['CC'],
metric_weight=[1] * 2,
radius_or_number_of_bins=[32] + [4],
convergence_threshold=[1.e-8, 1.e-9],
convergence_window_size=[20] + [10],
smoothing_sigmas=[[2, 1, 0], [4, 2, 1, 0]],
sigma_units=['vox'] * 2,
shrink_factors=[[4, 2, 1], [6, 4, 2, 1]],
use_histogram_matching=[False] + [True],
use_estimate_learning_rate_once=[True, True],
output_warped_image=True),
name='reg')
coreg.connect([(seg, reg, [('restored_image', 'moving_image')])
]) # connect segmentation node to registration node
merge1 = Node(niu.Merge(2), iterfield=['in2'],
name='merge1') # merge the linear and nonlinear transforms
coreg.connect([(f2s_c3d, merge1, [('itk_transform', 'in2')])])
coreg.connect([(reg, merge1, [('composite_transform', 'in1')])])
# warp the functional images into MNI space using the transforms from FLIRT and SYN
warp = Node(ApplyTransforms(
reference_image='default_images/MNI152_T1_2mm_brain.nii.gz',
input_image_type=3),
name='warp')
coreg.connect([(moco, warp, [('out_file', 'input_image')])])
coreg.connect([(merge1, warp, [('out', 'transforms')])])
preproc.connect([(selectfiles, coreg, [('anat', 'bet_anat.in_file')])])
preproc.connect([(fproc, coreg, [('moco.out_file', 'mean.in_file')])])
'''
Scrubbing workflow - find the motion outliers, bandpass filter, re-mean the data after bpf
'''
scrub = Workflow(name='scrub')
# Generate the Scrubbing Regressor
scrub_metrics = Node(MotionOutliers(dummy=4,
out_file='FD_outliers.1D',
metric='fd',
threshold=0.4),
name="scrub_metrics")
# regress out timepoints
scrub_frames = Node(Bandpass(highpass=0,
lowpass=99999,
outputtype='NIFTI_GZ'),
name='scrub_frames')
scrub.connect([(scrub_metrics, scrub_frames, [('out_file',
'orthogonalize_file')])])
preproc.connect([(coreg, scrub, [('warp.output_image',
'scrub_frames.in_file')])])
preproc.connect([(selectfiles, scrub, [('func', 'scrub_metrics.in_file')])
])
# mean image for remeaning after bandpass
premean = Node(TStat(args='-mean', outputtype='NIFTI_GZ'), name='premean')
# remean the image
remean2 = Node(Calc(expr='a+b', outputtype='NIFTI_GZ'), name='remean2')
scrub.connect([(scrub_frames, remean2, [('out_file', 'in_file_a')])])
scrub.connect([(premean, remean2, [('out_file', 'in_file_b')])])
preproc.connect([(coreg, scrub, [('warp.output_image', 'premean.in_file')])
])
'''
Regressors for final cleaning steps
'''
regressors = Workflow(name='regressors')
# Using registered structural image to create the masks for both WM and CSF
regbet = Node(BET(robust=True, frac=0.37, output_type='NIFTI_GZ'),
name='regbet')
regseg = Node(FAST(img_type=1,
output_type='NIFTI_GZ',
no_pve=True,
no_bias=True,
segments=True),
name='regseg')
regressors.connect([(regbet, regseg, [('out_file', 'in_files')])])
preproc.connect([(coreg, regressors, [('reg.warped_image',
'regbet.in_file')])])
'''
Create a cerebrospinal fluid (CSF) regressor
'''
# subtract subcortical GM from the CSF mask
subcortgm = Node(BinaryMaths(
operation='sub',
operand_file='default_images/subcortical_gm_mask_bin.nii.gz',
output_type='NIFTI_GZ',
args='-bin'),
name='subcortgm')
regressors.connect([(regseg, subcortgm, [(('tissue_class_files', pickindex,
0), 'in_file')])])
# Fill the mask holes
fillcsf = Node(MaskTool(fill_holes=True, outputtype='NIFTI_GZ'),
name='fillcsf')
regressors.connect([(subcortgm, fillcsf, [('out_file', 'in_file')])])
# Erode the mask
erocsf = Node(MaskTool(outputtype='NIFTI_GZ', dilate_inputs='-1'),
name='erocsf')
regressors.connect([(fillcsf, erocsf, [('out_file', 'in_file')])])
# Take mean csf signal from functional image
meancsf = Node(ImageMeants(output_type='NIFTI_GZ'), name='meancsf')
regressors.connect([(erocsf, meancsf, [('out_file', 'mask')])])
preproc.connect([(coreg, regressors, [('warp.output_image',
'meancsf.in_file')])])
bpf_dt_csf = Node(CSFbpfdt(), name='bpf_dt_csf')
regressors.connect([(meancsf, bpf_dt_csf, [('out_file', 'in_file')])])
'''
Creates a local white matter regressor
'''
# subtract subcortical gm
subcortgm2 = Node(BinaryMaths(
operation='sub',
operand_file='default_images/subcortical_gm_mask_bin.nii.gz',
output_type='NIFTI_GZ',
args='-bin'),
name='subcortgm2')
regressors.connect([(regseg, subcortgm2, [(('tissue_class_files',
pickindex, 2), 'in_file')])])
# fill mask
fillwm = Node(MaskTool(fill_holes=True, outputtype='NIFTI_GZ'),
name='fillwm')
regressors.connect([(subcortgm2, fillwm, [('out_file', 'in_file')])])
# erod mask
erowm = Node(MaskTool(outputtype='NIFTI_GZ', dilate_inputs='-1'),
name='erowm')
regressors.connect([(fillwm, erowm, [('out_file', 'in_file')])])
# generate local wm
localwm = Node(Localstat(neighborhood=('SPHERE', 25),
stat='mean',
nonmask=True,
outputtype='NIFTI_GZ'),
name='localwm')
regressors.connect([(erowm, localwm, [('out_file', 'mask_file')])])
preproc.connect([(coreg, regressors, [('warp.output_image',
'localwm.in_file')])])
# bandpass filter the local wm regressor
localwm_bpf = Node(Fourier(highpass=0.01,
lowpass=0.08,
args='-retrend',
outputtype='NIFTI_GZ'),
name='loacwm_bpf')
regressors.connect([(localwm, localwm_bpf, [('out_file', 'in_file')])])
# detrend the local wm regressor
localwm_bpf_dt = Node(Detrend(args='-polort 2', outputtype='NIFTI_GZ'),
name='localwm_bpf_dt')
regressors.connect([(localwm_bpf, localwm_bpf_dt, [('out_file', 'in_file')
])])
'''
Clean up your functional image with the regressors you have created above
'''
# create a mask for blurring filtering, and detrending
clean = Workflow(name='clean')
mask = Node(BET(mask=True, functional=True), name='mask')
mean_mask = Node(MCFLIRT(mean_vol=True, output_type='NIFTI_GZ'),
name="mean_mask")
dilf = Node(DilateImage(operation='max', output_type='NIFTI_GZ'),
name='dilf')
clean.connect([(mask, dilf, [('mask_file', 'in_file')])])
preproc.connect([(scrub, clean, [('remean2.out_file', 'mask.in_file')])])
fill = Node(MaskTool(in_file='default_images/MNI152_T1_2mm_brain.nii.gz',
fill_holes=True,
outputtype='NIFTI_GZ'),
name='fill')
axb = Node(Calc(expr='a*b', outputtype='NIFTI_GZ'), name='axb')
clean.connect([(dilf, axb, [('out_file', 'in_file_a')])])
clean.connect([(fill, axb, [('out_file', 'in_file_b')])])
bxc = Node(Calc(expr='ispositive(a)*b', outputtype='NIFTI_GZ'), name='bxc')
clean.connect([(mean_mask, bxc, [('mean_img', 'in_file_a')])])
clean.connect([(axb, bxc, [('out_file', 'in_file_b')])])
preproc.connect([(scrub, clean, [('remean2.out_file', 'mean_mask.in_file')
])])
#### BLUR, FOURIER BPF, and DETREND
blurinmask = Node(BlurInMask(fwhm=6, outputtype='NIFTI_GZ'),
name='blurinmask')
clean.connect([(bxc, blurinmask, [('out_file', 'mask')])])
preproc.connect([(scrub, clean, [('remean2.out_file', 'blurinmask.in_file')
])])
fourier = Node(Fourier(highpass=0.01,
lowpass=0.08,
retrend=True,
outputtype='NIFTI_GZ'),
name='fourier')
clean.connect([(blurinmask, fourier, [('out_file', 'in_file')])])
tstat = Node(TStat(args='-mean', outputtype='NIFTI_GZ'), name='tstat')
clean.connect([(fourier, tstat, [('out_file', 'in_file')])])
detrend = Node(Detrend(args='-polort 2', outputtype='NIFTI_GZ'),
name='detrend')
clean.connect([(fourier, detrend, [('out_file', 'in_file')])])
remean = Node(Calc(expr='a+b', outputtype='NIFTI_GZ'), name='remean')
clean.connect([(detrend, remean, [('out_file', 'in_file_a')])])
clean.connect([(tstat, remean, [('out_file', 'in_file_b')])])
concat = Node(ConcatModel(), name='concat')
# Removes nuisance regressors via regression function
clean_rs = Node(Bandpass(highpass=0, lowpass=99999, outputtype='NIFTI_GZ'),
name='clean_rs')
clean.connect([(concat, clean_rs, [('out_file', 'orthogonalize_file')])])
remean1 = Node(Calc(expr='a+b', outputtype='NIFTI_GZ'), name='remean1')
clean.connect([(clean_rs, remean1, [('out_file', 'in_file_a')])])
clean.connect([(tstat, remean1, [('out_file', 'in_file_b')])])
preproc.connect([(regressors, clean, [('bpf_dt_csf.out_file',
'concat.in_file_a')])])
preproc.connect([(fproc, clean, [('moco_bpfdt.out_file',
'concat.in_file_b')])])
preproc.connect([(regressors, clean, [('localwm_bpf_dt.out_file',
'clean_rs.orthogonalize_dset')])])
clean.connect([(remean, clean_rs, [('out_file', 'in_file')])])
'''
Write graphical output detailing the workflows and nodes
'''
fproc.write_graph(graph2use='flat',
format='png',
simple_form=True,
dotfilename='./fproc.dot')
fproc.write_graph(graph2use='colored',
format='png',
simple_form=True,
dotfilename='./fproc_color.dot')
coreg.write_graph(graph2use='flat',
format='png',
simple_form=True,
dotfilename='./coreg.dot')
coreg.write_graph(graph2use='colored',
format='png',
simple_form=True,
dotfilename='./coreg_color.dot')
scrub.write_graph(graph2use='flat',
format='png',
simple_form=True,
dotfilename='./scrub.dot')
scrub.write_graph(graph2use='colored',
format='png',
simple_form=True,
dotfilename='./scrub_color.dot')
regressors.write_graph(graph2use='flat',
format='png',
simple_form=True,
dotfilename='./reg.dot')
regressors.write_graph(graph2use='colored',
format='png',
simple_form=True,
dotfilename='./reg_color.dot')
preproc.write_graph(graph2use='flat',
format='png',
simple_form=True,
dotfilename='./preproc.dot')
preproc.write_graph(graph2use='colored',
format='png',
simple_form=True,
dotfilename='./preproc_color.dot')
return preproc
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=0.55, 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],
for kx in range(25):
subject_list=[subject_list0[kx]]
#------------------------------------6.1 Nodos de seleccion de sujetos-------------------
infosource = Node(IdentityInterface(fields=['asubject_id', 'session_num']), name="infosource")
infosource.iterables = [('asubject_id', subject_list), ('session_num', session)]
selectfiles = Node(SelectFiles(templates, base_directory=Subjects_dir), name="selectfiles")
#------------------------------------6.2 Nodos de Corregistro--------------------------------
bet_anat = Node(BET(frac=0.5, robust=True, mask=True, output_type='NIFTI_GZ'),
name="bet_anat")
segmentation = Node(FAST(output_type='NIFTI_GZ'),
name="segmentation")
threshold = Node(Threshold(thresh=0.5, args='-bin', output_type='NIFTI_GZ'),
name="threshold")
coreg_pre = Node(FLIRT(dof=dofx, output_type='NIFTI_GZ'),
name="coreg_pre")
coreg_bbr = Node(FLIRT(dof=dofx, cost='bbr', schedule=opj(os.getenv('FSLDIR'),'etc/flirtsch/bbr.sch'), output_type='NIFTI_GZ'),
name="coreg_bbr")
MNI=Node(Function(input_names=[], output_names=['out_file'], function=pif.template_MNI),
name='Tamplated_MNI')
Normalization = Node(FLIRT(dof=dofx, output_type='NIFTI_GZ'),
def preprocessing(*argu):
argu = argu[0]
json_file = argu[1]
with open(json_file, 'r') as jsonfile:
info = json.load(jsonfile, object_pairs_hook=OrderedDict)
subject_list = info["subject_list"]
experiment_dir = info["experiment_dir"]
output_dir = 'datasink'
working_dir = 'workingdir'
task_list = info["task_list"]
fwhm = [*map(int, info["fwhm"])]
TR = float(info["TR"])
iso_size = 4
slice_list = [*map(int, info["slice order"])]
# ExtractROI - skip dummy scans
extract = Node(ExtractROI(t_min=int(info["dummy scans"]),
t_size=-1,
output_type='NIFTI'),
name="extract")
slicetime = Node(SliceTiming(num_slices=len(slice_list),
ref_slice=int(median(slice_list)),
slice_order=slice_list,
time_repetition=TR,
time_acquisition=TR - (TR / len(slice_list))),
name="slicetime")
mcflirt = Node(MCFLIRT(mean_vol=True, save_plots=True,
output_type='NIFTI'),
name="mcflirt")
# Smooth - image smoothing
smooth = Node(Smooth(), name="smooth")
smooth.iterables = ("fwhm", fwhm)
# 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="art")
# BET - Skullstrip anatomical Image
bet_anat = Node(BET(frac=0.5, robust=True, output_type='NIFTI_GZ'),
name="bet_anat")
# FAST - Image Segmentation
segmentation = Node(FAST(output_type='NIFTI_GZ'),
name="segmentation",
mem_gb=4)
# Select WM segmentation file from segmentation output
def get_wm(files):
return files[-1]
# Threshold - Threshold WM probability image
threshold = Node(Threshold(thresh=0.5, args='-bin',
output_type='NIFTI_GZ'),
name="threshold")
# FLIRT - pre-alignment of functional images to anatomical images
coreg_pre = Node(FLIRT(dof=6, output_type='NIFTI_GZ'), name="coreg_pre")
# 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="coreg_bbr")
# Apply coregistration warp to functional images
applywarp = Node(FLIRT(interp='spline',
apply_isoxfm=iso_size,
output_type='NIFTI'),
name="applywarp")
# Apply coregistration warp to mean file
applywarp_mean = Node(FLIRT(interp='spline',
apply_isoxfm=iso_size,
output_type='NIFTI_GZ'),
name="applywarp_mean")
# Create a coregistration workflow
coregwf = Workflow(name='coregwf')
coregwf.base_dir = opj(experiment_dir, working_dir)
# Connect all components of the coregistration workflow
coregwf.connect([
(bet_anat, segmentation, [('out_file', 'in_files')]),
(segmentation, threshold, [(('partial_volume_files', get_wm),
'in_file')]),
(bet_anat, 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')]),
(bet_anat, applywarp, [('out_file', 'reference')]),
(coreg_bbr, applywarp_mean, [('out_matrix_file', 'in_matrix_file')]),
(bet_anat, applywarp_mean, [('out_file', 'reference')]),
])
# Infosource - a function free node to iterate over the list of subject names
infosource = Node(IdentityInterface(fields=['subject_id', 'task_name']),
name="infosource")
infosource.iterables = [('subject_id', subject_list),
('task_name', task_list)]
# SelectFiles - to grab the data (alternativ to DataGrabber)
anat_file = opj('sub-{subject_id}', 'anat', 'sub-{subject_id}_T1w.nii.gz')
func_file = opj('sub-{subject_id}', 'func',
'sub-{subject_id}_task-{task_name}_bold.nii.gz')
templates = {'anat': anat_file, 'func': func_file}
selectfiles = Node(SelectFiles(templates,
base_directory=info["base directory"]),
name="selectfiles")
# Datasink - creates output folder for important outputs
datasink = Node(DataSink(base_directory=experiment_dir,
container=output_dir),
name="datasink")
## Use the following DataSink output substitutions
substitutions = [
('_subject_id_', 'sub-'),
('_task_name_', '/task-'),
('_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
# Create a preprocessing workflow
preproc = Workflow(name='preproc')
preproc.base_dir = opj(experiment_dir, working_dir)
# Connect all components of the preprocessing workflow
preproc.connect([
(infosource, selectfiles, [('subject_id', 'subject_id'),
('task_name', 'task_name')]),
(selectfiles, extract, [('func', 'in_file')]),
(extract, slicetime, [('roi_file', 'in_files')]),
(slicetime, mcflirt, [('timecorrected_files', 'in_file')]),
(selectfiles, coregwf, [('anat', 'bet_anat.in_file'),
('anat', 'coreg_bbr.reference')]),
(mcflirt, coregwf, [('mean_img', 'coreg_pre.in_file'),
('mean_img', 'coreg_bbr.in_file'),
('mean_img', 'applywarp_mean.in_file')]),
(mcflirt, coregwf, [('out_file', 'applywarp.in_file')]),
(coregwf, smooth, [('applywarp.out_file', 'in_files')]),
(mcflirt, datasink, [('par_file', 'preproc.@par')]),
(smooth, datasink, [('smoothed_files', 'preproc.@smooth')]),
(coregwf, datasink, [('applywarp_mean.out_file', 'preproc.@mean')]),
(coregwf, art, [('applywarp.out_file', 'realigned_files')]),
(mcflirt, art, [('par_file', 'realignment_parameters')]),
(coregwf, datasink, [('coreg_bbr.out_matrix_file',
'preproc.@mat_file'),
('bet_anat.out_file', 'preproc.@brain')]),
(art, datasink, [('outlier_files', 'preproc.@outlier_files'),
('plot_files', 'preproc.@plot_files')]),
])
# Create preproc output graph# Creat # Create
preproc.write_graph(graph2use='colored', format='png', simple_form=True)
# Visualize the graph
img1 = imread(opj(preproc.base_dir, 'preproc', 'graph.png'))
plt.imshow(img1)
plt.xticks([]), plt.yticks([])
plt.show()
# Visualize the detailed graph# Visua # Visual
preproc.write_graph(graph2use='flat', format='png', simple_form=True)
img2 = imread(opj(preproc.base_dir, 'preproc', 'graph_detailed.png'))
plt.imshow(img2)
plt.xticks([]), plt.yticks([])
plt.show()
print("Workflow all set. Check the workflow image :)")
response = input('Should run the workflow? Enter yes or no :')
if response == 'yes':
preproc.run('MultiProc', plugin_args={'n_procs': 10})
elif response == 'no':
print('Exits the program since you entered no')
else:
raise RuntimeError('Should enter either yes or no')
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()
def transform(self, X, y=None):
initcwd = os.getcwd()
in_files_search_param = self.gather_steps[1]
if type(X[0]) == str and \
self.backend == 'fsl' and \
self.gather_steps[0] != 'source':
in_files_dir = os.path.join(self.project_path, 'derivatives',
self.pipeline_name, 'steps',
self.gather_steps[0])
layout = BIDSLayout(in_files_dir)
X.copy()
for subject in X:
in_files = []
for path in layout.get(subject=subject,
**in_files_search_param):
path = path.filename
in_files.append(path)
for in_file in in_files:
dirname = os.path.dirname(in_file)
prev_files = os.listdir(dirname)
os.chdir(dirname)
fastfsl = FAST(in_files=in_file, **self.backend_param)
fastfsl.run()
step_dir = os.path.join(self.project_path, 'derivatives',
self.pipeline_name, 'steps',
self.transformer_name)
curr_files = os.listdir(dirname)
for dir_file in curr_files:
if dir_file not in prev_files:
filepath_source = os.path.join(dirname, dir_file)
filepath_destination = get_destination_path_for_fast(
step_dir, filepath_source)
os.rename(filepath_source, filepath_destination)
elif type(X[0]) == str and \
self.backend == 'fsl' and \
self.gather_steps[0] == 'source':
in_files_dir = self.project_path
layout = BIDSLayout(in_files_dir)
X = X.copy()
for subject in X:
print('\n\nSUBJECT: {}'.format(subject))
in_files = []
for path in layout.get(subject=subject,
**in_files_search_param):
path = path.filename
if 'derivatives' not in path.split(os.sep):
in_files.append(path)
for in_file in in_files:
print('in_file: {}'.format(in_file))
dirname = os.path.dirname(in_file)
prev_files = os.listdir(dirname)
print('prev_files: {}'.format(prev_files))
print('Processing...')
os.chdir(dirname)
fastfsl = FAST(in_files=in_file, **self.backend_param)
fastfsl.run()
step_dir = os.path.join(self.project_path, 'derivatives',
self.pipeline_name, 'steps',
self.transformer_name)
curr_files = os.listdir(dirname)
print('step_dir: {}'.format(step_dir))
print('curr_files: {}'.format(curr_files))
for dir_file in curr_files:
if dir_file not in prev_files:
filepath_source = os.path.join(dirname, dir_file)
filepath_destination = get_destination_path_for_fast(
step_dir, filepath_source)
print(
'filepath_source: {}'.format(filepath_source))
print('filepath_destination: {}'.format(
filepath_destination))
os.rename(filepath_source, filepath_destination)
os.chdir(initcwd)
return X
def get_wf_tissue_masks(name='wf_tissue_masks'):
'''
This Function gives a workflow that resamples the T1 brains, extracts the
tissue types thresholds at 0.5 and registers them to T2* space
It then registers the tissue priors to the T2* space and then performs a
bitwise AND between two maps.
'''
# csf_tissue_prior_path, gm_tissue_prior_path, wm_tissue_prior_path,
# threshold = 0.5
wf_tissue_masks = Workflow(name=name)
inputspec = Node(IdentityInterface(fields=[
'resampled_anat_file_path', 'func2anat_mat_path', 'std2func_mat_path',
'reference_func_file_path', 'brain_mask_eroded', 'threshold'
]),
name="inputspec")
# FSL FAST node to segment the T1 brain
fast = Node(FAST(out_basename='fast_'), name='fast')
# probability_maps=True,segments=True,
wf_tissue_masks.connect(inputspec, 'resampled_anat_file_path', fast,
'in_files')
# Invert the func2anat matrix to get anat2func
inv_mat = Node(ConvertXFM(invert_xfm=True), name='inv_mat')
wf_tissue_masks.connect(inputspec, 'func2anat_mat_path', inv_mat,
'in_file')
# Transform the above segmented tissue masks to the functional space using the inverse matrix
anat2func_xform_csf = Node(FLIRT(output_type='NIFTI',
apply_xfm=True,
interp='sinc'),
name='anat2func_xform_csf')
wf_tissue_masks.connect(inputspec, 'reference_func_file_path',
anat2func_xform_csf, 'reference')
wf_tissue_masks.connect(inv_mat, 'out_file', anat2func_xform_csf,
'in_matrix_file')
anat2func_xform_wm = Node(FLIRT(output_type='NIFTI',
apply_xfm=True,
interp='sinc'),
name='anat2func_xform_wm')
wf_tissue_masks.connect(inputspec, 'reference_func_file_path',
anat2func_xform_wm, 'reference')
wf_tissue_masks.connect(inv_mat, 'out_file', anat2func_xform_wm,
'in_matrix_file')
std2func_xform_eroded_brain = Node(FLIRT(output_type='NIFTI',
apply_xfm=True,
interp='nearestneighbour'),
name='std2func_xform_eroded_brain')
wf_tissue_masks.connect(inputspec, 'reference_func_file_path',
std2func_xform_eroded_brain, 'reference')
wf_tissue_masks.connect(inputspec, 'std2func_mat_path',
std2func_xform_eroded_brain, 'in_matrix_file')
def select_item_from_array(arr, index=0):
import numpy as np
arr = np.array(arr)
return arr[index]
wf_tissue_masks.connect(
fast, ('partial_volume_files', select_item_from_array, 0),
anat2func_xform_csf, 'in_file')
wf_tissue_masks.connect(
fast, ('partial_volume_files', select_item_from_array, 2),
anat2func_xform_wm, 'in_file')
wf_tissue_masks.connect(inputspec, 'brain_mask_eroded',
std2func_xform_eroded_brain, 'in_file')
# Threshold
def get_opstring(threshold):
op = '-thr ' + str(threshold) + ' -bin'
return op
# print(inputspec.outputs)
# ----- CSF ------
threshold_csf = Node(interface=ImageMaths(suffix='_thresh'),
name='threshold_csf')
# threshold_csf.inputs.op_string = '-thresh '+str(inputspec.outputs.threshold)+' -bin'
wf_tissue_masks.connect(inputspec, ('threshold', get_opstring),
threshold_csf, 'op_string')
wf_tissue_masks.connect(anat2func_xform_csf, 'out_file', threshold_csf,
'in_file')
# ------- GM --------
# threshold_gm = Node(interface=ImageMaths(op_string='-thresh',
# suffix='_thresh'),
# name='threshold_gm')
#
#
# wf_tissue_priors.connect(inputspec, ('threshold', get_opstring), threshold_gm, 'op_string' )
# wf_tissue_priors.connect(fast, partial_volume_map[1], threshold_gm, 'in_file')
#
# -------- WM --------
threshold_wm = Node(interface=ImageMaths(suffix='_thresh'),
name='threshold_wm')
wf_tissue_masks.connect(inputspec, ('threshold', get_opstring),
threshold_wm, 'op_string')
wf_tissue_masks.connect(anat2func_xform_wm, 'out_file', threshold_wm,
'in_file')
# -------------------
#
# wf_tissue_masks.connect(threshold_csf, 'out_file', std2func_xform_csf, 'in_file')
# wf_tissue_masks.connect(threshold_wm, 'out_file', std2func_xform_wm, 'in_file')
# Masking the outer brain CSF
csf_mask = Node(interface=ApplyMask(), name='csf_mask')
wf_tissue_masks.connect(threshold_csf, 'out_file', csf_mask, 'in_file')
wf_tissue_masks.connect(std2func_xform_eroded_brain, 'out_file', csf_mask,
'mask_file')
# Masking the outer brain WM that might be present due to poor BET
wm_mask = Node(interface=ApplyMask(), name='wm_mask')
wf_tissue_masks.connect(threshold_wm, 'out_file', wm_mask, 'in_file')
wf_tissue_masks.connect(std2func_xform_eroded_brain, 'out_file', wm_mask,
'mask_file')
# wm_mask = Node(interface=ApplyMask(),
# name='wm_mask')
# wf_tissue_masks.connect(std2func_xform_wm, 'out_file', wm_mask, 'in_file')
# wf_tissue_masks.connect(std2func_xform_wm_prior, 'out_file', wm_mask, 'mask_file')
outputspec = Node(IdentityInterface(fields=['csf_mask', 'wm_mask']),
name="outputspec")
wf_tissue_masks.connect(csf_mask, 'out_file', outputspec, 'csf_mask')
# wf_tissue_priors.connect(threshold_gm, 'out_file', outputspec, 'gm_tissue_prior_path')
wf_tissue_masks.connect(wm_mask, 'out_file', outputspec, 'wm_mask')
return wf_tissue_masks
"versions": [{
"title": BET().version or "1.0",
"description":
f"Default BET version for nipype {_NIPYPE_VERSION}.", # noqa: E501
"input": BET_INPUT_SPECIFICATION,
"output": BET_OUTPUT_SPECIFICATION,
"nested_results_attribute": "outputs.get_traitsfree",
}],
},
{
"title":
"FAST",
"description":
"FAST (FMRIB's Automated Segmentation Tool) segments a 3D image of the brain into different tissue types (Grey Matter, White Matter, CSF, etc.), whilst also correcting for spatial intensity variations (also known as bias field or RF inhomogeneities).", # noqa: E501
"versions": [{
"title": FAST().version or "1.0",
"description":
f"Default FAST version for nipype {_NIPYPE_VERSION}.", # noqa: E501
"input": FAST_INPUT_SPECIFICATION,
"output": FAST_OUTPUT_SPECIFICATION,
}],
},
{
"title":
"FLIRT",
"description":
"FLIRT (FMRIB's Linear Image Registration Tool) is a fully automated robust and accurate tool for linear (affine) intra- and inter-modal brain image registration.", # noqa: E501
"versions": [{
"title": FLIRT().version or "1.0",
"description":
f"Default FLIRT version for nipype {_NIPYPE_VERSION}.", # noqa: E501
def pals(config: dict):
# Get config file defining workflow
# configs = json.load(open(config_file, 'r'))
print('Starting: initializing workflow.')
# Build pipelie
wf = Workflow(name='PALS')
# bidsLayout = bids.BIDSLayout(config['BIDSRoot'])
# Get data
loader = BIDSDataGrabber(index_derivatives=False)
loader.inputs.base_dir = config['BIDSRoot']
loader.inputs.subject = config['Subject']
if (config['Session'] is not None):
loader.inputs.session = config['Session']
loader.inputs.output_query = {
't1w': dict(**config['T1Entities'], invalid_filters='allow')
}
loader.inputs.extra_derivatives = [config['BIDSRoot']]
loader = Node(loader, name='BIDSgrabber')
entities = {
'subject': config['Subject'],
'session': config['Session'],
'suffix': 'T1w',
'extension': '.nii.gz'
}
# Reorient to radiological
if (config['Analysis']['Reorient']):
radio = MapNode(
Reorient(orientation=config['Analysis']['Orientation']),
name="reorientation",
iterfield='in_file')
if ('Reorient' in config['Outputs'].keys()):
reorient_sink = MapNode(Function(function=copyfile,
input_names=['src', 'dst']),
name='reorient_copy',
iterfield='src')
path_pattern = 'sub-{subject}/ses-{session}/anat/sub-{subject}_ses-{session}_desc-' + config[
'Analysis']['Orientation'] + '_{suffix}{extension}'
reorient_filename = join(config['Outputs']['Reorient'],
path_pattern.format(**entities))
pathlib.Path(os.path.dirname(reorient_filename)).mkdir(
parents=True, exist_ok=True)
reorient_sink.inputs.dst = reorient_filename
wf.connect([(radio, reorient_sink, [('out_file', 'src')])])
else:
radio = MapNode(Function(function=infile_to_outfile,
input_names='in_file',
output_names='out_file'),
name='identity',
iterfield='in_file')
# Brain extraction
bet = node_fetch.extraction_node(config, **config['BrainExtraction'])
if ('BrainExtraction' in config['Outputs'].keys()):
path_pattern = 'sub-{subject}/ses-{session}/anat/sub-{subject}_ses-{session}_space-' + \
config['Outputs']['StartRegistrationSpace'] + '_desc-brain_mask{extension}'
brain_mask_sink = MapNode(Function(function=copyfile,
input_names=['src', 'dst']),
name='brain_mask_sink',
iterfield='src')
brain_mask_out = join(config['Outputs']['BrainExtraction'],
path_pattern.format(**entities))
pathlib.Path(os.path.dirname(brain_mask_out)).mkdir(parents=True,
exist_ok=True)
brain_mask_sink.inputs.dst = brain_mask_out
## Lesion load calculation
# Registration
reg = node_fetch.registration_node(config, **config['Registration'])
if ('RegistrationTransform' in config['Outputs'].keys()):
path_pattern = 'sub-{subject}/ses-{session}/anat/sub-{subject}_ses-{session}_space-' + \
config['Outputs']['StartRegistrationSpace'] + '_desc-transform.mat'
registration_transform_filename = join(
config['Outputs']['RegistrationTransform'],
path_pattern.format(**entities))
registration_transform_sink = MapNode(Function(
function=copyfile, input_names=['src', 'dst']),
name='registration_transf_sink',
iterfield='src')
pathlib.Path(os.path.dirname(registration_transform_filename)).mkdir(
parents=True, exist_ok=True)
registration_transform_sink.inputs.dst = registration_transform_filename
wf.connect([(reg, registration_transform_sink, [('out_matrix_file',
'src')])])
# Get mask
mask_path_fetcher = Node(BIDSDataGrabber(
base_dir=config['LesionRoot'],
subject=config['Subject'],
index_derivatives=False,
output_query={
'mask': dict(**config['LesionEntities'], invalid_filters='allow')
},
extra_derivatives=[config['LesionRoot']]),
name='mask_grabber')
if (config['Session'] is not None):
mask_path_fetcher.inputs.session = config['Session']
# Apply reg file to lesion mask
apply_xfm = node_fetch.apply_xfm_node(config)
# Lesion load calculation
if (config['Analysis']['LesionLoadCalculation']):
lesion_load = MapNode(Function(function=overlap,
input_names=['ref_mask', 'roi_list'],
output_names='out_list'),
name='overlap_calc',
iterfield=['ref_mask'])
roi_list = []
if (os.path.exists(config['ROIDir'])):
buf = os.listdir(config['ROIDir'])
roi_list = [
os.path.abspath(os.path.join(config['ROIDir'], b)) for b in buf
]
else:
warnings.warn(f"ROIDir ({config['ROIDir']}) doesn't exist.")
buf = config['ROIList']
roi_list += [os.path.abspath(b) for b in buf]
lesion_load.inputs.roi_list = roi_list
# CSV output
csv_output = MapNode(Function(
function=csv_writer,
input_names=['filename', 'data_dict', 'subject', 'session']),
name='csv_output',
iterfield=['data_dict'])
csv_output.inputs.subject = config['Subject']
csv_output.inputs.session = config['Session']
path_pattern = 'sub-{subject}/ses-{session}/anat/sub-{subject}_ses-{session}_desc-LesionLoad.csv'
csv_out_filename = join(config['Outputs']['RegistrationTransform'],
path_pattern.format(**entities))
csv_output.inputs.filename = csv_out_filename
wf.connect([(apply_xfm, lesion_load, [('out_file', 'ref_mask')]),
(lesion_load, csv_output, [('out_list', 'data_dict')])])
## Lesion correction
if (config['Analysis']['LesionCorrection']):
## White matter removal node. Does the white matter correction; has multiple inputs that need to be supplied.
wm_removal = MapNode(Function(
function=white_matter_correction,
input_names=[
'image', 'wm_mask', 'lesion_mask', 'max_difference_fraction'
],
output_names=['out_data', 'corrected_volume']),
name='wm_removal',
iterfield=['image', 'wm_mask', 'lesion_mask'])
wm_removal.inputs.max_difference_fraction = config['LesionCorrection'][
'WhiteMatterSpread']
## File loaders
# Loads the subject image, passes it to wm_removal node
subject_image_loader = MapNode(Function(function=image_load,
input_names=['in_filename'],
output_names='out_image'),
name='file_load0',
iterfield='in_filename')
wf.connect([
(radio, subject_image_loader, [('out_file', 'in_filename')]),
(subject_image_loader, wm_removal, [('out_image', 'image')])
])
# Loads the mask image, passes it to wm_removal node
mask_image_loader = MapNode(Function(function=image_load,
input_names=['in_filename'],
output_names='out_image'),
name='file_load2',
iterfield='in_filename')
wf.connect([
(mask_path_fetcher, mask_image_loader, [('mask', 'in_filename')]),
(mask_image_loader, wm_removal, [('out_image', 'lesion_mask')])
])
# Save lesion mask with white matter voxels removed
output_image = MapNode(Function(
function=image_write,
input_names=['image', 'reference', 'file_name']),
name='image_writer0',
iterfield=['image', 'reference'])
path_pattern = 'sub-{subject}/ses-{session}/anat/sub-{subject}_ses-{session}_space-' + \
config['Outputs']['StartRegistrationSpace'] + '_desc-CorrectedLesion_mask{extension}'
lesion_corrected_filename = join(config['Outputs']['LesionCorrected'],
path_pattern.format(**entities))
output_image.inputs.file_name = lesion_corrected_filename
wf.connect([(wm_removal, output_image, [('out_data', 'image')]),
(mask_path_fetcher, output_image, [('mask', 'reference')])
])
## CSV output
csv_output_corr = MapNode(Function(function=csv_writer,
input_names=[
'filename', 'subject',
'session', 'data', 'data_name'
]),
name='csv_output_corr',
iterfield=['data'])
csv_output_corr.inputs.subject = config['Subject']
csv_output_corr.inputs.session = config['Session']
csv_output_corr.inputs.data_name = 'CorrectedVolume'
path_pattern = 'sub-{subject}/ses-{session}/anat/sub-{subject}_ses-{session}_desc-LesionLoad.csv'
csv_out_filename = join(config['Outputs']['RegistrationTransform'],
path_pattern.format(**entities))
csv_output_corr.inputs.filename = csv_out_filename
wf.connect([(wm_removal, csv_output_corr, [('corrected_volume', 'data')
])])
## White matter segmentation; either do segmentation or load the file
if (config['Analysis']['WhiteMatterSegmentation']):
# Config is set to do white matter segmentation
# T1 intensity normalization
t1_norm = MapNode(Function(
function=rescale_image,
input_names=['image', 'range_min', 'range_max', 'save_image'],
output_names='out_file'),
name='normalization',
iterfield=['image'])
t1_norm.inputs.range_min = config['LesionCorrection'][
'ImageNormMin']
t1_norm.inputs.range_max = config['LesionCorrection'][
'ImageNormMax']
t1_norm.inputs.save_image = True
wf.connect([(bet, t1_norm, [('out_file', 'image')])])
# White matter segmentation
wm_seg = MapNode(FAST(), name="wm_seg", iterfield='in_files')
wm_seg.inputs.out_basename = "segmentation"
wm_seg.inputs.img_type = 1
wm_seg.inputs.number_classes = 3
wm_seg.inputs.hyper = 0.1
wm_seg.inputs.iters_afterbias = 4
wm_seg.inputs.bias_lowpass = 20
wm_seg.inputs.segments = True
wm_seg.inputs.no_pve = True
ex_last = MapNode(Function(function=extract_last,
input_names=['in_list'],
output_names='out_entry'),
name='ex_last',
iterfield='in_list')
file_load1 = MapNode(Function(function=image_load,
input_names=['in_filename'],
output_names='out_image'),
name='file_load1',
iterfield='in_filename')
# White matter output; only necessary if white matter is segmented
wm_map = MapNode(Function(
function=image_write,
input_names=['image', 'reference', 'file_name']),
name='image_writer1',
iterfield=['image', 'reference'])
path_pattern = 'sub-{subject}/ses-{session}/anat/sub-{subject}_ses-{session}_space-' + \
config['Outputs']['StartRegistrationSpace'] + '_desc-WhiteMatter_mask{extension}'
wm_map_filename = join(config['Outputs']['LesionCorrected'],
path_pattern.format(**entities))
wm_map.inputs.file_name = wm_map_filename
wf.connect([(file_load1, wm_map, [('out_image', 'image')]),
(mask_path_fetcher, wm_map, [('mask', 'reference')])])
# Connect nodes in workflow
wf.connect([
(wm_seg, ex_last, [('tissue_class_files', 'in_list')]),
(t1_norm, wm_seg, [('out_file', 'in_files')]),
# (ex_last, wm_map, [('out_entry', 'image')]),
(ex_last, file_load1, [('out_entry', 'in_filename')]),
(file_load1, wm_removal, [('out_image', 'wm_mask')])
])
elif (config['Analysis']['LesionCorrection']):
# No white matter segmentation should be done, but lesion correction is expected.
# White matter segmentation must be supplied
wm_seg_path = config['WhiteMatterSegmentationFile']
if (len(wm_seg_path) == 0 or not os.path.exists(wm_seg_path)):
# Check if file exists at output
path_pattern = 'sub-{subject}/ses-{session}/anat/sub-{subject}_ses-{session}_space-' + \
config['Outputs']['StartRegistrationSpace'] + '_desc-WhiteMatter_mask{extension}'
wm_map_filename = join(config['Outputs']['LesionCorrected'],
path_pattern.format(**entities))
if (os.path.exists(wm_map_filename)):
wm_seg_path = wm_map_filename
else:
raise ValueError(
'Config file is inconsistent; if WhiteMatterSegmentation is false but LesionCorrection'
' is true, then WhiteMatterSegmentationFile must be defined and must exist.'
)
file_load1 = MapNode(Function(function=image_load,
input_names=['in_filename'],
output_names='out_image'),
name='file_load1',
iterfield='in_filename')
file_load1.inputs.in_filename = wm_seg_path
# Connect nodes in workflow
wf.connect([(file_load1, wm_removal, [('out_image', 'wm_mask')])])
# Connecting workflow.
wf.connect([
# Starter
(loader, radio, [('t1w', 'in_file')]),
(radio, bet, [('out_file', 'in_file')]),
(bet, reg, [('out_file', 'in_file')]),
(reg, apply_xfm, [('out_matrix_file', 'in_matrix_file')]),
(mask_path_fetcher, apply_xfm, [('mask', 'in_file')]),
])
try:
graph_out = config['Outputs'][
'LesionCorrected'] + '/sub-{subject}/ses-{session}/anat/'.format(
**entities)
wf.write_graph(graph2use='orig',
dotfilename=join(graph_out, 'graph.dot'),
format='png')
os.remove(graph_out + 'graph.dot')
os.remove(graph_out + 'graph_detailed.dot')
except OSError:
warnings.warn(
"graphviz not installed; can't produce graph. See http://www.graphviz.org/download/ for "
"installation instructions.")
wf.run()
return wf
del d["partial_volume_files"]
return d
interfaces = {
"BET": {
BET().version: BET
},
"CAT12 Segmentation": {
"12.6": Cat12Segmentation
},
"fslreorient2std": {
Reorient2Std().version: Reorient2Std
},
"FAST": {
FAST().version: FastWrapper
},
"FLIRT": {
FLIRT().version: FLIRT
},
"FNIRT": {
FNIRT().version: FNIRT
},
"FSL Anatomical Processing Script": {
FslAnat.__version__: FslAnat
},
"SUSAN": {
SUSAN().version: SUSAN
},
"ReconAll": {
ReconAll().version: ReconAll
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()