def apply_xfm_node(config: dict, **kwargs):
'''
Parses config file to return desired apply_xfm node.
Parameters
----------
config : dict
PALS config file
kwargs
Keyword arguments to send to registration method.
Returns
-------
MapNode
'''
if (not config['Analysis']['Registration']):
# No registration; no xfm to apply.
n = MapNode(Function(function=infile_to_outfile,
input_names=['in_file', 'in_matrix_file'],
output_names='out_file'),
name='transformation_skip',
iterfield=['in_file', 'in_matrix_file'])
else:
n = MapNode(fsl.FLIRT(apply_xfm=True,
reference=config['Registration']['reference']),
name='transformation_flirt',
iterfield=['in_file', 'in_matrix_file'])
return n
def registration_node(config: dict, **kwargs):
'''
Parses config file to return desired registration method.
Parameters
----------
config : dict
PALS config file
kwargs
Keyword arguments to send to registration method.
Returns
-------
MapNode
'''
# Get registration method
reg_method = config['Analysis']['RegistrationMethod']
if (not config['Analysis']['Registration']):
# No registration; in -> out
n = MapNode(Function(function=reg_no_reg,
input_names=['in_file'],
output_names=['out_file', 'out_matrix_file']),
name='registration_identity',
iterfield='in_file')
elif (reg_method.lower() == 'flirt'):
# Use FLIRT
n = MapNode(fsl.FLIRT(),
name='registration_flirt',
iterfield='in_file')
for k, v in kwargs.items():
setattr(n.inputs, k, v)
else:
raise (NotImplementedError(
f'Registration method {reg_method} not implemented.'))
return n
def extraction_node(config: dict, **kwargs):
'''
Parses config file to return desired brain extraction method.
Parameters
----------
config : dict
PALS config file
kwargs
Keyword arguments to send to brain extraction method.
Returns
-------
MapNode
'''
# Get extraction type
extract_type = config['Analysis']['BrainExtractionMethod']
if (not config['Analysis']['BrainExtraction']):
# No brain extraction; in-> out
n = MapNode(Function(function=infile_to_outfile,
input_names='in_file',
output_names='out_file'),
name='extract_skip',
iterfield='in_file')
return n
elif (extract_type.lower() == 'bet'):
n = MapNode(fsl.BET(**kwargs),
name='extraction_bet',
iterfield='in_file')
return n
else:
raise (NotImplementedError(
f'Extraction method {extract_type} not implemented.'))
def fristons_twenty_four_wf(wf_name='fristons_twenty_four'):
""" The main purpose of this workflow is to calculate 24 parameters including
the 6 motion parameters of the current volume and the preceeding volume,
plus each of these values squared.
Parameters
----------
wf_name: str
Workflow name
Returns
-------
wf: workflow object
Nipype Inputs
-------------
f24_input.in_file: str
Path to the input movement file from motion correction.
Nipype Outputs
-------------
f24_output.out_file: str
Path to 1D file containing the friston 24 parameters.
References
----------
.. [1] Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S., & Turner, R. (1996).
Movement-related effects in fMRI time-series. Magnetic Resonance in Medicine, 35(3),346-355
"""
wf = pe.Workflow(name=wf_name)
# specify input and output fields
in_fields = [
"in_file",
]
out_fields = [
"out_file",
]
f24_input = setup_node(IdentityInterface(fields=in_fields,
mandatory_inputs=True),
name='f24_input')
calc_friston = setup_node(Function(input_names=['in_file'],
output_names=['out_file'],
function=calc_friston_twenty_four),
name='calc_friston')
f24_output = setup_node(IdentityInterface(fields=out_fields),
name='f24_output')
# Connect the nodes
wf.connect([
(f24_input, calc_friston, [("in_file", "in_file")]),
(calc_friston, f24_output, [("out_file", "out_file")]),
])
return wf
def create_volatlas_workflow(wf_name, wf_base_dir, subject_list, cnxn_names,
fstr_dict, ref_file, agg_cnxn_names_dict):
from nipype.pipeline import engine as pe
from nipype.pipeline.engine import Node, JoinNode, MapNode, Workflow
from nipype.pipeline.engine.utils import IdentityInterface
from nipype.interfaces import Function
from nipype.interfaces.io import DataSink
"""
Variables
"""
dpy_fstr = fstr_dict['dpy']
warp_fstr = fstr_dict['warp']
parc_fstr = fstr_dict['parc']
cnxn_mapping_fstr = fstr_dict['cnxn_mapping']
"""
Node: Infosource
"""
# (iterates over subjects)
def mirror(subject_id):
return subject_id
mirror_npfunc = Function(['subject_id'], ['subject_id'], mirror)
node__infosource = Node(interface=mirror_npfunc, name="infosource")
node__infosource.iterables = [("subject_id", subject_list)]
"""
Node: Get data
"""
# (also iterates over cnxn ids)
def get_sub_files_func(subject_id, cnxn_name, dpy_fstr, warp_fstr,
parc_fstr, cnxn_mapping_fstr):
dpy_file = dpy_fstr % subject_id
cnxn_mapping_file = cnxn_mapping_fstr % subject_id
parc_file = parc_fstr % subject_id
warp_file = warp_fstr % subject_id
return dpy_file, parc_file, warp_file, cnxn_mapping_file, cnxn_name, subject_id
get_sub_files_npfunc = Function([
'subject_id', 'cnxn_name', 'dpy_fstr', 'warp_fstr', 'parc_fstr',
'cnxn_mapping_fstr'
], [
'dpy_file', 'parc_file', 'warp_file', 'cnxn_mapping_file', 'cnxn_name',
'subject_id'
], get_sub_files_func)
node__datasource = Node(interface=get_sub_files_npfunc, name='datasource')
node__datasource.inputs.dpy_fstr = dpy_fstr
node__datasource.inputs.parc_fstr = parc_fstr
node__datasource.inputs.warp_fstr = warp_fstr
node__datasource.inputs.cnxn_mapping_fstr = cnxn_mapping_fstr
node__datasource.iterables = [('cnxn_name', cnxn_names)]
"""
Node: Make sub cnxn visitation map
"""
make_sub_vismap_npfunc = Function([
'sub', 'dpy_file', 'parc_file', 'warp_file', 'ref_file',
'cnxn_inds_file', 'cnxn_name', 'vismap_fstr'
], ['sub_vismap_file'], make_sub_cnxn_visitation_map)
node__makesubvismap = Node(interface=make_sub_vismap_npfunc,
name="make_sub_vismap")
node__makesubvismap.inputs.ref_file = ref_file
node__makesubvismap.inputs.vismap_fstr = 'temp_vismap_%s.nii.gz'
#node__makesubvismap.inputs.overwrite=True
"""
Node: make grp cnxn visitation map
"""
make_grp_vismap_npfunc = Function(
['cnxn_name', 'sub_vismaps', 'grp_vismap_fstr', 'subs_list'],
['grp_vismap_fpath', 'grp_vismap_norm_fpath', 'subs_list_file'],
make_group_cnxn_visitation_map)
node__makegrpvismap = JoinNode(interface=make_grp_vismap_npfunc,
name='make_grp_vismap',
joinsource="infosource",
joinfield=["sub_vismaps",
'subs_list']) #subject_id")
node__makegrpvismap.inputs.grp_vismap_fstr = 'grp_vismap_%s.nii.gz' # this needs to be changed to come from previous node in wf
"""
Node: aggregate group cnxn visitation map
"""
# (to do...)
agg_grp_vismap_npfunc = Function(['in_files', 'cnxn_names', 'outfname'],
['agg_image_file', 'agg_list_file'],
aggregate_grp_vismap,
imports=['import os'])
node__agggrpvismap = JoinNode(interface=agg_grp_vismap_npfunc,
name='agg_grp_vismap',
joinsource="datasource",
joinfield=["in_files"])
node__agggrpvismap.iterables = [("cnxn_names",
agg_cnxn_names_dict.values()),
("outfname", agg_cnxn_names_dict.keys())]
node__agggrpvismap.synchronize = True
"""
Node: datasink
"""
# I want to use a mapnode for this, but can't get it to work
# so have to settle with this followed by a command line copy...
# (if you don't have a mapnode, just get same result as outputs of agggrpvismap node...)
node__datasink = Node(DataSink(), name='datasink')
node__datasink.inputs.base_directory = wf_base_dir
#node__datasinkniifile = MapNode(DataSink(infields=['agg_image_file']),name='ds_nii', iterfield=['agg_image_file'])
#node__datasinkniifile.inputs.base_directory=wf_base_dir
#node__datasinktxtfile = MapNode(DataSink(infields=['agg_list_file']),name='ds_txt', iterfield=['agg_list_file'])
#node__datasinktxtfile.inputs.base_directory=wf_base_dir
"""
Workflow: put it all together
"""
wf = pe.Workflow(name=wf_name)
wf.base_dir = wf_base_dir
wf.connect(node__infosource, 'subject_id', node__datasource, 'subject_id')
wf.connect(node__datasource, 'subject_id', node__makesubvismap, 'sub')
wf.connect(node__datasource, 'dpy_file', node__makesubvismap, 'dpy_file')
wf.connect(node__datasource, 'parc_file', node__makesubvismap, 'parc_file')
wf.connect(node__datasource, 'warp_file', node__makesubvismap, 'warp_file')
wf.connect(node__datasource, 'cnxn_mapping_file', node__makesubvismap,
'cnxn_inds_file')
wf.connect(node__datasource, 'cnxn_name', node__makesubvismap, 'cnxn_name')
wf.connect(node__makesubvismap, 'sub_vismap_file', node__makegrpvismap,
'sub_vismaps')
wf.connect(node__datasource, 'cnxn_name', node__makegrpvismap, 'cnxn_name')
wf.connect(node__datasource, 'subject_id', node__makegrpvismap,
'subs_list')
wf.connect(node__makegrpvismap, 'grp_vismap_norm_fpath',
node__agggrpvismap, 'in_files')
wf.connect(node__agggrpvismap, 'agg_image_file', node__datasink,
'@agg_image_file')
wf.connect(node__agggrpvismap, 'agg_list_file', node__datasink,
'@agg_list_file')
#wf.connect(node__agggrpvismap, 'agg_image_file', node__datasinkniifile, '@agg_image_file')
#wf.connect(node__agggrpvismap, 'agg_list_file', node__datasinktxtfile, '@agg_list_file')
return wf
def create_workflow(files,
target_file,
subject_id,
TR,
slice_times,
norm_threshold=1,
num_components=5,
vol_fwhm=None,
surf_fwhm=None,
lowpass_freq=-1,
highpass_freq=-1,
subjects_dir=None,
sink_directory=os.getcwd(),
target_subject=['fsaverage3', 'fsaverage4'],
name='resting'):
wf = Workflow(name=name)
# Rename files in case they are named identically
name_unique = MapNode(Rename(format_string='rest_%(run)02d'),
iterfield=['in_file', 'run'],
name='rename')
name_unique.inputs.keep_ext = True
name_unique.inputs.run = list(range(1, len(files) + 1))
name_unique.inputs.in_file = files
realign = Node(interface=spm.Realign(), name="realign")
realign.inputs.jobtype = 'estwrite'
num_slices = len(slice_times)
slice_timing = Node(interface=spm.SliceTiming(), name="slice_timing")
slice_timing.inputs.num_slices = num_slices
slice_timing.inputs.time_repetition = TR
slice_timing.inputs.time_acquisition = TR - TR / float(num_slices)
slice_timing.inputs.slice_order = (np.argsort(slice_times) + 1).tolist()
slice_timing.inputs.ref_slice = int(num_slices / 2)
# Comute TSNR on realigned data regressing polynomials upto order 2
tsnr = MapNode(TSNR(regress_poly=2), iterfield=['in_file'], name='tsnr')
wf.connect(slice_timing, 'timecorrected_files', tsnr, 'in_file')
# Compute the median image across runs
calc_median = Node(Function(input_names=['in_files'],
output_names=['median_file'],
function=median,
imports=imports),
name='median')
wf.connect(tsnr, 'detrended_file', calc_median, 'in_files')
"""Segment and Register
"""
registration = create_reg_workflow(name='registration')
wf.connect(calc_median, 'median_file', registration,
'inputspec.mean_image')
registration.inputs.inputspec.subject_id = subject_id
registration.inputs.inputspec.subjects_dir = subjects_dir
registration.inputs.inputspec.target_image = target_file
"""Use :class:`nipype.algorithms.rapidart` to determine which of the
images in the functional series are outliers based on deviations in
intensity or movement.
"""
art = Node(interface=ArtifactDetect(), name="art")
art.inputs.use_differences = [True, True]
art.inputs.use_norm = True
art.inputs.norm_threshold = norm_threshold
art.inputs.zintensity_threshold = 9
art.inputs.mask_type = 'spm_global'
art.inputs.parameter_source = 'SPM'
"""Here we are connecting all the nodes together. Notice that we add the merge node only if you choose
to use 4D. Also `get_vox_dims` function is passed along the input volume of normalise to set the optimal
voxel sizes.
"""
wf.connect([
(name_unique, realign, [('out_file', 'in_files')]),
(realign, slice_timing, [('realigned_files', 'in_files')]),
(slice_timing, art, [('timecorrected_files', 'realigned_files')]),
(realign, art, [('realignment_parameters', 'realignment_parameters')]),
])
def selectindex(files, idx):
import numpy as np
from nipype.utils.filemanip import filename_to_list, list_to_filename
return list_to_filename(
np.array(filename_to_list(files))[idx].tolist())
mask = Node(fsl.BET(), name='getmask')
mask.inputs.mask = True
wf.connect(calc_median, 'median_file', mask, 'in_file')
# get segmentation in normalized functional space
def merge_files(in1, in2):
out_files = filename_to_list(in1)
out_files.extend(filename_to_list(in2))
return out_files
# filter some noise
# Compute motion regressors
motreg = Node(Function(
input_names=['motion_params', 'order', 'derivatives'],
output_names=['out_files'],
function=motion_regressors,
imports=imports),
name='getmotionregress')
wf.connect(realign, 'realignment_parameters', motreg, 'motion_params')
# Create a filter to remove motion and art confounds
createfilter1 = Node(Function(
input_names=['motion_params', 'comp_norm', 'outliers', 'detrend_poly'],
output_names=['out_files'],
function=build_filter1,
imports=imports),
name='makemotionbasedfilter')
createfilter1.inputs.detrend_poly = 2
wf.connect(motreg, 'out_files', createfilter1, 'motion_params')
wf.connect(art, 'norm_files', createfilter1, 'comp_norm')
wf.connect(art, 'outlier_files', createfilter1, 'outliers')
filter1 = MapNode(fsl.GLM(out_f_name='F_mcart.nii',
out_pf_name='pF_mcart.nii',
demean=True),
iterfield=['in_file', 'design', 'out_res_name'],
name='filtermotion')
wf.connect(slice_timing, 'timecorrected_files', filter1, 'in_file')
wf.connect(slice_timing, ('timecorrected_files', rename, '_filtermotart'),
filter1, 'out_res_name')
wf.connect(createfilter1, 'out_files', filter1, 'design')
createfilter2 = MapNode(Function(input_names=[
'realigned_file', 'mask_file', 'num_components', 'extra_regressors'
],
output_names=['out_files'],
function=extract_noise_components,
imports=imports),
iterfield=['realigned_file', 'extra_regressors'],
name='makecompcorrfilter')
createfilter2.inputs.num_components = num_components
wf.connect(createfilter1, 'out_files', createfilter2, 'extra_regressors')
wf.connect(filter1, 'out_res', createfilter2, 'realigned_file')
wf.connect(registration,
('outputspec.segmentation_files', selectindex, [0, 2]),
createfilter2, 'mask_file')
filter2 = MapNode(fsl.GLM(out_f_name='F.nii',
out_pf_name='pF.nii',
demean=True),
iterfield=['in_file', 'design', 'out_res_name'],
name='filter_noise_nosmooth')
wf.connect(filter1, 'out_res', filter2, 'in_file')
wf.connect(filter1, ('out_res', rename, '_cleaned'), filter2,
'out_res_name')
wf.connect(createfilter2, 'out_files', filter2, 'design')
wf.connect(mask, 'mask_file', filter2, 'mask')
bandpass = Node(Function(
input_names=['files', 'lowpass_freq', 'highpass_freq', 'fs'],
output_names=['out_files'],
function=bandpass_filter,
imports=imports),
name='bandpass_unsmooth')
bandpass.inputs.fs = 1. / TR
bandpass.inputs.highpass_freq = highpass_freq
bandpass.inputs.lowpass_freq = lowpass_freq
wf.connect(filter2, 'out_res', bandpass, 'files')
"""Smooth the functional data using
:class:`nipype.interfaces.spm.Smooth`.
"""
smooth = Node(interface=spm.Smooth(), name="smooth")
smooth.inputs.fwhm = vol_fwhm
wf.connect(bandpass, 'out_files', smooth, 'in_files')
collector = Node(Merge(2), name='collect_streams')
wf.connect(smooth, 'smoothed_files', collector, 'in1')
wf.connect(bandpass, 'out_files', collector, 'in2')
"""
Transform the remaining images. First to anatomical and then to target
"""
warpall = MapNode(ants.ApplyTransforms(),
iterfield=['input_image'],
name='warpall')
warpall.inputs.input_image_type = 3
warpall.inputs.interpolation = 'Linear'
warpall.inputs.invert_transform_flags = [False, False]
warpall.inputs.terminal_output = 'file'
warpall.inputs.reference_image = target_file
warpall.inputs.args = '--float'
warpall.inputs.num_threads = 1
# transform to target
wf.connect(collector, 'out', warpall, 'input_image')
wf.connect(registration, 'outputspec.transforms', warpall, 'transforms')
mask_target = Node(fsl.ImageMaths(op_string='-bin'), name='target_mask')
wf.connect(registration, 'outputspec.anat2target', mask_target, 'in_file')
maskts = MapNode(fsl.ApplyMask(), iterfield=['in_file'], name='ts_masker')
wf.connect(warpall, 'output_image', maskts, 'in_file')
wf.connect(mask_target, 'out_file', maskts, 'mask_file')
# map to surface
# extract aparc+aseg ROIs
# extract subcortical ROIs
# extract target space ROIs
# combine subcortical and cortical rois into a single cifti file
#######
# Convert aparc to subject functional space
# Sample the average time series in aparc ROIs
sampleaparc = MapNode(
freesurfer.SegStats(default_color_table=True),
iterfield=['in_file', 'summary_file', 'avgwf_txt_file'],
name='aparc_ts')
sampleaparc.inputs.segment_id = ([8] + list(range(10, 14)) +
[17, 18, 26, 47] + list(range(49, 55)) +
[58] + list(range(1001, 1036)) +
list(range(2001, 2036)))
wf.connect(registration, 'outputspec.aparc', sampleaparc,
'segmentation_file')
wf.connect(collector, 'out', sampleaparc, 'in_file')
def get_names(files, suffix):
"""Generate appropriate names for output files
"""
from nipype.utils.filemanip import (split_filename, filename_to_list,
list_to_filename)
out_names = []
for filename in files:
_, name, _ = split_filename(filename)
out_names.append(name + suffix)
return list_to_filename(out_names)
wf.connect(collector, ('out', get_names, '_avgwf.txt'), sampleaparc,
'avgwf_txt_file')
wf.connect(collector, ('out', get_names, '_summary.stats'), sampleaparc,
'summary_file')
# Sample the time series onto the surface of the target surface. Performs
# sampling into left and right hemisphere
target = Node(IdentityInterface(fields=['target_subject']), name='target')
target.iterables = ('target_subject', filename_to_list(target_subject))
samplerlh = MapNode(freesurfer.SampleToSurface(),
iterfield=['source_file'],
name='sampler_lh')
samplerlh.inputs.sampling_method = "average"
samplerlh.inputs.sampling_range = (0.1, 0.9, 0.1)
samplerlh.inputs.sampling_units = "frac"
samplerlh.inputs.interp_method = "trilinear"
samplerlh.inputs.smooth_surf = surf_fwhm
# samplerlh.inputs.cortex_mask = True
samplerlh.inputs.out_type = 'niigz'
samplerlh.inputs.subjects_dir = subjects_dir
samplerrh = samplerlh.clone('sampler_rh')
samplerlh.inputs.hemi = 'lh'
wf.connect(collector, 'out', samplerlh, 'source_file')
wf.connect(registration, 'outputspec.out_reg_file', samplerlh, 'reg_file')
wf.connect(target, 'target_subject', samplerlh, 'target_subject')
samplerrh.set_input('hemi', 'rh')
wf.connect(collector, 'out', samplerrh, 'source_file')
wf.connect(registration, 'outputspec.out_reg_file', samplerrh, 'reg_file')
wf.connect(target, 'target_subject', samplerrh, 'target_subject')
# Combine left and right hemisphere to text file
combiner = MapNode(Function(input_names=['left', 'right'],
output_names=['out_file'],
function=combine_hemi,
imports=imports),
iterfield=['left', 'right'],
name="combiner")
wf.connect(samplerlh, 'out_file', combiner, 'left')
wf.connect(samplerrh, 'out_file', combiner, 'right')
# Sample the time series file for each subcortical roi
ts2txt = MapNode(Function(
input_names=['timeseries_file', 'label_file', 'indices'],
output_names=['out_file'],
function=extract_subrois,
imports=imports),
iterfield=['timeseries_file'],
name='getsubcortts')
ts2txt.inputs.indices = [8] + list(range(10, 14)) + [17, 18, 26, 47] +\
list(range(49, 55)) + [58]
ts2txt.inputs.label_file = \
os.path.abspath(('OASIS-TRT-20_jointfusion_DKT31_CMA_labels_in_MNI152_'
'2mm_v2.nii.gz'))
wf.connect(maskts, 'out_file', ts2txt, 'timeseries_file')
######
substitutions = [('_target_subject_', ''),
('_filtermotart_cleaned_bp_trans_masked', ''),
('_filtermotart_cleaned_bp', '')]
regex_subs = [
('_ts_masker.*/sar', '/smooth/'),
('_ts_masker.*/ar', '/unsmooth/'),
('_combiner.*/sar', '/smooth/'),
('_combiner.*/ar', '/unsmooth/'),
('_aparc_ts.*/sar', '/smooth/'),
('_aparc_ts.*/ar', '/unsmooth/'),
('_getsubcortts.*/sar', '/smooth/'),
('_getsubcortts.*/ar', '/unsmooth/'),
('series/sar', 'series/smooth/'),
('series/ar', 'series/unsmooth/'),
('_inverse_transform./', ''),
]
# Save the relevant data into an output directory
datasink = Node(interface=DataSink(), name="datasink")
datasink.inputs.base_directory = sink_directory
datasink.inputs.container = subject_id
datasink.inputs.substitutions = substitutions
datasink.inputs.regexp_substitutions = regex_subs # (r'(/_.*(\d+/))', r'/run\2')
wf.connect(realign, 'realignment_parameters', datasink,
'resting.qa.motion')
wf.connect(art, 'norm_files', datasink, 'resting.qa.art.@norm')
wf.connect(art, 'intensity_files', datasink, 'resting.qa.art.@intensity')
wf.connect(art, 'outlier_files', datasink, 'resting.qa.art.@outlier_files')
wf.connect(registration, 'outputspec.segmentation_files', datasink,
'resting.mask_files')
wf.connect(registration, 'outputspec.anat2target', datasink,
'resting.qa.ants')
wf.connect(mask, 'mask_file', datasink, 'resting.mask_files.@brainmask')
wf.connect(mask_target, 'out_file', datasink, 'resting.mask_files.target')
wf.connect(filter1, 'out_f', datasink, 'resting.qa.compmaps.@mc_F')
wf.connect(filter1, 'out_pf', datasink, 'resting.qa.compmaps.@mc_pF')
wf.connect(filter2, 'out_f', datasink, 'resting.qa.compmaps')
wf.connect(filter2, 'out_pf', datasink, 'resting.qa.compmaps.@p')
wf.connect(bandpass, 'out_files', datasink,
'resting.timeseries.@bandpassed')
wf.connect(smooth, 'smoothed_files', datasink,
'resting.timeseries.@smoothed')
wf.connect(createfilter1, 'out_files', datasink,
'resting.regress.@regressors')
wf.connect(createfilter2, 'out_files', datasink,
'resting.regress.@compcorr')
wf.connect(maskts, 'out_file', datasink, 'resting.timeseries.target')
wf.connect(sampleaparc, 'summary_file', datasink,
'resting.parcellations.aparc')
wf.connect(sampleaparc, 'avgwf_txt_file', datasink,
'resting.parcellations.aparc.@avgwf')
wf.connect(ts2txt, 'out_file', datasink,
'resting.parcellations.grayo.@subcortical')
datasink2 = Node(interface=DataSink(), name="datasink2")
datasink2.inputs.base_directory = sink_directory
datasink2.inputs.container = subject_id
datasink2.inputs.substitutions = substitutions
datasink2.inputs.regexp_substitutions = regex_subs # (r'(/_.*(\d+/))', r'/run\2')
wf.connect(combiner, 'out_file', datasink2,
'resting.parcellations.grayo.@surface')
return wf
def analyze_openfmri_dataset(data_dir, subject=None, model_id=None,
task_id=None, output_dir=None, subj_prefix='*',
hpcutoff=120., use_derivatives=True,
fwhm=6.0, subjects_dir=None, target=None):
"""Analyzes an open fmri dataset
Parameters
----------
data_dir : str
Path to the base data directory
work_dir : str
Nipype working directory (defaults to cwd)
"""
"""
Load nipype workflows
"""
preproc = create_featreg_preproc(whichvol='first')
modelfit = create_modelfit_workflow()
fixed_fx = create_fixed_effects_flow()
if subjects_dir:
registration = create_fs_reg_workflow()
else:
registration = create_reg_workflow()
"""
Remove the plotting connection so that plot iterables don't propagate
to the model stage
"""
preproc.disconnect(preproc.get_node('plot_motion'), 'out_file',
preproc.get_node('outputspec'), 'motion_plots')
"""
Set up openfmri data specific components
"""
subjects = sorted([path.split(os.path.sep)[-1] for path in
glob(os.path.join(data_dir, subj_prefix))])
infosource = pe.Node(niu.IdentityInterface(fields=['subject_id',
'model_id',
'task_id']),
name='infosource')
if len(subject) == 0:
infosource.iterables = [('subject_id', subjects),
('model_id', [model_id]),
('task_id', task_id)]
else:
infosource.iterables = [('subject_id',
[subjects[subjects.index(subj)] for subj in subject]),
('model_id', [model_id]),
('task_id', task_id)]
subjinfo = pe.Node(niu.Function(input_names=['subject_id', 'base_dir',
'task_id', 'model_id'],
output_names=['run_id', 'conds', 'TR'],
function=get_subjectinfo),
name='subjectinfo')
subjinfo.inputs.base_dir = data_dir
"""
Return data components as anat, bold and behav
"""
contrast_file = os.path.join(data_dir, 'models', 'model%03d' % model_id,
'task_contrasts.txt')
has_contrast = os.path.exists(contrast_file)
if has_contrast:
datasource = pe.Node(nio.DataGrabber(infields=['subject_id', 'run_id',
'task_id', 'model_id'],
outfields=['anat', 'bold', 'behav',
'contrasts']),
name='datasource')
else:
datasource = pe.Node(nio.DataGrabber(infields=['subject_id', 'run_id',
'task_id', 'model_id'],
outfields=['anat', 'bold', 'behav']),
name='datasource')
datasource.inputs.base_directory = data_dir
datasource.inputs.template = '*'
if has_contrast:
datasource.inputs.field_template = {'anat': '%s/anatomy/T1_001.nii.gz',
'bold': '%s/BOLD/task%03d_r*/bold.nii.gz',
'behav': ('%s/model/model%03d/onsets/task%03d_'
'run%03d/cond*.txt'),
'contrasts': ('models/model%03d/'
'task_contrasts.txt')}
datasource.inputs.template_args = {'anat': [['subject_id']],
'bold': [['subject_id', 'task_id']],
'behav': [['subject_id', 'model_id',
'task_id', 'run_id']],
'contrasts': [['model_id']]}
else:
datasource.inputs.field_template = {'anat': '%s/anatomy/T1_001.nii.gz',
'bold': '%s/BOLD/task%03d_r*/bold.nii.gz',
'behav': ('%s/model/model%03d/onsets/task%03d_'
'run%03d/cond*.txt')}
datasource.inputs.template_args = {'anat': [['subject_id']],
'bold': [['subject_id', 'task_id']],
'behav': [['subject_id', 'model_id',
'task_id', 'run_id']]}
datasource.inputs.sort_filelist = True
"""
Create meta workflow
"""
wf = pe.Workflow(name='openfmri')
wf.connect(infosource, 'subject_id', subjinfo, 'subject_id')
wf.connect(infosource, 'model_id', subjinfo, 'model_id')
wf.connect(infosource, 'task_id', subjinfo, 'task_id')
wf.connect(infosource, 'subject_id', datasource, 'subject_id')
wf.connect(infosource, 'model_id', datasource, 'model_id')
wf.connect(infosource, 'task_id', datasource, 'task_id')
wf.connect(subjinfo, 'run_id', datasource, 'run_id')
wf.connect([(datasource, preproc, [('bold', 'inputspec.func')]),
])
def get_highpass(TR, hpcutoff):
return hpcutoff / (2 * TR)
gethighpass = pe.Node(niu.Function(input_names=['TR', 'hpcutoff'],
output_names=['highpass'],
function=get_highpass),
name='gethighpass')
wf.connect(subjinfo, 'TR', gethighpass, 'TR')
wf.connect(gethighpass, 'highpass', preproc, 'inputspec.highpass')
"""
Setup a basic set of contrasts, a t-test per condition
"""
def get_contrasts(contrast_file, task_id, conds):
import numpy as np
import os
contrast_def = []
if os.path.exists(contrast_file):
with open(contrast_file, 'rt') as fp:
contrast_def.extend([np.array(row.split()) for row in fp.readlines() if row.strip()])
contrasts = []
for row in contrast_def:
if row[0] != 'task%03d' % task_id:
continue
con = [row[1], 'T', ['cond%03d' % (i + 1) for i in range(len(conds))],
row[2:].astype(float).tolist()]
contrasts.append(con)
# add auto contrasts for each column
for i, cond in enumerate(conds):
con = [cond, 'T', ['cond%03d' % (i + 1)], [1]]
contrasts.append(con)
return contrasts
contrastgen = pe.Node(niu.Function(input_names=['contrast_file',
'task_id', 'conds'],
output_names=['contrasts'],
function=get_contrasts),
name='contrastgen')
art = pe.MapNode(interface=ra.ArtifactDetect(use_differences=[True, False],
use_norm=True,
norm_threshold=1,
zintensity_threshold=3,
parameter_source='FSL',
mask_type='file'),
iterfield=['realigned_files', 'realignment_parameters',
'mask_file'],
name="art")
modelspec = pe.Node(interface=model.SpecifyModel(),
name="modelspec")
modelspec.inputs.input_units = 'secs'
def check_behav_list(behav, run_id, conds):
from nipype.external import six
import numpy as np
num_conds = len(conds)
if isinstance(behav, six.string_types):
behav = [behav]
behav_array = np.array(behav).flatten()
num_elements = behav_array.shape[0]
return behav_array.reshape(num_elements/num_conds, num_conds).tolist()
reshape_behav = pe.Node(niu.Function(input_names=['behav', 'run_id', 'conds'],
output_names=['behav'],
function=check_behav_list),
name='reshape_behav')
wf.connect(subjinfo, 'TR', modelspec, 'time_repetition')
wf.connect(datasource, 'behav', reshape_behav, 'behav')
wf.connect(subjinfo, 'run_id', reshape_behav, 'run_id')
wf.connect(subjinfo, 'conds', reshape_behav, 'conds')
wf.connect(reshape_behav, 'behav', modelspec, 'event_files')
wf.connect(subjinfo, 'TR', modelfit, 'inputspec.interscan_interval')
wf.connect(subjinfo, 'conds', contrastgen, 'conds')
if has_contrast:
wf.connect(datasource, 'contrasts', contrastgen, 'contrast_file')
else:
contrastgen.inputs.contrast_file = ''
wf.connect(infosource, 'task_id', contrastgen, 'task_id')
wf.connect(contrastgen, 'contrasts', modelfit, 'inputspec.contrasts')
wf.connect([(preproc, art, [('outputspec.motion_parameters',
'realignment_parameters'),
('outputspec.realigned_files',
'realigned_files'),
('outputspec.mask', 'mask_file')]),
(preproc, modelspec, [('outputspec.highpassed_files',
'functional_runs'),
('outputspec.motion_parameters',
'realignment_parameters')]),
(art, modelspec, [('outlier_files', 'outlier_files')]),
(modelspec, modelfit, [('session_info',
'inputspec.session_info')]),
(preproc, modelfit, [('outputspec.highpassed_files',
'inputspec.functional_data')])
])
# Comute TSNR on realigned data regressing polynomials upto order 2
tsnr = MapNode(TSNR(regress_poly=2), iterfield=['in_file'], name='tsnr')
wf.connect(preproc, "outputspec.realigned_files", tsnr, "in_file")
# Compute the median image across runs
calc_median = Node(Function(input_names=['in_files'],
output_names=['median_file'],
function=median,
imports=imports),
name='median')
wf.connect(tsnr, 'detrended_file', calc_median, 'in_files')
"""
Reorder the copes so that now it combines across runs
"""
def sort_copes(copes, varcopes, contrasts):
import numpy as np
if not isinstance(copes, list):
copes = [copes]
varcopes = [varcopes]
num_copes = len(contrasts)
n_runs = len(copes)
all_copes = np.array(copes).flatten()
all_varcopes = np.array(varcopes).flatten()
outcopes = all_copes.reshape(len(all_copes)/num_copes, num_copes).T.tolist()
outvarcopes = all_varcopes.reshape(len(all_varcopes)/num_copes, num_copes).T.tolist()
return outcopes, outvarcopes, n_runs
cope_sorter = pe.Node(niu.Function(input_names=['copes', 'varcopes',
'contrasts'],
output_names=['copes', 'varcopes',
'n_runs'],
function=sort_copes),
name='cope_sorter')
pickfirst = lambda x: x[0]
wf.connect(contrastgen, 'contrasts', cope_sorter, 'contrasts')
wf.connect([(preproc, fixed_fx, [(('outputspec.mask', pickfirst),
'flameo.mask_file')]),
(modelfit, cope_sorter, [('outputspec.copes', 'copes')]),
(modelfit, cope_sorter, [('outputspec.varcopes', 'varcopes')]),
(cope_sorter, fixed_fx, [('copes', 'inputspec.copes'),
('varcopes', 'inputspec.varcopes'),
('n_runs', 'l2model.num_copes')]),
(modelfit, fixed_fx, [('outputspec.dof_file',
'inputspec.dof_files'),
])
])
wf.connect(calc_median, 'median_file', registration, 'inputspec.mean_image')
if subjects_dir:
wf.connect(infosource, 'subject_id', registration, 'inputspec.subject_id')
registration.inputs.inputspec.subjects_dir = subjects_dir
registration.inputs.inputspec.target_image = fsl.Info.standard_image('MNI152_T1_2mm_brain.nii.gz')
if target:
registration.inputs.inputspec.target_image = target
else:
wf.connect(datasource, 'anat', registration, 'inputspec.anatomical_image')
registration.inputs.inputspec.target_image = fsl.Info.standard_image('MNI152_T1_2mm.nii.gz')
registration.inputs.inputspec.target_image_brain = fsl.Info.standard_image('MNI152_T1_2mm_brain.nii.gz')
registration.inputs.inputspec.config_file = 'T1_2_MNI152_2mm'
def merge_files(copes, varcopes, zstats):
out_files = []
splits = []
out_files.extend(copes)
splits.append(len(copes))
out_files.extend(varcopes)
splits.append(len(varcopes))
out_files.extend(zstats)
splits.append(len(zstats))
return out_files, splits
mergefunc = pe.Node(niu.Function(input_names=['copes', 'varcopes',
'zstats'],
output_names=['out_files', 'splits'],
function=merge_files),
name='merge_files')
wf.connect([(fixed_fx.get_node('outputspec'), mergefunc,
[('copes', 'copes'),
('varcopes', 'varcopes'),
('zstats', 'zstats'),
])])
wf.connect(mergefunc, 'out_files', registration, 'inputspec.source_files')
def split_files(in_files, splits):
copes = in_files[:splits[0]]
varcopes = in_files[splits[0]:(splits[0] + splits[1])]
zstats = in_files[(splits[0] + splits[1]):]
return copes, varcopes, zstats
splitfunc = pe.Node(niu.Function(input_names=['in_files', 'splits'],
output_names=['copes', 'varcopes',
'zstats'],
function=split_files),
name='split_files')
wf.connect(mergefunc, 'splits', splitfunc, 'splits')
wf.connect(registration, 'outputspec.transformed_files',
splitfunc, 'in_files')
if subjects_dir:
get_roi_mean = pe.MapNode(fs.SegStats(default_color_table=True),
iterfield=['in_file'], name='get_aparc_means')
get_roi_mean.inputs.avgwf_txt_file = True
wf.connect(fixed_fx.get_node('outputspec'), 'copes', get_roi_mean, 'in_file')
wf.connect(registration, 'outputspec.aparc', get_roi_mean, 'segmentation_file')
get_roi_tsnr = pe.MapNode(fs.SegStats(default_color_table=True),
iterfield=['in_file'], name='get_aparc_tsnr')
get_roi_tsnr.inputs.avgwf_txt_file = True
wf.connect(tsnr, 'tsnr_file', get_roi_tsnr, 'in_file')
wf.connect(registration, 'outputspec.aparc', get_roi_tsnr, 'segmentation_file')
"""
Connect to a datasink
"""
def get_subs(subject_id, conds, run_id, model_id, task_id):
subs = [('_subject_id_%s_' % subject_id, '')]
subs.append(('_model_id_%d' % model_id, 'model%03d' %model_id))
subs.append(('task_id_%d/' % task_id, '/task%03d_' % task_id))
subs.append(('bold_dtype_mcf_mask_smooth_mask_gms_tempfilt_mean_warp',
'mean'))
subs.append(('bold_dtype_mcf_mask_smooth_mask_gms_tempfilt_mean_flirt',
'affine'))
for i in range(len(conds)):
subs.append(('_flameo%d/cope1.' % i, 'cope%02d.' % (i + 1)))
subs.append(('_flameo%d/varcope1.' % i, 'varcope%02d.' % (i + 1)))
subs.append(('_flameo%d/zstat1.' % i, 'zstat%02d.' % (i + 1)))
subs.append(('_flameo%d/tstat1.' % i, 'tstat%02d.' % (i + 1)))
subs.append(('_flameo%d/res4d.' % i, 'res4d%02d.' % (i + 1)))
subs.append(('_warpall%d/cope1_warp.' % i,
'cope%02d.' % (i + 1)))
subs.append(('_warpall%d/varcope1_warp.' % (len(conds) + i),
'varcope%02d.' % (i + 1)))
subs.append(('_warpall%d/zstat1_warp.' % (2 * len(conds) + i),
'zstat%02d.' % (i + 1)))
subs.append(('_warpall%d/cope1_trans.' % i,
'cope%02d.' % (i + 1)))
subs.append(('_warpall%d/varcope1_trans.' % (len(conds) + i),
'varcope%02d.' % (i + 1)))
subs.append(('_warpall%d/zstat1_trans.' % (2 * len(conds) + i),
'zstat%02d.' % (i + 1)))
subs.append(('__get_aparc_means%d/' % i, '/cope%02d_' % (i + 1)))
for i, run_num in enumerate(run_id):
subs.append(('__get_aparc_tsnr%d/' % i, '/run%02d_' % run_num))
subs.append(('__art%d/' % i, '/run%02d_' % run_num))
subs.append(('__dilatemask%d/' % i, '/run%02d_' % run_num))
subs.append(('__realign%d/' % i, '/run%02d_' % run_num))
subs.append(('__modelgen%d/' % i, '/run%02d_' % run_num))
subs.append(('/model%03d/task%03d/' % (model_id, task_id), '/'))
subs.append(('/model%03d/task%03d_' % (model_id, task_id), '/'))
subs.append(('_bold_dtype_mcf_bet_thresh_dil', '_mask'))
subs.append(('_output_warped_image', '_anat2target'))
subs.append(('median_flirt_brain_mask', 'median_brain_mask'))
subs.append(('median_bbreg_brain_mask', 'median_brain_mask'))
return subs
subsgen = pe.Node(niu.Function(input_names=['subject_id', 'conds', 'run_id',
'model_id', 'task_id'],
output_names=['substitutions'],
function=get_subs),
name='subsgen')
wf.connect(subjinfo, 'run_id', subsgen, 'run_id')
datasink = pe.Node(interface=nio.DataSink(),
name="datasink")
wf.connect(infosource, 'subject_id', datasink, 'container')
wf.connect(infosource, 'subject_id', subsgen, 'subject_id')
wf.connect(infosource, 'model_id', subsgen, 'model_id')
wf.connect(infosource, 'task_id', subsgen, 'task_id')
wf.connect(contrastgen, 'contrasts', subsgen, 'conds')
wf.connect(subsgen, 'substitutions', datasink, 'substitutions')
wf.connect([(fixed_fx.get_node('outputspec'), datasink,
[('res4d', 'res4d'),
('copes', 'copes'),
('varcopes', 'varcopes'),
('zstats', 'zstats'),
('tstats', 'tstats')])
])
wf.connect([(modelfit.get_node('modelgen'), datasink,
[('design_cov', 'qa.model'),
('design_image', 'qa.model.@matrix_image'),
('design_file', 'qa.model.@matrix'),
])])
wf.connect([(preproc, datasink, [('outputspec.motion_parameters',
'qa.motion'),
('outputspec.motion_plots',
'qa.motion.plots'),
('outputspec.mask', 'qa.mask')])])
wf.connect(registration, 'outputspec.mean2anat_mask', datasink, 'qa.mask.mean2anat')
wf.connect(art, 'norm_files', datasink, 'qa.art.@norm')
wf.connect(art, 'intensity_files', datasink, 'qa.art.@intensity')
wf.connect(art, 'outlier_files', datasink, 'qa.art.@outlier_files')
wf.connect(registration, 'outputspec.anat2target', datasink, 'qa.anat2target')
wf.connect(tsnr, 'tsnr_file', datasink, 'qa.tsnr.@map')
if subjects_dir:
wf.connect(registration, 'outputspec.min_cost_file', datasink, 'qa.mincost')
wf.connect([(get_roi_tsnr, datasink, [('avgwf_txt_file', 'qa.tsnr'),
('summary_file', 'qa.tsnr.@summary')])])
wf.connect([(get_roi_mean, datasink, [('avgwf_txt_file', 'copes.roi'),
('summary_file', 'copes.roi.@summary')])])
wf.connect([(splitfunc, datasink,
[('copes', 'copes.mni'),
('varcopes', 'varcopes.mni'),
('zstats', 'zstats.mni'),
])])
wf.connect(calc_median, 'median_file', datasink, 'mean')
wf.connect(registration, 'outputspec.transformed_mean', datasink, 'mean.mni')
wf.connect(registration, 'outputspec.func2anat_transform', datasink, 'xfm.mean2anat')
wf.connect(registration, 'outputspec.anat2target_transform', datasink, 'xfm.anat2target')
"""
Set processing parameters
"""
preproc.inputs.inputspec.fwhm = fwhm
gethighpass.inputs.hpcutoff = hpcutoff
modelspec.inputs.high_pass_filter_cutoff = hpcutoff
modelfit.inputs.inputspec.bases = {'dgamma': {'derivs': use_derivatives}}
modelfit.inputs.inputspec.model_serial_correlations = True
modelfit.inputs.inputspec.film_threshold = 1000
datasink.inputs.base_directory = output_dir
return wf
def motion_power_stats_wf(wf_name='gen_motion_stats'):
""" The main purpose of this workflow is to get various statistical measures from the
movement/motion parameters obtained in functional preprocessing.
These parameters (FD calculations) are also required to carry out scrubbing.
Order of commands:
- Calculate Frame Wise Displacement FD as per power et al., 2012
Differentiating head realignment parameters across frames yields a six dimensional timeseries that represents
instantaneous head motion.
Rotational displacements are converted from degrees to millimeters by calculating displacement on the surface of
a sphere of radius 50 mm.[R5]
- Calculate Frame wise Displacement FD as per jenkinson et al., 2002
- Calculate Frames to exclude
Remove all frames which are below the threshold
- Calculate Frames to include
Include all the frames which are above the threshold
- Calculate DVARS
DVARS (D temporal derivative of timecourses, VARS referring to RMS variance over voxels) indexes
the rate of change of BOLD signal across the entire brain at each frame of data.To calculate
DVARS, the volumetric timeseries is differentiated (by backwards differences) and RMS signal
change is calculated over the whole brain.DVARS is thus a measure of how much the intensity
of a brain image changes in comparison to the previous timepoint (as opposed to the global
signal, which is the average value of a brain image at a timepoint).[R5]
- Calculate Power parameters::
MeanFD : Mean (across time/frames) of the absolute values for Framewise Displacement (FD),
computed as described in Power et al., Neuroimage, 2012)
rootMeanSquareFD : Root mean square (RMS; across time/frames) of the absolute values for FD
NumFD >=threshold : Number of frames (time points) where movement (FD) exceeded threshold
rmsFD : Root mean square (RMS; across time/frames) of the absolute values for FD
FDquartile(top 1/4th FD) : Mean of the top 25% highest FD values
PercentFD( > threshold) : Number of frames (time points) where movement (FD) exceeded threshold
expressed as a percentage of the total number of frames (time points)
MeanDVARS : Mean of voxel DVARS
- Calculate Motion Parameters
Following motion parameters are calculated::
Subject, Scan, Mean Relative RMS Displacement, Max Relative RMS Displacement,
Movements >threshold, Mean Relative Mean Rotation, Mean Relative Maxdisp,
Max Relative Maxdisp, Max Abs Maxdisp, Max Relative Roll,Max Relative Pitch,
Max Relative Yaw, Max Relative dS-I, Max Relative dL-R,Max Relative dP-A,
Mean Relative Roll, Mean Relative Pitch,Mean Relative Yaw, Mean Relative dS-I,
Mean Relative dL-R, Mean Relative dP-A, Max Abs Roll, Max Abs Pitch, Max Abs Yaw,
Max Abs dS-I, Max Abs dL-R, Max Abs dP-A, Mean Abs Roll,Mean Abs Pitch,Mean Abs Yaw,
Mean Abs dS-I,Mean Abs dL-R,Mean Abs dP-A
Parameters
----------
wf_name: workflow object
Workflow name
Returns
-------
param_wf: workflow object
Workflow object containing various movement/motion and power parameters estimates.
Nipype inputs
-------------
inputspec.motion_correct : string (func/rest file or a list of func/rest nifti file)
Path to motion corrected functional data
inputspec.mask : string (nifti file)
Path to field contianing brain-only mask for the functional data
inputspec.max_displacement : string (Mat file)
maximum displacement (in mm) vector for brain voxels in each volume.
This file is obtained in functional preprocessing step
inputspec.movement_parameters : string (Mat file)
1D file containing six movement/motion parameters(3 Translation, 3 Rotations)
in different columns (roll pitch yaw dS dL dP), obtained in functional preprocessing step
scrubbing_input.threshold : a float
scrubbing threshold
scrubbing_input.remove_frames_before : an integer
count of preceding frames to the offending time
frames to be removed (i.e.,those exceeding FD threshold)
scrubbing_input.remove_frames_after : an integer
count of subsequent frames to the offending time
frames to be removed (i.e., those exceeding FD threshold)
Nipype outputs
--------------
outputspec.FD_1D : 1D file
mean Framewise Displacement (FD)
outputspec.frames_ex_1D : 1D file
Number of frames that would be censored ("scrubbed")
also removing the offending time frames (i.e., those exceeding the threshold),
the preceeding frame, and the two subsequent frames
outputspec.frames_in_1D : 1d file
Number of frames left after removing for scrubbing
outputspec.power_params : txt file
Text file various power parameters for scrubbing.
outputspec.motion_params : txt file
Text file containing various movement parameters
References
----------
.. [1] Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious
but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage, 59(3),
2142-2154. doi:10.1016/j.neuroimage.2011.10.018
.. [2] Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Steps
toward optimizing motion artifact removal in functional connectivity MRI; a reply to Carp.
NeuroImage. doi:10.1016/j.neuroimage.2012.03.017
.. [3] Jenkinson, M., Bannister, P., Brady, M., Smith, S., 2002. Improved optimization for the robust
and accurate linear registration and motion correction of brain images. Neuroimage 17, 825-841.
"""
wf = pe.Workflow(name=wf_name)
# specify input and output fields
in_fields = [
"in_files",
"anat",
"atlas_anat",
"coreg_target",
"tissues",
"lowpass_freq",
"highpass_freq",
]
out_fields = [
"motion_corrected",
"motion_params",
"tissues",
"anat",
"time_filtered",
"smooth",
"time_filtered_mni",
"smooth_mni",
"tsnr_file",
"epi_brain_mask",
"tissues_brain_mask",
"motion_regressors",
"compcor_regressors",
"gsr_regressors",
"nuis_corrected",
"epi_mni",
"epi_mni_warpfield",
]
inputNode = setup_node(IdentityInterface(fields=[
'subject_id', 'scan_id', 'movement_parameters', 'max_displacement',
'motion_correct', 'mask', 'oned_matrix_save'
]),
name='inputspec')
scrubbing_input = setup_node(IdentityInterface(
fields=['threshold', 'remove_frames_before', 'remove_frames_after']),
name='scrubbing_input')
outputNode = setup_node(IdentityInterface(fields=[
'FD_1D', 'FDJ_1D', 'frames_ex_1D', 'frames_in_1D', 'power_params',
'motion_params'
]),
name='outputspec')
# calculate mean DVARS
cal_DVARS = setup_node(Function(input_names=['in_file', 'mask'],
output_names=['out_file'],
function=calculate_DVARS),
name='cal_DVARS')
wf.connect(inputNode, 'motion_correct', cal_DVARS, 'rest')
wf.connect(inputNode, 'mask', cal_DVARS, 'mask')
# Calculating mean Framewise Displacement as per power et al., 2012
calculate_FD = setup_node(Function(input_names=['in_file'],
output_names=['out_file'],
function=calculate_FD_P),
name='calculate_FD')
wf.connect(inputNode, 'movement_parameters', calculate_FD, 'in_file')
wf.connect(calculate_FD, 'out_file', outputNode, 'FD_1D')
# Calculating mean Framewise Displacement as per jenkinson et al., 2002
calculate_FDJ = setup_node(Function(input_names=['in_file'],
output_names=['out_file'],
function=calculate_FD_J),
name='calculate_FDJ')
wf.connect(inputNode, 'oned_matrix_save', calculate_FDJ, 'in_file')
wf.connect(calculate_FDJ, 'out_file', outputNode, 'FDJ_1D')
##calculating frames to exclude and include after scrubbing
exclude_frames = setup_node(Function(
input_names=['in_file', 'threshold', 'frames_before', 'frames_after'],
output_names=['out_file'],
function=set_frames_ex),
name='exclude_frames')
wf.connect(calculate_FD, 'out_file', exclude_frames, 'in_file')
wf.connect(scrubbing_input, 'threshold', exclude_frames, 'threshold')
wf.connect(scrubbing_input, 'remove_frames_before', exclude_frames,
'frames_before')
wf.connect(scrubbing_input, 'remove_frames_after', exclude_frames,
'frames_after')
wf.connect(exclude_frames, 'out_file', outputNode, 'frames_ex_1D')
include_frames = setup_node(Function(
input_names=['in_file', 'threshold', 'exclude_list'],
output_names=['out_file'],
function=set_frames_in),
name='include_frames')
wf.connect(calculate_FD, 'out_file', include_frames, 'in_file')
wf.connect(scrubbing_input, 'threshold', include_frames, 'threshold')
wf.connect(exclude_frames, 'out_file', include_frames, 'exclude_list')
wf.connect(include_frames, 'out_file', outputNode, 'frames_in_1D')
calc_motion_parameters = setup_node(Function(
input_names=[
"subject_id", "scan_id", "movement_parameters", "max_displacement"
],
output_names=['out_file'],
function=gen_motion_parameters),
name='calc_motion_parameters')
wf.connect(inputNode, 'subject_id', calc_motion_parameters, 'subject_id')
wf.connect(inputNode, 'scan_id', calc_motion_parameters, 'scan_id')
wf.connect(inputNode, 'movement_parameters', calc_motion_parameters,
'movement_parameters')
wf.connect(inputNode, 'max_displacement', calc_motion_parameters,
'max_displacement')
wf.connect(calc_motion_parameters, 'out_file', outputNode, 'motion_params')
calc_power_parameters = setup_node(Function(input_names=[
"subject_id", "scan_id", "FD_1D", "FDJ_1D", "threshold", "DVARS"
],
output_names=['out_file'],
function=gen_power_parameters),
name='calc_power_parameters')
wf.connect(inputNode, 'subject_id', calc_power_parameters, 'subject_id')
wf.connect(inputNode, 'scan_id', calc_power_parameters, 'scan_id')
wf.connect(cal_DVARS, 'out_file', calc_power_parameters, 'DVARS')
wf.connect(calculate_FD, 'out_file', calc_power_parameters, 'FD_1D')
wf.connect(calculate_FDJ, 'out_file', calc_power_parameters, 'FDJ_1D')
wf.connect(scrubbing_input, 'threshold', calc_power_parameters,
'threshold')
wf.connect(calc_power_parameters, 'out_file', outputNode, 'power_params')
return wf
def create_epi_t1_nonlinear_pipeline(name='epi_t1_nonlinear'):
"""Creates a pipeline that performs nonlinear EPI to T1 registration using
the antsRegistration tool. Beforehand, the T1 image has to be processed in
freesurfer and the EPI timeseries should be realigned.
Example
-------
>>> nipype_epi_t1_nonlin = create_epi_t1_nonlinear_pipeline('nipype_epi_t1_nonlin')
>>> nipype_epi_t1_nonlin.inputs.inputnode.fs_subject_id = '123456'
>>> nipype_epi_t1_nonlin.inputs.inputnode.fs_subjects_dir = '/project/data/freesurfer'
>>> nipype_epi_t1_nonlin.inputs.inputnode.realigned_epi = 'mcflirt.nii.gz'
>>> nipype_epi_t1_nonlin.run()
Inputs::
inputnode.fs_subject_id # subject id used in freesurfer
inputnode.fs_subjects_dir # path to freesurfer output
inputnode.realigned_epi # realigned EPI timeseries
Outputs::
outputnode.lin_epi2anat # ITK format
outputnode.lin_anat2epi # ITK format
outputnode.nonlin_epi2anat # ANTs specific 5D deformation field
outputnode.nonlin_anat2epi # ANTs specific 5D deformation field
"""
nonreg = Workflow(name='epi_t1_nonlinear')
# input
inputnode = Node(interface=util.IdentityInterface(
fields=['fs_subject_id', 'fs_subjects_dir', 'realigned_epi']),
name='inputnode')
# calculate the temporal mean image of the realigned timeseries
tmean = Node(interface=fsl.maths.MeanImage(dimension='T',
output_type='NIFTI_GZ'),
name='tmean')
nonreg.connect(inputnode, 'realigned_epi', tmean, 'in_file')
# import brain.mgz and ribbon.mgz from freesurfer directory
fs_import = Node(interface=nio.FreeSurferSource(),
name='freesurfer_import')
nonreg.connect(inputnode, 'fs_subjects_dir', fs_import, 'subjects_dir')
nonreg.connect(inputnode, 'fs_subject_id', fs_import, 'subject_id')
# convert brain.mgz to niigz
mriconvert = Node(interface=fs.MRIConvert(out_type='niigz'),
name='mriconvert')
nonreg.connect(fs_import, 'brain', mriconvert, 'in_file')
# calculate rigid transformation of mean epi to t1 with bbregister
bbregister = Node(interface=fs.BBRegister(init='fsl',
contrast_type='t2',
out_fsl_file=True),
name='bbregister')
nonreg.connect(inputnode, 'fs_subjects_dir', bbregister, 'subjects_dir')
nonreg.connect(inputnode, 'fs_subject_id', bbregister, 'subject_id')
nonreg.connect(tmean, 'out_file', bbregister, 'source_file')
# convert linear transformation to itk format compatible with ants
itk = Node(interface=c3.C3dAffineTool(fsl2ras=True,
itk_transform='epi2anat_affine.txt'),
name='itk')
nonreg.connect(tmean, 'out_file', itk, 'source_file')
nonreg.connect(mriconvert, 'out_file', itk, 'reference_file')
nonreg.connect(bbregister, 'out_fsl_file', itk, 'transform_file')
# get aparc aseg mask
# create brainmask from aparc+aseg
def get_aparc_aseg(files):
for name in files:
if 'aparc+aseg' in name:
return name
aparc_aseg_mask = Node(fs.Binarize(min=0.1,
dilate=10,
erode=7,
out_type='nii.gz',
binary_file='aparc_aseg_mask.nii.gz'),
name='aparc_aseg_mask')
# fill holes in mask
fillholes = Node(fsl.maths.MathsCommand(args='-fillh'), name='fillholes')
nonreg.connect([(fs_import, aparc_aseg_mask, [
(('aparc_aseg', get_aparc_aseg), 'in_file')
]), (aparc_aseg_mask, fillholes, [('binary_file', 'in_file')])])
#create bounding box mask and rigidly transform into anatomical (fs) space
fov = Node(interface=fs.model.Binarize(min=0.0, out_type='nii.gz'),
name='fov')
nonreg.connect(tmean, 'out_file', fov, 'in_file')
fov_trans = Node(interface=ants.resampling.ApplyTransforms(
dimension=3, interpolation='NearestNeighbor'),
name='fov_trans')
nonreg.connect(itk, ('itk_transform', filename_to_list), fov_trans,
'transforms')
nonreg.connect(fov, 'binary_file', fov_trans, 'input_image')
nonreg.connect(fillholes, 'out_file', fov_trans, 'reference_image')
#nonreg.connect(ribbon, 'binary_file', fov_trans, 'reference_image')
# intersect both masks
intersect = Node(interface=fsl.maths.BinaryMaths(operation='mul'),
name='intersect')
nonreg.connect(fillholes, 'out_file', intersect, 'in_file')
#nonreg.connect(ribbon, 'binary_file', intersect, 'in_file')
nonreg.connect(fov_trans, 'output_image', intersect, 'operand_file')
# inversly transform mask and mask original epi
mask_trans = Node(interface=ants.resampling.ApplyTransforms(
dimension=3,
interpolation='NearestNeighbor',
invert_transform_flags=[True]),
name='mask_trans')
nonreg.connect(itk, ('itk_transform', filename_to_list), mask_trans,
'transforms')
nonreg.connect(intersect, 'out_file', mask_trans, 'input_image')
nonreg.connect(tmean, 'out_file', mask_trans, 'reference_image')
maskepi = Node(interface=fs.utils.ApplyMask(), name='maskepi')
nonreg.connect(mask_trans, 'output_image', maskepi, 'mask_file')
nonreg.connect(tmean, 'out_file', maskepi, 'in_file')
# mask anatomical image (brain)
maskanat = Node(interface=fs.utils.ApplyMask(), name='maskanat')
nonreg.connect(intersect, 'out_file', maskanat, 'mask_file')
nonreg.connect(mriconvert, 'out_file', maskanat, 'in_file')
# invert masked anatomical image
anat_min_max = Node(interface=fsl.utils.ImageStats(op_string='-R'),
name='anat_min_max')
epi_min_max = Node(interface=fsl.utils.ImageStats(op_string='-r'),
name='epi_min_max')
nonreg.connect(maskanat, 'out_file', anat_min_max, 'in_file')
nonreg.connect(tmean, 'out_file', epi_min_max, 'in_file')
def calc_inversion(anat_min_max, epi_min_max):
mul = -(epi_min_max[1] - epi_min_max[0]) / (anat_min_max[1] -
anat_min_max[0])
add = abs(anat_min_max[1] * mul) + epi_min_max[0]
return mul, add
calcinv = Node(interface=Function(
input_names=['anat_min_max', 'epi_min_max'],
output_names=['mul', 'add'],
function=calc_inversion),
name='calcinv')
nonreg.connect(anat_min_max, 'out_stat', calcinv, 'anat_min_max')
nonreg.connect(epi_min_max, 'out_stat', calcinv, 'epi_min_max')
mulinv = Node(interface=fsl.maths.BinaryMaths(operation='mul'),
name='mulinv')
addinv = Node(interface=fsl.maths.BinaryMaths(operation='add'),
name='addinv')
nonreg.connect(maskanat, 'out_file', mulinv, 'in_file')
nonreg.connect(calcinv, 'mul', mulinv, 'operand_value')
nonreg.connect(mulinv, 'out_file', addinv, 'in_file')
nonreg.connect(calcinv, 'add', addinv, 'operand_value')
# nonlinear transformation of masked anat to masked epi with ants
antsreg = Node(interface=ants.registration.Registration(
dimension=3,
invert_initial_moving_transform=True,
metric=['CC'],
metric_weight=[1.0],
radius_or_number_of_bins=[4],
sampling_strategy=['None'],
transforms=['SyN'],
args='-g .1x1x.1',
transform_parameters=[(0.10, 3, 0)],
number_of_iterations=[[10, 5]],
convergence_threshold=[1e-06],
convergence_window_size=[10],
shrink_factors=[[2, 1]],
smoothing_sigmas=[[1, 0.5]],
sigma_units=['vox'],
use_estimate_learning_rate_once=[True],
use_histogram_matching=[True],
collapse_output_transforms=True,
output_inverse_warped_image=True,
output_warped_image=True),
name='antsreg')
nonreg.connect(itk, 'itk_transform', antsreg, 'initial_moving_transform')
nonreg.connect(maskepi, 'out_file', antsreg, 'fixed_image')
nonreg.connect(addinv, 'out_file', antsreg, 'moving_image')
# output
def second_element(file_list):
return file_list[1]
def first_element(file_list):
return file_list[0]
outputnode = Node(interface=util.IdentityInterface(fields=[
'lin_epi2anat', 'lin_anat2epi', 'nonlin_epi2anat', 'nonlin_anat2epi'
]),
name='outputnode')
nonreg.connect(itk, 'itk_transform', outputnode, 'lin_epi2anat')
nonreg.connect(antsreg, ('forward_transforms', first_element), outputnode,
'lin_anat2epi')
nonreg.connect(antsreg, ('forward_transforms', second_element), outputnode,
'nonlin_anat2epi')
nonreg.connect(antsreg, ('reverse_transforms', second_element), outputnode,
'nonlin_epi2anat')
return nonreg
def attach_concat_canica(main_wf, wf_name="canica", **kwargs):
""" Attach a Concat and a nilearn CanICA interface to `main_wf`.
The Concat node will merge all the files together in one 4D volume before delivering it to CanICA.
Parameters
----------
main_wf: nipype Workflow
wf_name: str
Name of the preprocessing workflow
kwargs: dict[str]->str
input_node: str
Name of the input node from where to connect the source `input_connect`.
input_connection: str
Name of the connection to obtain the source files.
Nipype Inputs for `main_wf`
---------------------------
datasink: nipype Node
Returns
-------
main_wf: nipype Workflow
"""
# Dependency workflows
srcwf_name = kwargs['input_node']
srcconn_name = kwargs['input_connection']
src_wf = main_wf.get_node(srcwf_name)
datasink = get_datasink(main_wf, name='datasink')
base_outdir = datasink.inputs.base_directory
ica_datasink = pe.Node(DataSink(parameterization=False,
base_directory=base_outdir,),
name="ica_datasink".format(wf_name))
ica_datasink.inputs.container = 'ica_{}'.format(wf_name)
# the list of the raw pet subjects
ica_subjs = pe.JoinNode(interface=IdentityInterface(fields=["ica_subjs"]),
joinsource="infosrc",
joinfield="ica_subjs",
name="ica_subjs")
# concat images
concat = setup_node(Function(function=concat_3D_imgs,
input_names=["in_files"],
output_names=["out_file"],
imports=['from pypes.interfaces.nilearn import ni2file']),
name="concat")
# warp each subject to the group template
ica = setup_node(CanICAInterface(), name="{}_ica".format(wf_name),)
algorithm = get_config_setting("{}_ica.algorithm".format(wf_name),
default=get_config_setting('canica.algorithm',
default=''))
if algorithm:
ica.inputs.algorithm = algorithm
# Connect the nodes
main_wf.connect([
# file list input
(src_wf, ica_subjs, [(srcconn_name, "ica_subjs")]),
# concat images
(ica_subjs, concat, [("ica_subjs", "in_files")]),
# canica
(concat, ica, [(("out_file", _check_list), "in_files")]),
# canica output
(ica, ica_datasink, [("components", "@components"),
("loadings", "@loadings"),
("score", "@score"),
]),
])
# plot the ICA results?
do_plot = get_config_setting('canica_extra.plot', default=True)
if not do_plot:
return main_wf
# get the plot threshold from the ICA node or the config file (in that order).
plot_thr = get_config_setting('canica_extra.plot_thr', default=0)
plot_thr = get_trait_value(ica.inputs, 'threshold', default=plot_thr)
# plto ica results images
plot_ica = setup_node(Function(function=plot_ica_results,
input_names=["ica_result", "application", "mask_file", "zscore", "bg_img"],
output_names=["all_icc_plot", "iccs_plot", "sliced_ic_plots"],),
name="plot_ica")
plot_ica.inputs.zscore = plot_thr
plot_ica.inputs.mask_file = get_trait_value(ica.inputs, 'mask')
plot_ica.inputs.application = 'nilearn'
# Connect the plotting nodes
main_wf.connect([
# canica
(ica, plot_ica, [("components", "ica_result")]),
# canica output
(plot_ica, ica_datasink, [("all_icc_plot", "@all_icc_plot"),
("iccs_plot", "@iccs_plot"),
("sliced_ic_plots", "@sliced_ic_plots"),
]),
])
return main_wf