def fix_preparation_pipeline(self, **kwargs):
pipeline = self.create_pipeline(
name='prepare_fix',
inputs=[
DatasetSpec('melodic_ica', directory_format),
DatasetSpec('filtered_data', nifti_gz_format),
DatasetSpec('coreg_to_atlas_mat', text_matrix_format),
DatasetSpec('coreg_matrix', text_matrix_format),
DatasetSpec('preproc', nifti_gz_format),
DatasetSpec('brain', nifti_gz_format),
DatasetSpec('coreg_ref_brain', nifti_gz_format),
DatasetSpec('mc_par', par_format),
DatasetSpec('brain_mask', nifti_gz_format)
],
outputs=[
DatasetSpec('fix_dir', directory_format),
DatasetSpec('hand_label_noise', text_format)
],
desc=("Pipeline to create the right folder structure before "
"running FIX"),
version=1,
citations=[fsl_cite],
**kwargs)
struct_ants2fsl = pipeline.create_node(ANTs2FSLMatrixConversion(),
name='struct_ants2fsl',
requirements=[c3d_req])
struct_ants2fsl.inputs.ras2fsl = True
struct_ants2fsl.inputs.reference_file = self.parameter('MNI_template')
pipeline.connect_input('coreg_to_atlas_mat', struct_ants2fsl,
'itk_file')
pipeline.connect_input('coreg_ref_brain', struct_ants2fsl,
'source_file')
epi_ants2fsl = pipeline.create_node(ANTs2FSLMatrixConversion(),
name='epi_ants2fsl',
requirements=[c3d_req])
epi_ants2fsl.inputs.ras2fsl = True
pipeline.connect_input('brain', epi_ants2fsl, 'source_file')
pipeline.connect_input('coreg_matrix', epi_ants2fsl, 'itk_file')
pipeline.connect_input('coreg_ref_brain', epi_ants2fsl,
'reference_file')
MNI2t1 = pipeline.create_node(ConvertXFM(),
name='MNI2t1',
wall_time=5,
requirements=[fsl509_req])
MNI2t1.inputs.invert_xfm = True
pipeline.connect(struct_ants2fsl, 'fsl_matrix', MNI2t1, 'in_file')
struct2epi = pipeline.create_node(ConvertXFM(),
name='struct2epi',
wall_time=5,
requirements=[fsl509_req])
struct2epi.inputs.invert_xfm = True
pipeline.connect(epi_ants2fsl, 'fsl_matrix', struct2epi, 'in_file')
meanfunc = pipeline.create_node(ImageMaths(op_string='-Tmean',
suffix='_mean'),
name='meanfunc',
wall_time=5,
requirements=[fsl509_req])
pipeline.connect_input('preproc', meanfunc, 'in_file')
prep_fix = pipeline.create_node(PrepareFIX(), name='prep_fix')
pipeline.connect_input('melodic_ica', prep_fix, 'melodic_dir')
pipeline.connect_input('coreg_ref_brain', prep_fix, 't1_brain')
pipeline.connect_input('mc_par', prep_fix, 'mc_par')
pipeline.connect_input('brain_mask', prep_fix, 'epi_brain_mask')
pipeline.connect_input('preproc', prep_fix, 'epi_preproc')
pipeline.connect_input('filtered_data', prep_fix, 'filtered_epi')
pipeline.connect(epi_ants2fsl, 'fsl_matrix', prep_fix, 'epi2t1_mat')
pipeline.connect(struct_ants2fsl, 'fsl_matrix', prep_fix, 't12MNI_mat')
pipeline.connect(MNI2t1, 'out_file', prep_fix, 'MNI2t1_mat')
pipeline.connect(struct2epi, 'out_file', prep_fix, 't12epi_mat')
pipeline.connect(meanfunc, 'out_file', prep_fix, 'epi_mean')
pipeline.connect_output('fix_dir', prep_fix, 'fix_dir')
pipeline.connect_output('hand_label_noise', prep_fix,
'hand_label_file')
return pipeline
def motion_correction_pipeline(self, **name_maps):
if 'struct2align' in self.input_names:
StructAlignment = True
else:
StructAlignment = False
pipeline = self.new_pipeline(
name='pet_mc',
desc=("Given a folder with reconstructed PET data, this "
"pipeline will generate a motion corrected PET"
"image using information extracted from the MR-based "
"motion detection pipeline"),
citations=[fsl_cite],
name_maps=name_maps)
check_pet = pipeline.add(
'check_pet_data',
CheckPetMCInputs(),
inputs={
'pet_data': ('pet_data_prepared', directory_format),
'reference': ('ref_brain', nifti_gz_format)
},
requirements=[fsl_req.v('5.0.9'),
mrtrix_req.v('3.0rc3')])
if self.branch('dynamic_pet_mc'):
pipeline.connect_input('fixed_binning_mats', check_pet,
'motion_mats')
else:
pipeline.connect_input('average_mats', check_pet, 'motion_mats')
pipeline.connect_input('correction_factors', check_pet,
'corr_factors')
if StructAlignment:
struct_reg = pipeline.add('ref2structural_reg',
FLIRT(dof=6,
cost_func='normmi',
cost='normmi',
output_type='NIFTI_GZ'),
inputs={
'reference':
('ref_brain', nifti_gz_format),
'in_file':
('struct2align', nifti_gz_format)
},
requirements=[fsl_req.v('5.0.9')])
if self.branch('dynamic_pet_mc'):
pet_mc = pipeline.add('pet_mc',
PetImageMotionCorrection(),
inputs={
'pet_image': (check_pet, 'pet_images'),
'motion_mat': (check_pet, 'motion_mats'),
'pet2ref_mat': (check_pet, 'pet2ref_mat')
},
requirements=[fsl_req.v('5.0.9')],
iterfield=['pet_image', 'motion_mat'])
else:
pet_mc = pipeline.add(
'pet_mc',
PetImageMotionCorrection(),
inputs={'corr_factor': (check_pet, 'corr_factors')},
requirements=[fsl_req.v('5.0.9')],
iterfield=['corr_factor', 'pet_image', 'motion_mat'])
if StructAlignment:
pipeline.connect(struct_reg, 'out_matrix_file', pet_mc,
'structural2ref_regmat')
pipeline.connect_input('struct2align', pet_mc, 'structural_image')
if self.parameter('PET2MNI_reg'):
mni_reg = True
else:
mni_reg = False
if self.branch('dynamic_pet_mc'):
merge_mc = pipeline.add(
'merge_pet_mc',
fsl.Merge(dimension='t'),
inputs={'in_files': (pet_mc, 'pet_mc_image')},
requirements=[fsl_req.v('5.0.9')])
merge_no_mc = pipeline.add(
'merge_pet_no_mc',
fsl.Merge(dimension='t'),
inputs={'in_files': (pet_mc, 'pet_no_mc_image')},
requirements=[fsl_req.v('5.0.9')])
else:
static_mc = pipeline.add('static_mc_generation',
StaticPETImageGeneration(),
inputs={
'pet_mc_images':
(pet_mc, 'pet_mc_image'),
'pet_no_mc_images':
(pet_mc, 'pet_no_mc_image')
},
requirements=[fsl_req.v('5.0.9')])
merge_outputs = pipeline.add(
'merge_outputs',
Merge(3),
inputs={'in1': ('mean_displacement_plot', png_format)})
if not StructAlignment:
cropping = pipeline.add(
'pet_cropping',
PETFovCropping(x_min=self.parameter('crop_xmin'),
x_size=self.parameter('crop_xsize'),
y_min=self.parameter('crop_ymin'),
y_size=self.parameter('crop_ysize'),
z_min=self.parameter('crop_zmin'),
z_size=self.parameter('crop_zsize')))
if self.branch('dynamic_pet_mc'):
pipeline.connect(merge_mc, 'merged_file', cropping,
'pet_image')
else:
pipeline.connect(static_mc, 'static_mc', cropping, 'pet_image')
cropping_no_mc = pipeline.add(
'pet_no_mc_cropping',
PETFovCropping(x_min=self.parameter('crop_xmin'),
x_size=self.parameter('crop_xsize'),
y_min=self.parameter('crop_ymin'),
y_size=self.parameter('crop_ysize'),
z_min=self.parameter('crop_zmin'),
z_size=self.parameter('crop_zsize')))
if self.branch('dynamic_pet_mc'):
pipeline.connect(merge_no_mc, 'merged_file', cropping_no_mc,
'pet_image')
else:
pipeline.connect(static_mc, 'static_no_mc', cropping_no_mc,
'pet_image')
if mni_reg:
if self.branch('dynamic_pet_mc'):
t_mean = pipeline.add(
'PET_temporal_mean',
ImageMaths(op_string='-Tmean'),
inputs={'in_file': (cropping, 'pet_cropped')},
requirements=[fsl_req.v('5.0.9')])
reg_tmean2MNI = pipeline.add(
'reg2MNI',
AntsRegSyn(num_dimensions=3,
transformation='s',
out_prefix='reg2MNI',
num_threads=4,
ref_file=self.parameter('PET_template_MNI')),
wall_time=25,
requirements=[ants_req.v('2')])
if self.branch('dynamic_pet_mc'):
pipeline.connect(t_mean, 'out_file', reg_tmean2MNI,
'input_file')
merge_trans = pipeline.add('merge_transforms',
Merge(2),
inputs={
'in1': (reg_tmean2MNI,
'warp_file'),
'in2':
(reg_tmean2MNI, 'regmat')
},
wall_time=1)
apply_trans = pipeline.add(
'apply_trans',
ApplyTransforms(
reference_image=self.parameter('PET_template_MNI'),
interpolation='Linear',
input_image_type=3),
inputs={
'input_image': (cropping, 'pet_cropped'),
'transforms': (merge_trans, 'out')
},
wall_time=7,
mem_gb=24,
requirements=[ants_req.v('2')])
pipeline.connect(apply_trans, 'output_image',
merge_outputs, 'in2'),
else:
pipeline.connect(cropping, 'pet_cropped', reg_tmean2MNI,
'input_file')
pipeline.connect(reg_tmean2MNI, 'reg_file', merge_outputs,
'in2')
else:
pipeline.connect(cropping, 'pet_cropped', merge_outputs, 'in2')
pipeline.connect(cropping_no_mc, 'pet_cropped', merge_outputs,
'in3')
else:
if self.branch('dynamic_pet_mc'):
pipeline.connect(merge_mc, 'merged_file', merge_outputs, 'in2')
pipeline.connect(merge_no_mc, 'merged_file', merge_outputs,
'in3')
else:
pipeline.connect(static_mc, 'static_mc', merge_outputs, 'in2')
pipeline.connect(static_mc, 'static_no_mc', merge_outputs,
'in3')
# mcflirt = pipeline.add('mcflirt', MCFLIRT())
# 'in_file': (merge_mc_ps, 'merged_file'),
# cost='normmi',
copy2dir = pipeline.add('copy2dir',
CopyToDir(),
inputs={'in_files': (merge_outputs, 'out')})
if self.branch('dynamic_pet_mc'):
pipeline.connect_output('dynamic_motion_correction_results',
copy2dir, 'out_dir')
else:
pipeline.connect_output('static_motion_correction_results',
copy2dir, 'out_dir')
return pipeline
def fix_preparation_pipeline(self, **name_maps):
pipeline = self.new_pipeline(
name='prepare_fix',
desc=("Pipeline to create the right folder structure before "
"running FIX"),
citations=[fsl_cite],
name_maps=name_maps)
if self.branch('coreg_to_tmpl_method', 'ants'):
struct_ants2fsl = pipeline.add(
'struct_ants2fsl',
ANTs2FSLMatrixConversion(ras2fsl=True),
inputs={
'reference_file': ('template_brain', nifti_gz_format),
'itk_file': ('coreg_to_tmpl_ants_mat', text_matrix_format),
'source_file': ('coreg_ref_brain', nifti_gz_format)
},
requirements=[c3d_req.v('1.0.0')])
struct_matrix = (struct_ants2fsl, 'fsl_matrix')
else:
struct_matrix = ('coreg_to_tmpl_fsl_mat', text_matrix_format)
# if self.branch('coreg_method', 'ants'):
# epi_ants2fsl = pipeline.add(
# 'epi_ants2fsl',
# ANTs2FSLMatrixConversion(
# ras2fsl=True),
# inputs={
# 'source_file': ('brain', nifti_gz_format),
# 'itk_file': ('coreg_ants_mat', text_matrix_format),
# 'reference_file': ('coreg_ref_brain', nifti_gz_format)},
# requirements=[c3d_req.v('1.0.0')])
MNI2t1 = pipeline.add('MNI2t1',
ConvertXFM(invert_xfm=True),
inputs={'in_file': struct_matrix},
wall_time=5,
requirements=[fsl_req.v('5.0.9')])
struct2epi = pipeline.add(
'struct2epi',
ConvertXFM(invert_xfm=True),
inputs={'in_file': ('coreg_fsl_mat', text_matrix_format)},
wall_time=5,
requirements=[fsl_req.v('5.0.9')])
meanfunc = pipeline.add(
'meanfunc',
ImageMaths(op_string='-Tmean',
suffix='_mean',
output_type='NIFTI_GZ'),
inputs={'in_file': ('series_preproc', nifti_gz_format)},
wall_time=5,
requirements=[fsl_req.v('5.0.9')])
pipeline.add('prep_fix',
PrepareFIX(),
inputs={
'melodic_dir': ('melodic_ica', directory_format),
't1_brain': ('coreg_ref_brain', nifti_gz_format),
'mc_par': ('mc_par', par_format),
'epi_brain_mask': ('brain_mask', nifti_gz_format),
'epi_preproc': ('series_preproc', nifti_gz_format),
'filtered_epi': ('filtered_data', nifti_gz_format),
'epi2t1_mat': ('coreg_fsl_mat', text_matrix_format),
't12MNI_mat': (struct_ants2fsl, 'fsl_matrix'),
'MNI2t1_mat': (MNI2t1, 'out_file'),
't12epi_mat': (struct2epi, 'out_file'),
'epi_mean': (meanfunc, 'out_file')
},
outputs={
'fix_dir': ('fix_dir', directory_format),
'hand_label_noise': ('hand_label_file', text_format)
})
return pipeline
def create_t1_based_unwarp(name='unwarp'):
"""
Unwarp an fMRI time series based on non-linear registration to T1.
NOTE: AS IT STANDS THIS METHOD DID NOT PRODUCE ACCEPTABLE RESULTS
IF BRAIN COVERAGE IS NOT COMPLETE ON THE EPI IMAGE.
ALSO: NEED TO ADD AUTOMATIC READING OF EPI RESOLUTION TO GET
"""
unwarpflow = pe.Workflow(name=name)
inputnode = pe.Node(
interface=util.IdentityInterface(fields=['epi', 'T1W']),
name='inputspec')
outputnode = pe.Node(interface=util.IdentityInterface(
fields=['unwarped_func', 'warp_files']),
name='outputspec')
tmedian = pe.Node(interface=ImageMaths(), name='tmedian')
tmedian.inputs.op_string = '-Tmedian'
epi_brain_ext = pe.Node(interface=util.Function(
function=epi_brain_extract,
input_names=['in_file'],
output_names=['out_vol', 'out_mask']),
name='epi_brain_ext')
fast_debias = pe.Node(interface=FAST(), name='FAST_debias')
fast_debias.inputs.output_biascorrected = True
robex = pe.Node(interface=util.Function(
function=my_robex,
input_names=['in_file'],
output_names=['out_file', 'out_mask']),
name='robex')
downsample_T1 = pe.Node(MRIConvert(), name='downsample_dti')
downsample_T1.inputs.vox_size = (3.438, 3.438, 3.000)
downsample_T1.inputs.out_type = 'niigz'
contrast_invert = pe.Node(interface=util.Function(
function=invert_contrast,
input_names=['in_t1_brain', 'in_b0_brain'],
output_names=['out_fn']),
name='contrast_invert')
ants_syn = pe.Node(interface=util.Function(
function=my_ants_registration_syn,
input_names=['in_T1W', 'in_epi'],
output_names=['out_transforms']),
name='ants_syn')
ants_warp = pe.Node(interface=WarpTimeSeriesImageMultiTransform(),
name='ants_warp')
'''connections'''
# unwarpflow.connect(inputnode, 'T1W', robex, 'in_file')
unwarpflow.connect(inputnode, 'T1W', fast_debias, 'in_files')
# unwarpflow.connect(robex, 'out_file', fast_debias, 'in_files')
unwarpflow.connect(fast_debias, 'restored_image', robex, 'in_file')
# unwarpflow.connect(fast_debias, 'restored_image', downsample_T1, 'in_file')
unwarpflow.connect(robex, 'out_file', downsample_T1, 'in_file')
unwarpflow.connect(downsample_T1, 'out_file', contrast_invert,
'in_t1_brain')
unwarpflow.connect(inputnode, 'epi', tmedian, 'in_file')
unwarpflow.connect(tmedian, 'out_file', epi_brain_ext, 'in_file')
unwarpflow.connect(epi_brain_ext, 'out_vol', contrast_invert,
'in_b0_brain')
unwarpflow.connect(contrast_invert, 'out_fn', ants_syn, 'in_T1W')
unwarpflow.connect(epi_brain_ext, 'out_vol', ants_syn, 'in_epi')
unwarpflow.connect(ants_syn, 'out_transforms', outputnode,
'out_transforms')
unwarpflow.connect(inputnode, 'epi', ants_warp, 'input_image')
unwarpflow.connect(contrast_invert, 'out_fn', ants_warp, 'reference_image')
unwarpflow.connect(ants_syn, 'out_transforms', ants_warp,
'transformation_series')
unwarpflow.connect(ants_syn, 'out_transforms', outputnode, 'warp_files')
unwarpflow.connect(ants_warp, 'output_image', outputnode, 'unwarped_func')
return unwarpflow
def firstlevel_wf(subject_id, sink_directory, name='wmaze_frstlvl_wf'):
frstlvl_wf = Workflow(name='frstlvl_wf')
info = dict(task_mri_files=[['subject_id', 'wmaze']],
motion_noise_files=[['subject_id', 'filter_regressor']])
subject_info = Node(Function(input_names=['subject_id'],
output_names=['output'],
function=subjectinfo),
name='subject_info')
subject_info.inputs.ignore_exception = False
subject_info.inputs.subject_id = subject_id
getcontrasts = Node(Function(input_names=['subject_id', 'info'],
output_names=['contrasts'],
function=get_contrasts),
name='getcontrasts')
getcontrasts.inputs.ignore_exception = False
getcontrasts.inputs.subject_id = subject_id
frstlvl_wf.connect(subject_info, 'output', getcontrasts, 'info')
#fFunction node to substitute names of folders and files created during pipeline
getsubs = Node(Function(input_names=['cons'],
output_names=['subs'],
function=get_subs),
name='getsubs')
getsubs.inputs.ignore_exception = False
getsubs.inputs.subject_id = subject_id
frstlvl_wf.connect(subject_info, 'output', getsubs, 'info')
frstlvl_wf.connect(getcontrasts, 'contrasts', getsubs, 'cons')
#datasource node to get the task_mri and motion-noise files
datasource = Node(DataGrabber(infields=['subject_id'],
outfields=info.keys()),
name='datasource')
datasource.inputs.template = '*'
datasource.inputs.subject_id = subject_id
datasource.inputs.base_directory = os.path.abspath(
'/home/data/madlab/data/mri/wmaze/preproc/')
datasource.inputs.field_template = dict(
task_mri_files='%s/func/realigned/*%s*.nii.gz',
motion_noise_files='%s/noise/%s*.txt')
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = True
datasource.inputs.ignore_exception = False
datasource.inputs.raise_on_empty = True
#function node to remove last three volumes from functional data
fslroi_epi = MapNode(
ExtractROI(t_min=0,
t_size=197), #start from first volume and end on -3 volume
iterfield=['in_file'],
name='fslroi_epi')
fslroi_epi.output_type = 'NIFTI_GZ'
fslroi_epi.terminal_output = 'stream'
frstlvl_wf.connect(datasource, 'task_mri_files', fslroi_epi, 'in_file')
#function node to modify motion and noise files to be single regressors
motionnoise = Node(Function(input_names=['subjinfo', 'files'],
output_names=['subjinfo'],
function=motion_noise),
name='motionnoise')
motionnoise.inputs.ignore_exception = False
frstlvl_wf.connect(subject_info, 'output', motionnoise, 'subjinfo')
frstlvl_wf.connect(datasource, 'motion_noise_files', motionnoise, 'files')
# Makes a model specification compatible with spm/fsl designers
# Requires subjectinfo to be received in the form of a Bunch of a list of Bunch
specify_model = Node(SpecifyModel(), name='specify_model')
specify_model.inputs.high_pass_filter_cutoff = -1.0
specify_model.inputs.ignore_exception = False
specify_model.inputs.input_units = 'secs'
specify_model.inputs.time_repetition = 2.0
frstlvl_wf.connect(fslroi_epi, 'roi_file', specify_model,
'functional_runs')
frstlvl_wf.connect(motionnoise, 'subjinfo', specify_model, 'subject_info')
#basic interface class generates identity mappings
modelfit_inputspec = Node(IdentityInterface(fields=[
'session_info', 'interscan_interval', 'contrasts', 'film_threshold',
'functional_data', 'bases', 'model_serial_correlations'
],
mandatory_inputs=True),
name='modelfit_inputspec')
modelfit_inputspec.inputs.bases = {'dgamma': {'derivs': False}}
modelfit_inputspec.inputs.film_threshold = 0.0
modelfit_inputspec.inputs.interscan_interval = 2.0
modelfit_inputspec.inputs.model_serial_correlations = True
frstlvl_wf.connect(fslroi_epi, 'roi_file', modelfit_inputspec,
'functional_data')
frstlvl_wf.connect(getcontrasts, 'contrasts', modelfit_inputspec,
'contrasts')
frstlvl_wf.connect(specify_model, 'session_info', modelfit_inputspec,
'session_info')
#MapNode for first level SPM design matrix to demonstrate contrasts and motion/noise regressors
level1_design = MapNode(Level1Design(),
iterfield=['contrasts', 'session_info'],
name='level1_design')
level1_design.inputs.ignore_exception = False
frstlvl_wf.connect(modelfit_inputspec, 'interscan_interval', level1_design,
'interscan_interval')
frstlvl_wf.connect(modelfit_inputspec, 'session_info', level1_design,
'session_info')
frstlvl_wf.connect(modelfit_inputspec, 'contrasts', level1_design,
'contrasts')
frstlvl_wf.connect(modelfit_inputspec, 'bases', level1_design, 'bases')
frstlvl_wf.connect(modelfit_inputspec, 'model_serial_correlations',
level1_design, 'model_serial_correlations')
#MapNode to generate a design.mat file for each run
generate_model = MapNode(FEATModel(),
iterfield=['fsf_file', 'ev_files'],
name='generate_model')
generate_model.inputs.environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'}
generate_model.inputs.ignore_exception = False
generate_model.inputs.output_type = 'NIFTI_GZ'
generate_model.inputs.terminal_output = 'stream'
frstlvl_wf.connect(level1_design, 'fsf_files', generate_model, 'fsf_file')
frstlvl_wf.connect(level1_design, 'ev_files', generate_model, 'ev_files')
#MapNode to estimate the model using FILMGLS -- fits the design matrix to the voxel timeseries
estimate_model = MapNode(FILMGLS(),
iterfield=['design_file', 'in_file', 'tcon_file'],
name='estimate_model')
estimate_model.inputs.environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'}
estimate_model.inputs.ignore_exception = False
estimate_model.inputs.mask_size = 5
estimate_model.inputs.output_type = 'NIFTI_GZ'
estimate_model.inputs.results_dir = 'results'
estimate_model.inputs.smooth_autocorr = True
estimate_model.inputs.terminal_output = 'stream'
frstlvl_wf.connect(modelfit_inputspec, 'film_threshold', estimate_model,
'threshold')
frstlvl_wf.connect(modelfit_inputspec, 'functional_data', estimate_model,
'in_file')
frstlvl_wf.connect(generate_model, 'design_file', estimate_model,
'design_file')
frstlvl_wf.connect(generate_model, 'con_file', estimate_model, 'tcon_file')
#merge node to merge the contrasts - necessary for fsl 5.0.7 and greater
merge_contrasts = MapNode(Merge(2),
iterfield=['in1'],
name='merge_contrasts')
frstlvl_wf.connect(estimate_model, 'zstats', merge_contrasts, 'in1')
#MapNode to transform the z2pval
z2pval = MapNode(ImageMaths(), iterfield=['in_file'], name='z2pval')
z2pval.inputs.environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'}
z2pval.inputs.ignore_exception = False
z2pval.inputs.op_string = '-ztop'
z2pval.inputs.output_type = 'NIFTI_GZ'
z2pval.inputs.suffix = '_pval'
z2pval.inputs.terminal_output = 'stream'
frstlvl_wf.connect(merge_contrasts, ('out', pop_lambda), z2pval, 'in_file')
#outputspec node using IdentityInterface() to receive information from estimate_model, merge_contrasts, z2pval, generate_model, and estimate_model
modelfit_outputspec = Node(IdentityInterface(fields=[
'copes', 'varcopes', 'dof_file', 'pfiles', 'parameter_estimates',
'zstats', 'design_image', 'design_file', 'design_cov', 'sigmasquareds'
],
mandatory_inputs=True),
name='modelfit_outputspec')
frstlvl_wf.connect(estimate_model, 'copes', modelfit_outputspec, 'copes')
frstlvl_wf.connect(estimate_model, 'varcopes', modelfit_outputspec,
'varcopes')
frstlvl_wf.connect(merge_contrasts, 'out', modelfit_outputspec, 'zstats')
frstlvl_wf.connect(z2pval, 'out_file', modelfit_outputspec, 'pfiles')
frstlvl_wf.connect(generate_model, 'design_image', modelfit_outputspec,
'design_image')
frstlvl_wf.connect(generate_model, 'design_file', modelfit_outputspec,
'design_file')
frstlvl_wf.connect(generate_model, 'design_cov', modelfit_outputspec,
'design_cov')
frstlvl_wf.connect(estimate_model, 'param_estimates', modelfit_outputspec,
'parameter_estimates')
frstlvl_wf.connect(estimate_model, 'dof_file', modelfit_outputspec,
'dof_file')
frstlvl_wf.connect(estimate_model, 'sigmasquareds', modelfit_outputspec,
'sigmasquareds')
#datasink node to save output from multiple points in the pipeline
sinkd = MapNode(DataSink(),
iterfield=[
'substitutions', 'modelfit.contrasts.@copes',
'modelfit.contrasts.@varcopes', 'modelfit.estimates',
'modelfit.contrasts.@zstats'
],
name='sinkd')
sinkd.inputs.base_directory = sink_directory
sinkd.inputs.container = subject_id
frstlvl_wf.connect(getsubs, 'subs', sinkd, 'substitutions')
frstlvl_wf.connect(modelfit_outputspec, 'parameter_estimates', sinkd,
'modelfit.estimates')
frstlvl_wf.connect(modelfit_outputspec, 'sigmasquareds', sinkd,
'modelfit.estimates.@sigsq')
frstlvl_wf.connect(modelfit_outputspec, 'dof_file', sinkd, 'modelfit.dofs')
frstlvl_wf.connect(modelfit_outputspec, 'copes', sinkd,
'modelfit.contrasts.@copes')
frstlvl_wf.connect(modelfit_outputspec, 'varcopes', sinkd,
'modelfit.contrasts.@varcopes')
frstlvl_wf.connect(modelfit_outputspec, 'zstats', sinkd,
'modelfit.contrasts.@zstats')
frstlvl_wf.connect(modelfit_outputspec, 'design_image', sinkd,
'modelfit.design')
frstlvl_wf.connect(modelfit_outputspec, 'design_cov', sinkd,
'modelfit.design.@cov')
frstlvl_wf.connect(modelfit_outputspec, 'design_file', sinkd,
'modelfit.design.@matrix')
frstlvl_wf.connect(modelfit_outputspec, 'pfiles', sinkd,
'modelfit.contrasts.@pstats')
return frstlvl_wf
def firstlevel_wf(subject_id, sink_directory, name='wmaze_frstlvl_wf'):
# Create the frstlvl workflow
frstlvl_wf = Workflow(name='frstlvl_wf')
# Dictionary holding the wildcard used in datasource
info = dict(task_mri_files=[['subject_id', 'wmaze']],
motion_noise_files=[['subject_id']])
# Calls the subjectinfo function with the name, onset, duration, and amplitude info
subject_info = Node(Function(input_names=['subject_id'],
output_names=['output'],
function=subjectinfo),
name='subject_info')
subject_info.inputs.ignore_exception = False
subject_info.inputs.subject_id = subject_id
# Create another Function node to define the contrasts for the experiment
getcontrasts = Node(
Function(
input_names=['subject_id', 'info'],
output_names=['contrasts'],
# Calls the function 'get_contrasts'
function=get_contrasts),
name='getcontrasts')
getcontrasts.inputs.ignore_exception = False
# Receives subject_id as input
getcontrasts.inputs.subject_id = subject_id
frstlvl_wf.connect(subject_info, 'output', getcontrasts, 'info')
#### subject_info (output) ----> getcontrasts (info)
# Create a Function node to substitute names of folders and files created during pipeline
getsubs = Node(
Function(
input_names=['cons'],
output_names=['subs'],
# Calls the function 'get_subs'
function=get_subs),
name='getsubs')
getsubs.inputs.ignore_exception = False
# Receives subject_id as input
getsubs.inputs.subject_id = subject_id
frstlvl_wf.connect(subject_info, 'output', getsubs, 'info')
frstlvl_wf.connect(getcontrasts, 'contrasts', getsubs, 'cons')
# Create a datasource node to get the task_mri and motion-noise files
datasource = Node(DataGrabber(infields=['subject_id'],
outfields=info.keys()),
name='datasource')
# Indicates the string template to match (in this case, any that match the field template)
datasource.inputs.template = '*'
# Receives subject_id as an input
datasource.inputs.subject_id = subject_id
# Base directory to allow branching pathways
datasource.inputs.base_directory = os.path.abspath(
'/home/data/madlab/data/mri/wmaze/preproc/')
datasource.inputs.field_template = dict(
task_mri_files='%s/func/smoothed_fullspectrum/_maskfunc2*/*%s*.nii.gz',
# Filter regressor noise files
motion_noise_files='%s/noise/filter_regressor*.txt')
# Inputs from the infields argument ('subject_id') that satisfy the template
datasource.inputs.template_args = info
# Forces DataGrabber to return data in sorted order when using wildcards
datasource.inputs.sort_filelist = True
# Do not ignore exceptions
datasource.inputs.ignore_exception = False
datasource.inputs.raise_on_empty = True
# Function to remove last three volumes from functional data
# Start from the first volume and end on the -3 volume
fslroi_epi = MapNode(ExtractROI(t_min=0, t_size=197),
iterfield=['in_file'],
name='fslroi_epi')
fslroi_epi.output_type = 'NIFTI_GZ'
fslroi_epi.terminal_output = 'stream'
frstlvl_wf.connect(datasource, 'task_mri_files', fslroi_epi, 'in_file')
# Function node to modify the motion and noise files to be single regressors
motionnoise = Node(
Function(
input_names=['subjinfo', 'files'],
output_names=['subjinfo'],
# Calls the function 'motion_noise'
function=motion_noise),
name='motionnoise')
motionnoise.inputs.ignore_exception = False
# The bunch from subject_info function containing regressor names, onsets, durations, and amplitudes
frstlvl_wf.connect(subject_info, 'output', motionnoise, 'subjinfo')
frstlvl_wf.connect(datasource, 'motion_noise_files', motionnoise, 'files')
# Makes a model specification compatible with spm/fsl designers
# Requires subjectinfo to be received in the form of a Bunch of a list of Bunch
specify_model = Node(SpecifyModel(), name='specify_model')
# High-pass filter cutoff in seconds
specify_model.inputs.high_pass_filter_cutoff = -1.0
specify_model.inputs.ignore_exception = False
# input units in either 'secs' or 'scans'
specify_model.inputs.input_units = 'secs'
# Time between start of one volume and the start of following volume
specify_model.inputs.time_repetition = 2.0
# Editted data files for model -- list of 4D files
frstlvl_wf.connect(fslroi_epi, 'roi_file', specify_model,
'functional_runs')
# List of event description files in 3 column format corresponding to onsets, durations, and amplitudes
frstlvl_wf.connect(motionnoise, 'subjinfo', specify_model, 'subject_info')
# Basic interface class generates identity mappings
modelfit_inputspec = Node(IdentityInterface(fields=[
'session_info', 'interscan_interval', 'contrasts', 'film_threshold',
'functional_data', 'bases', 'model_serial_correlations'
],
mandatory_inputs=True),
name='modelfit_inputspec')
# Set bases to a dictionary with a second dictionary setting the value of dgamma derivatives as 'False'
modelfit_inputspec.inputs.bases = {'dgamma': {'derivs': False}}
# Film threshold
modelfit_inputspec.inputs.film_threshold = 0.0
# Interscan_interval
modelfit_inputspec.inputs.interscan_interval = 2.0
# Create model serial correlations for Level1Design
modelfit_inputspec.inputs.model_serial_correlations = True
frstlvl_wf.connect(fslroi_epi, 'roi_file', modelfit_inputspec,
'functional_data')
frstlvl_wf.connect(getcontrasts, 'contrasts', modelfit_inputspec,
'contrasts')
frstlvl_wf.connect(specify_model, 'session_info', modelfit_inputspec,
'session_info')
# Creates a first level SPM design matrix to demonstrate contrasts and motion/noise regressors
level1_design = MapNode(Level1Design(),
iterfield=['contrasts', 'session_info'],
name='level1_design')
level1_design.inputs.ignore_exception = False
# Inputs the interscan interval (in secs)
frstlvl_wf.connect(modelfit_inputspec, 'interscan_interval', level1_design,
'interscan_interval')
# Session specific information generated by ``modelgen.SpecifyModel``
frstlvl_wf.connect(modelfit_inputspec, 'session_info', level1_design,
'session_info')
# List of contrasts with each contrast being a list of the form -[('name', 'stat', [condition list], [weight list], [session list])].
# If session list is None or not provided, all sessions are used.
frstlvl_wf.connect(modelfit_inputspec, 'contrasts', level1_design,
'contrasts')
# Name of basis function and options e.g., {'dgamma': {'derivs': True}}
frstlvl_wf.connect(modelfit_inputspec, 'bases', level1_design, 'bases')
# Option to model serial correlations using an autoregressive estimator (order 1)
# Setting this option is only useful in the context of the fsf file
frstlvl_wf.connect(modelfit_inputspec, 'model_serial_correlations',
level1_design, 'model_serial_correlations')
# Create a MapNode to generate a design.mat file for each run
generate_model = MapNode(FEATModel(),
iterfield=['fsf_file', 'ev_files'],
name='generate_model')
generate_model.inputs.environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'}
generate_model.inputs.ignore_exception = False
generate_model.inputs.output_type = 'NIFTI_GZ'
generate_model.inputs.terminal_output = 'stream'
# File specifying the feat design spec file
frstlvl_wf.connect(level1_design, 'fsf_files', generate_model, 'fsf_file')
# Event spec files generated by level1design (condition information files)
frstlvl_wf.connect(level1_design, 'ev_files', generate_model, 'ev_files')
# Create a MapNode to estimate the model using FILMGLS -- fits the design matrix to the voxel timeseries
estimate_model = MapNode(FILMGLS(),
iterfield=['design_file', 'in_file', 'tcon_file'],
name='estimate_model')
estimate_model.inputs.environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'}
estimate_model.inputs.ignore_exception = False
# Susan-smooth mask size
estimate_model.inputs.mask_size = 5
estimate_model.inputs.output_type = 'NIFTI_GZ'
estimate_model.inputs.results_dir = 'results'
# Smooth auto-correlation estimates
estimate_model.inputs.smooth_autocorr = True
estimate_model.inputs.terminal_output = 'stream'
frstlvl_wf.connect(modelfit_inputspec, 'film_threshold', estimate_model,
'threshold')
frstlvl_wf.connect(modelfit_inputspec, 'functional_data', estimate_model,
'in_file')
# Mat file containing ascii matrix for design
frstlvl_wf.connect(generate_model, 'design_file', estimate_model,
'design_file')
# Contrast file containing contrast vectors
frstlvl_wf.connect(generate_model, 'con_file', estimate_model, 'tcon_file')
# Create a merge node to merge the contrasts - necessary for fsl 5.0.7 and greater
merge_contrasts = MapNode(Merge(2),
iterfield=['in1'],
name='merge_contrasts')
frstlvl_wf.connect(estimate_model, 'zstats', merge_contrasts, 'in1')
# Create a MapNode to transform the z2pval
z2pval = MapNode(ImageMaths(), iterfield=['in_file'], name='z2pval')
z2pval.inputs.environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'}
# Do not ignore exceptions
z2pval.inputs.ignore_exception = False
# Defines the operation used
z2pval.inputs.op_string = '-ztop'
# Set the outfile type to nii.gz
z2pval.inputs.output_type = 'NIFTI_GZ'
# Out-file suffix
z2pval.inputs.suffix = '_pval'
# Set output to stream in terminal
z2pval.inputs.terminal_output = 'stream'
frstlvl_wf.connect(merge_contrasts, ('out', pop_lambda), z2pval, 'in_file')
# Create an outputspec node using IdentityInterface() to receive information from estimate_model,
# merge_contrasts, z2pval, generate_model, and estimate_model
modelfit_outputspec = Node(IdentityInterface(fields=[
'copes', 'varcopes', 'dof_file', 'pfiles', 'parameter_estimates',
'zstats', 'design_image', 'design_file', 'design_cov', 'sigmasquareds'
],
mandatory_inputs=True),
name='modelfit_outputspec')
# All lvl1 cope files
frstlvl_wf.connect(estimate_model, 'copes', modelfit_outputspec, 'copes')
# All lvl1 varcope files
frstlvl_wf.connect(estimate_model, 'varcopes', modelfit_outputspec,
'varcopes')
# All zstats across runs
frstlvl_wf.connect(merge_contrasts, 'out', modelfit_outputspec, 'zstats')
#
frstlvl_wf.connect(z2pval, 'out_file', modelfit_outputspec, 'pfiles')
# Graphical representation of design matrix
frstlvl_wf.connect(generate_model, 'design_image', modelfit_outputspec,
'design_image')
# Mat file containing ascii matrix for design
frstlvl_wf.connect(generate_model, 'design_file', modelfit_outputspec,
'design_file')
# Graphical representation of design covariance
frstlvl_wf.connect(generate_model, 'design_cov', modelfit_outputspec,
'design_cov')
# Parameter estimates for each column of the design matrix
frstlvl_wf.connect(estimate_model, 'param_estimates', modelfit_outputspec,
'parameter_estimates')
# Degrees of freedom
frstlvl_wf.connect(estimate_model, 'dof_file', modelfit_outputspec,
'dof_file')
# Summary of residuals
frstlvl_wf.connect(estimate_model, 'sigmasquareds', modelfit_outputspec,
'sigmasquareds')
# Create a datasink node to save output from multiple points in the pipeline
sinkd = MapNode(DataSink(),
iterfield=[
'substitutions', 'modelfit.contrasts.@copes',
'modelfit.contrasts.@varcopes', 'modelfit.estimates',
'modelfit.contrasts.@zstats'
],
name='sinkd')
sinkd.inputs.base_directory = sink_directory
sinkd.inputs.container = subject_id
frstlvl_wf.connect(getsubs, 'subs', sinkd, 'substitutions')
frstlvl_wf.connect(modelfit_outputspec, 'parameter_estimates', sinkd,
'modelfit.estimates')
frstlvl_wf.connect(modelfit_outputspec, 'sigmasquareds', sinkd,
'modelfit.estimates.@sigsq')
frstlvl_wf.connect(modelfit_outputspec, 'dof_file', sinkd, 'modelfit.dofs')
frstlvl_wf.connect(modelfit_outputspec, 'copes', sinkd,
'modelfit.contrasts.@copes')
frstlvl_wf.connect(modelfit_outputspec, 'varcopes', sinkd,
'modelfit.contrasts.@varcopes')
frstlvl_wf.connect(modelfit_outputspec, 'zstats', sinkd,
'modelfit.contrasts.@zstats')
frstlvl_wf.connect(modelfit_outputspec, 'design_image', sinkd,
'modelfit.design')
frstlvl_wf.connect(modelfit_outputspec, 'design_cov', sinkd,
'modelfit.design.@cov')
frstlvl_wf.connect(modelfit_outputspec, 'design_file', sinkd,
'modelfit.design.@matrix')
frstlvl_wf.connect(modelfit_outputspec, 'pfiles', sinkd,
'modelfit.contrasts.@pstats')
return frstlvl_wf
def firstlevel_wf(subject_id, sink_directory, name='ds008_R2_frstlvl_wf'):
frstlvl_wf = Workflow(name='frstlvl_wf')
info = dict(task_mri_files=[['subject_id', 'stopsignal']],
motion_noise_files=[['subject_id', 'filter_regressor']])
# Create a Function node to define stimulus onsets, etc... for each subject
subject_info = Node(Function(input_names=['subject_id'],
output_names=['output'],
function=subjectinfo),
name='subject_info')
subject_info.inputs.ignore_exception = False
subject_info.inputs.subject_id = subject_id
# Create another Function node to define the contrasts for the experiment
getcontrasts = Node(Function(input_names=['subject_id'],
output_names=['contrasts'],
function=get_contrasts),
name='getcontrasts')
getcontrasts.inputs.ignore_exception = False
getcontrasts.inputs.subject_id = subject_id
# Create a Function node to substitute names of files created during pipeline
getsubs = Node(Function(input_names=['subject_id', 'cons', 'info'],
output_names=['subs'],
function=get_subs),
name='getsubs')
getsubs.inputs.ignore_exception = False
getsubs.inputs.subject_id = subject_id
frstlvl_wf.connect(subject_info, 'output', getsubs, 'info')
frstlvl_wf.connect(getcontrasts, 'contrasts', getsubs, 'cons')
# Create a datasource node to get the task_mri and motion-noise files
datasource = Node(DataGrabber(infields=['subject_id'],
outfields=info.keys()),
name='datasource')
datasource.inputs.template = '*'
datasource.inputs.subject_id = subject_id
#datasource.inputs.base_directory = os.path.abspath('/scratch/PSB6351_2017/ds008_R2.0.0/preproc/')
#datasource.inputs.field_template = dict(task_mri_files='%s/func/realigned/*%s*.nii.gz',
# motion_noise_files='%s/noise/%s*.txt')
datasource.inputs.base_directory = os.path.abspath(
'/scratch/PSB6351_2017/students/salo/data/preproc/')
datasource.inputs.field_template = dict(
task_mri_files=
'%s/preproc/func/smoothed/corr_*_task-%s_*_bold_bet_smooth_mask.nii.gz',
motion_noise_files='%s/preproc/noise/%s*.txt')
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = True
datasource.inputs.ignore_exception = False
datasource.inputs.raise_on_empty = True
# Create a Function node to modify the motion and noise files to be single regressors
motionnoise = Node(Function(input_names=['subjinfo', 'files'],
output_names=['subjinfo'],
function=motion_noise),
name='motionnoise')
motionnoise.inputs.ignore_exception = False
frstlvl_wf.connect(subject_info, 'output', motionnoise, 'subjinfo')
frstlvl_wf.connect(datasource, 'motion_noise_files', motionnoise, 'files')
# Create a specify model node
specify_model = Node(SpecifyModel(), name='specify_model')
specify_model.inputs.high_pass_filter_cutoff = 128.
specify_model.inputs.ignore_exception = False
specify_model.inputs.input_units = 'secs'
specify_model.inputs.time_repetition = 2.
frstlvl_wf.connect(datasource, 'task_mri_files', specify_model,
'functional_runs')
frstlvl_wf.connect(motionnoise, 'subjinfo', specify_model, 'subject_info')
# Create an InputSpec node for the modelfit node
modelfit_inputspec = Node(IdentityInterface(fields=[
'session_info', 'interscan_interval', 'contrasts', 'film_threshold',
'functional_data', 'bases', 'model_serial_correlations'
],
mandatory_inputs=True),
name='modelfit_inputspec')
modelfit_inputspec.inputs.bases = {'dgamma': {'derivs': False}}
modelfit_inputspec.inputs.film_threshold = 0.0
modelfit_inputspec.inputs.interscan_interval = 2.0
modelfit_inputspec.inputs.model_serial_correlations = True
frstlvl_wf.connect(datasource, 'task_mri_files', modelfit_inputspec,
'functional_data')
frstlvl_wf.connect(getcontrasts, 'contrasts', modelfit_inputspec,
'contrasts')
frstlvl_wf.connect(specify_model, 'session_info', modelfit_inputspec,
'session_info')
# Create a level1 design node
level1_design = Node(Level1Design(), name='level1_design')
level1_design.inputs.ignore_exception = False
frstlvl_wf.connect(modelfit_inputspec, 'interscan_interval', level1_design,
'interscan_interval')
frstlvl_wf.connect(modelfit_inputspec, 'session_info', level1_design,
'session_info')
frstlvl_wf.connect(modelfit_inputspec, 'contrasts', level1_design,
'contrasts')
frstlvl_wf.connect(modelfit_inputspec, 'bases', level1_design, 'bases')
frstlvl_wf.connect(modelfit_inputspec, 'model_serial_correlations',
level1_design, 'model_serial_correlations')
# Create a MapNode to generate a model for each run
generate_model = MapNode(FEATModel(),
iterfield=['fsf_file', 'ev_files'],
name='generate_model')
generate_model.inputs.environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'}
generate_model.inputs.ignore_exception = False
generate_model.inputs.output_type = 'NIFTI_GZ'
generate_model.inputs.terminal_output = 'stream'
frstlvl_wf.connect(level1_design, 'fsf_files', generate_model, 'fsf_file')
frstlvl_wf.connect(level1_design, 'ev_files', generate_model, 'ev_files')
# Create a MapNode to estimate the model using FILMGLS
estimate_model = MapNode(FILMGLS(),
iterfield=['design_file', 'in_file', 'tcon_file'],
name='estimate_model')
frstlvl_wf.connect(generate_model, 'design_file', estimate_model,
'design_file')
frstlvl_wf.connect(generate_model, 'con_file', estimate_model, 'tcon_file')
frstlvl_wf.connect(modelfit_inputspec, 'functional_data', estimate_model,
'in_file')
# Create a merge node to merge the contrasts - necessary for fsl 5.0.7 and greater
merge_contrasts = MapNode(Merge(2),
iterfield=['in1'],
name='merge_contrasts')
frstlvl_wf.connect(estimate_model, 'zstats', merge_contrasts, 'in1')
# Create a MapNode to transform the z2pval
z2pval = MapNode(ImageMaths(), iterfield=['in_file'], name='z2pval')
z2pval.inputs.environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'}
z2pval.inputs.ignore_exception = False
z2pval.inputs.op_string = '-ztop'
z2pval.inputs.output_type = 'NIFTI_GZ'
z2pval.inputs.suffix = '_pval'
z2pval.inputs.terminal_output = 'stream'
frstlvl_wf.connect(merge_contrasts, ('out', pop_lambda), z2pval, 'in_file')
# Create an outputspec node
modelfit_outputspec = Node(IdentityInterface(fields=[
'copes', 'varcopes', 'dof_file', 'pfiles', 'parameter_estimates',
'zstats', 'design_image', 'design_file', 'design_cov', 'sigmasquareds'
],
mandatory_inputs=True),
name='modelfit_outputspec')
frstlvl_wf.connect(estimate_model, 'copes', modelfit_outputspec, 'copes')
frstlvl_wf.connect(estimate_model, 'varcopes', modelfit_outputspec,
'varcopes')
frstlvl_wf.connect(merge_contrasts, 'out', modelfit_outputspec, 'zstats')
frstlvl_wf.connect(z2pval, 'out_file', modelfit_outputspec, 'pfiles')
frstlvl_wf.connect(generate_model, 'design_image', modelfit_outputspec,
'design_image')
frstlvl_wf.connect(generate_model, 'design_file', modelfit_outputspec,
'design_file')
frstlvl_wf.connect(generate_model, 'design_cov', modelfit_outputspec,
'design_cov')
frstlvl_wf.connect(estimate_model, 'param_estimates', modelfit_outputspec,
'parameter_estimates')
frstlvl_wf.connect(estimate_model, 'dof_file', modelfit_outputspec,
'dof_file')
frstlvl_wf.connect(estimate_model, 'sigmasquareds', modelfit_outputspec,
'sigmasquareds')
# Create a datasink node
sinkd = Node(DataSink(), name='sinkd')
sinkd.inputs.base_directory = sink_directory
sinkd.inputs.container = subject_id
frstlvl_wf.connect(getsubs, 'subs', sinkd, 'substitutions')
frstlvl_wf.connect(modelfit_outputspec, 'parameter_estimates', sinkd,
'modelfit.estimates')
frstlvl_wf.connect(modelfit_outputspec, 'sigmasquareds', sinkd,
'modelfit.estimates.@sigsq')
frstlvl_wf.connect(modelfit_outputspec, 'dof_file', sinkd, 'modelfit.dofs')
frstlvl_wf.connect(modelfit_outputspec, 'copes', sinkd,
'modelfit.contrasts.@copes')
frstlvl_wf.connect(modelfit_outputspec, 'varcopes', sinkd,
'modelfit.contrasts.@varcopes')
frstlvl_wf.connect(modelfit_outputspec, 'zstats', sinkd,
'modelfit.contrasts.@zstats')
frstlvl_wf.connect(modelfit_outputspec, 'design_image', sinkd,
'modelfit.design')
frstlvl_wf.connect(modelfit_outputspec, 'design_cov', sinkd,
'modelfit.design.@cov')
frstlvl_wf.connect(modelfit_outputspec, 'design_file', sinkd,
'modelfit.design.@matrix')
frstlvl_wf.connect(modelfit_outputspec, 'pfiles', sinkd,
'modelfit.contrasts.@pstats')
return frstlvl_wf
