def test_ExtractROI_outputs():
output_map = dict(roi_file=dict(), )
outputs = ExtractROI.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_ExtractROI_inputs():
input_map = dict(
args=dict(argstr="%s"),
crop_list=dict(
argstr="%s", position=2, xor=["x_min", "x_size", "y_min", "y_size", "z_min", "z_size", "t_min", "t_size"]
),
environ=dict(nohash=True, usedefault=True),
ignore_exception=dict(nohash=True, usedefault=True),
in_file=dict(argstr="%s", mandatory=True, position=0),
output_type=dict(),
roi_file=dict(argstr="%s", genfile=True, hash_files=False, position=1),
t_min=dict(argstr="%d", position=8),
t_size=dict(argstr="%d", position=9),
terminal_output=dict(mandatory=True, nohash=True),
x_min=dict(argstr="%d", position=2),
x_size=dict(argstr="%d", position=3),
y_min=dict(argstr="%d", position=4),
y_size=dict(argstr="%d", position=5),
z_min=dict(argstr="%d", position=6),
z_size=dict(argstr="%d", position=7),
)
inputs = ExtractROI.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_ExtractROI_outputs():
output_map = dict(roi_file=dict())
outputs = ExtractROI.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_ExtractROI_inputs():
input_map = dict(z_size=dict(position=7,
argstr='%d',
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
t_min=dict(position=8,
argstr='%d',
),
y_min=dict(position=4,
argstr='%d',
),
y_size=dict(position=5,
argstr='%d',
),
args=dict(argstr='%s',
),
z_min=dict(position=6,
argstr='%d',
),
t_size=dict(position=9,
argstr='%d',
),
x_size=dict(position=3,
argstr='%d',
),
terminal_output=dict(mandatory=True,
nohash=True,
),
environ=dict(nohash=True,
usedefault=True,
),
in_file=dict(position=0,
mandatory=True,
argstr='%s',
),
x_min=dict(position=2,
argstr='%d',
),
output_type=dict(),
crop_list=dict(argstr='%s',
xor=['x_min', 'x_size', 'y_min', 'y_size', 'z_min', 'z_size', 't_min', 't_size'],
position=2,
),
roi_file=dict(hash_files=False,
genfile=True,
position=1,
argstr='%s',
),
)
inputs = ExtractROI.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def _fmap_cleanup(sub_folder, fmap_tp):
files = glob.glob(os.path.join([sub_folder, "**", "*"]), recursive=True)
files = [os.path.abspath(file) for file in files if 'epi.nii' in file]
logging.debug(files)
for file in files:
fslroi = ExtractROI(in_file=file,
roi_file=file,
t_min=0,
t_size=fmap_tp)
logging.debug(fslroi.cmdline)
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']], #dictionary used in datasource
motion_noise_files=[['subject_id']])
#function node to call subjectinfo function with 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
#function node to define contrasts
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')
#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
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 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/smoothed_fullspectrum/_maskfunc2*/*%s*.nii.gz', #functional files
motion_noise_files='%s/noise/filter_regressor??.txt'
) #filter regressor noise files
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
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'],
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')
#node to create model specifications compatible with spm/fsl designers (requires subjectinfo to be received in the form of a Bunch)
specify_model = Node(SpecifyModel(), name='specify_model')
specify_model.inputs.high_pass_filter_cutoff = -1.0 #high-pass filter cutoff in seconds
specify_model.inputs.ignore_exception = False
specify_model.inputs.input_units = 'secs' #input units in either 'secs' or 'scans'
specify_model.inputs.time_repetition = 2.0 #TR
frstlvl_wf.connect(
fslroi_epi, 'roi_file', specify_model,
'functional_runs') #editted data files for model -- list of 4D files
#list of event description files in 3 column format corresponding to onsets, durations, and amplitudes
frstlvl_wf.connect(motionnoise, 'subjinfo', specify_model, 'subject_info')
#node for basic interface class generating 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')
#node 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 #Susan-smooth mask size
estimate_model.inputs.output_type = 'NIFTI_GZ'
estimate_model.inputs.results_dir = 'results'
estimate_model.inputs.smooth_autocorr = True #smooth auto-correlation estimates
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') #mat file containing ascii matrix for design
frstlvl_wf.connect(generate_model, 'con_file', estimate_model,
'tcon_file') #contrast file containing contrast vectors
#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' #defines the operation used
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') #lvl1 cope files
frstlvl_wf.connect(estimate_model, 'varcopes', modelfit_outputspec,
'varcopes') #lvl1 varcope files
frstlvl_wf.connect(merge_contrasts, 'out', modelfit_outputspec,
'zstats') #zstats across runs
frstlvl_wf.connect(z2pval, 'out_file', modelfit_outputspec, 'pfiles')
frstlvl_wf.connect(
generate_model, 'design_image', modelfit_outputspec,
'design_image') #graphical representation of design matrix
frstlvl_wf.connect(
generate_model, 'design_file', modelfit_outputspec,
'design_file') #mat file containing ascii matrix for design
frstlvl_wf.connect(
generate_model, 'design_cov', modelfit_outputspec,
'design_cov') #graphical representation of design covariance
frstlvl_wf.connect(estimate_model, 'param_estimates', modelfit_outputspec,
'parameter_estimates'
) #parameter estimates for columns of design matrix
frstlvl_wf.connect(estimate_model, 'dof_file', modelfit_outputspec,
'dof_file') #degrees of freedom
frstlvl_wf.connect(estimate_model, 'sigmasquareds', modelfit_outputspec,
'sigmasquareds') #summary of residuals
#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
output_names=['subject_info', 'event_names'],
function=get_subject_info),
name='subject_info',
iterfield=['events', 'confounds'])
# set expected thread and memory usage for the node:
subject_info.interface.num_threads = 1
subject_info.interface.mem_gb = 0.1
# subject_info.inputs.events = selectfiles_results.outputs.events
# subject_info.inputs.confounds = selectfiles_results.outputs.confounds
# subject_info_results = subject_info.run()
# subject_info_results.outputs.subject_info
# ======================================================================
# DEFINE NODE: REMOVE DUMMY VARIABLES (USING FSL ROI)
# ======================================================================
# function: extract region of interest (ROI) from an image
trim = MapNode(ExtractROI(), name='trim', iterfield=['in_file'])
# define index of the first selected volume (i.e., minimum index):
trim.inputs.t_min = num_dummy
# define the number of volumes selected starting at the minimum index:
trim.inputs.t_size = -1
# define the fsl output type:
trim.inputs.output_type = 'NIFTI'
# set expected thread and memory usage for the node:
trim.interface.num_threads = 1
trim.interface.mem_gb = 3
# ======================================================================
# DEFINE NODE: LEAVE-ONE-RUN-OUT SELECTION OF DATA
# ======================================================================
leave_one_run_out = Node(Function(
input_names=['subject_info', 'event_names', 'data_func', 'run'],
output_names=['subject_info', 'data_func', 'contrasts'],
def test_ExtractROI_inputs():
input_map = dict(
args=dict(argstr='%s', ),
crop_list=dict(
argstr='%s',
position=2,
xor=[
'x_min', 'x_size', 'y_min', 'y_size', 'z_min', 'z_size',
't_min', 't_size'
],
),
environ=dict(
nohash=True,
usedefault=True,
),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
in_file=dict(
argstr='%s',
mandatory=True,
position=0,
),
output_type=dict(),
roi_file=dict(
argstr='%s',
genfile=True,
hash_files=False,
position=1,
),
t_min=dict(
argstr='%d',
position=8,
),
t_size=dict(
argstr='%d',
position=9,
),
terminal_output=dict(nohash=True, ),
x_min=dict(
argstr='%d',
position=2,
),
x_size=dict(
argstr='%d',
position=3,
),
y_min=dict(
argstr='%d',
position=4,
),
y_size=dict(
argstr='%d',
position=5,
),
z_min=dict(
argstr='%d',
position=6,
),
z_size=dict(
argstr='%d',
position=7,
),
)
inputs = ExtractROI.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
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
