def test_Level1Design_outputs():
output_map = dict(ev_files=dict(),
fsf_files=dict(),
)
outputs = Level1Design.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_Level1Design_outputs():
output_map = dict(ev_files=dict(),
fsf_files=dict(),
)
outputs = Level1Design.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_Level1Design_inputs():
input_map = dict(ignore_exception=dict(nohash=True,
usedefault=True,
),
session_info=dict(mandatory=True,
),
interscan_interval=dict(mandatory=True,
),
bases=dict(mandatory=True,
),
model_serial_correlations=dict(mandatory=True,
),
contrasts=dict(),
)
inputs = Level1Design.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_Level1Design_inputs():
input_map = dict(bases=dict(mandatory=True,
),
contrasts=dict(),
ignore_exception=dict(nohash=True,
usedefault=True,
),
interscan_interval=dict(mandatory=True,
),
model_serial_correlations=dict(mandatory=True,
),
session_info=dict(mandatory=True,
),
)
inputs = Level1Design.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'):
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
def l1(
preprocessing_dir,
highpass_sigma=225,
include={},
exclude={},
keep_work=False,
l1_dir="",
nprocs=10,
mask="/home/chymera/ni_data/templates/ds_QBI_chr_bin.nii.gz",
per_stimulus_contrast=False,
habituation="",
tr=1,
workflow_name="generic",
):
"""Calculate subject level GLM statistics.
Parameters
----------
include : dict
A dictionary with any combination of "sessions", "subjects", "trials" as keys and corresponding identifiers as values.
If this is specified ony matching entries will be included in the analysis.
exclude : dict
A dictionary with any combination of "sessions", "subjects", "trials" as keys and corresponding identifiers as values.
If this is specified ony non-matching entries will be included in the analysis.
habituation : string
One value of "confound", "in_main_contrast", "separate_contrast", "" indicating how the habituation regressor should be handled.
"" or any other value which evaluates to False will mean no habituation regressor is used int he model
"""
preprocessing_dir = path.expanduser(preprocessing_dir)
if not l1_dir:
l1_dir = path.abspath(path.join(preprocessing_dir, "..", "..", "l1"))
datafind = nio.DataFinder()
datafind.inputs.root_paths = preprocessing_dir
datafind.inputs.match_regex = '.+/sub-(?P<sub>.+)/ses-(?P<ses>.+)/func/.*?_trial-(?P<scan>.+)\.nii.gz'
datafind_res = datafind.run()
iterfields = zip(*[
datafind_res.outputs.sub, datafind_res.outputs.ses,
datafind_res.outputs.scan
])
if include:
iterfields = iterfield_selector(iterfields, include, "include")
if exclude:
iterfields = iterfield_selector(iterfields, exclude, "exclude")
infosource = pe.Node(
interface=util.IdentityInterface(fields=['subject_session_scan']),
name="infosource")
infosource.iterables = [('subject_session_scan', iterfields)]
datafile_source = pe.Node(
name='datafile_source',
interface=util.Function(
function=sss_to_source,
input_names=inspect.getargspec(sss_to_source)[0],
output_names=['out_file']))
datafile_source.inputs.base_directory = preprocessing_dir
datafile_source.inputs.source_format = "sub-{0}/ses-{1}/func/sub-{0}_ses-{1}_trial-{2}.nii.gz"
eventfile_source = pe.Node(
name='eventfile_source',
interface=util.Function(
function=sss_to_source,
input_names=inspect.getargspec(sss_to_source)[0],
output_names=['out_file']))
eventfile_source.inputs.base_directory = preprocessing_dir
eventfile_source.inputs.source_format = "sub-{0}/ses-{1}/func/sub-{0}_ses-{1}_trial-{2}_events.tsv"
specify_model = pe.Node(interface=SpecifyModel(), name="specify_model")
specify_model.inputs.input_units = 'secs'
specify_model.inputs.time_repetition = tr
specify_model.inputs.high_pass_filter_cutoff = highpass_sigma
specify_model.inputs.one_condition_file = not per_stimulus_contrast
specify_model.inputs.habituation_regressor = bool(habituation)
level1design = pe.Node(interface=Level1Design(), name="level1design")
level1design.inputs.interscan_interval = tr
level1design.inputs.bases = {
"custom": {
"bfcustompath": "/mnt/data/ni_data/irfs/chr_beta1.txt"
}
}
# level1design.inputs.bases = {'gamma': {'derivs':False, 'gammasigma':10, 'gammadelay':5}}
level1design.inputs.orthogonalization = {
1: {
0: 0,
1: 0,
2: 0
},
2: {
0: 1,
1: 1,
2: 0
}
}
level1design.inputs.model_serial_correlations = True
if per_stimulus_contrast:
level1design.inputs.contrasts = [
('allStim', 'T', ["e0", "e1", "e2", "e3", "e4",
"e5"], [1, 1, 1, 1, 1, 1])
] #condition names as defined in specify_model
elif habituation == "separate_contrast":
level1design.inputs.contrasts = [
('allStim', 'T', ["e0"], [1]), ('allStim', 'T', ["e1"], [1])
] #condition names as defined in specify_model
elif habituation == "in_main_contrast":
level1design.inputs.contrasts = [
('allStim', 'T', ["e0", "e1"], [1, 1])
] #condition names as defined in specify_model
elif habituation == "confound":
level1design.inputs.contrasts = [
('allStim', 'T', ["e0"], [1])
] #condition names as defined in specify_model
else:
level1design.inputs.contrasts = [
('allStim', 'T', ["e0"], [1])
] #condition names as defined in specify_model
modelgen = pe.Node(interface=fsl.FEATModel(), name='modelgen')
glm = pe.Node(interface=fsl.GLM(), name='glm', iterfield='design')
glm.inputs.out_cope = "cope.nii.gz"
glm.inputs.out_varcb_name = "varcb.nii.gz"
#not setting a betas output file might lead to beta export in lieu of COPEs
glm.inputs.out_file = "betas.nii.gz"
glm.inputs.out_t_name = "t_stat.nii.gz"
glm.inputs.out_p_name = "p_stat.nii.gz"
if mask:
glm.inputs.mask = mask
cope_filename = pe.Node(
name='cope_filename',
interface=util.Function(
function=sss_to_source,
input_names=inspect.getargspec(sss_to_source)[0],
output_names=['filename']))
cope_filename.inputs.source_format = "sub-{0}_ses-{1}_trial-{2}_cope.nii.gz"
varcb_filename = pe.Node(
name='varcb_filename',
interface=util.Function(
function=sss_to_source,
input_names=inspect.getargspec(sss_to_source)[0],
output_names=['filename']))
varcb_filename.inputs.source_format = "sub-{0}_ses-{1}_trial-{2}_varcb.nii.gz"
tstat_filename = pe.Node(
name='tstat_filename',
interface=util.Function(
function=sss_to_source,
input_names=inspect.getargspec(sss_to_source)[0],
output_names=['filename']))
tstat_filename.inputs.source_format = "sub-{0}_ses-{1}_trial-{2}_tstat.nii.gz"
zstat_filename = pe.Node(
name='zstat_filename',
interface=util.Function(
function=sss_to_source,
input_names=inspect.getargspec(sss_to_source)[0],
output_names=['filename']))
zstat_filename.inputs.source_format = "sub-{0}_ses-{1}_trial-{2}_zstat.nii.gz"
pstat_filename = pe.Node(
name='pstat_filename',
interface=util.Function(
function=sss_to_source,
input_names=inspect.getargspec(sss_to_source)[0],
output_names=['filename']))
pstat_filename.inputs.source_format = "sub-{0}_ses-{1}_trial-{2}_pstat.nii.gz"
pfstat_filename = pe.Node(
name='pfstat_filename',
interface=util.Function(
function=sss_to_source,
input_names=inspect.getargspec(sss_to_source)[0],
output_names=['filename']))
pfstat_filename.inputs.source_format = "sub-{0}_ses-{1}_trial-{2}_pfstat.nii.gz"
datasink = pe.Node(nio.DataSink(), name='datasink')
datasink.inputs.base_directory = path.join(l1_dir, workflow_name)
datasink.inputs.parameterization = False
workflow_connections = [
(infosource, datafile_source, [('subject_session_scan',
'subject_session_scan')]),
(infosource, eventfile_source, [('subject_session_scan',
'subject_session_scan')]),
(eventfile_source, specify_model, [('out_file', 'event_files')]),
(datafile_source, specify_model, [('out_file', 'functional_runs')]),
(specify_model, level1design, [('session_info', 'session_info')]),
(level1design, modelgen, [('ev_files', 'ev_files')]),
(level1design, modelgen, [('fsf_files', 'fsf_file')]),
(datafile_source, glm, [('out_file', 'in_file')]),
(modelgen, glm, [('design_file', 'design')]),
(modelgen, glm, [('con_file', 'contrasts')]),
(infosource, datasink, [(('subject_session_scan', ss_to_path),
'container')]),
(infosource, cope_filename, [('subject_session_scan',
'subject_session_scan')]),
(infosource, varcb_filename, [('subject_session_scan',
'subject_session_scan')]),
(infosource, tstat_filename, [('subject_session_scan',
'subject_session_scan')]),
(infosource, zstat_filename, [('subject_session_scan',
'subject_session_scan')]),
(infosource, pstat_filename, [('subject_session_scan',
'subject_session_scan')]),
(infosource, pfstat_filename, [('subject_session_scan',
'subject_session_scan')]),
(cope_filename, glm, [('filename', 'out_cope')]),
(varcb_filename, glm, [('filename', 'out_varcb_name')]),
(tstat_filename, glm, [('filename', 'out_t_name')]),
(zstat_filename, glm, [('filename', 'out_z_name')]),
(pstat_filename, glm, [('filename', 'out_p_name')]),
(pfstat_filename, glm, [('filename', 'out_pf_name')]),
(glm, datasink, [('out_pf', '@pfstat')]),
(glm, datasink, [('out_p', '@pstat')]),
(glm, datasink, [('out_z', '@zstat')]),
(glm, datasink, [('out_t', '@tstat')]),
(glm, datasink, [('out_cope', '@cope')]),
(glm, datasink, [('out_varcb', '@varcb')]),
]
workdir_name = workflow_name + "_work"
workflow = pe.Workflow(name=workdir_name)
workflow.connect(workflow_connections)
workflow.base_dir = l1_dir
workflow.config = {
"execution": {
"crashdump_dir": path.join(l1_dir, "crashdump")
}
}
workflow.write_graph(dotfilename=path.join(workflow.base_dir, workdir_name,
"graph.dot"),
graph2use="hierarchical",
format="png")
workflow.run(plugin="MultiProc", plugin_args={'n_procs': nprocs})
if not keep_work:
shutil.rmtree(path.join(l1_dir, workdir_name))
def runglmperun(self, subject, trtimeinsec):
s = SpecifyModel()
# loop on all runs and models within each run
modelfiles = subject._modelfiles
for model in modelfiles:
# Make directory results to store the results of the model
results_dir = os.path.join(subject._path, 'model', model[0],
'results', model[1])
dir_util.mkpath(results_dir)
os.chdir(results_dir)
s.inputs.event_files = model[2]
s.inputs.input_units = 'secs'
s.inputs.functional_runs = os.path.join(subject._path, 'BOLD',
model[1],
'bold_mcf_hp.nii.gz')
# use nibable to get the tr of from the .nii file
s.inputs.time_repetition = trtimeinsec
s.inputs.high_pass_filter_cutoff = 128.
# find par file that has motion
motionfiles = glob(
os.path.join(subject._path, 'BOLD', model[1], "*.par"))
s.inputs.realignment_parameters = motionfiles
#info = [Bunch(conditions=['cond1'], onsets=[[2, 50, 100, 180]], durations=[[1]]), Bunch(conditions=['cond1'], onsets=[[30, 40, 100, 150]], durations=[[1]])]
#s.inputs.subject_info = None
res = s.run()
res.runtime.cwd
print ">>>> preparing evs for model " + model[
1] + "and run " + model[0]
sessionInfo = res.outputs.session_info
level1design = Level1Design()
level1design.inputs.interscan_interval = trtimeinsec
level1design.inputs.bases = {'dgamma': {'derivs': False}}
level1design.inputs.session_info = sessionInfo
level1design.inputs.model_serial_correlations = True
#TODO: add contrasts to level 1 design so that I have just condition vs rest for each ev
#TODO: Look into changign this to FILM instead of FEAT - this also has the option of setting output directory
# http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT/UserGuide#Contrasts
#http://nipy.org/nipype/interfaces/generated/nipype.interfaces.fsl.model.html#filmgls
resLevel = level1design.run()
featModel = FEATModel()
featModel.inputs.fsf_file = resLevel.outputs.fsf_files
featModel.inputs.ev_files = resLevel.outputs.ev_files
resFeat = featModel.run()
print ">>>> creating fsf design files for " + model[
1] + "and run " + model[0]
# TODO: give mask here
glm = fsl.GLM(in_file=s.inputs.functional_runs[0],
design=resFeat.outputs.design_file,
output_type='NIFTI')
print ">>>> running glm for " + model[1] + "and run " + model[0]
resGlm = glm.run()
print ">>>> finished running glm for " + model[
1] + "and run " + model[0]
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
datasource.inputs.sort_filelist = False
results = datasource.run()
print results.outputs
cont1 = ['Bundling-Control', 'T', ['Bundling', 'Control'], [1, -1]]
s = SpecifyModel()
s.inputs.input_units = 'secs'
s.inputs.functional_runs = results.outputs.func
s.inputs.time_repetition = 2
s.inputs.high_pass_filter_cutoff = 128.
s.inputs.event_files = results.outputs.evs
model = s.run()
level1design = Level1Design()
level1design.inputs.interscan_interval = 2.5
level1design.inputs.bases = {'dgamma': {'derivs': False}}
level1design.inputs.model_serial_correlations = False
level1design.inputs.session_info = model.outputs.session_info
level1design.inputs.contrasts = [cont1]
l1d = level1design.run()
print l1d.outputs.ev_files
modelgen = FEATModel()
modelgen.inputs.ev_files = l1d.outputs.ev_files
modelgen.inputs.fsf_file = l1d.outputs.fsf_files
model = modelgen.run()
fgls = fsl.FILMGLS()
fgls.inputs.in_file = results.outputs.func
def seed_fc(
preprocessing_dir,
exclude={},
habituation='confound',
highpass_sigma=225,
lowpass_sigma=False,
include={},
keep_work=False,
out_dir="",
mask="",
match_regex='sub-(?P<sub>[a-zA-Z0-9]+)/ses-(?P<ses>[a-zA-Z0-9]+)/func/.*?_task-(?P<task>[a-zA-Z0-9]+)_acq-(?P<acq>[a-zA-Z0-9]+)_(?P<mod>[a-zA-Z0-9]+)\.(?:tsv|nii|nii\.gz)',
nprocs=N_PROCS,
tr=1,
workflow_name="generic",
modality="cbv",
):
"""Calculate subject level seed-based functional connectivity via the `fsl_glm` command.
Parameters
----------
exclude : dict
A dictionary with any combination of "sessions", "subjects", "tasks" as keys and corresponding identifiers as values.
If this is specified matching entries will be excluded in the analysis.
habituation : {"", "confound", "separate_contrast", "in_main_contrast"}, optional
How the habituation regressor should be handled.
Anything which evaluates as False (though we recommend "") means no habituation regressor will be introduced.
highpass_sigma : int, optional
Highpass threshold (in seconds).
include : dict
A dictionary with any combination of "sessions", "subjects", "tasks" as keys and corresponding identifiers as values.
If this is specified only matching entries will be included in the analysis.
keep_work : bool, optional
Whether to keep the work directory (containing all the intermediary workflow steps, as managed by nipypye).
This is useful for debugging and quality control.
out_dir : str, optional
Path to the directory inside which both the working directory and the output directory will be created.
mask : str, optional
Path to the brain mask which shall be used to define the brain volume in the analysis.
This has to point to an existing NIfTI file containing zero and one values only.
match_regex : str, optional
Regex matching pattern by which to select input files. Has to contain groups named "sub", "ses", "acq", "task", and "mod".
n_procs : int, optional
Maximum number of processes which to simultaneously spawn for the workflow.
If not explicitly defined, this is automatically calculated from the number of available cores and under the assumption that the workflow will be the main process running for the duration that it is running.
tr : int, optional
Repetition time, in seconds.
workflow_name : str, optional
Name of the workflow; this will also be the name of the final output directory produced under `out_dir`.
"""
preprocessing_dir = path.abspath(path.expanduser(preprocessing_dir))
if not out_dir:
out_dir = path.join(bids_base, 'l1')
else:
out_dir = path.abspath(path.expanduser(out_dir))
datafind = nio.DataFinder()
datafind.inputs.root_paths = preprocessing_dir
datafind.inputs.match_regex = match_regex
datafind_res = datafind.run()
out_paths = [
path.abspath(path.expanduser(i))
for i in datafind_res.outputs.out_paths
]
data_selection = zip(*[
datafind_res.outputs.sub, datafind_res.outputs.ses,
datafind_res.outputs.acq, datafind_res.outputs.task,
datafind_res.outputs.mod, out_paths
])
data_selection = [list(i) for i in data_selection]
data_selection = pd.DataFrame(data_selection,
columns=('subject', 'session', 'acquisition',
'task', 'modality', 'path'))
if exclude:
for key in exclude:
data_selection = data_selection[~data_selection[key].
isin(exclude[key])]
if include:
for key in include:
data_selection = data_selection[data_selection[key].isin(
include[key])]
bids_dictionary = data_selection[
data_selection['modality'] ==
modality].drop_duplicates().T.to_dict().values()
infosource = pe.Node(
interface=util.IdentityInterface(fields=['bids_dictionary']),
name="infosource")
infosource.iterables = [('bids_dictionary', bids_dictionary)]
datafile_source = pe.Node(
name='datafile_source',
interface=util.Function(
function=select_from_datafind_df,
input_names=inspect.getargspec(select_from_datafind_df)[0],
output_names=['out_file']))
datafile_source.inputs.bids_dictionary_override = {'modality': modality}
datafile_source.inputs.df = data_selection
seed_timecourse = pe.Node(
name='seed_timecourse',
interface=util.Function(
function=select_from_datafind_df,
input_names=inspect.getargspec(select_from_datafind_df)[0],
output_names=['out_file']))
specify_model = pe.Node(interface=SpecifyModel(), name="specify_model")
specify_model.inputs.input_units = 'secs'
specify_model.inputs.time_repetition = tr
specify_model.inputs.high_pass_filter_cutoff = highpass_sigma
specify_model.inputs.habituation_regressor = bool(habituation)
level1design = pe.Node(interface=Level1Design(), name="level1design")
level1design.inputs.interscan_interval = tr
if bf_path:
bf_path = path.abspath(path.expanduser(bf_path))
level1design.inputs.bases = {"custom": {"bfcustompath": bf_path}}
# level1design.inputs.bases = {'gamma': {'derivs':False, 'gammasigma':10, 'gammadelay':5}}
level1design.inputs.orthogonalization = {
1: {
0: 0,
1: 0,
2: 0
},
2: {
0: 1,
1: 1,
2: 0
}
}
level1design.inputs.model_serial_correlations = True
if habituation == "separate_contrast":
level1design.inputs.contrasts = [
('allStim', 'T', ["e0"], [1]), ('allStim', 'T', ["e1"], [1])
] #condition names as defined in specify_model
elif habituation == "in_main_contrast":
level1design.inputs.contrasts = [
('allStim', 'T', ["e0", "e1"], [1, 1])
] #condition names as defined in specify_model
elif habituation == "confound" or not habituation:
level1design.inputs.contrasts = [
('allStim', 'T', ["e0"], [1])
] #condition names as defined in specify_model
else:
raise ValueError(
'The value you have provided for the `habituation` parameter, namely "{}", is invalid. Please choose one of: {"confound","in_main_contrast","separate_contrast"}'
.format(habituation))
modelgen = pe.Node(interface=fsl.FEATModel(), name='modelgen')
modelgen.inputs.ignore_exception = True
glm = pe.Node(interface=fsl.GLM(), name='glm', iterfield='design')
glm.inputs.out_cope = "cope.nii.gz"
glm.inputs.out_varcb_name = "varcb.nii.gz"
#not setting a betas output file might lead to beta export in lieu of COPEs
glm.inputs.out_file = "betas.nii.gz"
glm.inputs.out_t_name = "t_stat.nii.gz"
glm.inputs.out_p_name = "p_stat.nii.gz"
if mask:
glm.inputs.mask = path.abspath(path.expanduser(mask))
glm.inputs.ignore_exception = True
cope_filename = pe.Node(
name='cope_filename',
interface=util.Function(
function=bids_dict_to_source,
input_names=inspect.getargspec(bids_dict_to_source)[0],
output_names=['filename']))
cope_filename.inputs.source_format = "sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_{modality}_cope.nii.gz"
varcb_filename = pe.Node(
name='varcb_filename',
interface=util.Function(
function=bids_dict_to_source,
input_names=inspect.getargspec(bids_dict_to_source)[0],
output_names=['filename']))
varcb_filename.inputs.source_format = "sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_{modality}_varcb.nii.gz"
tstat_filename = pe.Node(
name='tstat_filename',
interface=util.Function(
function=bids_dict_to_source,
input_names=inspect.getargspec(bids_dict_to_source)[0],
output_names=['filename']))
tstat_filename.inputs.source_format = "sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_{modality}_tstat.nii.gz"
zstat_filename = pe.Node(
name='zstat_filename',
interface=util.Function(
function=bids_dict_to_source,
input_names=inspect.getargspec(bids_dict_to_source)[0],
output_names=['filename']))
zstat_filename.inputs.source_format = "sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_{modality}_zstat.nii.gz"
pstat_filename = pe.Node(
name='pstat_filename',
interface=util.Function(
function=bids_dict_to_source,
input_names=inspect.getargspec(bids_dict_to_source)[0],
output_names=['filename']))
pstat_filename.inputs.source_format = "sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_{modality}_pstat.nii.gz"
pfstat_filename = pe.Node(
name='pfstat_filename',
interface=util.Function(
function=bids_dict_to_source,
input_names=inspect.getargspec(bids_dict_to_source)[0],
output_names=['filename']))
pfstat_filename.inputs.source_format = "sub-{subject}_ses-{session}_task-{task}_acq-{acquisition}_{modality}_pfstat.nii.gz"
datasink = pe.Node(nio.DataSink(), name='datasink')
datasink.inputs.base_directory = path.join(out_dir, workflow_name)
datasink.inputs.parameterization = False
workflow_connections = [
(infosource, datafile_source, [('bids_dictionary', 'bids_dictionary')
]),
(infosource, eventfile_source, [('bids_dictionary', 'bids_dictionary')
]),
(eventfile_source, specify_model, [('out_file', 'event_files')]),
(specify_model, level1design, [('session_info', 'session_info')]),
(level1design, modelgen, [('ev_files', 'ev_files')]),
(level1design, modelgen, [('fsf_files', 'fsf_file')]),
(modelgen, glm, [('design_file', 'design')]),
(modelgen, glm, [('con_file', 'contrasts')]),
(infosource, datasink, [(('bids_dictionary', bids_dict_to_dir),
'container')]),
(infosource, cope_filename, [('bids_dictionary', 'bids_dictionary')]),
(infosource, varcb_filename, [('bids_dictionary', 'bids_dictionary')]),
(infosource, tstat_filename, [('bids_dictionary', 'bids_dictionary')]),
(infosource, zstat_filename, [('bids_dictionary', 'bids_dictionary')]),
(infosource, pstat_filename, [('bids_dictionary', 'bids_dictionary')]),
(infosource, pfstat_filename, [('bids_dictionary', 'bids_dictionary')
]),
(cope_filename, glm, [('filename', 'out_cope')]),
(varcb_filename, glm, [('filename', 'out_varcb_name')]),
(tstat_filename, glm, [('filename', 'out_t_name')]),
(zstat_filename, glm, [('filename', 'out_z_name')]),
(pstat_filename, glm, [('filename', 'out_p_name')]),
(pfstat_filename, glm, [('filename', 'out_pf_name')]),
(glm, datasink, [('out_pf', '@pfstat')]),
(glm, datasink, [('out_p', '@pstat')]),
(glm, datasink, [('out_z', '@zstat')]),
(glm, datasink, [('out_t', '@tstat')]),
(glm, datasink, [('out_cope', '@cope')]),
(glm, datasink, [('out_varcb', '@varcb')]),
]
if highpass_sigma or lowpass_sigma:
bandpass = pe.Node(interface=fsl.maths.TemporalFilter(),
name="bandpass")
bandpass.inputs.highpass_sigma = highpass_sigma
if lowpass_sigma:
bandpass.inputs.lowpass_sigma = lowpass_sigma
else:
bandpass.inputs.lowpass_sigma = tr
workflow_connections.extend([
(datafile_source, bandpass, [('out_file', 'in_file')]),
(bandpass, specify_model, [('out_file', 'functional_runs')]),
(bandpass, glm, [('out_file', 'in_file')]),
])
else:
workflow_connections.extend([
(datafile_source, specify_model, [('out_file', 'functional_runs')
]),
(datafile_source, glm, [('out_file', 'in_file')]),
])
workdir_name = workflow_name + "_work"
workflow = pe.Workflow(name=workdir_name)
workflow.connect(workflow_connections)
workflow.base_dir = out_dir
workflow.config = {
"execution": {
"crashdump_dir": path.join(out_dir, "crashdump")
}
}
workflow.write_graph(dotfilename=path.join(workflow.base_dir, workdir_name,
"graph.dot"),
graph2use="hierarchical",
format="png")
workflow.run(plugin="MultiProc", plugin_args={'n_procs': nprocs})
if not keep_work:
shutil.rmtree(path.join(out_dir, workdir_name))
def create_firstlevel_workflow_FEAT(name='level1feat'):
input_node = pe.Node(IdentityInterface(fields=[
'events_file', 'single_trial', 'sort_by_onset', 'exclude', 'func_file',
'TR', 'confound_file', 'which_confounds', 'extend_motion_pars',
'model_serial_correlations', 'hrf_base', 'hp_filter', 'contrasts'
]),
name='inputspec')
output_node = pe.Node(
IdentityInterface(fields=['fsf_file', 'ev_file', 'feat_dir']),
name='outputspec')
level1_design = pe.MapNode(interface=Level1Design(
bases={'dgamma': {
'derivs': True
}},
interscan_interval=2.0,
model_serial_correlations=True),
iterfield=['contrasts', 'session_info'],
name='level1_design')
feat = pe.MapNode(interface=FEAT(), iterfield=['fsf_file'], name='FEAT')
extract_task = pe.MapNode(interface=Extract_task,
iterfield=['in_file'],
name='extract_task')
rename_feat_dir = pe.MapNode(interface=Rename_feat_dir,
iterfield=['feat_dir', 'task'],
name='rename_feat_dir')
firstlevel_wf = pe.Workflow(name=name)
modelgen_wf = create_modelgen_workflow()
firstlevel_wf.connect(input_node, 'events_file', modelgen_wf,
'inputspec.events_file')
firstlevel_wf.connect(input_node, 'func_file', modelgen_wf,
'inputspec.func_file')
firstlevel_wf.connect(input_node, 'TR', modelgen_wf, 'inputspec.TR')
firstlevel_wf.connect(input_node, 'single_trial', modelgen_wf,
'inputspec.single_trial')
firstlevel_wf.connect(input_node, 'sort_by_onset', modelgen_wf,
'inputspec.sort_by_onset')
firstlevel_wf.connect(input_node, 'extend_motion_pars', modelgen_wf,
'inputspec.extend_motion_pars')
firstlevel_wf.connect(input_node, 'exclude', modelgen_wf,
'inputspec.exclude')
firstlevel_wf.connect(input_node, 'confound_file', modelgen_wf,
'inputspec.confound_file')
firstlevel_wf.connect(input_node, 'which_confounds', modelgen_wf,
'inputspec.which_confounds')
firstlevel_wf.connect(input_node, 'hp_filter', modelgen_wf,
'inputspec.hp_filter')
firstlevel_wf.connect(input_node, 'TR', level1_design,
'interscan_interval')
firstlevel_wf.connect(input_node, 'model_serial_correlations',
level1_design, 'model_serial_correlations')
firstlevel_wf.connect(input_node, 'hrf_base', level1_design, 'bases')
firstlevel_wf.connect(input_node, 'contrasts', level1_design, 'contrasts')
firstlevel_wf.connect(modelgen_wf, 'outputspec.session_info',
level1_design, 'session_info')
firstlevel_wf.connect(level1_design, 'fsf_files', feat, 'fsf_file')
firstlevel_wf.connect(level1_design, 'fsf_files', output_node, 'fsf_file')
firstlevel_wf.connect(level1_design, 'ev_files', output_node, 'ev_file')
firstlevel_wf.connect(input_node, 'func_file', extract_task, 'in_file')
firstlevel_wf.connect(extract_task, 'task_name', rename_feat_dir, 'task')
firstlevel_wf.connect(feat, 'feat_dir', rename_feat_dir, 'feat_dir')
firstlevel_wf.connect(rename_feat_dir, 'feat_dir', output_node, 'feat_dir')
return firstlevel_wf
def l1(preprocessing_dir,
bf_path = '~/ni_data/irfs/chr_beta1.txt',
debug=False,
exclude={},
habituation='confound',
highpass_sigma=225,
lowpass_sigma=False,
include={},
keep_work=False,
out_base="",
mask="",
match={},
tr=1,
workflow_name="generic",
modality="cbv",
n_jobs_percentage=1,
invert=False,
):
"""Calculate subject level GLM statistic scores.
Parameters
----------
bf_path : str, optional
Basis set path. It should point to a text file in the so-called FEAT/FSL "#2" format (1 entry per volume).
exclude : dict
A dictionary with any combination of "sessions", "subjects", "tasks" as keys and corresponding identifiers as values.
If this is specified matching entries will be excluded in the analysis.
debug : bool, optional
Whether to enable nipype debug mode.
This increases logging.
habituation : {"", "confound", "separate_contrast", "in_main_contrast"}, optional
How the habituation regressor should be handled.
Anything which evaluates as False (though we recommend "") means no habituation regressor will be introduced.
highpass_sigma : int, optional
Highpass threshold (in seconds).
include : dict
A dictionary with any combination of "sessions", "subjects", "tasks" as keys and corresponding identifiers as values.
If this is specified only matching entries will be included in the analysis.
invert : bool
If true the values will be inverted with respect to zero.
This is commonly used for iron nano-particle Cerebral Blood Volume (CBV) measurements.
keep_work : bool, optional
Whether to keep the work directory (containing all the intermediary workflow steps, as managed by nipypye).
This is useful for debugging and quality control.
out_base : str, optional
Path to the directory inside which both the working directory and the output directory will be created.
mask : str, optional
Path to the brain mask which shall be used to define the brain volume in the analysis.
This has to point to an existing NIfTI file containing zero and one values only.
n_jobs_percentage : float, optional
Percentage of the cores present on the machine which to maximally use for deploying jobs in parallel.
tr : int, optional
Repetition time, in seconds.
workflow_name : str, optional
Name of the workflow; this will also be the name of the final output directory produced under `out_dir`.
"""
from samri.pipelines.utils import bids_data_selection
preprocessing_dir = path.abspath(path.expanduser(preprocessing_dir))
out_base = path.abspath(path.expanduser(out_base))
data_selection = bids_data_selection(preprocessing_dir, structural_match=False, functional_match=match, subjects=False, sessions=False)
ind = data_selection.index.tolist()
out_dir = path.join(out_base,workflow_name)
workdir_name = workflow_name+'_work'
workdir = path.join(out_base,workdir_name)
if not os.path.exists(workdir):
os.makedirs(workdir)
data_selection.to_csv(path.join(workdir,'data_selection.csv'))
get_scan = pe.Node(name='get_scan', interface=util.Function(function=get_bids_scan,input_names=inspect.getargspec(get_bids_scan)[0], output_names=['scan_path','scan_type','task', 'nii_path', 'nii_name', 'events_name', 'subject_session', 'metadata_filename', 'dict_slice']))
get_scan.inputs.ignore_exception = True
get_scan.inputs.data_selection = data_selection
get_scan.inputs.bids_base = preprocessing_dir
get_scan.iterables = ("ind_type", ind)
eventfile = pe.Node(name='eventfile', interface=util.Function(function=corresponding_eventfile,input_names=inspect.getargspec(corresponding_eventfile)[0], output_names=['eventfile']))
if invert:
invert = pe.Node(interface=fsl.ImageMaths(), name="invert")
invert.inputs.op_string = '-mul -1'
specify_model = pe.Node(interface=SpecifyModel(), name="specify_model")
specify_model.inputs.input_units = 'secs'
specify_model.inputs.time_repetition = tr
specify_model.inputs.high_pass_filter_cutoff = highpass_sigma
level1design = pe.Node(interface=Level1Design(), name="level1design")
level1design.inputs.interscan_interval = tr
if bf_path:
bf_path = path.abspath(path.expanduser(bf_path))
level1design.inputs.bases = {"custom": {"bfcustompath":bf_path}}
else:
# We are not adding derivatives here, as these conflict with the habituation option.
# !!! This is not difficult to solve, and would only require the addition of an elif condition to the habituator definition, which would add multiple column copies for each of the derivs.
level1design.inputs.bases = {'gamma': {'derivs':True, 'gammasigma':30, 'gammadelay':10}}
level1design.inputs.model_serial_correlations = True
modelgen = pe.Node(interface=fsl.FEATModel(), name='modelgen')
#modelgen.inputs.ignore_exception = True
glm = pe.Node(interface=fsl.GLM(), name='glm', iterfield='design')
# glm.inputs.out_cope = "cope.nii.gz"
# glm.inputs.out_varcb_name = "varcb.nii.gz"
# #not setting a betas output file might lead to beta export in lieu of COPEs
# glm.inputs.out_file = "betas.nii.gz"
# glm.inputs.out_t_name = "t_stat.nii.gz"
# glm.inputs.out_p_name = "p_stat.nii.gz"
if mask == 'mouse':
mask = '/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii'
else:
glm.inputs.mask = path.abspath(path.expanduser(mask))
glm.interface.mem_gb = 6
#glm.inputs.ignore_exception = True
out_file_name_base = 'sub-{{subject}}_ses-{{session}}_task-{{task}}_acq-{{acquisition}}_run-{{run}}_{{modality}}_{}.{}'
betas_filename = pe.Node(name='betas_filename', interface=util.Function(function=bids_dict_to_source,input_names=inspect.getargspec(bids_dict_to_source)[0], output_names=['filename']))
betas_filename.inputs.source_format = out_file_name_base.format('betas','nii.gz')
cope_filename = pe.Node(name='cope_filename', interface=util.Function(function=bids_dict_to_source,input_names=inspect.getargspec(bids_dict_to_source)[0], output_names=['filename']))
cope_filename.inputs.source_format = out_file_name_base.format('cope','nii.gz')
varcb_filename = pe.Node(name='varcb_filename', interface=util.Function(function=bids_dict_to_source,input_names=inspect.getargspec(bids_dict_to_source)[0], output_names=['filename']))
varcb_filename.inputs.source_format = out_file_name_base.format('varcb','nii.gz')
tstat_filename = pe.Node(name='tstat_filename', interface=util.Function(function=bids_dict_to_source,input_names=inspect.getargspec(bids_dict_to_source)[0], output_names=['filename']))
tstat_filename.inputs.source_format = out_file_name_base.format('tstat','nii.gz')
zstat_filename = pe.Node(name='zstat_filename', interface=util.Function(function=bids_dict_to_source,input_names=inspect.getargspec(bids_dict_to_source)[0], output_names=['filename']))
zstat_filename.inputs.source_format = out_file_name_base.format('zstat','nii.gz')
pstat_filename = pe.Node(name='pstat_filename', interface=util.Function(function=bids_dict_to_source,input_names=inspect.getargspec(bids_dict_to_source)[0], output_names=['filename']))
pstat_filename.inputs.source_format = out_file_name_base.format('pstat','nii.gz')
pfstat_filename = pe.Node(name='pfstat_filename', interface=util.Function(function=bids_dict_to_source,input_names=inspect.getargspec(bids_dict_to_source)[0], output_names=['filename']))
pfstat_filename.inputs.source_format = out_file_name_base.format('pfstat','nii.gz')
design_filename = pe.Node(name='design', interface=util.Function(function=bids_dict_to_source,input_names=inspect.getargspec(bids_dict_to_source)[0], output_names=['filename']))
design_filename.inputs.source_format = out_file_name_base.format('design','mat')
design_rename = pe.Node(interface=util.Rename(), name='design_rename')
datasink = pe.Node(nio.DataSink(), name='datasink')
datasink.inputs.base_directory = path.join(out_base,workflow_name)
datasink.inputs.parameterization = False
workflow_connections = [
(get_scan, eventfile, [('nii_path', 'timecourse_file')]),
(specify_model, level1design, [('session_info', 'session_info')]),
(level1design, modelgen, [('ev_files', 'ev_files')]),
(level1design, modelgen, [('fsf_files', 'fsf_file')]),
(modelgen, glm, [('design_file', 'design')]),
(modelgen, glm, [('con_file', 'contrasts')]),
(get_scan, datasink, [(('dict_slice',bids_dict_to_dir), 'container')]),
(get_scan, betas_filename, [('dict_slice', 'bids_dictionary')]),
(get_scan, cope_filename, [('dict_slice', 'bids_dictionary')]),
(get_scan, varcb_filename, [('dict_slice', 'bids_dictionary')]),
(get_scan, tstat_filename, [('dict_slice', 'bids_dictionary')]),
(get_scan, zstat_filename, [('dict_slice', 'bids_dictionary')]),
(get_scan, pstat_filename, [('dict_slice', 'bids_dictionary')]),
(get_scan, pfstat_filename, [('dict_slice', 'bids_dictionary')]),
(get_scan, design_filename, [('dict_slice', 'bids_dictionary')]),
(betas_filename, glm, [('filename', 'out_file')]),
(cope_filename, glm, [('filename', 'out_cope')]),
(varcb_filename, glm, [('filename', 'out_varcb_name')]),
(tstat_filename, glm, [('filename', 'out_t_name')]),
(zstat_filename, glm, [('filename', 'out_z_name')]),
(pstat_filename, glm, [('filename', 'out_p_name')]),
(pfstat_filename, glm, [('filename', 'out_pf_name')]),
(modelgen, design_rename, [('design_file', 'in_file')]),
(design_filename, design_rename, [('filename', 'format_string')]),
(glm, datasink, [('out_pf', '@pfstat')]),
(glm, datasink, [('out_p', '@pstat')]),
(glm, datasink, [('out_z', '@zstat')]),
(glm, datasink, [('out_t', '@tstat')]),
(glm, datasink, [('out_cope', '@cope')]),
(glm, datasink, [('out_varcb', '@varcb')]),
(glm, datasink, [('out_file', '@betas')]),
(design_rename, datasink, [('out_file', '@design')]),
]
if habituation:
level1design.inputs.orthogonalization = {1: {0:0,1:0,2:0}, 2: {0:1,1:1,2:0}}
specify_model.inputs.bids_condition_column = 'samri_l1_regressors'
specify_model.inputs.bids_amplitude_column = 'samri_l1_amplitude'
add_habituation = pe.Node(name='add_habituation', interface=util.Function(function=eventfile_add_habituation,input_names=inspect.getargspec(eventfile_add_habituation)[0], output_names=['out_file']))
# Regressor names need to be prefixed with "e" plus a numerator so that Level1Design will be certain to conserve the order.
add_habituation.inputs.original_stimulation_value='1stim'
add_habituation.inputs.habituation_value='2habituation'
workflow_connections.extend([
(eventfile, add_habituation, [('eventfile', 'in_file')]),
(add_habituation, specify_model, [('out_file', 'bids_event_file')]),
])
if not habituation:
specify_model.inputs.bids_condition_column = ''
level1design.inputs.contrasts = [('allStim','T', ['ev0'],[1])]
workflow_connections.extend([
(eventfile, specify_model, [('eventfile', 'bids_event_file')]),
])
#condition names as defined in eventfile_add_habituation:
elif habituation=="separate_contrast":
level1design.inputs.contrasts = [('stim','T', ['1stim','2habituation'],[1,0]),('hab','T', ['1stim','2habituation'],[0,1])]
elif habituation=="in_main_contrast":
level1design.inputs.contrasts = [('all','T', ['1stim','2habituation'],[1,1])]
elif habituation=="confound":
level1design.inputs.contrasts = [('stim','T', ["1stim", "2habituation"],[1,0])]
else:
print(habituation)
raise ValueError('The value you have provided for the `habituation` parameter, namely "{}", is invalid. Please choose one of: {{None, False,"","confound","in_main_contrast","separate_contrast"}}'.format(habituation))
if highpass_sigma or lowpass_sigma:
bandpass = pe.Node(interface=fsl.maths.TemporalFilter(), name="bandpass")
bandpass.inputs.highpass_sigma = highpass_sigma
bandpass.interface.mem_gb = 16
if lowpass_sigma:
bandpass.inputs.lowpass_sigma = lowpass_sigma
else:
bandpass.inputs.lowpass_sigma = tr
if invert:
workflow_connections.extend([
(get_scan, invert, [('nii_path', 'in_file')]),
(invert, bandpass, [('out_file', 'in_file')]),
(bandpass, specify_model, [('out_file', 'functional_runs')]),
(bandpass, glm, [('out_file', 'in_file')]),
(bandpass, datasink, [('out_file', '@ts_file')]),
(get_scan, bandpass, [('nii_name', 'out_file')]),
])
else:
workflow_connections.extend([
(get_scan, bandpass, [('nii_path', 'in_file')]),
(bandpass, specify_model, [('out_file', 'functional_runs')]),
(bandpass, glm, [('out_file', 'in_file')]),
(bandpass, datasink, [('out_file', '@ts_file')]),
(get_scan, bandpass, [('nii_name', 'out_file')]),
])
else:
if invert:
workflow_connections.extend([
(get_scan, invert, [('nii_path', 'in_file')]),
(invert, specify_model, [('out_file', 'functional_runs')]),
(invert, glm, [('out_file', 'in_file')]),
(invert, datasink, [('out_file', '@ts_file')]),
(get_scan, invert, [('nii_name', 'out_file')]),
])
else:
workflow_connections.extend([
(get_scan, specify_model, [('nii_path', 'functional_runs')]),
(get_scan, glm, [('nii_path', 'in_file')]),
(get_scan, datasink, [('nii_path', '@ts_file')]),
])
workflow_config = {'execution': {'crashdump_dir': path.join(out_base,'crashdump'),}}
if debug:
workflow_config['logging'] = {
'workflow_level':'DEBUG',
'utils_level':'DEBUG',
'interface_level':'DEBUG',
'filemanip_level':'DEBUG',
'log_to_file':'true',
}
workflow = pe.Workflow(name=workdir_name)
workflow.connect(workflow_connections)
workflow.base_dir = out_base
workflow.config = workflow_config
workflow.write_graph(dotfilename=path.join(workflow.base_dir,workdir_name,"graph.dot"), graph2use="hierarchical", format="png")
n_jobs = max(int(round(mp.cpu_count()*n_jobs_percentage)),2)
workflow.run(plugin="MultiProc", plugin_args={'n_procs' : n_jobs})
if not keep_work:
shutil.rmtree(path.join(out_base,workdir_name))
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
