A pipeline example that data from the FSL FEEDS set. Single subject, two stimuli.
You can find it at http://www.fmrib.ox.ac.uk/fsl/feeds/doc/index.html
import os # system functions
import nipype.interfaces.io as nio # Data i/o
import nipype.interfaces.fsl as fsl # fsl
import nipype.interfaces.utility as util # utility
import nipype.pipeline.engine as pe # pypeline engine
import nipype.algorithms.modelgen as model # model generation
from nipype.externals.pynifti import load
Confirm package dependencies are installed. (This is only for the tutorial, rarely would you put this in your own code.)
from nipype.utils.misc import package_check
package_check('numpy', '1.3', 'tutorial1')
package_check('scipy', '0.7', 'tutorial1')
package_check('networkx', '1.0', 'tutorial1')
package_check('IPython', '0.10', 'tutorial1')
Setup any package specific configuration. The output file format for FSL routines is being set to compressed NIFTI.
fsl.FSLCommand.set_default_output_type('NIFTI_GZ')
In this tutorial we will be setting up a hierarchical workflow for fsl analysis. This will demonstrate how pre-defined workflows can be setup and shared across users, projects and labs.
This is a generic fsl feat preprocessing workflow encompassing skull stripping, motion correction and smoothing operations.
preproc = pe.Workflow(name='preproc')
Set up a node to define all inputs required for the preprocessing workflow
inputnode = pe.Node(interface=util.IdentityInterface(fields=['func',
'struct',]),
name='inputspec')
Convert functional images to float representation. Since there can be more than one functional run we use a MapNode to convert each run.
img2float = pe.MapNode(interface=fsl.ImageMaths(out_data_type='float',
op_string = '',
suffix='_dtype'),
iterfield=['in_file'],
name='img2float')
preproc.connect(inputnode, 'func', img2float, 'in_file')
Extract the middle volume of the first run as the reference
extract_ref = pe.Node(interface=fsl.ExtractROI(t_size=1),
name = 'extractref')
Define a function to pick the first file from a list of files
def pickfirst(files):
if isinstance(files, list):
return files[0]
else:
return files
preproc.connect(img2float, ('out_file', pickfirst), extract_ref, 'in_file')
Define a function to return the 1 based index of the middle volume
def getmiddlevolume(func):
funcfile = func
if isinstance(func, list):
funcfile = func[0]
_,_,_,timepoints = load(funcfile).get_shape()
return (timepoints/2)-1
preproc.connect(inputnode, ('func', getmiddlevolume), extract_ref, 't_min')
Realign the functional runs to the middle volume of the first run
motion_correct = pe.MapNode(interface=fsl.MCFLIRT(save_mats = True,
save_plots = True),
name='realign',
iterfield = ['in_file'])
preproc.connect(img2float, 'out_file', motion_correct, 'in_file')
preproc.connect(extract_ref, 'roi_file', motion_correct, 'ref_file')
Extract the mean volume of the first functional run
meanfunc = pe.Node(interface=fsl.ImageMaths(op_string = '-Tmean',
suffix='_mean'),
name='meanfunc')
preproc.connect(motion_correct, ('out_file', pickfirst), meanfunc, 'in_file')
Strip the skull from the mean functional to generate a mask
meanfuncmask = pe.Node(interface=fsl.BET(mask = True,
no_output=True,
frac = 0.3),
name = 'meanfuncmask')
preproc.connect(meanfunc, 'out_file', meanfuncmask, 'in_file')
Mask the functional runs with the extracted mask
maskfunc = pe.MapNode(interface=fsl.ImageMaths(suffix='_bet',
op_string='-mas'),
iterfield=['in_file'],
name = 'maskfunc')
preproc.connect(motion_correct, 'out_file', maskfunc, 'in_file')
preproc.connect(meanfuncmask, 'mask_file', maskfunc, 'in_file2')
Determine the 2nd and 98th percentile intensities of each functional run
getthresh = pe.MapNode(interface=fsl.ImageStats(op_string='-p 2 -p 98'),
iterfield = ['in_file'],
name='getthreshold')
preproc.connect(maskfunc, 'out_file', getthresh, 'in_file')
Threshold the first run of the functional data at 10% of the 98th percentile
threshold = pe.Node(interface=fsl.ImageMaths(out_data_type='char',
suffix='_thresh'),
name='threshold')
preproc.connect(maskfunc, ('out_file', pickfirst), threshold, 'in_file')
Define a function to get 10% of the intensity
def getthreshop(thresh):
return '-thr %.10f -Tmin -bin'%(0.1*thresh[0][1])
preproc.connect(getthresh, ('out_stat', getthreshop), threshold, 'op_string')
Determine the median value of the functional runs using the mask
medianval = pe.MapNode(interface=fsl.ImageStats(op_string='-k %s -p 50'),
iterfield = ['in_file'],
name='medianval')
preproc.connect(motion_correct, 'out_file', medianval, 'in_file')
preproc.connect(threshold, 'out_file', medianval, 'mask_file')
Dilate the mask
dilatemask = pe.Node(interface=fsl.ImageMaths(suffix='_dil',
op_string='-dilF'),
name='dilatemask')
preproc.connect(threshold, 'out_file', dilatemask, 'in_file')
Mask the motion corrected functional runs with the dilated mask
maskfunc2 = pe.MapNode(interface=fsl.ImageMaths(suffix='_mask',
op_string='-mas'),
iterfield=['in_file'],
name='maskfunc2')
preproc.connect(motion_correct, 'out_file', maskfunc2, 'in_file')
preproc.connect(dilatemask, 'out_file', maskfunc2, 'in_file2')
Determine the mean image from each functional run
meanfunc2 = pe.MapNode(interface=fsl.ImageMaths(op_string='-Tmean',
suffix='_mean'),
iterfield=['in_file'],
name='meanfunc2')
preproc.connect(maskfunc2, 'out_file', meanfunc2, 'in_file')
Merge the median values with the mean functional images into a coupled list
mergenode = pe.Node(interface=util.Merge(2, axis='hstack'),
name='merge')
preproc.connect(meanfunc2,'out_file', mergenode, 'in1')
preproc.connect(medianval,'out_stat', mergenode, 'in2')
Smooth each run using SUSAN with the brightness threshold set to 75% of the median value for each run and a mask consituting the mean functional
smooth = pe.MapNode(interface=fsl.SUSAN(),
iterfield=['in_file', 'brightness_threshold','usans'],
name='smooth')
Define a function to get the brightness threshold for SUSAN
def getbtthresh(medianvals):
return [0.75*val for val in medianvals]
preproc.connect(maskfunc2, 'out_file', smooth, 'in_file')
preproc.connect(medianval, ('out_stat', getbtthresh), smooth, 'brightness_threshold')
preproc.connect(mergenode, ('out', lambda x: [[tuple([val[0],0.75*val[1]])] for val in x]), smooth, 'usans')
Mask the smoothed data with the dilated mask
maskfunc3 = pe.MapNode(interface=fsl.ImageMaths(suffix='_mask',
op_string='-mas'),
iterfield=['in_file'],
name='maskfunc3')
preproc.connect(smooth, 'smoothed_file', maskfunc3, 'in_file')
preproc.connect(dilatemask, 'out_file', maskfunc3, 'in_file2')
Scale each volume of the run so that the median value of the run is set to 10000
intnorm = pe.MapNode(interface=fsl.ImageMaths(suffix='_intnorm'),
iterfield=['in_file','op_string'],
name='intnorm')
preproc.connect(maskfunc3, 'out_file', intnorm, 'in_file')
Define a function to get the scaling factor for intensity normalization
def getinormscale(medianvals):
return ['-mul %.10f'%(10000./val) for val in medianvals]
preproc.connect(medianval, ('out_stat', getinormscale), intnorm, 'op_string')
Perform temporal highpass filtering on the data
highpass = pe.MapNode(interface=fsl.ImageMaths(suffix='_tempfilt'),
iterfield=['in_file'],
name='highpass')
preproc.connect(intnorm, 'out_file', highpass, 'in_file')
Generate a mean functional image from the first run
meanfunc3 = pe.MapNode(interface=fsl.ImageMaths(op_string='-Tmean',
suffix='_mean'),
iterfield=['in_file'],
name='meanfunc3')
preproc.connect(highpass, ('out_file', pickfirst), meanfunc3, 'in_file')
Strip the structural image a coregister the mean functional image to the structural image
nosestrip = pe.Node(interface=fsl.BET(frac=0.3),
name = 'nosestrip')
skullstrip = pe.Node(interface=fsl.BET(mask = True),
name = 'stripstruct')
coregister = pe.Node(interface=fsl.FLIRT(dof=6),
name = 'coregister')
preproc.connect([(inputnode, nosestrip,[('struct','in_file')]),
(nosestrip, skullstrip, [('out_file','in_file')]),
(skullstrip, coregister,[('out_file','in_file')]),
(meanfunc2, coregister,[(('out_file',pickfirst),'reference')]),
])
modelfit = pe.Workflow(name='modelfit')
Use nipype.algorithms.modelgen.SpecifyModel to generate design information.
modelspec = pe.Node(interface=model.SpecifyModel(), name="modelspec")
modelspec.inputs.concatenate_runs = False
Use nipype.interfaces.fsl.Level1Design to generate a run specific fsf file for analysis
level1design = pe.Node(interface=fsl.Level1Design(), name="level1design")
Use nipype.interfaces.fsl.FEATModel to generate a run specific mat file for use by FILMGLS
modelgen = pe.MapNode(interface=fsl.FEATModel(), name='modelgen',
iterfield = ['fsf_file'])
Set the model generation to run everytime. Since the fsf file, which is the input to modelgen only references the ev files, modelgen will not run if the ev file contents are changed but the fsf file is untouched.
modelgen.overwrite = True
Use nipype.interfaces.fsl.FILMGLS to estimate a model specified by a mat file and a functional run
modelestimate = pe.MapNode(interface=fsl.FILMGLS(smooth_autocorr=True,
mask_size=5,
threshold=1000),
name='modelestimate',
iterfield = ['design_file','in_file'])
Use nipype.interfaces.fsl.ContrastMgr to generate contrast estimates
conestimate = pe.MapNode(interface=fsl.ContrastMgr(), name='conestimate',
iterfield = ['tcon_file','fcon_file','stats_dir'])
modelfit.connect([
(modelspec,level1design,[('session_info','session_info')]),
(level1design,modelgen,[('fsf_files','fsf_file')]),
(modelgen,modelestimate,[('design_file','design_file')]),
(modelgen,conestimate,[('con_file','tcon_file')]),
(modelgen,conestimate,[('fcon_file','fcon_file')]),
(modelestimate,conestimate,[('results_dir','stats_dir')]),
])
fixed_fx = pe.Workflow(name='fixedfx')
Use nipype.interfaces.fsl.Merge to merge the copes and varcopes for each condition
copemerge = pe.MapNode(interface=fsl.Merge(dimension='t'),
iterfield=['in_files'],
name="copemerge")
varcopemerge = pe.MapNode(interface=fsl.Merge(dimension='t'),
iterfield=['in_files'],
name="varcopemerge")
Use nipype.interfaces.fsl.L2Model to generate subject and condition specific level 2 model design files
level2model = pe.Node(interface=fsl.L2Model(),
name='l2model')
Use nipype.interfaces.fsl.FLAMEO to estimate a second level model
flameo = pe.MapNode(interface=fsl.FLAMEO(run_mode='fe'), name="flameo",
iterfield=['cope_file','var_cope_file'])
fixed_fx.connect([(copemerge,flameo,[('merged_file','cope_file')]),
(varcopemerge,flameo,[('merged_file','var_cope_file')]),
(level2model,flameo, [('design_mat','design_file'),
('design_con','t_con_file'),
('design_grp','cov_split_file')]),
])
def sort_copes(files):
numelements = len(files[0])
outfiles = []
for i in range(numelements):
outfiles.insert(i,[])
for j, elements in enumerate(files):
outfiles[i].append(elements[i])
return outfiles
def num_copes(files):
return len(files)
firstlevel = pe.Workflow(name='firstlevel')
firstlevel.connect([(preproc, modelfit, [('highpass.out_file', 'modelspec.functional_runs'),
('highpass.out_file','modelestimate.in_file')]),
(preproc, fixed_fx, [('coregister.out_file', 'flameo.mask_file')]),
(modelfit, fixed_fx,[(('conestimate.copes', sort_copes),'copemerge.in_files'),
(('conestimate.varcopes', sort_copes),'varcopemerge.in_files'),
(('conestimate.copes', num_copes),'l2model.num_copes'),
])
])
This tutorial does a single subject analysis so we are not using infosource and iterables
# Specify the location of the FEEDS data. You can find it at http://www.fmrib.ox.ac.uk/fsl/feeds/doc/index.html
feeds_data_dir = os.path.abspath('feeds_data')
# Specify the subject directories
# Map field names to individual subject runs.
info = dict(func=[['fmri']],
struct=[['structural']])
Now we create a nipype.interfaces.io.DataSource object and fill in the information from above about the layout of our data. The nipype.pipeline.Node module wraps the interface object and provides additional housekeeping and pipeline specific functionality.
datasource = pe.Node(interface=nio.DataGrabber(outfields=['func', 'struct']),
name = 'datasource')
datasource.inputs.base_directory = feeds_data_dir
datasource.inputs.template = '%s.nii.gz'
datasource.inputs.template_args = info
firstlevel.inputs.preproc.smooth.fwhm = 5
hpcutoff = 100
TR = 3.
firstlevel.inputs.preproc.highpass.suffix = '_hpf'
firstlevel.inputs.preproc.highpass.op_string = '-bptf %d -1'%(hpcutoff/TR)
Setup a function that returns subject-specific information about the experimental paradigm. This is used by the nipype.interfaces.spm.SpecifyModel to create the information necessary to generate an SPM design matrix. In this tutorial, the same paradigm was used for every participant. Other examples of this function are available in the doc/examples folder. Note: Python knowledge required here.
from nipype.interfaces.base import Bunch
firstlevel.inputs.modelfit.modelspec.subject_info = [Bunch(conditions=['Visual','Auditory'],
onsets=[range(0,180*TR,60),range(0,180*TR,90)],
durations=[[30], [45]],
amplitudes=None,
tmod=None,
pmod=None,
regressor_names=None,
regressors=None)]
Setup the contrast structure that needs to be evaluated. This is a list of lists. The inner list specifies the contrasts and has the following format - [Name,Stat,[list of condition names],[weights on those conditions]. The condition names must match the names listed in the subjectinfo function described above.
cont1 = ['Visual>Baseline','T', ['Visual','Auditory'],[1,0]]
cont2 = ['Auditory>Baseline','T', ['Visual','Auditory'],[0,1]]
cont3 = ['Task','F', [cont1, cont2]]
contrasts = [cont1,cont2,cont3]
firstlevel.inputs.modelfit.modelspec.input_units = 'secs'
firstlevel.inputs.modelfit.modelspec.output_units = 'secs'
firstlevel.inputs.modelfit.modelspec.time_repetition = TR
firstlevel.inputs.modelfit.modelspec.high_pass_filter_cutoff = hpcutoff
firstlevel.inputs.modelfit.modelspec.subject_id = 'whatever'
firstlevel.inputs.modelfit.level1design.interscan_interval = TR
firstlevel.inputs.modelfit.level1design.bases = {'dgamma':{'derivs': True}}
firstlevel.inputs.modelfit.level1design.contrasts = contrasts
l1pipeline = pe.Workflow(name= "level1")
l1pipeline.base_dir = os.path.abspath('./fsl_feeds/workingdir')
l1pipeline.config = dict(crashdump_dir=os.path.abspath('./fsl_feeds/crashdumps'))
l1pipeline.connect([(datasource, firstlevel, [('struct','preproc.inputspec.struct'),
('func', 'preproc.inputspec.func'),
]),
])
Setup the datasink
datasink = pe.Node(interface=nio.DataSink(parameterization=False), name="datasink")
datasink.inputs.base_directory = os.path.abspath('./fsl_feeds/l1out')
datasink.inputs.substitutions = [('dtype_mcf_mask_mean', 'meanfunc'),
('brain_brain_flirt','coregistered')]
# store relevant outputs from various stages of the 1st level analysis
l1pipeline.connect([(firstlevel, datasink,[('fixedfx.flameo.stats_dir','@stats'),
('preproc.coregister.out_file','coregstruct'),
('preproc.meanfunc2.out_file','meanfunc'),
])
])
The code discussed above sets up all the necessary data structures with appropriate parameters and the connectivity between the processes, but does not generate any output. To actually run the analysis on the data the nipype.pipeline.engine.Pipeline.Run function needs to be called.
if __name__ == '__main__':
l1pipeline.run()
# l2pipeline.run()
See also
The full source code of this example is included in the Nipype source distribution (../examples/fsl_feeds_tutorial.py).