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volsurf.py
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volsurf.py
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# Import processing relevant modules
import nipype.algorithms.rapidart as ra # artifact detection
import nipype.interfaces.spm as spm # spm
import nipype.interfaces.freesurfer as fs # freesurfer
import nipype.interfaces.io as nio # i/o routines
import nipype.algorithms.modelgen as model # model generation
import nipype.interfaces.utility as util # utility
import nipype.pipeline.engine as pe # pypeline engine
"""
Setup preprocessing workflow
----------------------------
This is a generic preprocessing workflow that can be used by different analyses
"""
preproc = pe.Workflow(name='preproc')
"""Use :class:`nipype.interfaces.spm.Realign` for motion correction
and register all images to the mean image.
"""
realign = pe.Node(interface=spm.Realign(), name="realign")
realign.inputs.register_to_mean = True
"""Use :class:`nipype.algorithms.rapidart` to determine which of the
images in the functional series are outliers based on deviations in
intensity or movement.
"""
art = pe.Node(interface=ra.ArtifactDetect(), name="art")
#art.inputs.use_differences = [False,True]
#art.inputs.use_norm = True
#art.inputs.norm_threshold = 0.5
#art.inputs.zintensity_threshold = 3
art.inputs.mask_type = 'file'
#run FreeSurfer's BBRegister
surfregister = pe.Node(interface=fs.BBRegister(),name='surfregister')
surfregister.inputs.init = 'fsl'
surfregister.inputs.contrast_type = 't2'
# Get information from the FreeSurfer directories (brainmask, etc)
FreeSurferSource = pe.Node(interface=nio.FreeSurferSource(), name='fssource')
# Allow inversion of brainmask.mgz to volume (functional) space for alignment
ApplyVolTransform = pe.Node(interface=fs.ApplyVolTransform(),
name='applyreg')
ApplyVolTransform.inputs.inverse = True
# Allow for thresholding of volumized brainmask
Threshold = pe.Node(interface=fs.Binarize(),name='threshold')
Threshold.inputs.min = 10
convert2nii = pe.Node(interface=fs.MRIConvert(out_type='nii'),name='convert2nii')
"""Smooth the functional data using
:class:`nipype.interfaces.spm.Smooth`.
"""
volsmooth = pe.Node(interface=spm.Smooth(), name = "volsmooth")
surfsmooth = pe.MapNode(interface=fs.Smooth(proj_frac_avg=(0,1,0.1)), name = "surfsmooth",
iterfield=['in_file'])
preproc.connect([(realign, surfregister,[('mean_image', 'source_file')]),
(FreeSurferSource, ApplyVolTransform,[('brainmask','target_file')]),
(surfregister, ApplyVolTransform,[('out_reg_file','reg_file')]),
(realign, ApplyVolTransform,[('mean_image', 'source_file')]),
(ApplyVolTransform, Threshold,[('transformed_file','in_file')]),
(Threshold, convert2nii, [('binary_file', 'in_file')]),
(realign, art,[('realignment_parameters','realignment_parameters'),
('realigned_files','realigned_files')]),
(convert2nii, art, [('out_file', 'mask_file')]),
(realign, volsmooth, [('realigned_files', 'in_files')]),
(realign, surfsmooth, [('realigned_files', 'in_file')]),
(surfregister, surfsmooth, [('out_reg_file','reg_file')]),
])
"""
Set up volume analysis workflow
-------------------------------
"""
volanalysis = pe.Workflow(name='volanalysis')
"""Generate SPM-specific design information using
:class:`nipype.interfaces.spm.SpecifyModel`.
"""
modelspec = pe.Node(interface=model.SpecifyModel(), name= "modelspec")
modelspec.inputs.concatenate_runs = True
modelspec.overwrite = True
"""Generate a first level SPM.mat file for analysis
:class:`nipype.interfaces.spm.Level1Design`.
"""
level1design = pe.Node(interface=spm.Level1Design(), name= "level1design")
level1design.inputs.bases = {'hrf':{'derivs': [0,0]}}
"""Use :class:`nipype.interfaces.spm.EstimateModel` to determine the
parameters of the model.
"""
level1estimate = pe.Node(interface=spm.EstimateModel(), name="level1estimate")
level1estimate.inputs.estimation_method = {'Classical' : 1}
"""Use :class:`nipype.interfaces.spm.EstimateContrast` to estimate the
first level contrasts specified in a few steps above.
"""
contrastestimate = pe.Node(interface = spm.EstimateContrast(), name="contrastestimate")
volanalysis.connect([(modelspec,level1design,[('session_info','session_info')]),
(level1design,level1estimate,[('spm_mat_file','spm_mat_file')]),
(level1estimate,contrastestimate,[('spm_mat_file','spm_mat_file'),
('beta_images','beta_images'),
('residual_image','residual_image')]),
])
"""
Set up surface analysis workflow
--------------------------------
"""
surfanalysis = volanalysis.clone(name='surfanalysis')
"""
Set up volume normalization workflow
------------------------------------
"""
volnorm = pe.Workflow(name='volnormconimages')
convert = pe.Node(interface=fs.MRIConvert(out_type='nii'),name='convert2nii')
convert2 = pe.MapNode(interface=fs.MRIConvert(in_type='nifti1',out_type='nii'),
iterfield=['in_file'],
name='convertnifti12nii')
segment = pe.Node(interface=spm.Segment(), name='segment')
normwreg = pe.MapNode(interface=fs.ApplyVolTransform(),
iterfield=['source_file'],
name='applyreg2con')
normalize = pe.Node(interface=spm.Normalize(jobtype='write'),
name='norm2mni')
volnorm.connect([(convert, segment, [('out_file','data')]),
(convert2, normwreg, [('out_file','source_file')]),
(segment, normalize, [('transformation_mat', 'parameter_file')]),
(normwreg, normalize, [('transformed_file','apply_to_files')]),
])
"""
Preproc + Analysis pipeline
---------------------------
"""
inputnode = pe.Node(interface=util.IdentityInterface(fields=['struct',
'func',
'subject_id',
'session_info',
'contrasts']),
name='inputnode')
"""
Use :class:`nipype.algorithms.rapidart` to determine if stimuli are correlated with motion or intensity parameters (STIMULUS CORRELATED MOTION).
"""
stimcorr = pe.Node(interface=ra.StimulusCorrelation(),name='stimcorr')
stimcorr.inputs.concatenated_design = True
"""
Merge con images and T images into a single list that will then be normalized
"""
mergefiles = pe.Node(interface=util.Merge(2),
name='mergeconfiles')
l1pipeline = pe.Workflow(name='firstlevel')
l1pipeline.connect([(inputnode,preproc,[('func','realign.in_files'),
('subject_id','surfregister.subject_id'),
('subject_id','fssource.subject_id'),
]),
(inputnode, volanalysis,[('session_info','modelspec.subject_info'),
('subject_id','modelspec.subject_id'),
('contrasts','contrastestimate.contrasts')]),
(inputnode, surfanalysis,[('session_info','modelspec.subject_info'),
('subject_id','modelspec.subject_id'),
('contrasts','contrastestimate.contrasts')]),
])
# attach volume and surface model specification and estimation components
l1pipeline.connect([(preproc, volanalysis, [('realign.realignment_parameters',
'modelspec.realignment_parameters'),
('volsmooth.smoothed_files',
'modelspec.functional_runs'),
('art.outlier_files',
'modelspec.outlier_files'),
('convert2nii.out_file',
'level1design.mask_image')]),
(preproc, surfanalysis, [('realign.realignment_parameters',
'modelspec.realignment_parameters'),
('surfsmooth.smoothed_file',
'modelspec.functional_runs'),
('art.outlier_files',
'modelspec.outlier_files'),
('convert2nii.out_file',
'level1design.mask_image')]),
(preproc, stimcorr,[('realign.realignment_parameters',
'realignment_parameters'),
('art.intensity_files','intensity_values')]),
(volanalysis, stimcorr, [('level1design.spm_mat_file',
'spm_mat_file')]),
])
# attach volume contrast normalization components
l1pipeline.connect([(preproc, volnorm, [('fssource.orig','convert2nii.in_file'),
('surfregister.out_reg_file','applyreg2con.reg_file'),
('fssource.orig','applyreg2con.target_file')]),
(volanalysis, mergefiles,[('contrastestimate.con_images','in1'),
('contrastestimate.spmT_images','in2'),
]),
(mergefiles, volnorm, [('out',
'convertnifti12nii.in_file')]),
])