Paper: Smoothing comparison¶
from builtins import range
import nipype.interfaces.io as nio # Data i/o
import nipype.interfaces.spm as spm # spm
import nipype.interfaces.freesurfer as fs # freesurfer
import nipype.interfaces.nipy as nipy
import nipype.interfaces.utility as util
import nipype.pipeline.engine as pe # pypeline engine
import nipype.algorithms.modelgen as model # model specification
import nipype.workflows.fmri.fsl as fsl_wf
from nipype.interfaces.base import Bunch
import os # system functions
preprocessing = pe.Workflow(name="preprocessing")
iter_fwhm = pe.Node(interface=util.IdentityInterface(fields=["fwhm"]),
name="iter_fwhm")
iter_fwhm.iterables = [('fwhm', [4, 8])]
iter_smoothing_method = pe.Node(interface=util.IdentityInterface(fields=["smoothing_method"]),
name="iter_smoothing_method")
iter_smoothing_method.iterables = [('smoothing_method', ['isotropic_voxel',
'anisotropic_voxel',
'isotropic_surface'])]
realign = pe.Node(interface=spm.Realign(), name="realign")
realign.inputs.register_to_mean = True
isotropic_voxel_smooth = pe.Node(interface=spm.Smooth(),
name="isotropic_voxel_smooth")
preprocessing.connect(realign, "realigned_files", isotropic_voxel_smooth,
"in_files")
preprocessing.connect(iter_fwhm, "fwhm", isotropic_voxel_smooth, "fwhm")
compute_mask = pe.Node(interface=nipy.ComputeMask(), name="compute_mask")
preprocessing.connect(realign, "mean_image", compute_mask, "mean_volume")
anisotropic_voxel_smooth = fsl_wf.create_susan_smooth(name="anisotropic_voxel_smooth",
separate_masks=False)
anisotropic_voxel_smooth.inputs.smooth.output_type = 'NIFTI'
preprocessing.connect(realign, "realigned_files", anisotropic_voxel_smooth,
"inputnode.in_files")
preprocessing.connect(iter_fwhm, "fwhm", anisotropic_voxel_smooth,
"inputnode.fwhm")
preprocessing.connect(compute_mask, "brain_mask", anisotropic_voxel_smooth,
'inputnode.mask_file')
recon_all = pe.Node(interface=fs.ReconAll(), name="recon_all")
surfregister = pe.Node(interface=fs.BBRegister(), name='surfregister')
surfregister.inputs.init = 'fsl'
surfregister.inputs.contrast_type = 't2'
preprocessing.connect(realign, 'mean_image', surfregister, 'source_file')
preprocessing.connect(recon_all, 'subject_id', surfregister, 'subject_id')
preprocessing.connect(recon_all, 'subjects_dir', surfregister, 'subjects_dir')
isotropic_surface_smooth = pe.MapNode(interface=fs.Smooth(proj_frac_avg=(0, 1, 0.1)),
iterfield=['in_file'],
name="isotropic_surface_smooth")
preprocessing.connect(surfregister, 'out_reg_file', isotropic_surface_smooth,
'reg_file')
preprocessing.connect(realign, "realigned_files", isotropic_surface_smooth,
"in_file")
preprocessing.connect(iter_fwhm, "fwhm", isotropic_surface_smooth,
"surface_fwhm")
preprocessing.connect(iter_fwhm, "fwhm", isotropic_surface_smooth, "vol_fwhm")
preprocessing.connect(recon_all, 'subjects_dir', isotropic_surface_smooth,
'subjects_dir')
merge_smoothed_files = pe.Node(interface=util.Merge(3),
name='merge_smoothed_files')
preprocessing.connect(isotropic_voxel_smooth, 'smoothed_files',
merge_smoothed_files, 'in1')
preprocessing.connect(anisotropic_voxel_smooth, 'outputnode.smoothed_files',
merge_smoothed_files, 'in2')
preprocessing.connect(isotropic_surface_smooth, 'smoothed_file',
merge_smoothed_files, 'in3')
select_smoothed_files = pe.Node(interface=util.Select(),
name="select_smoothed_files")
preprocessing.connect(merge_smoothed_files, 'out', select_smoothed_files,
'inlist')
def chooseindex(roi):
return {'isotropic_voxel': list(range(0, 4)), 'anisotropic_voxel': list(range(4, 8)),
'isotropic_surface': list(range(8, 12))}[roi]
preprocessing.connect(iter_smoothing_method, ("smoothing_method", chooseindex),
select_smoothed_files, 'index')
rename = pe.MapNode(util.Rename(format_string="%(orig)s"), name="rename",
iterfield=['in_file'])
rename.inputs.parse_string = "(?P<orig>.*)"
preprocessing.connect(select_smoothed_files, 'out', rename, 'in_file')
specify_model = pe.Node(interface=model.SpecifyModel(), name="specify_model")
specify_model.inputs.input_units = 'secs'
specify_model.inputs.time_repetition = 3.
specify_model.inputs.high_pass_filter_cutoff = 120
specify_model.inputs.subject_info = [Bunch(conditions=['Task-Odd', 'Task-Even'],
onsets=[list(range(15, 240, 60)),
list(range(45, 240, 60))],
durations=[[15], [15]])] * 4
level1design = pe.Node(interface=spm.Level1Design(), name="level1design")
level1design.inputs.bases = {'hrf': {'derivs': [0, 0]}}
level1design.inputs.timing_units = 'secs'
level1design.inputs.interscan_interval = specify_model.inputs.time_repetition
level1estimate = pe.Node(interface=spm.EstimateModel(), name="level1estimate")
level1estimate.inputs.estimation_method = {'Classical': 1}
contrastestimate = pe.Node(interface=spm.EstimateContrast(),
name="contrastestimate")
contrastestimate.inputs.contrasts = [('Task>Baseline', 'T',
['Task-Odd', 'Task-Even'], [0.5, 0.5])]
modelling = pe.Workflow(name="modelling")
modelling.connect(specify_model, 'session_info', level1design, 'session_info')
modelling.connect(level1design, 'spm_mat_file', level1estimate, 'spm_mat_file')
modelling.connect(level1estimate, 'spm_mat_file', contrastestimate,
'spm_mat_file')
modelling.connect(level1estimate, 'beta_images', contrastestimate, 'beta_images')
modelling.connect(level1estimate, 'residual_image', contrastestimate,
'residual_image')
main_workflow = pe.Workflow(name="main_workflow")
main_workflow.base_dir = "smoothing_comparison_workflow"
main_workflow.connect(preprocessing, "realign.realignment_parameters",
modelling, "specify_model.realignment_parameters")
main_workflow.connect(preprocessing, "select_smoothed_files.out",
modelling, "specify_model.functional_runs")
main_workflow.connect(preprocessing, "compute_mask.brain_mask",
modelling, "level1design.mask_image")
datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
outfields=['func', 'struct']),
name='datasource')
datasource.inputs.base_directory = os.path.abspath('data')
datasource.inputs.template = '%s/%s.nii'
datasource.inputs.template_args = info = dict(func=[['subject_id',
['f3', 'f5', 'f7', 'f10']]],
struct=[['subject_id', 'struct']])
datasource.inputs.subject_id = 's1'
datasource.inputs.sort_filelist = True
main_workflow.connect(datasource, 'func', preprocessing, 'realign.in_files')
main_workflow.connect(datasource, 'struct', preprocessing,
'recon_all.T1_files')
datasink = pe.Node(interface=nio.DataSink(), name="datasink")
datasink.inputs.base_directory = os.path.abspath('smoothing_comparison_workflow/output')
datasink.inputs.regexp_substitutions = [("_rename[0-9]", "")]
main_workflow.connect(modelling, 'contrastestimate.spmT_images', datasink,
'contrasts')
main_workflow.connect(preprocessing, 'rename.out_file', datasink,
'smoothed_epi')
main_workflow.run()
main_workflow.write_graph()
Example source code
You can download the full source code of this example.
This same script is also included in the Nipype source distribution under the
examples directory.