Nipype doesn't just allow you to create your own workflows. It also already comes with predefined workflows, developed by the community, for the community. For a full list of all workflows, look under the Workflows section of the main homepage.
But to give you a short overview, there are workflows about:
Functional MRI workflows:
fsl about resting state, fixed_effects, modelfit, featreg, susan_smooth and many morespm about DARTEL and VBMStructural MRI workflows
ants about ANTSBuildTemplate and antsRegistrationBuildTemplatefreesurfer about bem, recon and tessellationDiffusion workflows:
camino about connectivity_mapping, diffusion and group_connectivitydipy about denoisefsl about artifacts, dti, epi, tbss and many moremrtrix about connectivity_mapping, diffusion and group_connectivity
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from nipype.workflows.fmri.fsl.preprocess import create_susan_smooth
smoothwf = create_susan_smooth()
Once a workflow is created, we need to make sure that the mandatory inputs are specified. To see which inputs we have to define, we can use the command:
create_susan_smooth?
Which gives us the output:
Create a SUSAN smoothing workflow
Parameters
----------
Inputs:
inputnode.in_files : functional runs (filename or list of filenames)
inputnode.fwhm : fwhm for smoothing with SUSAN
inputnode.mask_file : mask used for estimating SUSAN thresholds (but not for smoothing)
Outputs:
outputnode.smoothed_files : functional runs (filename or list of filenames)As we can see, we also need a mask file. For the sake of convenience, let's take the mean image of a functional image and threshold it at the 50% percentil:
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!fslmaths /data/ds000114/sub-01/ses-test/func/sub-01_ses-test_task-fingerfootlips_bold.nii.gz \
-Tmean -thrP 50 /output/sub-01_ses-test_task-fingerfootlips_mask.nii.gz
Now, we're ready to finish up our smooth workflow.
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smoothwf.inputs.inputnode.in_files = '/data/ds000114/sub-01/ses-test/func/sub-01_ses-test_task-fingerfootlips_bold.nii.gz'
smoothwf.inputs.inputnode.mask_file = '/output/sub-01_ses-test_task-fingerfootlips_mask.nii.gz'
smoothwf.inputs.inputnode.fwhm = 4
smoothwf.base_dir = '/output'
Before we run it, let's visualize the graph:
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%pylab inline
from IPython.display import Image
smoothwf.write_graph(graph2use='colored', format='png', simple_form=True)
Image(filename='/output/susan_smooth/graph.dot.png')
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And we're ready to go:
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smoothwf.run('MultiProc', plugin_args={'n_procs': 4})
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Once it's finished, we can look at the results:
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!fslmaths /data/ds000114/sub-01/ses-test/func/sub-01_ses-test_task-fingerfootlips_bold.nii.gz -Tmean fmean.nii.gz
!fslmaths /output/susan_smooth/smooth/mapflow/_smooth0/sub-01_ses-test_task-fingerfootlips_bold_smooth.nii.gz \
-Tmean smean.nii.gz
from nilearn import image, plotting
plotting.plot_epi(
'fmean.nii.gz', title="mean (no smoothing)", display_mode='z',
cmap='gray', cut_coords=(-45, -30, -15, 0, 15))
plotting.plot_epi(
'smean.nii.gz', title="mean (susan smoothed)", display_mode='z',
cmap='gray', cut_coords=(-45, -30, -15, 0, 15))
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print(smoothwf.list_node_names())
Ok. Hmm, what if we want to change the 'median' node, from 50% to 99%? For this, we first need to get the node.
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median = smoothwf.get_node('median')
Now that we have the node, we can change it's value as we want:
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median.inputs.op_string = '-k %s -p 99'
And we can run the workflow again...
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smoothwf.run('MultiProc', plugin_args={'n_procs': 4})
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And now the output is:
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!fslmaths /output/susan_smooth/smooth/mapflow/_smooth0/sub-01_ses-test_task-fingerfootlips_bold_smooth.nii.gz \
-Tmean mmean.nii.gz
from nilearn import image, plotting
plotting.plot_epi(
'smean.nii.gz', title="mean (susan smooth)", display_mode='z',
cmap='gray', cut_coords=(-45, -30, -15, 0, 15))
plotting.plot_epi(
'mmean.nii.gz', title="mean (smoothed, median=99%)", display_mode='z',
cmap='gray', cut_coords=(-45, -30, -15, 0, 15))
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