The integrates several interfaces to perform a first and second level analysis on a two-subject data set. The tutorial can be found in the examples folder. Run the tutorial from inside the nipype tutorial directory:


Import necessary modules from nipype.

from __future__ import print_function
from builtins import str
from builtins import range

import as nio  # Data i/o
import nipype.interfaces.spm as spm  # spm
import niflow.nipype1.workflows.fmri.spm as spm_wf  # spm
import nipype.interfaces.fsl as fsl  # fsl
from nipype.interfaces import utility as niu  # Utilities
import nipype.pipeline.engine as pe  # pypeline engine
import nipype.algorithms.rapidart as ra  # artifact detection
import nipype.algorithms.modelgen as model  # model specification
import os  # system functions


Set any package specific configuration. The output file format for FSL routines is being set to uncompressed NIFTI and a specific version of matlab is being used. The uncompressed format is required because SPM does not handle compressed NIFTI.

# Tell fsl to generate all output in uncompressed nifti format

# Set the way matlab should be called
# mlab.MatlabCommand.set_default_matlab_cmd("matlab -nodesktop -nosplash")
# mlab.MatlabCommand.set_default_paths('/software/spm8')

Setting up workflows

In this tutorial we will be setting up a hierarchical workflow for spm analysis. This will demonstrate how pre-defined workflows can be setup and shared across users, projects and labs.

Setup preprocessing workflow

This is a generic preprocessing workflow that can be used by different analyses

preproc = pe.Workflow(name='preproc')

Use nipype.interfaces.spm.Realign for motion correction and register all images to the mean image.

realign = pe.Node(spm.Realign(), name="realign")
realign.inputs.register_to_mean = True

Use 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(ra.ArtifactDetect(), name="art")
art.inputs.use_differences = [True, False]
art.inputs.use_norm = True
art.inputs.norm_threshold = 1
art.inputs.zintensity_threshold = 3
art.inputs.mask_type = 'file'
art.inputs.parameter_source = 'SPM'

Skull strip structural images using nipype.interfaces.fsl.BET.

skullstrip = pe.Node(fsl.BET(), name="skullstrip")
skullstrip.inputs.mask = True

Use nipype.interfaces.spm.Coregister to perform a rigid body registration of the functional data to the structural data.

coregister = pe.Node(spm.Coregister(), name="coregister")
coregister.inputs.jobtype = 'estimate'

Normalize and smooth functional data using DARTEL template

normalize_and_smooth_func = pe.Node(
    spm.DARTELNorm2MNI(modulate=True), name='normalize_and_smooth_func')
fwhmlist = [4]
normalize_and_smooth_func.iterables = ('fwhm', fwhmlist)

Normalize structural data using DARTEL template

normalize_struct = pe.Node(
    spm.DARTELNorm2MNI(modulate=True), name='normalize_struct')
normalize_struct.inputs.fwhm = 2

    (realign, coregister, [('mean_image', 'source'), ('realigned_files',
    (coregister, normalize_and_smooth_func, [('coregistered_files',
    (normalize_struct, skullstrip, [('normalized_files', 'in_file')]),
    (realign, art, [('realignment_parameters', 'realignment_parameters')]),
    (normalize_and_smooth_func, art, [('normalized_files',
    (skullstrip, art, [('mask_file', 'mask_file')]),

Set up analysis workflow

l1analysis = pe.Workflow(name='analysis')

Generate SPM-specific design information using nipype.interfaces.spm.SpecifyModel.

modelspec = pe.Node(model.SpecifySPMModel(), name="modelspec")
modelspec.inputs.concatenate_runs = True

Generate a first level SPM.mat file for analysis nipype.interfaces.spm.Level1Design.

level1design = pe.Node(spm.Level1Design(), name="level1design")
level1design.inputs.bases = {'hrf': {'derivs': [0, 0]}}

Use nipype.interfaces.spm.EstimateModel to determine the parameters of the model.

level1estimate = pe.Node(spm.EstimateModel(), name="level1estimate")
level1estimate.inputs.estimation_method = {'Classical': 1}

Use nipype.interfaces.spm.EstimateContrast to estimate the first level contrasts specified in a few steps above.

contrastestimate = pe.Node(spm.EstimateContrast(), name="contrastestimate")

Use :class: nipype.interfaces.utility.Select to select each contrast for reporting.

selectcontrast = pe.Node(niu.Select(), name="selectcontrast")

Use nipype.interfaces.fsl.Overlay to combine the statistical output of the contrast estimate and a background image into one volume.

overlaystats = pe.Node(fsl.Overlay(), name="overlaystats")
overlaystats.inputs.stat_thresh = (3, 10)
overlaystats.inputs.show_negative_stats = True
overlaystats.inputs.auto_thresh_bg = True

Use nipype.interfaces.fsl.Slicer to create images of the overlaid statistical volumes for a report of the first-level results.

slicestats = pe.Node(fsl.Slicer(), name="slicestats")
slicestats.inputs.all_axial = True
slicestats.inputs.image_width = 750

l1analysis.connect([(modelspec, level1design,
                       'session_info')]), (level1design, level1estimate,
                                           [('spm_mat_file', 'spm_mat_file')]),
                    (level1estimate, contrastestimate,
                     [('spm_mat_file', 'spm_mat_file'), ('beta_images',
                       'residual_image')]), (contrastestimate, selectcontrast,
                                             [('spmT_images', 'inlist')]),
                    (selectcontrast, overlaystats,
                     [('out', 'stat_image')]), (overlaystats, slicestats,
                                                [('out_file', 'in_file')])])

Preproc + Analysis pipeline

l1pipeline = pe.Workflow(name='firstlevel')
    (preproc, l1analysis,
     [('realign.realignment_parameters', 'modelspec.realignment_parameters'),
       'modelspec.functional_runs'), ('art.outlier_files',
       'level1design.mask_image'), ('normalize_struct.normalized_files',

Data specific components

The nipype tutorial contains data for two subjects. Subject data is in two subdirectories, s1 and s2. Each subject directory contains four functional volumes: f3.nii, f5.nii, f7.nii, f10.nii. And one anatomical volume named struct.nii.

Below we set some variables to inform the datasource about the layout of our data. We specify the location of the data, the subject sub-directories and a dictionary that maps each run to a mnemonic (or field) for the run type (struct or func). These fields become the output fields of the datasource node in the pipeline.

In the example below, run ‘f3’ is of type ‘func’ and gets mapped to a nifti filename through a template ‘%s.nii’. So ‘f3’ would become ‘f3.nii’.

# Specify the location of the data.
# data_dir = os.path.abspath('data')
# Specify the subject directories
subject_list = ['s1', 's3']
# Map field names to individual subject runs.
info = dict(
    func=[['subject_id', ['f3', 'f5', 'f7', 'f10']]],
    struct=[['subject_id', 'struct']])

infosource = pe.Node(
    niu.IdentityInterface(fields=['subject_id']), name="infosource")

Here we set up iteration over all the subjects. The following line is a particular example of the flexibility of the system. The datasource attribute iterables tells the pipeline engine that it should repeat the analysis on each of the items in the subject_list. In the current example, the entire first level preprocessing and estimation will be repeated for each subject contained in subject_list.

infosource.iterables = ('subject_id', subject_list)

Now we create a object and fill in the information from above about the layout of our data. The nipype.pipeline.NodeWrapper module wraps the interface object and provides additional housekeeping and pipeline specific functionality.

inputnode = pe.Node(
    niu.IdentityInterface(fields=['in_data']), name='inputnode')
datasource = pe.Node(
    nio.DataGrabber(infields=['subject_id'], outfields=['func', 'struct']),
datasource.inputs.template = 'nipype-tutorial/data/%s/%s.nii'
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = True

We need to create a separate workflow to make the DARTEL template

datasource_dartel = pe.MapNode(
    nio.DataGrabber(infields=['subject_id'], outfields=['struct']),
datasource_dartel.inputs.template = 'nipype-tutorial/data/%s/%s.nii'
datasource_dartel.inputs.template_args = dict(
    struct=[['subject_id', 'struct']])
datasource_dartel.inputs.sort_filelist = True
datasource_dartel.inputs.subject_id = subject_list

Here we make sure that struct files have names corresponding to the subject ids. This way we will be able to pick the right field flows later.

rename_dartel = pe.MapNode(
    iterfield=['in_file', 'subject_id'],
rename_dartel.inputs.subject_id = subject_list
rename_dartel.inputs.keep_ext = True

dartel_workflow = spm_wf.create_DARTEL_template(name='dartel_workflow')
dartel_workflow.inputs.inputspec.template_prefix = "template"

This function will allow to pick the right field flow for each subject

def pickFieldFlow(dartel_flow_fields, subject_id):
    from nipype.utils.filemanip import split_filename
    for f in dartel_flow_fields:
        _, name, _ = split_filename(f)
        if name.find("subject_id_%s" % subject_id):
            return f

    raise Exception

pick_flow = pe.Node(
        input_names=['dartel_flow_fields', 'subject_id'],

Experimental paradigm specific components

Here we create 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.

def subjectinfo(subject_id):
    from nipype.interfaces.base import Bunch
    from copy import deepcopy
    print("Subject ID: %s\n" % str(subject_id))
    output = []
    names = ['Task-Odd', 'Task-Even']
    for r in range(4):
        onsets = [list(range(15, 240, 60)), list(range(45, 240, 60))]
                          durations=[[15] for s in names],
    return output

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 = ('Task>Baseline', 'T', ['Task-Odd', 'Task-Even'], [0.5, 0.5])
cont2 = ('Task-Odd>Task-Even', 'T', ['Task-Odd', 'Task-Even'], [1, -1])
contrasts = [cont1, cont2]

# set up node specific inputs
modelspecref = l1pipeline.inputs.analysis.modelspec
modelspecref.input_units = 'secs'
modelspecref.output_units = 'secs'
modelspecref.time_repetition = 3.
modelspecref.high_pass_filter_cutoff = 120

l1designref = l1pipeline.inputs.analysis.level1design
l1designref.timing_units = modelspecref.output_units
l1designref.interscan_interval = modelspecref.time_repetition

l1pipeline.inputs.analysis.contrastestimate.contrasts = contrasts

# Iterate over each contrast and create report images.
selectcontrast.iterables = ('index', [[i] for i in range(len(contrasts))])

Setup the pipeline

The nodes created above do not describe the flow of data. They merely describe the parameters used for each function. In this section we setup the connections between the nodes such that appropriate outputs from nodes are piped into appropriate inputs of other nodes.

Use the nipype.pipeline.engine.Pipeline to create a graph-based execution pipeline for first level analysis. The config options tells the pipeline engine to use workdir as the disk location to use when running the processes and keeping their outputs. The use_parameterized_dirs tells the engine to create sub-directories under workdir corresponding to the iterables in the pipeline. Thus for this pipeline there will be subject specific sub-directories.

The nipype.pipeline.engine.Pipeline.connect function creates the links between the processes, i.e., how data should flow in and out of the processing nodes.

level1 = pe.Workflow(name="level1")
level1.base_dir = os.path.abspath('spm_dartel_tutorial/workingdir')

    (inputnode, datasource, [('in_data', 'base_directory')]),
    (inputnode, datasource_dartel, [('in_data', 'base_directory')]),
    (datasource_dartel, rename_dartel, [('struct', 'in_file')]),
    (rename_dartel, dartel_workflow, [('out_file',
    (infosource, datasource, [('subject_id', 'subject_id')]),
    (datasource, l1pipeline,
     [('func', 'preproc.realign.in_files'), ('struct',
      ('struct', 'preproc.normalize_struct.apply_to_files')]),
    (dartel_workflow, l1pipeline,
     [('outputspec.template_file', 'preproc.normalize_struct.template_file'),
    (infosource, pick_flow, [('subject_id', 'subject_id')]),
    (dartel_workflow, pick_flow, [('outputspec.flow_fields',
    (pick_flow, l1pipeline,
     [('dartel_flow_field', 'preproc.normalize_struct.flowfield_files'),
    (infosource, l1pipeline, [(('subject_id', subjectinfo),

Setup storage results

Use to store selected outputs from the pipeline in a specific location. This allows the user to selectively choose important output bits from the analysis and keep them.

The first step is to create a datasink node and then to connect outputs from the modules above to storage locations. These take the following form directory_name[.[@]subdir] where parts between [] are optional. For example ‘realign.@mean’ below creates a directory called realign in ‘l1output/subject_id/’ and stores the mean image output from the Realign process in the realign directory. If the @ is left out, then a sub-directory with the name ‘mean’ would be created and the mean image would be copied to that directory.

datasink = pe.Node(nio.DataSink(), name="datasink")
datasink.inputs.base_directory = os.path.abspath(
report = pe.Node(nio.DataSink(), name='report')
report.inputs.base_directory = os.path.abspath('spm_dartel_tutorial/report')
report.inputs.parameterization = False

def getstripdir(subject_id):
    import os
    return os.path.join(
        '_subject_id_%s' % subject_id)

# store relevant outputs from various stages of the 1st level analysis
    (infosource, datasink, [('subject_id', 'container'),
                            (('subject_id', getstripdir), 'strip_dir')]),
    (l1pipeline, datasink,
     [('analysis.contrastestimate.con_images', 'contrasts.@con'),
      ('analysis.contrastestimate.spmT_images', 'contrasts.@T')]),
    (infosource, report, [('subject_id', 'container'),
                          (('subject_id', getstripdir), 'strip_dir')]),
    (l1pipeline, report, [('analysis.slicestats.out_file', '@report')]),

Execute the pipeline

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__':{'n_procs': 4})

Setup level 2 pipeline

Use to extract the contrast images across a group of first level subjects. Unlike the previous pipeline that iterated over subjects, this pipeline will iterate over contrasts.

# collect all the con images for each contrast.
contrast_ids = list(range(1, len(contrasts) + 1))
l2source = pe.Node(nio.DataGrabber(infields=['fwhm', 'con']), name="l2source")
# we use .*i* to capture both .img (SPM8) and .nii (SPM12)
l2source.inputs.template = os.path.abspath(
# iterate over all contrast images
l2source.iterables = [('fwhm', fwhmlist), ('con', contrast_ids)]
l2source.inputs.sort_filelist = True

Use nipype.interfaces.spm.OneSampleTTestDesign to perform a simple statistical analysis of the contrasts from the group of subjects (n=2 in this example).

# setup a 1-sample t-test node
onesamplettestdes = pe.Node(spm.OneSampleTTestDesign(), name="onesampttestdes")
l2estimate = pe.Node(spm.EstimateModel(), name="level2estimate")
l2estimate.inputs.estimation_method = {'Classical': 1}
l2conestimate = pe.Node(spm.EstimateContrast(), name="level2conestimate")
cont1 = ('Group', 'T', ['mean'], [1])
l2conestimate.inputs.contrasts = [cont1]
l2conestimate.inputs.group_contrast = True

As before, we setup a pipeline to connect these two nodes (l2source -> onesamplettest).

l2pipeline = pe.Workflow(name="level2")
l2pipeline.base_dir = os.path.abspath('spm_dartel_tutorial/l2output')
    (l2source, onesamplettestdes, [('outfiles', 'in_files')]),
    (onesamplettestdes, l2estimate, [('spm_mat_file', 'spm_mat_file')]),
    (l2estimate, l2conestimate,
     [('spm_mat_file', 'spm_mat_file'), ('beta_images', 'beta_images'),
      ('residual_image', 'residual_image')]),

Execute the second level pipeline

if __name__ == '__main__':

Example source code

You can download the full source code of this example. This same script is also included in Nipype1 Examples Niflow under the package/niflow/nipype1/examples directory.