The integrates several interfaces to perform a first level analysis on a two-subject data set. It is very similar to the spm_tutorial with the difference of using nipy for fitting GLM model and estimating contrasts. The tutorial can be found in the examples folder. Run the tutorial from inside the nipype tutorial directory:

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

from nipype.interfaces.nipy.model import FitGLM, EstimateContrast
from nipype.interfaces.nipy.preprocess import ComputeMask

Import necessary modules from nipype.

import as nio  # Data i/o
import nipype.interfaces.spm as spm  # spm
import nipype.interfaces.matlab as mlab  # how to run matlab
import nipype.interfaces.utility as util  # utility
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.

# Set the way matlab should be called
mlab.MatlabCommand.set_default_matlab_cmd("matlab -nodesktop -nosplash")

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

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

infosource = pe.Node(
    interface=util.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)

Preprocessing pipeline nodes

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.

datasource = pe.Node(
        infields=['subject_id'], outfields=['func', 'struct']),
datasource.inputs.base_directory = data_dir
datasource.inputs.template = '%s/%s.nii'
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = True

Use 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

compute_mask = pe.Node(interface=ComputeMask(), name="compute_mask")

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(interface=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'

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

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

Smooth the functional data using nipype.interfaces.spm.Smooth.

smooth = pe.Node(interface=spm.Smooth(), name="smooth")
smooth.inputs.fwhm = 4

Set up analysis 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]

Generate design information using nipype.interfaces.spm.SpecifyModel. nipy accepts only design specified in seconds so “output_units” has always have to be set to “secs”.

modelspec = pe.Node(interface=model.SpecifySPMModel(), name="modelspec")
modelspec.inputs.concatenate_runs = True
modelspec.inputs.input_units = 'secs'
modelspec.inputs.output_units = 'secs'
modelspec.inputs.time_repetition = 3.
modelspec.inputs.high_pass_filter_cutoff = 120

Fit the GLM model using nipy and ordinary least square method

model_estimate = pe.Node(interface=FitGLM(), name="model_estimate")
model_estimate.inputs.TR = 3.
model_estimate.inputs.model = "spherical"
model_estimate.inputs.method = "ols"

Estimate the contrasts. The format of the contrasts definition is the same as for FSL and SPM

contrast_estimate = pe.Node(
    interface=EstimateContrast(), name="contrast_estimate")
cont1 = ('Task>Baseline', 'T', ['Task-Odd', 'Task-Even'], [0.5, 0.5])
cont2 = ('Task-Odd>Task-Even', 'T', ['Task-Odd', 'Task-Even'], [1, -1])
contrast_estimate.inputs.contrasts = [cont1, cont2]

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.

l1pipeline = pe.Workflow(name="level1")
l1pipeline.base_dir = os.path.abspath('nipy_tutorial/workingdir')

    [(infosource, datasource, [('subject_id', 'subject_id')]),
     (datasource, realign, [('func', 'in_files')]), (realign, compute_mask, [
         ('mean_image', 'mean_volume')
     ]), (realign, coregister, [('mean_image', 'source'),
                                 'apply_to_files')]), (datasource, coregister,
                                                       [('struct', 'target')]),
     (coregister, smooth,
      [('coregistered_files', 'in_files')]), (realign, modelspec, [
          ('realignment_parameters', 'realignment_parameters')
      ]), (smooth, modelspec,
           [('smoothed_files', 'functional_runs')]), (realign, art, [
               ('realignment_parameters', 'realignment_parameters')
           ]), (coregister, art, [('coregistered_files', 'realigned_files')]),
     (compute_mask, art, [('brain_mask', 'mask_file')]), (art, modelspec, [
         ('outlier_files', 'outlier_files')
     ]), (infosource, modelspec, [
         (("subject_id", subjectinfo), "subject_info")
     ]), (modelspec, model_estimate,
          [('session_info', 'session_info')]), (compute_mask, model_estimate,
                                                [('brain_mask', 'mask')]),
     (model_estimate, contrast_estimate,
      [("beta", "beta"), ("nvbeta", "nvbeta"), ("s2", "s2"), ("dof", "dof"),
       ("axis", "axis"), ("constants", "constants"), ("reg_names",

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.