fMRI: SPM Auditory dataset


The recreates the classical workflow described in the SPM8 manual using auditory dataset that can be downloaded from


Import necessary modules from nipype.

from builtins import range

import as nio  # Data i/o
import nipype.interfaces.spm as spm  # spm
import nipype.interfaces.fsl as fsl  # fsl
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.modelgen as model  # model specification
import os  # system functions


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

Setting up workflows

In this tutorial we will be setting up a hierarchical workflow for SPM analysis. This will demonstrate how predefined 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')

We strongly encourage to use 4D files instead of series of 3D for fMRI analyses for many reasons (cleanness and saving and filesystem inodes are among them). However, the the workflow presented in the SPM8 manual which this tutorial is based on uses 3D files. Therefore we leave converting to 4D as an option. We are using merge_to_4d variable, because switching between 3D and 4dD requires some additional steps (explained later on). Use Merge to merge a series of 3D files along the time dimension creating a 4D file.

merge_to_4d = True

if merge_to_4d:
    merge = pe.Node(interface=fsl.Merge(), name="merge")
    merge.inputs.dimension = "t"

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

realign = pe.Node(interface=spm.Realign(), name="realign")

Use 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'

segment = pe.Node(interface=spm.Segment(), name="segment")

Uncomment the following line for faster execution

# segment.inputs.gaussians_per_class = [1, 1, 1, 4]

Warp functional and structural data to SPM’s T1 template using Normalize. The tutorial data set includes the template image, T1.nii.

normalize_func = pe.Node(interface=spm.Normalize(), name="normalize_func")
normalize_func.inputs.jobtype = "write"

normalize_struc = pe.Node(interface=spm.Normalize(), name="normalize_struc")
normalize_struc.inputs.jobtype = "write"

Smooth the functional data using Smooth.

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

write_voxel_sizes is the input of the normalize interface that is recommended to be set to the voxel sizes of the target volume. There is no need to set it manually since we can infer it from data using the following function:

def get_vox_dims(volume):
    import nibabel as nb
    if isinstance(volume, list):
        volume = volume[0]
    nii = nb.load(volume)
    hdr = nii.header
    voxdims = hdr.get_zooms()
    return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]

Here we are connecting all the nodes together. Notice that we add the merge node only if you choose to use 4D. Also, the get_vox_dims function is passed along the input volume of Normalize to set the optimal voxel sizes.

if merge_to_4d:
    preproc.connect([(merge, realign, [('merged_file', 'in_files')])])

    (realign, coregister, [('mean_image', 'target')]),
    (coregister, segment, [('coregistered_source', 'data')]),
    (segment, normalize_func, [('transformation_mat', 'parameter_file')]),
    (segment, normalize_struc,
     [('transformation_mat', 'parameter_file'), ('modulated_input_image',
      (('modulated_input_image', get_vox_dims), 'write_voxel_sizes')]),
    (realign, normalize_func, [('realigned_files', 'apply_to_files'),
                               (('realigned_files', get_vox_dims),
    (normalize_func, smooth, [('normalized_files', 'in_files')]),

Set up analysis workflow

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

Generate SPM-specific design information using SpecifySPMModel.

modelspec = pe.Node(interface=model.SpecifySPMModel(), name="modelspec")

Generate a first level SPM.mat file for analysis Level1Design.

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

Use EstimateModel to determine the parameters of the model.

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

threshold = pe.Node(interface=spm.Threshold(), name="threshold")

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

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

    (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')]),
    (contrastestimate, threshold, [('spm_mat_file', 'spm_mat_file'),
                                   ('spmT_images', 'stat_image')]),

Preprocessing and analysis pipeline

l1pipeline = pe.Workflow(name='firstlevel')
l1pipeline.connect([(preproc, l1analysis,

Plugging in functional_runs is a bit more complicated, because model spec expects a list of runs. Every run can be a 4D file or a list of 3D files. Therefore for 3D analysis we need a list of lists and to make one we need a helper function.

if merge_to_4d:
    l1pipeline.connect([(preproc, l1analysis,

    def makelist(item):
        return [item]

    l1pipeline.connect([(preproc, l1analysis,
                         [(('smooth.smoothed_files', makelist),

Data specific components

In this tutorial there is only one subject M00223. 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.

# Specify the location of the data downloaded from
data_dir = os.path.abspath('spm_auditory_data')
# Specify the subject directories
subject_list = ['M00223']
# Map field names to individual subject runs.
info = dict(
    func=[['f', 'subject_id', 'f', 'subject_id',
           list(range(16, 100))]],
    struct=[['s', 'subject_id', 's', 'subject_id', 2]])

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)

Now we create a DataGrabber object and fill in the information from above about the layout of our data.

datasource = pe.Node(
        infields=['subject_id'], outfields=['func', 'struct']),
datasource.inputs.base_directory = data_dir
datasource.inputs.template = '%s%s/%s%s_%03d.img'
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = True

Experimental paradigm specific components

Here we create a structure that provides information about the experimental paradigm. This is used by the SpecifySPMModel to create the information necessary to generate an SPM design matrix.

from nipype.interfaces.base import Bunch
subjectinfo = [
        conditions=['Task'], onsets=[list(range(6, 84, 12))], durations=[[6]])

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 = ('active > rest', 'T', ['Task'], [1])
contrasts = [cont1]

# set up node specific inputs
modelspecref = l1pipeline.inputs.analysis.modelspec
modelspecref.input_units = 'scans'
modelspecref.output_units = 'scans'
modelspecref.time_repetition = 7
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.preproc.smooth.fwhm = [6, 6, 6]
l1pipeline.inputs.analysis.modelspec.subject_info = subjectinfo
l1pipeline.inputs.analysis.contrastestimate.contrasts = contrasts
l1pipeline.inputs.analysis.threshold.contrast_index = 1

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 Workflow to create a graph-based execution pipeline for first level analysis. Set the base_dir option to instruct the pipeline engine to use spm_auditory_tutorial/workingdir as the filesystem location to use when running the processes and keeping their outputs. Other options can be set via the configuration file. For example, use_parameterized_dirs tells the engine to create sub-directories under base_dir, corresponding to the iterables in the pipeline. Thus, for this pipeline there will be subject specific sub-directories.

When building a workflow, interface objects are wrapped within a Node so that they can be inserted in the workflow.

The connect() method creates the links between Node instances, 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_auditory_tutorial/workingdir')

level1.connect([(infosource, datasource, [('subject_id', 'subject_id')]),
                (datasource, l1pipeline, [('struct',
if merge_to_4d:
    level1.connect([(datasource, l1pipeline, [('func',
    level1.connect([(datasource, l1pipeline, [('func',

Setup storage results

Use DataSink 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(interface=nio.DataSink(), name="datasink")
datasink.inputs.base_directory = os.path.abspath(

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')]),

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 run() method needs to be called.

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.