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# fMRI: SPM Auditory dataset

## Introduction

The fmri_spm_auditory.py recreates the classical workflow described in the SPM8 manual (http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf) using auditory dataset that can be downloaded from http://www.fil.ion.ucl.ac.uk/spm/data/auditory/:

> python fmri_spm_auditory.py

Import necessary modules from nipype.

    import nipype.interfaces.io 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 matlabimport nipype.interfaces.fsl as fsl          # fsl
    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

### Preliminaries

    # 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 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')

We strongly encourage to use 4D files insteead 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 4d requires some additional steps (explauned later on). Use `nipype.interfaces.fsl.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 `nipype.interfaces.spm.Realign` for motion correction and register all images to the mean image.

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

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'

    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 `nipype.interfaces.spm.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 `nipype.interfaces.spm.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 van 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.get_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 get_vox_dims function is passed along the input volume of normalise to set the optimal voxel sizes.

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

    preproc.connect([(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', 'apply_to_files'),
                                                 (('modulated_input_image', get_vox_dims), 'write_voxel_sizes')]),
                     (realign, normalize_func, [('realigned_files', 'apply_to_files'),
                                                (('realigned_files', get_vox_dims), 'write_voxel_sizes')]),
                     (normalize_func, smooth, [('normalized_files', 'in_files')]),
                     ])

### Set up analysis workflow

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

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

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

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

    level1design = pe.Node(interface=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(interface=spm.EstimateModel(), name="level1estimate")
    level1estimate.inputs.estimation_method = {'Classical' : 1}

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

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

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

    l1analysis.connect([(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')]),
                      ])

### Preproc + Analysis pipeline

    l1pipeline = pe.Workflow(name='firstlevel')
    l1pipeline.connect([(preproc, l1analysis, [('realign.realignment_parameters',
                                                'modelspec.realignment_parameters')])])

Pluging 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, [('smooth.smoothed_files',
                                                    'modelspec.functional_runs')])])
    else:
        def makelist(item):
            return [item]
        l1pipeline.connect([(preproc, l1analysis, [(('smooth.smoothed_files',makelist),
                                                    'modelspec.functional_runs')])])

### 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 http://www.fil.ion.ucl.ac.uk/spm/data/auditory/
    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', 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 `nipype.interfaces.io.DataGrabber` 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(interface=nio.DataGrabber(infields=['subject_id'],
                                                   outfields=['func', 'struct']),
                         name = 'datasource')
    datasource.inputs.base_directory = data_dir
    datasource.inputs.template = '%s%s/%s%s_%03d.img'
    datasource.inputs.template_args = info

### Experimental paradigm specific components

Here we create a structure that provides 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.

    from nipype.interfaces.base import Bunch
    subjectinfo = [Bunch(conditions=['Task'],
                                onsets=[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 `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_auditory_tutorial/workingdir')

    level1.connect([(infosource, datasource, [('subject_id', 'subject_id')]),
                    (datasource,l1pipeline,[('struct', 'preproc.coregister.source')])
                    ])
    if merge_to_4d:
        level1.connect([(datasource,l1pipeline,[('func','preproc.merge.in_files')])])
    else:
        level1.connect([(datasource,l1pipeline,[('func','preproc.realign.in_files')])])

### Setup storage results

Use `nipype.interfaces.io.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('spm_auditory_tutorial/l1output')

    def getstripdir(subject_id):
        import os
        return os.path.join(os.path.abspath('spm_auditory_tutorial/workingdir'),'_subject_id_%s' % subject_id)

    # store relevant outputs from various stages of the 1st level analysis
    level1.connect([(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 `nipype.pipeline.engine.Pipeline.Run` function needs to be called.

    if __name__ == '__main__':
        level1.run()
        level1.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.