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# fMRI: surface smooth - FreeSurfer, SPM

This tutorial illustrates how to perform surface-based smoothing of cortical data using FreeSurfer and then perform firstlevel model and contrast estimation using SPM. A surface-based second level glm illustrates the use of spherical registration and freesurfer’s glm functions.

## Preparing environment

### Step 0

In order to run this tutorial you need to have SPM and FreeSurfer tools installed and accessible from matlab/command line. Check by calling mri_info from the command line.

### Step 1

Link the fsaverage directory for your freesurfer distribution. To do this type:

    cd nipype-tutorial/fsdata
    ln -s $FREESURFER_HOME/subjects/fsaverage
    cd ..

## Defining the workflow

    import os                                    # system functions

    import nipype.algorithms.modelgen as model   # model generation
    import nipype.algorithms.rapidart as ra      # artifact detection
    import nipype.interfaces.freesurfer as fs    # freesurfer
    import nipype.interfaces.io as nio           # i/o routines
    import nipype.interfaces.matlab as mlab      # how to run matlab
    import nipype.interfaces.spm as spm          # spm
    import nipype.interfaces.utility as util     # utility
    import nipype.pipeline.engine as pe          # pypeline engine

### Preliminaries

Set any package specific configuration.

Setting the subjects directory and the appropriate matlab command to use. if you want to use a different spm version/path, it should also be entered here.

These are currently being set at the class level, so every node will inherit these settings. However, these can also be changed or set for an individual node.

    # Tell freesurfer what subjects directory to use
    subjects_dir = os.path.abspath('fsdata')
    fs.FSCommand.set_default_subjects_dir(subjects_dir)

    # Set the way matlab should be called
    mlab.MatlabCommand.set_default_matlab_cmd("matlab -nodesktop -nosplash")
    # If SPM is not in your MATLAB path you should add it here
    mlab.MatlabCommand.set_default_paths('/software/spm8')

### Setup preprocessing workflow

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

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

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.freesurfer.BBRegister` to coregister the mean functional image generated by realign to the subjects’ surfaces.

    surfregister = pe.Node(interface=fs.BBRegister(),name='surfregister')
    surfregister.inputs.init = 'fsl'
    surfregister.inputs.contrast_type = 't2'

Use `nipype.interfaces.io.FreeSurferSource` to retrieve various image files that are automatically generated by the recon-all process.

    FreeSurferSource = pe.Node(interface=nio.FreeSurferSource(), name='fssource')

Use `nipype.interfaces.freesurfer.ApplyVolTransform` to convert the brainmask generated by freesurfer into the realigned functional space.

    ApplyVolTransform = pe.Node(interface=fs.ApplyVolTransform(),
                                name='applyreg')
    ApplyVolTransform.inputs.inverse = True

Use `nipype.interfaces.freesurfer.Binarize` to extract a binary brain mask.

    Threshold = pe.Node(interface=fs.Binarize(),name='threshold')
    Threshold.inputs.min = 10
    Threshold.inputs.out_type = 'nii'

Two different types of functional data smoothing are performed in this workflow. The volume smoothing option performs a standard SPM smoothin. using `nipype.interfaces.spm.Smooth`. In addition, we use a smoothing routine from freesurfer (`nipype.interfaces.freesurfer.Binarize`) to project the functional data from the volume to the subjects’ surface, smooth it on the surface and fit it back into the volume forming the cortical ribbon. The projection uses the average value along a “cortical column”. In addition to the surface smoothing, the rest of the volume is smoothed with a 3d gaussian kernel.

Note

It is very important to note that the projection to the surface takes a 3d manifold to a 2d manifold. Hence the reverse projection, simply fills the thickness of cortex with the smoothed data. The smoothing is not performed in a depth specific manner. The output of this branch should only be used for surface-based analysis and visualization.

    volsmooth = pe.Node(interface=spm.Smooth(), name = "volsmooth")
    surfsmooth = pe.MapNode(interface=fs.Smooth(proj_frac_avg=(0,1,0.1)), name = "surfsmooth",
                            iterfield=['in_file'])

We connect up the different nodes to implement the preprocessing workflow.

    preproc.connect([(realign, surfregister,[('mean_image', 'source_file')]),
                     (FreeSurferSource, ApplyVolTransform,[('brainmask','target_file')]),
                     (surfregister, ApplyVolTransform,[('out_reg_file','reg_file')]),
                     (realign, ApplyVolTransform,[('mean_image', 'source_file')]),
                     (ApplyVolTransform, Threshold,[('transformed_file','in_file')]),
                     (realign, art,[('realignment_parameters','realignment_parameters'),
                                   ('realigned_files','realigned_files')]),
                     (Threshold, art, [('binary_file', 'mask_file')]),
                     (realign, volsmooth, [('realigned_files', 'in_files')]),
                     (realign, surfsmooth, [('realigned_files', 'in_file')]),
                     (surfregister, surfsmooth, [('out_reg_file','reg_file')]),
                     ])

### Set up volume analysis workflow

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

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

    modelspec = pe.Node(interface=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(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}

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")

    volanalysis.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')]),
                      ])

### Set up surface analysis workflow

We simply clone the volume analysis workflow.

    surfanalysis = volanalysis.clone(name='surfanalysis')

### Set up volume normalization workflow

The volume analysis is performed in individual space. Therefore, post analysis we normalize the contrast images to MNI space.

    volnorm = pe.Workflow(name='volnormconimages')

Use `nipype.interfaces.freesurfer.MRIConvert` to convert the brainmask, an mgz file and the contrast images (nifti-1 img/hdr pairs), to single volume nifti images.

    convert = pe.Node(interface=fs.MRIConvert(out_type='nii'),name='convert2nii')
    convert2 = pe.MapNode(interface=fs.MRIConvert(out_type='nii'),
                          iterfield=['in_file'],
                          name='convertimg2nii')

Use `nipype.interfaces.spm.Segment` to segment the structural image and generate the transformation file to MNI space.

Note

Segment takes longer than usual because the nose is wrapped behind the head in the structural image.

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

Use `nipype.interfaces.freesurfer.ApplyVolTransform` to convert contrast images into freesurfer space.

    normwreg = pe.MapNode(interface=fs.ApplyVolTransform(),
                          iterfield=['source_file'],
                          name='applyreg2con')

Use `nipype.interfaces.spm.Normalize` to normalize the contrast images to MNI space

    normalize = pe.Node(interface=spm.Normalize(jobtype='write'),
                        name='norm2mni')

Connect up the volume normalization components

    volnorm.connect([(convert, segment, [('out_file','data')]),
                     (convert2, normwreg, [('out_file', 'source_file')]),
                     (segment, normalize, [('transformation_mat', 'parameter_file')]),
                     (normwreg, normalize, [('transformed_file','apply_to_files')]),
                     ])

### Preproc + Analysis + VolumeNormalization workflow

Connect up the lower level workflows into an integrated analysis. In addition, we add an input node that specifies all the inputs needed for this workflow. Thus, one can import this workflow and connect it to their own data sources. An example with the nifti-tutorial data is provided below.

For this workflow the only necessary inputs are the functional images, a freesurfer subject id corresponding to recon-all processed data, the session information for the functional runs and the contrasts to be evaluated.

    inputnode = pe.Node(interface=util.IdentityInterface(fields=['func',
                                                                 'subject_id',
                                                                 'session_info',
                                                                 'contrasts']),
                        name='inputnode')

Connect the components into an integrated workflow.

    l1pipeline = pe.Workflow(name='firstlevel')
    l1pipeline.connect([(inputnode,preproc,[('func','realign.in_files'),
                                            ('subject_id','surfregister.subject_id'),
                                            ('subject_id','fssource.subject_id'),
                                            ]),
                        (inputnode, volanalysis,[('session_info','modelspec.subject_info'),
                                                 ('contrasts','contrastestimate.contrasts')]),
                        (inputnode, surfanalysis,[('session_info','modelspec.subject_info'),
                                                  ('contrasts','contrastestimate.contrasts')]),
                        ])

    # attach volume and surface model specification and estimation components
    l1pipeline.connect([(preproc, volanalysis, [('realign.realignment_parameters',
                                                'modelspec.realignment_parameters'),
                                               ('volsmooth.smoothed_files',
                                                'modelspec.functional_runs'),
                                               ('art.outlier_files',
                                                'modelspec.outlier_files'),
                                               ('threshold.binary_file',
                                                'level1design.mask_image')]),
                        (preproc, surfanalysis, [('realign.realignment_parameters',
                                                  'modelspec.realignment_parameters'),
                                                 ('surfsmooth.smoothed_file',
                                                  'modelspec.functional_runs'),
                                                 ('art.outlier_files',
                                                  'modelspec.outlier_files'),
                                                 ('threshold.binary_file',
                                                  'level1design.mask_image')])
                        ])

    # attach volume contrast normalization components
    l1pipeline.connect([(preproc, volnorm, [('fssource.orig','convert2nii.in_file'),
                                            ('surfregister.out_reg_file','applyreg2con.reg_file'),
                                            ('fssource.orig','applyreg2con.target_file')]),
                        (volanalysis, volnorm, [('contrastestimate.con_images',
                                                 'convertimg2nii.in_file'),
                                                ])
                      ])

### 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(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.nii'
    datasource.inputs.template_args = info
    datasource.inputs.sort_filelist = True

### Set preprocessing parameters

    l1pipeline.inputs.preproc.fssource.subjects_dir = subjects_dir
    l1pipeline.inputs.preproc.volsmooth.fwhm = 4
    l1pipeline.inputs.preproc.surfsmooth.surface_fwhm = 5
    l1pipeline.inputs.preproc.surfsmooth.vol_fwhm = 4

### 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 = [range(15,240,60),range(45,240,60)]
            output.insert(r,
                          Bunch(conditions=names,
                                onsets=deepcopy(onsets),
                                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

We replicate the modelspec parameters separately for the surface- and volume-based analysis.

    modelspecref = l1pipeline.inputs.volanalysis.modelspec
    modelspecref.input_units             = 'secs'
    modelspecref.time_repetition         = 3.
    modelspecref.high_pass_filter_cutoff = 120

    modelspecref = l1pipeline.inputs.surfanalysis.modelspec
    modelspecref.input_units             = 'secs'
    modelspecref.time_repetition         = 3.
    modelspecref.high_pass_filter_cutoff = 120

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

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

    l1pipeline.inputs.inputnode.contrasts = 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.Workfow` to create a graph-based execution pipeline for first level analysis.

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

    level1.connect([(infosource, datasource, [('subject_id', 'subject_id')]),
                    (datasource,l1pipeline,[('func','inputnode.func')]),
                    (infosource,l1pipeline,[('subject_id','inputnode.subject_id'),
                                           (('subject_id', subjectinfo),
                                            'inputnode.session_info')]),
                    ])

### Store the output

Create a datasink node to store the contrast images and registration info

    datasink = pe.Node(interface=nio.DataSink(), name="datasink")
    datasink.inputs.base_directory = os.path.abspath('volsurf_tutorial/l1out')
    datasink.inputs.substitutions = []

    def getsubs(subject_id):
        subs = [('_subject_id_%s/'%subject_id,'')]
        return subs

    # store relevant outputs from various stages of the 1st level analysis
    level1.connect([(infosource, datasink,[('subject_id','container'),
                                           (('subject_id', getsubs), 'substitutions')
                                           ]),
                    (l1pipeline, datasink,[('surfanalysis.contrastestimate.con_images','contrasts'),
                                           ('preproc.surfregister.out_reg_file','registrations'),
                                           ])
                    ])

Run the analysis pipeline and also create a dot+png (if graphviz is available) that visually represents the workflow.

    if __name__ == '__main__':
        level1.run()
        level1.write_graph(graph2use='flat')

### Level2 surface-based pipeline

Create a level2 workflow

    l2flow = pe.Workflow(name='l2out')
    l2flow.base_dir = os.path.abspath('volsurf_tutorial')

Setup a dummy node to iterate over contrasts and hemispheres

    l2inputnode = pe.Node(interface=util.IdentityInterface(fields=['contrasts',
                                                                   'hemi']),
                          name='inputnode')
    l2inputnode.iterables = [('contrasts', range(1,len(contrasts)+1)),
                             ('hemi', ['lh','rh'])]

Use a datagrabber node to collect contrast images and registration files

    l2source = pe.Node(interface=nio.DataGrabber(infields=['con_id'],
                                                 outfields=['con','reg']),
                       name='l2source')
    l2source.inputs.base_directory = os.path.abspath('volsurf_tutorial/l1out')
    l2source.inputs.template = '*'
    l2source.inputs.field_template = dict(con='*/contrasts/con_%04d.img',
                                          reg='*/registrations/*.dat')
    l2source.inputs.template_args = dict(con=[['con_id']],reg=[[]])
    l2source.inputs.sort_filelist = True

    l2flow.connect(l2inputnode, 'contrasts', l2source, 'con_id')

Merge contrast images and registration files

    mergenode = pe.Node(interface=util.Merge(2, axis='hstack'),
                        name='merge')

    def ordersubjects(files, subj_list):
        outlist = []
        for s in subj_list:
            for f in files:
                if '/%s/'%s in f:
                    outlist.append(f)
                    continue
        print outlist
        return outlist

    l2flow.connect(l2source,('con', ordersubjects, subject_list), mergenode, 'in1')
    l2flow.connect(l2source,('reg', ordersubjects, subject_list), mergenode, 'in2')

Concatenate contrast images projected to fsaverage

    l2concat = pe.Node(interface=fs.MRISPreproc(), name='concat')
    l2concat.inputs.target = 'fsaverage'
    l2concat.inputs.fwhm = 5

    def list2tuple(listoflist):
        return [tuple(x) for x in listoflist]
    l2flow.connect(l2inputnode, 'hemi', l2concat, 'hemi')
    l2flow.connect(mergenode, ('out', list2tuple), l2concat, 'vol_measure_file')

Perform a one sample t-test

    l2ttest = pe.Node(interface=fs.OneSampleTTest(), name='onesample')
    l2flow.connect(l2concat, 'out_file', l2ttest, 'in_file')

Run the analysis pipeline and also create a dot+png (if graphviz is available) that visually represents the workflow.

    if __name__ == '__main__':
        l2flow.run()
        l2flow.write_graph(graph2use='flat')

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.