#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: """ =================== fMRI: NiPy GLM, SPM =================== The fmri_nipy_glm.py 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: python fmri_nipy_glm.py """ from nipype.interfaces.nipy.model import FitGLM, EstimateContrast from nipype.interfaces.nipy.preprocess import ComputeMask """Import necessary modules from nipype.""" import nipype.interfaces.io 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 """ Preliminaries ------------- 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 'f3.nii'. """ # 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 :class:`nipype.interfaces.io.DataSource` object and fill in the information from above about the layout of our data. The :class:`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 """Use :class:`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 :class:`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 :class:`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 :class:`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 :class:`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], amplitudes=None, tmod=None, pmod=None, regressor_names=None, regressors=None)) 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 :class:`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 :class:`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') l1pipeline.connect([(infosource, datasource, [('subject_id', 'subject_id')]), (datasource,realign,[('func','in_files')]), (realign, compute_mask, [('mean_image','mean_volume')]), (realign, coregister,[('mean_image', 'source'), ('realigned_files','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", "reg_names")]) ]) if __name__ == '__main__': l1pipeline.run()