#!/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: DARTEL, SPM ================= The fmri_spm_dartel.py integrates several interfaces to perform a first and second level analysis on a two-subject data set. The tutorial can be found in the examples folder. Run the tutorial from inside the nipype tutorial directory:: python fmri_spm_dartel.py Import necessary modules from nipype.""" from __future__ import print_function from builtins import range import nipype.interfaces.io as nio # Data i/o import nipype.interfaces.spm as spm # spm import nipype.workflows.fmri.spm as spm_wf # spm import nipype.interfaces.fsl as fsl # fsl 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. """ # Tell fsl to generate all output in uncompressed nifti format fsl.FSLCommand.set_default_output_type('NIFTI') # Set the way matlab should be called #mlab.MatlabCommand.set_default_matlab_cmd("matlab -nodesktop -nosplash") #mlab.MatlabCommand.set_default_paths('/software/spm8') """ 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') """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 """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' """Skull strip structural images using :class:`nipype.interfaces.fsl.BET`. """ skullstrip = pe.Node(interface=fsl.BET(), name="skullstrip") skullstrip.inputs.mask = True """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' """Normalize and smooth functional data using DARTEL template """ normalize_and_smooth_func = pe.Node(spm.DARTELNorm2MNI(modulate=True), name='normalize_and_smooth_func') fwhmlist = [4] normalize_and_smooth_func.iterables = ('fwhm',fwhmlist) """Normalize structural data using DARTEL template """ normalize_struct = pe.Node(spm.DARTELNorm2MNI(modulate=True), name='normalize_struct') normalize_struct.inputs.fwhm = 2 preproc.connect([(realign,coregister,[('mean_image', 'source'), ('realigned_files','apply_to_files')]), (coregister, normalize_and_smooth_func, [('coregistered_files','apply_to_files')]), (normalize_struct,skullstrip,[('normalized_files','in_file')]), (realign,art,[('realignment_parameters','realignment_parameters')]), (normalize_and_smooth_func,art,[('normalized_files','realigned_files')]), (skullstrip,art,[('mask_file','mask_file')]), ]) """ Set up analysis workflow ------------------------ """ l1analysis = pe.Workflow(name='analysis') """Generate SPM-specific design information using :class:`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 :class:`nipype.interfaces.spm.Level1Design`. """ level1design = pe.Node(interface=spm.Level1Design(), name= "level1design") level1design.inputs.bases = {'hrf':{'derivs': [0,0]}} """Use :class:`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 :class:`nipype.interfaces.spm.EstimateContrast` to estimate the first level contrasts specified in a few steps above. """ contrastestimate = pe.Node(interface = spm.EstimateContrast(), name="contrastestimate") """Use :class: `nipype.interfaces.utility.Select` to select each contrast for reporting. """ selectcontrast = pe.Node(interface=util.Select(), name="selectcontrast") """Use :class:`nipype.interfaces.fsl.Overlay` to combine the statistical output of the contrast estimate and a background image into one volume. """ overlaystats = pe.Node(interface=fsl.Overlay(), name="overlaystats") overlaystats.inputs.stat_thresh = (3,10) overlaystats.inputs.show_negative_stats = True overlaystats.inputs.auto_thresh_bg = True """Use :class:`nipype.interfaces.fsl.Slicer` to create images of the overlaid statistical volumes for a report of the first-level results. """ slicestats = pe.Node(interface=fsl.Slicer(), name="slicestats") slicestats.inputs.all_axial = True slicestats.inputs.image_width = 750 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,selectcontrast,[('spmT_images','inlist')]), (selectcontrast,overlaystats,[('out','stat_image')]), (overlaystats,slicestats,[('out_file','in_file')]) ]) """ Preproc + Analysis pipeline --------------------------- """ l1pipeline = pe.Workflow(name='firstlevel') l1pipeline.connect([(preproc, l1analysis, [('realign.realignment_parameters', 'modelspec.realignment_parameters'), ('normalize_and_smooth_func.normalized_files', 'modelspec.functional_runs'), ('art.outlier_files', 'modelspec.outlier_files'), ('skullstrip.mask_file', 'level1design.mask_image'), ('normalize_struct.normalized_files', 'overlaystats.background_image')]), ]) """ 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 :class:`nipype.interfaces.io.DataGrabber` 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 datasource.inputs.sort_filelist = True """We need to create a separate workflow to make the DARTEL template """ datasource_dartel = pe.MapNode(interface=nio.DataGrabber(infields=['subject_id'], outfields=['struct']), name = 'datasource_dartel', iterfield = ['subject_id']) datasource_dartel.inputs.base_directory = data_dir datasource_dartel.inputs.template = '%s/%s.nii' datasource_dartel.inputs.template_args = dict(struct=[['subject_id','struct']]) datasource_dartel.inputs.sort_filelist = True datasource_dartel.inputs.subject_id = subject_list """Here we make sure that struct files have names corresponding to the subject ids. This way we will be able to pick the right field flows later. """ rename_dartel = pe.MapNode(util.Rename(format_string="subject_id_%(subject_id)s_struct"), iterfield=['in_file', 'subject_id'], name = 'rename_dartel') rename_dartel.inputs.subject_id = subject_list rename_dartel.inputs.keep_ext = True dartel_workflow = spm_wf.create_DARTEL_template(name='dartel_workflow') dartel_workflow.inputs.inputspec.template_prefix = "template" """This function will allow to pick the right field flow for each subject """ def pickFieldFlow(dartel_flow_fields, subject_id): from nipype.utils.filemanip import split_filename for f in dartel_flow_fields: _, name, _ = split_filename(f) if name.find("subject_id_%s" %subject_id): return f raise Exception pick_flow = pe.Node(util.Function(input_names=['dartel_flow_fields', 'subject_id'], output_names=['dartel_flow_field'], function = pickFieldFlow), name = "pick_flow") """ Experimental paradigm specific 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 = [list(range(15,240,60)),list(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] # set up node specific inputs modelspecref = l1pipeline.inputs.analysis.modelspec modelspecref.input_units = 'secs' modelspecref.output_units = 'secs' modelspecref.time_repetition = 3. 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.analysis.contrastestimate.contrasts = contrasts # Iterate over each contrast and create report images. selectcontrast.iterables = ('index',[[i] for i in range(len(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 :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. """ level1 = pe.Workflow(name="level1") level1.base_dir = os.path.abspath('spm_dartel_tutorial/workingdir') level1.connect([(datasource_dartel, rename_dartel, [('struct', 'in_file')]), (rename_dartel, dartel_workflow, [('out_file','inputspec.structural_files')]), (infosource, datasource, [('subject_id', 'subject_id')]), (datasource,l1pipeline,[('func','preproc.realign.in_files'), ('struct', 'preproc.coregister.target'), ('struct', 'preproc.normalize_struct.apply_to_files')]), (dartel_workflow, l1pipeline, [('outputspec.template_file', 'preproc.normalize_struct.template_file'), ('outputspec.template_file', 'preproc.normalize_and_smooth_func.template_file')]), (infosource, pick_flow, [('subject_id', 'subject_id')]), (dartel_workflow, pick_flow, [('outputspec.flow_fields', 'dartel_flow_fields')]), (pick_flow, l1pipeline, [('dartel_flow_field', 'preproc.normalize_struct.flowfield_files'), ('dartel_flow_field', 'preproc.normalize_and_smooth_func.flowfield_files')]), (infosource,l1pipeline,[(('subject_id', subjectinfo), 'analysis.modelspec.subject_info')]), ]) """ Setup storage results --------------------- Use :class:`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_dartel_tutorial/l1output') report = pe.Node(interface=nio.DataSink(), name='report') report.inputs.base_directory = os.path.abspath('spm_dartel_tutorial/report') report.inputs.parameterization = False def getstripdir(subject_id): import os return os.path.join(os.path.abspath('spm_dartel_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')]), (infosource, report,[('subject_id', 'container'), (('subject_id', getstripdir),'strip_dir')]), (l1pipeline, report,[('analysis.slicestats.out_file', '@report')]), ]) """ 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(plugin_args={'n_procs': 4}) level1.write_graph() """ Setup level 2 pipeline ---------------------- Use :class:`nipype.interfaces.io.DataGrabber` to extract the contrast images across a group of first level subjects. Unlike the previous pipeline that iterated over subjects, this pipeline will iterate over contrasts. """ # collect all the con images for each contrast. contrast_ids = list(range(1,len(contrasts)+1)) l2source = pe.Node(nio.DataGrabber(infields=['fwhm', 'con']), name="l2source") # we use .*i* to capture both .img (SPM8) and .nii (SPM12) l2source.inputs.template = os.path.abspath('spm_dartel_tutorial/l1output/*/con*/*/_fwhm_%d/con_%04d.*i*') # iterate over all contrast images l2source.iterables = [('fwhm',fwhmlist), ('con',contrast_ids)] l2source.inputs.sort_filelist = True """Use :class:`nipype.interfaces.spm.OneSampleTTestDesign` to perform a simple statistical analysis of the contrasts from the group of subjects (n=2 in this example). """ # setup a 1-sample t-test node onesamplettestdes = pe.Node(interface=spm.OneSampleTTestDesign(), name="onesampttestdes") l2estimate = pe.Node(interface=spm.EstimateModel(), name="level2estimate") l2estimate.inputs.estimation_method = {'Classical': 1} l2conestimate = pe.Node(interface = spm.EstimateContrast(), name="level2conestimate") cont1 = ('Group','T', ['mean'],[1]) l2conestimate.inputs.contrasts = [cont1] l2conestimate.inputs.group_contrast = True """As before, we setup a pipeline to connect these two nodes (l2source -> onesamplettest). """ l2pipeline = pe.Workflow(name="level2") l2pipeline.base_dir = os.path.abspath('spm_dartel_tutorial/l2output') l2pipeline.connect([(l2source,onesamplettestdes,[('outfiles','in_files')]), (onesamplettestdes,l2estimate,[('spm_mat_file','spm_mat_file')]), (l2estimate,l2conestimate,[('spm_mat_file','spm_mat_file'), ('beta_images','beta_images'), ('residual_image','residual_image')]), ]) """ Execute the second level pipeline --------------------------------- """ if __name__ == '__main__': l2pipeline.run()