nipype.interfaces.fsl.fix module

The fix module provides classes for interfacing with the FSL FIX <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIX/index.html> command line tools.

This was written to work with FSL version v5.0

The following example assumes that melodic has already been run, so the datagrabber is configured to start from there (a list of melodic output directories). If no hand_labels_noise.txt exists already, this will fail and comment on that.

EXAMPLE: subject_list = [‘1’, ‘2’, ‘3’]

fix_pipeline = pe.Workflow(name=’fix_pipeline’) fix_pipeline.base_dir = os.path.abspath(‘./’)

info = dict(mel_ica=[[‘subject_id’]])

datasource = pe.Node(interface=nio.DataGrabber(infields=[‘subject_id’], outfields=[‘mel_ica’]), name=’datasource’) datasource.inputs.base_directory = os.path.abspath(‘<path_to_base_directory>’) datasource.inputs.template = ‘%s/<path_to_melodic_ica>’ datasource.inputs.template_args = info datasource.inputs.subject_id = subject_list datasource.inputs.sort_filelist = True datasource.iterables = (‘subject_id’, subject_list)

# create training set by looking into which mel_icas have hand_labels_noise.txt files in them create_training_set = pe.JoinNode(interface=fix.TrainingSetCreator(), joinfield=[‘mel_icas_in’], joinsource=’datasource’, name=’trainingset_creator’)

# train the classifier train_node = pe.Node(interface=fix.Training(trained_wts_filestem=’foo’), name=’train_node’)

# test accuracy. Probably not necessary, and also failing on my setup because of fix itself (no error msg) accuracy_tester = pe.Node(interface=fix.AccuracyTester(output_directory=’more_foo’), name=’accuracy_tester’)

# classify components classify_node = pe.Node(interface=fix.Classifier(), name=’classify’)

# apply cleanup cleaner_node = pe.Node(interface=fix.Cleaner(), name=’cleaner’)

fix_pipeline.connect(datasource, ‘mel_ica’, create_training_set, ‘mel_icas_in’) fix_pipeline.connect(create_training_set, ‘mel_icas_out’, train_node, ‘mel_icas’) fix_pipeline.connect(train_node, ‘trained_wts_file’, accuracy_tester, ‘trained_wts_file’) fix_pipeline.connect(datasource, ‘mel_ica’, accuracy_tester, ‘mel_icas’) fix_pipeline.connect(train_node, ‘trained_wts_file’, classify_node, ‘trained_wts_file’) fix_pipeline.connect(datasource, ‘mel_ica’, classify_node, ‘mel_ica’) fix_pipeline.connect(classify_node, ‘artifacts_list_file’, cleaner_node, ‘artifacts_list_file’)

fix_pipeline.write_graph() outgraph = fix_pipeline.run()

class nipype.interfaces.fsl.fix.Classifier(command=None, terminal_output=None, write_cmdline=False, **inputs)

Bases: CommandLine

Classify ICA components using a specific training dataset (<thresh> is in the range 0-100, typically 5-20).

Mandatory Inputs

threshan integer

Threshold for cleanup. Maps to a command-line argument: %d (position: -1).

trained_wts_filea pathlike object or string representing an existing file

Trained-weights file. Maps to a command-line argument: %s (position: 2).

Optional Inputs

argsa string

Additional parameters to the command. Maps to a command-line argument: %s.

artifacts_list_filea pathlike object or string representing a file

Text file listing which ICs are artifacts; can be the output from classification or can be created manually.

environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

Environment variables. (Nipype default value: {})

mel_icaa pathlike object or string representing an existing directory

Melodic output directory or directories. Maps to a command-line argument: %s (position: 1).

Outputs

artifacts_list_filea pathlike object or string representing a file

Text file listing which ICs are artifacts; can be the output from classification or can be created manually.

cmd = 'fix -c'

class nipype.interfaces.fsl.fix.Cleaner(command=None, terminal_output=None, write_cmdline=False, **inputs)

Bases: CommandLine

Extract features (for later training and/or classifying)

Mandatory Inputs

artifacts_list_filea pathlike object or string representing an existing file

Text file listing which ICs are artifacts; can be the output from classification or can be created manually. Maps to a command-line argument: %s (position: 1).

Optional Inputs

aggressivea boolean

Apply aggressive (full variance) cleanup, instead of the default less-aggressive (unique variance) cleanup. Maps to a command-line argument: -A (position: 3).

argsa string

Additional parameters to the command. Maps to a command-line argument: %s.

cleanup_motiona boolean

Cleanup motion confounds, looks for design.fsf for highpass filter cut-off. Maps to a command-line argument: -m (position: 2).

confound_filea pathlike object or string representing a file

Include additional confound file. Maps to a command-line argument: -x %s (position: 4).

confound_file_1a pathlike object or string representing a file

Include additional confound file. Maps to a command-line argument: -x %s (position: 5).

confound_file_2a pathlike object or string representing a file

Include additional confound file. Maps to a command-line argument: -x %s (position: 6).

environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

Environment variables. (Nipype default value: {})

highpassa float

Cleanup motion confounds. Maps to a command-line argument: -m -h %f (position: 2). (Nipype default value: 100)

Outputs

cleaned_functional_filea pathlike object or string representing an existing file

Cleaned session data.

cmd = 'fix -a'

class nipype.interfaces.fsl.fix.FeatureExtractor(command=None, terminal_output=None, write_cmdline=False, **inputs)

Bases: CommandLine

Extract features (for later training and/or classifying)

Optional Inputs

argsa string

Additional parameters to the command. Maps to a command-line argument: %s.

environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

Environment variables. (Nipype default value: {})

mel_icaa pathlike object or string representing an existing directory

Melodic output directory or directories. Maps to a command-line argument: %s (position: -1).

Outputs

mel_icaa pathlike object or string representing an existing directory

Melodic output directory or directories. Maps to a command-line argument: %s (position: -1).

cmd = 'fix -f'

class nipype.interfaces.fsl.fix.Training(command=None, terminal_output=None, write_cmdline=False, **inputs)

Bases: CommandLine

Train the classifier based on your own FEAT/MELODIC output directory.

Optional Inputs

argsa string

Additional parameters to the command. Maps to a command-line argument: %s.

environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

Environment variables. (Nipype default value: {})

looa boolean

Full leave-one-out test with classifier training. Maps to a command-line argument: -l (position: 2).

mel_icasa list of items which are a pathlike object or string representing an existing directory

Melodic output directories. Maps to a command-line argument: %s (position: -1).

trained_wts_filestema string

Trained-weights filestem, used for trained_wts_file and output directories. Maps to a command-line argument: %s (position: 1).

Outputs

trained_wts_filea pathlike object or string representing an existing file

Trained-weights file.

cmd = 'fix -t'

class nipype.interfaces.fsl.fix.TrainingSetCreator(from_file=None, resource_monitor=None, ignore_exception=False, **inputs)

Bases: BaseInterface

Goes through set of provided melodic output directories, to find all the ones that have a hand_labels_noise.txt file in them.

This is outsourced as a separate class, so that the pipeline is rerun every time a handlabeled file has been changed, or a new one created.

Optional Inputs

mel_icas_ina list of items which are a pathlike object or string representing an existing directory

Melodic output directories. Maps to a command-line argument: %s (position: -1).

Outputs

mel_icas_outa list of items which are a pathlike object or string representing an existing directory

Hand labels for noise vs signal. Maps to a command-line argument: %s (position: -1).