interfaces.fsl.aroma

ICA_AROMA

Link to code

Wraps command ICA_AROMA.py

Interface for the ICA_AROMA.py script.

ICA-AROMA (i.e. ‘ICA-based Automatic Removal Of Motion Artifacts’) concerns a data-driven method to identify and remove motion-related independent components from fMRI data. To that end it exploits a small, but robust set of theoretically motivated features, preventing the need for classifier re-training and therefore providing direct and easy applicability.

See link for further documentation: https://github.com/rhr-pruim/ICA-AROMA

Example

>>> from nipype.interfaces.fsl import ICA_AROMA
>>> from nipype.testing import example_data
>>> AROMA_obj = ICA_AROMA()
>>> AROMA_obj.inputs.in_file = 'functional.nii'
>>> AROMA_obj.inputs.mat_file = 'func_to_struct.mat'
>>> AROMA_obj.inputs.fnirt_warp_file = 'warpfield.nii'
>>> AROMA_obj.inputs.motion_parameters = 'fsl_mcflirt_movpar.txt'
>>> AROMA_obj.inputs.mask = 'mask.nii.gz'
>>> AROMA_obj.inputs.denoise_type = 'both'
>>> AROMA_obj.inputs.out_dir = 'ICA_testout'
>>> AROMA_obj.cmdline  # doctest: +ELLIPSIS
'ICA_AROMA.py -den both -warp warpfield.nii -i functional.nii -m mask.nii.gz -affmat func_to_struct.mat -mc fsl_mcflirt_movpar.txt -o .../ICA_testout'

Inputs:

[Mandatory]
denoise_type: ('nonaggr' or 'aggr' or 'both' or 'no', nipype default
         value: nonaggr)
        Type of denoising strategy:
        -no: only classification, no denoising
        -nonaggr (default): non-aggresssive denoising, i.e. partial
        component regression
        -aggr: aggressive denoising, i.e. full component regression
        -both: both aggressive and non-aggressive denoising (two outputs)
        flag: -den %s
feat_dir: (an existing directory name)
        If a feat directory exists and temporal filtering has not been run
        yet, ICA_AROMA can use the files in this directory.
        flag: -feat %s
        mutually_exclusive: in_file, mat_file, fnirt_warp_file,
         motion_parameters
in_file: (an existing file name)
        volume to be denoised
        flag: -i %s
        mutually_exclusive: feat_dir
motion_parameters: (an existing file name)
        motion parameters file
        flag: -mc %s
        mutually_exclusive: feat_dir
out_dir: (a directory name, nipype default value: out)
        output directory
        flag: -o %s

[Optional]
TR: (a float)
        TR in seconds. If this is not specified the TR will be extracted
        from the header of the fMRI nifti file.
        flag: -tr %.3f
args: (a unicode string)
        Additional parameters to the command
        flag: %s
dim: (an integer (int or long))
        Dimensionality reduction when running MELODIC (defualt is automatic
        estimation)
        flag: -dim %d
environ: (a 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', nipype default value: {})
        Environment variables
fnirt_warp_file: (an existing file name)
        File name of the warp-file describing the non-linear registration
        (e.g. FSL FNIRT) of the structural data to MNI152 space (.nii.gz)
        flag: -warp %s
        mutually_exclusive: feat_dir
mask: (an existing file name)
        path/name volume mask
        flag: -m %s
        mutually_exclusive: feat_dir
mat_file: (an existing file name)
        path/name of the mat-file describing the affine registration (e.g.
        FSL FLIRT) of the functional data to structural space (.mat file)
        flag: -affmat %s
        mutually_exclusive: feat_dir
melodic_dir: (an existing directory name)
        path to MELODIC directory if MELODIC has already been run
        flag: -meldir %s

Outputs:

aggr_denoised_file: (an existing file name)
        if generated: aggressively denoised volume
nonaggr_denoised_file: (an existing file name)
        if generated: non aggressively denoised volume
out_dir: (an existing directory name)
        directory contains (in addition to the denoised files): melodic.ica
        + classified_motion_components + classification_overview +
        feature_scores + melodic_ic_mni)