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nipype.workflows.fsl.preprocess

create_featreg_preproc()

Create a FEAT preprocessing workflow with registration to one volume of the first run

Parameters

name : name of workflow (default: featpreproc) highpass : boolean (default: True) whichvol : which volume of the first run to register to (‘first’, ‘middle’, ‘mean’)

Inputs:

inputspec.func : functional runs (filename or list of filenames)
inputspec.fwhm : fwhm for smoothing with SUSAN
inputspec.highpass : HWHM in TRs (if created with highpass=True)

Outputs:

outputspec.reference : volume to which runs are realigned
outputspec.motion_parameters : motion correction parameters
outputspec.realigned_files : motion corrected files
outputspec.motion_plots : plots of motion correction parameters
outputspec.mask : mask file used to mask the brain
outputspec.smoothed_files : smoothed functional data
outputspec.highpassed_files : highpassed functional data (if highpass=True)
outputspec.mean : mean file

Example

>>> from nipype.workflows.fsl import create_featreg_preproc
>>> import os
>>> preproc = create_featreg_preproc()
>>> preproc.inputs.inputspec.func = ['f3.nii', 'f5.nii']
>>> preproc.inputs.inputspec.fwhm = 5
>>> preproc.inputs.inputspec.highpass = 128./(2*2.5)
>>> preproc.base_dir = '/tmp'
>>> preproc.run() 
>>> preproc = create_featreg_preproc(highpass=False, whichvol='mean')
>>> preproc.inputs.inputspec.func = 'f3.nii'
>>> preproc.inputs.inputspec.fwhm = 5
>>> preproc.base_dir = '/tmp'
>>> preproc.run() 

Graph

digraph featpreproc{

  label="featpreproc";

  featpreproc_inputspec[label="inputspec.IdentityInterface.utility"];

  featpreproc_img2float[label="img2float.ImageMaths.fsl"];

  featpreproc_extractref[label="extractref.ExtractROI.fsl"];

  featpreproc_realign[label="realign.MCFLIRT.fsl"];

  featpreproc_plot_motion[label="plot_motion.PlotMotionParams.fsl", style=filled, colorscheme=greys7 color=2];

  featpreproc_meanfunc[label="meanfunc.ImageMaths.fsl"];

  featpreproc_meanfuncmask[label="meanfuncmask.BET.fsl"];

  featpreproc_maskfunc[label="maskfunc.ImageMaths.fsl"];

  featpreproc_getthreshold[label="getthreshold.ImageStats.fsl"];

  featpreproc_threshold[label="threshold.ImageMaths.fsl"];

  featpreproc_medianval[label="medianval.ImageStats.fsl"];

  featpreproc_dilatemask[label="dilatemask.ImageMaths.fsl"];

  featpreproc_maskfunc2[label="maskfunc2.ImageMaths.fsl"];

  featpreproc_maskfunc3[label="maskfunc3.ImageMaths.fsl"];

  featpreproc_concat[label="concat.Merge.utility"];

  featpreproc_select[label="select.Select.utility"];

  featpreproc_meanscale[label="meanscale.ImageMaths.fsl"];

  featpreproc_highpass[label="highpass.ImageMaths.fsl"];

  featpreproc_meanfunc3[label="meanfunc3.ImageMaths.fsl"];

  featpreproc_outputspec[label="outputspec.IdentityInterface.utility"];

  featpreproc_inputspec -> featpreproc_img2float;

  featpreproc_inputspec -> featpreproc_highpass;

  featpreproc_inputspec -> featpreproc_select;

  featpreproc_img2float -> featpreproc_extractref;

  featpreproc_img2float -> featpreproc_extractref;

  featpreproc_img2float -> featpreproc_realign;

  featpreproc_extractref -> featpreproc_outputspec;

  featpreproc_extractref -> featpreproc_realign;

  featpreproc_realign -> featpreproc_maskfunc2;

  featpreproc_realign -> featpreproc_maskfunc;

  featpreproc_realign -> featpreproc_meanfunc;

  featpreproc_realign -> featpreproc_medianval;

  featpreproc_realign -> featpreproc_plot_motion;

  featpreproc_realign -> featpreproc_outputspec;

  featpreproc_realign -> featpreproc_outputspec;

  featpreproc_plot_motion -> featpreproc_outputspec;

  featpreproc_meanfunc -> featpreproc_meanfuncmask;

  featpreproc_meanfuncmask -> featpreproc_maskfunc;

  featpreproc_maskfunc -> featpreproc_getthreshold;

  featpreproc_maskfunc -> featpreproc_threshold;

  featpreproc_getthreshold -> featpreproc_threshold;

  featpreproc_threshold -> featpreproc_medianval;

  featpreproc_threshold -> featpreproc_dilatemask;

  featpreproc_medianval -> featpreproc_meanscale;

  featpreproc_dilatemask -> featpreproc_maskfunc2;

  featpreproc_dilatemask -> featpreproc_maskfunc3;

  featpreproc_dilatemask -> featpreproc_outputspec;

  featpreproc_maskfunc2 -> featpreproc_concat;

  subgraph cluster_featpreproc_susan_smooth {

      label="susan_smooth";

    featpreproc_susan_smooth_inputnode[label="inputnode.IdentityInterface.utility"];

    featpreproc_susan_smooth_median[label="median.ImageStats.fsl"];

    featpreproc_susan_smooth_mask[label="mask.ImageMaths.fsl"];

    featpreproc_susan_smooth_meanfunc2[label="meanfunc2.ImageMaths.fsl"];

    featpreproc_susan_smooth_merge[label="merge.Merge.utility"];

    featpreproc_susan_smooth_smooth[label="smooth.SUSAN.fsl"];

    featpreproc_susan_smooth_outputnode[label="outputnode.IdentityInterface.utility"];

    featpreproc_susan_smooth_inputnode -> featpreproc_susan_smooth_median;

    featpreproc_susan_smooth_inputnode -> featpreproc_susan_smooth_median;

    featpreproc_susan_smooth_inputnode -> featpreproc_susan_smooth_smooth;

    featpreproc_susan_smooth_inputnode -> featpreproc_susan_smooth_smooth;

    featpreproc_susan_smooth_inputnode -> featpreproc_susan_smooth_mask;

    featpreproc_susan_smooth_inputnode -> featpreproc_susan_smooth_mask;

    featpreproc_susan_smooth_median -> featpreproc_susan_smooth_merge;

    featpreproc_susan_smooth_median -> featpreproc_susan_smooth_smooth;

    featpreproc_susan_smooth_mask -> featpreproc_susan_smooth_meanfunc2;

    featpreproc_susan_smooth_meanfunc2 -> featpreproc_susan_smooth_merge;

    featpreproc_susan_smooth_merge -> featpreproc_susan_smooth_smooth;

    featpreproc_susan_smooth_smooth -> featpreproc_susan_smooth_outputnode;

  }

  featpreproc_maskfunc3 -> featpreproc_concat;

  featpreproc_concat -> featpreproc_select;

  featpreproc_select -> featpreproc_meanscale;

  featpreproc_select -> featpreproc_outputspec;

  featpreproc_meanscale -> featpreproc_highpass;

  featpreproc_highpass -> featpreproc_outputspec;

  featpreproc_highpass -> featpreproc_meanfunc3;

  featpreproc_meanfunc3 -> featpreproc_outputspec;

  featpreproc_maskfunc2 -> featpreproc_susan_smooth_inputnode;

  featpreproc_susan_smooth_outputnode -> featpreproc_maskfunc3;

  featpreproc_dilatemask -> featpreproc_susan_smooth_inputnode;

  featpreproc_inputspec -> featpreproc_susan_smooth_inputnode;

}

create_fsl_fs_preproc()

Create a FEAT preprocessing workflow together with freesurfer

Parameters

name : name of workflow (default: preproc) highpass : boolean (default: True) whichvol : which volume of the first run to register to (‘first’, ‘middle’, ‘mean’)

Inputs:

inputspec.func : functional runs (filename or list of filenames)
inputspec.fwhm : fwhm for smoothing with SUSAN
inputspec.highpass : HWHM in TRs (if created with highpass=True)
inputspec.subject_id : freesurfer subject id
inputspec.subjects_dir : freesurfer subjects dir

Outputs:

outputspec.reference : volume to which runs are realigned
outputspec.motion_parameters : motion correction parameters
outputspec.realigned_files : motion corrected files
outputspec.motion_plots : plots of motion correction parameters
outputspec.mask_file : mask file used to mask the brain
outputspec.smoothed_files : smoothed functional data
outputspec.highpassed_files : highpassed functional data (if highpass=True)
outputspec.reg_file : bbregister registration files
outputspec.reg_cost : bbregister registration cost files

Example

>>> import os
>>> from nipype.workflows.fsl import create_fsl_fs_preproc
>>> preproc = create_fsl_fs_preproc(whichvol='first')
>>> preproc.inputs.inputspec.highpass = 128./(2*2.5)
>>> preproc.inputs.inputspec.func = ['f3.nii', 'f5.nii']
>>> preproc.inputs.inputspec.subjects_dir = '.'
>>> preproc.inputs.inputspec.subject_id = 's1'
>>> preproc.inputs.inputspec.fwhm = 6
>>> preproc.run() 

Graph

digraph preproc{

  label="preproc";

  preproc_inputspec[label="inputspec.IdentityInterface.utility"];

  preproc_img2float[label="img2float.ImageMaths.fsl"];

  preproc_extractref[label="extractref.ExtractROI.fsl"];

  preproc_realign[label="realign.MCFLIRT.fsl"];

  preproc_medianval[label="medianval.ImageStats.fsl"];

  preproc_plot_motion[label="plot_motion.PlotMotionParams.fsl", style=filled, colorscheme=greys7 color=2];

  preproc_maskfunc[label="maskfunc.ImageMaths.fsl"];

  preproc_maskfunc3[label="maskfunc3.ImageMaths.fsl"];

  preproc_concat[label="concat.Merge.utility"];

  preproc_select[label="select.Select.utility"];

  preproc_meanscale[label="meanscale.ImageMaths.fsl"];

  preproc_highpass[label="highpass.ImageMaths.fsl"];

  preproc_outputspec[label="outputspec.IdentityInterface.utility"];

  preproc_inputspec -> preproc_highpass;

  preproc_inputspec -> preproc_select;

  preproc_inputspec -> preproc_img2float;

  preproc_img2float -> preproc_extractref;

  preproc_img2float -> preproc_extractref;

  preproc_img2float -> preproc_realign;

  preproc_extractref -> preproc_outputspec;

  preproc_extractref -> preproc_realign;

  preproc_realign -> preproc_outputspec;

  preproc_realign -> preproc_outputspec;

  preproc_realign -> preproc_medianval;

  preproc_realign -> preproc_plot_motion;

  preproc_realign -> preproc_maskfunc;

  subgraph cluster_preproc_getmask {

      label="getmask";

    preproc_getmask_inputspec[label="inputspec.IdentityInterface.utility"];

    preproc_getmask_fssource[label="fssource.FreeSurferSource.io"];

    preproc_getmask_register[label="register.BBRegister.freesurfer"];

    preproc_getmask_threshold[label="threshold.Binarize.freesurfer"];

    preproc_getmask_transform[label="transform.ApplyVolTransform.freesurfer"];

    preproc_getmask_dilate[label="dilate.DilateImage.fsl"];

    preproc_getmask_threshold2[label="threshold2.Binarize.freesurfer"];

    preproc_getmask_outputspec[label="outputspec.IdentityInterface.utility"];

    preproc_getmask_inputspec -> preproc_getmask_fssource;

    preproc_getmask_inputspec -> preproc_getmask_fssource;

    preproc_getmask_inputspec -> preproc_getmask_register;

    preproc_getmask_inputspec -> preproc_getmask_register;

    preproc_getmask_inputspec -> preproc_getmask_register;

    preproc_getmask_inputspec -> preproc_getmask_register;

    preproc_getmask_inputspec -> preproc_getmask_transform;

    preproc_getmask_inputspec -> preproc_getmask_transform;

    preproc_getmask_fssource -> preproc_getmask_threshold;

    preproc_getmask_register -> preproc_getmask_transform;

    preproc_getmask_register -> preproc_getmask_outputspec;

    preproc_getmask_register -> preproc_getmask_outputspec;

    preproc_getmask_threshold -> preproc_getmask_transform;

    preproc_getmask_transform -> preproc_getmask_dilate;

    preproc_getmask_dilate -> preproc_getmask_threshold2;

    preproc_getmask_threshold2 -> preproc_getmask_outputspec;

  }

  preproc_medianval -> preproc_meanscale;

  preproc_plot_motion -> preproc_outputspec;

  preproc_maskfunc -> preproc_concat;

  subgraph cluster_preproc_susan_smooth {

      label="susan_smooth";

    preproc_susan_smooth_inputnode[label="inputnode.IdentityInterface.utility"];

    preproc_susan_smooth_median[label="median.ImageStats.fsl"];

    preproc_susan_smooth_mask[label="mask.ImageMaths.fsl"];

    preproc_susan_smooth_meanfunc2[label="meanfunc2.ImageMaths.fsl"];

    preproc_susan_smooth_merge[label="merge.Merge.utility"];

    preproc_susan_smooth_smooth[label="smooth.SUSAN.fsl"];

    preproc_susan_smooth_outputnode[label="outputnode.IdentityInterface.utility"];

    preproc_susan_smooth_inputnode -> preproc_susan_smooth_median;

    preproc_susan_smooth_inputnode -> preproc_susan_smooth_median;

    preproc_susan_smooth_inputnode -> preproc_susan_smooth_mask;

    preproc_susan_smooth_inputnode -> preproc_susan_smooth_mask;

    preproc_susan_smooth_inputnode -> preproc_susan_smooth_smooth;

    preproc_susan_smooth_inputnode -> preproc_susan_smooth_smooth;

    preproc_susan_smooth_median -> preproc_susan_smooth_smooth;

    preproc_susan_smooth_median -> preproc_susan_smooth_merge;

    preproc_susan_smooth_mask -> preproc_susan_smooth_meanfunc2;

    preproc_susan_smooth_meanfunc2 -> preproc_susan_smooth_merge;

    preproc_susan_smooth_merge -> preproc_susan_smooth_smooth;

    preproc_susan_smooth_smooth -> preproc_susan_smooth_outputnode;

  }

  preproc_maskfunc3 -> preproc_concat;

  preproc_concat -> preproc_select;

  preproc_select -> preproc_outputspec;

  preproc_select -> preproc_meanscale;

  preproc_meanscale -> preproc_highpass;

  preproc_highpass -> preproc_outputspec;

  preproc_maskfunc -> preproc_susan_smooth_inputnode;

  preproc_inputspec -> preproc_susan_smooth_inputnode;

  preproc_inputspec -> preproc_getmask_inputspec;

  preproc_inputspec -> preproc_getmask_inputspec;

  preproc_getmask_outputspec -> preproc_maskfunc;

  preproc_getmask_outputspec -> preproc_medianval;

  preproc_getmask_outputspec -> preproc_susan_smooth_inputnode;

  preproc_getmask_outputspec -> preproc_outputspec;

  preproc_getmask_outputspec -> preproc_outputspec;

  preproc_getmask_outputspec -> preproc_outputspec;

  preproc_getmask_outputspec -> preproc_maskfunc3;

  preproc_extractref -> preproc_getmask_inputspec;

  preproc_susan_smooth_outputnode -> preproc_maskfunc3;

}

create_parallelfeat_preproc()

Create a FEAT preprocessing workflow that preprocess each run independently of the others

Parameters

name : name of workflow (default: featpreproc) highpass : boolean (default: True)

Inputs:

inputspec.func : functional runs (filename or list of filenames)
inputspec.fwhm : fwhm for smoothing with SUSAN
inputspec.highpass : HWHM in TRs (if created with highpass=True)

Outputs:

outputspec.reference : volume to which runs are realigned
outputspec.motion_parameters : motion correction parameters
outputspec.realigned_files : motion corrected files
outputspec.motion_plots : plots of motion correction parameters
outputspec.mask : mask file used to mask the brain
outputspec.smoothed_files : smoothed functional data
outputspec.highpassed_files : highpassed functional data (if highpass=True)
outputspec.mean : mean file

Example

>>> from nipype.workflows.fsl import create_parallelfeat_preproc
>>> import os
>>> preproc = create_parallelfeat_preproc()
>>> preproc.inputs.inputspec.func = ['f3.nii', 'f5.nii']
>>> preproc.inputs.inputspec.fwhm = 5
>>> preproc.inputs.inputspec.highpass = 128./(2*2.5)
>>> preproc.base_dir = '/tmp'
>>> preproc.run() 
>>> preproc = create_parallelfeat_preproc(highpass=False)
>>> preproc.inputs.inputspec.func = 'f3.nii'
>>> preproc.inputs.inputspec.fwhm = 5
>>> preproc.base_dir = '/tmp'
>>> preproc.run() 

Graph

digraph featpreproc{

  label="featpreproc";

  featpreproc_inputspec[label="inputspec.IdentityInterface.utility"];

  featpreproc_img2float[label="img2float.ImageMaths.fsl"];

  featpreproc_extractref[label="extractref.ExtractROI.fsl"];

  featpreproc_realign[label="realign.MCFLIRT.fsl"];

  featpreproc_meanfunc[label="meanfunc.ImageMaths.fsl"];

  featpreproc_meanfuncmask[label="meanfuncmask.BET.fsl"];

  featpreproc_maskfunc[label="maskfunc.ImageMaths.fsl"];

  featpreproc_getthreshold[label="getthreshold.ImageStats.fsl"];

  featpreproc_threshold[label="threshold.ImageMaths.fsl"];

  featpreproc_dilatemask[label="dilatemask.ImageMaths.fsl"];

  featpreproc_plot_motion[label="plot_motion.PlotMotionParams.fsl", style=filled, colorscheme=greys7 color=2];

  featpreproc_medianval[label="medianval.ImageStats.fsl"];

  featpreproc_maskfunc2[label="maskfunc2.ImageMaths.fsl"];

  featpreproc_maskfunc3[label="maskfunc3.ImageMaths.fsl"];

  featpreproc_concat[label="concat.Merge.utility"];

  featpreproc_select[label="select.Select.utility"];

  featpreproc_meanscale[label="meanscale.ImageMaths.fsl"];

  featpreproc_highpass[label="highpass.ImageMaths.fsl"];

  featpreproc_meanfunc3[label="meanfunc3.ImageMaths.fsl"];

  featpreproc_outputspec[label="outputspec.IdentityInterface.utility"];

  featpreproc_inputspec -> featpreproc_highpass;

  featpreproc_inputspec -> featpreproc_img2float;

  featpreproc_inputspec -> featpreproc_select;

  featpreproc_img2float -> featpreproc_extractref;

  featpreproc_img2float -> featpreproc_extractref;

  featpreproc_img2float -> featpreproc_realign;

  featpreproc_extractref -> featpreproc_outputspec;

  featpreproc_extractref -> featpreproc_realign;

  featpreproc_realign -> featpreproc_meanfunc;

  featpreproc_realign -> featpreproc_maskfunc;

  featpreproc_realign -> featpreproc_plot_motion;

  featpreproc_realign -> featpreproc_outputspec;

  featpreproc_realign -> featpreproc_outputspec;

  featpreproc_realign -> featpreproc_medianval;

  featpreproc_realign -> featpreproc_maskfunc2;

  featpreproc_meanfunc -> featpreproc_meanfuncmask;

  featpreproc_meanfuncmask -> featpreproc_maskfunc;

  featpreproc_maskfunc -> featpreproc_getthreshold;

  featpreproc_maskfunc -> featpreproc_threshold;

  featpreproc_getthreshold -> featpreproc_threshold;

  featpreproc_threshold -> featpreproc_medianval;

  featpreproc_threshold -> featpreproc_dilatemask;

  featpreproc_dilatemask -> featpreproc_outputspec;

  featpreproc_dilatemask -> featpreproc_maskfunc3;

  featpreproc_dilatemask -> featpreproc_maskfunc2;

  featpreproc_plot_motion -> featpreproc_outputspec;

  featpreproc_medianval -> featpreproc_meanscale;

  featpreproc_maskfunc2 -> featpreproc_concat;

  subgraph cluster_featpreproc_susan_smooth {

      label="susan_smooth";

    featpreproc_susan_smooth_inputnode[label="inputnode.IdentityInterface.utility"];

    featpreproc_susan_smooth_mask[label="mask.ImageMaths.fsl"];

    featpreproc_susan_smooth_meanfunc2[label="meanfunc2.ImageMaths.fsl"];

    featpreproc_susan_smooth_median[label="median.ImageStats.fsl"];

    featpreproc_susan_smooth_merge[label="merge.Merge.utility"];

    featpreproc_susan_smooth_smooth[label="smooth.SUSAN.fsl"];

    featpreproc_susan_smooth_outputnode[label="outputnode.IdentityInterface.utility"];

    featpreproc_susan_smooth_inputnode -> featpreproc_susan_smooth_median;

    featpreproc_susan_smooth_inputnode -> featpreproc_susan_smooth_median;

    featpreproc_susan_smooth_inputnode -> featpreproc_susan_smooth_smooth;

    featpreproc_susan_smooth_inputnode -> featpreproc_susan_smooth_smooth;

    featpreproc_susan_smooth_inputnode -> featpreproc_susan_smooth_mask;

    featpreproc_susan_smooth_inputnode -> featpreproc_susan_smooth_mask;

    featpreproc_susan_smooth_mask -> featpreproc_susan_smooth_meanfunc2;

    featpreproc_susan_smooth_meanfunc2 -> featpreproc_susan_smooth_merge;

    featpreproc_susan_smooth_median -> featpreproc_susan_smooth_merge;

    featpreproc_susan_smooth_median -> featpreproc_susan_smooth_smooth;

    featpreproc_susan_smooth_merge -> featpreproc_susan_smooth_smooth;

    featpreproc_susan_smooth_smooth -> featpreproc_susan_smooth_outputnode;

  }

  featpreproc_maskfunc3 -> featpreproc_concat;

  featpreproc_concat -> featpreproc_select;

  featpreproc_select -> featpreproc_outputspec;

  featpreproc_select -> featpreproc_meanscale;

  featpreproc_meanscale -> featpreproc_highpass;

  featpreproc_highpass -> featpreproc_outputspec;

  featpreproc_highpass -> featpreproc_meanfunc3;

  featpreproc_meanfunc3 -> featpreproc_outputspec;

  featpreproc_dilatemask -> featpreproc_susan_smooth_inputnode;

  featpreproc_inputspec -> featpreproc_susan_smooth_inputnode;

  featpreproc_maskfunc2 -> featpreproc_susan_smooth_inputnode;

  featpreproc_susan_smooth_outputnode -> featpreproc_maskfunc3;

}

create_susan_smooth()

Create a SUSAN smoothing workflow

Parameters

name : name of workflow (default: susan_smooth) separate_masks : separate masks for each run

Inputs:

inputnode.in_files : functional runs (filename or list of filenames)
inputnode.fwhm : fwhm for smoothing with SUSAN
inputnode.mask_file : mask used for estimating SUSAN thresholds (but not for smoothing)

Outputs:

outputnode.smoothed_files : functional runs (filename or list of filenames)

Example

>>> from nipype.workflows.fsl import create_susan_smooth
>>> smooth = create_susan_smooth()
>>> smooth.inputs.inputnode.in_files = 'f3.nii'
>>> smooth.inputs.inputnode.fwhm = 5
>>> smooth.inputs.inputnode.mask_file = 'mask.nii'
>>> smooth.run() 

Graph

digraph susan_smooth{

  label="susan_smooth";

  susan_smooth_inputnode[label="inputnode.IdentityInterface.utility"];

  susan_smooth_mask[label="mask.ImageMaths.fsl"];

  susan_smooth_meanfunc2[label="meanfunc2.ImageMaths.fsl"];

  susan_smooth_median[label="median.ImageStats.fsl"];

  susan_smooth_merge[label="merge.Merge.utility"];

  susan_smooth_smooth[label="smooth.SUSAN.fsl"];

  susan_smooth_outputnode[label="outputnode.IdentityInterface.utility"];

  susan_smooth_inputnode -> susan_smooth_mask;

  susan_smooth_inputnode -> susan_smooth_mask;

  susan_smooth_inputnode -> susan_smooth_median;

  susan_smooth_inputnode -> susan_smooth_median;

  susan_smooth_inputnode -> susan_smooth_smooth;

  susan_smooth_inputnode -> susan_smooth_smooth;

  susan_smooth_mask -> susan_smooth_meanfunc2;

  susan_smooth_meanfunc2 -> susan_smooth_merge;

  susan_smooth_median -> susan_smooth_merge;

  susan_smooth_median -> susan_smooth_smooth;

  susan_smooth_merge -> susan_smooth_smooth;

  susan_smooth_smooth -> susan_smooth_outputnode;

}