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OutlierCount brick

Using AFNI 3dToutcount, computes outliers for all sub-bricks (3D volumes for each TR) in the input dataset

Mandatory inputs parameters:

  • in_file (a string representing an existing file)

    Input image.

    ex. '/home/username/data/derived_data/reg_func_valid.nii'
    

Optional inputs with default value parameters:

  • autoclip (a boolean, optional, default value is False)

    Clip off small voxels. Mutually exclusive with mask.

    ex. False
    
  • automask (a boolean, optional, default value is False)

    Clip off small voxels. Mutually exclusive with mask.

    ex. False
    
  • fraction (a boolean, optional, default value is True)

    Combine the final measurements along each axis.

    ex. True
    
  • interval (a boolean or an integer, optional, default value is False)

    Write out the median + 3.5 MAD of outlier count with each timepoint.

    ex. False
    
  • legendre (a boolean or an integer, optional, default value is False)

    Use Legendre polynomials.

    ex. False
    
  • out_prefix (a string, optional, default value is ‘outliers’)

    Specify the string to be prepended to the filename of the output file.

    ex. 'outliers_'
    
  • qthr (a float between 0.0 and 1.0, default value is 0.001)

    Indicate a value for q to compute alpha.

    ex. 0.001
    

Optional inputs:

  • mask_file (a string representing an existing file, optional)

    Mask image. Compute correlation only across masked voxels. Mutually exclusive with automask and autoclip.

    ex. '/home/username/data/derived_data/automask_mean_reg_func_valid.nii'
    
  • polort (an integer, optional)

    Detrend each voxel timeseries with polynomials.

    ex. 3
    

Outputs parameters:

  • out_file (a strings representing a file)

    Out file.

    ex. '/home/username/data/derived_data/outliers_reg_func_valid.out'
    

Useful links:

AFNI 3dToutcount

AFNI OutlierCount - nipype