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'

Usefull links: