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: