Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.

8.5.7. nilearn.image.high_variance_confounds

nilearn.image.high_variance_confounds(imgs, n_confounds=5, percentile=2.0, detrend=True, mask_img=None)

Return confounds signals extracted from input signals with highest variance.

Parameters:

imgs : Niimg-like object

mask_img : Niimg-like object

If not provided, all voxels are used. If provided, confounds are extracted from voxels inside the mask. See http://nilearn.github.io/manipulating_images/input_output.html.

n_confounds : int

Number of confounds to return.

percentile : float

Highest-variance signals percentile to keep before computing the singular value decomposition, 0. <= percentile <= 100. mask_img.sum() * percentile / 100 must be greater than n_confounds.

detrend : bool

If True, detrend signals before processing.

Returns:

numpy.ndarray

Highest variance confounds. Shape: (number_of_scans, n_confounds).

See also

nilearn.signal.high_variance_confounds

Notes

This method is related to what has been published in the literature as ‘CompCor’ (Behzadi NeuroImage 2007).

The implemented algorithm does the following:

  • Computes the sum of squares for each signal (no mean removal).
  • Keeps a given percentile of signals with highest variance (percentile).
  • Computes an SVD of the extracted signals.
  • Returns a given number (n_confounds) of signals from the SVD with highest singular values.